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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
➢ Challenges
➢ Solutions
Promises and hurdles in Precision Oncology
➢ Challenges
➢ Solutions
- Logistics
- Identifying the target
- Exploiting the target
- Identifying better and more cost/effective strategies to
stratify patients
Promises and hurdles in Precision Oncology
➢ Challenges
➢ Solutions
- Logistics
- Identifying the target
- Exploiting the target
- Identifying better and more cost/effective strategies to
stratify patients
Promises and hurdles in Precision Oncology
Zehir et al. Nature Medicine 2017
Promises and hurdles in Precision Oncology:
Logistics
Promises and hurdles in Precision Oncology:
Logistics
Moorcraft et al. Annals of Oncology 2018
Promises and hurdles in Precision Oncology:
Logistics
Moorcraft et al. Annals of Oncology 2018
Promises and hurdles in Precision Oncology:
Logistics
Moorcraft et al. Annals of Oncology 2018
➢ Challenges
➢ Solutions
- Logistics
- Identifying the target
- Exploiting the target
- Identifying better and more cost/effective strategies to
stratify patients
Promises and hurdles in Precision Oncology
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
Promises and hurdles in Precision Oncology:
Can we identify a target?
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
Promises and hurdles in Precision Oncology:
Can we identify a target?
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
Promises and hurdles in Precision Oncology:
Can we identify a target?
➢ Challenges
➢ Solutions
- Logistics
- Identifying the target
- Exploiting the target
- Identifying better and more cost/effective strategies to
stratify patients
Promises and hurdles in Precision Oncology
Promises and hurdles in Precision Oncology:
Is patient’s selection = clinical benefit?
1st line trials of novel agents in gastroesophageal cancer since 2010Trial 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 CapeOXOS 0.91 (0.73-1.12) 0.35
10.5
CapeOx+lapatinib 12.2
RILOMET ECXOS 1.37 (1.06-1.78) 0.016
11.5
ECX+rilotumumab 9.6
METMab FOLFOX
Onartuzumab+FOLFOXOS 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 EOXOS 1.37 (1.07-1.76) 0.013
11.3
EOX+panitumumab 8.8
EXPAND CXPFS 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
Promises and hurdles in Precision Oncology:
Is patient’s selection = clinical benefit?
Promises and hurdles in Precision Oncology:
Is patient’s selection = clinical benefit?
Promises and hurdles in Precision Oncology:
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
Images courtesy of Neil R Smith
Intra-patient heterogeneity of biomarker expression affects outcome even in
biomarker selected populations.
Promises and hurdles in Precision Oncology:
FGFR2 inhibition in gastro-oesophageal cancer
a tale of two trials
Pearson et al. Cancer Discovery 2016
0,50
1,00
2,00
4,00
8,00
16,00
32,00
99 12 135 214 206 87 316 269 21
FG
FR
2 C
NV
pla
sm
a
FGFR2 plasma CNV (OG)
*pt no 99 = no pretreatment sample
= response
Promises and hurdles in Precision Oncology:
FGFR2 CNV in plasma predicts response to
AZD4547
Pearson et al. Cancer Discovery 2016
Conclusions I: Current limitations of
personalized medicine
➢ Logistics: access to material, degraded FFPE
tissues, turnaround time, cost/effectiveness
➢Biology: cancer heterogeneity, cancer evolution
➢ Challenges
➢ Solutions
- Logistics
- Identifying the target
- Exploiting the target
- Identifying better and more cost/effective strategies to
stratify patients
Promises and hurdles in Precision Oncology
Liquid Biopsy
Invasiveness Low
Compliance High
Cost/Effectiveness High
Time 3 days
Repeated biopsy Possible
Open issuesSensitivity/
reproducibility
Promises in Precision Oncology:
Liquid biopsies
Corcoran & Chabner. NEJM 2018
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
Testing RAS pathway abnormalities in
metastatic colon cancer
Misale et al. Cancer Discovery 2014
Khan et al. Cancer Discovery 2018
Multi-region Sequencing and Cancer
Heterogeneity in the RAS pathway
Metastatic colorectal
cancer patientTreatment (Cetuximab EGFR inhibitor)
Blood
Tissue
biopsy
Diagnostic tissue
sampleTissue
biopsy
Tissue
biopsy
Scan Scan Scan
Khan et al. Cancer Discovery 2018
Longitudinal biopsies to monitor anti-EGFR
response: the PROSPECT-C Trial
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)
Limitations of a tiered approach for cfDNA
testing
Digital-droplet PCR
(Cohort I)
Khan et al. Cancer Discovery 2018
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)
Khan et al. Cancer Discovery 2018
Mutations in the RAS pathway in pre-treatment
bloods are associated with no status benefit
from EGFR inhibition
Khan et al. Cancer Discovery 2018
Mutations in the RAS pathway in pre-treatment
bloods are associated with no status benefit
from EGFR inhibition
NGS of post-treatment bloods identifies drivers
of resistance to EGFR inhibitors
Khan et al. Cancer Discovery 2018
NGS of post-treatment bloods identifies drivers
of resistance to EGFR inhibitors
Khan et al. Cancer Discovery 2018
Comparison of solid and liquid biopsies
Khan et al. Cancer Discovery 2018
Comparison of solid and liquid biopsies
Khan et al. Cancer Discovery 2018
ERBB2 CNV
validation in tissues
and bloods
Comparison of solid and liquid biopsies
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
Analysis of liquid and solid biopsies confirms
that resistance to EGFR inhibition is polyclonal
Khan et al. Cancer Discovery 2018
Analysis of liquid and solid biopsies confirms
that resistance to EGFR inhibition is polyclonal
Khan et al. Cancer Discovery 2018
Khan et al. Cancer Discovery 2018
Using liquid biopsies to inform clinical decisions
Khan et al. Cancer Discovery 2018
Using liquid biopsies to inform clinical decisions
Khan et al. Cancer Discovery 2018
Using liquid biopsies to inform clinical decisions
Khan et al. Cancer Discovery 2018
Using liquid biopsies to inform clinical decisions:
monitoring ctDNA to forecast evolution
Monitoring ctDNA to forecast evolution from
academic exercise to relevant tool
Monitoring ctDNA to forecast evolution from
academic exercise to relevant tool
Monitoring ctDNA to forecast evolution from
academic exercise to relevant tool
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
050302011 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
Khan et al. Cancer Discovery 2018
Blood-Based Prediction of Tumour Relapse: The
ctDNA Forecast
From “Precision Medicine” to “Tight Medicine”
Health/Economic implications of personalised
medicine
49
Cost of Trial without patient selection = £642.141
From “Precision Medicine” to “Tight Medicine”
Health/Economic implications of personalised
medicine
50
Cost of Trial without patient selection = £642.141
Estimated cost of Trial with patient selection = £437.108
From “Precision Medicine” to “Tight Medicine”
Health/Economic implications of personalised
medicine
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.
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