IPATIMUP Translational Research Unit Matching IPATIMUP s Expertise with Company s Research/Clinical Strategy

Transcription

1 IPATIMUP Translational Research Unit Matching IPATIMUP s Expertise with Company s Research/Clinical Strategy Innovative Projects Questions & Needs André Albergaria INDUSTRY Pharma R&D Clinical Research HOSPITALS Clinicians & Patients

2 IPATIMUP Translational Research Unit Matching IPATIMUP s Expertise with Company s Research/Clinical Strategy

3 The alignment with the market outside. The New Paradigm of Drug Development in Pharma

4 TRU s Pipeline New Projects Project Project Presentation/Submission Project Presentation/Submission Discussion/Evaluation Project Presentation/Submission Discussion/Evaluation Contratualization

5 The era of NGS as an approach to improve gastric cancer management An Integrative Translational Research approach. Industry Axis Academy Axis Innovation Sweet Spot

6 How could we (rationally) select GC cases for high-throughput analysis and generate relevant data with impact in GC?

7 Next Generation Sequencing published works: 2013 REFERENCE SEQ. STRATEGY Nº OF CASES MOST FREQUENTLY MUTATED GENES Wang et al, Nat Genet 2011 Whole exome seq 22T + 22 N Zang et al, Nat Genet 2012 Exome seq 15 T + 15 N Nagarajan et al, Genome Biology 2013 Whole genome seq 2 T + 2 N (validation in 40 exomes) TP53 PTEN ARID1A RPL22 TTK FMN2 SPRR2B PTN ACVR2A PMS2L3 TP53 PIK3CA PKHD1 (cell-adhesion genes) CTNNB1 (cell-adhesion genes) CNTN1 (cell-adhesion genes) FAT4 (cell-adhesion genes) TP53 PTEN AQP7 ACVR2A STAU2 CTNNB1 PIK3CA TTK COPB2 DNAH7 TTN FSCB CTNNB1 (cell-adhesion genes) SEMA3E MCHR1 SPANXN2 METTL3 EIF3A EPB4IL3 ARID1A (epigenetic modifier) MLL3 (epigenetic modifier) MLL (epigenetic modifier) DNMT3A (epigenetic modifier) SETD1A (epigenetic modifier) KDM2B (epigenetic modifier) BAZ1B (epigenetic modifier) CHD4 (epigenetic modifier) DHX36 CCDC73 PCDH15 FMN2 ARID1A PAPPA SPTA1 RP1L1 EVPL Mutated genes Bulky genomic analysis of GC: Low number of samples studied Heterogeneity of histological type Any pre-selection strategy has been used OVCH1-CCDC91 COPG2-AGBL3 ZC3H15-ITGAV INTS4-RSF1 SOX5-OVCH1 YWHAB-BCAS1 FHIT del and rearrang. WWOX deletion KRAS amplification RASSF8 deletion GSTM1 deletion Structural variations

8 The answer to the question:.. STRATIFICATION! Molecular stratification based in prognostic and therapeutic markers: 1- HER2 amplification/overexpression (target therapy): Patients have a short increase in OS 2- Microsatellite instability (MSI) (good prognosis marker): Poor response to conventional therapy 3- CDH1 LOH: Patients with the worse prognosis in GC

9 TCGA, Nature 2014 Massive highthroughput anaylsis of 300 cases to find different subtypes This study reaches a division of GC into subtypes but (again) no stratification was included We will be addressing the real-world GC patients landscape

10 Why are we innovative? We used a pragmatic, simple and cost effective approach for a priori stratification of GC patients We produce a systematic molecular GC landscape of 3molecularly homogeneous GC sub-groups using WGS, RNAseq, Methylseq and Bioinformatics. We have clinical, pathological and survival data for these highly selected cases. Our pre-stratified GC-specific signatures will be validated in 4 GC independent cohorts, to derive subgroup-specific targetable signatures (the ones that will improve therapeutic efficacy) We will then test targetable factors in multimodal therapy regimens, using pre-clinical models mimicking specific subgroup- signatures.

11 Project Deliverables for (Patients) Community New biomarkers for gastric cancer, of potential use in/as: 1. Targets for new drugs; 2. Non-invasive diagnosis; 3. More accurate prognosis; 4. Stratification for clinical trials. Overall, a set of genetic tools that can significantly improve the management and treatment of gastric cancer.

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