3.10
4.30
8.70
5.306.00
6.40
4.30
5.156.40
8.60
7.45
4.30
5.30
4.25
0.00
2.00
4.00
6.00
8.00
10.00
12.00
0 3 6 9 12 15
TMB
med
ain
Brain Breast CervicalColorectal Endometrium Esoph.& GastricGIST & soft Head & neck LiverLung Melanoma OvaryPancreas Thyroid
Identification of Lung Cancer Mutational Signatures and Tumor Drivers Associated with Specific Bimodal PD-L1/TMB Status
Methods424 FFPE clinical samples from lung cancer patients were analyzed using a CLIA-validated NGS-based assay that interrogates SNVs, indels using a 323 gene panel and by IHC for PD-L1 using theFDA approved PharmDx assay. TMB (mutations/Mb) is categorized as low (≤7), intermediate(7<TMB≤15) and high: (TMB> 15). NGS results were paired with PD-L1 status which was definedby tumor proportion scores (TPS) as: negative (TPS<1%), Low expressing (≥1-49%) and High(≥50%). In silico analyses were also performed on 5939 lung cancer samples from publicdatabases. The study was approved by Neogenomics Institution’s Ethics Board and external IRB,approval number 420160280.
NeoTYPE Discovery 323 + MSI + TMB Assay Clinical Validation Summary
Figure 1. NeoTYPE Discovery 323 +MSI +TMB assay.
145 solid tumor oncogenes and tumor suppressors 41 Homologous Recombination Repair (HRR) and
DNA damage repair genes 40 NCI-MATCH trial arms directly addressed Cancer pathways: EGFR/ RAS/ RAF MAPK/ PI3K/
MTOR, CDK/ Rb/ FGF and other RTK signaling cascades* Full gene list available upon request
Tumor Mutation Burden
TMB scores TMB category
Mut/Mb
≤7 Low
>7, <15 Intermediate
≥15 High
NeoTYPE Discovery 323 + MSI + TMB
Panel size 323 genes (all exons)
Genomic size 1 Mb
Chemistry Amplicon based
Sequencing platform Novaseq 6000
Microsatellites 27 target regions
Validated samples FFPE block/slides
Min tumor content 20% (30% MSI)
Recommended DNA input 100 ng
Minimum DNA input 50 ng
Type of test CLIA validated LDT
Reimbursement Status CMS covered
SNV/ InDel Clinical Performancevariants/
parameterSensitivity
(5% AF)Specificity Reprod.
SNVs 98.60% 98.10%99.2%
InDel 96.80% 94.30%
Tumor Mutation Burden Clinical Performance
metricAccuracy vs. WES
Reprod.intra assay
Reprod. inter assay
TMB score91.4% Pearson
coeff. - -TMB category
100%100% 100%
Microsatellite instability Clinical PerformanceScore Sensitivity Specificity Reprod.
MSI (p>0.95) 87.50% 100% 100%
Technical Assay performanceAverage coverage 1908XMinimum average coverage ≥500X
Limit of Detection SNVs3% hotspots, 5% all
others
Limit of detection InDel3% hotspots, 5% all
othersAnalytical Specifiticy 99.9996%
Analytical Sensitivity (LOD: AF, 95% CI)
2.5% (SNVs), 2.8% (InDel)
reproducibility intra-assay 98.66%reproducibility inter-assay 99.19%
NSCLC
Figure 2. Tumor Mutation Burden (TMB) across cancer types. TMB median of serially tested NSCLC tumors without any preselection criteria and compared with other tumor types processed in the same manner in the same period. In addition, all lung cancer tumors were Microsatellite Stable (MSS) as determined according to the assay specifications. Cervical and lung cancers presented the highest median values off all tumor sites. Sphere size represents the sample size for each cancer type (n=784)
NSCLC Tumor Mutation Burden
Min 0
25% lower quartile 5.3
median 8.6
75% upper Quartile 16
max 126.2
NSCLC Tumor Mutation Burden
TMB scores Category%
Samples
TMB≤7 Low 38.6 %
7<TMB <15 Intermediate 34.7 %
TMB≥15 High 26.7 %
26%
44%
31%
0%
10%
20%
30%
40%
50%
<1% 1%-49% 50-100%
NSC
LC p
atie
nts
(%
)
PDL1 TPS Categories
0
50
100
PD
-L1
TP
S (
%)
Samples
PDL1 Expression (22C3, FDA CDx)
R² = -0.266
0
20
40
60
80
100
0 50 100 150
PD
-L1
exp
resi
on
TMB
PD-L1 vs TMB correlation
PD-L1 - TMB Correlation in NSCLC
TMB
TMB-LowTMB-
Interm. TMB-High
Total
PD-L1
PD-L1 Neg. 40 37 31 108
PD-L1-Positive
73 67 46 186
PD-L1-High
51 43 36 130
Total 164 147 113 424
Tumor Mutation Burden in the Lung Cancer Cohort
Neg Positive High
Median=7%
Expression:
Figure 3. Mutation signatures and correlations between TMB and PD-L1 expression ina cohort of 424 NSCLC patients. TMB and PD-L1 expression lineal regression wasperformed. Samples were divided according to combined TMB and PD-L1 results. Thetop 12 most frequently mutated genes in each subcategory were plotted.
TPS:
Cervical
29%
24%20%
12%8%10%8% 8% 8%
4% 4% 6%
0%5%
10%15%20%25%30%35%
TP5
3
KR
AS
NO
TCH
2
EGFR
MET
RA
NB
P2
AR
ID1
A
PIK
3C
2B
TOP
2A
FAT1
FLT4
KM
T2A
mu
tate
d s
amp
les
(%)
PD-L1 High & TMB Low (n=51)
65%
42%
65%
29%23%19%16%19%16%13%16%16%
0%
10%
20%
30%
40%
50%
60%
70%
TP5
3
LRP
1B
SPTA
1
SMAR…
GN
AS
ALK
FGFR
2
SLIT
2
RO
S1
AM
ER1
FAT1
MED
12
mu
tate
d t
um
ors
(%
)
PD-L1 Neg. & TMB High (n=31)
28%
18%20%
10%10%8%
5%8% 8% 8%
5% 5%
0%
5%
10%
15%
20%
25%
30%
EGFR
KR
AS
TP5
3
RA
NB
P2
RB
M1
0
CTN
NB
1
SETD
2
SMA
RC
A4
SPEN
STK
11
AR
ID1
A
AR
ID1
Bmu
tate
d s
amp
les
(%
)
PD-L1 Neg. & TMB Low (n=40)
75%
58%
39%36%36%33%33%28%28%25%22%
17%
0%10%20%30%40%50%60%70%80%
TP5
3
LRP
1B
GN
AS
KR
AS
RU
NX
1T1
SPTA
1
KM
T2C
FAT1
KM
T2D
NTR
K3
AR
ID1
A
BR
CA
2mu
tate
d s
amp
les
(%)
PD-L1 High & TMB High (n=36)
Genomic Signatures Define Combined PD-L1 & TMB neg/Low vs High Categories
MEK 1/2
BRAF class 1/2/3ARAF/ RAF1
Figure 6. Genomic signatures on NSCLC patients with intermediate Mutation Burden classified by PD-L1 expression. The 12 most mutated genes associated with varying PDL1 expression are shown. Kaplan-Meyer regressions show progression free survival (left) or Overall Survival (right) on patients with EGFR vs. KEAP1 or STK11 mutant tumors publically available with or without alterations on these genes (cbioportal). Samples with overlapping mutation on these genes are excluded.
Figure 5. Genomic signatures on NSCLC samples divided by combined Tumor Mutation Burden (TMB) and PD-L1 expression. The top-12 most frequently mutated genes in each subcategory were included. Mutated samples were counted only once even if more than 1 mutation in the gene was detected. The genes were ordered by the total number of mutations detected. Overall survival from 5939 NSCLC patients with mutations vs wt status on the top 5 mutated genes on PDL1 Neg &TMB High, was estimated using Kaplan-Meyer regressions using cbioportal.
Results We found poor correlation between PD-L1 expression and TMB in NSCLC (r2=0.266) and
identified gene mutation signatures specific to groups defined by PD-L1 expression
combined with TMB scores.
KRAS mutations are constant across PDL1 TPS and less frequent on TMB High tumors, while
EGFR mutation frequency negatively correlates to TMB and less to PD-L1 TPS..
In PD-L1 Low/TMB High tumors (n=46) EGFR and KRAS were found mutated on 15% and 9%
of samples, respectively (not part of the top 12 genes), while NTRK and NOTCH signaling are
altered on 37% and 52%, respectively (65% combined).
In tumors with an intermediate TMB (7<TMB≤15), we find that KRAS mutations are more
frequent (43%) in PDL-1 High expressing tumors..
EGFR alterations have highest frequency on TMB intermediate/ PD-L1 Low tumors.
KEAP1 mutations, which have been recently proposed as poor prognosis biomarkers upon
immunotherapy and other therapies in NSCLC, are found on up to 22% of lung tumors with
TMB intermediate, including on 17% of tumors with TMB-intermediate and high PD-L1
expression.
STK11 mutations on TMB intermediate tumors are present on 16% of PDL1 negative tumors
but are rare (2%) on High PDL-1 expressing tumors.
KEAP1 mutations predict better PFS than EGFR and STK11, but EGFR mutations predict
longer OS than KEAP1 or STK11 according to publically available data
Mutations on chromatin remodeling /DNA repair SMARCA4 and ARID1A/B genes are
frequently found on the TMB intermediate group.
We also identified a 5 genes set specifically mutated on PDL1-Neg/TMB High tumors with
potential prognostic/predictive value.
Figure 4. EGFR and KRAS alterations. Mutation frequency of the 2 genes was segregated by TMB or PD-L1 categories.
EGFR/KRAS Signaling in NSCLC Cohort
Conclusions Genomic alteration signatures might define subsets of lung cancer tumors with no PD-L1
expression to complement TMB and PD-L1 on the selection criteria for patients whom maybenefit from checkpoint inhibitors.
Further studies are needed to evaluate specific signatures or single genes as biomarkers ofimmunotherapy response in NSCLC patients as well as to identify the molecularmechanisms enabling cancer cells therapy resistance and disease progression.
0%
5%
10%
15%
20%
25%
30%
Low Interm. High Neg. Pos. High
TMB PD-L1
mu
tate
d s
amp
les
(%)
EGFR /KRAS mutations vs. TMB / PDL1
EGFR KRAS
Fernando J. Lopez-Diaz*, Lauryn Keeler, Sally Agersborg, Lawrence Weiss, Vincent Funari. Neogenomics Laboratories, Carlsbad, CA. *[email protected]
HRR
DDR
Neo_323
NCI-MATCH
65%
30%22%19%19%16%16%16%14%14%14%14%
0%
20%
40%
60%
80%
TP5
3
LRP
1B
KEA
P1
KR
AS
FAT1
SPTA
1
KM
T2D
STK
11
RB
M1
0
KM
T2A
SMA
RC
A4
PTC
H1
mu
tate
d s
amp
les
(%)
TMB Int. & PD-L1 Neg. (n=37)
67%43%
21%19%19%17%17%14%14%14%14%14%
0%
20%
40%
60%
80%
TP5
3
KR
AS
LRP
1B
CD
KN
2A
RB
M1
0
FAT1
KEA
P1
NO
TCH
2
NF1
SPTA
1
AR
ID1
B
AR
ID1
A
mu
tate
d s
amp
les
(%)
TMB Int. & PD-L1 high (n=43)
60%
30%24%15%15%13%12%12% 9% 12%10% 9%
0%
20%
40%
60%
80%
TP5
3
LRP
1B
KR
AS
EGFR
KEA
P1
SPTA
1
KM
T2D
CD
KN
2A
ATR
X
NO
TCH
2
SLIT
2
ATM
Mu
tate
d s
amp
les
(%)
TMB Int. & PD-L1 Low (n=67)
24%21%20%
14%
7% 7% 6% 5% 5% 5% 4% 4%
0%
5%
10%
15%
20%
25%
30%
TP5
3
EGFR
KR
AS
NOTC
…
RA
NB
P2
STK
11
RB
M1
0
KEA
P1
LRP
1B
AR
ID1
A
CD
KN
2A
BR
CA
2mu
tate
d s
amp
les
(%)
TMB Low (n=164)
63%
34%28%
18%18%16%14%11%14%14%12%11%
0%
20%
40%
60%
80%
TP5
3
LRP
1B
KR
AS
KEA
P1
SPTA
1
FAT1
RB
M1
0
EGFR
KM
T2D
NO
TCH
2
AR
ID1
A
CD
KN
2A
mu
tate
d s
amp
les
(%) TMB Int. (n=147)
29%22%
19%12%12%12%12%10% 9% 9% 9% 9%
0%
10%
20%
30%
40%
TP5
3
LRP
1B
SPTA
1
NOTC
…
KM
T2D
KEA
P1
NTR
K3
NOTC
…
RUNX…
RANB…
EPH
A3
NF1
mu
tate
d s
amp
les
(%) TMB High (n=113)
50%
28%
18%16%17%15%11%12%11%11%11%11%
0%
20%
40%
60%
TP5
3
LRP
1B
SPTA
1
SMA…
KR
AS
EGFR
RBM…
FAT1
KMT…
KEA
P1
NOT…
STK
11
mu
tate
d s
amp
les
(%)
PDL1 Neg (n=108)
48%
27%17%17%19%18%15%12%10% 9% 11% 9%
0%
20%
40%
60%
TP5
3
LRP
1B
SPTA
1
EGFR
KR
AS
NOTC
…
KEA
P1
KM
T2D
RANB…
PR
KD
C
CDKN…
RB
M1
0
mu
tate
d s
amp
les
(%) PD-L1 Pos. (n=186)
32%
20%19%13%12%13%12%
8% 12%12% 9% 10%
0%
10%
20%
30%
40%
TP5
3
LRP
1B
KR
AS
SPTA
1
FAT1
NOTC
…
GN
AS
NTR
K3
KM
T2C
KM
T2D
PIK3C…
AR
ID1
Amu
tate
d s
amp
les
(%) PD-L1 High (n=130)
The Genomic Signatures on Different TMB Intermediate NSCLC Scenarios
5-genespredictor
TP53
LRP1B
SPTA1
SMARCA4
GNAS
72%
54%
41%
30% 28% 26% 24% 24% 22% 22% 22% 22%
0%10%20%30%40%50%60%70%80%
TP5
3
LRP
1B
SPTA
1
NO
TCH
2
NTR
K3
KEA
P1
KM
T2D
NO
TCH
3
RA
NB
P2
RU
NX
1T1
EPH
A3
NF1
mu
tate
d s
amp
les
(%)
PD-L1 Pos. & TMB High (n=46)
26% 25%
18% 17%
8% 8% 8% 8% 8% 7% 7% 7%
0%
5%
10%
15%
20%
25%
30%
EGFR
TP5
3
KR
AS
NO
TCH
2
CD
KN
2A
LRP
1B
STK
11
RA
NB
P2
KEA
P1
PIK
3C
2B
RB
M1
0
EPH
A7
Mu
tate
d s
amp
les
(%)
PD-L1 Pos. & TMB Low (n=72)
PFS Overall Survival Overall Survival
EGFR EGFR
EGFRKEAP1
STK11
KEAP1STK11
Overall Survival
Altered
Non-altered
DataBackgroundPD-L1 expression and Tumor Mutation Burden (TMB) have independently emergedas prospective biomarkers of response to anti PD1-/PDL1 checkpoint inhibitors.Combined use of TMB, PD-L1 protein levels has been proposed. However, how thetumor genomic landscape interplays with the tumor microenvironment in definingparticular predictive therapy response statuses is not clear. Moreover there is stilllittle real world data available regarding the combined prevalence of thesebiomarkers in specific genomic landscapes.