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Establishing guidelines to harmonize tumor mutational burden (TMB): In silico assessment of variation in TMB quantification across diagnostic platforms: Phase I of the Friends of Cancer Research TMB Harmonization Project

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  • National Cancer Institute, National Institutes of Health, DHHS

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Background Tumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, demonstrates predictive biomarker potential for the identification of patients with cancer most likely to respond to immune checkpoint inhibitors. TMB is optimally calculated by whole exome sequencing (WES), but next-generation sequencing targeted panels provide TMB estimates in a time-effective and cost-effective manner. However, differences in panel size and gene coverage, in addition to the underlying bioinformatics pipelines, are known drivers of variability in TMB estimates across laboratories. By directly comparing panel-based TMB estimates from participating laboratories, this study aims to characterize the theoretical variability of panel-based TMB estimates, and provides guidelines on TMB reporting, analytic validation requirements and reference standard alignment in order to maintain consistency of TMB estimation across platforms. Methods Eleven laboratories used WES data from The Cancer Genome Atlas Multi-Center Mutation calling in Multiple Cancers (MC3) samples and calculated TMB from the subset of the exome restricted to the genes covered by their targeted panel using their own bioinformatics pipeline (panel TMB). A reference TMB value was calculated from the entire exome using a uniform bioinformatics pipeline all members agreed on (WES TMB). Linear regression analyses were performed to investigate the relationship between WES and panel TMB for all 32 cancer types combined and separately. Variability in panel TMB values at various WES TMB values was also quantified using 95% prediction limits. Results Study results demonstrated that variability within and between panel TMB values increases as the WES TMB values increase. For each panel, prediction limits based on linear regression analyses that modeled panel TMB as a function of WES TMB were calculated and found to approximately capture the intended 95% of observed panel TMB values. Certain cancer types, such as uterine, bladder and colon cancers exhibited greater variability in panel TMB values, compared with lung and head and neck cancers. Conclusions Increasing uptake of TMB as a predictive biomarker in the clinic creates an urgent need to bring stakeholders together to agree on the harmonization of key aspects of panel-based TMB estimation, such as the standardization of TMB reporting, standardization of analytical validation studies and the alignment of panel-based TMB values with a reference standard. These harmonization efforts should improve consistency and reliability of panel TMB estimates and aid in clinical decision-making.
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MerinoDM, etal. J Immunother Cancer 2020;8:e000147. doi:10.1136/jitc-2019-000147
Open access
Establishing guidelines to harmonize
tumor mutational burden (TMB): in
silico assessment of variation in TMB
quantication across diagnostic
platforms: phase I of the Friends of
Cancer Research TMB
Harmonization Project
Diana M Merino ,1 Lisa M McShane,2 David Fabrizio,3 Vincent Funari,4
Shu- Jen Chen,5 James R White,6 Paul Wenz,7 Jonathan Baden,8 J Carl Barrett,9
Ruchi Chaudhary,10 Li Chen,11 Wangjuh (Sting) Chen,12 Jen- Hao Cheng,5
Dinesh Cyanam,10 Jennifer S Dickey,13 Vikas Gupta,14 Matthew Hellmann,15
Elena Helman,16 Yali Li,3 Joerg Maas,17 Arnaud Papin,18 Rajesh Patidar,11
Katie J Quinn,16 Naiyer Rizvi,19 Hongseok Tae,12 Christine Ward,8 Mingchao Xie,20
Ahmet Zehir,15 Chen Zhao,7 Manfred Dietel,17 Albrecht Stenzinger,21 Mark Stewart,1
Jeff Allen,1 On behalf of the TMB Harmonization Consortium
To cite: MerinoDM,
McShaneLM, FabrizioD, etal.
Establishing guidelines to
harmonize tumor mutational
burden (TMB): in silico
assessment of variation in TMB
quantication across diagnostic
platforms: phase I of the Friends
of Cancer Research TMB
Harmonization Project. Journal
for ImmunoTherapy of Cancer
2020;8:e000147. doi:10.1136/
jitc-2019-000147
Additional material is
published online only. To view,
please visit the journal online
(http:// dx. doi. org/ 10. 1136/ jitc-
2019- 000147).
Accepted 11 February 2020
For numbered afliations see
end of article.
Correspondence to
Dr Diana M Merino;
dmerino@ focr. org
Original research
© Author(s) (or their
employer(s)) 2020. Re- use
permitted under CC BY- NC. No
commercial re- use. See rights
and permissions. Published by
BMJ.
ABSTRACT
Background Tumor mutational burden (TMB), dened
as the number of somatic mutations per megabase of
interrogated genomic sequence, demonstrates predictive
biomarker potential for the identication of patients with
cancer most likely to respond to immune checkpoint
inhibitors. TMB is optimally calculated by whole exome
sequencing (WES), but next- generation sequencing
targeted panels provide TMB estimates in a time- effective
and cost- effective manner. However, differences in panel
size and gene coverage, in addition to the underlying
bioinformatics pipelines, are known drivers of variability
in TMB estimates across laboratories. By directly
comparing panel- based TMB estimates from participating
laboratories, this study aims to characterize the
theoretical variability of panel- based TMB estimates, and
provides guidelines on TMB reporting, analytic validation
requirements and reference standard alignment in order to
maintain consistency of TMB estimation across platforms.
Methods Eleven laboratories used WES data from The
Cancer Genome Atlas Multi- Center Mutation calling in
Multiple Cancers (MC3) samples and calculated TMB from
the subset of the exome restricted to the genes covered by
their targeted panel using their own bioinformatics pipeline
(panel TMB). A reference TMB value was calculated from
the entire exome using a uniform bioinformatics pipeline
all members agreed on (WES TMB). Linear regression
analyses were performed to investigate the relationship
between WES and panel TMB for all 32 cancer types
combined and separately. Variability in panel TMB values
at various WES TMB values was also quantied using 95%
prediction limits.
Results Study results demonstrated that variability within
and between panel TMB values increases as the WES
TMB values increase. For each panel, prediction limits
based on linear regression analyses that modeled panel
TMB as a function of WES TMB were calculated and found
to approximately capture the intended 95% of observed
panel TMB values. Certain cancer types, such as uterine,
bladder and colon cancers exhibited greater variability in
panel TMB values, compared with lung and head and neck
cancers.
Conclusions Increasing uptake of TMB as a predictive
biomarker in the clinic creates an urgent need to bring
stakeholders together to agree on the harmonization of
key aspects of panel- based TMB estimation, such as
the standardization of TMB reporting, standardization
of analytical validation studies and the alignment of
panel- based TMB values with a reference standard.
These harmonization efforts should improve consistency
and reliability of panel TMB estimates and aid in clinical
decision- making.
BACKGROUND
Immune checkpoint inhibitors (ICIs) have
recently emerged as a pillar of cancer care,
providing the potential for durable responses
and improved survival for patients across
multiple cancer types.1–3 An intensive clinical
development pipeline investigating ICIs is
ongoing as a result. However, not all patients
with cancer respond to ICIs, with modest
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response rates for several approved indications (approx-
imately 20% or less in lung cancer, bladder cancer and
cancers of the head and neck, among others) and high
treatment costs. There is a crucial interest in the devel-
opment of biomarker assays to predict which patients
are most likely to respond and benefit from ICIs, and to
improve clinical decision- making and disease manage-
ment.4 5
Expression of the programmed cell death ligand
protein-1 (PD- L1) by immunohistochemistry (IHC) has
been studied extensively as a biomarker of response to
anti- PD- L1 and programmed cell death protein 1 (PD-1)
therapy. Several assays have been developed to quantify
tumor PD- L1 immuno- positivity; however, quantitation is
imperfect, and lack of standardization across platforms
and scoring systems precludes assay interchangeability.6
Tumor mutational burden (TMB), which measures the
number of somatic mutations per megabase (Mb) of the
interrogated genomic sequence of a tumor, has been
most recently identified as a biomarker of response
to ICIs in several cancer types. High TMB is associated
with improved outcomes in patients with melanoma
treated with cytotoxic T- lymphocyte- associated protein 4
(CTLA-4) blockade7–9 and PD-1/PD- L1 blockade across
several cancer types, including melanoma,10 11 non- small-
cell lung carcinoma,12–15 bladder cancer,16 microsatellite
instability cancers3 17 and pan- tumor cohorts.18–20 High
TMB has also been associated with improved outcomes in
patients treated with a combination of PD-1/PD- L1 and
CTLA-4 inhibitors.21–24
Initial assessments of TMB involved whole exome
sequencing (WES) of matched tumor tissue and normal
specimens using next- generation sequencing (NGS).3 8–10
However, WES is not currently routine in clinical practice
due to substantial cost and turnaround time, which has
led assay manufacturers and commercial and academic
labs to develop targeted NGS panels. These targeted
panels, which cover several hundred genes, are already
routinely used in clinical practice, and are currently being
adapted to estimate TMB. TMB estimated from targeted
NGS panels has generally correlated well with TMB deter-
mined by WES, however the reliability of this technology
is still being assessed.13–16 20 22 25–30
There are several targeted NGS panels at different
stages of development that estimate TMB. To date,
the Foundation Medicine FoundationOne CDx test31
is currently the only Food and Drug Administration
(FDA)- approved panel, which includes TMB as part
of its tumor profiling claim, while the Memorial Sloan
Kettering Cancer Center MSK- IMPACT (Integrated
Mutation Profiling of Actionable Cancer Targets)32
has received FDA authorization. Additionally, there
are many more commercial and laboratory- developed
test panels currently under development. Each panel
has unique features integrated into their design that
may impact TMB estimation. For example, each panel
may include different numbers and types of genes,
use different sequencing platforms, have different
methods of filtering germline mutations, incorporate
different mutation types in the quantification of TMB
and use proprietary bioinformatics protocols to calcu-
late TMB.33 34 Thus, TMB estimates will vary according
to the targeted panel used.35 This is a crucial time to
understand the differences in TMB estimation across
panels, standardize the way TMB is reported, begin to
harmonize methods for TMB quantification and iden-
tify optimal approaches to promote TMB alignment
across different targeted NGS panels.
Friends of Cancer Research (Friends) convened a
consortium of key stakeholders, including diagnostic
manufacturers, academics, pharmaceutical companies,
the National Cancer Institute and the FDA, to recom-
mend best practices and approaches for TMB measure-
ment, validation, alignment and reporting well ahead
of the adoption of this powerful biomarker in clinical
decision- making. Leveraging the expertise and insights
of this comprehensive group of stakeholders, the Friends
TMB harmonization project seeks to establish a uniform
approach to measure and report TMB across different
sequencing panels by harmonizing the definition of
TMB, proposing best practices for analytic validation
studies and ensuring consistency of TMB calculation
through alignment with a universal reference standard.
The project consists of a stepwise approach broken down
into three phases: phase I, reported here, comprises the
in silico analysis, which by using publicly available data
from The Cancer Genome Atlas (TCGA) representing 32
cancer types, aims to identify the theoretical variability of
panel- derived TMB estimates (panel TMB) relative to a
common, standardized WES- derived TMB (WES TMB)
across various panels. Building on the results of the in
silico analysis, phase II will analyze human tumor clinical
sample material to objectively measure variation across
panels using patient formalin- fixed paraffin- embedded
(FFPE) tissue samples. This empirical analysis will also
compare panel TMB results to an agreed on universal
reference standard, consisting of a collection of human
tumor- derived reference cell lines that span a clinically
meaningful TMB dynamic range. FFPE tissue samples will
also be used to validate the use of the cell line standard.
Finally, phase III will involve a clinical study that seeks to
retrospectively analyze samples from patients treated with
ICIs to evaluate optimal cut- off values that will help guide
the clinical application of TMB (see online supplemen-
tary figure 1).
The need for harmonization of TMB is a global effort,
which is portrayed by the representation of national and
international diagnostic companies in the consortium.
Moreover, in seeking to complement the consortium’s
work, the Friends TMB harmonization project has part-
nered with the technical comparability study conducted
by Quality in Pathology in Germany,36 leading to the
identification of common and panel- specific factors
that influence TMB estimation and the development of
global recommendations, which have been published
previously.33
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MerinoDM, etal. J Immunother Cancer 2020;8:e000147. doi:10.1136/jitc-2019-000147
Open access
Due to the large scale and collaborative nature of
this effort, study results will greatly contribute to under-
standing and refining how to best quantify and interpret
TMB as a biomarker, help establish standards that will
facilitate harmonization across different testing platforms
and inform future harmonization efforts that seek to
ensure consistency across diagnostic platforms.
METHODS
In silico dataset
Mutation calls generated using Multi- Center Mutation
calling in Multiple Cancers (MC3) WES data from TCGA
project were used for this analysis.37 Variants that over-
lapped with the CCDS, using bedtools (- wa option)38
were extracted from the publicly available mc3. v0. 2. 8.
PUBLIC. maf file (https:// gdc. cancer. gov/ about- data/
publications/ pancanatlas). Finally, the data were filtered
for any overlap or redundancy using the ‘merge’ func-
tion. The consortium created a final bed file that covered
32.102 Mb of the genome after intersecting the data
found in the MAF files and filtering for any overlap or
redundancy (see online supplementary methods). The
final bed file size was used as the denominator for calcu-
lating WES TMB in this study. Three different consortium
laboratories independently calculated WES TMB using
the same dataset and analytical methodology with 100%
concordance.
Ten thousand two hundred ninety- five tumor samples
with matched normal initially composed part of the
cohort. Only samples with at least one variant which
PASSED variant review filter were used (see online
supplementary methods for variant quality filters). Low
quality samples based on variant filters and those with
low purity were also removed from further analysis. The
remaining cases (n=8291) were randomly assigned to
training (n=4157) and validation (n=4134) datasets with
similar median candidate mutations and cancer types
(online supplementary figure 2). Participants, though
not required, could use the ‘training’ set for their own
algorithm or parameter testing. However, all analyses
described herein were conducted using the validation
dataset.
The evaluations reported in the present study are those
comparing panel TMB to WES TMB on the validation
set, with no adjustments made to the panel TMB algo-
rithms once the validation set analyses began. All analyses
focused on tumors for which WES TMB was 40 because
>98% of the TCGA dataset tumors investigated had TMB
40 in the TCGA dataset and all members of the consor-
tium agreed that this range would have the greatest rele-
vance for clinical decision- making. Of the 4134 tumors
initially represented in the validation set, 4065 remained
after excluding those with WES TMB >40. All results were
blinded to the entire consortium, with the exception of
the project statistician and data manager (LMMS and
DMM) who were regarded as neutral parties not affiliated
with any of the participating laboratories.
Statistical analysis
Statistical analyses interrogated the relationship between
WES TMB and panel TMB values. The first analysis
focused on the combined data from all 32 tumor types.
Spearman’s R correlation values were calculated, and
scatterplots and difference plots were created to assess
linearity of the relationship between panel TMB and WES
TMB and to evaluate whether variance of panel TMB was
constant across the range of WES TMB values.
Next, the 32 tumors were divided into three strata
according to the number of samples within each tumor
type that had TMB values spanning the range 0–40 mut/
Mb (see online supplementary methods and figure 3).
Stratum 1 contained eight tumor types (see online supple-
mentary table 1A—stratum 1) displaying a good distribu-
tion of TMB values spanning the range of interest (0–40
mut/Mb). Seventy- seven per cent of samples (1257/1627)
had TMB 10 mut/Mb, 19% (306/1627) had TMB 10–40
mut/Mb and 4% (64/1627) had TMB 40 mut/Mb and
were thus eliminated from further analyses. Stratum 2 was
represented by 11 tumor types (see online supplemen-
tary table 1B—stratum 2) whose samples had generally
low TMB values (10 mut/Mb, 98%, 1723/1754), and
only 1.5% (26/1754) of samples had TMB 10–40 mut/
Mb. Only five samples (0.29%) had TMB 40 mut/Mb
and were thus eliminated from further analyses. Stratum
3 was represented by 13 tumor types (see online supple-
mentary table 1C—stratum 3) whose samples had very
low TMB values (5 mut/Mb, 99.5%, 749/753) and only
4 samples (0.5%) had samples with TMB between 5 and
10 mut/Mb. Regression modeling using weighted least
squares was implemented to account for the heterosce-
dasticity in errors, referring to the variability in panel
TMB values about the fitted regression line, which was
observed to increase with the mean and with WES TMB.
This modeling was conducted for all strata, although we
focused on stratum 1 considering strata 2 and 3 provided
less stable and unreliable estimates due to the large
number of samples that concentrated in the lower end of
the TMB range.
For each regression, the mean panel TMB was modeled
as a simple linear function of the WES TMB, and five
different models for the error variance were consid-
ered (see online supplementary methods). Restricted
maximum likelihood analysis using the gls function avail-
able in the R package nlme was performed to estimate
the model parameters and select a best fitting variance
structure based on minimum Akaike information and
Bayesian information criteria.
Whole exome analysis
The whole exome analysis of the TCGA MC3 validation
dataset used an agreed on methodology to calculate the
WES TMB values, termed the Uniform TMB Calculation
Method (see online supplementary table 2). The goal of
phase I of this harmonization study is to assess the theo-
retical variability across panels. Given that the partici-
pating panels were at different stages of development and
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Table 1 Description of the 11 participating diagnostic NGS panels
Laboratory Panel name # genes
Total region
covered
(Mb)
TMB region
covered*
(Mb)
Type of exonic
mutations included in
TMB estimation
Published
performance
characteristics
(ref.)
ACT Genomics ACTOnco+ 440 1.8 1.12 Non- synonymous†,
synonymous
NA
AstraZeneca AZ600 607 1.72 1.72 Non- synonymous,
synonymous
NA
Caris SureSelect XT 592 1.60 1.40 Non- synonymous Vanderwalde et al40
Foundation
Medicine
FoundationOne CDx‡ 324 2.20 0.80 Non- synonymous,
synonymous
Frampton et al41
Chalmers et al25
Fabrizio et al42
US FDA SSED31
Guardant Health GuardantOMNI§ 500 2.15 1.00 Non- synonymous,
synonymous
Quinn et al43
Illumina TSO500 (TruSight Oncology
500)
523 1.97 1.33 Non- synonymous,
synonymous
NA
Memorial Sloan
Kettering Cancer
Center
MSK- IMPACT¶ 468 1.53 1.14 Non- synonymous Cheng et al,44 Zehir et
al,30 US FDA32
NeoGenomics NeoTYPE Discovery Prole
for Solid Tumors
372 1.10 1.03 Non- synonymous,
synonymous
NA
Personal Genome
Diagnostics
PGDx elio tissue complete 507 2.20 1.33 Non- synonymous,
synonymous
Wood et al45
QIAGEN QIAseq TMB panel 486 1.33 1.33 Non- synonymous,
synonymous
NA
Thermo Fisher
Scientic
Oncomine Tumor Mutation
Load Assay
409 1.70 1.20 Non- synonymous Chaudhary et al46
Endris et al35
*Coding region used to estimate TMB regardless of the size of the region assessed by the panel.
†Non- synonymous mutations include single nucleotide variants, splice- site variants and short insertions and deletions (indels).
‡FoundationOne CDx assay has been approved by the US FDA as an IVD.31
§GuardantOMNI is a plasma- based circulating tumor DNA assay.
¶MSK- IMPACT assay has been authorized by the US FDA32
NA, not available.
had different sensitivity levels, the consortium decided
to use the Uniform TMB Calculation Method, which
would enable the selection of high- quality variants that
all laboratories were able to assess as part of their panels.
The consortium created a custom bed file covering
32.102 Mb of the genome which was used to calculate the
reference WES TMB values. The calculated WES TMB
values comprised the reference dataset for this study.
The uniform method for analysis of WES TMB included
minimum thresholds for median target coverage (median
300X as this was identified as the point where sensitivity
for the lower allele frequency variants drops drasti-
cally) (see online supplementary figure 4), variant allele
frequency (0.05), read depth (25) and variant count
(3), and synonymous variants were excluded.
Panel analysis
Each participating laboratory calculated TMB from the
subset of the exome restricted to the genes covered by
their targeted panel and using their own unique bioinfor-
matics pipeline (panel TMB). If available, the laborato-
ry’s bioinformatics analysis has been reported in table 1.
The panel- derived TMB datasets were sent to a neutral
third party (DMM) who assigned coded identifiers to the
laboratories to mask which laboratory contributed each
dataset. All subsequent data analyses were conducted by
LMMS and DMM. Participating laboratories were not
involved in the analyses and were not provided the key to
the coded lab identifiers.
RESULTS
In silico assessment of theoretical TMB variation across
panels
Eleven academic and commercial laboratories with
targeted gene panels in different stages of development
participated in this study (table 1). The size of the coding
region used to estimate TMB from these gene panels
ranged between 0.80 and 1.72 Mb. And the number of
genes in each of the gene panels ranged between 324 and
607 genes. All participating laboratories included exonic
somatic non- synonymous, frameshift and splice site vari-
ants and short indels when estimating TMB. Eight panels
(8/11, 73%) also included synonymous variants in their
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Open access
Figure 1 Estimated regression lines for panel tumor mutational burden (TMB) as a function of whole exome sequencing
(WES) TMB for each of the 11 participating laboratories analyzing (A) all cancer types combined and (B) stratum 1 cancer types
combined. Solid lines represent the tted regression lines. Red dashed line represents 45o line.
estimation. Each laboratory used their own bioinfor-
matics algorithms and workflows, which were optimized
using the sequencing methods, mutation types and filters
that best suited their own panel specifications. Since the
participating panels were in different stages of develop-
ment, only a few had published panel performance char-
acteristics (table 1).
The WES TMB values were calculated using the TCGA
MC3 Mutation Annotation Format (MAF) validation
dataset and an agreed on methodology (see ‘Whole
exome analysis’ section and online supplementary table
2). The panel TMB values on the same validation dataset
were estimated by down- sampling to the regions covered
by each of the laboratories’ panels and applying their own
bioinformatics algorithms. To prevent the misinterpre-
tation of this study’s results as an interlab performance
study, all laboratories agreed for the results to be blinded
with respect to the lab generating each dataset.
First, all 32 cancer types in the TCGA MC3 dataset
were investigated together using weighted linear regres-
sion analysis (generalized least squares, see ‘Methods’
section). Some variation was observed across panels, with
Spearman’s rank correlation values (R) ranging from
0.79 to 0.88, and slope values ranging from 0.87 to 1.47
(figure 1A, online supplementary figure 5). Eight labo-
ratories (73%) had slope values >1, demonstrating an
overestimation of TMB. Panel factors that may influence
TMB overestimation were not assessed due to the blinded
study design but may have included the type of mutations
counted for the panel TMB value (eg, synonymous alter-
ations included in panel TMB that were excluded from
the WES estimation), among others.
In silico assessment of theoretical TMB variation across
panels by cancer type
A limitation of analyzing all cancer types together is the
variable distribution of TMB across different cancer types,
with some cancer types displaying large dynamic ranges of
TMB values up to several hundred mutations per Mb and
others with very limited distributions with very few samples
reaching 20 mutations per Mb (see online supplementary
figure 3). To account for this limitation, cancer types were
categorized into strata by their distribution of WES TMB
values. Stratum 1 (n=1563 samples with <40 mut/Mb)
had samples with a good distribution of WES TMB values
covering 0–40 mut/Mb, which enabled a more robust
regression analysis across a clinically relevant TMB range.
The eight cancer types in stratum 1 were: bladder urothe-
lial carcinoma (BLCA, n=195), colon adenocarcinoma
(COAD, n=128), head and neck squamous cell carcinoma
(HNSC, n=232), lung adenocarcinoma (LUAD, n=228),
lung squamous cell carcinoma (LUSC, n=228), skin cuta-
neous melanoma (SKCM, n=166), stomach adenocarci-
noma (STAD, n=189) and uterine corpus endometrial
carcinoma (UCEC, n=197).
Regression analyses restricted to stratum 1 tumors
revealed an association between WES TMB and panel
TMB similar to that for all cancer types analyzed together
(Spearman’s R: 0.81–0.90 and slope 0.80–1.32, figure 1B,
per laboratory online supplementary figure 6 and table
3). The slopes calculated when stratum 1 tumors were
analyzed were consistently lower than when all cancers
were analyzed. The greatest differences in slope values
when comparing slopes estimated for all cancers and
for stratum 1 tumors only, were observed for labs 8 (all
cancers 1.47 vs stratum 1 1.32) and 9 (all cancers 1.24 vs
stratum 1 1.1) (both 0.15), while labs 4 (all cancers 0.904
vs stratum 1 0.897) and 2 (all cancers 1.087 vs stratum 1
1.076) had the least differences ( 0.007 and 0.01, respec-
tively). When stratum 1 tumors were analyzed, only six
laboratories (55%) reported overestimation of TMB with
slope values >1.
Regression analyses with stratum 2 and 3 were not
robust, as the WES TMB values did not adequately cover
the entire clinically meaningful range (see online supple-
mentary figures 7 and 8, and table 3).
Lastly, the eight cancers in stratum 1 were analyzed sepa-
rately. UCEC, BLCA and COAD had the broadest range of
slope values (UCEC: range 0.755–1.602, 0.847; BLCA:
range 1.042–1.79, 0.748; COAD: range 0.75–1.486,
0.736) (figure 2, online supplementary table 4), and most
laboratories consistently overestimated these cancer types,
with BLCA as the only cancer type for which all 11 labora-
tories (100%) consistently overestimated their panel TMB
values relative to WES TMB. Conversely, LUAD, LUSC
and HNSC had the tightest range of slope values with no
consistent bias to overestimating or underestimating TMB
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Figure 2 Estimated regression lines for panel tumor mutational burden (TMB) as a function of whole exome sequencing (WES)
TMB for the eight cancer types within stratum 1. All cancer types had a good distribution of WES TMB values from 0 to 40 mut/
Mb. Solid lines represent the tted regression lines. Red dashed line represents 45o line. BLCA, bladder urothelial carcinoma;
COAD, colon adenocarcinoma; HNSC, head and neck squamous cell carcinoma; LUAD, lung adenocarcinoma ; LUSC,
lung squamous cell carcinoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; UCEC, uterine corpus
endometrial carcinoma.
(LUAD: range 0.817–1.135, 0.318; LUSC: range 0.741–
1.099, 0.358; HNSC: range 0.854–1.244, 0.39).
Dening the theoretical variation in TMB across panels and by
cancer type
Prediction limits for the observed panel TMB at fixed
WES TMB (5, 10, 15 and 20 mut/Mb) were calculated
to quantify the variability around the regression line at
those selected WES TMB values. The limits were designed
to capture approximately 95% of the panel TMB values
expected to be observed at a given WES TMB. Some
laboratories had consistently tighter (narrower) predic-
tion intervals, while others demonstrated more variability
(wider intervals), but for all laboratories, the prediction
intervals became tighter with decreasing WES TMB value,
indicating greater variability in panel TMB at larger WES
TMB values (figure 3). Generally, the prediction intervals
observed for each participating laboratory were similar,
with laboratories demonstrating intervals that spanned as
small as ±4.7 mut/Mb or as large as ±12.3 mut/Mb when
the WES TMB was 10 mut/Mb, which is a TMB threshold
that has been previously used to define a TMB- high cohort
using NGS panels.33 When prediction limits were assessed
by strata, the variability of the intervals was very large for
cancer types in strata 2 and 3 compared with stratum 1
because most TMB values for the cancers in these strata
accumulate in the lower end of the TMB spectrum, thus
resulting in more uncertainty in the fitted regression lines
and wide scatter in panel TMB values around those lines
(see online supplementary figure 9). When the eight
stratum 1 cancers were analyzed separately, prediction
intervals at the discreet value of WES TMB=10 mut/Mb
were observed to be wider for BLCA and UCEC, while
LUAD, LUSC, HNSC and SKCM had the tightest inter-
vals (figure 4). This is similar to the observed variation
in fitted regression lines for BLCA and UCEC across
laboratories (figure 2). The theoretical variability around
the regression was also seen to increase (wider intervals)
with increasing TMB value in individual cancer types (see
online supplementary figure 10).
DISCUSSION
Eleven laboratories with distinct NGS targeted gene
panels and bioinformatic approaches participated in
phase I of the Friends of Cancer Research TMB Harmo-
nization Project and provided early insights into the theo-
retical variability across different targeted gene panels
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Figure 3 Ninety- ve per cent prediction intervals for panel tumor mutational burden (TMB) estimated at discreet whole exome
sequencing (WES) TMB values (5, 10, 15 and 20 mut/Mb), by laboratory across all laboratories. Blue arrows represent the
estimated mean panel TMB for each laboratory. Red dashed line represents the discreet WES TMB value at which prediction
interval is calculated.
that estimate TMB. The goal of the first phase of the
project was to describe the variability in TMB estimates
across several uniquely designed panel- based diagnostic
assays and to further elucidate the theoretical variation
in TMB quantification using an in silico approach with
a large publicly available dataset with high- quality reads
and a common reference TMB standard calculated from
the entire exome. Moreover, dependence of the associa-
tion between panel TMB and WES TMB on cancer type
was investigated.
Variability in panel TMB across different panels was
observed, with some panels consistently overestimating
or underestimating TMB, suggesting that panel size and
composition, as well as laboratories’ bioinformatics algo-
rithms, including types of mutations counted and variant
filters used in the TMB calculation, were likely contribu-
tors to the differences. Because of the blinded design of
this study, the influence of these factors on panel TMB
variability was not evaluated in this early phase of the
project but will be assessed in the following empirical
phase to be reported subsequently. Additionally, other
studies have recently reported on the impact of panel
size, DNA input and variant filtering on panel- based TMB
estimates.35 39
The study evaluated a robust dataset containing 32
cancer types, with very few cases having TMB >40 mut/Mb
(n=69/4134, 1.7%), so it was not possible to robustly esti-
mate the association between panel TMB and WES TMB
for cases with values >40 mut/Mb. TMB data were thus
capped at 40 mut/Mb and a linear relationship was used
to model the relationship in that range. Factoring the
limited dynamic range of TMB values observed in some
cancer types, a subset of eight cancers was identified and
named stratum one for the primary analysis. Stratum 1
cancer types included lung, bladder, head and neck, skin,
colon, uterine and gastric cancers, all of which have been
shown to respond to immune checkpoint inhibitors. Eval-
uating these cancers separately revealed distinct levels
of variability in the association between panel TMB and
WES TMB across panels, with some cancer types having
less variability (eg, lung and head and neck cancers), and
some having greater variability (eg, uterine, bladder and
colon cancers). As our initial findings suggest that panels
may perform differently on certain cancer types, further
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Figure 4 Ninety- ve per cent prediction intervals for panel TMB (x- axis) of stratum 1 tumor types at whole exome sequencing
tumor mutational burden (WES TMB) 10 mut/Mb by laboratory (y- axis). Blue arrows represent the estimated mean panel TMB
for each laboratory. Red dashed line indicates WES TMB value=10 mut/Mb. BLCA, bladder urothelial carcinoma; COAD, colon
adenocarcinoma; HNSC, head and neck squamous cell carcinoma; LUAD, lung adenocarcinoma ; LUSC, lung squamous cell
carcinoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; UCEC, uterine corpus endometrial carcinoma.
work is required to understand the factors contributing to
any disease- specific TMB variability, and the relationships
beyond the analyzed TMB range. However, the composi-
tion of the panels’ genes, types of mutations counted or
methods used to train their respective TMB algorithms
could be future areas of focus.
Despite these cancer- dependent findings, our study
found that panel TMB values were strongly correlated
with WES TMB across laboratories. Additionally, the
calculated 95% prediction intervals permitted estimation
of the linear relationship between panel TMB and WES
TMB as well as quantification of the range in which 95%
of the observed TMB panel values would be expected to
fall for tumors with various fixed WES TMB values. This
provides a framework for understanding the theoretical
variability likely to be incurred in the clinical applica-
tion of TMB estimation across panels, but also suggests
that harmonization of TMB estimates could be achieved
through alignment using external reference materials.
There is still, however, much that can be done to improve
the reliability of using NGS panels for TMB estimation.
The selection of high- quality variants from the TCGA
MC3 dataset was used to assess the theoretical variability
of TMB across panels in this study ensuring the interpret-
ability of the findings where the assessment of variability
was limited to factors such as panel size and composition
or bioinformatics pipeline, instead of perceived differ-
ences regarding sensitivity and specificity of individual
variant calling. However, we acknowledge that in a clin-
ical setting the estimation of TMB from FFPE tissue may
introduce variants of lesser quality and panels should
aim to validate the sensitivity and specificity of individual
variant calling separately from TMB validation.
As TMB measurements are most likely to be impactful
in treatment decisions for stratum one cancer types,
including these tumors as part of a laboratory’s analytical
validation studies to achieve optimal accuracy and consis-
tency is critical. On the other hand, it is also important
to recognize that there are cancer types with generally
low TMB values that may have a few cases with high TMB
values that may benefit from reliable panel TMB results.
Moreover, because of the cancer type- dependent distri-
bution of TMB values, studies aiming to evaluate the clin-
ical utility of TMB and determine optimal TMB cut- offs
for treatment decisions may need to account for specific
cancer types. This would be consistent with a recent
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Table 2 Consensus recommendations for the standardization of analytical validation studies of targeted NGS panels that estimate TMB
Parameters Recommendations
Accuracy* Accuracy or agreement should be measured by comparing the TMB values generated by the assay requiring validation against reference TMB
values generated either from:
A comparable companion diagnostic approved by a regulatory agency, such as the FDA, if available, OR
A WES assay with validated performance characteristics and using an accepted WES TMB calculation method, such as the common method
reported in this study (see online supplementary table 1).
The minimum number of samples used for evaluation of accuracy should be at least 30. Samples should have TMB values that span the entire
analytical range being investigated (0–40 mut/Mb is recommended currently).
TMB as a continuous score: This analysis will characterize the analytical performance of the assay over the analytical range of interest per the
intended use.
Quantication of performance based on TMB as a continuous variable should include an appropriate regression analysis and a scatter plot
showing the association between the panel and reference TMB values.
Additionally, quantication of performance should examine the pointwise prediction intervals for panel TMB values as obtained from the
regression analysis, at predened reference TMB values. A description of the absolute deviation from the mean should also be reported.
For TMB as a categorical call: accuracy should be analytically validated using a single or multiple discreet TMB cut- off values within the analytical
range of interest.
Quantication of performance of TMB as a categorical call should be based on 2×2 agreement tables to inform the positive per cent agreement,
negative per cent agreement and overall per cent agreement informed by 2×2 agreement tables.
For assays pursuing a companion diagnostic claim, accuracy should be examined using a predetermined discrete cut- off value investigated in a
study using clinical samples covering the spectrum of conditions from a dened, or intent- to- treat (ITT) population. If the ITT population includes
multiple cancer types, stratied analyses should be conducted.
Alteration level agreement: as a supplemental analysis, the agreement between alteration level calls (including single nucleotide variants (SNVs)
and short indels) between the platform and the reference should be provided for each alteration included in the TMB panel and restricted to the
overlapping genomic regions between the two assays.
Report the concordance of variant calls between variants identied by WES and panel as a function of the panel variant allele frequency (VAF).
Characterize the percentage of tests passing QC by reporting rst pass acceptability rate and overall acceptability rate (after samples have been
retested, if necessary).
Continued
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Parameters Recommendations
Precision* Precision should be evaluated using several samples. For each sample, separate analyses should be performed as described in the TMB as a
continuous score and TMB as a categorical call sections below.
Analytical validation of precision of TMB as both a continuous score and a categorical call will improve reliability of TMB as a biomarker.
Because TMB is a composite estimate composed of different variants, its precision should be evaluated as a composite score (mut/Mb).
TMB as a continuous score:
Precision studies of quantitative TMB estimates should evaluate the mean, SD and coefcient of variation of TMB values obtained from testing
aliquots of the same sample under stipulated precision conditions (eg, replicates, runs, instruments, lots, operators) for a range of samples (5–6
samples with 20 TMB results distributed across the precision conditions each) with TMB values within the analytical range (0–40 mut/Mb is
recommended currently), and include different levels of tumor content and VAF values.
Note: identication of the TMB range to be evaluated should be guided by the most recently published clinically relevant studies.
Precision of the TMB score should be estimated using a variance component analysis to estimate between- run, within- run, between instruments,
between lots and between operator SD for each sample.
Quantication of performance should include calculation of repeatability and within- lab SD for each sample corresponding to several discreet
TMB values, given that a single average value of variation (eg, coefcient of variation pooled across several samples having different TMB levels)
may not best reect the changing variability across the TMB range.
TMB as a categorical call: consistency of the categorical calls (eg, TMB high vs TMB low according to a discreet cut- off) should be evaluated based
on both repeatability and within- laboratory precision for the TMB results according to a single or multiple discreet cut- off values.
For repeatability, calculate the per cent of TMB high calls (if majority call is high) or per cent of TMB low calls (if majority call is low) between
replicate samples tested under the same lab conditions.
For within laboratory precision, calculate the per cent of TMB high calls (if majority call is high) or per cent of TMB low calls (if majority call is low)
and the mean TMB score from replicate samples tested under varying within- lab conditions.
Note: the number of aliquots tested per sample should be sufcient to account for the various sources of assay variability, such as the ones
described above (TMB as a continuous score). Moreover, the number of samples tested should be similar.
Emphasis should be placed on evaluating samples with TMB values:
Signicantly below cut- off (approximates limit of blank, expect TMB low almost 100% of time).
Near and below cut- off (expect TMB low 95% of time).
Near and above cut- off (expect TMB high 95% of time).
Signicantly above cut- off (expect very high TMB almost 100% of time).
Alteration level precision: the evaluation of precision for each individual alteration call used to estimate TMB is not necessary but may be performed
as an exploratory analysis to provide insight into the mechanisms that contribute to the TMB score variability.
Per cent tumor content should be collected when evaluating precision and reported, if applicable.
Characterize the percentage of tests passing QC by reporting rst pass acceptability rate and overall acceptability rate (after samples have been
retested, if necessary).
Table 2 Continued
Continued
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Parameters Recommendations
Sensitivity* The impact of tumor content of a sample on the TMB categorical call (high, low) should be evaluated using multiple samples, taking into
consideration the precision of the TMB score as a function of decreasing tumor content.
Undiluted samples should have a range of expected TMB scores, a range of VAF values for somatic mutations and a ratio of SNVs and indels
that are representative of clinical samples.
The evaluation of panel sensitivity to tumor content should be done using:
Samples: 6–10 undiluted samples where each sample is diluted to at least 5 levels of tumor content.
Each sample should have a dilution series ranging from well above and below the expected sensitivity limits for tumor content.
Note: It is likely that matched normal will be required to generate each respective dilution value. Consideration should be given to technical
and biological factors that may impact the choice of the normal sample and design of the dilution series.
At each dilution level, at least 10 replicate samples should be tested.
For each sample, the evaluation should include a calculation of the per cent of TMB high (above a predened TMB threshold) across replicates at
each dilution and a probit regression of per cent TMB high versus tumor content. From the regression, report the estimated tumor content where the
probability of detecting TMB high is 95%.
Limit of blank* Non- tumor samples should be used to establish the limit of blank for TMB, yielding results close to, but not always equal to 0 mut/Mb.
Considerations should be given to technical and biological factors, such as age of patient and distance from tumor lesion, among others.
Percentage of
tests passing QC
Report percentage of tests passing TMB QC metrics in routine testing.
Example QC metrics for TMB might include: median exon coverage, coverage uniformity, contamination rate.
*The denitions of the terms used in this table are based on the Clinical and Laboratory Standards Institute Harmonized Terminology Database at http://htd.clsi.org/.
FDA, Food and Drug Administration; NGS, next- generation sequencing; QC, quality control; TMB, tumor mutational burden ; WES, whole exome sequencing .
Table 2 Continued
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report that found that in patients who received ICI, those
who had high TMB had longer survival than those who
had low TMB, but TMB- high cut- offs were cancer- type
dependent.20
The Friends of Cancer Research TMB Harmonization
Consortium includes the participation of several leading
commercial and academic laboratories as well as a diverse
group of stakeholders, who together identified oppor-
tunities for standardization to promote the harmoniza-
tion of TMB estimation. These have led the consortium
to recommend best practices for panel developers that
seek to promote consistency in alignment and facilitate
commutability across panels table 2. These recommenda-
tions revolve around the following three items.
1. Ensure reporting consistency: TMB should be reported in
mutations/megabase (mut/Mb)
The current practice of reporting WES- derived TMB
values as number of somatic mutations, while panel-
derived TMB values are reported as a density of somatic
mutations per Mb of genomic region covered by the
panel (mut/Mb), precludes the aggregate analysis of
TMB being derived from WES or targeted panels, espe-
cially since the size of the exome interrogated using
different platforms may not be consistent. Reporting
TMB as mutations/megabase (mut/Mb) in order to keep
these values consistent and comparable is recommended
by the consortium.
2. Analytical validation studies for TMB estimation should be
standardized to include assessment of analytical accuracy,
precision and sensitivity
Size of targeted gene panel, technical sensitivity of the
assay and pre- analytical and analytical variables are
known to contribute to variability in panel- based TMB
estimates.33 The same in silico data were used by every
participating laboratory, which created a theoretical
setting that focused the investigation on potential sources
of variability that are unique to the technical specifica-
tions of the panel (eg, size and composition), and the
bioinformatics approaches of each laboratory (eg, muta-
tion types counted and germline and hotspot mutation
filtering). Some of these factors cannot be easily modified
and standardized across laboratories as panel assays are,
for the most part, proprietary and have been designed
to optimize their respective technical specifications and
conditions. However, harmonization of TMB estimates
may be achieved across laboratories by ensuring that the
analytical validation studies for each panel follow a stan-
dard approach including alignment of panel TMB values
to an external reference standard. Recommendations for
analyzing accuracy, precision and sensitivity of TMB values
to tumor content when used both as a continuous score
and a categorical call have been proposed by the consor-
tium (table 2). These recommendations will ensure that
regardless of the type of panel or bioinformatics pipeline
a laboratory decides to use, TMB estimates are held to a
standard of acceptable reliability.
3. Consistency across panels could be ensured through
alignment of panel TMB values to WES-derived universal
reference standard
Comparison to WES TMB is currently the most recog-
nized way to determine accuracy of panel TMB. However,
it should be noted that differences in performance
between panel TMB and WES TMB are to be expected
based on differences in coverage depth between the two
methods, with typically greater depth and higher vari-
ability observed in panels.
Universal reference standards, with TMB values span-
ning a clinically relevant range (eg, 0–40 mut/Mb),
represent a promising tool to achieve alignment or cali-
bration in order to ensure consistency of the TMB esti-
mation across platforms, regardless of known sources of
variability. An ideal reference standard for TMB estima-
tion should be generated from a renewable source and its
TMB values should be calculated using WES. To mitigate
differences resulting from comparisons using multiple
different WES assays, a universally accepted, predefined
bioinformatics pipeline and statistical methods should
be implemented. A calibration curve generated using
the reference standard should be used to normalize and
compare across panels, which should promote alignment
and aid in the analytical validation of panel TMB values.
CONCLUSIONS
Harmonization of methodologies for the accurate
measurement of complex continuous biomarkers is an
ongoing effort. The Friends of Cancer Research TMB
Harmonization Project has convened key stakeholders
early in the development of NGS assays that estimate
TMB to more effectively identify avenues for the harmo-
nization of estimation approaches and to emphasize the
need for the uptake and implementation of these harmo-
nization recommendations. The results included in this
report are the initial results from this stepwise approach,
but future studies will focus on assessing the feasibility of
using tumor- derived cell lines as external reference stan-
dards to help facilitate alignment of panel TMB values.
Additional empirical analyses will also be conducted to
investigate the influence of biologic factors (eg, specimen
type, cancer type) and technical factors (eg, sequencing
technology) on panel TMB, continue refining best prac-
tices for panel assessment of TMB, and developing align-
ment approaches to improve interchangeability between
TMB estimates generated from different targeted gene
panels.
Lastly, the collaborative efforts of the TMB Harmoni-
zation Consortium will serve as a framework for future
harmonization initiatives that seek to standardize complex
quantitative biomarker assays and promote the reliability
of biomarker testing.
Author afliations
1Friends of Cancer Research, Washington, DC, USA
2National Cancer Institute, Bethesda, Maryland, USA
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3Foundation Medicine Inc, Cambridge, Massachusetts, USA
4NeoGenomics Laboratories, Aliso Viejo, California, USA
5ACT Genomics, Taipei, Taiwan
6Resphera Biosciences, Baltimore, Maryland, USA
7Clinical Genomics, Illumina Inc, San Diego, California, USA
8Bristol- Myers Squibb Co, Princeton, New Jersey, USA
9Translational Medicine, Oncology Research and Early Development, AstraZeneca
Pharmaceuticals LP, Boston, Massachusetts, USA
10Clinical Sequencing Division, Thermo Fisher Scientic, Ann Arbor, Michigan, USA
11Molecular Characterization Laboratory, Frederick National Laboratory for Cancer
Research, Frederick, Maryland, USA
12Caris Life Sciences Inc, Phoenix, Arizona, USA
13Personal Genome Diagnostics, Baltimore, Maryland, USA
14QIAGEN Inc, Aarhus, Denmark
15Memorial Sloan Kettering Cancer Center, New York, New York, USA
16Bioinformatics, Guardant Health Inc, Redwood City, California, USA
17Quality in Pathology (QuIP), Berlin, Germany
18QIAGEN Inc, Waltham, Massachusetts, USA
19Division of Hematology/Oncology, Department of Medicine, Columbia University,
New York, New York, USA
20AstraZeneca Pharmaceuticals LP, Waltham, Massachusetts, USA
21Institute of Pathology, University Hospital Heidelberg, Heidelberg, Baden-
Württemberg, Germany
Twitter Diana M Merino @d2merino
Acknowledgements The authors would like to thank all members of the Friends
of Cancer Research TMB Harmonization Consortium for their support and scientic
contributions to the study design and interpretation. The authors would also like to
thank Dr Alexander Lazar for helpful discussions.
Contributors DMM: conception, design, acquisition, analysis, interpretation, draft
and manuscript preparation. LMMS: design, acquisition, analysis, interpretation,
draft and manuscript preparation. DF: conception, design, acquisition, analysis,
interpretation, draft and manuscript preparation. VF: analysis, interpretation,
draft and manuscript preparation. S- JC: analysis, interpretation, draft and
manuscript preparation. JRW: design, acquisition, analysis, interpretation, draft
and manuscript preparation. PW: analysis, interpretation, draft and manuscript
preparation. JB: analysis, interpretation, draft and manuscript preparation. JCB:
conception, design, acquisition, analysis, interpretation, draft and manuscript
preparation. RC: analysis, interpretation, draft and manuscript preparation.
LC: analysis, interpretation, draft and manuscript preparation. WSC: analysis,
interpretation, draft and manuscript preparation. J- HC: analysis, interpretation,
draft and manuscript preparation. DC: analysis, interpretation, draft and manuscript
preparation. JSD: analysis, interpretation, draft and manuscript preparation. VG:
analysis, interpretation, draft and manuscript preparation. MH: conception, design,
interpretation, draft and manuscript preparation. EH: analysis, interpretation,
draft and manuscript preparation. YL: design, acquisition, analysis, interpretation,
draft and manuscript preparation. JM: conception, design, interpretation, draft
and manuscript preparation. AP: analysis, interpretation, draft and manuscript
preparation. RP: analysis, interpretation, draft and manuscript preparation. KJQ:
analysis, interpretation, draft and manuscript preparation. NR: conception, design,
interpretation, draft and manuscript preparation. HT: analysis, interpretation,
draft and manuscript preparation. CW: conception, design, acquisition, analysis,
interpretation, draft and manuscript preparation. MX: analysis, interpretation, draft
and manuscript preparation. AZ: analysis, interpretation, draft and manuscript
preparation. CZ: analysis, interpretation, draft and manuscript preparation.
MD: conception, design, interpretation, draft and manuscript preparation.
AS: conception, design, interpretation, draft and manuscript preparation. MS:
conception, design, acquisition, analysis, interpretation, draft and manuscript
preparation. JA: conception, design, acquisition, analysis, interpretation, draft and
manuscript preparation. All authors have approved the submitted version of the
manuscript.
Funding The Friends initiative uses a distributed research model and costs
incurred are borne by each participating organization. Additional sources of funding
were provided by AstraZeneca; Bristol- Myers Squibb; EMD Serono; Genentech and
Merck & Company Inc.
Competing interests DF: employment with Foundation Medicine and stockholder
in Roche. VF: employment with NeoGenomics Inc and stockholder in NeoGenomics
Inc. S- JC: employment with ACT Genomics and stockholder in ACT Genomics.
JRW: founder and owner of Resphira Biosciences and paid consultant to PGDx. PW:
employment with Illumina and stockholder in Illumina. JB: employment with Bristol-
Myers Squibb, shareholder in Bristol- Myers Squibb and shareholder in Johnson
& Johnson. JCB: employment with AstraZeneca Pharmaceuticals and stocks in
AstraZeneca Pharmaceuticals. RC: employment with Thermo Fisher Scientic.
WSC: employment with Caris Life Sciences. JHC: employment with ACT Genomics.
DC: employment with Thermo Fisher Scientic and stockholder in Thermo Fisher
Scientic. JSD: employment with Personal Genome Diagnostics. VG: employment
with QIAGEN. MH: received research funding from Bristol- Myers Squibb; is paid
a consultant to Merck, Bristol- Myers Squibb, AstraZeneca, Genentech/Roche,
Janssen, Nektar, Syndax, Mirati and Shattuck Labs; has received travel support/
honoraria from AztraZeneca and BMS and a patent has been led by MSK related
to the use of tumor mutation burden to predict response to immunotherapy (PCT/
US2015/062208), which has received licensing fees from PGDx. EH: employment
with Guardant Health Inc and stockholder in Guardant Health Inc. YL: employment
with Foundation Medicine while engaged in the research project (March 2019).
Currently, an employee of Thrive Sciences, Inc and stockholder of Thrive Sciences,
Inc. AP: employment with QIAGEN. KJQ: employment with Guardant Health Inc and
stockholder in Guardant Health Inc. NR: NR and Memorial Sloan Kettering Cancer
Center have a patent ling (PCT/US2015/062208) for the use of tumor mutation
burden and HLA for prediction of immunotherapy efcacy, which is licensed to
Personal Genome Diagnostics. NR is a founder, shareholder and serves on the
scientic advisory board of Gritstone Oncology. NR has also consulted for AbbVie,
AstraZeneca, BMS, EMD Sorono, Genentech, GSK, Janssen, Lilly, Merck, Novartis,
Pzer, Regeneron. HT: employment with Caris Life Sciences. CW: employment
with Bristol- Myers Squibb, shareholder in Bristol- Myers Squibb ad shareholder
in Johnson & Johnson. MX: employment with AstraZeneca Pharmaceuticals. CZ:
employment with Illumina and stockholder in Illumina. AS: advisory board and/or
speech honoraria from: Bayer, BMS, MSD, Novartis, AstraZeneca, Roche, Seattle
Genomics, Illumina, Thermo Fisher, Takeda. Research funding from: Chugai, BMS.
Patient consent for publication Not required.
Ethics approval As leaders in the forefront of the collection and analysis of human
tissue specimens, the TCGA formed the Ethics, Law and Policy Group to generate
and oversee policies related to ethical and logistical considerations of the samples
they have made publicly available. The TCGA Ethics and Policies was originally
published by the National Cancer Institute. The TCGA study received ethics approval
for conducting their research work. No additional ethics approval was requested to
use the publicly available TCGA data.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data are available in a public, open access
repository. The mutation le ( mc3. v0. 2. 8. PUBLIC. maf. gz) analyzed in this study was
downloaded from the National Cancer Institute Genomics Data Commons: https://
gdc. cancer. gov/ about- data/ publications/ pancanatlas. All datasets generated by the
TMB Harmonization Consortium as part of this Project have been made available at
https:// precision. fda. gov/. Interested users should make a precision. FDA account
and request access to the data.
Open access This is an open access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non- commercially,
and license their derivative works on different terms, provided the original work is
properly cited, appropriate credit is given, any changes made indicated, and the use
is non- commercial. See http:// creativecommons. org/ licenses/ by- nc/ 4. 0/.
ORCID iD
Diana MMerino http:// orcid. org/ 0000- 0002- 6173- 6957
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... As a biomarker that affects treatment decisions, the accuracy, reliability, and comparability of TMB estimation is of paramount importance (14)(15)(16)(17). Currently, TMB is typically detected by whole exome sequencing (WES) and comprehensive genomic profiling. ...
... A series of U.S. Food and Drug Administration approved kits and laboratory-developed tests have been applied in clinical practice (18)(19)(20). However, as the testing panels, sequencing platforms, and bioinformatic algorithms differ widely across assays, and the mutation types considered for TMB estimation can vary from one laboratory to another, significant differences in TMB levels were always noticed between different assays and laboratories, especially when TMB values were around levels that may be clinical decision points (14,15,17,21). Although many attempts have been made in recent years to improve the measurement of TMB, the inconsistency of testing is still an important problem that is yet to be resolved (14,15,17,21). ...
... However, as the testing panels, sequencing platforms, and bioinformatic algorithms differ widely across assays, and the mutation types considered for TMB estimation can vary from one laboratory to another, significant differences in TMB levels were always noticed between different assays and laboratories, especially when TMB values were around levels that may be clinical decision points (14,15,17,21). Although many attempts have been made in recent years to improve the measurement of TMB, the inconsistency of testing is still an important problem that is yet to be resolved (14,15,17,21). For clinical laboratories to improve the consistency and reliability of these tests, qualified reference materials that provide ground-truth data in harmonizing these measurements are of prime importance (21). ...
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As a biomarker that affects treatment decisions of immune checkpoint inhibitors, the accuracy, reliability, and comparability of tumor mutational burden (TMB) estimation is of paramount importance. To improve the consistency and reliability of these tests, qualified reference materials providing ground-truth data are crucial. In this study, we developed a set of formalin-fixed and paraffin-embedded (FFPE) samples with different TMB values as the novel reference materials for TMB estimation. By introducing several clinically relevant variants in MutS Homolog 2 (MSH2) gene and DNA polymerase epsilon (POLE) gene into human cell lines using CRISPR/Cas9 technology, we first constructed four typical cell lines which verified with hypermutator or ultramutator phenotype. Followed by cell mixing and paraffin embedding, the novel FFPE samples were prepared. It was confirmed that our novel FFPE samples have sufficient quantity of cells, high reproducibility, and they can provide matched wild type sample as the genetic background. The double-platform whole exome sequencing validation showed that our FFPE samples were also highly flexible as they containing different TMB values spanning a clinically relevant range (2.0–106.1 mut/Mb). Without limitations on production and TMB values, our novel FFPE samples based on CRISPR/Cas9 editing are suitable as candidate reference materials. From a practical point of view, these samples can be used for the validation, verification, internal quality control, and proficiency testing of TMB assessment.
... The factors involved at different steps that may affect TMB estimates are summarized in Table 2. According to the results, the FoCR harmonization study indicated that panel-based tissue TMB estimates are comparable with WES TMB [19,20]. ...
... Although this approach only showed a minimal effect on the correlation between WES TMB based on the FoCR harmonization studies, the data also exhibited reduced inter-panel variability when calculating both synonymous and non-synonymous mutations. The FoCR harmonization study also evaluated the effects of the approach when excluding known somatic pathogenic variants and found that TMB would be overestimated without the filtration of known pathogenic mutation variants [19,20]. The inclusion strategies of mutation types among participating laboratories and panels in the phase II FoCR harmonization study are summarized in Table 2. ...
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As tumor mutational burden (TMB) has been approved as a predictive biomarker for immune checkpoint inhibitors (ICIs), next-generation sequencing (NGS) TMB panels are being increasingly used clinically. However, only a few of them have been validated in clinical trials or authorized by administration. The harmonization and standardization of TMB panels are thus essential for clinical implementation. In this review, preanalytic, sequencing, bioinformatics and interpretative factors are summarized to provide a comprehensive picture of how the different factors affect the estimation of panel-based TMB. Among the factors, poor DNA quality, improper formalin fixation and residual germline variants after filtration may overestimate TMB, while low tumor purity may decrease the sensitivity of the TMB panel. In addition, a small panel size leads to more variability when comparing with true TMB values detected by whole-exome sequencing (WES). A panel covering a genomic region of more than 1Mb is more stable for harmonization and standardization. Because the TMB estimate reflects the sum of effects from multiple factors, deliberation based on laboratory and specimen quality, as well as clinical information, is essential for decision making.
... The plot of single nucleotide variants (SNVs) and small insertions or deletions (INDELs) was also made for the genes that were significantly differentially expressed (Figure 4), and Fisher's exact tests were performed on the number of SNVs and INDELs in these genes between all pairs of RAS groups. To compare the mutation rate across the RAS groups, the tumour mutation burden (TMB) was calculated using the suggested guidelines by Merino et al. 15 In summary, the 'frameshift', 'inframe', 'missense', and 'nonsense' mutations located at exons with tumour depth greater than or equal to 25, alternative variant count greater than or equal to 3, and variant allele frequency greater than or equal to 0.05 were filtered. Then, the number of mutations per patient was divided by 33 Mb to obtain the number of mutations per megabase of exome otherwise known as the TMB. ...
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Introduction In metastatic colorectal cancer (mCRC), RAS mutations impart inferior survival and resistance to anti-epidermal growth factor receptor (EGFR) antibodies. KRAS G12C inhibitors have been developed and we evaluated how KRAS G12C differs from other RAS mutations. Patients and Methods This retrospective review evaluated patients in British Columbia, Canada with mCRC and RAS testing performed between 1 January 2016 and 31 December 2018. Sequencing information from The Cancer Genome Analysis (TCGA) was also obtained and analysed. Results Age at diagnosis, sex, anatomic location and stage at diagnosis did not differ by RAS mutation type. Progression free survival on first chemotherapy for patients with metastatic KRAS G12C tumours was 11 months. Median overall survival did not differ by RAS mutation type but was worse for both KRAS G12C (27 months) and non-G12C alterations (29 months) than wildtype (43 months) ( p = 0.01). Within the TCGA, there was no differential gene expression between KRAS G12C and other RAS mutations. However, eight genes with copy number differences between the G12C and non-G12C RAS mutant groups were identified after adjusting for multiple comparisons ( FITM2, PDRG1, POFUT1, ERGIC3, EDEM2, PIGU, MANBAL and PXMP4). We also noted that other RAS mutant mCRCs had a higher tumour mutation burden than those with KRAS G12C mutations (median 3.05 vs 2.06 muts/Mb, p = 4.2e–3) and that KRAS G12C/other RAS had differing consensus molecular subtype distribution from wildtype colorectal cancer (CRC) ( p < 0.0001) but not each other ( p = 0.14). Conclusion KRAS G12C tumours have similar clinical presentation to other RAS mutant tumours, however, are associated with differential copy number alterations.
... In a subsequent phase II (KEYNOTE-158) study, pembrolizumab monotherapy had an ORR of only 3.7%, a median PFS of 4.1 mo and a median OS of 24.2 mo in patients with previously treated advanced well-differentiated NENs [79]. Pembrolizumab is also proposed for patients with tumor progression after previous treatment, tumors with high tumor mutational burden and no adequate alternative treatment regimens [80,81] [90]. These regimens are also recommended for GEP-NEC patients in the 2021 NCCN guideline as first-line chemotherapy. ...
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Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are a rare group of tumors originating from neuroendocrine cells of the digestive system. Their incidence has increased over the last decades. The specific pathogenetic mechanisms underlying GEP-NEN development have not been completely revealed. Unfunctional GEP-NENs are usually asymptomatic; some grow slowly and thus impede early diagnosis, which ultimately results in a high rate of misdiagnosis. Therefore, many GEP-NEN patients present with later staged tumors. Motivated hereby, research attention for diagnosis and treatment for GEP-NENs increased in recent years. The result of which is great progress in clinical diagnosis and treatment. According to the most recent clinical guidelines, improved grading standards can accurately define poorly differentiated grade 3 neuroendocrine tumors and neuroendocrine carcinomas (NECs), which are subclassified into large and small cell NECs. Combining different functional imaging methods facilitates precise diagnosis. The expression of somatostatin receptors helps to predict prognosis. Genetic analyses of mutations affecting death domain associated protein (DAXX), multiple endocrine neoplasia type 1 (MEN 1), alpha thalassemia/intellectual disability syndrome X-linked (ATRX), retinoblastoma transcriptional corepressor 1 (RB 1), and mothers against decapentaplegic homolog 4 (SMAD 4) help distinguishing grade 3 NENs from poorly differentiated NECs. The aim of this review is to summarize the latest research progress on diagnosis and treatment of GEP-NENs.
... Measuring TMB requires a comprehensive genomic profiling assay, e.g., by applying a broad multigene panel (usually covering a region of > 1 Mb) (Merino et al. 2020). Using a 10 mut/Mb cutoff, the prevalence of tissue TMB-H has been reported as being present in 13-17% of samples in randomized phase 3 studies in patients with GC or GEJ cancer (Marabelle et al. 2020a;Shitara et al. 2021;Wyrwicz et al. 2020). ...
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Despite new therapeutic options, advanced gastric cancer remains associated with a poor prognosis compared with other cancers. Recent gains in the treatment of gastric cancer were accompanied by the identification of novel biomarkers associated with various cellular pathways and corresponding diagnostic technologies. It is expected that the standardization of clinical workflow and technological refinements in biomarker assessment will support greater personalization and further improve treatment outcomes. In this article, we review the current state of prognostic and predictive biomarkers in gastric cancer.
... Measuring TMB requires a comprehensive genomic profiling assay, e.g., by applying a broad multigene panel (usually covering a region of > 1 Mb) (Merino et al. 2020). Using a 10 mut/Mb cutoff, the prevalence of tissue TMB-H has been reported as being present in 13-17% of samples in randomized phase 3 studies in patients with GC or GEJ cancer (Marabelle et al. 2020a;Shitara et al. 2021;Wyrwicz et al. 2020). ...
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Despite new therapeutic options, advanced gastric cancer remains associated with a poor prognosis compared with other cancers. Recent gains in the treatment of gastric cancer were accompanied by the identification of novel biomarkers associated with various cellular pathways and corresponding diagnostic technologies. It is expected that the standardization of clinical workflow and technological refinements in biomarker assessment will support greater personalization and further improve treatment outcomes. In this article, we review the current state of prognostic and predictive biomarkers in gastric cancer.
... In recent advances in medicine, immune checkpoint inhibitors have proven to be a key therapeutic measure for malignancies (32). Programmed death protein 1 (PD-1), PD-L1, PD-L2, and cytotoxic T lymphocyte-associated protein 4 (CTLA4) are common immune checkpoints (33,34). PD-L1 is extensively expressed throughout the body, especially in cancer cells and immune cells (3). ...
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Background Increasing evidence has demonstrated that pyroptosis exerts key roles in the occurrence, development, and prognosis of uterine corpus endometrial carcinoma (UCEC). However, the mechanism of pyroptosis and its predictive value for prognosis remain largely unknown. Methods UCEC data were acquired from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes in UCEC vs. normal cases were selected to perform a weighted correlation network analysis (WGCNA). Forty-two UCEC-associated pyroptosis-related genes were identified via applying differential expression analysis. Protein–protein interaction (PPI) and gene correlation analyses were applied to explore the relationship between 21 UCEC key genes and 42 UCEC-associated pyroptosis-related genes. The expression of 42 UCEC-associated pyroptosis-related genes of different grades was also calculated. The immune environment of UCEC was evaluated. Furthermore, pyroptosis-related genes were filtered out by the co-expression. Univariate and a least absolute shrinkage and selection operator (LASSO) Cox analyses were implemented to yield a pyroptosis-related gene model. We also performed consensus classification to regroup UCEC samples into two clusters. A clinically relevant heatmap and survival analysis curve were implemented to explore the clinicopathological features and relationship between two clusters. Furthermore, a Kaplan–Meier survival analysis was implemented to analyze the risk model. Results Twenty-one UCEC key genes and 42 UCEC-associated pyroptosis-related genes were identified. The PPI and gene correlation analysis showed a clear relationship. The expression of 42 UCEC-associated pyroptosis-related genes of different grades was also depicted. A risk model based on pyroptosis-related genes was then developed to forecast overall survival among UCEC patients. Finally, Cox regression analysis verified this model as an independent risk factor for UCEC patients. Conclusions The expression of pyroptosis-related gene may influence UCEC occurrence, development, and prognosis.
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Background: Recent data suggest that BRAFV600E-mutated metastatic colorectal cancer (mCRC) patients with right-sided tumours and ECOG-PS = 0 may achieve benefit from the triplet regimen differently than those with left-sided tumours and ECOG-PS > 0. Methods: The predictive impact of primary sidedness and ECOG-PS was evaluated in a large real-life dataset of 296 BRAFV600E-mutated mCRC patients treated with upfront triplet or doublet ± bevacizumab. Biological differences between right- and left-sided BRAFV600E-mutated CRCs were further investigated in an independent cohort of 1162 samples. Results: A significant interaction effect between primary sidedness and treatment intensity was reported in terms of both PFS (p = 0.010) and OS (p = 0.003), with a beneficial effect of the triplet in the right-sided group and a possible detrimental effect in the left-sided. No interaction effect was observed between ECOG-PS and chemo-backbone. In the MSS/pMMR population, a consistent trend for a side-related subgroup effect was observed when FOLFOXIRI ± bevacizumab was compared to oxaliplatin-based doublets±bevacizumab (p = 0.097 and 0.16 for PFS and OS, respectively). Among MSS/pMMR tumours, the BM1 subtype was more prevalent in the right-sided group (p = 0.0019, q = 0.0139). No significant differences were observed according to sidedness in the MSI-H/dMMR population. Conclusions: Real-life data support the use of FOLFOXIRI ± bevacizumab only in BRAFV600E-mutated mCRC patients with right-sided tumours.
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The heterogeneity of each individual oncologic disease can be mirrored by molecular analysis of a simple blood draw in real time. Liquid biopsy testing has been shown useable for cancer detection, proof of minimal residual disease, therapy decision making and monitoring. However, an individual blood analyte does not present a comprehensive picture of the disease. It was recently shown that multi-modal/multi-parametric/multi-analyte liquid biopsy testing has the advantage of generating a high-resolution snapshot of the disease complexity. The different blood analytes such as circulating tumor cells, circulating immune cells, tumor-educated platelets, extracellular vesicles, cell-free DNA, cell-free RNA and circulating proteins complement each other and have additive value for clinical cancer management. We, here, like to review the studies leading to these promising conclusions and like to, at the end, mention that many challenges lie ahead before the translation into the clinic can be accomplished, including issues concerning clinical utility, method standardization, cost reimbursement and data management.
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Introduction Tumor mutational burden (TMB) is a quantitative assessment of the number of somatic mutations within a tumor genome. Immunotherapy benefit has been associated with TMB assessed by whole exome sequencing (wesTMB) and by gene panel sequencing (psTMB). The initiatives of Quality in Pathology (QuIP) and Friends of Cancer Research (FoCR) have jointly addressed the need for harmonization between TMB testing options in tissues. This QuIP study identifies critical sources of variation in psTMB assessment. Methods Twenty samples from three tumor types (LUAD, HNSC, COAD) with available WES data were analyzed for psTMB, using six panels across 15 testing centers. Inter-laboratory and inter-platform variation including agreement on variant calling and TMB classification were investigated. Bridging factors to transform psTMB to wesTMB values were empirically derived. The impact of germline filtering was evaluated. Results Sixteen samples demonstrated low interlaboratory and interpanel psTMB variation with 87.7% of pairwise comparisons showing a Spearman’s ρ>0.6. A wesTMB cutpoint of 199 missense mutations projected to psTMB cutpoints between 7.8 and 12.6 muts/Mbp; the corresponding psTMB and wesTMB classifications agreed in 74.9% of cases. For three-tier classification with cutpoints of 100 and 300 mutations, agreement was observed in 76.7%, weak misclassification in 21.8%, and strong misclassification in 1.5% of cases. Confounders of psTMB estimation included fixation artifacts, DNA input, sequencing depth, genome coverage, and variant allele frequency cutpoints. Conclusions This study provides real-world evidence that all evaluated panels can be used to estimate TMB in a routine diagnostic setting and identifies important parameters for reliable tissue TMB assessment that require careful control. As complex/composite biomarkers beyond TMB are likely playing an increasing role in therapy prediction, the efforts by QuIP and FoCR also delineate a general framework and blueprint for the evaluation of such assays.
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Characterization of tumors utilizing next‐generation sequencing methods, including assessment of the number of somatic mutations (tumor mutational burden [TMB]), is currently at the forefront of the field of personalized medicine. Recent clinical studies have associated high TMB with improved patient response rates and survival benefit from immune checkpoint inhibitors; hence, TMB is emerging as a biomarker of response for these immunotherapy agents. However, variability in current methods for TMB estimation and reporting is evident, demonstrating a need for standardization and harmonization of TMB assessment methodology across assays and centers. Two uniquely placed organizations, Friends of Cancer Research (Friends) and the Quality Assurance Initiative Pathology (QuIP), have collaborated to coordinate efforts for international multistakeholder initiatives to address this need. Friends and QuIP, who have partnered with several academic centers, pharmaceutical organizations, and diagnostic companies, have adopted complementary, multidisciplinary approaches toward the goal of proposing evidence‐based recommendations for achieving consistent TMB estimation and reporting in clinical samples across assays and centers. Many factors influence TMB assessment, including preanalytical factors, choice of assay, and methods of reporting. Preliminary analyses highlight the importance of targeted gene panel size and composition, and bioinformatic parameters for reliable TMB estimation. Herein, Friends and QuIP propose recommendations toward consistent TMB estimation and reporting methods in clinical samples across assays and centers. These recommendations should be followed to minimize variability in TMB estimation and reporting, which will ensure reliable and reproducible identification of patients who are likely to benefit from immune checkpoint inhibitors. This article is protected by copyright. All rights reserved.
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Immune checkpoint inhibitor (ICI) treatments benefit some patients with metastatic cancers, but predictive biomarkers are needed. Findings in selected cancer types suggest that tumor mutational burden (TMB) may predict clinical response to ICI. To examine this association more broadly, we analyzed the clinical and genomic data of 1,662 advanced cancer patients treated with ICI, and 5,371 non-ICI-treated patients, whose tumors underwent targeted next-generation sequencing (MSK-IMPACT). Among all patients, higher somatic TMB (highest 20% in each histology) was associated with better overall survival. For most cancer histologies, an association between higher TMB and improved survival was observed. The TMB cutpoints associated with improved survival varied markedly between cancer types. These data indicate that TMB is associated with improved survival in patients receiving ICI across a wide variety of cancer types, but that there may not be one universal definition of high TMB. © 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.
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Background: Treatment with immune checkpoint blockade (ICB) with agents such as anti-programmed cell death protein 1 (PD-1), anti-programmed death-ligand 1 (PD-L1), and/or anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) can result in impressive response rates and durable disease remission but only in a subset of patients with cancer. Expression of PD-L1 has demonstrated utility in selecting patients for response to ICB and has proven to be an important biomarker for patient selection. Tumor mutation burden (TMB) is emerging as a potential biomarker. However, refinement of interpretation and contextualization is required. Materials and methods: In this review, we outline the evolution of TMB as a biomarker in oncology, delineate how TMB can be applied in the clinic, discuss current limitations as a diagnostic test, and highlight mechanistic insights unveiled by the study of TMB. We review available data to date studying TMB as a biomarker for response to ICB by tumor type, focusing on studies proposing a threshold for TMB as a predictive biomarker for ICB activity. Results: High TMB consistently selects for benefit with ICB therapy. In lung, bladder and head and neck cancers, the current predictive TMB thresholds proposed approximates 200 non-synonymous somatic mutations by whole exome sequencing (WES). PD-L1 expression influences response to ICB in high TMB tumors with single agent PD-(L)1 antibodies; however, response may not be dependent on PD-L1 expression in the setting of anti-CTLA4 or anti-PD-1/CTLA-4 combination therapy. Disease-specific TMB thresholds for effective prediction of response in various other malignancies are not well established. Conclusions: TMB, in concert with PD-L1 expression, has been demonstrated to be a useful biomarker for ICB selection across some cancer types; however, further prospective validation studies are required. TMB determination by selected targeted panels has been correlated with WES. Calibration and harmonization will be required for optimal utility and alignment across all platforms currently used internationally. Key challenges will need to be addressed prior to broader use in different tumor types.
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Background: Tumor mutational burden (TMB) is an increasingly important biomarker for immune checkpoint inhibitors. Recent publications have described strong association between high TMB and objective response to mono- and combination immunotherapies in several cancer types. Existing methods to estimate TMB require large amount of input DNA, which may not always be available. Methods: In this study, we develop a method to estimate TMB using the Oncomine Tumor Mutation Load (TML) assay with 20 ng of DNA, and we characterize the performance of this method on various formalinfixed, paraffin-embedded (FFPE) research samples of several cancer types. We measure the analytical performance of TML workflow through comparison with control samples with known truth, and we compare performance with an orthogonal method which uses matched normal sample to remove germline variants. We perform whole exome sequencing (WES) on a batch of FFPE samples and compare the WES TMB values with TMB estimates by the TML assay. Results: In-silico analyses demonstrated the Oncomine TML panel has sufficient genomic coverage to estimate somatic mutations with a strong correlation (r2 =0.986) to WES. Further, in silico prediction using WES data from three separate cohorts and comparing with a subset of the WES overlapping with the TML panel, confirmed the ability to stratify responders and non-responders to immune checkpoint inhibitors with high statistical significance. We found the rate of somatic mutations with the TML assay on cell lines and control samples were similar to the known truth. We verified the performance of germline filtering using only a tumor sample in comparison to a matched tumor-normal experimental design to remove germline variants. We compared TMB estimates by the TML assay with that from WES on a batch of FFPE research samples and found high correlation (r2 =0.83). We found biologically interesting tumorigenesis signatures on FFPE research samples of colorectal cancer (CRC), lung, and melanoma origin. Further, we assessed TMB on a cohort of FFPE research samples including lung, colon, and melanoma tumors to discover the biologically relevant range of TMB values. Conclusions: These results show that the TML assay targeting a 1.7-Mb genomic footprint can accurately predict TMB values that are comparable to the WES. The TML assay workflow incorporates a simple workflow using the Ion GeneStudio S5 System. Further, the AmpliSeq chemistry allows the use of low input DNA to estimate mutational burden from FFPE samples. This TMB assay enables scalable, robust research into immuno-oncology biomarkers with scarce samples. Keywords: Cancer genomics; checkpoint inhibitors; tumor mutational burden (TMB); Oncomine Tumor Mutation Load Assay (TML); immuno-oncology
Conference Paper
Background High TMB is predictive of PFS benefit with anti-PD-(L)1 ± anti-CTLA-4 therapy in mNSCLC. Preliminary results from MYSTIC (NCT02453282), an open-label, Phase III trial of first-line durvalumab (D; anti-PD-L1), ± tremelimumab (T; anti-CTLA-4), vs platinum-based chemotherapy (CT) in mNSCLC, indicate that blood TMB (bTMB, ≥16 mut/Mb; GuardantOMNI [Guardant Health]) from circulating tumor DNA (ctDNA) correlates positively with tissue (t) TMB (≥10 mut/Mb) (Spearman’s correlation coefficient = 0.6) and is predictive of survival benefit with D±T vs CT. Efficacy outcomes were assessed using additional exploratory cut-offs for bTMB and ≥10 mut/Mb for tTMB. Methods Pts with EGFR and wild-type, immunotherapy/CT-naïve mNSCLC were randomized (1:1:1) to D (20 mg/kg i.v. q4w); D (20 mg/kg i.v. q4w) + T (1 mg/kg i.v. q4w up to 4 doses); or CT. bTMB was evaluated with the GuardantOMNI platform. tTMB was evaluated with the FoundationOne tissue NGS platform. bTMB cut-off was defined as ≥20 mut/Mb (bTMB≥20). Data cut-off: Oct 4, 2018 (OS); Jun 1, 2017 (PFS). Results 1118 pts were randomized. Baseline sample datasets were: bTMB 809; tTMB 460 pts, with baseline characteristics balanced between treatment arms. bTMB≥20 was associated with improved OS (D+T vs CT: HR 0.49 [95% CI 0.32, 0.74]; D vs CT: HR 0.72 [95% CI 0.50, 1.05]) and PFS (D+T vs CT: HR 0.53 [95% CI 0.34, 0.81]; D vs CT: HR 0.77; [95% CI 0.52, 1.13]); 24-mo survival: D+T 48.1% (95% CI 35.5, 59.7), D 33.8% (95% CI 23.4, 44.5) and CT 19.4% (95% CI 11.0, 29.5). Data for tTMB are shown (table). Additional cut-offs will be presented. Conclusion MYSTIC provides the most comprehensive data set to date supporting TMB as a predictive biomarker of OS benefit with immunotherapy. In exploratory analyses, bTMB≥20 was associated with OS and PFS benefit with D±T vs CT, with the greatest magnitude of benefit observed for pts receiving D+T. bTMB ≥20 mut/Mb bTMB <20 mut/Mb tTMB ≥10 mut/Mb tTMB <10 mut/Mb D+T D CT D+T D CT D+T D CT D+T D CT n 64 77 70 204 209 185 60 60 67 104 85 84 OS Median, mos 21.9 12.6 10.0 8.5 11.0 11.6 16.6 18.6 11.9 8.4 10.1 13.8 95% CI 11.4, 32.8 7.8, 18.6 8.1, 11.7 6.7, 9.8 8.9, 14.9 9.6, 13.1 9.7, 27.3 9.3, 22.0 9.1, 16.0 5.3, 10.3 6.4, 14.6 10.1, 16.3 HR* 0.49 0.72 - 1.16 0.93 - 0.72 0.70 - 1.39 1.26 - 95% CI 0.32, 0.74 0.50, 1.05 - 0.93, 1.45 0.74, 1.16 - 0.48, 1.09 0.47, 1.06 - 1.00, 1.92 0.90, 1.77 - PFS Median, mos 4.2 2.7 4.4 2.0 2.8 5.0 3.1 3.1 5.1 2.4 2.5 5.6 95% CI 2.8, NR 1.8, 4.4 4.1, 5.4 1.7, 2.8 2.2, 3.1 4.2, 5.5 1.5, 6.8 2.3, 12.7 4.2, 5.6 1.6, 3.0 1.8, 3.4 4.2, 7.0 HR* 0.53 0.77 - 1.55 1.19 - 0.97 0.86 - 1.98 1.49 - 95% CI 0.34, 0.81 0.52, 1.13 - 1.23, 1.94 0.94, 1.50 - 0.63, 1.49 0.55, 1.33 - 1.42, 2.78 1.05, 2.13 - • *HRs refer to comparison of experimental regimen to CT; NR, not reached Citation Format: Solange Peters, Byoung Chul Cho, Niels Reinmuth, Ki Hyeong Lee, Alexander Luft, Myung-Ju Ahn, Paul Baas, Manuel Cobo Dols, Alexey Smolin, David Vicente, Vladimir Moiseyenko, Scott J. Antonia, Kazuhiko Nakagawa, Sarah B. Goldberg, Edward Kim, Rajiv Raja, Philip Brohawn, Delyth Clemett, Piruntha Thiyagarajah, Urban Scheuring, Feng Liu, Naiyer Rizvi. Tumor mutational burden (TMB) as a biomarker of survival in metastatic non-small cell lung cancer (mNSCLC): Blood and tissue TMB analysis from MYSTIC, a Phase III study of first-line durvalumab ± tremelimumab vs chemotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr CT074.
Article
Assessment of Tumor Mutational Burden (TMB) for response stratification of cancer patients treated with immune checkpoint inhibitors is emerging as a new biomarker. Commonly defined as the total number of exonic somatic mutations, TMB approximates the amount of neoantigens that potentially are recognized by the immune system. While whole exome sequencing (WES) is an unbiased approach to quantify TMB, implementation in diagnostics is hampered by tissue availability as well as time and cost constrains. Conversely, panel‐based targeted sequencing is nowadays widely used in routine molecular diagnostics, but only very limited data are available on its performance for TMB estimation. Here, we evaluated three commercially available larger gene panels with covered genomic regions of 0.39 Megabase pairs (Mbp), 0.53 Mbp and 1.7 Mbp using I) in silico analysis of TCGA data and II) wet‐lab sequencing of a total of 92 formalin‐fixed and paraffin‐embedded (FFPE) cancer samples grouped in three independent cohorts (NSCLC, CRC and mixed cancer types) for which matching WES data were available. We observed a strong correlation of the panel data with WES mutation counts especially for the gene panel > 1Mbp. Sensitivity and specificity related to TMB cutpoints for checkpoint inhibitor response in non‐small cell lung cancer (NSCLC) determined by wet‐lab experiments well reflected the in silico data. Additionally, we highlight potential pitfalls in bioinformatics pipelines and provide recommendations for variant filtering. In summary, this study is a valuable data source for researchers working in the field of immuno‐oncology as well as for diagnostic laboratories planning TMB testing. This article is protected by copyright. All rights reserved.
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Programmed cell death protein–1 (PD-1) and programmed cell death ligand–1 (PD-L1) checkpoint blockade immunotherapy elicits durable antitumor effects in multiple cancers, yet not all patients respond. We report the evaluation of >300 patient samples across 22 tumor types from four KEYNOTE clinical trials. Tumor mutational burden (TMB) and a T cell–inflamed gene expression profile (GEP) exhibited joint predictive utility in identifying responders and nonresponders to the PD-1 antibody pembrolizumab. TMB and GEP were independently predictive of response and demonstrated low correlation, suggesting that they capture distinct features of neoantigenicity and T cell activation. Analysis of The Cancer Genome Atlas database showed TMB and GEP to have a low correlation, and analysis by joint stratification revealed biomarker-defined patterns of targetable-resistance biology. These biomarkers may have utility in clinical trial design by guiding rational selection of anti–PD-1 monotherapy and combination immunotherapy regimens.