Biomarkers for immuno-oncology: tumourmutational load and beyond.
The impact of a multidimensional approachPhilip Jermann, PhD
Head of Molecular Assay Development UnitInstitute of Medical Genetics and Pathology
University Hospital Basel, Switzerland
Understanding Tumor-Immune Interactions Through NGSUsing genomic assays to improve the success of immunotherapy
Tumor-Immune interactions are complex
and warrants a multi-marker approach
NGS assays broaden our understanding of the
biology beyond traditional IHC biomarkers
PD-L1
IHC
Source: Thermo Fisher Scientific
Understanding Tumor-Immune Interactions Through NGSUsing genomic assays to improve the success of immunotherapy
Tumor-Immune interactions are complex
and warrants a multi-marker approach
NGS assays broaden our understanding of the
biology beyond traditional IHC biomarkers
PD-L1
IHC
Source: Thermo Fisher Scientific
Tumor mutational burden (TMB)An assessment of the number of somatic mutations within a tumor genome
High somatic mutation load Higher neoantigen load Greater response to immunotherapy
Recognition of
neoantigens by T cells
More likely to
form neoantigens
Mutational landscape determines sensitivity to
PD-1 blockade in non-small cell lung cancer1
Genetic basis for clinical response to
CTLA-4 blockade in melanoma2
Genomic correlation of response to CTLA-4
blockade in metastatic melanoma3
Ove
rall
su
rviv
al
0 500 1000 1500
Nivolumab plus Ipilimumab in Lung Cancer
with a High Tumor Mutational Burden
(Checkmate 227)5
Whole exome sequencing studies:
Tumor mutational burden (TMB) as a
biomarker for clinical benefit from dual immune
checkpoint … (Checkmate 568)4
Targeted NGS studies:
1Rizvi et al. Science (2015); 2Snyder et al. N Eng J Med (2014); 3Van Allen at al. Science (2015); 4Ramalingam et al. AACR 2018; 5Hellman et al. N Eng J Med (2018); Neoantigen artwork credit: Dr. Bjoern Peters
High TMB has been found to correlate with response to immune checkpoint inhibitor treatment
Retrospective analysis of NSCLC samples treated with ICIs
Retrospective study to evaluate TMB as a potential biomarker for ICI treatment
76 clinical samples, advanced NSCLC, treatedwith immune checkpoint inhibitors.
Retrospective sample collection. Tissueblocks prior to treatment.
Sample characteristics
All patients
(n = 76)
No. (%)
TMB
Low & Int
(n = 51)
No. (%)
TMB
High
(n = 25)
No. (%)
p - value
Age (yr) 0.907
Median (Range) 66 (31-90) 65 (49-79) 67 (31-90)
Sex (N) 0.615
Male 47 (62) 30 (59) 16 (68)
Female 29 (38) 21 (41) 8 (32)
Tumor histology at diagnosis (N) >0.999
Adenocarcinoma 70 (92) 47 (92) 23 (92)
Squamous cell carcinoma 6 (8) 4 (8) 2 (8)
Tumor type (N) 0.043
Primary tumor 47 (62) 36 (71) 11 (44)
Metastasis/ Lymph node 29 (38) 15 (29) 14 (56)
Tumor cell content (%) 0.213
Median (Range) 60 (20 - 95) 60 (20-95) 60 (20-90)
Immunotherapy (N) >0.999
Nivolumab 60 (79) 40 (78) 20 (80)
Pembrolizumab 10 (13) 9 (18) 1 (4)
Atezolizumab 3 (4) 2 (4) 1 (4)
Other 3 (4) 0 (0) 3 (12)
Number of lines before I-O (N) 0.724
First (0) 11 (14) 7 (14) 4 (16)
Second (1) 39 (51) 30 (59) 9 (36)
Third (2) 10 (13) 6 (12) 4 (16)
Fourth (3) 2 (3) 0 (0) 2 (8)
not available 13 (17) 8 (16) 5 (20)
Smoking status (N) 0.155
Never 10 (13) 9 (18) 1 (4)
Current/former 60 (79) 39 (76) 21 (84)
not available 6 (8) 3 (6) 3 (12)
PD-L1 (N) >0.999
< 1% 28 (37) 19 (37) 9 (36)
≥ 1% 39 (51) 27 (53) 12 (48)
not available 9 (12) 5 (10) 4 (16)
Durable clinical benefit (N)
DCB 32 (42) 16 (31) 16 (64) 0.013
No DCB 44 (58) 35 (69) 9 (36)Alborelli et al., The Journal of Pathology, 2019
Retrospective analysis of NSCLC samples treated with ICIs
Retrospective study to evaluate TMB as a potential biomarker for ICI treatment
76 clinical samples, advanced NSCLC, treatedwith immune checkpoint inhibitors.
Retrospective sample collection. Tissueblocks prior to treatment.
Target enrichment using Oncomine™ TML Assay*
Sequencing 1000X depth of coverage.
TMB calculation based on non-synonymousSNVs and InDels using Ion Reporter™ Software*.
• 1.22 Mb coding regions
• 409 cancer-related genes
• Low DNA input requirement (20 ng)
• Automated analysis workflow in Ion
Reporter Software*
• Detection of clinically relevant mutation
*For Research Use Only. Not For Use in Diagnostic Procedures
Alborelli et al., The Journal of Pathology, 2019
Retrospective analysis of NSCLC samples treated with ICIs
Evaluation of pre-analytical factors for TMB analysis
*Potential deamination artefacts defined as G:C>A:T mutations with < 15% allelic frequency
UDG treatment reduces rejection rate due todeamination artefacts*
Separation of TMB values in high, intermediate, and low TMB based on tertiles.
Retrospective analysis of NSCLC samples treated with ICIs
Correlation of TMB with clinical outcome
*DCB: PFS > 6 months
TMB correlates with response to ICI therapy
*
Alborelli et al., The Journal of Pathology, 2019
(≥ 9 mut/mb)
High TMB associated with increased response rate
Retrospective analysis of NSCLC samples treated with ICIs
Correlation of TMB with clinical outcome
*DCB: PFS > 6 months
TMB correlates with response to ICI therapy Increased PFS in TMB-high samples
*
(≥ 9 mut/mb)
(≤ 8 mut/mb)
Alborelli et al., The Journal of Pathology, 2019
*
Retrospective analysis of NSCLC samples treated with ICIs
Combination of TMB and PD-L1 improves predictive value
No correlation between PD-L1 and TMBImproved stratification upon
combination of PD-L1 and TMBROC analysis confirms feasibility of
combinatorial approach
But is it good enough?
Alborelli et al., The Journal of Pathology, 2019
Understanding Tumor-Immune Interactions Through NGSFocusing on T-Cells
Tumor-Immune interactions are complex
and warrants a multi-marker approach
NGS assays broaden our understanding of the
biology beyond traditional IHC biomarkers
PD-L1
IHC
Source: Thermo Fisher Scientific
V-Gene CDR3 AA CDR3 NT Frequency
TRBV7-8 ASSLGQAYEQY GCCAGCAGCTTAGGTCAGGCATACGAGCAGTAC 1.8E-03
TRBV7-8 ASSLGQAYEQY GCCAGCAGCTTGGGACAGGCCTACGAGCAGTAC 4.8E-04
TRBV7-8 ASSLGQAYEQY GCCAGCAGCTTAGGGCAGGCCTACGAGCAGTAC 9.9E-05
Sequencing Beta Chain of T Cell Receptors to
Characterize Immune Status
TCRComplex.png used under creative commons license: https://creativecommons.org/licenses/by-sa/3.0
Investigating the T-Cell Repertoire in the TME
Oncomine™ TCR Beta Short Read Assay*
Short Read assay capturing CDR3 region, compatible with
DNA or RNA from FFPE samples
V-Gene CDR3 AA CDR3 NT Frequency
TRBV7-8 ASSLGQAYEQY GCCAGCAGCTTAGGTCAGGCATACGAGCAGTAC 1.8E-03
TRBV7-8 ASSLGQAYEQY GCCAGCAGCTTGGGACAGGCCTACGAGCAGTAC 4.8E-04
TRBV7-8 ASSLGQAYEQY GCCAGCAGCTTAGGGCAGGCCTACGAGCAGTAC 9.9E-05
TCR Convergence TCR Evenness (normalized Shannon Entropy)
*For Research Use Only. Not For Use in Diagnostic Procedures
TCR Beta Short Read Assay
Subset of NSCLC FFPE Cohort (N=37)
Note: Durable Clinical Benefit defined as PFS ≥ 6 months.
Dashed lines indicate optimal cutoff based on Youden’s J method.
No DCB DCB
0.00
0.05
0.10
0.15
Convergence
p = .028
Con
verg
ent
TC
R F
req
uen
cy
No DCB DCB
0
20
40
60
80
100
PD-L1 IHC
p = .058
Pe
rce
nta
ge
PD
-L1
po
sitiv
e
No DCB DCB
0.70
0.75
0.80
0.85
0.90
0.95
1.00
Evenness
p = .036
Evenn
ess
No DCB DCB
0
5
10
15
20
25
TMB
p = .105
Muta
tions p
er
Mb
Note: PD-L1 IHC available for only 33 samples.
TCR Convergence vs. TMB / PD-L1
Subset of NSCLC FFPE Cohort (N=37)
• TCR Convergence identifies 5 responders who are missed by TMB.
• TMB identifies 1 responder who is missed by TCR Convergence.
0 5 10 15 20
0.00
0.02
0.04
0.06
0.08
0.10
TCR Convergence vs TMB
Mutations per Mb
Co
nve
rgent
TC
R F
requ
en
cy
DCB
No DCB
No DCB DCB
0.00
0.05
0.10
0.15
Convergence
p = .028
Con
verg
ent
TC
R F
req
uen
cy
TCR Covergence vs. TCR Evenness
Subset of NSCLC FFPE Cohort (N=37)
0.80 0.85 0.90 0.95
0.00
0.02
0.04
0.06
0.08
0.10
TCR Convergence vs Evenness
Evenness
Co
nve
rge
nt
TC
R F
req
uen
cy
DCB
No DCB
No DCB DCB
0.70
0.75
0.80
0.85
0.90
0.95
1.00
Evenness
p = .036
Evenn
ess
TCR Covergence vs. TCR Evenness
Subset of NSCLC FFPE Cohort (N=37)
• TCR Convergence fails to identify two
responders who are identified by TCR
Evenness.
• TCR Convergence identifies a responder who
is missed by TCR Evenness.
0.80 0.85 0.90 0.95
0.00
0.02
0.04
0.06
0.08
0.10
TCR Convergence vs Evenness
Evenness
Co
nve
rge
nt
TC
R F
req
uen
cy
DCB
No DCB
Combination of TCR Convergence and Evenness?
TCR Model Outperforms TMB as a Predictor of Response
Combining TCR Convergence and Evenness yields highest predictive value
Dashed line indicates optimal cutoff based on Youden’s J method.
No DCB DCB
0.0
0.2
0.4
0.6
0.8
1.0
TCR Score
p = .014
TC
R S
core
0 5 10 15 20
0.2
0.3
0.4
0.5
0.6
0.7
0.8
TCR Score vs TMB
Mutations per Mb
Mode
l S
core
DCB
No DCB
Conclusions and Outlook
Where are we and what still needs to be improved?
1. The Oncomine™ TML assay is easy to integrate into an existing Ion Torrent™ workflow.
2. Mind the pre-analytics! Deamination artefacts may severely affect TMB values. DNA should be treated with UDG prior to library prep.
3. High TMB correlates with increased PFS and OS, but predictive power is still limited.
4. TCR Convergence and Evenness show similar predictive power as TMB.
5. A multi-biomarker approach may be most predictive.
Acknowledgments
Molecular Assay Development Unit:
Dr. Ilaria Alborelli, Dr. Katharina
Leonards, Dr. Byron Calgua, Laura
Leuenberger BSc, Ramon Benitez
Contact: [email protected]
Molecular Pathologists:
Dr. Spasenija Savic, Dr. Lukas
Bubendorf, Dr. Kirsten Mertz, Dr. M.
Matter, Prof. L. Terracciano, Dr. Michel
Bihl
Institute of Medical Genetics and Pathology
Oncology:
Dr. Sacha Rothschild, Dr. Alfred
Zippelius, Dr. Severin Poechtrager, Dr.
Andreas Wicki
Thermo Fisher:
Dr. Timothy Looney, Dr. Luca Quagliata
The data presented here represent a sole property of the author and his institution. Thermo Fisher Scientific and its affiliates are not endorsing, recommending, or promoting any use or
application of Thermo Fisher Scientific products presented by third parties during this seminar. Information and materials presented or provided by third parties are provided as-is and
without warranty of any kind, including regarding intellectual property rights and reported results. Parties presenting images, text and material represent they have the rights to do so.
Speaker was provided travel support by Thermo Fisher Scientific for this presentation, but no remuneration.
