Software development for the use of a mathematical prediction tool for chemotherapy responsiveness in a clinical and in a research environment

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1 Royal College of Surgeons in Ireland MSc by research theses Theses and Dissertations Software development for the use of a mathematical prediction tool for chemotherapy responsiveness in a clinical and in a research environment Elisabeth Zink Royal College of Surgeons in Ireland, zink.elisabeth@web.de Citation Zink E. Software development for the use of a mathematical prediction tool for chemotherapy responsiveness in a clinical and in a research environment [MSc Thesis]. Dublin: Royal College of Surgeons in Ireland; This Thesis is brought to you for free and open access by the Theses and Dissertations at e-publications@rcsi. It has been accepted for inclusion in MSc by research theses by an authorized administrator of e- publications@rcsi. For more information, please contact epubs@rcsi.ie.

2 Use Licence Creative Commons Licence: This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License. This thesis is available at e-publications@rcsi:

3 Software development for the use of a mathematical prediction tool for chemotherapy responsiveness in a clinical and in a research environment Elisabeth Zink Dissertation submitted to the Royal College of Surgeons in Ireland for the acquisition of the academic degree Master of Science May 2014 Department of Physiology and Medical Physics Royal College of Surgeons in Ireland 121 St. Stephen s Green Dublin 2, Ireland Supervisors: Dr. Markus Rehm & Prof. Jochen Prehn

4 Candidate thesis declaration I declare that this thesis, which I submit to RCSI for examination in consideration of the award of a higher degree MSc is my own personal effort. Where any of the content presented is the result of input or data from a related collaborative research programme this is duly acknowledged in the text such that it is possible to ascertain how much of the work is my own. I have not already obtained a degree in RCSI or elsewhere on the basis of this work. Furthermore, I took reasonable care to ensure that the work is original, and, to the best of my knowledge, does not breach copyright law, and has not been taken from other sources except where such work has been cited and acknowledged within the text. Signed RCSI Student Number Date IP Declaration The contents of the enclosed manuscript are confidential and should not be disclosed, or disseminated in any way, to any third party other than to staff or students of the Royal College of Surgeons in Ireland or an external examiner appointed for the purpose of reviewing the manuscript. 2

5 Table of contents Summary... 6 List of authored publications... 7 Acknowledgement... 8 Abbreviations... 9 List of figures List of tables Introduction Cell Death Apoptosis Extrinsic pathway Intrinsic pathway Cell death impairment Colorectal cancer and its treatment Systems biology and systems medicine Aims of this thesis Material and Methods APOPTO-CELL: a mathematical model implementation of the apoptosis execution pathway Programming environment Experimental data/ clinical cohort Analysis methods for patient data Kaplan Meier plots ROC curves Statistics Results Refactoring of the APOPTO-CELL code

6 Introduction Restructuring of the APOPTO-CELL model code for use with a graphical user interface Conclusion Validation of the APOPTO-CELLup model code Introduction APOPTO-CELLup can reproduce the protein profiles of intrinsic apoptosis pathway in HeLa cells APOPTO-CELLup can reproduce the modelled influence of XIAP over-expression on cell death in HeLa cells Prediction of treatment responsiveness in different cell lines with APOPTO-CELL was reproduced with APOPTO-CELLup Prediction of targeted treatment strategies for colorectal cancer patients could be reproduced by APOPTO-CELLup Conclusion Implementation of a graphical user interface for APOPTO-CELLup to facilitate its usage in a cancer research and diagnostic environment Introduction GUI development for the application of APOPTO-CELLup in a research environment GUI development for the application of APOPTO-CELLup in a clinical environment Update of GUI functionalities according to user feedback Changes in the menu bar from APOPTO-GUI 1.0 to APOPTO- GUI Changes for the manual data input mode of APOPTO-GUI Changes for the automated data input mode of APOPTO-GUI Conclusion APOPTO-CELLup as a predictive marker in the clinical data set Ni

7 Introduction Aim of this chapter General Ni240 characteristics RPPA-based determination of absolute protein concentration is only for caspases-3 and -9, SMAC and XIAP possible Single proteins fail as predictive biomarkers for chemotherapy outcome in stage II and stage III CRC patients Successful evaluation of APOPTO-CELLup as a predictive marker in stage III CRC patients treated with chemotherapy Conclusion Discussion Bibliography Appendix APOPTO-CELL code used for refactoring

8 Summary Since cancer is the second most common cause of death in Ireland (Irish Cancer Society, 2013), there is great potential to improve personal treatment of cancer patients and to reduce the mortality rate. The search for biomarkers to predict the responsiveness to chemotherapy fails for certain cancer types due to their high heterogeneity. Integrating systems knowledge into biomarker research can improve the classification of patients into responder and non-responder. For instance the systems model APOPTO-CELL, which was developed in our group previously, predicts the treatment outcome of stage II and stage III colorectal cancer patients (Hector et al, 2012). This mathematical model outperforms classical statistical methods and is able to calculate whether targeted therapeutics such as apoptosis sensitizers may improve the responsiveness of individual patients to chemotherapy. Interviews with physicians and pathologists revealed that there is a need for prediction tools such as APOPTO-CELL that can support the consultant in the treatment decision process. APOPTO-CELLup could be validated successfully against the original published version. We therefore investigated how APOPTO-CELL may be integrated into a clinical diagnostic environment. For this purpose a requirement analysis was performed and as a result two possible use cases, one in the clinical pathology and the other in the clinical research setting were defined. Based on the use cases, a prototype for a graphical user interface was developed. Feedback from potential users resulted in a refined user interface that can be integrated into existing clinical workflows. APOPTO-CELL provides the prediction of treatment responsiveness in different standardized exchange formats. Here, I present a workflow integration strategy and a functional prototype for the APOPTO-CELL response prediction. A final application of the model to a patient set with 221 stage II and III CRC patients revealed that the model is able to predict chemotherapy responsiveness in patients with stage III CRC and classify correctly responders and non- responders to chemotherapy. 6

9 List of authored publications The work presented in this thesis is related to the following publication: Wuerstle ML, Zink E, Prehn J, Rehm M (2014) From computational modelling of the intrinsic apoptosis pathway to a systems-based analysis of chemotherapy resistance: achievements, perspectives and challenges in systems medicine. Cell Death and Disease, 5. doi: /cddis

10 Acknowledgement First of all, I would like to thank Prof. Jochen Prehn and Dr. Markus Rehm for giving me the great opportunity to work at the RCSI. Thank you for your guidance and support during the last year. Furthermore, thanks to the Health Research Board in Ireland, it funded this work. I would particularly like to thank also my interview partners Prof. Elaine Kay and Dr. Sandra van Schaeybroeck. For their work on RPPA and for providing the Ni240 data I have to thank Prof. Patrick Johnston, Dr. Daniel Longley, Dr. Bryan Hennessy, Dr. Clare Morgan, Sarah Curry, Dr. Mattia Cremona, Dr. Aine Murphy, and Dr. Lorna Flanagan. Moreover, I am very grateful for the support and the friendship I experienced by my colleagues in the department. Especially I would like to name my subgroup members Max, Christian, Eugenia, Frank, Elodie and Emilie. I also give thanks to my colleagues in the office and tea bag friends Andi, Niamh, Manuela, Guiyeom, Natalia, Britta, Franziska, and Jasmin for having a nice time. Thanks to Andrew Rooney for reviewing my thesis. Vor allem möchte ich mich auch bei meinen Eltern und meiner Familie bedanken. Mama, Papa ihr habt es mir ermöglicht zu studieren und diesen Weg zu gehen. Danke Michi R. für deine tatkräftige Unterstützung während meiner Abwesenheit. Du warst meiner Familie eine große Hilfe. Ich liebe dich! Thanks to everyone who contributed to this work! 8

11 Abbreviations Apaf-1 Apoptotic protease activation factor 1 ATP adenosine-5'-triphosphate Bcl-2 B cell lymphoma protein 2 Bid BIR caspase BH3 interacting-domain death agonist baculoviral IAP repeat cysteine-dependent aspartate-specific protease CD95 cluster of differentiation 95 CRC Cyt-C DISC FADD FLIP GUI IAP IHC mcrc MOMP NCCD ODE ROC SMAC TMA Colorectal cancer cytochrome-c Death-inducing signalling complex Fas(CD95)-associated death domain containing protein Flice-like inhibitory protein Graphical user interface Inhibitor of apoptosis protein Immunohistochemistry metastatic colorectal cancer Mitochondrial outer membrane permeabilization Nomenclature Committee on Cell Death ordinary differential equation Receiver-operating characteristics Second mitochondrial-derived activator of caspases Tissue microarray 9

12 TNFR1 TRAIL UML VEGF XIAP tumour necrosis factor receptor-1 TNF-related apoptosis-inducing ligand unified modeling language Vascular Endothelial Growth Factor X-linked inhibitor of apoptosis protein 10

13 List of figures Figure 1-1: Apoptotic pathways Figure 1-2: Activation of initiator and effector caspases Figure 1-3: Simplified overview from apoptosis induction to execution Figure 1-4: Simplified scheme for the integration of APOPTO-CELL into a clinical diagnostic environment Figure 2-1: Schematic representation of the intrinsic apoptosis pathway downstream of MOMP as implemented in the mathematical model Figure 2-2: Example of a substrate cleavage trace in cells undergoing apoptosis produced by APOPTO-CELL Figure 2-3: Example Kaplan Meier curve to analyse patient survival Figure 2-4: Example of a ROC curve to analyse performance of a diagnostic test Figure 3-1: Published protein profiles (left) of (A) apoptosome formation and SMAC release could be reproduced with APOPTO-CELLup (right) as well as (B) SMACs interaction with XIAP Figure 3-2: Published protein profiles (left) of (A) XIAP and its binding to caspase-3 and -9 could be reproduced with APOPTO-CELLup (right) as well as (B) the cleavage of XIAP and the binding of BIR-fragments to caspases. 60 Figure 3-3: Published protein profiles (left) of (A) activation of caspases could be reproduced with APOPTO-CELLup (right) as well as (B) modelled substrate cleavage Figure 3-4: Altering initial XIAP concentrations showed minor substrate cleavage for XIAP concentrations above 0.3 μm Figure 3-5: Published model predictions (left) for the specific XIAP concentrations were approved experimentally and could be reproduced with APOPTO-CELLup (right) Figure 3-6: Prediction of the response to MOMP for different cell lines can be reproduced with APOPTO-CELLup Figure 3-7: Prediction of the response to MOMP for different cell lines can be reproduced with APOPTO-CELLup Figure 3-8: Published effects of alternative, targeted therapeutics showed the same classification of responders and non-responders for different treatment options as the APOPTO-CELLup modelled treatment responses

14 Figure 3-9: Suggested workflow for the use of APOPTO-CELL in a research setting for analysing patient cohorts Figure 3-10: GUI for automated data input enables user to load a file containing the required data Figure 3-11: Template file for automated data input in APOPTO-GUI Figure 3-12: Data sheet for automated data input mode of APOPTO-GUI 1.0 (example) Figure 3-13: GUI response after model calculation for automated data input Figure 3-14: A summary file contains the model input and a reduced set of calculated output for all patients per run Figure 3-15: Example output file of APOPTO-GUI 1.0 containing traces of caspase-9, caspase-3 and substrate cleavage for all calculated patients Figure 3-16: Example of a figure that can be saved for every patient Figure 3-17: Suggested workflow for use of APOPTO-CELL by consultants analysing a patient s treatment responsiveness Figure 3-18: Workflow suggestion for an application of APOPTO-CELL in a pathological setting Figure 3-19: The GUI for manual data input contains mandatory fields for model input and patient information Figure 3-20: User interface shows after successful calculation the result in the right part of APOPTO-GUI Figure 3-21: GUI for manual data input in case of a bad outcome prediction shows the influence of targeted therapeutics Figure 3-22: Updated functionalities for the APOPTO-CELL GUI are implemented in the options menu Figure 3-23: File with all traces for a single patient Figure 3-24: The patient report created by APOPTO-GUI can be saved either as Word or PDF document Figure 3-25: Overview file for all analysed patients by APOPTO-CELL Figure 3-26 Different Help dialogs provide information on use of the tool Figure 3-27 About menus

15 Figure 3-28 Screenshot of start window for manual data input Figure 3-29: Second version of GUI for manual data input after calculation Figure 3-30: Second version of GUI for automated data input Figure 3-31: APOPTO-GUI 1.1 for automated data input after calculation Figure 3-32: Recurrence occurs earlier and more often in advanced disease stage Figure 3-33: Chemotherapy significantly prolongs the time to recurrence for stage III CRC patients, but not for stage II CRC patients Figure 3-34: Tumours in the ascending colon and rectum are more likely to have recurrence than other tumour sites Figure 3-35: RPPA determined protein levels correlate with Western Blot determined protein concentrations of four cell lines Figure 3-36: SMAC and caspase-3 could be used as biomarkers for recurrence, whereas caspase-9 and XIAP levels have no significant segregation of patients with and without recurrence Figure 3-37: Using SMAC levels to separate patients according to their recurrence status works for stage II as well as for stage III Figure 3-38: SMAC levels predict clinical outcome of patients that were observed only after surgery despite their stage Figure 3-39: Substrate cleavage traces produced by APOPTO-CELLup for RPPA-based protein concentrations of the Ni240 cohort Figure 3-40: Kaplan Meier analysis of predicted recurrence revealed a good separation between patients predicted with (green line) and without recurrence (blue line) for an optimized threshold of 1.5% but not for predefined value of 25% Figure 3-41: The optimized threshold value of 1.5% final substrate cleavage can separate patients regarding their recurrence status in stage III but not in stage II Figure 3-42: The optimized threshold value is predictive for treatment outcome in stage III Figure 3-43: ROC analysis identified the range around 5% final substrate cleavage as an optimal threshold for APOPTO-CELLup with high sensitivity (blue) and specificity (red)

16 Figure 3-44 APOPTO-CELLup is predictive for chemotherapy treated stage III patients

17 List of tables Table 1-1: Characteristics and treatment by CRC stages Table 1-2: CRC related survival rates by stage Table 2-1: Protein concentrations of the apoptotic proteins caspase-3, caspase-9, XIAP, Apaf-1 and SMAC for the 10 cell lines used in Schmid et al (2012) Table 2-2: Clinical characteristics of patients that were simulated by the APOPTO-CELLup model Table 3-1: Comparison of coding for rate constants and concentrations within different code versions Table 3-2: Requirements for a graphical user interface of APOPTO-CELL in a research setting Table 3-3: Requirements for a GUI of APOPTO-CELL in a clinical setting

18 1. Introduction 1.1. Cell Death In living organisms most cells have to die after a certain time, either because of external circumstances, e.g. cellular DNA was damaged by UV-irradiation, or because of internal circumstances. Internal circumstances can be for example the elimination of cells during embryonic development in order to delete unwanted structures or to form limbs. Scientific research on cell death commenced in the 19 th century when Carl Vogt reported physiological cell death processes in 1842 (Vogt, 1842). In 1858, Rudolf Virchow described differences in tissue degeneration. He detected a passive pathological event, which is named necrosis, and death brought on by altered life, a phenomenon he named necrobiosis (Virchow, 1858). Scientists discovered morphologically distinguishable types of cell death. The different morphological changes were the criteria used for a long time to classify different types of cell death. Two of the main types of cell death are necrosis and apoptosis (Clarke & Clarke, 2012). Necrotic cells show swollen cell organelles followed by cellular membrane disruption. During necrosis, large amounts of harmful cellular contents are released into the intercellular space, often inducing inflammatory reactions (Los et al, 2002). Necrosis can be caused by external factors like heat or cold and mechanical forces (Zong & Thompson, 2006). By contrast, a cell undergoing apoptosis shows specific symptoms, such as membrane blebbing, shrinking and DNA fragmentation (Kerr et al, 1972). Apoptosis was discovered only recently in 1965 when John Kerr observed a type of cell death that was unlike to that of necrosis (Kerr, 1965). He initially named this form of cell death shrinking necrosis because of the morphological changes (Kerr, 1971). In 1972, the final term apoptosis was given to this subtype of cell death by Kerr et al (1972). The word apoptosis is a Greek description of the falling of leaves from the tree. In 2002, Brenner, Horvitz and Sulston were honoured with the Nobel Prize in Physiology or Medicine for their in-depth investigation of the apoptosis mechanisms in the nematode C. elegans (Ellis & Horvitz, 1986). 16

19 Apoptosis Apoptosis is a form of programmed cell death that eliminates individual cells without significantly affecting surrounding tissue. This mechanism prevents unnecessary destruction of the surrounding tissue. There are two major pathways for apoptosis. The extrinsic pathway is triggered from outside the cell and the intrinsic pathway is triggered inside the cell. The intrinsic pathway of apoptosis is characterised by the permeabilization of the mitochondrial outer membrane and the release of mitochondrial content into the cytosol. Both pathways are complex and regulated through many proteins. The most important regulatory proteins for the intrinsic pathway downstream of mitochondrial outer membrane permeabilization (MOMP) are caspases (cysteine-dependent aspartate-specific proteases), XIAP (X-linked inhibitor of apoptosis protein), SMAC (second mitochondrial-derived activator of caspases) and Apaf-1 (apoptotic protease activation factor 1). The extrinsic pathway can trigger the intrinsic pathway as seen in Figure 1 below. Dysregulated apoptosis, e.g. through genetical defects of key proteins, can lead to autoimmune diseases, developmental defects and neurological disorders (Mattson, 2000; Meier et al, 2000; Mountz et al, 1994). Impaired apoptosis pathways can also lead to cancer (Hanahan & Weinberg, 2011). 17

20 Figure 1-1: Apoptotic pathways. This schematic shows the intrinsic (left) and the extrinsic (right) pathway of apoptosis. The intrinsic pathway is induced by apoptotic stimuli, which result in MOMP. MOMP leads to the release of cytochrome-c and SMAC from the mitochondrial inter-membrane space. Cytochrome-c then activates Apaf-1 that homooligomerises into an activation platform called apoptosome that activates caspase-9. Activated caspase-9 then activates caspase-3. Activated caspase-3 orchestrates cell death through the cleavage of numerous cellular substrates, e.g. DNA repair proteins. The extrinsic pathway is induced by the binding of ligands to death receptors, leading to the assembly of the death-inducing signalling complex (DISC). Once activated at the DISC, caspase-8 can trigger cell death directly by caspase-3 activation or indirectly by triggering MOMP through Bid (BH3 interacting-domain death antagonist) cleavage. Caspase-8 activation can be inhibited by cellular flicelike inhibitory protein (c-flip). Caspases-3 and -9 are antagonized by XIAP that is itself inhibited by SMAC. This figure is a simplification of the apoptosis pathways and does not show specific features, such as signalling feedbacks. Original figure was taken from Khan et al (2010). 18

21 Extrinsic pathway The extrinsic pathway of apoptosis is also called the receptor pathway and is initiated at the plasma membrane upon ligation of death receptors with extracellular cytokines (Fulda & Debatin, 2006). Death receptors like tumour necrosis factor receptor-1 (TNFR1), Fas/cluster of differentiation 95 (CD95) or TNF-related apoptosis-inducing ligand (TRAIL) receptors, which bind TNF, Fas-ligand, or TRAIL, respectively, contain a cytoplasmic death domain that mediates interaction of the receptors with death-domain(dd)-containing adapter proteins like Fas(CD95)-associated death domain containing protein (FADD) (Bodmer et al, 2002). Procaspase-8 and -10 bind to FADD. The generated death-inducing signalling complex (DISC) activates caspases-8 and -10, which in turn activate the effector caspases-3 and -7 that ultimately execute cell death (Peter & Krammer, 2003; Sprick et al, 2002). Cell death can be prevented by inhibitors that antagonize caspases, for instance by inhibitors of apoptosis proteins (IAPs) like XIAP. Another example for an inhibitor is cflip, an inhibitor which competitively binds to FADD and thereby disables the autocatalytic cleavage of caspases-8 and -10 (Chang et al, 2002; Tao et al, 2011) or members of the B cell lymphoma protein 2 (Bcl-2) family proteins, which control MOMP (Dewson et al, 2008) Intrinsic pathway Another term for the intrinsic pathway is the mitochondrial pathway, because it is initiated through the release of the mitochondrial proteins SMAC and cytochrome-c. The mitochondrial pore formation for the protein release is triggered by apoptotic stimuli, such as DNA damage, and stress of the endoplasmatic reticulum and impaired proteasomal protein degradation. The family of Bcl-2 proteins, whose members can be either pro- or anti-apoptotic, regulates MOMP. Once the Cyt-C is released into the cytosol it binds to Apaf- 1 that homo-oligomerises into a heptameric complex, called apoptosome. The apoptosome in turn recruits and activates procaspase-9 (Parthasarathy & Philipp, 2012). Activated caspase-9 cleaves and activates downstream effector caspases-3 and -7 (Reubold et al, 2011). Caspases-3,-6 and -7 19

22 cleave numerous cellular proteins, leading to the characteristical morphological features of apoptosis. The caspase inhibitor XIAP can be inhibited by SMAC as well as cleaved by caspase-3. Although cleavage of XIAP results in a fragment containing the BIR1 (baculovirus IAP repeat 1) and -2 domain and a fragment containing the BIR3-domain, the inhibitory effect of these fragments is not lost (Takahashi et al, 1998). The BIR12 fragment can bind to caspase-3 and -7 (Abhari & Davoodi, 2010) and the BIR3 fragment can still inhibit caspase-9 (Kulathila et al, 2009). Apaf-1 Apoptotic protease-activation factor-1 is a cytoplasmic protein in an inactive conformation. Upon binding of Cyt-C and ATP (adenosine-5'-triphosphate), the Apaf-1 monomers homo-oligomerise into a heptameric complex termed apoptosome (Acehan et al, 2002). The apoptosome then recruits and activates procaspase-9. The mechanisms of caspase-9 activation upon the apoptosome are currently undergoing scientific investigations (see in paragraph Caspases, page 20). Research on the apoptosome however is challenging due to its large size, its instability and its tendency to aggregate (Yuan & Akey, 2013). Caspases Caspase is an abbreviation for cysteine-dependent aspartate-specific protease and describes the process whereby the said protein cleaves its protein substrates only at specific cleavage sites that contain a short peptide sequence including a crucial aspartate residue. Caspases are expressed as zymogens ( procaspases ) and are named caspase-1 to caspase-14 in the chronological order of discovery. Caspases can be divided into two groups. One group is involved in inflammatory responses (1, 4, 5, 11 and 12) and the other group is involved in the apoptosis pathway (2, 3, 6, 7, 8, 9 and 10). Caspases involved in apoptosis are separated into effector caspases and initiator caspases (Shi, 2002). Members of the initiator group are caspases-2,- 20

23 8,-9,-10. Initiator caspases contain a long pro-domain and can be activated by the dimerization of two monomers (Figure 1-2) at an activation platform. For caspase-9 there are indications that activation occurs not by dimerisation but by an allosteric change induced by binding to the apoptosome and thereby activation of monomeric caspase-9 (Pop et al, 2006; Rodriguez & Lazebnik, 1999). Activated initiator caspases activate the effector or so-called executioner capases-3, -6 and -7 by proteolytic processing of specific cleavage sites within the linker region between the catalytic sub-units. Effector caspases contain only a short pro-domain and exist as inactive dimers in the cytosol and will form active heterotetramers, consisting of two large and two small catalytic domains, upon cleavage. They can then cleave their substrates (Adams, 2003; Fadeel & Orrenius, 2005). Caspase 3 is the most important executioner caspase, as it cleaves most cellular substrates and therefore drives apoptosis execution (Slee et al, 2001). 21

24 Figure 1-2: Activation of initiator and effector caspases. The red parts symbolize the pro-domains of procaspases. The pro-domain is followed by one large and one small catalytic domain (blue parts). In order to activate the initiator caspases, two procaspases have to dimerize. Effector procaspases have already dimerized and need to be cleaved between the small and the large sub-unit in order to become active. Matured caspases are more stable due to further proteolytic cleavage and can be regulated for example by the X-linked inhibitor of apoptosis protein. Original figure was taken from Pop and Salvesen (2009). XIAP X-linked inhibitor of apoptosis protein is one of the eight proteins of the family of inhibitor of apoptosis proteins (IAP). Characteristic for this protein family is the baculovirus IAP repeat (BIR) domain, a zinc-binding fold of approximately 70 amino acid residues that mediate protein-protein interactions. Every IAP has one to three copies of this domain (Clem & Miller, 1994). XIAP contains three BIR domains (Takahashi et al, 1998). IAPs inhibit caspases by blocking their active site. The third BIR domain, BIR3, binds to activated caspase-9, whereas the linker domain between BIR1 and BIR2 binds to the active site of caspases-3 and -7 (Shi, 2002). It has been shown that in cancer cells, XIAP is often overexpressed, a fact that can lead to a resistance to chemotherapy (de Almagro & Vucic, 2012). 22

25 SMAC Second mitochondria-derived activator of caspases, also known as Diablo, is released from the mitochondrial inter-membrane space after MOMP, together with several other mitochondrial proteins like Cyt-C. The mature form of SMAC binds via an IAP binding motif to XIAP and thereby antagonizes the inhibition of caspases (Fesik & Shi, 2001). Drugs that mimick the function of SMAC are called SMAC-mimetics. Their purpose is to inhibit IAP proteins and to sensitize cancer cells towards chemotherapeutic treatment (Park et al, 2005; Zobel et al, 2006). Recently, the Nomenclature Committee on Cell Death (NCCD) redefined the different types of cell death, because modern methods enable to determine differences not only on morphological levels but also on a biochemical level (Vanden Berghe et al, 2013). The NCCD was founded by editors of the journal Cell Death and Differentiation in 2005 in order to guide the scientific field of cell death in the use of cell death-related terms and to provide unified definitions for cell death processes (Kroemer et al, 2005). Extrinsic apoptosis is defined as a caspase-dependent cell death subroutine and can be suppressed by chemical pan-caspase inhibitors or by the overexpression of viral inhibitors of caspases. There are three major lethal signalling cascades for extrinsic apoptosis, which are always induced by death receptor signalling or ligand deprivation-induced dependence receptor signalling and which, further, activate the caspase-3 cascade. Intrinsic apoptosis is caspasedependent and is defined as a mitochondrial outer membrane permeabilization (MOMP)-mediated cell death process. MOMP is regulated by a system of pro-apoptotic and anti-apoptotic proteins which means MOMP will only occur if the stress-triggered pro-apoptotic signals are predominant. (Galluzzi et al, 2012) Cell death impairment Cell death may be enhanced in AIDS, ischemic stroke, and neurodegeneration, whereas apoptotic pathways in cancerous cells and during autoimmunity diseases are often down-regulated (Yuan & Akey, 2013). 23

26 Hanahan and Weinberg describe that in cancerous cells mechanisms, which should be protective for cells, are disturbed. Among their eight traits, there is also apoptosis listed, which is evaded in malignant cells.(hanahan & Weinberg, 2011). To stop the growing of defective cells radio- and chemotherapy try to trigger cell death by disturbing DNA replication or cell division. These therapies, however, affect not only impaired cells but also non-malignant cells, thereby causing undesired side effects for the patient (Ashkenazi, 2002) Colorectal cancer and its treatment Each year, around 230,000 people die of colorectal cancer (CRC) in Europe, which means CRC is the second most common cause for cancer-related deaths in Europe ( 2014). This type of cancer is also known as bowel cancer, because it affects parts of the large bowel (colon cancer) or parts of the back passage (rectal cancer). When this type of cancer is diagnosed, it is classified into different stages to determine its tumour progression status. The two most common staging systems are TNM and Dukes. TNM is an abbreviation for Tumour, Node, Metastasis and characterizes the size of the primary tumour, the number of lymph nodes involved and whether the cancer has already spread. The tumour size T is separated into 4 substages (T1-T4), the lymph node stage N is divided into 3 substages (N0-N2) and the stages M0 and M1 exist for metastases. The TNM staging is combined in a system called stage grouping ( -cancer-staged). These stages start with 0 as the least advanced stage and end with stage IV as the most advanced disease stage ( -cancer-staged). The Dukes staging system consist of only the groups A-D, describing how the tumour affects surrounding tissue. A is defined as a stage where the tumour grows only in the colon or rectum and group D stands for metastatic growth (Cancer_Research_UK, 2014). Dukes staging from A to D is congruent with stage grouping stage I to stage IV. The tumour is staged 24

27 only once during diagnosis and this staging is kept for the whole course of disease, it will not be adjusted to a higher-level stage during treatment or the progress of disease. Instead, additional information is added to the stage that was initially diagnosed. Depending on the response to treatment, a stage II cancer that came back after successful treatment as a new tumour located in bone tissue will be referred to as stage II with recurrent disease in the bones, or if treatment failed, as stage II with bone metastasis ( The typical strategy after the diagnosis of cancer is described in the following text. If a patient is diagnosed with colorectal cancer, he or she will undergo surgery and the removed cancer tissue will be analysed pathologically afterwards. When the disease is in an advanced stage, the patient will probably receive chemotherapy before surgery to shrink the tumour. This treatment is called neo-adjuvant chemotherapy ( Depending on the tumour stage, a patient will either receive only curative resection or curative resection and additional treatment with adjuvant chemotherapy or radiotherapy to induce cell death in cancerous cells remaining in the body. In the case of colorectal cancer, the standard chemotherapeutic treatment is a 5-FU based chemotherapy aiming at inducing cell death in remaining cancer tissue. 5-Fluorouracil is a nucleotide homologue, which is integrated into DNA and causes mismatches. It triggers apoptosis by disturbing DNA replication and repair. Nowadays, 5-FU is often combined with other chemotherapeutic drugs like irinotecan for instance, a drug that causes double-strand DNA breaks during DNA replication, or oxaliplatin, which also blocks DNA replication and transcription. Oxaliplatin causes crosslinking of DNA strands leading to apoptosis (McWhirter et al, 2013). The American Cancer Society gives a specific overview of the stage-related treatment of colorectal cancer on its website(see also Table 1-1). If a CRC tumour is discovered at a very early stage and has not yet grown beyond the inner lining of colon or rectum (stage 0), surgery is sufficient to remove the 25

28 tumour. If the tumour is already larger and has grown through several layers of the colon or rectum but not through the colon/rectum wall and has not yet spread, it is staged as stage I. Treatment for stage I patients consists primarily of surgery to remove the affected part of the tissue. If the tumour growth was underestimated, additional treatment with chemotherapy or radiation therapy will be necessary. Tumours that have grown through the wall but not affected the lymph nodes are stage II tumours. The recommended treatment for all stage II patients is surgery, but so far chemotherapy was not shown to provide a benefit for all stage II patients (ACS, 2014a; ACS, 2014b). There are several pathological features, which have been shown to be associated with poor prognosis in stage II disease such as extramural vascular invasion, poorly differentiated tumours, obstructed tumours, perineural invasion and low lymph node recovery from the resection specimen. These features have been used to identify high-risk patients and have become criteria for adjuvant chemotherapy in stage II disease but their value to predict the treatment outcome has not yet been established (NCCC, 2011). The expression of p21 WAF1, a regulator of the cell cycle, is also a high-risk marker for stage II patients that should receive chemotherapy (REERINK et al, 2004; Sulzyc- Bielicka et al, 2011). If the surgeon is not sure whether all cancer tissue was removed during surgery, the patient will be treated with a local radiation therapy. Patients staged as stage III have tumours grown through colon or rectum wall and already cancerous cells in their lymph nodes. Standard stage III treatment for rectal but not for colonic tumours is chemo-radiation before surgery to shrink the tumour. This type of treatment is called neoadjuvant therapy. For both tumour sites, patients will undergo surgery followed by adjuvant chemotherapy. Typically used treatment options are FOLFOX (5-FU, leucovorin and oxaliplatin) or CapeOx (capecitabine and oxaliplatin). For stage III patients the benefits of 5-FU based chemotherapy are more pronounced than for stage II patients (Gill et al, 2004). The most advanced stage is stage IV. In this case, the tumour has grown through the colon or rectum wall, has invaded nearby lymph nodes and spread to other parts of the body like liver, lung and distinct lymph nodes. Because of its progress, it is very unlikely that patients with a stage IV tumour may be cured by surgery. In early stage IV patients, surgery and chemotherapy before and after surgery 26

29 might be able to cure a patient. Most patients will receive palliative therapy to relieve their suffering. Chemotherapeutic treatment options for these patients are leucovorin, 5-FU, oxaliplatin, irinotecan, capecitabine, bevacizumab, cetuximab, panitumumab, and regorafenib. Depending on the patient s condition, he or she will receive one of these chemotherapeutics or a combination of them (ACS, 2014a; ACS, 2014b). Stage IV patients with metastatic colorectal cancer (mcrc) often receive the chemotherapeutic cetuximab instead of a 5-FU based approach. Cetuximab is an antibody that inhibits the Epidermial Growth Factor Receptor, which is found on most colorectal cells and is part of the deregulated signalling pathway in cancer cells. Recently it was shown that cetuximab should not be used in liver mcrc patients with the tumour genotype KRAS exon 2 wild-type, as it leads to shorter progression-free survival in these patients (Primrose et al, 2014). A second antibody chemotherapeutic for mcrc patients is bevacizumab, which inhibits the Vascular Endothelial Growth Factor (VEGF) that allows tumours to grow and to metastasise (Cassidy et al, 2010). VEGF is responsible for angiogenesis, which is the process of building new blood vessels. Tumour cells use VEGF to encourage angiogenesis in order to ensure their oxygen and nutritions supply via blood vessels. 27

30 Table 1-1: Characteristics and treatment by CRC stages. Table content is based on information given by the American Cancer Society (ACS, 2014a; ACS, 2014b) Stage Characteristics Treatment 0 Not grown beyond inner lining of colon Surgery I Grown through several layers of colon, but not yet through colon or rectum wall and has not spread Surgery, removing affected part of colon or rectum. If rectal cancer was classified wrongly, additional treatment might be necessary (chemo or radiotherapy) II Grown through colon wall, but has not spread to lymph nodes Surgery, for high-risk patients additional chemo (combinations of 5FU, leucovorin, capecitabine, oxaliplatin); if not sure whether all cancer tissue was removed also radiation therapy III Spread to nearby lymph nodes, but not other parts of body Surgery, followed by adjuvant chemo FOLFOX (5-FU, leucovorin and oxaliplatin) or CapeOX (capecitabine and oxaliplatin); radiation therapy, as for stage II. For rectal cancer: chemoradiation before surgery IV Spread to distant organs and tissues (liver, lung, distant lymph nodes) Surgery is unlikely to cure; in early cases curative resection together with chemo might help (before and/or after surgery; leucovorin, 5- FU, oxaliplatin, irinotecan, capecitabine, bevacizumab, cetuximab, panitumumab, regorafenib). Overall survival of cancer patients was shown to differ within different European countries (Sant et al, 2009). Table 1-2 lists the 5-year survival rates for colon and rectal cancer patients in America and for pooled colorectal cancer patients in the UK. Cancer staging was given in the Dukes system for the English survival rates. These 5-year survival rates are stated as 92.3% for stage I, 77% for stage II, 47.4% for stage III and 6.6% in stage IV. This 28

31 declining survival with advancing disease stage is also seen in the American 5-year survival list. The staging system used in America is more detailed than the Dukes system. For that reason, the survival rates are different. Stage I is stated with only 74% of 5-year survival, stage II survival rates can range from 67% to 32% depending on the grade of the stage, stage III ranges from 74% to 28% and stage IV has a 5-year survival of 6%. Table 1-2: CRC related survival rates by stage. Table content is based on information given by the American Cancer society and Cancer Research UK (ACS, 2014c; CR(UK), 2012). Staging for 5-year survival in England was originally given in the Dukes system (A-D). Stage I II III IV 5-year observed survival rate (colon cancer, America) 74% IIA: 67% IIB: 59% IIC: 37% IIIA: 73% IIIB: 46% IIIC: 28% 6% 5-year observed survival rate (rectal cancer, America) 74% IIA: 65% IIB: 52% IIC: 32% IIIA: 74% IIIB: 45% IIIC: 33% 6% Five year relative survival rates (England) 92.3% 77% 47.7% 6.6% The range of chemotherapeutics and their combination shows that not all patients respond equally to the same treatment. Moreover, chemotherapeutics have many unwanted side effects on the human body such as hair loss, vomiting, exhaustion and more. For that reason, it is important to differentiate between patients that might be cured by resection alone from those in need of chemotherapy. Moreover, the chosen chemotherapeutic should have the fewest side effects but should also be 29

32 capable of eliminating possibly remaining malignant cells and preventing a recurrence of the tumour Systems biology and systems medicine Employing computational approaches can help to understand signalling pathways and impairments in the case of cancer. This computational field is called systems biology. Molecular systems biology combines knowledge and tools from different disciplines such as biology, computer science, medicine, physics, chemistry and engineering (Medina, 2013). Systems biology began as early as the 1960s, when the first computational models of biological systems were being developed. Until the 1990s, however, the field of systems biology was only a minor area in research. Only with the progress of biotechnology and the development of high throughput methods, systems biology started to prosper (Schneider, 2013). Detailed information about single players in a system can be gained by high throughput methods. Besides that information, the dynamics of the system members have to be investigated in order to comprehend complex regulatory mechanisms (Kitano, 2002). Therefore, a knowledge of the molecules that are involved in a pathway and their interaction with other molecules is crucial, as well as the knowledge of how these same interactions lead to cell function (Bruggeman & Westerhoff, 2007). Bruggemann and Westerhoff (2007) described two different strategies for systems biology. The top down strategy starts with the measured data of a system and tries to find a model for describing the processes. The second strategy starts with a mathematical model and tries to verify this model afterwards with experimental data; that is why it is called bottom up. Both strategies aim at understanding and predicting the outcome of a biological process. However, it is important to connect a computational model with experimental data in order to validate the model (Bruggeman & Westerhoff, 2007). 30

33 In the case of apoptosis, many mathematical models have been developed. Most commonly used equations for the mathematical models are ordinary differential equations (ODEs). They represent the rates of production, degradation and consumption of the participating molecules in terms of mass action kinetics (Aldridge et al, 2006). Therefore, mathematical models are able to simulate kinetic signalling of biological processes such as enzymatic reactions, binding or dissociation of proteins. Fussenegger et al (2000) published the first apoptosis model in This study was theoretical and based on ODEs. In the years after, several studies followed that also included experimental data. Models for the extrinsic pathway investigate different parts of the pathway. The model presented by Eissing et al. in 2004 was adapted to experimental data and focuses on the feedback of caspase-8 and caspase-3 activation, which executes extrinsic apoptosis. Their model was able to conciliate the observed difference of fast caspase-3 activation kinetics in single cells and slower caspase-3 activation kinetics in a cell population (Eissing et al, 2004). Bentele also published a model investigating the apoptosis induction via the CD95 receptor signalling in Their work revealed that the amount of activated receptors has to reach a threshold in order to activate caspases for a successful apoptosis execution (Bentele et al, 2004). Since then the modelling of CD95 signalling has been investigated further. It was shown that this threshold is dependent on the concentrations of CD95 ligand and cflip (Lavrik et al, 2007). A more detailed model demonstrated that cflip long will have a proapoptotic role in CD95 mediated apoptosis if there is a high concentration of cflip short (Fricker et al, 2010). Caspase-8 activation at the DISC was investigated computationally and experimentally, which revealed that the DISC contains much more cflip and procaspases-8 and -10 than FADD (Schleich et al, 2012). The intrinsic apoptosis pathway can be roughly divided into three parts: the initiation, the regulation of MOMP, and apoptosis execution. So far, the initiation of the intrinsic apoptosis has not been modelled which might be due to the diversity of stress-inducing signals and the big field of BH3-only proteins and their numerous interactions, which contribute to apoptosis induction. The second step, the regulation of MOMP, is mostly dependent on 31

34 the family of Bcl-2 proteins, and results in models of pore-formation and the spatiotemporal spread of MOMP signals. The final step is apoptosis execution triggered by mitochondrial protein release through pores and resulting in cellular substrate cleavage. Figure 1-3: Simplified overview from apoptosis induction to execution. A) Cellular stress initiates the apoptosis signalling. B) Interaction of different Bcl-2 family members triggers MOMP C) MOMP-induced apoptosis execution leads to cell death. Original figure was taken from Wuerstle ML et al (2014). The regulation of MOMP was initially investigated by Chen et al. They used two different modelling approaches to investigate the switch-like role of Bax activation. One model is based on ODEs, whereas the other model is a cellular automaton. Both models showed that Bax activation might be a selfamplification process that regulates the mitochondrial pore formation (Chen et al, 2007a). Further investigations showed that the direct activation model of Bax is more likely than the indirect activation model(chen et al, 2007b). Another model investigating the interactions of Bcl-2 family members and supporting the direct Bax activation was published by Lindner et al (2013a). This ODE-based model was able to remodel the stress responses of cell lines based on absolute protein quantifications (Lindner et al, 2013b). A cellular automaton modelling of mitochondrial pore formation showed that only small 32

35 amounts of Bax need to be activated for successful pore formation (Dussmann et al, 2010). One model that deals with the apoptosis execution after MOMP is the APOPTO-CELL model published in 2006 by the Prehn group (Rehm et al, 2006). The computational model is based on experimental data and needs a certain number of protein concentrations as input. Since then, the mathematical ODE model of the intrinsic apoptosis pathway subsequent to MOMP has been part of several publications. The first paper about the APOPTO-CELL model demonstrated the former reported rapid activation of effector caspases in HeLa cells (Tyas et al, 2000). In single cell imaging experiments, observed caspase responses equalled the protein time profiles that were produced by the systems approach. Further examination of the time profiles of the protein levels showed that overexpression of XIAP could prevent apoptosis execution whereas the overexpression of SMAC does not enhance apoptosis execution. Both predictions were verified by single cell analysis, which measured the same protein quantities as the predicted protein levels (Rehm et al, 2006). Another paper published in the same year confirmed these findings. This model, also dependent on protein concentrations, showed that the positive feedback loops are controlled by XIAP (Legewie et al, 2006). The model s clinical capability was evaluated from lab data to patient-relevant data. Application of the APOPTO-CELL model to patient data revealed that the model could be used as a prediction tool, as in over 80% of the patients APOPTO-CELL predicted the correct outcome. Patients with predicted high caspase activation efficiency are linked with good clinical outcome, whereas prediction of low caspase activity is assigned with bad clinical outcome. Moreover, to show the influence of targeted therapeutics if a patient is predicted with bad outcome, the model was extended. The outcome prediction for the drugs differed depending on the different patients (Hector et al, 2012). All of the three targeted therapeutics, SMAC-mimetics, proteasome inhibition and caspase-3 activation compounds, were reported as being potential drugs that induce cell death and are already either in use or undergoing clinical trials. 33

36 Due to the wide range of varying types of cancer, it would be interesting to find out if there are differences between these types. In order to find out whether the model is able to also predict the outcome of different cancer types not only colorectal cancer but different cancer cell lines, including human colorectal cancer, breast cancer and cervical cancer, were tested. APOPTO-CELL was able to correctly differentiate between cell lines that were capable of apoptosis and those that were not capable of apoptosis. Even the predicted time points of onset as well as the duration of substrate cleavage were shown to fit the experimental data with a high significance (Schmid et al, 2012). The approach was also applied to glioblastoma cell lines in order to compare the predictive efficiency of the systems approach and single biomarkers. Each protein required for the model on its own was not as good in predicting the treatment outcome as the systems model. Inclusion of pathway knowledge into predictions is more successful than considering single parameters of a network (Murphy et al, 2013). In order to improve modelling, the apoptosis models mentioned above ignore spatial effects of signalling as well as many other published apoptosis models. However, there are also some models that do consider these spatial effects. The controversial observations of the TRAIL induced wave-like permeabilization of the mitochondrial outer membrane were described in a mathematical model. The model predicted that MOMP waves accelerate as they proceed through cells, a prediction that was proved by experimental data (Rehm et al, 2009). Another spatial model is an extension of the abovementioned APOPTO-CELL model and uses not only partial differential equations but also partial differential equations to simulate a wave-like Cyt-C release. As the model predicted, experiments showed that the spatial effect of MOMP is lost during the rapid effector caspases activation in the cytosol (Huber et al, 2010). However, there are not only mathematical models published concerning the intrinsic apoptosis pathway. A data driven model was recently presented by Passante et al. The authors integrated pathway knowledge into their model, thereby immensely improving the predictive efficiency. In order to find the best responsiveness prediction, they combined certain proteins into functional 34

37 groups depending on protein-protein interplays as known from the intrinsic pathway. An application of multivariate statistical analysis to these groups was able to show whether a cell dies more likely of TRAIL induced apoptosis or DTIC (standard drug for melanoma) induced apoptosis. Therefore, the best treatment option can be found in silicio (Passante et al, 2013). Some models consider not only the intrinsic, but a combination of the intrinsic and the extrinsic apoptosis pathway. The mathematical model presented by Albeck et al. in 2008 showed that for the intrinsic pathway, releases of SMAC and Cyt-C from mitochondria are necessary for rapid caspase activation, but that the snap-action behaviour of MOMP is not dependent on feedback loops of tbid and Bax activation (Albeck et al, 2008). Other models combine the intrinsic and the extrinsic pathway, involving the DISC formation with caspase- 8 activation, caspase-3 activation, mitochondrial engagement and the caspase-6 feedback loop (Albeck et al, 2008). After the in vitro validation of systems biology models with cells, they are now being tested for patient data and undergoing clinical trials. This new step has led to the new term of systems medicine, which was defined in 2010 by the European Commission upon holding a workshop, termed From systems biology to systems medicine. Its definition for systems medicine is the following: "Systems medicine is the application of systems biology approaches to medical research and medical practice. Its objective is to integrate a variety of biological/medical data at all relevant levels of cellular organization using the power of computational and mathematical modelling, to enable understanding of the pathophysiological mechanisms, prognosis, diagnosis and treatment of disease. The Commission acknowledges the advantages in systems medicine to build a prospective medicine that will be predictive, personalized, preventive and participatory (also known as P4 medicine ). (Kyriakopoulou C, 2010) 35

38 1.4. Aims of this thesis As the APOPTO-CELL model is able to distinguish in a small patient set (n=20) between patients that benefit from chemotherapy and those who do not (Hector et al, 2012), the use of this model in a clinical context would be of great benefit as it can improve and personalize cancer treatment (Figure 1-4). The model has to be presented in a way that makes it easily usable in a clinical diagnostic environment without requiring preliminary knowledge on programming or mathematical models. The use of this model in research will be improved by providing modelling results in appropriate formats for further investigations. The first aim of this thesis is to develop a graphical user interface for the model of intrinsic apoptosis execution. For this purpose, the model code needs to be revised for its usability with a graphical user interface. The revised code has to be validated by checking its consistency with previously published model results. The creation of a graphical user interface (GUI) that will be well-accepted by the end-user requires the feedback of potential users that has to be integrated into the implementation of a functional prototype of the GUI. Another aim is to test the models prediction efficiency by comparing modelling results with the clinical outcome for a big patient set. The given Ni240 cohort consists of colorectal cancer patients diagnosed with stage II and III. The proteins required by the APOPTO-CELL model were quantified by RPPA for all patients. The available raw data of RPPA analysis need to be converted into absolute protein concentrations, which can be handled by the implemented APOPTO-CELL GUI. For every patient of the Ni240 cohort, the model prediction for treatment outcome has to be compared with the recorded clinical outcome in order to get the model s prediction efficiency. 36

39 Figure 1-4: Simplified scheme for the integration of APOPTO-CELL into a clinical diagnostic environment. A patient suffering from a disease will consult a doctor. Tumour tissue will be taken for diagnosis either by biopsy or by surgery. Protein concentrations of the tissue have to be determined in order to run the APOPTO_CELL model. Model predictions about chemotherapy responsiveness have to be returned to the consultant, who will use the model findings for a personalized treatment finding. The model supported treatment option will at least prolong the time to recurrence of the disease or even cure the patient. 37

40 2. Material and Methods 2.1. APOPTO-CELL: a mathematical model implementation of the apoptosis execution pathway The APOPTO-CELL model was published in 2006 by Rehm et al. and consists of 53 reactions, 19 reaction partners and 74 reaction parameters. As already mentioned in the introduction, the model predicts observed apoptosis execution kinetics in HeLa cells. Figure 2-1 shows a diagram of the modelled protein interactions. Because the model implements the apoptosis execution after MOMP, the protein concentrations of procaspase-3, caspase-9, XIAP, SMAC and Apaf-1 have to be provided as model input. For the apoptosis model, the formation of the caspase-9 activation platform called apoptosome was simplified. Apoptosome formation is characterized by the release of cytochrome-c, Apaf-1 oligomerisation and the activation of procaspase-9. Autoproteolytic processing of activated caspase-9 results in caspase- 9(p35/p12).The resulting activated caspase-9(p35/p12) will cleave and activate procaspase-3. Active caspase-3 is implemented to cleave substrate with a DEVD amino acid sequence, which can be compared with experimental lab data. Furthermore it is implemented that the caspase-3 dependent feedback loop on the apoptosome cleaves caspase-9(p35/p12) into caspase- 9(p35/p10). Finally, the model implements the following protein interactions of XIAP. The cleavage of XIAP by caspase-3 results in the XIAP fragments BIR3 and BIR1-2. These fragments are, as well as XIAP itself, implemented with an inhibitory function on caspase-3 and activated caspase-9(p35/p12). BIR1-2 thereby inhibits caspase-3 and BIR3 interacts with caspase-9(p35/p12). Caspase-9(p35/p10) can be inhibited by XIAP and its fragments. XIAP and its cleavage fragments are antagonized by SMAC. The model outputs are traces over time for the different proteins involved in this pathway. The model output, which tells the user whether a cell undergoes apoptosis or not, is the caspase-3 substrate cleavage trace. This trace shows which percentage of the caspase-3 substrate is cleaved and this subsequently gives information on the progress of apoptosis execution. Figure 2-2 shows an example image of the calculated substrate cleavage for the time of 38

41 simulation. It was shown in imaging experiments that cells, which show less than 25% substrate cleavage display no apoptotic morphological changes at all (Rehm et al, 2006). For that reason, a threshold of 25% of calculated substrate cleavage was chosen to decide whether a cell or tissue is capable of apoptosis. The time of simulation is set to 300 minutes because effectorcaspase activation is usually a rapid process (Rehm et al, 2002). 300 minutes is therefore a sufficient duration to model the apoptosis execution after MOMP. 39

42 Figure 2-1: Schematic representation of the intrinsic apoptosis pathway downstream of MOMP as implemented in the mathematical model. Dashed lines show inhibition interactions, full lines activation. The activation of procaspase-9 was simplified in the model by an input function. This input function represents the cytochrome-c release and the Apaf-1 oligomerisation with the activation of procaspase-9. Activated caspase-9 can activate procaspases-3 and -7, which then start to cleave cellular substrates and activate procaspases-3, -7 and -9 further. Caspases can be inhibited by XIAP, which can be inhibited by mitochondrial released SMAC. Although XIAP can be cleaved by caspases-3 and -7, the inhibiting function of the XIAP fragments Bir1-2 and Bir3-RING is not lost. -7 is not part of APOPTO-CELL, the model implementation focuses on -3. Original figure taken from Rehm et al (2006). 40

43 Figure 2-2: Example of a substrate cleavage trace in cells undergoing apoptosis produced by APOPTO-CELL. The blue line shows the percentage of cleaved substrates over time. The more caspase-3 is activated, the more substrate will be cleaved and apoptosis will be executed successfully. Based on the final value of this trace, it will be decided whether the tissue is apoptosis sensitive or not. A threshold value of 25% substrate cleavage was determined in HeLa cells to be required to successfully undergo apoptosis (Rehm et al, 2006) and is highlighted as a line parallel to the x-axis in the figure. 41

44 2.2. Programming environment All implementations of the model and the graphical user interface were performed with MATLAB version 8.0 (release 2012b) from MathWorks, USA. MATLAB was used on a PC with a 64 bit system of Microsoft Windows 7 (Microsoft Windows version ). Implementation of the graphical elements of the GUI was performed with the MATLAB graphical user interface builder function guide Experimental data/ clinical cohort Remodelling of cell line responses In section the model was tested for the ability to predict the effector caspase activation in different cell lines. Their concentrations for the required apoptotic proteins are listed in Table 2-1. The quantification of the protein concentrations is described in Rehm et al (2006) and Schmid et al (2012). 42

45 Table 2-1: Protein concentrations of the apoptotic proteins caspase-3, caspase-9, XIAP, Apaf-1 and SMAC for the 10 cell lines used in Schmid et al (2012). The protein concentrations listed for HeLa were already quantified in Rehm et al (2006). Cell line [µm] Procaspase-3 Procaspase-9 [µm] XIAP [µm] Apaf-1 [µm] SMAC [µm] HeLa HeLa XIAP Adv DLD DLD XIAP 0/ HCT HCT116 XIAP 0/ HCT116 SMAC -/ MCF MCF7/C LoVo HT

46 Clinical data cohort Ni240 For the application of the APOPTO-CELLup model to clinical data, a data set from the NI240 trial at the Queens University Belfast was used. This cohort is part of the FP7 APO-DECIDE project and consists of 254 cases of stage II and III colorectal cancer patients. All patients underwent curative resection. The study, with a median follow up of 72 months, included a control group who did not receive chemotherapy and a group who received 5-FU-based chemotherapy. For every patient a formalin-fixed paraffin-embedded section of tumour and matched normal tissue was collected. The aim of the APO- DECIDE project is to compare the mathematical modelling outcome that is based on protein concentrations in the patient tissue with the clinical outcome of the patient to test if the model can be used to predict treatment outcome. Protein concentrations were calculated from RPPA staining results. RPPA was performed successfully by an APO-DECIDE project partner for 220 patients of the data set. Table 2-2 shows an overview of the clinical characteristics for these 220 patients. 44

47 Table 2-2: Clinical characteristics of patients that were simulated by the APOPTO-CELLup model. Gender Stage II Stage III Stage IV total n median Age (years) Chemotherapy Median survival time(months) male female yes no recurrence free survival Tumour location overall survival Caecum Ascending colon Transverse colon Descending colon Sigmoid colon Rectosigmoid Rectum Synchronous

48 2.4. Analysis methods for patient data Kaplan Meier plots In 1958 Kaplan and Meier presented a new tool for processing incomplete study observations (Kaplan & Meier, 1958). Kaplan Meier plots are usually used in studies to investigate survival times for patients with diseases such as cancer. This method allows demonstration of not only just the time to death for a patient but additional information such as time to recurrence of a cancer. The advantage of this kind of analysis is that even patients who left the study for other reasons than the monitored disease can be included for evaluation. On the x-axis the time to the specified event is plotted, which is in this case recurrence. The y-axis shows the cumulative survival, which is the number of patients expressed as a percentage. Day zero on the x-axis represents the start point of the observation time. In this thesis for the NI240 cohort, day zero is the date of surgery. Every time a patient shows recurrence, the curve will drop down one step; if a patient leaves the study without showing the event of interest, it will be marked by a vertical dash but the curve will not drop down. Reasons for leaving the study can be end of following up time, moving away or dying because of other reasons than the observed disease. This is called censoring. If another patient has recurrence after a patient was censored, the curve will not drop down one small step for this patient but it will drop down to the percentage of remaining patients in the study.(jager et al, 2008) ROC curves Receiver Operating Characteristics (ROC) analysis for diagnostic tests was introduced by Metz in 1978 (Metz, 1978). Sensitivity and specificity are commonly used values to access the performance of a diagnostic test, which classifies the result as positive (=sick) or negative (=healthy). Sensitivity is the ratio of people who are sick and were classified as having the disease, whereas specificity gives the ratio of people who are healthy and were classified as not suffering from the disease. ROC uses these values to visualize the diagnostic performance of a test: the true-positive rate (= sensitivity) is plotted against the false-positive rate (=1-specificity). If the test 46

49 result is not binary with a Yes or No outcome but a range of values, for example a numeric value between 0 and 100, ROC plots the true positive rate and the false-positive rate for different cut-off values of the test result. An optimal test with high sensitivity and high specificity would have a curve through the upper left edge of the panel, which means the test is significantly different from a random test, which would have a diagonal as curve. As sensitivity and specificity can have maximal values of 1, the area under the curve will be maximal 1. For a random test without a clear threshold, the area under the curve, which is the diagonal, will be 0.5. Therefore, the area under the ROC calculated curve for the investigated test should be more than 0.5 for a test with a clear cut-off. (Zweig & Campbell, 1993) To find the best threshold value, sensitivity and specificity are added for every investigated threshold point. The threshold with the highest result for the addition will be the best value for an optimal differentiation between a negative and a positive test result. 47

50 Figure 2-3: Example Kaplan Meier curve to analyse patient survival. On the x-axis the time to the observed event is plotted, whereas the y-axis shows which percentage of patients remains. Every time a patient shows the event of interest, the curve drops one step down. Patients that left the study and did not show the event are marked with a tick. These patients are called censored patients. Reasons for leaving the study can be different. Either their observation time ended or they can no longer be observed. This happens if they moved away or died for other reasons than the one observed. 48

51 Figure 2-4: Example of a ROC curve to analyse performance of a diagnostic test. The x-axis of a ROC curve shows the false positive rate (=1-specificity), while the y axis the true positive rate (sensitivity) of a diagnostic test. ROC analysis tries to find the best cut-off value for the classification between a positive and a negative result. If the test outcome is in a binary format, the curve will have only one point with one pair of a true positive and a false positive rate. If the test outcome is a range of values, which have to be classified into a positive or a negative test result by a threshold value, the curve will have several points with different true positive and false positive rates. The optimal threshold value for a good test result classification will be the point with the highest value for the addition of sensitivity and specificity. 49

52 2.5. Statistics Correlation analysis between the results of reverse phase protein arrays (RPPA) analysis and the Western Blot determined protein concentrations for the cell lines HeLa, MCF-7, HCT-116 Smac -/- and HCT-116 XIAP -/- was performed in Excel, using Pearson`s correlation. Analysis of the RPPA patient data and the APOPTO-CELLup model outcome was done in SPSS IBM, USA. To test the patient groups for significant differences in the Kaplan-Meier survival plots, the log-rank test of SPSS was applied. 50

53 3. Results 3.1. Refactoring of the APOPTO-CELL code Introduction Using complex computational, mathematical models usually requires programming knowledge. Therefore, using a mathematical tool by someone other than a systems biologist requires an interface, which enables the usability without programming knowledge. For instance in the case of using a mathematical model in a clinical diagnostic environment, there has to be an interface between the clinician interested in the model predictions and the mathematical model calculations itself. This interface will be a graphical user interface (GUI), in other words a software tool that can be used easily without knowledge about mathematical modelling. Moreover, this GUI cannot only ease the use of the model for users without modelling knowledge but it can also make the usage easier for the modeller. The GUI can provide several functions that will be required regularly by researchers using the model to analyse samples. By using a graphical user interface, they can choose one of the several functions depending on the purpose of their work without changing the code. Therefore, one of the aims of the thesis is the development of a graphical user interface for APOPTO-CELL. In order to do so, the APOPTO- CELL code has to be refactored to allow the integration of the model into a GUI environment. APOPTO-CELL was originally developed in a procedural way for use in a research environment and to test different features of the intrinsic pathway. The first reason why the procedural code has to be refactored is that the MATLAB code has to be modularised into smaller functions that can be called in response to GUI interactions by the user. For instance, the code for figure generation has to be separated from code that implements the model or code that defines functions for interaction with the user like storing modelling results. This has the advantage that the code provides a better expandability for future extensions of the GUI functionalities. Another reason for refactoring the code is the use of global variables in the original APOPTO-CELL code. Global variables are variables that can be overwritten in all functions within the program. This can lead to wrong 51

54 assignments of parameters. For example the global variable G is defined in a program with function A and function B. Function A uses the values written to G. Function B uses a local variable called g, but accidentally g was written as G after declaring g. When function B is executed before function A, the values of G will be changed, because every function has the right to read and to write to global variables. If the function A is executed, function A might return an error, although the mistake in the code is in function B. It is hard to identify the mistake caused by a global variable, because it might occur in a part of the code where it is not expected to be. For big codes it is even harder to identify the code line that causes troubles in the program (Heller, 2002). Therefore, maintenance of a code with local variables is easier and potential error sources are reduced because the use of the local variables is more transparent. To avoid inconsistencies in the code, it is recommended to define local variables that are only defined for some specific functions of the program and are handed over from function to function and to avoid by any means global variables (Stefanov, 2008). The original code was intended to be flexible in order to investigate several intrinsic pathway modes. This was achieved by defining global arrays variables, which contain for instance the stoichiometric information about the reactions and the reaction kinetics. Since all the parameters and interaction information are stored in different arrays that have to be multiplied first, it is hard to comprehend the interaction system of the proteins, which reduces the expandability of the model. Moreover, the use of global variables S, vel, kp, km, and v hinder a modularisation of the APOPTO-CELL code. Aside of that, the use of global variables entails a very complex model code, which leads to speed losses. Another consequence is the bad maintainability of the code Aim of this chapter In order to create a graphical user interface for APOPTO-CELL, the previously used MATLAB code of the intrinsic pathway model has to be refactored and simplified to be integrated into the GUI development system of MATLAB. 52

55 Restructuring of the APOPTO-CELL model code for use with a graphical user interface The code, which is used for the GUI, is based on the APOPTO-CELL code used for the publication of Schmid et al (2012) and has, for the reasons outlined, to be refactored. The variables, which were changed from global to local restrictions to avoid unwanted changes by other program functions, were the arrays containing the initial protein concentrations c and the rate constants for the forward (kp) and backward (km) reactions. These parameters are in the refactored code called APOPTO-CELLup replaced with ic, k and k_. Table 3-1: Comparison of coding for rate constants and concentrations within different code versions. Variable containing protein concentrations Array containing rate constants for forward reactions Array containing rate constants for backward reactions APOPTO-CELL c(1) = 0; kp( 1) = PCAS3*km(1);... km( 1) = ;... APOPTO-CELLup ic(1) = 0; k(1)= ;... k_(1)=0.0039;... The more important global variables that were restricted to local access are S and vel, which are components of the calculation of the protein profiles over time. The code for calculation of protein profiles over time is implemented in APOPTO-CELL as followed: change of protein concentration is dependent on the reaction velocity. Reaction velocity v is calculated as multiplication of reaction kinetics km and kp and the concentrations c of the involved proteins. If there is plenty of protein A, but no protein B, proteins A and B cannot react to the protein or protein-complex C. The information about the protein concentrations required is stored in the helper array vel. vel contains the 53

56 indices for the proteins in the concentrations array. A reaction can consist of forward and backward reaction. As there are no reactions with more than three reaction partners for a forward and a backward reaction, the vel array has the size of [number of reactions, 6]. The first three elements are the reaction partners for the forward reaction and the last three the partners for a backward reaction. The concentrations of the forward partners will be multiplied by kp, the concentrations of the backward partners by km. Information about the reaction stoichiometry is stored in the array S. S contains the information in which of the 53 reactions the reaction partner, which is one of 19 proteins or protein complexes, is included and which amount of the protein will react. It gives also information whether the protein or protein-complex will accumulate or will be degraded. For that reason, the total change of protein concentration is calculated as S*v, combining the information about reaction stoichiometry and reaction velocity. The following is an example of the explained: Reaction 3 in the APOPTO-CELL model code describes the production of caspase-3 by the cleavage of procaspase-3 by caspase-9: caspase-9 + procaspase-3 caspase-3 + caspase-9. caspase-9 concentration will not change in this reaction because of the catalytic activity of caspase-9. This reaction is a forward reaction with the forward constant kp(3) = 6 and the backward constant km(3) = 0. As caspase-3 production is dependent on the concentrations of available caspase-9 and procaspase-3, vel is defined for this reaction as vel(3) = [ ]. where 2 is the index of caspase-9 in the concentration array, 1 is the index of procaspase-3 and 25 is a dummy value, which defines the numerical value of 1. Velocity v is now calculated as v(3) = kp(3)*c(2)*c(1)*1 + km(3)*1*1*1. This reaction will increase the concentration of caspase-3 and decrease the concentration of procaspase-3. For this reason, the stoichiometric array S will 54

57 have the entry +1 at the place for caspase-3 in reaction 3 and the entry -1 at the place for procaspase-3 in reaction 3. The concentration change res for all involved proteins is then calculated as res = S*v. Instead of using the two variables S, v and vel that define the reaction partners and the reaction stoichiometry of a biochemical reaction, a new variable is dc is introduced in APOPTO-CELLup. dc summarizes all the different arrays in one array, which has the size of [number of reaction partners, 1]. This has the benefit to increase readability and flexibility of the implemented reactions. Array dc contains for the same reaction that was explained above the entries dc(casp3) = +1*kp(reaction 3)*iC(casp9)*iC(procasp3) dc(procasp3)= -1*kp(reaction 3)*iC(casp9)*iC(procasp3). In the original APOPTO-CELL code, the global variables S and vel were changed within the code for test purposes. If this code would be used with the GUI and would therefore be extended and modularised, errors could occur. In the new APOPTO-CELLup version, there are two modularised functions. The function newapotocell defines the variables for protein concentrations and kinetic rates and the subfunction aposim combines these variables in the variable dc, presenting the apoptosis model. In the refactored code all interactions for a protein are stored in one place and can be understood better than combinations of several arrays. Another advantage of refactoring is the reduction of code. The number of code lines was reduced from 243 lines in APOPTO-CELL to 29 in APOPTO-CELLup. The original APOPTO-CELL code can be seen in supplement

58 Conclusion In order to extend the APOPTO-CELL model with a graphical user interface, the code had to be refactored and simplified. The originally global variables for kinetic rates and protein concentrations were restricted to local access. Additionally, the actual apoptosis model implementation was changed from several arrays that have to be combined in the APOPTO-CELL code to one array containing all the information in the APOPTO-CELLup code. Modularised functions for the definition of modelling parameters and the model calculation itself were created in APOPTO-CELLup. The APOPTO- CELLup version of the intrinsic apoptosis pathway has an improved readability and comprehensibility. Moreover, avoiding global variables facilitates the maintenance of the code and the possible extension of the code in the future. The code defining the model calculations could be reduced by about 10 times. 56

59 3.2. Validation of the APOPTO-CELLup model code Introduction The APOPTO-CELL model was successfully applied to predict apoptosis execution in cell lines after MOMP (Schmid et al, 2012). A publication by Hector et al (2012) showed that APOPTO-CELL is also capable of predicting treatment outcome of stage II and III colorectal cancer patients based on the protein expression levels required for the model (hector gut 2012). Additionally the model was extended to suggest co-treatments such as SMAC-mimetics, proteasome inhibition and drugs that directly activate caspase-3, based on their efficacy to sensitize tumour cells for individual patients (Hector et al, 2012). These three co-treatments are implemented in the APOTO-CELL code in the manner described. To simulate the effect of SMAC-mimetics, the initial concentration of 0 μm SMAC available in cytosol was raised by the SMAC-mimetic concentration. The effect of proteasome inhibition, which leads to a reduced degradation of proteins, was simulated in the APOPTO-CELL model by switching off proteasome dependent protein degradation. Therefore degradation rates were altered. The treatment with caspase-3 activation drugs is implemented via an additional reaction partner that processes and activates caspase-3. Settings for these targeted therapeutics were the initial cytosolic SMAC concentrations of 0.1, 0.25 and 0.5 μm, proteasome inhibition was assumed to be either 10%, 50% or 100% and initial caspase-3 activator concentrations were set to 0.01, 0.05 and 0.1 μm (Hector et al, 2012). Since the first publication of the APOPTO-CELL model in 2006, the model code has changed as well as the programming environment. As already mentioned, the model code was extended in 2012 by the modelling of targeted therapeutics (Hector et al, 2012) and it was refactored for the use with a GUI. Moreover, the programming environment MATLAB was updated since It had therefore to be ensured that there were no major changes in the performance of MATLAB intern algorithms and that the modelling results were still the same as in the first model version. 57

60 In the following, four representative figures were chosen from three publications to be reproduced with the APOPTO-CELLup code in MATLAB R2012b in order to show that the currently used code APOPTO-CELLup produces the same results as the old codes used with previous MATLAB versions. These figures show the modelling of the intrinsic pathway in HeLa cells (Rehm et al, 2006), the apoptosis execution downstream of MOMP for nine additional cell lines (Schmid et al, 2012) and finally the predictions of patient responses to chemotherapy and targeted therapeutics (Hector et al, 2012) Aim of the chapter Aim of this chapter is to show that the refactoring of the APOPTO-CELL model code and the use of an updated programming environment have not changed the model behaviour when compared with the old version of the MATLAB code APOPTO-CELLup can reproduce the protein profiles of intrinsic apoptosis pathway in HeLa cells It was shown that caspase-3 activation is a fast process that occurs after MOMP induced Cyt-C release (Rehm et al, 2002; Tyas et al, 2000). Rehm et al (2006) developed the computational model APOPTO-CELL that is capable of predicting the apoptosis execution in HeLa-cells. The model predictions were verified by single cell experiments. In the paper, a mathematical model of apoptosis execution reproduces protein profiles over time for different proteins (Rehm et al, 2006). Inputs of the model were the absolute protein concentrations of HeLa cells which were previously determined by Western Blots (see Table 2-1 in section 2.3). In Figure 3-1 to Figure 3-3, the left hand side shows the published figures of APOPTO-CELL, whereas the right hand side shows the successful reproduced figures with APOPTO-CELLup. Figure 3-1 demonstrates the consequences of MOMP. Figure 3-1A visualizes the simplified function for apoptosome formation. The model output showed also that SMAC release 58

61 from mitochondria led to a constant increase of cytosolic SMAC, which was impaired by the binding of XIAP (Figure 3-1B). Moreover, the decrease of XIAP owing to its binding to caspases and its cleavage into the BIR1-2 and the BIR3 fragments could be remodelled in Figure 3-2. Protein profiles for the XIAP fragments showed rapidly increasing concentrations over time (Figure 3-2A). Figure 3-2B showed that with increasing BIR concentrations also the concentration for BIR bound to caspases increased. Final execution of apoptosis in HeLa cells is shown in Figure 3-3. Within 15 minutes after Cyt-C release the amount of inactive procaspase-3 decreases. At the same time, protein levels for active caspases (Figure 3-3A) and the amount of cleaved substrate (Figure 3-3B) rise. Figure 3-1: Published protein profiles (left) of (A) apoptosome formation and SMAC release could be reproduced with APOPTO-CELLup (right) as well as (B) SMACs interaction with XIAP. Original pictures were taken from (Rehm et al, 2006) 59

62 Figure 3-2: Published protein profiles (left) of (A) XIAP and its binding to caspase-3 and -9 could be reproduced with APOPTO-CELLup (right) as well as (B) the cleavage of XIAP and the binding of BIR-fragments to caspases. Original pictures were taken from (Rehm et al, 2006) Figure 3-3: Published protein profiles (left) of (A) activation of caspases could be reproduced with APOPTO-CELLup (right) as well as (B) modelled substrate cleavage. Original pictures were taken from (Rehm et al, 2006) 60

63 APOPTO-CELLup can reproduce the modelled influence of XIAP over-expression on cell death in HeLa cells XIAP has been shown to be a key regulatory protein of the apoptosis execution pathway (Harlin et al, 2001; Wilkinson et al, 2004). High XIAP expression levels can lead to resistance to treatment in cell lines (Ndozangue- Touriguine et al, 2008) and in patients (Schimmer et al, 2009). By varying the initial protein concentrations of the apoptotic proteins SMAC, procaspase-3 and XIAP, Rehm et al. investigated computationally the influence of single proteins on apoptosis execution mediated by the mitochondrial apoptosis pathway. The systems analysis predicted that XIAP expression levels regulate the rapid all-or-none apoptosis execution in HeLa cells but SMAC as XIAPantagonist only plays a minor role. These predictions and the according experimental results are illustrated in two figures of the paper by Rehm et al (2006). These are reproduced with APOPTO-CELLup here as Figure 3-4 and Figure 3-5. The first figure shows substrate cleavage for HeLa in a biologically relevant altered range of XIAP concentrations (from 0 to 0.5 μm). The range between 0.15 μm and 0.30 μm initial XIAP will differentiate between total substrate cleavage and insufficient substrate cleavage for a successful apoptosis execution. This means if there is only a small amount of the caspase substrates cleaved there is an impairment of the apoptosis execution and no cell death occurs. The APOPTO-CELLup code can reproduce the figure published in the paper as seen in Figure 3-4. Even though Figure 3-4 could not be rotated into the exact angle as the published figure, it can be seen that the critical initial concentration of 0.3 µm XIAP reaches only about 20% of final substrate cleavage. Lower XIAP concentrations than 0.3 µm cannot prevent a cell from undergoing apoptosis after MOMP. Concentrations beyond the 5-fold of the original XIAP concentration can save a cell from undergoing cell death by inhibiting caspase activity. 61

64 Figure 3-4: Altering initial XIAP concentrations showed minor substrate cleavage for XIAP concentrations above 0.3 μm. The published model predictions (left) could be reproduced with APOPTO-CELLup (right). Original image taken from Rehm et al (2006) Experiments with HeLa cells overexpressing XIAP were carried out in order to validate the model predictions. The modelling of the critical region around 0.30 μm, a 4.5 fold of the normal XIAP concentration in HeLa cells, was reproduced in Figure 3-5. The modelling results could be confirmed by real time imaging of apoptosis execution in HeLa cells overexpressing XIAP. Figure 3-5 shows the substrate cleavage for the specific concentrations of 0.26, 0.28 and 0.30 µm initial XIAP. This published figure could exactly be reproduced. Figure 3-5: Published model predictions (left) for the specific XIAP concentrations were approved experimentally and could be reproduced with APOPTO-CELLup (right). Original pictures taken from (Rehm et al, 2006). 62

65 Prediction of treatment responsiveness in different cell lines with APOPTO-CELL was reproduced with APOPTO- CELLup Schmid et al (2012) showed that APOPTO-CELL is not restricted to HeLa, as it can successfully resemble the kinetic response to treatment that leads to apoptosis execution. Treatment response could be predicted correctly in nine additional cell lines. For instance, the model correctly predicted that the colorectal cancer cell lines LoVo and HCT116 Smac -/- as well as the modified HeLa cell line that over-expresses XIAP had impairment of apoptosis execution subsequent to MOMP. The average protein concentrations for cells of the respective cell lines were given as shown in Table 2-1 in section 2.3. The apoptosis execution for these model inputs are plotted as dashed lines in the original figure and could also be re-plotted with the APOPTO-CELLup code. The original figures include also a grey area, which represents the plausible kinetics for single cells of the entire cell line population. These plausible kinetics are gained by the average concentration of the respective protein plus or minus one S.D. and are not further investigated here. The cell lines LoVo, HCT116 Smac -/- and HeLa XIAP Adv were predicted by APOPTO- CELLup with the identically impaired caspase activation (Figure 3-6). Other cell lines were predicted as having a rapid capase activation resulting in successful apoptosis execution. APOPTO-CELLup predicted the cell lines HT- 29, HeLa, MCF-7/C3, HCT116, HCT116 XIAP 0/, DLD-1 and DLD XIAP 0/- with the same rapid apoptosis execution as they were shown in the paper (Figure 3-7). 63

66 Figure 3-6: Prediction of the response to MOMP for different cell lines can be reproduced with APOPTO-CELLup. The dashed lines represent predictions for average protein concentrations of the particular cell line, whereas the grey area represents the plausible kinetics (average protein concentrations + or S.D.) for cells of the entire population of this cell line. The newly created figures by APOPTO-CELLup (blue dashed line without grey area) show the same impaired substrate cleavage for the 3 cell lines as the master depictions. Original panels were taken from Schmid et al (2012). 64

67 Figure 3-7: Prediction of the response to MOMP for different cell lines can be reproduced with APOPTO-CELLup. The dashed lines represent predictions for average protein concentrations of the particular cell line, whereas the grey area represents the plausible kinetics (average protein concentrations + or S.D.) for cells of the entire population of this cell line. The newly created figures by APOPTO-CELLup (blue dashed line without grey area) show the same rapid substrate cleavage for the 7 cell lines as the master depictions. Original panels were taken from Schmid et al (2012). 65

68 Prediction of targeted treatment strategies for colorectal cancer patients could be reproduced by APOPTO-CELLup By using APOPTO-CELL Hector et al. showed in 2012 that for a patient set of 30 patients the likelihood of colorectal cancer tumours to undergo apoptosis decreases with advancing disease state. Moreover, the systems model APOPTO-CELL predicted correctly the outcome of chemotherapy for these colorectal cancer patients in most of the cases. The additional modelling of individual patient responses to novel apoptosis-inducing therapeutics pointed out markedly different inter-individual responses. There are three approaches for the targeted therapeutics: freeing caspases from their inhibitor XIAP by SMAC-mimetics, decreasing protein degradation through proteasome inhibition and activation of caspase-3 independent of upstream caspase-9 by procaspase-3 activators. XIAP antagonists were predicted by the old APOPTO-CELL implementation to be effective in most of patients, whereas proteasome inhibition and procaspase-3 activators affected only some patients. Figure 3-8 below shows the seven patients of the patient set, who were predicted as not benefitting from chemotherapy treatment alone. Moreover, it shows the predicted influence that targeted therapeutics have on their treatment responses (Hector et al, 2012). The settings for the modelling of targeted therapeutics are shown in chapter The implementation of APOPTO-CELLup can reproduce these published results (Figure 3-8). In all of the seven patient cases, SMAC-mimetics are predicted to improve the responsiveness of patients predicted with bad clinical outcome. Caspase-3 activation will also enhance apoptosis execution for many patients, whereas proteasome inhibition is only in some cases a good alternative treatment option. 66

69 Figure 3-8: Published effects of alternative, targeted therapeutics showed the same classification of responders and non-responders for different treatment options as the APOPTO-CELLup modelled treatment responses. The upper part of the figure shows the predictions modelled with the APOPTO- CELLup version whereas the bottom shows the published figure produced with the old APOPTO-CELL code. Because there was no explicit assignment of the numbering to the available patient IDs obtainable, it is not absolutely guaranteed that the correct patients were chosen. These patients were predicted with a bad clinical outcome in the first place. For that reason, additional targeted therapeutics alternatives were investigated for them. SMAC-mimetics (green bars) can improve treatment responses better than caspase-3 activators (red bars) and proteasome inhibition (blue bars). Original image was taken from Hector et al (2012). 67

70 Conclusion To avoid inconsistent results with previously published data, the new code had to be validated. Predictions of the APOPTO-CELLup code that will be used for the GUI are congruent with published model predictions. This validation step showed that the modelled kinetics of the intrinsic apoptosis pathway in HeLa cells are the same for the original APOTO-CELL version and the APOPTO-CELLup version. Predictions of apoptosis execution in several cell lines could be validated for the code that will be used for the GUI. Moreover, the outcome predictions for co-treatment of patients predicted with apoptosis impairment did not change. Updates in the programming environment did not influence the modelling results of APOPTO-CELLup compared to APOPTO-CELL. Neither did the simplification of the model code. Therefore, the APOPTO-CELLup code can further be used for implementation in a graphical user interface. 68

71 3.3. Implementation of a graphical user interface for APOPTO-CELLup to facilitate its usage in a cancer research and diagnostic environment Introduction Developing software is a dynamic and repetitive process. It starts with defining requirements, followed by the implementation of a prototype, validating the first version, then constantly improving the implementation, and further testing it. However, it is very important to include the end user in the developing process. For the successful usage of the APOPTO-CELLup model, it is very important to know how the user will integrate the program in his/her workflow, how data are available to be processed by the model and which kind of results have to be returned. User feedback has therefore to be involved in the development from the beginning. This is a common workflow that was also used for other clinical software solutions (Cunningham et al, 2012). Currently, there is no software available to predict chemotherapy responsiveness of cancer cells based on systems modelling. The clinical use of APOPTO-CELL is an innovation in cancer treatment as well as the prediction for possible co-treatments Aims of chapter Currently, there is no graphical user interface available for the APOPTO-CELL model that would allow the use of the mathematical apoptosis model without a modelling or programming background. However, it was shown that the model would be of benefit in a clinical diagnostic environment based on its capability of predicting chemotherapy responsiveness (Hector et al, 2012). The aim of this chapter is to develop a functional prototype for a graphical user interface that can be used in a clinical and in a research setting. Software requirements have to be defined and user feedback obtained to implement an accepted graphical user interface. 69

72 GUI development for the application of APOPTO- CELLup in a research environment Requirement analysis The first step for every software development should be the specification of the software requirements. A requirement analysis ensures that the software developer understands the needs of the end-user and the user will accept the developed application (IEEE, 2009). Requirements of a software application can be categorized. The first category is the functional requirements that describe what the software is supposed to do. The second category handles non-functional requirements that describe how the system will do what it is supposed to do. Defining possible workflows of how the tool can be used supports the identification of features and properties. A common tool to visualize software and its interaction with the user is the unified modeling language (UML). UML has a wide range of diagrams for visualization of static code structure (structure diagrams) or dynamic behaviour of a system (behaviour diagrams)(omg, 2011). One kind of behaviour diagrams are use case diagrams. In the following use case-like diagrams will be used to show the possible interactions of the APOPTO-CELL model with a research and a clinical environment Suggested workflow for use of APOPTO-CELLup in a research setting The first use case of the apoptosis pathway model targets the research field, where it can be used to analyse tumour samples for validation of the model in clinical trials. It is quite common in biomarker research to use high throughput methods that processes over 100 samples. In this context, it is suggested to have an automated data input mode for the APOPTO-CELLup application. The workflow for the application of the model in a research setting can have the following structure, which can also be seen in Figure 3-9. Patient tissue samples have to be stained, the staining has to be analysed and scored. The results of the protein staining will be written to an Excel-file, which is a common file format in the field of research because it is compatible and 70

73 available for most researchers. Out of the staining file, a second Excel-file can be generated containing protein concentrations and the tissue IDs. The researcher can then load the file containing the protein concentrations and the tissue sample IDs and start the calculation. After finishing the computations, the model results will be stored to another Excel file that can be used for further investigations. For research projects including many projects partners, it is common to share the results in databases. Therefore, the modelling results can be combined with other analysis results and can be fed to a database. 71

74 Figure 3-9: Suggested workflow for the use of APOPTO-CELL in a research setting for analysing patient cohorts. For application of APOPTO-CELL in research, a higher number of patients has to be analysed at once than in a clinical setting. The patient tissue has to be stained and the staining has to be analysed, which might result in a scoring of the tissue samples. The analysis result will be stored in an Excel file. The staining for the proteins has to be converted to protein concentrations that can be used by APOPTO-CELLup. These protein concentrations will be stored in an Excel sheet as well. This file can be loaded into the APOPTO-CELL GUI in order to calculate chemotherapy responsiveness for the patients. The modelling results will be returned in the Excel file format. If data belong to a project that includes a database, the resulting file can be combined with staining results if necessary and can be imported into the database. 72

75 Functional Requirements Functional requirements describe what a software tool is supposed to do. The GUI for APOPTO-CELL should be used in a research environment to investigate the apoptosis pathway of many patients in order to find for example new targets to improved treatment and to predict treatment outcome. The requirements, which are defined in the following text, will be marked with a consecutive numbering starting with F1. As the workflow presented in Figure 3-9 revealed, there should be the possibility to process a data set containing up to hundreds of patients. The suggested workflow showed also that for bigger data sets in research, it is good to import a file that can be read by the GUI application of APOPTO- CELL and can be processed automatically. For that reason, the graphical user interface should have an automated data input mode (F1). This mode will give the user the possibility either to load an already existing file that contains the protein concentrations (F2) or to create a new file (F3). For every patient an ID should be given in order to create an unique identifier in case for later analysis (F4). Moreover, the drawn up workflow showed that Microsoft Office Excel is a common file format, which should be used for input and output files of the GUI (F5). For the research setting, there is not only the outcome prediction for a patient of interest but also detailed modelling information about the interaction of apoptosis pathway members (F6). This additional information should enable researchers to investigate protein interactions and the outcome for certain patients in detail. The output should therefore save the calculated traces for the most important proteins caspase-3, caspase-9 and cell substrate cleavage. Nevertheless, besides the detailed information a summary of modelling predictions will be important as well e.g. to compare modelling predictions with the recorded clinical outcome of a patient (F7). 73

76 Non-functional Requirements Non-functional requirements describe how a software tool is supposed to work. Identified non-functional requirements for the GUI are marked in the text below with an ongoing numbering starting with NF1. The mathematical APOPTO-CELL model for example requires the absolute protein concentrations of the five apoptotic proteins caspase-3, caspase-9, XIAP, Apaf-1 and SMAC. Therefore, these protein concentrations have to be provided by the user. The unit of the concentrations has to be in µm (NF1). For automated data processing, an input file has to contain the protein concentrations of a patient and the associated patient ID in a specific order (NF2). Defining a standardized order for the elements of the input file will prevent confusion within different users comparing their modelling inputs and outputs. Without a given input structure for the prediction tool, every user will have his own preference for the sequence of protein concentrations which can differ from the sequence of the other user. The GUI will provide this standard. If the user has to create a new input file, the software should give a template file that must be used. Before the calculation is started, the program has to check whether for each patient ID all protein concentrations are in a numerical format and complete, because otherwise the model cannot be executed (NF3). Model features were already explained in Material and Methods (chapter 2), nonetheless they will be described here again to clarify the next requirements. The output of APOPTO-CELL is the estimation whether the tissue with the entered protein concentrations is capable of apoptosis. This estimation is based on whether 25% of cell substrate is cleaved after 300 minutes. Low substrate cleavage indicates that apoptosis will not be executed successfully. These values were determined in Rehm, et al If the model predicts a patient as not capable of apoptosis, which means he/she would not benefit from chemotherapy, the influence of targeted therapeutics will be calculated. Currently the model is implemented to test SMAC-mimetics, procaspase-3 activation compounds and the inhibition of proteasome. After a successful simulation, the results of all patients have to be stored to a file. This overview 74

77 file, listing all patients from the input file, should contain the patient ID, the entered initial protein concentrations and the model outcome (NF4). The model outcome should include the final value of substrate cleavage and the decision whether the tissue analysed will be capable of apoptosis as Yes / No. In case the prediction was Not capable of apoptosis, the name of the targeted therapeutic that can improve responsiveness should be shown together with the regarding dosage. The overview file will also show the name of the input file, which was read for modelling purposes. This will give the user the possibility to check whether the correct input file was selected. As mentioned above, a researcher might be interested in different protein traces. For that reason, a second output file will be created to store the traces information (NF5). This file will contain the name of the input file. Moreover, information on the patient ID, the value of the final substrate cleavage after 300 minutes and the outcome prediction will provide an overview for every patient on top of the file. To reconstruct the protein profiles, the time points are required as well as the numerical values for the Caspases and the substrate cleavage at the specific time points. The GUI needs also the possibility to reset the input either to start a new calculation or to clear fields after a wrong input (NF6). The prototype of this graphical user interface that can be used in a research setting will be implemented in the same programming environment as the mathematical model, which is MATLAB. To enhance usability of the model, the graphical user interface will be kept as simple as possible. 75

78 Table 3-2: Requirements for a graphical user interface of APOPTO-CELL in a research setting. Functional requirements will be in the list referred to as F, non-functional requirements as NF. Reference number F1 F2 F3 F4 F5 F6 F7 NF1 NF2 NF3 NF4 NF5 NF6 Requirement Automated processing of big data sets Load an existing file Generate a new input file from a template Assign unique patient ID to modelling input File format for GUI input and output: Microsoft Excel Provide detailed information about modelling results for all patients Provide overview of modelling results for all modelled patients Input: Protein concentration of caspase-3,and -9, XIAP, Apaf-1 and SMAC in μm Standardized input file for automated data input Check completeness of input data Output file I: summary of modelling input and output for all patients such as ID, protein concentrations, prediction of apoptosis sensitivity, final value of substrate cleavage, name and dosage of co-treatment that can improve responsiveness Output file II: detailed modelling information for all patients such as protein profiles of the most important proteins caspase-3 and -9 and substrate cleavage, ID, outcome prediction and responsiveness improvement by targeted therapeutics Function to reset input 76

79 First prototype of automated data input mode for the application in a research setting The code of the first prototype will be referred to as APOPTO-GUI 1.0 in the following text. The graphical user interface of APOPTO-GUI 1.0 has two different input modes relating to the two different application environments that were defined in chapter 1.4. The current chapter presents the part of the GUI for the research environment, the automated data input mode. The defined requirements, which are fulfilled by the implementation, are referred to with the reference number given in Table 3-2. The abbreviation F describes a functional requirement, whereas NF describes a non-functional requirement. If the user selects the input mode Read data from file in the upper panel of the window (Figure 3-10), the user can either browse an Excel-file containing the required protein concentrations and patient IDs or generate a new file (requirements F2 and F3 in Table 3-2). For browsing a file, the user has to press the Browse button. This action opens a new browser window, which shows as default only Excel files (F5), but it is also possible to show all files of a folder. Selecting a file closes the browser and sets the file path in the provided field. It is also possible to enter the path manually without browsing. 77

80 Figure 3-10: GUI for automated data input enables user to load a file containing the required data. The GUI for automated data input enables user to browse an existing file via the Browse button or to generate a new file with pre-defined headings via the Generate file button. The simulation can be started by pressing the Calculate button. If the input file has the wrong format, an error dialog will show the user what has to be changed. Reset will set the file path and the file name for Generate file to ---. The GUI after a successful calculation can be seen in Figure If the user has not yet created an input file in the right format, this can be done by entering a file name in the second panel below the browse panel. The new file name must not contain the special characters. / \ : *? < > and ", and must contain least one character or digit. Otherwise, an information message pops up and the user has to enter the name again. Selecting the Generate file button will open a new Excel file with the specified name that contains only the column headers in the required order (requirement NF2, illustrated in Figure 3-11). After filling the file with data to analyse, it has to be saved by the user. Figure 3-12 shows a file that can be used by the GUI for calculation. To start the calculation, the user has to press the Calculate button. 78

81 Figure 3-11: Template file for automated data input in APOPTO-GUI 1.0 The automated data input requires the compliance of a certain order for the data. This order will be provided via the GUI by the template file that opens after the user selected the Generate file button. The first column has to contain the (unique) patient IDs; the following five columns will contain the initial protein concentrations of caspase-3, caspase-9, XIAP, Apaf-1 and SMAC. The concentration unit is each time μm. 79

82 Figure 3-12: Data sheet for automated data input mode of APOPTO-GUI 1.0 (example). This sheet is filled as specified by the template sheet. The protein concentrations are given in μm and the patient IDs are unique for this sheet. A patient ID has to contain at least one character to be recognized as text. Protein concentrations have to be numerical. For every patient entry, all fields defined in the header must be filled. Selecting the Calculate button in the right part of the GUI will start the calculation process. If MATLAB cannot read the chosen file as Excel-file, an error dialog will appear and calculation stops. After successfully reading the file, its content will be crosschecked (NF3). The used MATLAB function that reads the Excel file splits the content of the file into a text and a numeric part. The text array should contain the column headers and the patient IDs, whereas all the protein concentrations should be listed in the numeric array. The first row of the text array consists of the following column header: ID Casp3 [µm] Casp9 [µm] Xiap [µm] Apaf1 [µm] Smac [µm] The first column has to be filled with patient or tissue sample IDs, columns 2 to 6 the numeric concentrations for every ID entity. Empty fields are not allowed. At the end of the calculation process, an overview file of the 80

83 modelling results as shown in Figure 3-14 or a detailed traces file as shown in Figure 3-15 will be created (F6, F7). The overview file gives a summary of the modelling input for all patients and their most important modelling results. The modelling results are composed of the outcome prediction for apoptosis sensitivity, the final value of substrate cleavage and in case of a bad outcome prediction information about the influence of different modelled co-treatments (NF4). The second file contains the results for every patient showing the traces of caspase-3 and -9 and of the substrate cleavage. The assigned patient ID gives together with the outcome prediction and the probable influence of co-treatments an overview about the most important model results (NF5). Finally, a button to open the result file appears on the right part of the window (Figure 3-13). Pressing this button will open the Excel application and show the overview file. When the input is reset by clicking the Reset button (NF6), the result button will hide and the input path will be set to a default value for both options, browsing and generating an input file. 81

84 Figure 3-13: GUI response after model calculation for automated data input. The user can open the created overview file for all patients by clicking the button Open result file. This button will appear for calculation of a browsed file and for calculation of a new generated file. 82

85 Figure 3-14: A summary file contains the model input and a reduced set of calculated output for all patients per run. The first line of the file contains information with the save path of file that contains the initial concentrations. Moreover, the first line contains a timestamp when the result file was created. This timestamp helps to identify the current output file in case an input file was simulated twice. The third row consists of the column headers and beginning from the fourth row patient data are following. If an ID occurs twice or more times, the IDs are expanded with a counting extension to get a unique assignment for later identification (for example ID5_1, ID5_2, ). For every patient or tissue the unique ID, the initial concentrations, the outcome prediction, the final value for cleaved substrate and if necessary, additional information on targeted therapeutics will be stored. 83

86 Figure 3-15: Example output file of APOPTO-GUI 1.0 containing traces of caspase-9, caspase-3 and substrate cleavage for all calculated patients. The second row contains general information about the patient, like the ID the outcome prediction and which targeted therapeutic can improve responsiveness. The third row shows the column headers for the saved traces, the following rows contain the calculated numerical values. Because older Excel versions can only handle 255 columns per sheet, each created Excel file will have 255 columns per sheet although newer versions could have more. If the input file contains too many patients, all entries after the 255 th column will be written on the next sheet, which starts again with a first column for the time steps followed by the protein traces. 84

87 Some settings of the tool can be changed in the options menu on top of the GUI window. First of all, the user can change the simulation time for apoptosis execution. The default value is 300 minutes, but a shorter simulation time might be sufficient for outcome prediction. The next sub-item in the options menu enables the user to change the default input path. This path will be called by the browse function to find an input file. Moreover, the new generated input data file will be stored in the folder defined by this path. The last sub-item of the menu gives the user the opportunity to save a figure of the substrate cleavage trace for every entered entity. This figure will be saved as an Adobe Illustrator file in the format.ai and in the picture format.bmp. Adobe Illustrator files can be used for processing the image for scientific papers, whereas bitmap files can be immediately inserted in text documents. 85

88 Figure 3-16: Example of a figure that can be saved for every patient. Based on the blue substrate cleavage trace the model will decide whether the examined tissue is apoptosis sensitive. Substrate cleavage above the threshold (black line parallel to the x-axis) indicates apoptosis sensitivity, low substrate cleavage indicates treatment resistance (see explanation also in chapter 2.1.) 86

89 User feedback The thesis topic was part of a bigger project with international collaboration partners to improve treatment of cancer patients by predicting treatment outcome based on protein expressions. Dr. Maximilian Würstle, who is a member of our research group, is responsible for the communication with the project partners regarding the modelling data and protein quantifications. His exchange with Optimata, one of the project partners, revealed another need for a GUI for APOPTO-CELL in a research environment. Optimata developed the so-called virtual patient, which is software that predicts the progression of solid tumours with and without treatment. Optimata will perform a higher-level integration that combines data like protein level and gene data, as well as additional data about tumour size and age of a patient. The resulting mathematical model will be used to predict a patient s outcome, e.g. drug responsiveness. They will expand APOPTO-CELLup in order to include EGFR signalling and to integrate APOPTO-CELLup into their virtual patient software in order to improve their software for investigating tumour growth and drug responsiveness in metastatic CRC patients. This research group is not only interested in the most important traces for apoptosis execution, which are caspases-3 and -9 and the substrate cleavage, but they showed interest in storing all of the calculated protein profiles for a patient to exploit if those additional information might improve predictions. This means the already implemented option for saving a traces file has to be expanded in the next step. 87

90 GUI development for the application of APOPTO- CELLup in a clinical environment Requirement analysis The requirement analysis for the use case clinical diagnostic environment will follow the same procedure as already presented for the research workflow (see section ). Definition of workflows was used to find requirements and to implement a prototype for this setting Suggested workflows Another use case of APOPTO-CELLup will be in a clinical environment to improve treatment of patients via predicting the treatment outcome. The consultant will use the model predictions to decide which treatment a patient should receive. There are two different use-cases to use APOPTO-CELLup in a clinical environment. The first case is the application of APOPTO-CELLup to one specific patient by a consultant, where it is important to have fast results in order to find the best treatment for this specific person. This patient specific workflow is described in the following text and is illustrated in Figure The patient diagnosed with colorectal cancer will have a surgery, where cancer tissue will be removed and a small tissue sample will be sent to pathology for analysis. The pathologist has to determine the concentrations of required APOPTO-CELLup proteins and return the findings to the consultant. The consultant can start the APOPTO-CELLup application and enter the received protein concentrations, which are provided either in the report of the pathologist or in a database, where patient data are stored. Based on the predicted responsiveness by APOPTO-CELL, the consultant s decision for treatment in the case of this specific patient can be substantiated. The modelling result should also be stored in a file to be able to investigate or inspect the results at a later date. Expanding the usual clinical diagnostic workflow by the application of the APOPTO-CELL model would increase the workload for all parties concerned. Pathology has to determine the standard tissue examination in addition to the protein concentrations and has to send the result files to the consultant. The additional workload for the consultant 88

91 would be the application of the systems model and using the model predictions afterwards for the treatment finding. Figure 3-17: Suggested workflow for use of APOPTO-CELL by consultants analysing a patient s treatment responsiveness. To apply APOPTO-CELL in a clinical environment, the patient tissue to analyse has to be send to the pathology in charge. A pathologist has to determine protein concentrations of the cancer tissue taken during biopsy or surgery and return the results to the consultant. This data exchange can take place by sending the results as a document or storing the results in a clinical database. The consultant can then feed the protein concentrations of the examined tissue into the apoptosis pathway model and include the modelling results into his decision for a personalized cancer treatment. 89

92 Another solution, which increases only the workload of one party instead of two parties, would be to use the tool in pathology. Compared to the workflow described in Figure 3-17, this workflow is not as time-consuming for the consultant, because he/she will receive only the modelling results and has not to spend time using the software. Compared to the previous workflow, workload for pathology will increase only slightly by the model application. Figure 3-18 is a visualization of this suggested workflow. In this use-case, a patient is diagnosed with colorectal cancer and undergoes surgery. A sample of the tumour tissue will be sent to pathology, where it will be analysed. Besides the usual examination of the tissue, it will also be stained for the apoptotic proteins and their concentrations will be calculated. However, for the application of APOPTO-CELLup in a pathological environment are two ways conceivable. Either every patient s tissue sample is treated individually or many tissues are collected and treated together. In the first case, a manual data input would be easier because it might be more effort to create the input file than enter them direct to APOPTO-CELL. If there is more than one sample to analyse, it is easier to load a file containing the initial concentrations as described for the application of APOPTO-CELL in a research setting (chapter ). An Excel file containing the model predictions for a patient should be created and then returned to the consultant, who can find a personalized treatment option. 90

93 Figure 3-18: Workflow suggestion for an application of APOPTO-CELL in a pathological setting. APOPTO-CELL can be integrated into the workflow of a pathology department. The tissue to analyse has to be stained for the five apoptotic proteins required as model input. The staining results will be used for calculating the absolute protein concentrations. Depending on the number of patients and the storage of the calculated protein concentrations, a pathologist can choose between two ways of calculating the chemotherapy responsiveness. Either a file containing the concentrations can be load to AC or the values can be entered manually. A file should be created. Finally a physician will use the received model predictions for a treatment finding instead of protein quantification data as shown in Figure

94 Functional Requirements APOPTO-CELL could be used in a clinical diagnostic environment in order to find out whether a patient will benefit from a 5-FU based chemotherapy or not. If the patient does not benefit from this type of treatment, the consultant has to decide whether the patient receives additional targeted therapeutics to improve the responsiveness or if the patient will get another form of treatment for example radiotherapy. The presented workflows in Figure 3-17 and Figure 3-18 showed that the GUI should offer in a clinical environment the possibility to process data of a single patient (F1). The suggested workflow in Figure 3-18 revealed that it might be easier for a single patient prediction, to enter the required protein data manually and not via a data file. If the protein concentrations analysed in pathology are given in a different format than that is required for the automated data input, it might be more effort for either the consultant or a pathologist, to create a file in the required format than to enter the protein concentrations directly on the GUI. Moreover, it is important to have an ID assigned to the model output so that it can be connected to the corresponding patient (F2). Because only the prediction whether the examined tissue is apoptosis sensitive will be of interest for the treatment decision, it is important to save this final model prediction of chemotherapy responsiveness and information about the possible improvement by chemotherapeutics (F3). Hence, an output file should be provided; as mentioned above (section ), the file format will be Microsoft Office Excel. The file that can be created in the research mode as well will show an overview for the entered patient Non-functional Requirements For the application of APOPTO-CELL in research, it was already described that the five apoptotic proteins caspase-3, caspase-9, XIAP, Apaf-1 and SMAC are required for modelling. Their unit has to be μm (NF1). Therefore, these protein concentrations have to be provided by the user for a patient in every use case. In case of a manual data input, the GUI will have five fields labelled with the regarding protein name where the user can enter the 92

95 concentration values. One additional field will be provided on the GUI to enable the user to enter the patients ID (NF2). The calculation should be enabled only when all protein concentrations are given in a numerical format, because the model requires all five proteins for calculation (NF3). Decision for apoptosis sensitivity is made as described in the section of non-functional requirements for the automated data input mode in this thesis. After a successful simulation in the manual input mode, the results should be shown on the GUI (NF4) and they should be stored to a file (NF5). This overview file contains the patient ID, the initial protein concentrations needed for APOPTO-CELLup, the final decision whether the patient is apoptosis sensitive and if not, which dosage of which targeted therapeutic could improve responsiveness. The GUI for manual data input will offer the option to reset the input to facilitate a new calculation or the deletion of wrong inputs (NF6). 93

96 Table 3-3: Requirements for a GUI of APOPTO-CELL in a clinical setting. Functional requirements will be in the list referred to as F, non-functional requirements as NF. Reference number F1 F2 F3 NF1 NF2 NF3 NF4 NF5 NF6 Requirement Single patient specific application Assignment of ID to patient Provide outcome prediction for a patient Input: Protein concentration of caspase-3,and -9, XIAP, Apaf-1 and SMAC in μm Provide input fields for protein concentrations and patient ID Check completeness of input Show modelling output on GUI Output file: summary of modelling input and output for a patient Function to reset input 94

97 First prototype of manual data input This chapter will present the implementation of the manual data input mode for the APOPTO-CELL GUI in a clinical environment. The second data input mode was already presented in chapter and is primarily designed for an automated data input in a research environment. Here, the fulfilled requirements for a clinical usage of APOPTO-CELL are referred to with the reference number given in Table 3-3. The abbreviation F describes a functional requirement, whereas NF describes a non-functional requirement (see chapter ). Starting APOPTO-GUI 1.0 will show the window for pre-selected option Manual data input (Figure 3-19). The manual data input mode is designed for the investigation of chemotherapy responsiveness of a single patient (F1). It shows the mandatory fields for the patient ID and the input proteins (F2, NF1, and NF2). The user has to enter protein concentrations for every protein field in order to be able to start the calculation. If a non-numeric value is entered the entry will be set to ---- and the user has to re-enter the required value. In addition, the patient ID will be checked to contain at least one character and no special characters like. / \ : *? < > and ". The reason for this is that the ID will be used in the names of the result files, which allow no special characters. If there is an invalid character detected, the patient ID will be reset to ---- as well. Starting the calculation with invalid inputs will only show an error message dialog, telling the user to check his inputs (NF3). 95

98 Figure 3-19: The GUI for manual data input contains mandatory fields for model input and patient information. This graphical user interface is the first to show up after starting APOPTO-GUI 1.0. Calculation can be started via the Calculate button only if all fields are filled with valid values. Concentrations have to be numerical and the patient ID requires at least one character. Pressing the reset button will set all values in the fields to ---. Running the program with correct inputs will create an additional panel on the right side of the GUI, displaying model results (Figure 3-20, specified in requirement NF4). The upper part of this panel shows the prediction, whether the patient will benefit from chemotherapy (F3). A figure below shows the profile of cleaved substrate over time and a line indicating the threshold for outcome classification. If apoptosis is predicted to be executed successfully, no additional information on targeted therapeutics will be shown. Otherwise, the panel shows below the traces figure which targeted therapeutics could improve the responsiveness (Figure 3-21). If the apoptosis execution prediction is near the threshold value, the user can review the graphic and decide whether to give additional targeted therapeutics in this specific case. If the user wants to start a new calculation or delete the wrong input, he/she can 96

99 select Reset (NF6). Pressing the reset button on the bottom of the GUI will remove the results field and set all input fields to Figure 3-20: User interface shows after successful calculation the result in the right part of APOPTO-GUI 1.0. In case of a good outcome prediction no additional information about targeted therapeutics will be shown because the patient will benefit from chemotherapy anyways. If bad outcome is predicted the targeted therapeutics that improve the response will be shown. 97

100 Figure 3-21: GUI for manual data input in case of a bad outcome prediction shows the influence of targeted therapeutics. In case of a bad outcome prediction, the consultant can see which targeted therapeutic will have influence on apoptosis execution. He/she can select the targeted therapeutic from a drop-down menu, the minimum dosage required is shown below the drop-down menu on the right side of the GUI. If not selected differently in the options menu, the figure displayed on the GUI will be saved for later investigations ( Figure 3-16). For both input modes, two files with modelling results will be created automatically. An Excel-file will be created as shown in Figure 3-15 containing the patients ID and the protein concentrations over time for caspase-3, -9 and substrate. This file enables the user to recreate the protein profiles of caspases-3 and -9 and the cleaved substrate over time, in case the figure was not saved. In addition, an Excel file that shows the overview of all model inputs and outputs for this specific APOPTO-CELL run is created (requirement NF5, shown in Figure 3-14). 98

101 User feedback To gain feedback from users in a clinical diagnostic environment, two consultants were interviewed about the integration of the prediction tool APOPTO-CELL in a clinical diagnostic workflow. The first prototype of the GUI, APOPTO-GUI 1.0 was shown to them. In the following, summaries of their responses are given A consultants opinion about the integration of an APOPTO-CELL GUI into a clinical diagnostic environment Dr. Sandra van Schaeybroeck is a consultant in medical oncology at the Queens University in Belfast and works on increasing survival in particular groups of colorectal cancer patients by improving their treatment responses. Her research is based on a molecular level with focus on specific gene faults in order to identify predictive response markers. She could give the point of view of a treating consultant who would use the APOPTO-CELL model for a personalized treatment finding in his/her daily routine. The semi-structured interview was designed to consolidate the knowledge about the diagnosis workflow in a clinic and to cover open questions regarding the integration of an APOPTO-CELL GUI into this workflow. The following is a summary of Dr. van Schaeybroecks responses. Workflow for cancer diagnosis and treatment If a solid cancer is diagnosed in an early state, there will be a biopsy to analyse the current state in depth and cancer tissue will be available to determine the needed protein concentrations for APOPTO-CELL. Afterwards the patient will undergo surgery and receive chemotherapy. In some cases cancer is diagnosed in a late stage or metastasis already spread, then chemotherapy has to be given immediately without tissue analysis. But for majority of the cases paraffin embedded tissue is available. Therefore, the tumour stage plays a crucial role for cancer treatment. If there is tissue available to analyse, it will be sent to the pathology to do so. The results of pathology are given in a digital file with different sections 99

102 including information on the patient, a gross description of the tumour, a microscopic description of tumour, and an immunochemical analysis. This result report is fed into a clinic specific lab system where the consultant can see the results. Usually pathology is not part of the clinical lab system but they have access to it to upload their results. In this system, every patient has a unique, hospital specific number to assign data to the right person. To get access to a patient s data in the lab system, the clinician has to enter first a password and then he/she can search by the patients ID. The lab systems vary from clinic to clinic. Consultants will use the results of pathology in order to find the best treatment. For some cancer types, there is a standardized way to treat, and then it is the decision of the consultant which treatment the patient will receive. If there are complications or it is a not common type of cancer, the decision will be made by a group of physicians discussing this patient case. The decision to give additional targeted therapeutics depends on the genetics of the tumour; the kind of targeted therapeutic varies for different cancer types. Due to chemotherapy apoptotic protein concentrations in tumour tissue will change. Inclusion of APOPTO-CELL in a clinical workflow Because APOPTO-CELL can only be used after tissue was analysed, it can be included into the pathological part of the diagnosis and treatment workflow, where tissue is analysed and the report is created. The report could include a separate section with the outcome prediction and maybe a graphical representation of the result. In Dr. van Schaeybroeck s opinion a statistical evaluation is also very important for good result estimation, maybe more important than a graphical trace representation. For this purpose it is also important to be able to specify different patient characteristics to be able to compare the prediction with other patients. If a bad outcome is predicted, the influence of targeted therapeutics should be calculated and shown automatically. All in all, the result has to be presented in a simple way to the user. The storage of the result files can also be very interesting for following the development of the cancer and the response to the given treatment. To be able to link the calculated results to the specific patient, it is enough to have a 100

103 field where the user can enter either the unique and hospital specific patient ID or the patient s name. APOPTO-CELL could also be implemented as an online tool, because many physicians are already now used to apply online tools like Adjuvant! Online ( Advantages are that there are no installations on user workstations necessary and maintenance can be done central. For further information how to include APOPTO-CELL best into the pathological workflow, pathologists have to be consulted. Further important aspects for future integration of the GUI in a clinical environment To use APOPTO-CELL in a clinical environment the used protein concentrations have to be immunochemical quantified, because otherwise it would not be accepted among experts. If there are some ranges to define, they have to be validated first to prove a biological relevance. Moreover, it has to be proved whether concentrations of apoptotic proteins in primary tumour tissues are the same as in metastases, because there are cancers know where the concentrations alter. APOPTO-CELL has to consider this A histopathologists opinion about the integration of an APOPTO-CELL GUI into a pathological workflow Prof. Elaine Kay is a consultant histopathologist at the Beaumont Hospital in Dublin. She is interested in identifying biomarkers predicting treatment responses for human solid cancers (especially breast, bladder and colorectal cancer) and the translation of identified biomarkers into a routine diagnostic laboratory setting. Therefore, she is able to explain the routine in a pathological environment and the possible integration of APOPTO-CELL into this environment. This semi-structured interview was designed to find out how the workflow in pathology works and how the GUI for APOPTO-CELL could be integrated into this workflow. 101

104 General workflow in pathology A usual workflow for pathological analysis of tumour tissue is the following: pathology receives tissue, analyses it and send a report file back to the consultant. The report will include several sections describing the tumour and the analysed tissue. The result of this analysis will be described in the final report, which also can include some figures or statistical evaluations. Typically, there will be sections containing patient information, describing the tumour tissue and the lymph node status. This report will be uploaded to the clinic specific lab system; for some systems there is already the possibility of doing this automated, but it is not yet in use by every hospital. To support the finding of the best treatment for an individual patient, there are also diagnostic kits available. Genomic Health for example developed the kit called Oncotype DX ( ). The Oncotype DX test is a commercial genomic diagnostic test to get information whether to give chemotherapy or not. The test for colon cancer is specialised for patients with stages II and III and is analysing the likelihood of cancer recurrence by measuring the expression levels of 12 genes in a tumour sample. A block of paraffin-embedded tissue has to be sent to the Genomic Health laboratory, where it will be analysed. The tissue will be taken during surgical resection. The consultant can receive the test results within 14 calendar days by fax, overnight mail or secure online transfer. Inclusion of APOPTO-CELL into pathological workflow APOPTO-CELL should be used by the pathologist, who will include the results in his/her report, possibly as a supplementary. The result should be presented as simple as possible and a statistical evaluation of the result is very important. To include the APOPTO-CELL created file into the report, the result of APOPTO-CELL has to be a Word or PDF file, which contains the patients ID, or the ID of the tissue in pathology. It is not able to copy and paste the content of the APOPTO-CELL created file manually to the final pathology report. The risk to cause mistakes or to assign results to the wrong patient would be too high. So, the whole file will be included and not be inserted by copy & paste. It could be named as APOPTO-CELL report and could contain 102

105 the sections tissue tested, concentrations (etc) found, meaning of the result. The optimal scenario in Prof. Kay s opinion is the usage of the routine tool APOPTO-CELL in pathology: With a robust immunochemical staining, it is possible to stain and analyse the tissue automated. The analyzer will create a file with the input for APOPTO-CELL. APOPTO-CELL will read the file automatically and create a report, which will be included in the pathology report as a supplementary page. This report will be returned via a clinic specific lab system to the consultant, who will read the report and then make the ultimate decision for the best fitting treatment. The inclusion of the influence of targeted therapeutics would be an advancement that is not yet available but would be very useful. Further important aspects for future integration of the GUI in a clinical environment Acquisition of protein concentrations will be very important, because depending on the protein quantification method the usage of APOPTO-CELL might differ. To determine caspase levels of tumour tissue the best method would be TMAs whereas RPPA (procedures are similar to Western blot), would show different results. Using RPPA as quantification method requires, that the tumour cells to be analysed have to be taken by laser capture microdissection. This ensures that only tumour tissue is analysed. If the model input is dependent on fresh tissue, it is very difficult to prove that it is tumour tissue. This means increased additional workload for a pathologist. Very specific antibodies will be necessary to measure the protein concentrations. In this case, it would not be easy to integrate APOPTO-CELL into a clinical/pathological workflow. Decreasing the workload by measuring the RNA levels instead of protein concentrations is not possible. The reason is that the measured protein concentrations do not always correlate with the measured RNA levels. 103

106 To establish APOPTO-CELL in a clinical environment, the method to determine the input of APOPTO-CELL has to be settled and the whole system has to be validated. Some methods, like immunohistochemical measuring of protein concentrations using TMAs, can be easily included in the pathological workflow. This is because these methods are already part of the daily routine in a pathology department. For the final introduction of APOPTO-CELL to the Irish market, it has to be verified by the Irish Medicines Board and it has to bear the European CE mark. For that reason, some clinical trials will be needed Feedback that can be implemented in the prototype Potential clinical users of the APOPTO-CELL GUI confirmed that the workflow illustrated in Figure 3-18 is the most probable workflow. The best approach to integrate APOPTO-CELL optimally in a clinical workflow is the usage of the prediction tool in pathology. Providing the model result together with other pathology results in a summary report will not increase the workload for the consultant but still gives him/her the possibility to include the APOPTO-CELL findings in his/her treatment decision. A Word or a PDF document, which contains a link to the patient details, a description of the result, and the picture of the substrate cleavage trace, can extend the current interface output. This file can be provided together with tissue analysis results for a consultant to support the treatment finding. Within the next few years, there will be no automated tool implemented as mentioned by Prof. Kay which does everything from tissue analysis to outcome prediction by APOPTO-CELL to final report creation automated. However, there could be an interface provided which takes the output of a current analyzer, runs the APOPTO-CELL model and creates in the end e.g. a Word document, which can be included in the pathology report. There is already the option implemented to read Excel files and run the APOPTO- CELL model automated. Depending on the final chosen quantification 104

107 method, the output files of machines doing this kind of analysis have to be analysed and the input file for the model adapted Update of GUI functionalities according to user feedback The feedback obtained from interviewing potential users was integrated in a second, improved implementation of the GUI. The code of the updated GUI will be referred to as APOPTO-GUI 1.1. The most obvious changes from the first version 1.0 of the prototype to the second version 1.1 of the prototype are graphical changes on the GUI surface. The aesthetic changes were implemented to improve the GUIs style and to improve the look and feel of the user interface. Unnecessary GUI elements were removed in order to simplify the interface. The upper part of the prediction tool containing the selection of the data input type was changed. In APOPTO-GUI 1.1 it includes the name of the prediction model and a logo of the Centre for Systems Medicine. New improved functionality has been added to the menu bar as well. The options menu was expanded to include different functionality required by the user (see more in chapters and ). Extra menu items like About and Help provide additional information. Figure 3-22 shows the menu bar of APOPTO-GUI 1.1. The user s decision to choose another input mode will not lose any information; the user can switch between manual and automated data input. 105

108 Figure 3-22: Updated functionalities for the APOPTO-CELL GUI are implemented in the options menu. The selected options menu shows all the different options a user can select or change. The upper part of the options menu is more interesting in a research setting, whereas the lower part for saving the files is more of interest in a clinical setting. The About menu and the Help menu can be seen in Figure 3-26 and in Figure

109 Changes in the menu bar from APOPTO-GUI 1.0 to APOPTO-GUI Options menu (see in Figure 3-22) In APOPTO-GUI 1.0, the program created two result files automatically. In APOPTO-GUI 1.1, the user can select manually from different files offered in the options menu to save modelling results. The different options are described below. Calculation duration : this menu item enables the user to change the duration of the simulation. The unit is minutes and default value is 300 minutes. This option was already implemented in APOPTO-GUI 1.0. Record traces : as the use case for the systems biology approach is not only in a clinical setting expected but also in research, there might be the need to investigate the single protein traces of a patient to understand the protein interactions. For this reason, there was additionally the option implemented to save all the protein traces of a patient in a file instead of only specific traces for caspases-3, -9, and for substrate cleavage. One subitem of the selection gives the user still the opportunity to save the traces of the executioner caspases and their effect on the cellular substrate cleavage. This was already implemented in the first GUI version and saves the result for all patients in one file (Figure 3-15). The other subitem is implemented in the new version APOPTO-GUI 1.1 and saves all calculated protein traces for a single patient to a file (Figure 3-23). Besides the numerical values of traces at particular time points, the name of the patient is included as well. Both files are Excel files. 107

110 Figure 3-23: File with all traces for a single patient. This file will include all calculated traces for the intrinsic apoptosis pathway of a patient. The first column contains the time steps, the following columns the traces for all pathway players. The initial concentrations can be read from the first numerical row at time point zero. Default input path : this file path determines where files should be taken from and will be stored to. Choosing this option will open an explorer window where the user can select a folder he or she wishes the created files to be saved to. In case the user uses the manual data input mode, the results files will be stored in the selected folder. However, when the user selects the automated 108

111 input mode, the selected folder will open when the user browses for an already existing file. In case he/she generates a new file, this new file will be stored in the folder selected by this menu option. This path will be reset for every program start of the APOPTO-CELL GUI to the directory where the APOPTO-CELL program is located. This menu item was already implemented in APOPTO-GUI 1.0. Save figures : this selection will store an image of the substrate cleavage trace for each patient as an ai file and as a bmp file ( Figure 3-16). A checkmark beside the label will show the user whether this option is selected. Save report as : as the feedback from clinical staff revealed, the model should be used in the pathology setting where already a report for the consultant will be created. This report is usually assembled from Microsoft Office Word documents or PDF files, depending on the clinical labsystem. Therefore, the format of the model output has to be a document in one of the mentioned formats. The files created with the option Save as Word or Save as PDF will have the same contents but only a different file format. Figure 3-24 is an example of such a created report. The report starts with an ID in order to assign it to the correct person and the date when the report was created. The second section of the report includes a short description of APOPTO-CELL and for legal reasons a general warning that the predicted outcome by APOPTO-CELL does not warrant the real treatment outcome. The second part of the report file contains the model input followed by the results of the APOPTO-CELL results. The decision whether the modelled tissue is apoptosis sensitive is described in a short sentence. This sentence is chosen from a set of predefined sentences depending on the outcome. The set is defined as follows: - The tissue analysed is capable of apoptosis, so treating the patient with 5FU-based chemotherapy will be successful. - The tissue analysed is not capable of apoptosis, even treating the patient with 5FU-based chemotherapy will fail. Additional treatment of the patient with the targeted therapeutics SMAC-mimetics, proteasome 109

112 inhibition and caspase-3 activation compounds can not improve the chemotherapy responsiveness sufficiently. - The tissue analysed is not capable of apoptosis, treating the patient with 5FU-based chemotherapy will fail. Additional treatment of the patient with the following targeted therapeutics might be able to improve the chemotherapy responsiveness If the model suggests that there is an improvement of chemotherapy responsiveness by targeted therapeutics, a table is shown with the type of therapeutic and the minimum dosage needed. If the option Save figure is additionally chosen, an image with the substrate cleavage trace will be included at the end of the report (Figure 3-24). The third option Save as Excel will create an overview file as already implemented in the first version 1.0, where a summary of all patients and their modelling results is given (Figure 3-25). The file has nearly the same format as the overview file created in APOPTO-GUI 1.0. The file still contains the patient ID, the initial protein concentrations and the model outputs outcome prediction and final substrate cleavage. The only difference is the presentation of the co-treatment options. In APOPTO-GUI 1.1, there is a column for every co-treatment option. This column shows the minimum dosage required to exceed the threshold of final substrate cleavage. If the option to save a Word or an Excel file is selected, the extension for the created documents will be.doc or.xls and not the new versions.docx or.xlsx because it is not ensured that every client has the latest version of Microsoft Office package. Using the old document versions will assure the compatibility of the APOPTO-GUI 1.1 with workstations that have only older versions of Microsoft Office installed. 110

113 Figure 3-24: The patient report created by APOPTO-GUI can be saved either as Word or PDF document (example). The different sections of the document include the patient ID to assign it to the right person, a short description of the APOPTO-CELL tool, the modelling input and finally the model output. The output is presented as a sentence describing the modelled apoptosis sensitivity and the potential influence of targeted therapeutics. If there is an influence by targeted therapeutics predicted, the dosage for the co-treatment will be shown. Should a picture of the modelled substrate cleavage be included at the end of the file, the option Save picture needs to be selected. This file can be saved as a Word document as well as a PDF file. 111

114 Figure 3-25: Overview file for all analysed patients by APOPTO-CELL. This file is a summary of all analysed patients for one run of APOPTO-CELL. The modelling input like ID and initial protein concentrations are listed as well as the models prediction whether these concentrations could trigger apoptosis sufficiently. The final value of substrate cleavage is listed and in case of a bad outcome prediction the minimum dosage to improve responsiveness is given for the targeted therapeutics SMAC-mimetics, procaspase-3 activation compounds and the proteasome inhibition. 112

115 Help menu This menu item shows the user a short introduction how to use the APOPTO- CELL model and explains the options the user can select (Figure 3-26). The help menu was separated for information about the model input (Figure 3-26A) and the model output (Figure 3-26B) About RCSI, CSM This menu point provides some information about the Royal College of Surgeons in Ireland and its Centre for Systems Medicine, where the APOPTO-CELL model was developed. Both institutions are described briefly. A link to the relevant web pages provides more information for the interested user (Figure 3-27). 113

116 A B Figure 3-26 Different Help dialogs provide information on use of the tool. Help dialog window this window will provide some general information for the user. One option (A) gives information about the model and its different input modes and the other section (B) about the output possibilities. Figure 3-27 About menus. These menus give the user information about the institutions where the model and the tool were developed. 114

117 Changes for the manual data input mode of APOPTO-GUI 1.1 If the user chooses the manual data input, he/she has to enter an ID for the patient which is not allowed to contain special characters because the patient ID is used in filenames for the model output. Moreover, the initial concentrations for the apoptotic proteins caspase-3, caspase-9, XIAP, Apaf-1 and SMAC have to be provided. The input format for these concentrations is a numeric value in µm. If the entered value is not numerical it will be replaced by --- and the user has to enter the concentration again. Selecting the Calculate button after entering the values will check if the input is correct and start the calculation. If there are some inconsistencies detected, a new dialog window will pop up and show the user what is wrong and needs to be changed. The program stops further calculations in this case and calculation needs to be started again after changing the mentioned issues. If the input is okay, the program will continue modelling the protein interactions with the given concentrations. Depending on the selected options, different files containing the APOPTO-CELL results will be created or if nothing is chosen, the result will be shown on the right hand side of the graphical user interface. If there is at least one file selected to store modelling results, pressing the button Open result folder will open the folder where the result files are stored. This button will appear after a successful calculation beside the Calculate button in the bottom part of the GUI. 115

118 Figure 3-28 Screenshot of start window for manual data input. The fields for the patient ID and the measures (= proteins needed as model input) have to be filled correctly otherwise the wrong value will be replaced by --- and the user can not start the calculation. Changing the mistakes will enable the calculation. 116

119 Figure 3-29: Second version of GUI for manual data input after calculation. The only change for this result part is the additional button Open result folder, which will open the folder where all possibly created result files are stored. All other elements of the GUI stayed the same, only some graphical changes such as removing of frames were performed. 117

120 Changes for the automated data input mode of APOPTO-GUI 1.1 Elements for the interaction with the user did not change in the automated data input mode. If in the options menu no item is selected for storing a file, no calculation will be carried out. Also the functionalities for the two buttons Browse and Generate File did not change. Browse will open a dialog window showing the Excel files in the default folder, which is either set in the program or changed by the user via the options menu. A new Excel file with the required headers for the model can be created for a patient cohort by selecting the Generate File button. The entered file name for the new file is not allowed to contain special characters, which are not allowed in file names. The forbidden special characters in file names are /. \ : *? < > and ". 118

121 Figure 3-30: Second version of GUI for automated data input. Beside the stylistic, optical changes of the GUI, nothing has changed. In the automated data input mode, the user can still browse an existing file or create a new file with the specific name the user entered in the mandatory field. The Calculate button will start the calculation if the model input is valid and Reset will set the values in the fields for the file names to

122 Figure 3-31: APOPTO-GUI 1.1 for automated data input after calculation In the updated version of the GUI, the functionality of the Open result button changed. The previous version had the functionality of opening the created overview file for all patients. As the options menu changed and the user can select from different files that should be saved, the functionality of this button was changed as well. Now the button will open the folder, where all the result files are stored. 120

123 Conclusion In this chapter, preliminary requirements for the application of APOPTO- CELLup in a clinical as well as in a research setting were defined. For that reason, different possible workflows were drawn up. For the use of the GUI in a clinical setting there are two different places in a diagnostic workflow to integrate the model. One option would be the application of the tool by a consultant using the protein quantification results of pathology. The second option would be to use the tool in pathology and provide modelling results to the consultant. The workflow presented for the integration of APOPTO-CELL in research is similar to the integration of APOPTO-CELL in pathology. The difference for both settings is the number of patients to investigate and the focus on the modelling results. Research requires detailed modelling information for up to hundreds of patients, whereas in a clinical environment only the prediction of apoptosis sensitivity for a single patient is of interest. A first functional prototype was implemented regarding the specified features. To check the feasibility of integrating the GUI in a clinical diagnostic workflow and to refine clinical requirements, potential clinical users were interviewed. They confirmed that the model should be applied in pathology and a patient report provided by APOPTO-GUI will be included into the tissue analysis report created in pathology. The consultant will receive only modelling results but will not be the user of APOPTO-CELL. Feedback from a project partner in the research field revealed additional needs for the model output. This led to changed functionalities of the GUI. The final functional prototype of the APOPTO-CELL GUI has two different data input modes. One for data input of a single patient with a graphical presentation of the modelling results on the GUI surface. The second one provides the automated data input for at least one patient, whose initial protein concentrations have to be stored in a file with a specific format. The user can select from a variety of output files to store the modelling results. An image of the trace determining treatment outcome can be saved for every patient. Integration of APOPTO-GUI 1.1 into a research setting is ensured by providing output files with detailed modelling information for later investigations. Moreover, a summary file for the inputs and outputs of every modelled patient can be created. Integration of the prediction tool into a 121

124 clinical diagnostic environment is ensured by an individual patient report that can be integrated into analysis results of pathology. Customized functionalities of the GUI fulfil user specified requirements for an acceptable GUI in a research and a clinical diagnostic environment. 122

125 3.4. APOPTO-CELLup as a predictive marker in the clinical data set Ni Introduction The APOPTO-CELL model has already successfully been applied to a patient set (see section 3.2.5). This patient set contained 30 patients diagnosed with stage II and III, 20 of these patients received chemotherapy and were analysed by APOPTO-CELL (Hector et al, 2012). Although these were clinical data/ samples, the number of cases was too small to prove a clinical relevance. Therefore, a patient set with a bigger number of patients will be necessary to prove APOPTO-CELLs benefit for a clinical diagnostic environment. A clinical patient set, the Ni240 cohort, was part of this project. This cohort consists of 254 colorectal cancer patients diagnosed with disease stages II and III (even one patient with stage IV). The Ni240 patients were randomised for either curative resection followed by observation or for curative resection with chemotherapy treatment afterwards. For both stages, about half of the patients were treated with chemotherapy whereas the rest of the patients underwent only tumour resection. The clinical data included information regarding the treatment of the patient (chemotherapy, radiotherapy, and surgery), information about the tumour (site, stage, grade, and recurrence), dates of surgery and recurrence and when the patient was last seen. Part of the tumour tissue removed during surgery was prepared as a formalin-fixed, paraffin-embedded block. In this way preserved patient tissue can be examined years later by researchers for in depth investigations. To predict the treatment outcome by APOPTO-CELLup, the successful treatment of a cancer patient is defined as the ability of the chemotherapy to trigger MOMP in tumour cells and to reduce the tumour size due to apoptosis induction. It is therefore assumed that patients who received chemotherapy show less or later recurrence than patients without chemotherapy treatment. The non-treated patient group functions as a control group for the modelling. Their apoptosis pathway was not activated by chemotherapy and therefore 123

126 the systems model should not be able to predict the clinical outcome. The prediction efficiency of the apoptosis model can be tested by comparing the modelling results with the recorded clinical outcome. To run the systems approach of intrinsic apoptosis absolute protein concentrations are required. There are several methods available to determine absolute protein concentrations of tissue (Seevaratnam et al, 2009; Smith, 1984). For this project, the Reverse Phase Protein Array (RPPA) method was chosen, because many patient samples can be processed at the same time and RPPA does not require much tissue. For RPPA, the tissue has first to be lysed. Tissue lysates will be applied automatically on an array. The slide will then be stained with a specific antibody that can be used for such a high throughput method (Spurrier et al, 2008). For every tissue lysate, a dilution series is applied on the slide. Usually there are 5 dilution steps, each reducing the concentration by 50%. Dilution is used to overcome potential staining saturation of the pure lysate and to obtain more exact protein quantification (Liotta et al, 2003). Images are taken of the stained slides and analysed afterwards. Normalisation to β-actin enables the comparison of tissue within different slides. RPPA was also used to analyse molecular networks in ovarian cancer (Sheehan et al, 2005). The β-actin-normalised values at a specific dilution step are the results received from the collaboration partner Aim of this chapter Aim of this chapter was to test if APOPTO-CELL can be used as a predictive systems biomarker tool based on protein quantification by RPPA. 124

127 General Ni240 characteristics Due to the lack of tissue for some patients, only 211 of the 254 patients were further analysed. Before modelling results were analysed, general characteristics of the patient set were analysed to see if the expectations for colorectal cancer patients, explained in the following, were fulfilled for the Ni240 cohort. First of all, recurrence is expected to occur more often in advanced diseases than in early-discovered diseases (Lin et al, 2009). This was shown for the Ni240 patients by a Kaplan Meier curve investigating the time to recurrence for each disease stage. The blue trace for stage II patients in Figure 3-32 shows recurrence in about 20% of patients and recurrence appears much later than in the advanced stage. Stage III patients (green trace) have a median recurrence-free survival time of about 1500 days (roughly 4 years) and 60 % of patients had recurrence. These values are congruent with values in the literature (Lin et al, 2009). The only stage IV patient had recurrence after about 300 days. For patients with recurred tumours, the median time to recurrence is increased from about 500 days in stage III patients compared to about 1000 days in stage II patients. 125

128 Figure 3-32: Recurrence occurs earlier and more often in advanced disease stage. In this Kaplan Meier curve, the patient set was split by the criteria tumour stage for investigation of recurrence characteristics. Each step down in the curve represents a patient with recurrence. Every tick in a curve represents a patient who left the study and had so far no recurrence (censored). In stage III (green trace), about 60% of the patients showed recurrence. Stage III colorectal cancer patients had a median time to recurrence of about 1500 days, whereas stage II patients (blue trace) had only in nearly 25% recurrence. Patients that had recurrence had a median time of about 1000 days in stage II compared to only 500 days in stage III. The only stage IV patient (beige trace) had recurrence after 300 days. 126

129 A study conducted by Sargent et al (2009) could show that stage III patients have a clear benefit from chemotherapy. This benefit could not be shown in case of stage II patients. It is recommended to treat only high risk stage II patients with chemotherapy (Bastos et al, 2010). Because the Ni240 cohort includes patients with and without chemotherapy treatment, it is interesting to see whether chemotherapy had a significant influence on the time to recurrence of colorectal cancer patients with a specific stage. As there is already a difference in recurrence for different stages, the data set was split into groups regarding the stage. The stage-grouped patients were separated for the criteria chemotherapy received. In both staging groups, patients who received additional treatment to curative resection had recurrence later than the control group without additional treatment (Figure 3-33). However, for stage II the difference is not that distinctive as for the group stage III. In the later group the difference between chemotherapy-treated and -untreated patients is statistically significant. Because there are also other patient specifics given like tumour site, this was investigated as well. For rectal tumours, recurrence is expected to occur more often than for other tumour sites, because of anatomical restrictions it is difficult to resect tumours at this site (Rodriguez-Bigas et al, 2003). Kaplan Meier curves for the whole patient set revealed that the tumour sites ascending colon and rectum are more likely to have recurrence (Figure 3-34). Splitting the data set for characteristics as stage and chemotherapy received showed that this trend is in all groups whereas other tumour sites have different trends in different groups (data not shown). 127

130 Figure 3-33: Chemotherapy significantly prolongs the time to recurrence for stage III CRC patients, but not for stage II CRC patients. The Ni240 patient set was divided into tumour stages II (upper panel) and III (lower panel). For each stage, the patients were split by the criteria chemotherapy received. For both stages, additionally treated patients (green trace) experienced longer recurrence-free survival than patients, who had only resection (blue trace). Stage III patients with chemotherapy, in particular, showed recurrence later and less frequently than patients without adjuvant treatment. The differences in time to recurrence are not significant for stage II patients but significant for the group stage III. 128

131 Figure 3-34: Tumours in the ascending colon and rectum are more likely to have recurrence than other tumour sites. Analysing the time to recurrence for distinct tumour sites showed that the tumour sites ascending colon (blue line) and rectum (yellow line) had more often recurrence than patient with other tumour sites did. Splitting the data set for different criteria like stage and chemotherapy received showed that this trend is present in all of the groups (data not shown). 129

132 RPPA-based determination of absolute protein concentration is feasible for all required apoptotic proteins except for APAF-1 Normalised RPPA staining results for 220 patients and six cell lines were obtained from the RCSI group of Bryan Hennessey located at Beaumont Hospital in Dublin. But the APOPTO-CELL model requires absolute protein concentrations as input instead of staining raw data. For that reason, this work aimed to calculate absolute protein concentrations in patient tissue based on the received staining data and known protein concentrations for some cell lines (see in 2.3.1). The procedure to calculate the concentrations is described in the following text. To find the absolute protein concentrations in patient tissue with unknown concentrations, not only patient tissue was stained, but also cell lines. Because APOPTO-CELL uses the five apoptotic proteins caspase-3, caspase-9, XIAP, Apaf-1 and SMAC, cell lines lacking these proteins were included in order to know the signal if there is no protein present. The caspase-3 deficient cell line MCF-7, HCT-116 SMAC knockout and HCT-116 XIAP knockout were used as well as the Apaf-1 silenced SK-MEL cells and the caspase-9 deficient Jurkat cells. Every cell line lacking a protein is used in the staining for this particular protein to determine the signal in absence of the protein. This signal can then be subtracted from all other measured signals in order to get the clear protein signal. HeLa was already used in other publications to determine protein concentrations in patient tissue (Hector et al, 2012). Hence, HeLa was included on the slide as a reference cell line. Because for MCF-7, HCT-116 Smac -/- and HCT-116 XIAP -/- the protein concentrations were determined by Western Blots earlier as well (Schmid et al, 2012), the correlation between those concentrations and the results of the RPPA processing were investigated. Except for the Apaf-1 staining, a linear positive correlation trend could be shown for RPPA analysis results and Western Blot results as seen in Figure With increasing protein concentrations determined by Western Blot, the arbitrary RPPA units rose as well. But this correlation was not statistically significant at a level of 0.05 presumably because only 4 data points are available. Determining the 130

133 available cell lines more than once or including more than four cell lines with known protein concentrations will show whether the correlation is significant at a level of Because this linear trend was recognizable, this relationship could be used for calculating the protein concentrations in patient tissue. The recorded signal of the protein deficient cell lines was subtracted from all other measurements for the particular protein. This resulted in a regression curve through the origin. Now, the measurements could be related linearly to the reference point HeLa. The resulting formula to calculate the unknown protein concentration in tissue is the following: Concentrat ion RPPAunit ( tissue) ( tissue) concentration ( HeLa) RPPAunit ( HeLa) Equation 1 Because there was no correlation for the Apaf-1 staining, the staining for this protein could not be evaluated further. Apaf-1 s contribution to the apoptosis modelling is the formation of the apoptosome complex and activating caspase-9 on it. For APOPTO-CELLs simplified function representing the apoptosome formation it is assumed that a 1:1 ratio of caspase-9 and Apaf-1 is required. For all investigated cell lines, Apaf-1 was never the limiting factor but caspase-9. For that reason, the concentration of Apaf-1 was set for all patients to the average Apaf-1 concentration, which was measured in the patient set used in Hector et al (2012), instead of using concentrations calculated depending on the RPPA staining results. This replacement value for Apaf-1 is higher in all investigated patient samples than the RPPA-based calculated protein concentrations of caspase

134 Figure 3-35: RPPA determined protein levels correlate with Western Blot determined protein concentrations of four cell lines. A linear correlation between Western Blot determined protein concentrations and RPPA values could be shown for the proteins caspase-3, caspase-9, XIAP and SMAC in the cell lines HeLa, MCF-7, HCT-116 SMAC -/- and HCT-116 XIAP -/-. For the protein Apaf-1, there was no correlation between the different quantification methods. 132

135 Single proteins fail as predictive biomarkers for chemotherapy outcome in stage II and stage III CRC patients One might argue single proteins can be used as biomarker for treatment outcome. This might be cheaper and less workload than determining five proteins and additionally using a tool to determine treatment outcome. To check whether a single protein is sufficient to predict outcome of chemotherapy treatment, RPPA-measured protein levels of the apoptotic proteins were investigated for their capability of treatment outcome prediction (Figure 3-36). Apaf-1 was not considered because of its bad correlation with Western Blot results. Receiver operating characteristics (ROC, see section 2.4.2) analysis was employed for the measured protein levels to find a value to distinguish the modelling results between bad and good clinical outcome. The optimal threshold value suggested by this analysis was then used to split the patient set into two groups; one group with high level of protein expression (protein level above the threshold) and one group with low protein expression (protein level below the threshold). High protein level expression for the proapoptotic proteins caspase-3, -9 and SMAC is supposed to enhance apoptosis. A patient is supposed to benefit from chemotherapy in this case and to show reduced recurrence compared to an untreated patient. In the case of XIAP as an anti-apoptotic protein, low levels of protein are supposed to enhance cell death. Plotting the time to recurrence of the pooled data set for high and low protein expression levels with Kaplan-Meier curves revealed, that caspase-3 and SMAC could be used as prognostic markers. Both plots showed significant differences in time to recurrence for distinct protein levels. SMAC was even better than caspase-3, whereas for caspase-9 and XIAP no statistical significant differences in the time to recurrence could be detected for different protein expression levels. Furthermore, the number of patients classified by the optimal threshold into low and high levels was not balanced for these two proteins. For SMAC and caspase-3 in contrast, patient numbers were balanced. 133

136 Figure 3-36: SMAC and caspase-3 could be used as biomarkers for recurrence, whereas caspase-9 and XIAP levels have no significant segregation of patients with and without recurrence. Patients with low SMAC levels (green line in lower right panel) showed significant more often recurrence than patients with high SMAC levels (blue line in lower right panel). The distinction of the pooled patient set by caspase-3 levels was not that good as for SMAC, which is known to be a prognostic marker (De Oliveira Lima et al, 2009). Curves for high and low XIAP and caspase-9 levels intersect. Moreover, the ration of patients with high levels and patients with low levels is not balanced. 134

137 SMAC is also known as a prognostic marker for patients that were not treated with chemotherapy (Endo et al, 2009). Thus, SMAC was investigated further. The patient set was first split regarding the tumour stage and then regarding the chemotherapy treatment. Both stages had a good separation between patients with recurrence and patients without recurrence. For stage II, about 20% of patients with high SMAC expression levels had recurrence but for patients who had low SMAC levels only 45% had recurrence in real (Figure 3-37). For the stage III patients, analysis revealed that about 70% of patients with low SMAC level had real recurrence but also 55% of patients with high SMAC levels had recurrence. Because the prediction efficiency of the biomarker for treatment outcome should be investigated, the staging groups were split again for its chemotherapy treatment (Figure 3-38). SMAC was only able to distinguish the recurrence status in stage III patients who received no chemotherapy. In the patient group stage III, chemotherapy received there was not a great difference between the curves for patients with high and low SMAC levels. Overall, protein levels of caspases-3, -9 and XIAP failed as prognostic markers for treatment outcome. SMAC is a prognostic marker for stage III patients that received no chemotherapy but not predictive regarding the influence of chemotherapy. However, APOPTO-CELLup remodels the intrinsic apoptosis pathway after triggering MOMP. Therefore, it is of interest to have a biomarker for the patient group that received chemotherapy and activated thereby the apoptosis machinery. 135

138 Figure 3-37: Using SMAC levels to separate patients according to their recurrence status works for stage II as well as for stage III. Low SMAC levels (green lines) are in both staging groups more reliable markers than high levels (blue lines). Many patients that had recurrence were classified as having high SMAC levels, but high SMAC levels should enhance apoptosis, kill tumour cells and prevent recurrence. 136

139 Figure 3-38: SMAC levels predict clinical outcome of patients that were observed only after surgery despite their stage. In the additional treated patient group, SMAC levels fail as prognostic marker, because for low (green line) and high (blue trace) SMAC levels, recurrence was the same. In the groups without additional treatment, low SMAC levels are a reliable prognostic marker for recurrence. Many stage III patients that received no chemotherapy and had recurrence had high SMAC levels. 137

140 Successful evaluation of APOPTO-CELLup as a predictive marker in stage III CRC patients treated with chemotherapy To find out whether the systems model APOPTO-CELLup is predictive for chemotherapy responsiveness, the determined protein concentrations of the patients were fed into the systems model and the output was saved in different files. APOPTO-GUI 1.1 options were set to save a file with an overview of all patients and for every patient a second file containing all protein profiles calculated for the intrinsic apoptosis pathway. The model output of interest, which is the final percentage of substrate cleavage stored in the overview file, was compared with the real clinical patient outcome. The intrinsic apoptosis pathway downstream of MOMP was simulated for 300 minutes. The first threshold of the model to classify between responders and non-responders was set to 25% of substrate cleavage, because this value was used in the above mentioned publications (Hector et al, 2012; Rehm et al, 2006). Figure 3-39 shows the substrate cleavage traces of the RPPAanalysed patients produced by APOPTO-CELLup. In deed there were more stage II patients than stage II patients, but the traces for stage III seem to be cumulated in the area below 15% substrate cleavage. However, the calculated traces for stage II patients are spread over the whole area from 0% to 100% substrate cleavage. ROC analysis was employed to optimize the threshold value for the modelling approach to distinguish patients with recurrence from patients without recurrence. ROC analysis found the value of 1.5% final substrate cleavage as an optimal threshold. Figure 3-40 shows that patients predicted with final substrate cleavage below 1.5% had recurrence more frequently and earlier than patients with substrate cleavage above the threshold. Splitting the pooled APOPTO-CELLup predictions regarding the stage showed in Kaplan Meier plots that the optimized threshold value for the pooled patient set could distinguish the recurrence status of stage III patients but not of stage II patients (Figure 3-41). Kaplan Meier plots for the cohort predictions divided regarding tumour stage and chemotherapy treatment showed that the model 138

141 is predictive for treated stage III patients and also not for stage II patients (Figure 3-42). patients, but not for untreated stage III Figure 3-39: Substrate cleavage traces produced by APOPTO-CELLup for RPPA-based protein concentrations of the Ni240 cohort. Substrate cleavage traces for stage II (upper panels) show a bigger diversity than the traces for stage III (lower panels). For stage III, most of the traces cumulate in a range between 0% and 20% substrate cleavage after 300 minutes, whereas for stage II traces spread over the whole range of 0-100% final substrate cleavage. 139

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