1. 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

4. 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

5. 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?

6. 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?

7. 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

8. 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.

9. Patients Identify with Precision Medicine

10. 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):
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):
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.

11. 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

12. 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

13. 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