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Repeat Biopsies and the Potential Value of Biologically-Informed Acquired Resistance Therapy

Repeat Biopsies and the Potential Value of Biologically-Informed Acquired Resistance Therapy. Lecia V. Sequist, MD, MPH Associate Professor of Medicine, Harvard Medical School Mary B. Saltonstall Endowed Chair in Oncology, Massachusetts General Hospital. “The magic of EGFR inhibitors”.

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Repeat Biopsies and the Potential Value of Biologically-Informed Acquired Resistance Therapy

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  1. Repeat Biopsies and the Potential Value of Biologically-Informed Acquired Resistance Therapy Lecia V. Sequist, MD, MPH Associate Professor of Medicine, Harvard Medical School Mary B. Saltonstall Endowed Chair in Oncology, Massachusetts General Hospital

  2. “The magic of EGFR inhibitors”

  3. Treatment B Treatment A Treatment C Treatment D The promise of genotype-directed therapy

  4. The Concept of Oncogene Addiction Non-addicted case EGFR IGFR K-Ras gefitinib MAPK PTEN PI3K Jak/Stat Apoptosis EGFR-Addicted EGFR P42/44 MAPK PI3K Jak/Stat • EGFR mutant cancers are “simple”-one RTK controls all downstream signaling.

  5. Acquired Resistance Feb 2010 Diagnosis Dec 2010 TKI max response July 2011 Acq. resist Repeat Biopsy

  6. Clinical Information Targeted Therapy Biopsy Routine and Molecular Pathology

  7. Repeat Biopsies: EGFR mutants with AR to gefitinib, erlotinib Sequist et al Sci Transl Med 2011 8

  8. Two General Classes of TKI Resistance Target Alteration RTK mutation or amplification P P P P P P STAT ERK PI3K Sensitive/TKI-naïve Bypass Tracks Receptor TK Receptor TK RTK2 RTK1 RTK2 P P ? STAT ERK PI3K STAT ERK PI3K Specific TKI Slide courtesy of Alice Shaw

  9. Sci Transl Med; March 2011 • 37 consecutive samples with paired pre- and post- AR tissue • Comparative analyses for: • Histology with IHC • SNaPshot (most common mutations in 13 genes) • FISH for EGFR and MET amplification

  10. Updated MGH cohort: EGFR mutants with AR, n=106 EGFR Amp 15% with T790M 10% alone 4% with SCLC 1% SCLC 8% with EGFR amp 1% alone 4% with PI3K 3% PI3K 5% with SCLC3% alone 2% MET amp 5% BRAF 2% No identified AR mechanism 26% T790M 52% alone 42% with EGFR amp 10%

  11. Waxing/waning resistance in response to TKI selective pressure Sequist et al, Sci Transl Med 2011

  12. Waxing/waning resistance in response to TKI selective pressure Adenocarcinoma Sequist et al, Sci Transl Med 2011 High-grade neuroendocrine carcinoma

  13. EGFR transformed to SCLC is responsive to SCLC chemo Patient received carboplatin, etoposide and erlotinib

  14. T790M • Most common mechanism of resistance to EGFR TKIs (50-68%) • May have a better prognosis than non-T790M mechanisms (Oxnard, CCR 2010)

  15. Overcoming T790M: Irreversible TKIs Drug concentration (mM) gefitinib HKI-272 EKB-569 NCI-H1975 (L858R and T790M) HKI-272 (M) gefitinib (M) 120 untreated untreated 0.001 0.001 0.01 0.01 0.1 0.1 10 10 100 1 1 P-EGFR 80 P-AKT Relative cell viability (%) 60 P-MAPK 40 Total EGFR 20 Total AKT Total MAPK 0 0 .02 .2 2 20 Kwak, PNAS 102:7665, 2005

  16. Neratinib (HKI-272) RR 2%, PFS 15 weeks in TKI-resistant patients (Sequist, JCO 2010) Afatinib (BIBW-2992) RR 7%, PFS ~13 weeks in TKI-resistant pts (Miller, Lan Onc ‘12) Dacomitinib (PF-299804) RR 7% in TKI-resistant patients (Janne, ASCO ’09) ….novel T790M-specific TKIs are entering clinical trials CO-1686 AP26113 Irreversible TKIs (Pan-HER Inhibitors): Not highly effective for T790M

  17. Afatinib + cetuximab at MTD: Responses by T790M mutation T790M+ T790M– EGFR wt Uninformative for T790M 50 40 30 20 10 0 –10 –20 –30 –40 –50 –60 –70 –80 –90 –100 –110 Maximum percentage decrease from baseline (%) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Patient index sorted by maximum % decrease

  18. PFS at MTD 1.0 0.8 0.6 0.4 0.2 0.0 Estimated PFS4 probability 0 3 6 9 12 15 18 Time from treatment start (months) Number at risk Afatinib + cetuximab 96 59 32 12 5 3 0 MTD: Afatinib 40 mg daily + cetuximab 500 mg/m2 every 2 weeks MTD = maximum tolerated dose; PSF4 = progression-free survival at 4 months.

  19. AUY922 (Hsp90): best CT response: EGFR-mutant patients (n=25†/35) 100 80 60 40 20 0 -20 -40 -60 -80 -100 Best % change in target lesions * * * * * * *Confirmed responses; †Patients with at least one post-baseline scan;‡Including one PR not confirmed. Felip, et al. ESMO ‘12

  20. TKI Resistance via MET Amplification EGFR HGF MET Engelman et al., Science 2007: 316; 1040.

  21. Pre-Rx ‘08 Resistant ‘09 Proof of principle: 63 year old man with an EGFR mutant lung cancer Developed Resistance erlotinib 1/30/08 3/31/08 2/25/09 Rx on clinical trial

  22. Met Inhibitors in Clinical Trials • ARQ-197, specific MET inhibitor • Randomized phase II of erlotinib +/- ARQ-197 in TKI-naïve patients showed PFS benefit of combo but wasn’t designed to look at EGFR mutants or acquired resistance to EGFR TKIs (Sequist, JCO 2011) • Met-mab • Randomized phase II of erlotinib +/- MET-Mab in TKI-naïve paitents showed benefit of combo but again wasn’t designed to look at EGFR mutants or acquired resistance to EGFR TKIs (Spigel , ASCO 2011) • XL-184, MET + RET + VEGF • Randomized phase II of erlotinib +/- XL-184 in TKI-resistant patients, completed but not reported yet • Crizotinib: • We know it works in MET amp patients, but we don’t know about EGFR mutant,TKI resistant pts with MET amp

  23. Treatment of MET amp pt with Crizotinib Jan 2012 March 2012

  24. Clinical Strategies for Patients in the Clinic • Repeat biopsies whenever possible • Clinical trials whenever possible • Treatment beyond progression and local therapy for local progression • Continuing TKI beyond with other therapies

  25. Treatment Beyond Progression: appealing if PD is slow Oxnard, et al ASCO’12

  26. Summary and Future Directions Genotype-directed therapy paradigm has revolutionized NSCLC landscape Treatment of resistance has proven complicated Repeat biopsies of patients with AR will continue to greatly supplement lab-based research Prevention may be a potent strategy, especially since pre-disposition toward certain mechanisms may be identifiable. Need more ideal combination regimens Need to develop less-invasive ways of assessing tumor genotype

  27. Acknowledgments MGH Cancer Center Jeff Engelman Alice Shaw Daniel Haber Becca Heist Jerry Azzoli Jennifer Temel Inga Lennes Justin Gainor Panos Fidias Rachel Rosovsky Mike Lanuti Subba Digumarthy Michele Myers Marguerite Parkman Emily Howe MGH Pathology John Iafrate Mari Mino-Kenudson Dora Dias-Santagata Vicente Morales Vanderbilt William Pao Kadaoki Ohashi UCSF Belinda Waltman Haber/Toner Lab Shyamala Maheswaran Shannon Stott John Walsh James Sullivan Mike Rothenberg Funding Uniting Against Lung Cancer NIH/NCI (R21CA156000) MGH Thoracic Oncology MGH Pathology Yale Tom Lynch Scott Gettinger Sarah Goldberg Katie Politi Engelman Lab Tony Faber Matt Niederest Elizabeth Lockerman Germans Trias i Pujol, Barcelona Teresa Moran Stanford Joel Neal

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