Applying Genomic Profiling to Precision Cancer Medicine in Clinical Practice George D. Demetri MD Senior Vice-President for Experimental Therapeutics Dana-Farber Cancer Institute Professor of Medicine, Harvard Medical School Co-Director, Ludwig Center at Harvard Harvard Medical School Boston, Massachusetts USA george_demetri@dfci.harvard.edu

Lack of standards for conduct or interpretation of tumor testing Challenges to Clinical Application of New Diagnostic Genomic Technologies in Cancer Variations in testing Lack of standards for conduct or interpretation of tumor testing Lack of data (and overly enthusiastic hype) for value of genomic profiling of tumors Lack of patient/payor/physician demand

How to document and define the value of genomic profiling in cancer? What sort of profiling? Single gene testing “Limited” panel testing vs. “large” panel testing Whole exome? Whole genome? What defines “actionability”? Results may be interesting – but do they drive important changes in patient management?

Proving the value of genomic profiling in cancers What drives demand for genomic profiling? What determines the value of the most accurate diagnosis? An available therapy? A change in prognosis? How can we make accurate, large-scale profiling a reliable, inexpensive and necessary commodity?

Complexity in Cancer: The GIST Example 1 Dominant Mutation per patient – Site of mutation differs between patients KIT PDGFRA WILD TYPE in both KIT and PDGFRA (13%) – +/-RESISTANT TO TKIs Exon 9 (8%) - SENS Membrane Exon 11 (76%) - SENSITIVE Exon 12 (0.3%) - SENSITIVE Exon 13 (1%) - +/-SENS Cytoplasm Exon 17 (1%) – RESISTANT TO TKI Exon 18 (0.6%) - RESISTANT

GIST with different mutations behave differently KIT mutant Exon 11 KIT mutant Exon 9 No KIT Mutation Different Genotypic and Structural Variants Fail Imatinib Therapy at Different Rates Heinrich, Corless, Fletcher, Demetri et al.

Patients Identify with Precision Medicine

Clinically-Important Genomic Differences Between Individual Patients with GIST KIT mutation (75% to 80%) GIST PDGFRA mutation (approx 10%) SDH mutations or silencing (SDHA, SDHB, SDHC) (approx 10%) BRAF or NF1 mutations (<2%) SPECIFIC SUBTYPES of above mutations can also impact patient outcomes1 Point mutations in KIT exon 11 confer overall favorable prognosis KIT mutations in exon 9 associated with poor prognosis PDGFRA D842V mutation: good risk in primary GIST , worse outcomes in metastatic GIST 1. Corless CL, et al. Nat Rev Cancer. 2011;11(12):865-878. Up to 95% of GISTs are positive for KIT expression by immunohistochemistry.1,2 While these are KIT-positive tumors, they may not necessary have a mutation in the KIT gene (about 75% to 80% of all GISTs have a mutation in KIT).1 KIT mutations most frequently occur in exon 9 or exon 11, but have also been found in exons 13 or 17 in rare cases.1 There is evidence that mutations in exon 9 of KIT are associated with worse prognosis than those in exon 11.2 However, deletions in exon 11 are also associated with poor prognosis.2 A smaller proportion of patients (5% to 8%) may have mutations in platelet-derived growth factor receptor alpha (PDGFRA).1 In general, mutations in PDGFRA are associated with a more favorable prognosis than KIT mutations.1 A small subset of GISTs are wild type for both KIT and PDGFRA, but may have mutations in BRAF, SDHA, SDHB, or SDHC. These tumors are referred to as wild-type GIST and occur in approximately 12% to 15% of patients.1 References: Corless CL, et al. Gastrointestinal stromal tumors: origin and molecular oncology. Nat Rev Cancer, 2011;11(12):865-878. Demetri GD, et al. NCCN Task Force report: update on the management of patients with gastrointestinal stromal tumors. J Natl Compr Canc Netw. 2010;8(suppl 2):S1-S41.

A Biblical Analogy What is the minimum value for molecular profiling to become a necessary standard? The story of Abraham negotiating with G_d about what level of ”righteousness” would save Sodom. 50 ”righteous” people out of 100,000? 25? 10? 0.0001% would be acceptable. For 1.7 M cancer diagnoses per year in US, need to change management in only 170 patient cases

Other Barriers to Optimal Use of Genomic Profiling Education of physicians Lack of (educated/rational) demand from patients Indiscriminate interpretation of results Not all BRAF mutations have the same therapeutic implications Fear of legal action to obtain expensive drugs without adequate clinical justification

Summary on Status of Genomic Profiling to Enhance Meaningful Precision in Cancer Medicine Feasibility of expansion for the best profiling technologies Need large-scale access and rigorous analysis of impact Fairness principles should apply (not just who is willing and able to pay for profiling) Objective analytics on impact in clinical outcomes Define important changes in patient care and outcomes