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FIMM - Institiute for Molecular Medicine Finland Disclosures © FIMM - Institiute for Molecular Medicine Finland www.fimm.fi Disclosures › Research collaborations: .Labcyte Inc (liquid handling) .Pfizer, Roche .IMI-Predect public-private project (10 companies) › Off-label use of drugs will be discussed 2 Genomic and molecular landscape in AML Number of patient samples 23 significantly mutated genes in 200 AML patient samples The Cancer Genome Atlas (TCGA) dataset Panoramic view of AML, Chen and Chen; Nature Genetics, June 2013 Genomic and Epigenomic Landscapes of Adult De Novo Acute Myeloid Leukemia, New England Journal of Medicine 2013 3 Example of a translational gap in acute myeloid leukemia • AML genomes Novel therapy sequenced targets and Many <50% survival • Clonal therapeutics leukemia -Resistance evolution (e.g. FLT-3, patients common (10% characterized DNMTs) die due to survival) • Increasing disease/patient- disease -Classical data on specific and/or chemotherapy molecular molecular treatment used (from pathogenesis markers toxicity 1960s) with • Stem cell toxicity niche -Targeted treatments not available -Mutational profile often not helpful 4 US National Academy of Sciences 2012 5 Infrastructure and collaborative environment for individualized systems medicine in AML Finnish Hematology Society Genomic & molecular profiles Kimmo Porkka National Drug sensitivity testing Satu Mustjoki Clinic Biobank Data integration & models Mika Kontro Krister Wennerberg Tero Aittokallio Individualized and Caroline Heckman Jonathan Knowles improved therapy Olli Kallioniemi Why AML? Sampling over the course of disease Cancer always accessible Ex-vivo functional drug testing easier 6 Systems medicine to optimize drugs and drug combinations for individual AML patients • Repeated sampling at diagnosis, therapy response, relapse and drug resistance • Integration of genomic and molecular profiles and high-throughput ex-vivo drug testing for each patient (real-time) • Feedback therapeutic insights back to the clinic for tailoring personalized therapy or guiding clinical trials with new agents • Molecular and functional analysis of drug resistant samples after novel therapies 7 Biobanking all leukemia patients across the country: On average 46 sample aliquots per patient 8 Samples across time points provide molecular insights on disease progression in patients, including drug resistance DiagnosisRelapse 1 Treatment 1 Relapse 2 Relapse 3 Drug testing Drug testing Drug testing Exome seq Exome seq Exome seq Exome seq p-proteomics RNA seq RNA seq RNA seq p-proteomics p-proteomics p-proteomics 9 Individualized systems medicine (ISM) System medicine approach: - Data integration - Repeated sampling - Feedback to clinic - Learning system Pemovska et al. Cancer Discovery, on-line Sep 20, 2013 Rapid data analysis, integration and interpretation to provide therapeutic insights to the clinic Drug sensitivity testing Exome sequencing Integration & Interpretation & Feedback to clinic (4 days to 2 weeks) Gene expression Phosphoproteomics Fusion genes 11 High throughput screening infrastructure: - “repurposed” from functional genomics/drug discovery to drug sensitivity testing for individualized medicine BeckmanCoulter integrated robotic system Labcyte Access Workstation – fully automated assays – nl acoustic dispensing Plate readers Micro- 96 384 1536 Imaging arrays Flow cytometry Genomics Wennerberg, Östling et al. HTB facility at FIMM 12 The most direct way to ascertain patient treatment: test drugs individually on patient cells Drug sensitivity and resistance testing (DSRT) workflow Leukemia cells 4 day turnaround isolated from Fluorescent dyes blood or bone (HCS) marrow Measure cell growth/survival/ p- 30 x 106 cells proteins and calculate response 37°C, 72 h Pre-plated drug collection (1, 10, 100, 1000 and 10 000 nM) 13 Drug sensitivity and resistance testing with dose-response curves for each drug Detailed dose-response curves Probes for all oncology drugs and many 66 emerging cancer compounds for individual patient samples Approved 137 Investigati onal 103 14 (+)JQ1 Bimatoprost Dactinomycin GDC-0941 Mechlorethamine Omacetaxine Roscovitine Tosedostat 2-methoxyestradiol BKM-120 Danusertib GDC-0980 Megestrol OSI-027 Ruboxistaurin Tretinoin Abiraterone Bleomycin Dasatinib Gefitinib Melphalan OSI-906 Rucaparib Triethylenemelamine ABT-751 BMS-754807 Daunorubicin Gemcitabine Mepacrine Oxaliplatin Ruxolitinib UCN-01 Afatinib Bortezomib Decitabine Goserelin Mercaptopurine Paclitaxel S-trityl-L-cysteine Uracil mustard Allopurinol Bosutinib Dexamethasone Gossypol Methotrexate Panobinostat Saracatinib Valrubicin Altretamine Brivanib Dexrazoxone GSK1120212 Methoxsalen Patupilone Selumetinib Vandetanib Alvespimycin Bryostatin 1 Docetaxel Hydroxyurea Methylprednisolone Pazopanib Serdemetan Vargatef Alvocidib Busulfan Doramapimod Idarubicin MGCD-265 PD 0332991 Sirolimus Vatalanib Amifostine Cabozantinib Dovitinib Ifosfamide Midostaurin Pemetrexed SNS-032 Veliparib Aminoglutethimide CAL-101 Doxorubicin Imatinib Mitomycin C Pentostatin Sorafenib Vemurafenib Aminolevulinic acid Camptothecin EMD1214063 Imiquimod Mitotane PF-00477736 Sotrastaurin VER 155008 Amonafide Canertinib Entinostat Indibulin Mitoxantrone PF-04691502 Streptozocin Vinblastine Anagrelide Capecitabine Enzastaurin Iniparib MK-2206 PF-04708671 Sunitinib Vincristine Anastrozole Carboplatin Erlotinib Irinotecan MK1775 Pilocarpine Tacrolimus Vinorelbine Arsenic trioxide Carfilzomib Estramustine Ixabepilone MLN8237 Pipobroman Tamoxifen Vismodegib AS703026 Carmustine Etoposide JNJ-26481585 Mocetinostat Plerixafor Tandutinib Volasertib AT9283 Cediranib Everolimus Lapatinib Motesanib Plicamycin Tanespimycin Vorinostat Axitinib Celecoxib Exemestane Lasofoxifene Navitoclax Ponatinib Tarenflurbil XAV-939 AZ 3146 Chlorambucil Fasudil Lenalidomide Nelarabine Prednisolone Tasocitinib XL147 Azacitidine CI-994 Finasteride Lestaurtinib Neratinib Prednisone Temozolomide XL765 AZD1480 Cisplatin Fingolimod Letrozole Nilotinib PRIMA-1(Met) Temsirolimus YM155 AZD4547 Cladribine FK-866 Leucovorin calcium Nilutamide Procarbazine Teniposide Zolendronic acid AZD7762 Clofarabine Floxuridine Levamisole Nitrogen mustard PX-866 Thalidomide AZD8055 Clomifene Fludarabine Linifanib Nutlin-3 Quizartinib Thioguanine Barasertib Crizotinib Fluorouracil Lomustine NVP-AUY922 RAF265 Thiotepa Belinostat CUDC-101 Flutamide LY2157299 NVP-BEZ235 Raloxifene Tipifarnib Bexarotene Cyclophosphamide Foretinib LY2603618 NVP-LDE225 RDEA119 Tivozanib BI 2536 Cytarabine Fulvestrant LY2784544 Obatoclax Regorafenib Topotecan BIIB021 Dacarbazine Galiellalactone Masitinib Olaparib Ridaforolimus Toremifene 15 Identification of cancer-specific drug responses › Kill cancer cells more than normal cells (cancer-selective response) › We calculate DSS scores by comparing cancer’s response to controls (Healthy bone marrow) 100 control AML 75 50 Patient-specific cancer- % viability selective drug response 25 0 -9 -8 -7 -6 -5 Conc (log M) High DSS value => cancer cells have higher sensitivity to the drug 16 Summary of ex-vivo drug testing data from 27 chemorefractory AML patients Most frequent bioactive drugs in 27 AML patients 17 Guidance for personalized I II III IV V 600_1 252_5 252_1 560_1 560_9 600_2 1690 4701001 medicine and 1993 2095 1064 1145_3 800 393_1 393_3 1145_6 1145_1 718 1280 1497 600_3 252_3 252_2 1867 784_2 784_1 250 Disease stage DD D*RR D*DR D*RR D R R R D R R R R R R R R R D R Navitoclax clinical Ruxolitinib Dexamethasone $" & + '& + % &' $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ MEKi $" &+( ' ! $ ++) development of PI3K/mTORi Quizartinib emerging drugs Sunitinib DasatinibLink between MEK Topoisomeraseand JAK IIi inhibition $" )))*& &# sensitivity in DSRT Correlating drug TP53 RUNX1subtype II sensitivity with genomic DNMT3A NRAS/KRAS data: MLL-X fusions • Predictive biomarkers WT1 ETV6-NTRK3 • Novel therapeutics KIT MLL fusions link to NUP98-NSD1 • Drug combinations FLT3-ITD MEK and PI3K NPM1 inhibitor sensitivity • Novel drug targets IDH1/IDH2 PTPN11 • Insights on drug resistance Activating FLT3 Adverse karyotype mutations link to FAB subtypes M2 M1 M2 M2M2M2M5 M5 M5 M5sensitivity M5M5M5M5 to M5M5M5 FLT3 M1M1 inhibitors and dasatinib 18 Individualized systems medicine for AML to define optimal drugs for patients Using drugs as biological probes => direct opportunity for translation 19 Drug sensitivity Cancer-selective drugs testing report: summary of drugs Ineffective drugs showing selective cancer efficacy in an individual AML patients based on ex vivo testing 20 Clinical data: A treatment-refractory AML patient receiving a novel combination of clinical drugs based on ex-vivo profiling: Complete clinical response and subsequent progression Ex-vivo drug response data after relapse 21 Clinical implementation of ex-vivo drug-testing data in chemorefractory AML patients Leukemia‐selective responses observed in ex‐vivo drug testing Drugs available for human use (on‐ or off‐label) 14/17 (81%): ex‐vivo guided treatment possible 10 patients received personalized combinatorial treatments designed according to predictions 2 x CRi , 2 x leukemia free (hypoplasia) 40% response rate Drug combinations for patients: - Clo-Dasatinib-Vin - Sorafenib-Clo - Dasatinib-Sunitinib-Temsirolimus - Azacytidine-Sunitinib 22 Kontro et al, EHA 2013 Individualized systems medicine for AML to define optimal drugs for patients Molecular profiling to understand drug resistance and tx failure Drug-testing to understand drug (cross)-resistance and new vulnerabilities 23 Assesment of treatment response: clonal evolution by next-gen seq after
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