How To Help Patients With Cancer With Personalized Medicine

Transcription

1 Use case Personalized medicine of cancer BioMedBridges mid-term review 12 March 2014, Florence Imre Västrik Institute for Molecular Medicine Finland FIMM On behalf of WP8 team

2 Outline Need for personalized medicine Case Acute Myeloid Leukemia (AML) How can research infrastructures help IT of personalized medicine How is it working Thoughts on sustainability

3 One size doesn t fit all need for personalized medicine PERCENTAGE OF THE PATIENT POPULATION FOR WHICH A PARTICULAR DRUG IS INEFFECTIVE, ON AVERAGE ANTI-DEPRESSANTS 38% (SSRIs) ASTHMA DRUGS 40% DIABETES DRUGS 43% ARTHRITIS DRUGS 50% ALZHEIMER S DRUGS 70% CANCER DRUGS 75% Source: The Case for Personalized Medicine, 3 rd edition

4 Focus in this use case: Adult Acute Myeloid Leukemia (AML) Most common adult acute hematological malignancy Poor survival Immunological, molecular, genomic heterogeneity Molecular driver signals poorly known Survival, newly diagnosed AML v v. >60-v. Lack of targeted therapeutic strategies Recurrent AML Very poor prognosis Specific therapies for recurrent disease not available Survival, relapsed AML N = 73 Alive Elossa at 1 1v yr. 34% 34% Elossa 2v 21% Alive at 2 yrs. 21% Finnish Leukemia Group. AML2003 / E. Elonen

5 For comparison Chronic Myeloid Leukemia (CML) and potential of targeted therapy % surviving % surviving Months from therapy start BCR-ABL fusion targeted with Imatinib (Gleevec) 5

6 Driver of the use case What should I put here?

7 Research infrastructures to the rescue Patient s omics data: Germline and tumor genome sequence Tumor transcriptome sequence Ex vivo drug sensitivity Phosphoproteomics

8 Sequence data interpretation pipeline Normal genome sequence Tumor genome sequence Germline variants Copy Number Variation Somatic mutations Ensembl dbsnp Cancer Gene Census 1000 Genomes COSMIC SISu ChEMBL DrugBank Known & putative new predisposing variants Putative driver genes Drug suggestions Tumor transcriptome sequence Fusion transcripts MRD markers

9 Ex vivo Drug Sensitivity and Resistance Testing (DSRT) Leukemia cells Dose response curves Measure cell viability 72 h Compendium of drugs, drug candidates & molecular probes Currently 306 oncology-focused active substances/drugs 135 approved, 171 investigational 65 conventional chemotherapeutics 20 hormone therapy drugs 119 kinase inhibitors 33 epigenetic/differentiating drugs 55 other targeted drugs 10 immunosuppressants

10 Chemical screening data interpretation pipeline Leukemia Healthy bone marrow mononuclear cells Dose response curves Dose response curves Drug Sensitivity Score (DSS) Drug Sensitivity Score (DSS) Selective Drug Sensitivity Score (sdss) ChEMBL DrugBank Clinical Trials Mediator Sorted and annotated drug list % viability 100 control Healthy BM AML Leukemia Conc (log M) Multikinase inhibitor PI3K/mTOR KI Drug Sensitivity Namt inhibitor Score (DSS) VEGFR-1/2/3 TKI mtorc2/1 inhibitor PI3K/mTOR KI p53-activating drug EGFR/HER-2 inhibitor PI3K/mTOR KI Aurora A/B/C KI

11 Treatment, follow-up, counseling One patient s data All patients data PM pipeline Clinical pesentation Knowledgebases Physician s report

12 Requirements for personalized medicine IT system Enable fine-grained access control to the data By subject/person as well as their data fields Enable tiered access Provide/be able to use appropriate domain model Health record with omics data, time series Use relevant existing ontologies, vocabularies and terminologies Provide browser based user interface To also edit the data! Provide easy-to-use web app development framework (API) Affordable

13 Clinical translation Done if N=13 Patient consents, results arrive in time DSRT shows a distinct pattern of leukemia-selective response Drugs available (on/off-label, compassionate) No standard therapy available (refractory/relapsed patients) 2 Complete Remission 4 Partial Remission 3 No Change 3 Progressive Disease 1 non-evaluable Data courtesy of Prof. Kimmo Porkka

14 Patient case FHRB.600

15 Background 50- year- old male, previously healthy AML FAB M5, normal karyotype, FLT3- ITD Failed 3 consecuhve induchon therapies Febrile, pancytopenia, WHO 3 Molecular profiling: exome and transcriptome sequencing and targeted phospho- proteomics of serial samples DSRT: SelecHon of an off- label drug- combinahon as per compassionate use based on ex vivo drug screening results

16 Transcriptome sequencing detects NUP98- NSD1 fusion NUP98 (chr 11) NSD1 (chr 5) Pemovska, Kontro et al. Cancer Discovery, 2013

17 Ex vivo DSRT results 600: 1st DSRT Ponatinib - Broad TKI BIIB021 - HSP90 inhibitor Sunitinib - Broad TKI* Tanespimycin - HSP90 inhibitor Tivozanib - TKI (VEGFR) Dasatinib - Broad TKI* Lestaurtinib - Broad TKI NVP-AUY922 - HSP90 inhibitor Entinostat - HDAC inhibitor Cytarabine - Antimetabolite Quizartinib - Flt3 inhibitor Foretinib - TKI (VEGFR2/MET) PF mtor/pi3k inhibitor Mitoxantrone - Topoisomerase II inhibitor Alvespimycin - HSP90 inhibitor Navitoclax - Bcl-2/Bcl-xL inhibitor Temsirolimus - Rapalog* Sorafenib - TKI (B-Raf/VEGFR) Axitinib - TKI (VEGFR) Pazopanib - TKI (VEGFR) sdss Pemovska, Kontro, et al. Cancer Discovery, 2013

18 Treatment response SensiHve sample Exome sequencing CNA RNAseq Expression Phophoproteomics Resistant sample Exome sequencing CNA RNAseq Expression Phophoproteomics Bone marrow blast cell % Neutrophil count Bone marrow blasts + promonocyte count (%) Days from start of therapy Neutrophil count (10 9 /L) InfecHon DasaHnib SuniHnib Temsirolimus

19 Loss of drug effect in the pahents is reflected in ex vivo drug teshng Temsirolimus 20 Pre treatment Post treatment Pretreatment Postreatment Pre treatment DasaHnib Post treatment Pretreatment Postreatment SuniHnib 600_3 sdss Bleomycin _2 sdss Tivozanib Alvespimycin Lestaurtinib Dasatinib Cytarabine Pazopanib Entinostat Axitinib PF Sorafenib Vorinostat Etoposide Temsirolimus Valrubicin Quizartinib BIIB021 Tanespimycin Sunitinib Ponatinib Pre treatment Post treatment Pretreatment Postreatment Pemovska, Kontro, et al. Cancer Discovery, 2013

20 Genome and transcriptome sequencing reveals clonal evoluhon D 600_3 600_2 600_0 90% FLT3-ITD #1 WT1 #2 40% 20% 18% 10% 5% NUP98-NSD1 5% FLT3-ITD #2 WT1 #1 WT1 #4 12% 30% WT1 #3 Chemotherapy 70% Das-Sun-Tem Pemovska, Kontro, et al. Cancer Discovery, 2013

21 About sustainability Ultimate aim the tools/service taken up and paid for by the healthcare system Engaging pharma interest in ex vivo drug testing for early indication of efficacy and search for efficacy predicting biomarkers Funded by FP7 Capacities Specific Programme, grant agreement no

22 Summary Implementation of personalized medicine benefits from physical ESFRI research infrastructure as well as biomed data bridges

23 Acknowledgements Töresin Karakoyun, Benjamin Braasch, Wolfgang Kuchinke, Sebastian Seufert, Christian Öhman [Heinrich Heine University Düsseldorf] Nathalie Conte, Adam Faulconbridge, Julie McMurry, Helen Parkinson [EMBL-EBI] Bart Charbon, Joeri van der Velde, Pieter Neerincx,Morris Swertz [University Medical Center Groningen], Martin Fransson, Roxana Merino Martinez [Karolinska Institutet], Raffael Bild, Sabine Brunner, Ashish Lamichane, Safey Halim, Willi Mann, Klaus Kuhn [Technische Universität München], Tea Pemovska, Maxim M. Bespalov, Evgeny Kulesskiy,, Anna Lehto, Laura Turunen, Krister Wennerberg [Institute for Molecular Medicine Finland - FIMM] Mika Kontro, Erkki Elonen, Satu Mustjoki, Kimmo Porkka [Helsinki University Central Hospital], Bhagwan Yadav, Henrik Edgren, Samuli Eldfors, Agnieszka Szwajda, Henrikki Almusa, Pekka Ellonen,Riikka Karjalainen, Sonja Lagström, Maija Lepistö, Muntasir Mamun Majumder, Jesus M. Lopez Marti, Pirkko Mattila, Astrid Murumägi, Aino Palva, Alun Parsons, Minna Suvela, Imre Västrik, Maija Wolf, Jonathan Knowles, Tero Aittokallio, Caroline A. Heckman, Olli Kallioniemi [Institute for Molecular Medicine Finland - FIMM]

24 Questions & answers