Post on 25-Jan-2023
The evolving landscape of biomarkers for immune checkpoint inhibitors
in thoracic oncology
Paul Hofman
Laboratory of Clinical and Experimental PathologyFHU OncoAge, Inserm U1081/CNRS 7284
Côte d’Azur University, Nice, France
Biomarkers
Response Resistance Toxicity Hyperprogression
Biomarkers
Response
Two independent predictive biomarkers
IHC PD-L1 Tumor mutational burden
+ EGFR, ALK, ROS1 & BRAF
ValidatedDaily practice
Not ValidatedClinical trials
PD-L1 has limitations
– Patient with 0% of PD-L1 IHC positivity can be good responder– Patient with > 50% of PD-L1 IHC positivity can be non responder–Heterogeneity of PD-L1 IHC staining limits the assessment in small biopsies – Inter & intra oberver variability– PD-L1 IHC is not well-validated to date –Many PD-L1 clones, many devices, different performances–Many cut off (>1%, > 25%, > 50%)– Clinical value of positive immune cells for PD-L1 is controversial
PD-L1 IHC
<1%
>50%
Prembrolizumabfirst line
Chemotherapyfirst line
…..or…..!
> 50% TC ?
First line I-O according to the PD-L1 expression on TC (May 2018)
> 25% TC ? > 1% TC ? …All comers ?
Controversies about EMA decision
EMA excluded patients with PD-L1 <1%from access to durvalumab
Expands pembrolizumab indication for first-line treatment of NSCLC (TPS ≥1%). Accessed April 11, 2019. https://www.fda.gov/Drugs/InformationOnDrugs/ ApprovedDrugs/ucm635857.htm.FDA
Summary of interobserver concordance studies for PD-L1 IHC assessment in NSCLC
Low concordanceat TPS 1% !
Tumor mutational burden
What’s next ?
CheckMate 227: PFS in Patients With High TMB (≥10 mut/Mb) by Tumor PD-L1 Expression
≥1% PD-L1 expression <1% PD-L1 expression
Nivo + ipi(n = 38)
Chemo(n = 48)
Median PFS, mob 7.7 5.3
HR95% CI
0.480.27, 0.85
Chemotherapy
Nivolumab +ipilimumab
Months
0
20
40
60
80
100
0 6 12 183 9 15 21 24
1-y PFS = 45%
1-y PFS = 8%
1-y PFS = 42%
1-y PFS = 16%
PF
S (
%)
Chemotherapy
Nivolumab +ipilimumab
Months
0
20
40
60
80
100
0 6 12 183 9 15 21 24
Nivo + ipi(n = 101)
Chemo(n = 112)
Median PFS, moa 7.1 5.5
HR95% CI
0.620.44, 0.88
2018
CheckMate 227: PFS in Patients With High TMB (≥10 mut/Mb) by Tumor PD-L1 Expression
≥1% PD-L1 expression <1% PD-L1 expression
Nivo + ipi(n = 38)
Chemo(n = 48)
Median PFS, mob 7.7 5.3
HR95% CI
0.480.27, 0.85
Chemotherapy
Nivolumab +ipilimumab
Months
0
20
40
60
80
100
0 6 12 183 9 15 21 24
1-y PFS = 45%
1-y PFS = 8%
1-y PFS = 42%
1-y PFS = 16%
PF
S (
%)
Chemotherapy
Nivolumab +ipilimumab
Months
0
20
40
60
80
100
0 6 12 183 9 15 21 24
Nivo + ipi(n = 101)
Chemo(n = 112)
Median PFS, moa 7.1 5.5
HR95% CI
0.620.44, 0.88
The pros
The cons
1. Alternative / complementary biomarker to PD-L1 2. Compatible with targeted panel NGS tests
3. Less heterogeneity than PD-L1 (?)
1. Turnaround times for getting the results2. Sensitivity links to DNA quantity/quality
3. Proposed cutpoints for TMB High4. Reproducibility across sequencing platforms
5. Cost effectivness6. Accreditation is mandatory
TMB
Lung Cancer with a High Tumor Mutational BurdenVanderLaan PA, et al. N Engl J Med 2018
VanderLaan PA. N Engl J Med 2018; 379: 11.
In-House Testingversus
External to Referral Center?
TMBTMBMain challenge
in thoracic oncology
Estimated number of patients over 100 cases who will benefit to ICIs according to a high TMB
WithoutWithout TAT consideration
(>10 mut/Mb)
(<10 mut/Mb)
TCR repertoire variability may serve as a predictivebiomarker for immunotherapy in solid tumors,including those where TMB is not predictive of response
Biomarkers
Resistance
Skoulidis et al, Cancer Discovery May 2018
Patients treated by PD1/PD-L1 inhibitors
STK11 mutation
Biomarkers
Toxicity
TCR repertoire ?
Biomarkers
Hyperprogression
MDM2 amplification?EGFR amplification?
More and more biomarkersin I-O pipeline
PD-L1
TMB
TCR
GEP
?
Tumor mutational burden &genomic alterations
PD1/PD-L1 and other ICIs
Adaptative immunity TCR repertoire
SNPs (germline DNA)
Microbiome
Innate immunity
Combination oftherapies
Combination ofbiomarkers
J Clin Oncol. 2019 Feb 1;37(4):318-327
Which targets?Which methods?How many fields to assess per tumor?Primary and/or metastatic site (s)?How to quantify the different signals?How to assess the different cut off?
&How to integrate genomic associated data ?
Comment optimiser?How to optimize?
The evolving landscape of biomarkers for immune checkpoint inhibitors