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William Pao, MD, PhD Assistant Director, Personalized Cancer Medicine

Genotype-Driven Lung Cancer Treatment AAAS-FDLI Colloquium on Personalized Medicine October 27, 2009. William Pao, MD, PhD Assistant Director, Personalized Cancer Medicine Vanderbilt-Ingram Cancer Center Nashville, TN. Disclosure Information.

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William Pao, MD, PhD Assistant Director, Personalized Cancer Medicine

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  1. Genotype-Driven Lung Cancer TreatmentAAAS-FDLI Colloquium on Personalized MedicineOctober 27, 2009 William Pao, MD, PhD Assistant Director, Personalized Cancer Medicine Vanderbilt-Ingram Cancer Center Nashville, TN

  2. Disclosure Information I have the following financial relationships to disclose: Patent licensed to MolecularMD for EGFR T790M testing Consulting for MolecularMD

  3. Case Report Day 0 • 55 yo Caucasian woman • 9 pk-yr smoking history • s/p RLL and LUL lobectomies for lung adenocarcinoma • 2 years later, recurrence with bilateral pulm nodules • Progression on systemic chemotherapy

  4. Cancer in the United States, 2009 Jemal et al ‘09

  5. Risk factors 10% “never smokers” (<100 cigarettes in a lifetime) 50% former smokers NSCLC has 4 stages St I-IIIA – potentially curable by surgery But 60% diagnosed at incurable stages (IIIB/IV) Lung Cancer Facts Squam NSCLC histologies: Adenocarcinoma Squamous cell carcinoma Large cell carcinoma Large Adeno Small

  6. 5-Year Overall Survival (Clinical Stage) Goldstraw et al ‘07

  7. 30 Yrs’ Research: Effect of Standard Chemotherapy in Metastatic NSCLC Has Reached a Plateau 1207 pts Response rate – 19% Median TTP – 3.7 mos Median OS – 8 mos Schiller et al ‘02

  8. Gefitinib and Erlotinib –Related Quinazoline EGFR-TKIs ZD1839 Gefitinib Iressa OSI-774 Erlotinib Tarceva ATP

  9. K K Schematic of EGFR Signaling Pathway Ligand Ligand-binding domain RAS Gefitinib & erlotinib block signaling here RAF Grb-2 PI3K MEK SOS MAPK PTEN AKT Proliferation mTOR STAT 3/5 Survival

  10. Phase I/II/III Results • Phase I - gefitinib • Unexpected objective regressions in 10/100 patients with NSCLC • Phase II - gefitinib • 10/28% RRs in US/Japan for gefitinib – 2003 FDA approval (2nd-line) • Phase III – erlotinib vs placebo • 9% vs <1% RR; 6.7 vs. 4.7 mo OS – 2004 FDA approval (2nd-line) • Mild side effects • acneiform rash and diarrhea Baselga et al ’02; Herbst et al ’02; Ranson et al ’02; Nakagawa et al ’03; Fukuoka et al ’03; Kris et al ‘03; Shepherd et al ‘05

  11. Dramatic Response to Gefitinib

  12. Case Report Day 0 4 months • 55 yo Caucasian woman • 9 pk-yr smoking history • s/p RLL and LUL lobectomies for lung adenocarcinoma • 2 years later, recurrence with bilateral pulm nodules • Progression on systemic chemotherapy • Response to erlotinib

  13. Who Responds to Gefitinib or Erlotinib (2003)? • No clear association with EGFR expression • Clinical predictors • Female • Never smoker • Adenocarcinoma – esp. bronchioloalveolar subtype • Japanese (Miller et al JCO ’04; Fukuoka et al JCO ’03) • Are there molecular predictors of sensitivity?

  14. EGFR Mutations Associated with Sensitivity to Gefitinib/Erlotinib EGF ligand binding Tyrosine kinase autophos TM K DFG Y Y Y Y 718 745 858 861 964 GXGXXG K DFG L L Exon: 18 19 20 21 22 23 24 LREA G719A/C deletion L858R L861Q Lynch et al ’04; Paez et al ‘04; Pao et al ‘04

  15. Prospective Trialsof EGFR-TKIs in NSCLC *measured in months; **includes 5 atypical mutations; NR – not reached

  16. Rnd Ph III Trial: Iressa Pan ASian Study(IPASS: Gefitinib vs Chemo, upfront) EGFR mutation positive EGFR mutation negative Gefitinib (n=91)Carboplatin / paclitaxel (n=85) Gefitinib (n=132)Carboplatin / paclitaxel (n=129) 1.0 1.0 HR (95% CI) = 0.48 (0.36, 0.64) p<0.0001 No. events gefitinib: 97No. events Chemo: 111 HR (95% CI) = 2.85 (2.05, 3.98) p<0.0001 No. events gefitinib: 88No. events Chemo: 70 0.8 0.8 0.6 0.6 Probability of progression-free survival Probability of progression-free survival 0.4 0.4 0.2 0.2 0.0 0.0 0 4 8 12 16 20 24 0 4 8 12 16 20 24 Months Months At risk : Gefitinib 132 108 31 11 3 0 91 21 2 1 0 0 71 4 C/P 129 103 37 7 2 1 0 85 58 14 1 0 0 0 Gefitinib RR 1% Gefitinib RR 71% Mok et al ‘09

  17. Are There Molecular Predictors of Resistance to EGFR-TKIs? • Primary resistance • Tumors that are refractory to treatment with either gefitinib or erlotinib • The majority of patients • Are these all EGFR wildtype tumors?

  18. K K Mutations in the ERBB Pathway in NSCLC Ligand Ligand-binding domain 3 RAS 1 = both EGFR and HER2 1 4 RAF 2 Grb-2 PI3K MEK SOS MAPK PTEN AKT Proliferation mTOR STAT 3/5 Survival

  19. KRAS Mutations in NSCLC: A Negative Predictor for Response to EGFR TKIs

  20. Predictors of Response to Gefitinib/Erlotinib

  21. How to Select EGFR-TKI Therapy? • 50% of never smokers with adenoca have EGFR mutations • 19% of former smokers with adenoca have EGFR mutations • 4% of current smokers with adenoca have EGFR mutations • 15% of never smokers have KRAS mutations • Pham et al ‘06

  22. Case Report Day 0 4 months 25 months • 55 yo Caucasian woman • 9 pk-yr smoking history • s/p RLL and LUL lobectomies for lung adenocarcinoma • 2 years later, recurrence with bilateral pulm nodules • Progression on systemic chemotherapy • Response to erlotinib • Acquired resistance

  23. Case Report Day 0 4 months 25 months Growing bone lesion Growing lung lesion

  24. Drug Contact Residues Are Commonly Affected (T790M, T854A) 92% 4% 4% Adapted from Yun et al ’07; Bean et al ‘08

  25. MET Amplification Occurs in ~20% of Cases, With or Without T790M Mutations Engelman et al ’07; Bean et al ‘07

  26. Case Report Day 0 4 months 25 months T790M no MET Exon 19 del BIBW2992? Erlotinib

  27. Is this unique?Are there other examples?

  28. Fusions Involving the ALK Tyrosine Kinase Define Another Subset of NSCLC (Soda et al ‘07) 20 ALK KINASE 2 6 13 14 15 18 20 EML4 V1 KINASE V2 KINASE V3a/b KINASE V4 KINASE V5a/b KINASE V6 KINASE V7 KINASE “V4” KINASE “V5” KINASE

  29. Tumor Responses to PF-02341066 for NSCLC Evaluable Patients with ALK Fusions – Kwak et al PASCO ‘09 40 8+ 16 20 8+ 12 2+ 13+ 2+ 8+ 15+ 8+ 23+ 15+ 4+ One patient had clinical progression and discontinued without radiographic confirmation.

  30. Traditional View of Lung Cancer Small Cell Lung Cancer (SCLC) Non-Small Cell Lung Cancer (NSCLC): Adenocarcinoma Squamous cell carcinoma Large cell carcinoma Squam Large Adeno Small

  31. 1987: KRAS Mutations in Lung Adenoca KRAS Squam Large Adeno Unknown Small

  32. 2004: EGFR Mutations Identified EGFR KRAS Squam Large Adeno Unknown Small

  33. 2009: Lung Adenoca-Multiple Molecular Subsets ALK fusion ROS fusion PIK3CA BRAF PDGFR amp MEK1 KRAS HER2 EGFR Squam Large Adeno Unknown Small

  34. 2009: Lung Adenoca-Multiple Molecular Subsets ALK fusion ROS fusion PIK3CA BRAF PDGFR amp MEK1 KRAS HER2 EGFR • Mutations associated with drug sensitivity • G719X, exon 19 del, L858R, L861Q • Mutations associated with 1ry drug resistance • exon 20 dup • Mutations associated with 2ry drug resistance • L747S, D761Y, T854A, T790M • MET amplification Unknown

  35. Molecularly Tailored Therapy

  36. Version 1 SNaPShot: 36 Somatic Point Mutations in 8 Genes Relevant to Targeted Therapy in Lung Adenocarcinoma BRAF EGFR NRAS KRAS PIK3CA MEK1 (MAP2K1) AKT1 PTEN + PCR-based sizing assays for EGFR ex 19 dels, EGFR ex 20 ins’s, HER2 20 ins’s + ALK assessment

  37. Potential Benefits of Tailoring Therapy According to the Genetic Makeup of Tumors • Reduced healthcare costs Standard Approach: 2 cycles carbo/paclitaxel/bevacizumab $29,170 (wait 6 weeks to determine response) followed by 2 cycles of pemetrexed $21,868 (wait 6 weeks to determine response) Total: $51,038 Molecularly Tailored Approach: Multiplex mutation test $2,000 (>70% chance of response if known EGFR mutation) Erlotinib (90d) $13,671 Total: $17,671 Day 0

  38. Successes/Limitations Successes Limitations Some small molecular subsets (~1% = 2,100 pts) Diagnostic molecular assays labor-intensive May take 2-3 weeks to get result Different mutns assessed by different technologies Translocations/ Pt mutns/insertions/deletions RR/PFS vs OS Practicing oncologists not familiar with various tests • Molecular subsets of NSCLC defined • Greater likelihood of expected outcomes • Can prioritize treatment regimens • Will be necessary as more agents become available • Can rationally develop trials • Cost savings

  39. Acknowledgements Medicine David Johnson Jeff Sosman Bioinformatics Dan Masys Mia Levy Russ Waitman MGH A. John Iafrate Funding Anonymous Foundation VICC CCSG TJ Martell Foundation • Pao Lab • MarKeesa Duke • Laurel Fohn • Katie Hutchinson • Zengliu Su • Paula Woods • VICC • Jennifer Pientepol • Pathology • Cheryl Coffin • Cindy Vnencak-Jones

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