tumor mutation burden as predictive biomarker …...correlation between tmb and neoantigen load...
TRANSCRIPT
Tumor mutation burden as predictivebiomarker –
from discovery to standardization
Nicola Normanno
ISTITUTO NAZIONALE PER LO STUDIO E LA CURA DEI TUMORI FONDAZIONE G. Pascale – NAPOLI
SC Biologia Cellulare e Bioterapie
CENTRO RICERCHE ONCOLOGICHEMERCOGLIANO (AV)
Laboratorio di Farmacogenomica
Tumor Mutation Burden in NSCLC
Spigel ASCO 2016
Adeno(n=7,925)
SCC(n=1,324)
NSCLC NOS
(n=1,773)
SCLC(n=640)
EGFR mutation
(n=1,775)
ALK or ROS1 fusion(n=489)
METex14 alteration
(n=286)
BRAF mutation
(n=493)
KRAS mutation
(n=3,155)
MeanMutations/Mb
9.1 11.3 11.0 10.3 4.5 3.1 6.2 9.7 10.3
TMB >10 (%)2350 (30)
541 (41)
711 (40)
269 (42)
129 (7)
17 (3)
27 (9)
153 (31)
1,238 (39)
TMB >20 (%)760 (10)
113 (9)
233 (13)
42 (7)
21 (1)
4 (1)
4 (1)
51 (10)
298 (9)
Wilcoxon signed-rank test vs KRAS
p<0.001 p<0.001 p<0.001 p<0.001
Correlation Between TMB and NeoantigenLoad
TMB=tumor mutation burden.1. Hellmann M. Oral presentation at AACR 2017. 2. Van Allen EM et al. Science. 2015;350(6257):207-211. 3. Rooney MS et al. Cell. 2015;160:48-61.
• Higher levels of TMB have been shown to correlate with higher neoantigen load1-3
‒ Melanoma, lung, bladder, liver, and stomach cancers are predicted to have the highest neoantigen loads3
Pre
dic
ted
Ne
oan
tige
ns
Pan-Tumor1
Total Mutations
0
500
1000
0 1000 2000 3000 4000 5000
R2=0.91Spearman rho 0.92
Correlation between TMB and Response Rate with Anti–PD-1 or Anti–PDL1 Therapy in 27
Tumor Types
Yarchoan NEJM 2018
Whole Exome Sequencing Is a Technical and Complex Process
1. Frampton GM et al. Nat Biotechnol. 2013;31(11):1023-1031. 2. Ulahannan D. Br J Cancer. 2013; 109(4):827-835.
DNA Extraction
Clinical Report
Data Analysis
ACTGTGCGACGA
CGCGTAGATCGC
AACGTCGCGATA
AlignmentDNA
SequencingLibrary
PreparationHybrid Capture
• Sample attrition may occur within the process, and technical complexity may require specialty labs2
• Use of consolidated gene panels may alleviate some of the challenges associated with whole-exome sequencing2
NGS-based genomic profiling workflow1
TMB assessed by targeted NGS correlates with WES and durable clinical benefit (DCB)
Rizvi JCO 2018
MSK-IMPACT panel 468 genes
CheckMate 227: Nivo + Ipi in 1L NSCLC With High TMB (≥10 mut/Mb)
CheckMate 227 Part 1 Study Designa
Database lock: January 24, 2018; minimum follow-up: 11.2 months
N = 1189
<1% PD-L1expression
N = 550
Nivolumab 3 mg/kg Q2W Ipilimumab 1 mg/kg Q6W
n = 396
Histology-based chemotherapyb
n = 397
Nivolumab 240 mg Q2Wn = 396
Nivolumab 3 mg/kg Q2W Ipilimumab 1 mg/kg Q6W
n = 187
Histology-based chemotherapyb
n = 186
Nivolumab 360 mg Q3W + histology-based chemotherapyb
n = 177
R
1:1:1
Key Eligibility Criteria• Stage IV or recurrent NSCLC
• No prior systemic therapy
• No known sensitizing
EGFR/ALK alterations
• ECOG PS 0–1
Stratified by SQ vs NSQ
R
1:1:1
7
aNCT02477826 bNSQ: pemetrexed + cisplatin or carboplatin, Q3W for ≤4 cycles, with optional pemetrexed maintenance following chemotherapy or nivolumab + pemetrexed maintenance following nivolumab + chemotherapy; SQ: gemcitabine + cisplatin, or gemcitabine + carboplatin, Q3W for ≤4 cycles; cThe TMB co-primary analysis was conducted in the subset of patients randomized to nivolumab + ipilimumab or chemotherapy who had evaluable TMB ≥10 mut/Mb
≥1% PD-L1expression
Nivolumab + ipilimumab n = 396
Chemotherapyb
n = 397
Patients for PD-L1 co-primary analysis
Co-primary endpoints: Nivolumab +
ipilimumab vs chemotherapy
• OS in PD-L1–selected populations
• PFS in TMB-selected populations
Nivolumab + ipilimumab n = 139
Chemotherapyb
n = 160
Patients for TMB co-primary analysisc
CheckMate 568: Identification of Optimal TMB Cutoff (≥10 mut/Mb) for Nivo + Ipi in 1L NSCLC
TMB Analysis With FoundationOne CDx™
Assay
14FoundationOne CDx™ Technical Information. Cambridge, MA: Foundation Medicine Inc; 2018.
FoundationOne CDxTM (F1CDx)
FoundationOne CDxTM TMB result
Somatic mutations
per megabase
FoundationOne
CDxTM
Tumor sample
(FFPE)
• FoundationOne CDx™ uses next-generation
sequencing to detect substitutions, insertions and
deletions, and copy number alterations in 324 genes
and select gene rearrangements
– TMB: total number of synonymous and non-
synonymous variants (≥5% allele frequency)
after filtering germline mutations
CheckMate 568: Identification of Optimal TMB Cutoff (≥10 mut/Mb) for Nivo + Ipi in 1L NSCLC
ORR by TMBa,b
15
0
10
20
30
40
50
OR
R (
%)
n/N
TMB (mut/Mb)
<5c
2/23
9
≥5 to <10d
4/27
15
≥10e
21/48
44
≥15f
11/28
39
aIrrespective of PD-L1 expression; b12% ORR for <10 mut/Mb and 50% ORR for ≥10 to <15 mut/Mb; cCR = 0; dCR = 4%; eCR = 8%; fCR = 7%
CheckMate 568: Identification of Optimal TMB Cutoff (≥10 mut/Mb) for Nivo + Ipi in 1L NSCLC
ROC Curves by Objective Response
16
Tru
e-p
os
itiv
e f
rac
tio
n
False-positive fraction
AUC = 0.70
TMB (n = 98)
AUC = 0.73
9-10 mut/Mb
False-positive fraction
PD-L1 (n = 252)
0.00
0.25
0.50
0.75
0.00 0.25 0.50 0.75 1.00
1.00
0.00
0.25
0.50
0.75
0.00 0.25 0.50 0.75 1.00
1.00
Tru
e-p
os
itiv
e f
rac
tio
n
CheckMate 227: Nivo + Ipi in 1L NSCLC With High TMB (≥10 mut/Mb)
Co-primary Endpoint: PFS With Nivolumab + Ipilimumab vs Chemotherapy in Patients With High TMB (≥10 mut/Mb)a
17
Nivo + ipi 139 85 66 55 36 24 11 3 0
Chemo 160 103 51 17 7 6 4 0 0
Nivo + ipi
(n = 139)
Chemo
(n = 160)
Median PFS,b mo 7.2 5.4
HRc
97.5% CI
0.58
0.41, 0.81
P = 0.0002
Months
0
20
40
60
80
100
0 6 12 183 9 15 21 24
PF
S (
%)
Chemotherapy
Nivolumab +ipilimumab
1-y PFS = 43%
1-y PFS = 13%
aPer blinded independent central review (BICR); median (range) of follow-up in the co-primary analysis population was 13.6 mo (0.4, 25.1) for nivo + ipi and 13.2 mo (0.2, 26.0) for chemo;b95% CI: nivo + ipi (5.5, 13.2 mo), chemo (4.4, 5.8 mo); c95% CI: 0.43, 0.77 mo; dThe P-value for the treatment interaction was 0.0018
No. at risk
• In patients with TMB <10 mut/Mb treated with nivo + ipi vs chemo, the HR was 1.07 (95% CI: 0.84, 1.35)d
TMB testing: Critical issues
TMB is emerging as a relevant biomarker for response to immunotherapy. Standardization and harmonization of TMB testing are pivotal. Different open questions need to be answered:
1. Current methods for TMB measurement are not standardized and vary from WES to targeted sequencing panels (commercial kits, laboratory-developedassays…)
2. As a consequence, bioinformatic pipelines for TMB calculation differ amongthe different assays
3. No univocal cut-offs to identify low-medium-high TMB tumors exist (the onlyvalidated cut-off is ≥10mut/mb with FoundationOne®CDX, Checkmate-227)
FoundationOne CDx™ 324 genes - Threshold10 mut/Mb
FoundationOne 324 genes value 10
WES value ⁓200Mutations/Mb
Thermo Fisher 409 genes value 12
Qiagen⁓400 genes value 12
Illumina ⁓500 genes value 14
Aims of the study:a) to organize a pilot European EQA scheme for TMB;b) to harmonize the different TMB methods;c) to define recommendations for TMB assessment.
Academia: ESMO, AIOM, Gen&Tiss, ESP, UKNEQAS, EMQN, cIQc, RCPA QualityAssurance Programs
Sponsors: BMS, AstraZeneca, Roche/Foundation Medicine, Thermo Fisher Scientific, Illumina, Qiagen, Genentech
Tumour mutation burden:from recommendations for testing to external quality assessment schemes
Project Kick-off MeetingNaples, 10th May 2018
Participants:
ESMO BMSAIOM Thermo FisherGen&Tiss IlluminaESP QiagenEMQN GenentechcIQc Merck KGaA
Format of pilot EQA
Selection of participants
(survey)
EQA material validation study
EQA distribution
Results submission
- Reports- Data collection
EQA assessment
Release of EQA results and
Scheme Report
EQA sample validation• FFPE cell lines with different mutational load (high, intermediate, low TMB)• Different validating laboratories will be identified • Perform sample validation (DNA extraction + TMB testing)• Different TMB panels to test the samples (Thermo Fisher – Qiagen – Illumina -
FoundationOne)
EQA sample distribution• Identification of 30 laboratories with a survey
• taking into account labs with molecular pathology expertise, already performing TMB testing, with different methods
Analysis of EQA results/Issue of results • Specific scoring system• TMB result and clinical interpretation
Project Planninga) EQA Pilot scheme for TMB testing
a) Creation of an harmonization networka) Identification of a panel of experts to provide FFPE material from NSCLC
patients. b) Identify the appropriate number of centers to be involved
b) Sample selection and analysisa) Collection of samples in order to obtain a significant number of analyses
to allow the creation of a conversion table
c) Comparative analysisa) Performance of comparative analysis of the scores resulting from different
TMB tests to create a reference table that maps the score of one text to another, to enable harmonisation.
d) Publication of the results/Workshops
Project Planningb) Harmonization phase
Project Timeline
TMB project
Timeline
Jun/
Jul
2018
Aug/
Sept
2018
Oct/
Nov
2018
Dec
2018/
Jan
2019
Feb/
Mar
2019
Apr/
May
2019
Jun/
Jul
2019
Aug/
Sept
2019
Oct
2019
Val
idat
ion
ph
ase
(A)
Tender for control material
Identification of the participating
centers (survey)
Place order for EQA material
Material manufacture
Requirements for data collections
EQA sample validation
EQA sample distribution
Analysis of EQA results/Issue of
results
Har
mo
niz
atio
n
ph
ase
(B)
Creation of an harmonization
network
Sample selection and analysis
Comparative analyses
Publication of the
results/Workshops
27
Immuno-oncology Consortium with Leading EU Institutes
For Research Use Only. Not for use in diagnostic procedures.
The immuno-oncology consortia members:
Sabine Merkelbach-Bruse
Institute of Pathology, University Hospital Cologne International
Albrecht Stenzinger
Institute of Pathology, Heidelberg University Hospital
Michael Hummel
Institute of Pathology, Charité - University Medical Center
Berlin
Wilko Weichert and Nicole Pfarr
Institute of Pathology, Technical University of Munich
Bea Bellosillo
Hospital del Mar, Barcelona
Nicola Normanno
IRCCS National Cancer Institute "G. Pascale Foundation"
Jose Carlos Machado
i3S and Institute of Molecular Pathology and Immunology of
the University of Porto (IPATIMUP), Portugal
Maurice Jansen and Winand Dinjens
Erasmus MC Cancer Institute, Rotterdam
Empower clinical trials with
Ion Torrent™ immuno-oncology assays
28
Phase 1 - Controls
• 6 x cell line FFPE controls (AccuRef)• predicted to be high and low TMB plus 2 x Acrometric Oncology Hotspot Control (AOHC)
• check reproducible results across 8 labs
Phase 2 - Shared sample
• IPATIMUP providing 8 CRC samples for all labs which have MSI status,• no exome or clinical response data
• 8 shared samples run as standard TML workflow 8plex chip without normal
comparison
• TUM providing access to tumour-normal exome analysis via DKFZ
• Comparison to exome and interlay reproducibility
Phase 3 - Own samples
• 16 samples from own biobank
• high and low mutational burden status
• Ideally with clinical response data and/or any supporting biomarker/exome data
Tumour Mutational Load assay project - Experimental plan
29
Preliminary Phase I ResultsCell-Ref™ FFPE Slides and Acrometrix™ Control
TMB
(Mu
t/M
b)
A549 AOHC H2228 HCC2998 MCF7 SKMEL2 T47D
Average TMB 7.0 255.5 7.8 198.2 3.9 19.0 2.8
% CV 12% 9% 8% 6% 17% 9% 14%
Range 5.9-8.1
229.2-
270.8 6.8-8.4
176.5-
209.2 3.4-5.1 16.9-21.3 2.5-3.4
30
Preliminary data Phase II results (2 Laboratories)
R² = 0,9697
0
10
20
30
40
50
60
70
0 10 20 30 40 50 60 70
Re
p 2
(C
RO
M)
Rep 1 (IPATIMUP)
Reproducibility of Shared Clinical Samples
TMB testing in CRC cell lines (1)
MSI Status Mutation Count (cBbioPortal)
TML (Oncomine™TML Assay)
RKO MSI-H 382 105,62
LOVO MSI-H 267 80,64
HCT116 MSI-H 227 74,62
COLO320 MSS 23 15,89
HT29 MSS 64 15,89
SW1116 MSS 40 15,87
H1650 MSS 25 6,74
H1975 MSS 54 10,37
TMB testing in CRC cell lines (2)
R² = 0,979
0
50
100
150
200
250
300
350
400
450
0 20 40 60 80 100 120
Mu
tati
on
Co
un
t-
Cb
ioP
ort
al
TMB (Oncomine™TML Assay)
TMB in primary CRC FFPE samples
10
30
50
70
90
110
130
150
MSI MSI-L/MSS
Mu
tati
on
Lo
ad/M
B
(On
com
ine
™TM
LA
ssay
)
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