Tumor heterogeneity and liquid biopsy

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in partnership with

Tumour Heterogeneity and Liquid Biopsy

Naples, 24/05/2019 Nicola Valeri MD, PhD, FRCP

Associate Professor in Personalized Oncology

Team Leader, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK Consultant Medical Oncologist, The Royal Marsden Hospital, London, UK

8th meeting on EXTERNAL QUALITY ASSESSMENT IN MOLECULAR PATHOLOGY

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➢ Challenges

➢ Solutions

Promises and hurdles in Precision Oncology

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➢ Challenges

➢ Solutions

- Logistics

- Identifying the target - Exploiting the target

- Identifying better and more cost/effective strategies to stratify patients

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➢ Challenges

➢ Solutions

- Logistics

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Zehir et al. Nature Medicine 2017

Promises and hurdles in Precision Oncology:

Logistics

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Logistics

Moorcraft et al. Annals of Oncology 2018

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Logistics

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Logistics

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➢ Challenges

➢ Solutions

- Logistics

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Meric-Bernstam F et al. J Clin Oncol 2015; Ferté C et al. Cancer Res 2014; Le Tourneau C et al. Lancet Oncol 2015; Zehir et al. Nature Medicine 2017

Can we identify a target?

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Actionable Targets by NGS 30-40%

Genotype-matched treatment 4-11%

Clinical benefit (durable PR/SD) Anecdotal

Meric-Bernstam F et al. J Clin Oncol 2015; Ferté C et al. Cancer Res 2014; Le Tourneau C et al. Lancet Oncol 2015; Zehir et al. Nature Medicine 2017

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Actionable Targets by NGS 30-40

Genotype-matched treatment 4-11%

Clinical benefit (durable PR/SD) Anecdotal

DNA-Guided Precision Medicine for

Cancer: A Case of Irrational Exuberance?

Voest & Bernards. Cancer Discovery 2016

Estimation of the Percentage of US

Patients With Cancer Who Benefit From Genome-Driven Oncology.

Marquart et al. JAMA Oncology 2018

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➢ Challenges

➢ Solutions

- Logistics

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Is patient’s selection = clinical benefit?

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1^st line trials of novel agents in gastroesophageal cancer since 2010

Trial Regimens OS/PFS HR (95% CI) P value Months

BIOMARKER SELECTED

TOGA Cis-5FU OS 0.74 (0.60-0.91) 0.0046 11.1

Cis-5FU + trastuzumab 13.8

LOGiC CapeOX

OS 0.91 (0.73-1.12) 0.35 10.5

CapeOx+lapatinib 12.2

RILOMET ECX

OS 1.37 (1.06-1.78) 0.016 11.5

ECX+rilotumumab 9.6

METMab FOLFOX

Onartuzumab+FOLFOX OS 0.82 0.244 11.3

11.0 NON-BIOMARKER SELECTED

AVAGAST Cis-5FU OS 0.87 0.1001 10.1

Cis-5FU + bevacizumab 12.1

REAL-3 EOX

OS 1.37 (1.07-1.76) 0.013 11.3

EOX+panitumumab 8.8

EXPAND CX

PFS 1.09 (0.92-1.29) 0.32 10.7

CX+cetuximab 9.4

Bang et al. Lancet 2010; Hecht et al. J Clin Onc 2015; Cunningham et al. ASCO 2015; Shah et al. ASCO 2015;

Ohtsu et al. J Clin Onc 2011; Waddell et al. Lancet Oncol 2013; Lordick et al. Lancet Oncol 2013

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FGFR2 inhibition in gastro-oesophageal cancer a tale of two trials

RMH FGFR trial – met primary endpoint – 33% ORR

Pearson et al. Cancer Discovery 2016

AZ SHINE trial – closed for futility – no responders (1 mixed)

Van Cutsem et al. Annals of Oncology 2017

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Images courtesy of Neil R Smith

Intra-patient heterogeneity of biomarker expression affects outcome even in biomarker selected populations.

FGFR2 inhibition in gastro-oesophageal cancer a tale of two trials

Pearson et al. Cancer Discovery 2016

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0,50 1,00 2,00 4,00 8,00 16,00 32,00

99 12 135 214 206 87 316 269 21

FGFR2 CNV plasma

FGFR2 plasma CNV (OG)

*pt no 99 = no pretreatment sample

= response

FGFR2 CNV in plasma predicts response to AZD4547

Pearson et al. Cancer Discovery 2016

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Conclusions I: Current limitations of personalized medicine

➢ Logistics: access to material, degraded FFPE tissues, turnaround time, cost/effectiveness

➢ Biology: cancer heterogeneity, cancer evolution

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➢ Challenges

➢ Solutions

- Logistics

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Liquid Biopsy

Invasiveness Low

Compliance High

Cost/Effectiveness High

Time 3 days

Repeated biopsy Possible Open issues Sensitivity/

reproducibility

Promises in Precision Oncology:

Liquid biopsies

Corcoran & Chabner. NEJM 2018

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Should liquid biopsies substitute solid biopsies in clinical practice?

1) Using liquid biopsies to determine RAS status

2) Using liquid biopsies to monitor response and resistance 3) Using liquid biopsies to identify vulnerabilities

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Testing RAS pathway abnormalities in metastatic colon cancer

Misale et al. Cancer Discovery 2014

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Khan et al. Cancer Discovery 2018

Multi-region Sequencing and Cancer Heterogeneity in the RAS pathway

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Metastatic colorectal

cancer patient Treatment (Cetuximab EGFR inhibitor)

Blood

Tissue biopsy Diagnostic tissue

sample Tissue

biopsy

Tissue biopsy

Scan Scan Scan

Longitudinal biopsies to monitor anti-EGFR response: the PROSPECT-C Trial

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Methods for cell-free (cf)DNA analysis in the PROSPECT-C Trial

- Tiered approach: test for Individual hot-spots - Rapid Turnaround time

- Relatively inexpensive

- 77 genes panel including APC and TP53 - Rapid Turnaround time

- Relatively expensive Digital-droplet PCR

(Cohort I)

NGS “Avenio” panel (Cohort II)

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Limitations of a tiered approach for cfDNA testing

Digital-droplet PCR (Cohort I)

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Testing RAS status in pre-treatment bloods

- Approximately 25% of patients who tested as RAS wild-type on archival tissues show mutations in pre-treatment bloods

- Approximately 50% of patients harbour mutations in the RAS pathway (i.e. ERBB2) Digital-droplet PCR

(Cohort I)

NGS “Avenio” panel (Cohort II)

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Mutations in the RAS pathway in pre-treatment bloods are associated with no status benefit

from EGFR inhibition

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Mutations in the RAS pathway in pre-treatment bloods are associated with no status benefit

from EGFR inhibition

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NGS of post-treatment bloods identifies drivers of resistance to EGFR inhibitors

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NGS of post-treatment bloods identifies drivers of resistance to EGFR inhibitors

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Comparison of solid and liquid biopsies

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Khan et al. Cancer Discovery 2018 ERBB2 CNV

validation in tissues and bloods

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Most RAS mutations detected in blood are present at low VAF

in tissues

Khan et al. Cancer Discovery 2018 ERBB2 CNV

validation in tissues and bloods

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Analysis of liquid and solid biopsies confirms that resistance to EGFR inhibition is polyclonal

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Analysis of liquid and solid biopsies confirms that resistance to EGFR inhibition is polyclonal

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Using liquid biopsies to inform clinical decisions

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Using liquid biopsies to inform clinical decisions:

monitoring ctDNA to forecast evolution

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Monitoring ctDNA to forecast evolution from academic exercise to relevant tool

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Sensitive Resistant

Baseline Response Relapse

Lote, Spiteri, Ermini et al. Annals of Oncology 2017; Khan et al. Cancer Discovery 2018

Dating cancer progression and resistance

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0 5 0

3 0

2 0

1

1 40

0.01 0.11 0.21 0.31 0.41

Mutant detected in the blood

Predictive window of opportunity

Relapse

Weather prediction:

Wind Humidity Temperature

Resistance prediction:

Frequency in the driver Sensitivity of the method

Frequency of blood sampling

Blood-Based Prediction of Tumour Relapse: The ctDNA Forecast

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From “Precision Medicine” to “Tight Medicine”

Health/Economic implications of personalised medicine

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Cost of Trial without patient selection = £642.141 From “Precision Medicine” to “Tight Medicine”

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Cost of Trial without patient selection = £642.141

Estimated cost of Trial with patient selection = £437.108 From “Precision Medicine” to “Tight Medicine”

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Conclusions: next-generation biopsies to improve patient’s outcomes

1. Analysis of RAS status in pre-treatment bloods improves selection of patients candidate to anti- EGFR treatment.

2. Frequent serial blood sampling allows to

forecast treatment failure at single patient level and might identify vulnerabilities.

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Drug Discovery Unit ICR, UK Udai Banerji

Johann de Bono

Molecular Diagnostics RMH, UK Michael Hubank

Paula Proszek Sanna Hulkki

University of Padua IT Matteo Fassan

Massimo Rugge

Beatson Institute for Cancer Research, UK Owen Sansom

Royal Marsden NHS Trust Khurum Khan

Francesco Sclafani Lizzy Smyth

Shelize Khakoo Gayathri Anandappa Sing Yu Moorcraft Ian Chau

Ruwaida Begum Clare Peckitt Naureen Starling David Watkins Sheela Rao Asif Chaudry Nina Tunariu Dow-Mu Koh William Allum David Cunningham Valeri’s Lab

Andrea Lampis Jens Hahne

Mahnaz Darvish Damavandi George Vlachogiannis Hazel Lote

Somaieh Hedayat Massimiliano Salati

Institute of Cancer Research Andrea Sottoriva

Chiara Braconi

Anguraj Sadanandam Steven Whittaker Vladimir Kirkin Sue Eccles Simon Robinson Paul Clarke Rosemary Burke Paul Workman George Poulogiannis Gabriela Kramer-Marek Javier Fernandez Mateos Inma Spiteri Sagastume Yann Jamin

Janet Shipley Mel Greaves

Acknowledgments

NIHR RM/ICR Biomedical Research Centre