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Victor Cohen, MDCM, FRCPC Department of Oncology Segal Cancer Center PowerPoint Presentation
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Victor Cohen, MDCM, FRCPC Department of Oncology Segal Cancer Center

Victor Cohen, MDCM, FRCPC Department of Oncology Segal Cancer Center

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Victor Cohen, MDCM, FRCPC Department of Oncology Segal Cancer Center

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  1. Mechanisms of Acquired Resistance to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors (EGFR-TKI) in Non-Small Cell Lung Cancer (NSCLC) Victor Cohen, MDCM, FRCPC Department of Oncology Segal Cancer Center SMBD – Jewish General Hospital McGill University

  2. Potential Conflict of Interest • Dr. Victor Cohen • Roche Canada/Research Support(2004-present)

  3. Background • EGFR-TKI (gefitinib and erlotinib)developed as therapeutic agents for NSCLC • Members of a class of Quinazolium-derived agents • Inhibit EGFR pathway by binding (reversible) to ATP pocket domain • Antitumor activity in clinical trials but benefits modest

  4. Background • 10% of (Western) patients treated with EGFR-TKI have dramatic and durable responses • Clinical features predicting sensitivity; female, adenocarcinoma, Asian ethnicity and never-smoking history • 2004; EGFR mutations as a major determinant underlying dramatic responses following treatment with TKI

  5. EGFR Mutations

  6. EGFR Mutations • Mutations identified using DNA sequencing methods • Considered the “gold standard” • Non-sequencing assays offering ease of scoring and high sensitivity have been developed • Providing a robust and accessible approach to the rapid identification of EGFR mutations • Denaturing High Performance Liquid Chromatography (dHPLC)

  7. EGFR Mutations • Prospective trials treating therapy-naïve patients with EGFR mutations with EGFR-TKI (200 patients) • RR 55-82% and median TTP of 9-13 months (3 to 4-fold greater than observed with chemotherapy) • Despite dramatic efficacy, all patients will ultimately develop resistance (acquired) to the agents

  8. Acquired Resistance • Critical to understand mechanisms of acquired resistance • May lead to the development of effective therapies for patients who develop acquired resistance • Acquired resistance mechanisms have been studied most extensively in EGFR mutant cancers • Remains to be determined if mechanisms are shared with wild-type EGFR cancers

  9. EGFR T790M Mutation

  10. EGFR T790M Mutation • Detected from tumors of EGFR mutant NSCLC patients who have developed clinical resistance (in vitro EGFR-TKI resistant mutant cell lines) • Found in 50% of tumors • Analogous position to known resistance mutations to imatinib in other kinases; “gatekeeper mutation” • ? Steric hindrance

  11. Amplification of MET

  12. Amplification of MET • Redundant activation of Her3 permits cells to transmit same downstream signaling in the presence of EGFR-TKI • Concomitant inhibition of both EGFR and MET is required to kill resistant cells • 22% of NSCLC with acquired resistance had MET amplification in specimens

  13. Acquired Resistance • EGFR T790M and MET amplification account for 60-70% of all known causes of acquired resistance to EGFR-TKI • Other mechanisms are likely to be discovered • TGF-β IL-6 axis mediates selective and adaptive mechanisms of resistance to molecular targeted therapy in lung cancer* * Yao et al. PNAS 35, 15535-40 (2010)

  14. Acquired Resistance • Existence of subpopulation of cells intrinsically resistant to EGFR-TKI • Display features of EMT • Activation of TGF-β mediated signaling was sufficient to induce EMT phenotypes • Upregulation of TGF-β resulted in increased secretion of IL-6 (cells resisted treatment independently of EGFR pathway)

  15. Acquired Resistance • Produced during inflammatory response • Mouse model used to determine whether inflammation might impair sensitivity • Induction of inflammation stimulated IL-6 secretion and was sufficient to decrease tumor response • Data provide evidence indicating resistance could arise not only as a consequence of changes within cells but also through activation of tumor microenvironment

  16. Challenges • Important to continue to study preclinical models (and tumors) that have developed resistance to uncover novel resistance mechanisms • Several challenges in translating preclinical studies into effective clinical therapies

  17. Challenges • Accurately identifying which patients have which mechanisms of resistance • No repeated tumor biopsies • Critical in that the therapeutic strategy aimed at overcoming resistance may not be effective in all resistant patients E.g. Irreversible inhibitors not effective in resistance mediated by MET amplification

  18. Challenges • Multiple mechanisms of resistance can occur concurrently in same patient • Both EGFR T790M and MET have been detected in same specimens (occur independently in different metastatic sites in the same patient) • Therapeutic strategy aimed solely at one mechanism may not be effective or lead only to partial regressions • Combination strategies may be more comprehensive and potentially more effective

  19. Challenges • Biological definition and detection of resistance mechanisms • T790M can sometimes be present as minor allele and yet be sufficient to cause resistance but may go undetected • Challenges with detection of MET amplification. Definition of what constitutes clinically significant amplification not well defined

  20. Conclusion • Gefitinib and erlotinib are effective therapies for patients with EGFR mutant NSCLC • All patients ultimately develop resistance • Important to identify and study mechanisms of resistance as a means of rationally designing the next generation clinical studies • Several clinical trials (aimed at inhibiting known resistance mechanisms) are already underway

  21. Clinical data on EGFR-TKIs and Overcoming Resistance in Metastatic NSCLC2nd Quebec Conference on Therapeutic Resistance in Cancer Vera Hirsh, MD, FRCPC McGill University Health Centre Montreal, QC, CANADA

  22. Potential Conflict of Interest • Dr. Vera Hirsh • None

  23. EGFR expression in human tumours Tumours showinghigh EGFR expression High expressiongenerally associatedwith NSCLC 40-80% Prostate 40-80% Gastric 33-74% Head and neck 90-100% Breast 14-91% Colorectal 25-77% Pancreatic 30-50% Ovarian 35-70% Invasion Metastasis Late-stage disease Poor outcome

  24. EGFR mutation causes conformational change and increased activation Wild Type EGFR Mutant EGFR Ligand Extracellular domain Trans-membrane domain Tyrosine kinase domain ATP Tyrosine phosphorylation Ras-Raf-MAPK Proliferation Pi3K-AKT Survival EGFR internalisation Degradation/recycling EGFR signals for longer at the cell membrane Arteaga 2006, Gadzar et al 2004, Hendricks et al 2006, Sordella et al 2004

  25. The distribution of activating mutations among EGFR mutation positive patients is similar in Asian and non-Asian studies ATP binding cleft Regulatory domain Transmembrane region Extracellular domain C-lobe N-lobe TK domain A-loop Chelix P-loop 21 20 19 18 Some patients had more than one mutation type

  26. BR.21 Study Design Erlotinib* 150 mg daily RANDOM I ZE Stratified by: Centre PS, 0/1 vs 2/3 Response to prior Rx (CR/PR:SD:PD) Prior regimens, (1 vs 2) Prior platinum, (Yes vs no) Placebo “150 mg” daily *2:1 Randomization

  27. BR.21: Overall Survival 1.00 0.75 0.50 0.25 0 HR=0.70 (95% CI, 0.58-0.85); P < 0.001* Survival distribution function 31% 42.5% improvement in median survival Erlotinib Placebo 21% 0 5 10 15 20 25 30 Survival time (months) *HR and P-value adjusted for stratification factors at randomization plus HER1/EGFR status. Shepherd et al. N Engl J Med. 2005;353:123-132.

  28. IPASS: first-line study design Gefitinib(250 mg/day) 1:1 randomisation Carboplatin (AUC 5 or 6)/ paclitaxel (200 mg/m2) 3 weekly† Endpoints • Patients • Chemo-naïve • Age ≥18 years • Adenocarcinoma histology • Never or light ex-smokers* • Life expectancy≥12 weeks • PS 0-2 • Measurable stage IIIB/ IV disease • Primary • PFS (non-inferiority) • Secondary • ORR • OS • QoL • Disease-related symptoms • Safety and tolerability • Exploratory • Biomarkers • EGFR mutation • EGFR-gene-copy number • EGFR protein expression *Never smokers, <100 cigarettes in lifetime; light ex-smokers, stopped 15 years ago and smoked 10 pack years†Maximum of 6 cycles Carboplatin / paclitaxel was offered to gefitinib patients upon progression; AUC, area under curve Mok et al 2009

  29. IPASS: ORR Patients (%) (n=609) (n=608) Odds ratio >1 implies a greater chance of response on gefitinib Odds ratio and p-value from logistic regression with covariates Mok et al 2009

  30. IPASS: pre-planned analysis of ORR by EGFR mutation status Gefitinib Carboplatin / paclitaxel EGFR M+ OR (95% CI) 2.75 (1.65, 4.60), p=0.0001 EGFR M- OR (95% CI ) 0.04 (0.01, 0.27), p=0.0013 ORR (%) 71.2 47.3 23.5 1.1 (n=132) (n=129) (n=91) (n=85) ITT populationOR>1 implies greater chance of response on gefitinibOR and p-value from logistic regression with covariate Mok et al 2009

  31. IPASS: PFS Carboplatin /paclitaxel Gefitinib N Events 609 453 (74.4%) 608 497 (81.7%) HR (95% CI) = 0.741 (0.651, 0.845) p<0.0001 5.874%48%7% Median PFS (months)4 months progression-free6 months progression-free12 months progression-free 5.761%48%25% Probabilityof PFS 1.0 0.8 0.6 0.4 0.2 0.0 0 4 8 12 16 20 24 Months Primary Cox analysis with covariates; ITT population HR <1 implies a lower risk of progression on gefitinibITT, intent-to-treat Mok et al 2009

  32. IPASS: pre-planned analysis of PFSby EGFR mutation status EGFR M- 1.0 N Median (m) Gefitinib 91 1.5 Carboplatin / paclitaxel 85 5.5 0.8 HR (95% CI) = 2.85 (2.05, 3.98) p<0.0001 0.6 Probability of PFS 0.4 0.2 0.0 0 4 8 12 16 20 24 Time from randomisation (months) EGFR M+ 1.0 N Median (m) Gefitinib 132 9.5 Carboplatin / paclitaxel 129 6.3 0.8 HR (95% CI) = 0.48 (0.36, 0.64) p<0.0001 0.6 Probability of PFS 0.4 0.2 0.0 0 4 8 12 16 20 24 Time from randomisation (months) Primary Cox analysis with covariates; intent-to-treat (ITT) population Hazard ratio (HR) <1 implies a lower risk of progression on gefitinib Mok et al 2009

  33. IPASS: EGFR mutation is a strong predictor for differential PFS benefit between gefitinib and doublet chemotherapy Gefitinib EGFR M+ (n=132)Gefitinib EGFR M- (n=91)Carboplatin / paclitaxel EGFR M+ (n=129) Carboplatin / paclitaxel EGFR M- (n=85) Probabilityof PFS 1.0 EGFR M+HR=0.48, 95% CI 0.36, 0.64 p<0.0001 EGFR M- HR=2.85, 95% CI 2.05, 3.98 p<0.0001 0.8 Treatment by subgroup interaction test, p<0.0001 0.6 0.4 0.2 0.0 0 4 8 12 16 20 24 Time from randomisation (months) M+, mutation positive; M-, mutation negative

  34. PFS by EGFR mutation type: IPASS Exon 19 deletion L858R N N Gefitinib 64 Carboplatin / paclitaxel 47 Gefitinib 66 Carboplatin / paclitaxel 74 1.0 1.0 HR (95% CI) = 0.553 (0.352, 0.868) p=0.0101 HR (95% CI) = 0.337 (0.255, 0.560) p<0.0001 0.8 0.8 0.6 0.6 Probability of PFS Probability of PFS 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 Time from randomisation (months) Time from randomisation (months) Mok et al 2009 Post hoc Cox analysis with covariates; ITT population

  35. IPASS: QoL and symptom improvement rates for overall population Gefitinib (n=590) Carboplatin/paclitaxel (n=561) p=0.0148 p=0.3037 p<0.0001 % patients with sustained clinically relevant improvementa Evaluable for QoL population ; logistic regression model with covariatesa≥6-point improvement (FACT-L and TOI); ≥2-point improvement (LCS), maintained ≥21 days Mok et al 2009

  36. IPASS: post hoc QoL and symptom improvement rates for EGFR M+ patients Gefitinib (n=131) Carboplatin / paclitaxel (n=128) p<0.0001 p<0.0001 p=0.0003 % patients with sustained clinically relevant improvementa Evaluable for QoL population; logistic regression model with covariatesa6-point improvement (FACT-L and TOI); 2-point improvement (LCS),maintained ≥21 days Mok et al N Engl J Med 2009

  37. IPASS: 2010 updated OS analysis (ITT) Gefitinib (n=609)Carboplatin/ paclitaxel(n=608) HR (95% CI) 0.90(0.79, 1.02); p=0.109 No. events G 484 (80%) C / P 470 (77%)Median OS G 18.8 monthsC / P 17.4 months 1.0 Probabilityof survival 0.8 0.6 0.4 0.2 0.0 48 0 4 8 12 16 20 24 28 32 36 40 44 52 Patients at risk: Gefitinib 609 608 514 525 468 443 400 364 331 301 270 232 227 183 192 151 148 119 97 65 44 28 13 9 3 1 0 0 C / P Primary Cox analysis with covariates A hazard ratio <1 implies a lower risk of death on gefitinibNo formal adjustment made for multiple testing Yang CH et al. ESMO 2010

  38. IPASS: 2010 summary of subsequent systemic therapy (ITT) *% exclude 20 patients in the gefitinib arm with ongoing randomised treatment**Patients may have also received other chemotherapy and / or EGFR TKI during the study. Excludes single platinum based chemotherapy #Categories are not mutually exclusive Radiotherapy, surgery, medical procedures and other treatments excluded Yang CH et al. ESMO 2010

  39. IPASS: 2010OS by EGFR mutation status (ITT) EGFR mutation + 1.0 0.8 0.6 0.4 0.2 0.0 0 4 8 12 16 20 24 28 32 36 40 44 48 52 EGFR mutation - Gefitinib (n=91)Carboplatin /paclitaxel(n=85) HR (95% CI) 1.18(0.86, 1.63); p=0.309No. events G 82 (90%)C / P 74 (87%)Median OS G 11.2 monthsC / P 12.7 months Gefitinib (n=132)Carboplatin /paclitaxel(n=129) HR (95% CI) 1.00(0.76, 1.33); p=0.990No. events G 104 (79%)C / P 95 (74%)Median OS G 21.6 monthsC / P 21.9 months 1.0 0.8 0.6 Probability of survival Probability of survival 0.4 0.2 0.0 0 4 8 12 16 20 24 28 32 36 40 44 48 52 Time from randomisation (months) Time from randomisation (months) Patients at risk: Gefitinib C / P 132 129 126 123 121 112 103 95 88 80 70 68 58 55 46 48 38 40 24 26 11 15 6 7 3 0 69 76 52 57 40 44 29 33 26 25 19 19 16 16 11 11 8 3 5 1 1 1 0 1 0 0 91 85 0 0 Cox analysis with covariates; a hazard ratio <1 implies a lower risk of death on gefitinib No formal adjustment for multiple testing was made, therefore statistical significance at the traditional 5% level cannot be claimed Yang CH et al. ESMO 2010

  40. IPASS: 2010 overall survival: EGFR mutation non-evaluable (ITT) 1.0 Probabilityof survival 0.8 0.6 0.4 0.2 0.0 0 4 8 12 16 20 24 28 32 36 40 44 48 52 Time from randomisation (months) Patients at risk: Gefitinib 386 394 319 326 295 274 257 225 214 188 174 139 150 109 130 87 99 68 65 36 28 12 6 1 0 0 0 0 C / P Gefitinib (n=386)Carboplatin / paclitaxel(n=394) HR (95% CI) 0.82(0.70, 0.96); p=0.015*No. events G 298 (77%)C / P 301 (76%) Median OS G 18.9 monthsC / P 17.2 months Primary Cox analysis with covariates; a hazard ratio <1 implies a lower risk of death on gefitinib*No formal adjustment for multiple testing was made, therefore statistical significance at the traditional 5% level cannot be claimed Yang CH et al. ESMO 2010

  41. IPASS: PFS and OS by known EGFR mutation status OS (2010) 1.0 Gefitinib EGFR M+ Gefitinib EGFR M- C / P EGFR M+ C / P EGFR M- 0.8 Mutation + 0.6 Probability of survival 0.4 0.2 Mutation - 0.0 4 8 12 16 20 24 4 8 12 16 20 24 28 32 36 40 44 48 52 0 Time from randomisation (months) 126 69 123 76 121 52 112 57 103 40 95 44 88 29 80 33 70 26 68 25 58 19 55 19 46 16 48 16 38 11 40 11 24 8 26 3 11 5 15 1 6 1 7 1 3 0 0 1 0 0 0 0 132 91 129 85 108 21 103 58 71 4 37 14 31 2 7 1 11 1 2 0 3 0 1 0 0 0 0 0 PFS (2008) 1.0 0.8 0.6 Probability of progression-free survival 0.4 0.2 0.0 0 Time from randomisation (months) Patients at risk: Gefitinib M+ Gefitinib M- C / P M+ C / P M- 132 91 129 85 Yang CH et al. ESMO 2010 Patients at risk excludes censored patients and those who have experienced an event

  42. IPASS conclusions: updated survival analysis Mature OS (secondary endpoint) was similar for gefitinib and carboplatin / paclitaxel with no statistically significant difference between treatments in the overall population A consistent OS outcome was observed across clinical subgroups with no significant difference in OS There was no significant difference in OS across the EGFR biomarker subgroups The true effect of the initial randomised treatment on OS is likely to have been confounded by the subsequent therapy, in particular the switching of patients to the alternative study therapy Yang CH et al. ESMO 2010

  43. IPASS conclusions: overall summary IPASS has demonstrated that positive EGFR mutation status is predictive of benefit from treatment with gefitinib over chemotherapy in terms of PFS, ORR and HRQoL PFS is an endpoint unlikely to be confounded by subsequent treatments, therefore is a more appropriate endpoint for evaluation of treatment effect in first-line treatment of NSCLC than OS IPASS has demonstrated the importance of biomarker testing in NSCLC, making a significant step towards personalised medicine IPASS has changed clinical practice and treatment guidelines for patients with advanced NSCLC who harbour an EGFR mutation HRQoL, health-related quality of life; NSCLC, non-small-cell lung cancer; ORR, objective response rate Yang CH et al. ESMO 2010

  44. Recently reported Phase III studies of gefitinib as first-line treatment for NSCLC in selected populations First-SIGNAL (Korea) • Endpoints • Primary • OS • Secondary • PFS, ORR, QoL, disease-related symptomssafety and tolerability Gefitinib250 mg/day • Patients • Chemo-naïve • Adenocarcinoma • Never smokers • ECOG† PS 0–2 Gemcitabine (1250 mg/m2) & cisplatin (80 mg/m2) 3-weekly* NEJ002 (Japan) • Endpoints • Primary • PFS (superiority) • Secondary • OS, ORR, QoL, disease-related symptomssafety and tolerability Gefitinib250 mg/day • Patients • Chemo-naïve • EGFR mutation-positive • PS 0–1 Carboplatin AUC 6 & paclitaxel 200 mg/m2 3-weekly †Eastern Cooperative Oncology Group *Maximum 9 cycles • Kobayashi et al 2009; Lee et al2009

  45. PFS and ORR with first-line gefitinib versus doublet chemotherapy in EGFR mutation-positive Asian patients across three Phase III studies Gefitinib (n=132) C / P (n=129) Gefitinib (n=26) G / C (n=16) Gefitinib (n=98) C / P (n=100) HR (95% CI) = 0.613 (0.308, 1.221) p=0.084 HR (95% CI) = 0.48 (0.36, 0.64) p<0.0001 HR (95% CI) = 0.36 (0.25, 0.51) p<0.001 1.0 1.0 1.0 0.8 0.8 0.8 Probability of PFS Probability of PFS Probability of PFS 0.6 0.6 0.6 0.4 0.4 0.4 0.2 0.2 0.2 0.0 0.0 0.0 0 100 200 300 400 500 0 5 10 15 20 25 30 0 4 8 12 16 20 24 months days months First-SIGNAL IPASS NEJ002 p=0.002 p<0.001 p<0.0001 ORR % ORR % ORR % Mok et al 2009; Lee et al 2009; Kobayashi et al 2009

  46. 1st-line treatment for mutation positive patients with NSCLC EURTAC – Spanish Lung Cancer GroupPhase III1: Erlotinib IIIB/IV NSCLC chemotherapy-naïve EGFR mutation Platinum + taxane or gemcitabine

  47. New-generation erbB inhibitors BIBW 2992 – potency and selectivity (IC50): Solca F et al. Proceedings, AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics. 2005;118:A242 Solca F et al. Proceedings, AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics. 2005;118:A244

  48. Resistance mutations Mutations known to cause resistance to 1st-generation EGFRi include: Exon 20 in-frame insertions Exon 20 point-mutations (e.g. T790M) Sharma et al. Nat Rev Cancer. 2007;7:169–181

  49. BIBW 2992 – active against resistance mutation NCI-H1975 cells express L858R/T790M double-mutant EGFR Li et al. Oncogene. 2008;27:4702–4711

  50. LUX-Lung 1: Trial design • Patients with: • Adenocarcinoma of the lung • Stage IIIB/IV • Progressed after one or two lines of chemotherapy (incl. one platinum-based regimen) and ≥12 weeks of treatment with erlotinib or gefitinib • ECOG 0–2 • N=585 Randomization 2:1 (DoubleBlind) Oral afatinib 50 mg once daily plus BSC Oral placebo once daily plus BSC Primary endpoint: Overall survival (OS) Secondary: PFS, RECIST response, QoL (LC13 & C30), safety • Radiographic assessments at 4, 8, 12 wks and every 8 wks thereafter • Exploratory biomarkers: • Archival tissue testing for EGFR mutations (optional; central lab) • Serum EGFR mutational analysis (all patients)