How Systems Biology Can Advance Immune Oncology. Birgit Schoeberl, PhD NEDMG Summer Symposium May 31st

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1 How Systems Biology Can Advance Immune Oncology Birgit Schoeberl, PhD NEDMG Summer Symposium May 31st

2 Immuno-Oncology is One of the Most Promising Avenues in the Battle Against Cancer Conventional Therapy Immune-Checkpoint Blockade Checkpoint inhibitors induce long, durable responses in a subset of patients Merrimack Pharmaceuticals, Inc. All rights reserved. Champiat S, Ileana E, Giaccone G, Besse B, Mountzios G, Eggermont A, Soria J-C. Incorporating immune-checkpoint inhibitors into systemic therapy of NSCLC. J. Thorac. Oncol. 2014;9(2): [adapted from Ribas A (2012) 2 CCR 18:336]

3 Checkpoint-Inhibitors have Response Rates of 10-30% No clear biomarker identified Tumor microenvironment and neo-antigen load seem to define responsiveness Cold Tumors Hot Tumors 2016 Merrimack Pharmaceuticals, Inc. All rights reserved. 3

4 Number of Clinical Studies Initiated by Year Monotherapy In Combination Based on clinicaltrials.gov 2016 Merrimack Pharmaceuticals, Inc. All rights reserved. 4

5 Cancer is Complex

6 The way to go Nature Reviews, Clinical Oncology March 2016 Can we do something similar in Immuno-Oncology? 6

7 Possible Outcomes of Tumor Progression 1. Tumor dormancy 2. Partial immune escape 3. Complete immune escape 4. Error catastrophe

8 A Mathematical Framework of Tumor Cell Evolution during Immuno-Surveillance Tumor cell at time of seeding New tumor cell variants Immune cells specific to tumor cell variants Immune cell Iwami S, Haeno H, Michor F (2012) A Race between Tumor Immunoescape and Genome Maintenance Selects for Optimum Levels of (epi)genetic Instability. PLoS Comput Biol 8(2):

9 The Mathematical Model Tumor Cells Immune Cells f - dividing rate d - death rate u - probability to form variant N - total number of tumor variants p - rate of tumor cell inhibition by immune cells c - immune cell generation rate s interaction parameter

10 Possible Outcomes of Tumor Progression 1. Tumor dormancy 2. Partial immune escape 3. Complete immune escape 4. Error catastrophe Sensitivity analysis identifies most sensitive parameters: u Mutation rate N maximum number of tumor cell variants

11 Total Number of Tumor Cells During Tumorigenesis When number of tumor variants exceeds a threshold Nc -> error catastrophe

12 Steady-State Regime of Tumor Immuno-Escape and Error Catastrophe Number of variants Error Catastrophe Complete Immuno-Escape Partial Immuno-Escape Immune cells suppress all tumor cell variants Mutation Rate Sufficient tumor heterogeneity allows for complete immune escape

13 An Increased Division Rate of Variant Tumor Cells Promotes Immune Escape Increased tumor cell division rate

14 Using this simple model to simulate drug responses Baseline: Large number of tumor cells that achieved complete immuno-escape 1. Chemotherapy reduces tumor cell growth rate variant generation 2. Immunotherapy that increases # of immune cells Total cell number, X N=50 N=10 Time Time Tumor cells can NOT be eradicated

15 Using this simple model to simulate drug responses Baseline: Large number of tumor cells that achieved complete immuno-escape Combination therapy of chemo + immune therapy Total cell number, X N=10 N=50 Time Tumor cells can be eradicated

16 Summary: Summary & Model Extension A simple tumor-immune cell population model can help us: understand the drivers of immuno-escape explore desired mechanisms of action of combination therapies Next steps: Include innate immune cells More specialized immune cells Different growth rates

17 New Technologies Single cell RNAseq Mass cytometry PRO provides high-dimensional, singlecell data CON low numbers of cells loss of spatial information lab/scrna.seq.course/master/figures/rna-seq_workflow- 5.pdf.jpg. PRO high numbers of cells spatial information CON quantification of ~50 readouts at the single-cell level by combining metal isotope-labeled antibodies with Cell 169, , May 4, 2017

18 Challenge: Single cell RNAseq is still difficult to perform routinely on patient samples. Can we infer the (immune) cell content from bulk gene expression data using available single-cell RNAseq data sets?

19 Estimation of Immune Cell Content in Tumor Tissue Using Single-Cell RNA-seq Data Max Schelker, Sonia Feau, Jinyan Du, Nav Ranu, Edda Klipp, Gavin MacBeath, Birgit Schoeberl, Andreas Raue

20 Three Human Data-Sets 1. data-set of 4000 single cells derived from peripheral blood of 4 healthy subjects (Zheng, G. X. Y. et al. 2017) 2. data-set of 4645 tumor-derived single cells from 19 melanoma patient samples (Tirosh, I. 2016) 3. an unpublished data-set of 3114 single cells from 4 ovarian cancer ascites Total = 27 patient samples

21 Cellular Composition per Sample

22 Classification of cell types from scrna-seq expression profiles using decision trees

23 Benchmarking the Cell Type Classification FACS Ascites Literature Tirosh et al 2016

24 Five Reference Gene Expression Profiles (RGEPs) Basis for an accurate deconvolution is the choice of the signature gene set

25 Estimation Accuracy is Dependent on Different Signature Gene Sets 1. Table S12: 244 marker genes differentially expressed in regulatory T cells (Tirosh, I. 2016) 2. Table S3: 385 marker genes (Tirosh, I. 2016) 3. LM22: 547 signature genes that were found to maximally differentiate various cell types (Newman, A. M. et al. 2015) 4. Merged: 1076 unique genes combined from the LM22, Table S12 and Table and the 45 marker genes 5. All genes: 17,933 genes that have a non-zero expression in at least one sample.

26 Estimation Accuracy is Dependent on Deconvolution Algorithms 1. mldivide: exact solution using an algorithm for matrix inversion in MATLAB 2. SDPT3: a semidefinite-quadratic-linear programming algorithm from the CVX package20; 3. fitlm: fitting a linear model (y = a*x+b) to the data based on least-squares in MATLAB; 4. ν-svr: a support vector regression algorithm used in the CIBERSORT method.

27 True and Estimated Cell Proportions for all 27 Patient Samples

28 Estimation Accuracy of Cellular Composition for T Cell Subsets Depends on the RGEPs Indication specific RGEP3 performs close to Controls

29 Estimation Accuracy of Patient-Specific tumor Cell Gene Expression Profiles Deconvolution results in significantly improved gene expression profiles when tumor cell content is between 20-70%

30 Challenge: Single cell RNAseq is still difficult to perform routinely on patient samples Can we infer the immune cell content from bulk gene expression data using available single-cell RNAseq data sets? Yes but cell-specific and indication-specific reference gene expression profiles are necessary.

31 Summary Reference gene expression profiles derived from PBMCs are insufficient to enable accurate deconvolution With indication- and cell type-specific reference gene expression profiles deconvolution methods like CIBERSORT can accurately estimate the cellular composition Results in more accurate information about the tumor cell gene expression profiles by eliminating contamination from non-malignant cells Deconvolution approaches can be applied infer cellular composition from bulk gene expression data. Deconvolution provides a tool to link cellular heterogeneity to biological function or drug response from bulk gene expression data.

32 Now that we can quantify cellular heterogeneity we can start modeling it to understand treatment effects Thank you