David Tamborero, PhD

Lopez-Bigas' lab

Study of Tumor Genomes

Study of Tumor Genomes Study sequencing data of tumors to understand the biological mechanisms shaping the mutational processes observed at different levels of resolution Marc Rosenthal

Genome scale: Mutation signatures reflecting 'dominant' biological processes acting through the genome Megabase scale: Mutation burden depends on region characteristics (chromatin state, transcription level, replication time,..) Nucleotide(s) scale: Mutation peak in TFBS of melanocytes due to physical competition with nucleotide excision repair DNAbinding elements Sabarinathan et al. Nature 2016

Mutagenic processes (intrinsic/extrinsic) Mechanisms of DNA repair The observation of the mutation pattern in sequenced tumor cohorts provides new insights on the interaction of the mutagenic processes and the ability of the cell to repair them

Study of Tumor Genomes Identify 'functional units' that confer the cancer hallmarks upon genomic alterations Hanahan et al.

How we identify driver genes? Mutations are fixed during clonal expansion if they confer a selective advantage to the tumor cells Genes whose alterations show signals of positive selection across a tumor cohort reveal the driver genes of that cancer Computational methods are aimed to detect mutations that do not follow a random behavior as estimated by a background mutational model

Catalogs of driver genes per cancer Rubio-Perez, Tamborero et al., Cancer Cell 2015

Analysis of whole-genome sequencing data 2,500 tumors 37 cancer types

Context matters! Genomic/transcriptomic characteristics of tumors are associated with distinct immune infiltrate configurations Tamborero, R.Perez et al 2017

Study of Tumor Genomes How to interpret the data from a newly sequenced tumor?

How to (clinically) interpret sequencing data? Given a list of genomic alterations of a newly sequenced tumor sample: - which ones are more likely to drive that tumor? - which ones may be exploited therapeutically?

https://www.cancergenomeinterpreter.org Note that CGI analyses mutations, CNAs and fusions Several formats accepted Cancer type specific analyses

Alteration analysis interactive report (mutation tab) https://www.cancergenomeinterpreter.org All the annotations are included in the reports Variants are classified as: - already validated oncogenic/neutral - predicted driver/passenger for VUS Computational prediction of the effect of VUS based on the knowledge retrieved from the sequencing data of large tumor cohorts

Drug prescription interactive report (biomarkers tab) https://www.cancergenomeinterpreter.org Organized by distinct levels of clinical relevance Cancer Biomarkers database Rodrigo Dienstmann

Pan-cancer landscape of genomic biomarkers of drug response Proportion of tumors Tumors with exome-seq at diagnosis (e.g TCGA) n = 6,792 Proportion of tumors Advanced tumors with panel-seq (GENIE) n = 17,642 Tamborero et al. Bioarxiv 2017

CGI use cases CGI early adopters: research Improve predictive models to identify novel biomarkers of drug response Refine analyses of newly sequenced cohorts

CGI use cases CGI early adopters: translational Support of clinical decision-making Allocation of cancer patients to the most appropriate genomicguided clinical trial Exploration of drug re-purposing opportunities in pediatric cancers

https://www.cancergenomeinterpreter.org Website statistics (oct 2016-today) >5,500 users >60,000 page views

SUMMARY

The study of sequencing data retrieves new insights of the biology of cancer and provides the rationale for molecular-guided therapeutic strategies

Tumor material gene panel / WES / WGS Clinical bioinformatics - digest the info to support clinical actions - generate data to drive novel findings Good et al. Genome Biology 2014

Rodrigo Dienstmann Aura Muntasell JM Piulats Carmen de Torres