Learning from sensitivity and resistance in ovarian cancer trials - PowerPoint PPT Presentation

“The whole of science is nothing more than a refinement of everyday thinking” Albert Einstein Learning from sensitivity and resistance in ovarian cancer trials June 2, 2017 Stephanie Lheureux, MD PhD Clinician Investigator – Drug Development Program Staff Medical Oncologist, Gyne Medical Oncology Department of Medicine Princess Margaret Cancer Centre Assistant Professor, Faculty of Medicine, University of Toronto

Current therapeutic classes, pathways and molecular targets Lheureux S, Oza AM 2017

Study 19: PFS results Olaparib maintenance monotherapy significantly prolonged PFS in platinum-sensitive relapsed HGSOC – Patients with a BRCA1/2 mutation receive greater treatment benefit – Non-mutation carriers may also benefit from olaparib treatment 1.0 BRCAm (n=136) BRCAm (n=136) BRCAwt (n=118) BRCAwt (n=118) 0.9 Olaparib Olaparib Placebo Placebo Olaparib Olaparib Placebo Placebo Median PFS, Median PFS, 11.2 11.2 4.3 4.3 7.4 7.4 5.5 5.5 0.8 months months Proportion of patients 0.7 HR=0.18 HR=0.18 HR=0.53 HR=0.53 progression-free P <0.00001 P <0.00001 P =0.007 P =0.007 0.6 0.5 0.4 0.3 Olaparib BRCAm Placebo BRCAm 0.2 Olaparib BRCAwt 0.1 Placebo BRCAwt 0 0 3 6 9 12 15 Time from randomization (months) Ledermann J et al,. Lancet 2014

SOLO2: maintenance olaparib BRCA1/2 Pujade Lauraine E et al., SGO 2017

SOLO2: maintenance olaparib BRCA1/2 Pujade Lauraine E et al., SGO 2017

Refine biomarkers Beyond BRCA1/2 mutations NO HR HR DEFICIENCY DEFICIENCY Levine, D. The Cancer Genome Atlas 2011

Ariel 2: Response Rate RECIST ORR: 16% RECIST ORR: 75% RECIST ORR: 36% Swisher EM et al., Lancet Oncology 2017

ENGOT-OV16/ NOVA Trial: PFS results HRD-positive HRD-negative sBRCAmut BRCAwt % of % of % of Patients Patients Patients without without without PFS PFS Progression Progression Progressio PFS Median Hazard Median Hazard or Death or Death n or Death Median Hazard (95% CI) Ratio (95% CI) Ratio (95% CI) Ratio 12 18 12 18 12 18 Treatme (Month (95% CI) Treatme (Month (95% CI) Treatme (Month (95% CI) mo mo mo mo nt s) p-value nt s) p-value nt s) p-value mo mo Nirapari 20.9 Nirapari 9.3 Nirapari 6.9 0.27 0.38 0.58 62 52 45 27 27 19 b b (9.7, b (5.8, (5.6, % % % % % % (0.081, (0.231, (0.361, (N=35) NR) 15.4) (N=71) (N=92) 9.6) 0.903) 0.628) 0.922) 11.0 3.7 3.8 Placebo 19 19 Placebo Placebo 11 p=0.024 p=0.000 p=0.022 (2.0, 6% (3.3, (3.7, 7% 7% (N=12) % % % (N=44) (N=42) 8 1 6 NR) 5.6) 5.6) NR=Not reached Mirza MR et al. NEJM 2016

Extreme Responders – Non Responders OLALA Study Lheureux S et al, CCR 2017

Extreme Responders – Non Responders Lheureux S et al, CCR 2017

Evolution of the Targets: Heterogeneity  Given the evolutionary capabilities of tumors, monitoring disease evolution to guide therapeutic interventions and to understand evolutionary trajectories of individual tumors has become a vital research area. Giornelli GH, Springerplus 2016 McGranahan N, Cell. 2017

McGranahan N, Cell. 2017

McGranahan N, Cell. 2017

NEJM 2017 April

Overview of the Clinical Characteristics of the Patients

Jamal-Hanjani M et al. N Engl J Med 2017. DOI: 10.1056/NEJMoa1616288

Heterogeneity NSCLC  HGSOC • The onset of chromosome instability appears to have a considerable effect on the evolution of NSCLC  Instability appears to be the predominant driver of parallel evolution and can lead to both mutational and copy-number diversity among subclones. • Elevated copy-number heterogeneity was associated with shorter relapse- free survival  Patients who have early-stage tumors with high levels of copy-number heterogeneity may represent a high-risk group who may benefit from close monitoring and early therapeutic intervention during follow-up. • Understanding how mutational processes shape tumor evolution may inform strategies to limit tumor adaptation in the clinical setting.

Understanding Extreme • Short - Long term • Appropriate Tissue analysis – right time • Olaparib in recurrent high-grade serous/poorly differentiated ovarian carcinoma or triple-negative breast cancer: A phase 2, multicentre, open-label, non-randomised study OLALA Study Gelmon et al. Lancet Oncol 2011

PATIENT 1 1997 2005 2006 2008 2013 2015 Legend: HGSOC Initial BRCA1 LOH Death Signature diagnosis p53 R248W 1A 3 Primary HRD 4 15500 Signature 6 12500 7 9500 11 6500 3500 12 500 15 UI/mL 100 16 8 80 1 18 1 60 19 40 Progression 20 20 0 Unknown CA125 level over time CNVs+SNVs January 2009 June 2009 June 2010 December 2012 July 2015 25% Radiation 30 Gy in At the start of Response on Isolated spleen At progression 10 fractions olaparib olaparib progression on olaparib 10% 5% 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Cancer Cell Fraction BRCA1 Expression ● 2.5 0.5 1.0 1.5 2.0 ● Surgery Biopsy Recurrence Platinum-based chemotherapy Gemcitabine chemotherapy Olaparib Log 2 (TPM+1) ● − ● ● ● ● ● − 0 ● − ● CCNE1 Expression 0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0 Log 2 (TPM+1) BRCA1 Depth Ratio ● Progression ● ● Primary ● ● ● − ● 0 ● 0 X) ry n 0 20 40 60 80 Position (Mb) Lheureux S et al, JCO 2017

PATIENT 2 2002 2008 2015 HGSOC Initial Legend: diagnosis Signature BRCA2 LOH 1A Primary 10 400 15 Unknown 200 CNVs+SNVs 12 100 19 25% 1 7 Progression UI/mL 50 10% 5% 0 CA125 level over time August 2008 January 2009 April 2015 At the start of olaparib Response on olaparib At progression on olaparib 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Surgery Biopsy Recurrence Platinum-based chemotherapy Olaparib Cancer Cell Fraction − ● ● 0.5 1.0 1.5 2.0 2.5 3.0 ● 5 0 ● ● ● PARP1 Expression ● BRCA2 Expression 5 4 Log 2 (TPM+1) ● Log 2 (TPM+1) 0 3 5 2 − − 0 ● 1 ● ● ● ● 5 − ● ● ● 0 ● 0 0 ● ) Lheureux S et al, JCO 2017

PATIENT 3 Legend: A 2009 2016 Signature HGSOC Initial BRCA1 W353* diagnosis 1A 1B BRCA1 LOH HRD 3 Signature 4 100 Primary 7 80 12 60 Unknown UI/mL 40 20 CNVs+SNVs 0 25% CA125 level over time 10% June 2009 August 2016 H&E At the start of complete 5% 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Initial diagnosis olaparib response on Cancer Cell Fraction olaparib Surgery Recurrence Platinum-based chemotherapy Olaparib Chromosome Chromosome 17 16 18 20 22 chr1 chr2 chr3 chr4 chr5 chr6 chr7 chr8 chr9 chr10 chr11 chr12 chr13 chr14 chr15chr16chr17 chr18 chr19 chr20 chr21 chr22 chrX 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 17 21 X 19 4 4 0.5 1.0 1.5 2.0 BRCA1 W353* Copy-Number Copy-Number 3 3 Primary Primary ● ●●●●●●●●●●●●●●● ● ●●●●● 2 ●●●●●● ●● 2 ●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●● ●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●● ●● ● ●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●● ●●●● ●●● ●● ●● 1 1 0 0 0 0 20 40 60 80 Allele-A: Allele-B: Chromosome Position (Mb) Allele-A: Allele-B: Lheureux S et al, JCO 2017

Biomarkers in clinical trials Trial design: Determining whether a cancer driver event occurs early or late can indicate whether it is involved in tumor initiation or maintenance, and its clonality may inform potential therapeutic strategies Trial correlatives : Integration in clinical trial – Time biopsies Biomarkers in practice : Well defined, Reproducible, Robust, Sensitive, Clinically meaningful, cost effective assay, time impact Technique: Biomarkers test Type of tissue

Science – Clinic A Complementary Approach Preclinical Discovery Clinical practice Clinical trials validation

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