IPATIMUP
Translational
Research Unit Matching IPATIMUP’s
Expertise with Company’s
Research/Clinical Strategy
Innovative Projects
INDUSTRY Pharma R&D
HOSPITALS Clinicians & Patients
Clinical Research
Questions & Needs
André Albergaria
IPATIMUP
Translational
Research Unit Matching IPATIMUP’s
Expertise with Company’s
Research/Clinical Strategy
The alignment with the market…outside.
The New Paradigm of Drug Development in Pharma
TRU’s Pipeline 2014 - 2015
Discussion/Evaluation Presentation/Submission Project
Contratualization Discussion/Evaluation Presentation/Submission Project
Project 3 New Projects
Presentation/Submission Project
The era of NGS as an approach to improve gastric cancer management
An Integrative Translational Research approach.
Industry Axis
Aca
dem
y Axis
Innovation Sweet Spot
How could we (rationally) select GC cases for high-throughput analysis and generate
relevant data with impact in GC?
REFERENCE SEQ. STRATEGY Nº OF CASES MOST FREQUENTLY MUTATED GENES
Wang et al, Nat Genet 2011
Whole exome seq 22T + 22 N
TP53 PTEN ARID1A RPL22 TTK FMN2 SPRR2B PTN ACVR2A PMS2L3
DNAH7 TTN FSCB CTNNB1 (cell-adhesion genes) SEMA3E MCHR1 SPANXN2 METTL3 EIF3A EPB4IL3
Zang et al, Nat Genet 2012
Exome seq 15 T + 15 N
TP53 PIK3CA PKHD1 (cell-adhesion genes) CTNNB1 (cell-adhesion genes) CNTN1 (cell-adhesion genes) FAT4 (cell-adhesion genes)
ARID1A (epigenetic modifier) MLL3 (epigenetic modifier) MLL (epigenetic modifier) DNMT3A (epigenetic modifier) SETD1A (epigenetic modifier) KDM2B (epigenetic modifier) BAZ1B (epigenetic modifier) CHD4 (epigenetic modifier)
Nagarajan et al, Genome Biology
2013
Whole genome seq 2 T + 2 N
(validation in 40 exomes)
TP53 PTEN AQP7
ACVR2A STAU2
CTNNB1 PIK3CA
TTK COPB2
DHX36 CCDC73 PCDH15
FMN2 ARID1A PAPPA SPTA1 RP1L1 EVPL
Mutated genes
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
Next Generation Sequencing published works: 2013
Bulky genomic analysis of GC:
• Low number of samples studied
• Heterogeneity of histological type
• Any pre-selection strategy has been used
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
The answer to the question:….. STRATIFICATION!
We will be addressing the real-world GC patients landscape
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 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.
Why are we innovative?
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.
