Integrative and Functional Characterization of the Soft Tissue Sarcoma Genome

1. Integrative and Functional Characterization of theSoft Tissue Sarcoma Genome The Sarcoma Genome Project Jordi Barretina / Barry S. Taylor CTOS 15th Annual Meeting 6th November 2009

2. Soft Tissue Sarcomas • Derived from connective tissues • (mesenchymal tumors). • Rare (<1% of all cancers) • and heterogeneous. • STS have not yet been a focus of large-scale genomic efforts (low TCGA, ICGC priority). • Most of them caused by somatic mutations. • Gleevec as a paradigm of targeted cancer therapies.

3. From Cancer Genomics to Targeted Therapies • The need: • 50% of patients with newly diagnosed sarcoma eventually die of metastatic disease. • The majority of sarcoma subtypes are not very responsive to chemotherapy. • The solution: • Conduct a detailed genomic characterization of soft tissue sarcoma tumor samples. • Selective therapies against genomically altered targets can be highly effective and can exert fewer side effects.

4. The Sarcoma Genome Project Sample collection RNA and DNA extraction U133A Affy Expression arrays (n=141) 250K Affy (StyI) SNP arrays (n= 207) Sequencing (n=48) Candidate mutation validation (n=48 + 159*) Gene selection Computational analysis Data integration Targeted functional validation

5. TSGP Goals Elucidate the genetic alterations and signaling pathways associated with specific sarcoma subtypes. Improve sarcoma classification. Identify new therapeutic targets in sarcoma through integrative analysis.

6. Samples … + matched normals (205) Paired expression data for 141 of them.

7. Sequencing Summary • 48 Soft Tissue Sarcomas: • 21 liposarcomas • 10 myxofibrosarcomas • 6 GISTs • 11 synovial sarcomas • Genes chosen for exon resequencing: • Group 1: All Tyrosine Kinases • Group 2: Selected Cancer and Sarcoma genes • Group 3: All microRNAs • ( 224 genes + 496 microRNAs, • 3587 exons, 3830 amplicons)

8. Title line 1 Title line 2

9. Sequencing Summary • We found 37 somatic point mutations and 9 indels • (involving 21 genes • across 6 sarcoma subtypes). • Several previously described in sarcoma and other cancers (COSMIC). • 30 not previously reported. • 24 affected kinases. • 18 predicted to have a functional effect by in silico analysis EPHA5 NTRK1

10. PIK3CA mutations in ~18% of myxoid/round cell liposarcomas

11. Diverse NF1 alterations in soft tissue sarcoma

12. Nucleotide and copy number alterations in soft tissue sarcoma

13. Functional annotation of the Cancer Genome • Question: Which amplified genes in DDLPS are necessary for cancer cell proliferation/survival? • Answer: Genomics-driven RNAi screen in DDLPS • Systematic knockdown of ~400 significantly amplified genes • with shRNAs in 3 genotype-matched cell lines

14. Functional annotation of the Dedifferentiated Liposarcoma “Amplicome” • Genes essential for DDLPS cell proliferation.

15. CDK4 as a target inDedifferentiated Liposarcoma PD-0332991 CDK4/CDK6 inhibitor

16. Functional annotation of the Dedifferentiated Liposarcoma “Amplicome” • Genes essential for DDLPS cell proliferation.

17. YEATS4 and MDM2 co-amplification and potential cooperation in p53 pathway regulation

18. Summary Our study yields the most detailed map of molecular alterations across diverse sarcoma subtypes to date. Subtype-specific genomic alterations define new targets for soft tissue sarcoma therapy. - PI3K pathway in PIK3CA-mutant myxoid/round cell liposarcomas - mTOR pathway in NF1-deficient soft tissue sarcomas

19. Next-Gen sequencing is replacing capillary and array-based technologies Molecular Alterations in Cancer standard technology new technology DNA • Point mutations (substitutions/indels) • Chromosomal aberrations • Copy gains and losses • Loss of heterozygosity (LOH) • Rearrangements & fusion genes • Epigenetic modifications CANNOT BE DETECTED RNA Transcript expression level changes Differential alternative splicing Allele-specific expression changes NO GENOME-WIDE HIGH THROUGHPUT TECHNOLOGY Protein Protein expression level changes Protein modification changes Protein degradation/stability changes NO GENOME-WIDE HIGH THROUGHPUT TECHNOLOGY NO GENOME-WIDE HIGH THROUGHPUT TECHNOLOGY

20. Next steps in DDLPS… 50 Paired Patient Samples Affymetrix 250K SNP Array 5 Cell Lines 54,020 lentiviralshRNA pool screen (~11,000 genes) 12 Paired Patient Samples Whole Exome Sequencing ~186,000 baits (~16,000 target genes) & RNA-Seq Significantly Amplified Genes Genes Essential for Proliferation Recurrent Mutations and Fusion Genes Shantanu Banerji

21. Acknowledgements Cancer Genome Analysis Shantanu Banerji Alex Ramos Rameen Beroukhim Gaddy Getz Craig Mermel Derek Chiang Barbara Weir Kinjal Shah Lauren Ambrogio Tzu-Hsiu Chen Megan Hanna Laura MacConaill Project Management Wendy Winckler Comp Biology & Bioinf Jim Robinson David Twomey Ted Liefeld Michael Reich Pablo Tamayo Biological Samples Platform Scott Mahan Jennifer Franklin Jennifer Chen Alex Thomson Kristin Ardlie Genetic Analysis Platform Brendan Blumensteil Kristian Cibulskis Liuda Ziaugra Carrie Sougnez Stacey Gabriel Sequencing Platform Robb Onofrio Jen Baldwin RNAi Platform Hanh Le Pat Lizotte Brian Wong Alan Derr Jen Grenier Serena Silver David Root MSKCC Pennelope DeCarolis Mariana Lagos-Quintana Alan Ho Tsuyoshi Saito Neerav Shukla Christopher Lau Comp Biology Center Barry Taylor John Major Boris Reva Nick Socci Alex Lash Genomics Core Lab Agnes Viale Heidi Greulich Todd Golub Bill Hahn Levi Garraway Bill Sellers Eric Lander Matthew Meyerson Robert Maki Gary Schwartz Cristina Antonescu Chris Sander Marc Ladanyi Harold Varmus Sam Singer