Secuenciación masiva: papel en la toma de decisiones

Transcription

1 Secuenciación masiva: papel en la toma de decisiones

2 Cancer is a Genetic Disease Development of cancer is driven by the acquisition of somatic genetic alterations: Nonsynonymous point mutations: missense. P.e. AKT1 g.c>t E17K nonsense. P.e APC g.c>t Q1338* Indels: in frame (insertion of Aa in the protein) frameshifts (leading to premature STOP codons and truncated proteins) Somatic copy number alterations : amplifications deletions - focal (median: 1.8Mb) - arm-level (size of a chromosome arm) Translocations: gene fusions Epigenomic changes: hypermethilation of CpG islands leads to gene silencing. P.e. PTEN

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4 The revolution in Genomics: NGS First draft of human genome 2nd generation instruments

5 The revolution in Genomics: NGS 2000 Sanger Sequencing (1977-) 2016 NGS (2006-) ABIPrism (Applied Biosystems) Up to 2304 per day (96 sequences per hour) HiSeq X (Illumina) Up to 2 billion sequences per day (6 billion in 3 days) FOLD INCREASE PER DAY MiSeq (Illumina) Up to 50 million sequences per day

6 Comprehensive analysis of Cancer genomics: The Cancer Genome Atlas 25* forms of cancer glioblastoma multiforme (brain) squamous carcinoma (lung) serous cystadenocarcinoma (ovarian) Etc. Etc. Etc. Biospecimen Core Resource with more than 150 Tissue Source Sites 6 Cancer Genomic Characterization Centers 3 Genome Sequencing Centers 7 Genome Data Analysis Centers Data Coordinating Center Multiple data types Clinical diagnosis Treatment history Histologic diagnosis Pathologic report/images Tissue anatomic site Surgical history Gene expression/rna sequence Chromosomal copy number Loss of heterozygosity Methylation patterns mirna expression DNA sequence RPPA (protein) Subset for Mass Spec

7 Lessons Learnt from the TCGA Data

8 Lessons Learnt from the TCGA Data The long tail of mutations in Cancer Van Allen et al., Nature Medicine 20, (2014) doi: /nm.3559

9 Lessons Learnt from the TCGA Data Driver somatic mutations across human cancer types may be linked to cellular processes and signaling pathways -> Hallmarks of Cancer PanCancer project, TCGA, Nature Genetics 2013 M. Lawrence & G. Getz/Broad Institute

10 Lessons Learnt from the TCGA Data Shall we reconsider?

11 Lessons Learnt from the TCGA Data The prevalence of somatic mutations across human cancer types. LB Alexandrov et al. Nature 500, (2013) doi: /nature12477

12 Lessons Learnt from the TCGA Data Signatures of somatic mutations across tumors 10,952 exomes and 1,048 whole-genomes across 40 distinct types of human cancer LB Alexandrov et al. Nature 500, (2013) doi: /nature12477

13 Lessons Learnt from the TCGA Data Signatures of somatic mutations across tumors: MSI MSI LB Alexandrov et al. Nature 500, (2013) doi: /nature12477

14 Lessons Learnt from the TCGA Data Signatures of somatic mutations across tumors: BRCAness BRCAness Substantial numbers of large deletions (up to 50 bp) with overlapping microhomology at breakpoint junctions LB Alexandrov et al. Nature 500, (2013) doi: /nature12477

15 Lessons Learnt from the TCGA Data Novel mechanisms of DNA damage: Kataegis & APOBEC Nik-Zainal S, Alexandrov LB, Wedge DC, et al. May Cell 149 (5): doi: /j.cell

16 NGS in clinical practice?

17 Conceptual evolution of Cancer treatment Nowadays Clinical Oncology Pathological Oncology Molecular Oncology Personalized Medicine Few therapeutic options to treat tumors: - Surgery - Radiotherapy - Few chemotherapies Increase on therapeutic options allowed specific treatments for different tumor types: -Combined chemo-radiation -Specific protocols Targeted agents that work in specific molecular alterations: -Broad knowledge of molecular tumor biology Disease guided approach Pathological guided approach Molecular approach

18 Conceptual evolution of Cancer treatment Nowadays Clinical Oncology Pathological Oncology Molecular Oncology Personalized Medicine First magic bullets : BCR/ABL Translocation-imatinib HER 2 Amplification Trastuzumab Push in Molecular Biology of Cancer Social attention (Nixon declares war on cancer) Pharma expands the pipeline

19 Conceptual evolution of Cancer treatment Nowadays Clinical Oncology Pathological Oncology Molecular Oncology Personalized Medicine Identification of driver molecular alterations (oncogene addiction) Successful stories of targeted therapies FDA guidance on codevelopment of diagnostics

20 NSCLC esquamous Enabling Stratified Medicine in NSCLC NSCLC Adenocarcinoma

21 Clinical research environment Disease Molecular alteration Clinical trial Breast cancer HER2 positive Breast cancer HR positive Breast cancer Triple negative NSCLC EGFR mutant NSCLC ALK rearranged Squamous NSCLC CRC KRAS wild-type CRC KRAS mutant Ovarian cancer Endometrial cancer Melanoma BRAF mutant Melanoma BRAF wild-type Papillary thyroid cancer Medullary thyroid cancer Pancreatic cancer Gastric cancer HER2 positive Gastric cancer HER2 negative Bladder cancer Glioblastoma Medulloblastoma Basal cell carcinoma PIK3CA mutation PIK3CA amplification PTEN mutation PI3K inhibitor PTEN loss of expression BRAF inhibitor KRAS mutation MEK inhibitor NRAS mutation PI3K + MEK inhibitor HRAS mutation PARP inhibitor BRAF mutation MET inhibitor BRCA1/2 mutation SMO inhibitor FGFR2/3 mutation FGFR inhibitor FGFR1 amplification Irreversible EGFR/HER2 inhibitor MET mutation Second-generation EGFR antibody MET amplification HSP90 inhibitor RET mutation... ASCO Educational Book 2012,169-72

22 Testing precision-medicine strategies in clinical trials SHIVA SAFIR-01 Issues to overcome: Ongoing efforts: WINTHER SAFIR-02 (NSCLC) high attrition rates heavily pretreated patients Limited drug activity in the Molecular-guided treatment arms

23 Many options based on molecular alterations Standard of care Clinical Reseach HER2 ampl- HER2 inhibitors EGFR mut- EGFR inhibitors ALK/ROS1 ampl- ALK inhibitors CKIT mut- KIT inhibitors BRAF mut- BRAF inhibitors BRCA1/2 mut- PARPinhibitors FGFR1 ampl- FGFR inhibitors FGFR2 ampl- FGFR inhibitors FGFR1 mut- FGFR inhibitors FGFR2 mut- FGFR inhibitors FGFR3-TACC3 tras- FGFR inhibitors PTCH mut- SMO inhibitors SMO mut- SMO inhibitors KRAS mut- MEK inhibitors PIK3CA mut- PI3Kalpha inhibitors PTEN mut- PI3K beta inhibitors AKT1/2 mut- AKT inhibitors NOTCH1 mut- NOTCH inhibitors IDH1 mut- IDH inhibitors MET ampl- MET inhibitors HER2 mut- HER2 inhibitors

24 Technical complexity of detecting multiple alterations IHC derived techniques Point mutations & indels Copy Number Alterations Gene fusions Hybridisation & detectionbased techniques Protein presence or loss Protein phosphorilation levels Gene expression Sequencingbased techniques NGS

25 What can be detected by NGS? Dienstmann et al. J Clin Oncol 2013

26 How is detection through NGS? Meyerson et al. Nature Rev Genet 2010

27 What can be detected? It all depends on the application Amplicon-seq Capture approaches Exome-seq Whole genome sequecying

28 Modern times: NGS in practice or in research? CLINICAL PRACTICE Amplicon-seq Capture approaches Exome-seq Whole genome sequecying RESEARCH Specific regions are multiplex-pcr amplified and sequenced. Customized pannels (p.e 350 regions in 70 genes) Quick Up to 2.5 and Mb cheap (200k probes) are sequencingready in 1 working day. Allows good intronexon coverage. Allows panels containing 400 cancer genes. Aprox Mb. Allows mutation detection as well as copy number calling. Expensive, needs time for bioinformatics ABL1 AKT1 AKT2 ALK APC BRAF CDH1 CDK4 CDKN2A CSF1R CTNNB1 Dear1 EGFR ERa ERBB2 FBXW7 FGFR1 FGFR2 FGFR3 FLT3 FRAP GATA1 GNA11 GNAQ GNAS GSK3B HIF1A HRAS IDH1 IDH2 IGF1R JAK1 JAK2 JAK3 KIT KRAS MAG MAP2K4 MEK1 MET MLH1 MPL MSH6 MYC NF2 NF3 NOTCH1 NOTCH4 NRAS PDGFRA PIK3CA PIK3R1 PIK3R5 PRKAG1 PRKAG2 PTCH1 PTEN RB1 RET RICTOR RUNX1 SMAD4 SMARCB1 SMO SRC STK11 TNK2 TP53 VHL WT1

29 Small gene panels vs Exomes Assessing the clinical utility of cancer genomic and proteomic data across tumor types. Y Yuan et al. Nature Biotechnology 32, (2014)

30 NGS is starting to gain formal rights Genes in VHIO-Card v3 ABL1 ERBB3 IDH1 MYC RNF43 AKT1 ESR1 IDH2 NF2 RUNX1 AKT2 FBXW7 JAK1 NOTCH1 SMAD4 AKT3 FGFR1 JAK3 NOTCH4 SMARCB1 ALK FGFR2 KIT NRAS SRC APC FGFR3 KRAS PDGFRA STK11 BRAF FGFR4 MAG PIK3CA TP53 CDH1 FLT3 MAP2K1 PIK3R1 TSC1 MET + E14 splice CDKN2A GATA1 PIK3R5 site TSC2 VHL CSF1R GNA11 MLH1 PTCH1 CTNNB1 GNAQ MPL PTEN ZNRF3 EGFR GNAS MSH6 RB1 BRCA1 ERBB2 HRAS MTOR RET BRCA2 July 2012 Application March 2013 ENAC Audit July 2013 Accreditation May 2016 NGS Ampliconseq March 2012 Kick off 30

31 NGS testing in the clinical setting today Gingras, I. et al. The current use and attitudes towards tumor genome sequencing in breast cancer. Sci. Rep. 6, 22517; (2016).

32 Challenges in Setting up NGS approaches in the Clinic

33 Dealing with FFPE tissues 99.9% of patient s samples 1- TUMOR PURITY 2- DNA/RNA QUALITY: FRAGMENTATION AND CHEMICAL DAMAGE 3- AMOUNTS

34 TUMOR PURITY Selecting a cutoff for minimum tumor area NGS sensitivity: 5% minimum mutated alleles N (2n): stroma, lymphocytes, normal surrounding tissue Mutated allele TUMOR CELLS Mutations may be present in all tumor cells: CLONAL OR May be present in a subset of cells: SUBCLONAL. In some instances, resistance mechanisms ex. EGFR T790M Some tumor types are stroma enriched, ex. pancreas

35 Challenges: Variant interpretation VUS (variants of uncertain significance): oncogenes vs tumor supressors Increases with panel size Published data in silico prediction tools VAF (variant allele fraction) Protein domain Protein residue +-±/*~

36 Challenges: the germline issue The need of germline DNA Reference genome Alignment Reference genome (major chromosomal contigs with chrs1-22, X, Y) Increases with panel size Variant calling (mutation vs polymorphism) Reference genome (major chromosomal contigs with chrs1-22, X, Y) Germline DNA from subject

37 To conclude: So, most likely, in the relatively short term (1-3 yrs): Small gene panels (<120 genes) will be part of the routine in the clinics Medium-sized gene panels will be used in all clinical research settings Larger options (Exome-seq, WGS) will be adopted when costs and timeline issues are solved

38 THANKS!!!!!