Emerging Biomarkers of VEGF and mtor Inhibitors in 2015

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1 Emerging Biomarkers of VEGF and mtor Inhibitors in 2015 Laurence Albiges Institut Gustave Roussy, France Fourteenth International Kidney Cancer Symposium Miami, Florida, USA November 6-7,

2 Biomarker use Early diagnosis Prognosis Risk of recurrence prediction (Recurrence score, MET polymorphism ) Early detection of recurrence (ct DNA?) Predictive Selection of treatment (efficacy/toxicity) Therapeutic window (drug exposure: PK/PD) Response/resistance marker (under Tx)

3 Biomarker use Early diagnosis Prognosis Risk of recurrence prediction (Recurrence score, MET polymorphism ) Early detection of recurrence (ct DNA?) Predictive Selection of treatment (efficacy/toxicity) Therapeutic window (drug exposure: PK/PD) How did we explore biomakers so far? What is new in 2015? Response/resistance marker (under Tx) VEGF inhibition mtor inhibition (no immunotherapy!)

4 Where do we stand? Tumor Host: germline Blood Imaging VHL HIF1a, HIF2a CAIX PTEN, ps6k Polymorphism VEGF-A, IL8, HIF1a, VEGFR2 VEGF, VEGFR2, CAIX CAF (OPN, VEGF,CAIX,VEGFR2, TRAIL) IL6 DCE-US,PET CT Sonpavde G and Choueiri TK. Urol Oncol. 2014;32:5-15. To date: no biological maker is routinely used to guide therapy choice Toxicity under treatement is a routinely used biomarker Still at the biomarker identification step for prediction!

5 Moving forward with biomarker identification? Large molecular screening (e.g. TCGA) Extreme responders (e.g. mtor) Large scale study population (e.g. IMDC) Pre-clinical work (e.g. MET, RANK) Clinical trial (e.g. Comparz, RECORD3) Mandatory samples collection & Dedicated trial for biomarker validation

6 Moving forward with biomarker identification? Large molecular screening (e.g. TCGA) Extreme responders (e.g. mtor) Large scale study population (e.g. IMDC) Pre-clinical work (e.g. MET, RANK) Clinical trial (e.g. Comparz, RECORD3) Mandatory samples collection & Dedicated trial for biomarker validation

7 From biological datasets to clinical mrcc trials Rationale: New molecular classifications from TCGA and other groups has widely been investigated in localised disease Few datasets included metastatic disease let alone treatement response to systemic therapy Approach: Explore the impact of PBRM1, SETD2, BAP1 mutations in a prospective dataset cohort of mrcc patients treated with targeted therapy

8 Identification of efficacy biomarkers through next generation sequencing (NGS): Results from RECORD-3 Somatic mutations in exons of 341 cancer related genes were identified by an NGS assay (MSK IMPACT platform) at ~530X coverage on tumor (archival tissues, 260 patients) DNA matched germline (181 patients) DNA Association between genotypes and first-line PFS was assessed by Cox PH models and log-rank tests Hsieh J, et al. ASCO Abstract 4509.

9 RECORD 3 : BAP1 and 1st Line PFS BAP1: BRCA1 Associated Protein Mutations of BAP1 correlates with poor prognosis ( similar findings were seen with SETD2) Hsieh J, et al. ASCO Abstract 4509.

10 RECORD 3: PBMR1 and 1st Line PFS PBMR1: Mutations of PBMR1 may correlate with improved efficacy to everolimus Hsieh J, et al. ASCO Abstract 4509.

11 RECORD 3: KDM5C and 1st Line PFS KDM5C: Mutations of KDM5C may correlate with improved efficacy to sunitinib: first biomarker to predict upfront who should be treated with sunitinib? Hsieh J, et al. ASCO Abstract 4509.

12 Moving forward with biomarker identification? Large molecular screening (e.g. TCGA) Extreme responders (e.g. mtor) Large scale study population (e.g. IMDC) Pre-clinical work (e.g. RANK) Clinical trial (e.g. Comparz, RECORD3) Mandatory samples collection & Dedicated trial for biomarker validation

13 Wagle et al. Cancer Discovery, 2014 Voss et al, Clinical Cancer Res, 2014 Wagle wt al. N Engl J Med, 2014 Iyer et al. Science, 2012

14 mtor Mutations in mrcc: Study Design Dataset of 79 mrcc patients treated with temsirolimus or everolimus Responders: CR or PR (RECIST 1.0) No tumor growth or any tumor shrinkage for at least 6 months Non-Responders: PD at first restaging (first 3 months) while on therapy Targeted sequencing was performed to investigate genes of interest across the mtor pathway Primary endpoint: mutations in core mtor pathway Exploratory endpoints: mutations in PI3K-mTOR pathway genes, mutational status in patients with extreme response (PR/CR > 6 mos) vs. pts with PD Fay AP, et al. ASCO Abstract 4519.

15 mtor Mutations in mrcc According to Response Status Trend toward higher response rate in pts with MTOR, TSC1, and TSC2 mutations: Pts, % Responders (n = 43) MTOR, TSC1, TSC2 Mutation Status Nonresponders (n = 36) Mutation detected Mutation not detected P Value Odds Ratio Association between mutation status, responses were significant when comparing pts with robust responses (CR/PR > 6 mos) vs primary refractory Pts, % CR/PR > 6 mos (n = 12) MTOR, TSC1, TSC2 Mutation Status P. Refractory (n = 35) Mutation detected Mutation not detected Odds Ratio Mutations in MTOR, TSC1, and TSC2 may be associated with good responses to mtor inhibitors in mrcc P Value Results need validation but suggest an strategy for selecting more rational and tailored therapies for patients with mrcc Fay AP, et al. ASCO Abstract 4519.

16 Extreme responders to VEGF TT Whole-exome sequencing to predict two extreme phenotypes of response to VEGF-targeted therapies Gene Mutation Extreme Responder Total=13 n(%) Primary Refractory Total=14 n(%) p-value* VHL 9(64.3) 7(50) p=0.44 PBRM1** 7(53.8) 1(7.1) p=0.012 SETD2 ** 6(46.1) 2(14.2) p=0.10 BAP1** 1(7.7) 2(14.2) - KDM5C** 1(7.7) 2(14.2) - KDM6A** 1(7.7) 0(0) - MLL2** 1(7.7) 0(0) - HIF1A 0(0) 1(7.1) - MTOR 2(14.2) 1(7.1) - PTEN 1(7.7) 2(14.2) - TSC2 1(7.7) 0(0) - ATM 2(15.4) 0(0) p=0.22 TP53 0(0) 4(28.5) p=0.097 Fay, MERIT AWARD recipient ASCO GU Symposium 2015

17 Moving forward with biomarker identification? Large molecular screening (e.g. TCGA) Extreme responders (e.g. mtor) Large scale study population (e.g. IMDC) Pre-clinical work (e.g. MET, RANK) Clinical trial (e.g. Comparz, RECORD3) Mandatory samples collection & Dedicated trial for biomarker validation

18 Probability BMI Correlates with Outcome in IMDC Cohort High BMI in the IMDC (N = 1975) is independently associated with OS and TTF after adjustment for IMDC prognostic risk 1.0 Obese Overweight Normal Underweight Median, months OS HR a (95% CI) P Value Median, months TTF HR a (95% CI) P Value No. of patients at risk Months Since Therapy Initiation BMI <25 BMI ( ) 8.1 ( ) Underweight Normal Overweight Obese a After adjustment for IMDC prognostic risk group. TTF, time to treatment failure Albiges L et al. Presented at: 2014 ASCO annual meeting; 30 May 30-3 June 2014; Chicago, IL. Abstract

19 Proportion of Patients Alive Proportion of Patients Alive BMI Correlates with Outcome in an External Validation Set BMI correlated with OS, PFS and ORR in an external validation set (N = 4567) of Phase II-III clinical trial data after adjustment a OS PFS ORR BMI 25: 23.4 months BMI< 25: 14.5 months HR a (95% CI) 0.83 ( ) P = BMI 25: 8.2 months BMI< 25: 5.5 months HR a (95% CI) 0.82 ( ) P < BMI 25: 25.3% BMI <25: 17.6% OR a (95% CI) ( ) P < Survival, (months) Survival, months OR, objective response; ORR, objective response rate. *Adjustment factors included age, gender, ethnicity, prior nephrectomy, histology, ECOG PS, IMDC risk groups, liver and bone metastases, baseline statins, dyslipidaemia, hypertension, angiotensin system inhibitors. Albiges L et al. Presented at: 2015 ASCO-GU annual meeting; February 2015; Orlando, FL. Abstract 405.

20 Proportion of Patients Alive Proportion of Patients Alive Predicitive value of NLR: Neutrophil to lymphocyte ratio IMDC Cohort a (N = 1199) Median OS (95% CI) NLR > months ( ) NLR months ( ) HR b (95% CI) 1.45 ( ) P < Validation Cohort a (N = 4350) Median OS (95% CI) NLR > months ( ) NLR months ( ) HR a (95% CI) 1.47 ( ) P < Survival, months Survival, months a IMDC cohort data was collected retrospectively from 9 sites, the validation cohort data included 12 prospective randomised trials. b HR was calculated after adjustment for IMDC variables. Alimohamed NS et al ASCO annual meeting; 29 May-3 June 2015; Chicago, IL. Abstract 404.

21 Moving forward with biomarker identification? Large molecular screening (e.g. TCGA) Extreme responders (e.g. mtor) Large scale study population (e.g. IMDC) Pre-clinical work (e.g. MET, RANK) Clinical trial (e.g. Comparz, RECORD3) Mandatory samples collection & Dedicated trial for biomarker validation

22 Cumulative Survival Prop 0 >median CS (64 e vents/ 70 cases) Prop The c-met Pathway: c-met Expression Retrospective Analysis of Tumour c-met Expression with Sunitinib Treatment (N = 137) MET high OS MET low + 1. Peltola KJ, et al. Presented at: 2014 ESMO; September; Madrid, Spain. Poster 822P N at Risk <=median CS >median CS Years since 1st TKI start Retrospective Analysis RCC of cohort Tumour c-met Expression with cmet:max VEGF Combined TT Score (N, n=143= 143) 2 Open Questions Years since 1st TKI start RCC cohort Prognostic or predictive? Interest in the context of cmet:tp: cabozantinib Max Combined Score, approval n=143 Proprotion free of treatment failure (%) <=median CS (66 e vents/ 75 cases) >median CS (62 e vents/ 68 cases) N at Risk <=median CS >median CS ure (%) 100 Albiges L et al. Presented at: 2015 IKCS Miami; 6-7 November ; Florida N at Ri Low:0 High:2 Proprotion free of treatment failure (%) 1 N at Ri Low:0 High:2 ure (%) 1

23 Moving forward with biomarker identification? Large molecular screening (e.g. TCGA) Large scale study population (e.g. IMDC) Extreme responders (e.g. mtor) Pre-clinical work (e.g. MET, RANK) Clinical trial with samples collection & Dedicated trial for biomarker validation

24 Probability of OS PD- L1 expression Prospective RCT with PD-L1 expression assessment: a prognostic value of PD-L1, no predictive role PD-L1 expression in COMPARZ: VEGFR-TKI (N = 453) High tumour PD-L1 expression was associated with shorter OS with sunitinib and pazopanib PD-L1 expression in CHECKMATE 025 EVEROLIMUS pts No predictive role of response to everolimus Pazopanib low Pazopanib high Sunitinib low Sunitinib high Median OS, months (95% CI) 35.6 (27.2, 40.8) 15.1 (9.4, 45.1) 27.8 (23.7, 32.9) 15.3 (11.2, 30.5) P = H-score; low ( 55) vs high (>55) Time, months 60 Motzer et al. NEJM 2015 Choueiri et al, Clin Can Res 2015

25 Conclusion In a emerging era where both VEGF and mtor inhibition will be challenged in the management of mrcc, the question of biomarker able to predict response / guide patients toward TT is crucial. The biomarqueur quest is hampered by tissue collection and prospective collection of treatement response What shall we do in 2016? Large scale data base (clinical response) with matching annotated samples Sequential biopsy in responders with secondary resistance Validation datasets : Role of company sponsored trials

26 Fourteenth International Kidney Cancer Symposium Miami, Florida, USA November 6-7,