apresentação - Health Cluster Portugal

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