Univ. Helsinki Molecular Medicine (2008-) Karolinska Institutet Molecular Precision Medicine Stockholm, Sweden (2015-)

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1 Univ. Helsinki Molecular Medicine (2008-) Karolinska Institutet Molecular Precision Medicine Stockholm, Sweden (2015-)

2 Disclosures Patents on on molecular cytogenetic tools licensed by UC to Abbot Founder in Medisapiens Inc. (medical bioinformatics) Collaborations with pharma: Bayer, Roche, Novartis; Astra-Zeneca Collaborations with technology companies: Labcyte Inc., Pelago Discussion on drug repositioning: off-label treatment opportunities

3 04/07/2017

4 How often will DNA sequencing reveal actionable treatment opportunities? Interim Analysis presented at AACR

5 Currently open are 19 treatment arms in the NCI Match Trial (35-70 pts/arm)

6 Currently open are 19 treatment arms in the NCI Match Trial (35-70 pts/arm)

7 Systems Medicine Basis Precision medicine aims to collect, connect, and apply vast amounts of scientific research data and information about our health to understand why individuals respond differently to treatments and therapies, and help guide more precise and predictive medicine (UCSF definition). Drug testing Proteomics Imaging / Clinical data Functional testing 04/07/2017

8 Precision medicine Real-time data and allocation & adjustment of therapies US National Academy of Sciences 2012 FIMM 2013-

9 Elements assembled to take precision systems medicine to the clinical setting Clinical laboratory Companies Clinical trials Clinical expertise & data Living biobanks & ex-vivo models Individualized and improved cancer therapy Genomics & other technologies Bioinformatics / decision support Biomedical expertise Ethical Legal Regulatory Team science Real-time translation Patient

10 Academic and Industrial Collaborators

11 Individual Systems Medicine (ISM) for real-time impact on treatment for leukemia (clinical drugs) Database New drug development Clinical trials Biobanking Clinical system medicine: - Data integration - Repeated sampling - Feedback to clinic - Learning system Pemovska et al. Cancer Discovery, 2013 Pemovska et al. Nature, 2015 Malani et al., Leukemia, 2017

12 Big data management, integration and rapid interpretation needed to give real-time feedback to the clinic Drug sensitivity testing Exome sequencing Integration & Interpretation & Feedback to clinic (4 days to 2 weeks) N=1 Gene expression Phosphoproteomics Fusion genes

13 Percent inhibition Drug Sensitivity and Resistance Testing (DSRT) Platform: Towards nanoliter-scale HT drug testing of patient-derived cells 540 active substances (FO4) 143 approved drugs 319 investigational drugs Drug testing data analysis Nanoliter acoustics (Labcyte) 72h Viability /death by CellTiter-Glo / CellTox (Promega) nm 70 conventional chemotherapeutics 22 hormone therapy drugs 231 kinase inhibitors 51 epigenetic/differentiating drugs 75 other targeted drugs 13 immunosuppressants Pemovska et al., Cancer Discovery 2013 Image-based testing (Opera Phenix) Slope Drug concentration Drug Sensitivity Score (DSS) Modified AUC Yadav B et al., Sci Rep :5193 Flow cytometry (ique HD 384 well FACS screens) 1000 nm 100 nm 10 nm 1 nm

14 Pharmacopeia-wide drug sensitivity and resistance testing with dose-response curves for each drug Detailed dose-response curves for all oncology drugs and many emerging cancer compounds for individual patient samples Investigational Clinical drugs 04/07/2017

15 _3 393_ _1 1145_ _ _ _ _ _1 600_ _5 252_2 560_ _1 600_2 560_9 1408_ DSS Axitinib Kinase Kd (nm) ABL1 36 ABL1(T315I) 1.5 ABL1(H396P) 20 ABL1(M351T) 36 ABL1(E255K) 63 ABL1(Y253F) 230 ABL1(F317I) 800 VEGFR Molecular mechanisms 30 Axitinib T315I mutated patient samples Ph+ patient samples AML patient samples Healthy control samples 3. Clinical proof of concept Drug efficacy in a subgroup ex-vivo: - T315I gate-keeper mutation in BCR-ABL - Resistance to ABL inhibitors

16 Example of a translational gap in acute myeloid leukemia AML genomes and clonal evolution characterized Increasing data on molecular pathogenesis Stem cell niche Novel therapy targets and therapeutics (e.g. FLT-3, DNMTs) disease/patientspecific molecular markers Many leukemia patients die due to disease and/or treatment toxicity <30% survival -Resistance common (<10% survival) -Classical chemotherapy -Targeted treatments not much applied 16

17 Eight Functional Categories of Genes Are Commonly Mutated in Acute Myeloid Leukemia (AML) FLT3-ITD DNMT3A IDH2 NPM1 FLT3 NRAS WT1 ASXL1 PTPN11 IDH1 TET2 RUNX1 KRAS BCOR CBL STAG2 NF1 SRSF2 JAK2 GATA2 BRAF ATRX TP53 ATXN7L1 PHF6 NUP98 IKZF1 EZH2 EP3 CREBBP ABCA12 U2AF1 RAD5 NSD1 NOTCH1 MLL5 KIT KDM5A KDM2B ETV6 ELF1 CSF2 CHGA CEBPA Activated signaling 2. Tumor suppressors 3. DNA methylation 4. Chromatin modifications 5. Myeloid TF fusions 6. Cohesin complex 7. Spliceosome 8. Nucleophosmins Ref: Döhner H et al. N Engl J Med 2015;373: Mutation Mutation percentage in 128 in AML 128 AML samples samples

18 Drug sensitive (e.g. Flt-3, Mek) Bcl-2 only Drug resistant Bcl-2 mixed Mek Mek, Jak Glucocorticoids Six drug response subgroups in AML: resistant sensitive Chemotherapeutic drugs BCL-2 inhibitors BET inhibitors JAK inhibitors Glucocorticoids FLT-3/TK inhibitors mtor/pi3k inhibitors MEK inhibitors p38-mapk kinase inhibitors

19 Six major drug response subgroups in AML: association with driver mutations resistant sensitive Chemotherapeutic drugs BCL-2 inhibitors BET inhibitors JAK inhibitors Glucocorticoids FLT-3/TK inhibitors mtor/pi3k inhibitors MEK inhibitors p38-mapk kinase inhibitors

20 Opportunities for drug repositioning and clinical trials in AML: corticosteroids (Malani et al., Leukemia, 2016) CTG_sDSS Venetoclax Omipalisib Navitoclax AZD8055 Dexamethasone PF AZD2014 BIIB021 Omacetaxine ZSTK474 Ralimetinib Mocetinostat Pictilisib INK128 Doramapimod SNS-032 Teniposide Methylprednisolone Luminespib Entinostat Camptotechin Tretinoin Pomalidomide Deferoxamine aq Trametinib MK1775 Bosutinib Crenolanib AT 101 Selumetinib Danusertib Chloroquine aq Docetaxel Midostaurin C646 Dasatinib Nintedanib Paclitaxel Stattic PF FHRB.4172_2 Clinical Proof of concept: Subgroup responding: - FLT-3 WT - Relapse after cytarabine

21 Clinical translation of ISM results AML Pemovska et al., Cancer Disc 3: , 2013 Malani et al., in progress, 2017 Clinical translation in 19 out of 52 relapsed and refractory cases 7/19 (37%) led to complete remission or morphologic leukemia-free state Non-randomized observational study: Each patient was given different targeted drugs or combinations (short-term responses)

22 Monitoring clonal evolution by next-gen sequencing during treatment

23 Ultra-deep amplicon sequencing: Drugresistance arises from pre-existing rare cell 70 variants ULTRA-DEEP amplicon sequencing PHF6 706 Relapse TET2_ TET2_1, DNMT3A, INPPA4, DLEC PHF _Diagnosis_2011 0,8 706_Relapse_ PHF6 mutation 31% frequency at relapse by amplicon seq (8751/28229 reads) Poojitha Ojamies, Pekka Ellonen, Maija Wolf, Mika Kontro, Kimmo Porkka et al. Leukemia, 2016

24 Absolute read count Ultra-deep amplicon sequencing: Drugresistance arises from pre-existing rare 70 cell variants : PHF6 A>G variant at relapse after filtering Freq: 0.5% Variant coverage: 67/12468 reads Variant position TET2_2 PHF _Diagnosis_2011 TET2_1, DNMT3A, INPPA4, DLEC1 G A C T ,8 706_Relapse_ We identified 16 mutations from four patients (frequency 0,54-2%) that were not detected by exome sequencing Poojitha Ojamies, Pekka Ellonen, Maija Wolf, Mika Kontro, Kimmo Porkka et al. Leukemia, on-line, 2016

25 Variant allele frequency % Blast % Subclone-specific differential responses to targeted therapies 100 Day => Opportunity for subclone-specific precision cancer medicine DNMT3A, FLT3 40 NPM1 20 MTOR, STAT5A, CBL 0 Allotransplantation Day Cytotoxic Azacitidine+sunitinb/imatinib Dasatinib Axitinib-Everolimus Pt.1886_D Pt.1886_R1 Pt.1886_R2 Pt.1886_R3 Pt.1886_R4

26 After treatment Cross-resistance and vulnerabilities to previously ineffective drugs after relapse Drug crossresistance New vulnerabilities Before treatment 26

27 Precision medicine: stratified, individualized and subclone-based Stratified Individualized Subclone-based

28 Conclusions on systems medicine in AML Identification of drug repositioning opportunities (dexamethasone, dasatinib) in subgroups of patients (responsive subgroup, biomarker, mechanism of action, clinical proof of concept) Identification and implementation of individualized therapy recommendations No durable responses in these highly advanced chemorefractory patients, but could help bridge patients to BMT or immunotherapy Relapses due to rare pre-existing genomically defined subclones that expand during therapy. After relapse, resistance seen ex-vivo to the drugs applied, but also new drug vulnerabilities

29 Drug sensitivity testing has been promoted for decades, but has often failed, why would it work now? Previously: Solid tumors, chemotherapeutic agents, poorly representative cell cultures, a few drugs at a time, no molecular data, primitive assays and data analysis Now: Starting from hematological malignancies, repositioning targeted agents, ex-vivo primary cultures (representativity confirmed), comparison to healthy cells, profiling many drugs at a time, dose response, multiple assays, genomics and molecular profiling integrated, multiple samples during disease progression, deep systems data analysis Still needed: further assay technology development, consistency and QC, better / more complex models

30 Comparisons of drug testing data at FIMM with Broad and Sanger 30

31 Single cell phenomics in Drug Profiling 1 2 Cell seeding to pre-plated drugs on 384-well plates Immunostaining with 4 labels & HT-imaging 3 Multiparametric image analysis 4 & machine learning Dose response curve fitting & Drug Sensitivity Score (DSS) in Breeze. WebDSRT for images and data viewing.

32 Precision medicine: stratified, individualized, subclonebased, single cell-based Stratified Individualized Subclonebased Single cell -based

33 Slide: Päivi Östling Hematology => solid tumors 04/07/2017

34 Ovarian cancer: Astrid Murumägi, Ralf Butzov et al. Progenitor cells isolated from ovarian cancer ascites for functional drug testing Excellent representation of genomics between conditionally reprogrammed cells ex-vivo and patient s primary tumor Both Primary tumor sample from 2012 (FFPE) and conditionally reprogrammed cells carry KRAS (G12V) hotspot mutation and TP53 (S215N) mutation 04/07/

35 Individual systems medicine (ISM) grand challenge Precision cancer medicine is not just about genome sequencing, powerful systems medicine capabilities can complement the approach Requires consortia & multidisciplinary collaborations: biology-technology-clinic-industry-patients In hematological malignancies: identification of subgroups of drug-responsive patients, biomarkers, mechanism of action and clinical proof of concept ISM has facilitated drug repositioning, clinical trials, drug development

36 Acknowledgements Senior scientists: Päivi Östling, Vilja Pietiäinen, Maija Wolf, Teijo Pellinen, Astrid Murumägi

37 Kimmo Porkka, Mika Kontro, Satu Mustjoki Dept. Hematology, Helsinki University Hospital, Comprehensive Cancer Center Hematology Research Unit, Univ. Helsinki Antti Rannikko, Harry Nisen, Tuomas Mirtti Jing Tang Quantitative Systems Pharmacology Dept. Urology, Dept Pathology, Helsinki University Hospital Johanna Tapper, Riitta Koivisto-Korander, Ralf Butzow Women s hospital, Dept Pathology, Helsinki University Hospital Petri Bono, Heikki Joensuu, Katriina Peltola Comprehensive Cancer Center, Helsinki