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Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Computational Biological Modeling Identifies PD-(L)1 Immunotherapy Sensitivity Among Molecular Subgroups of
Sukhmani K Padda1, Jacqueline V Aredo1, Shireen Vali2
1Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA.
Purpose:
KRAS-mutated (KRASMUT) non-small-cell lung cancer (NSCLC) is emerging as a heterogeneous disease defined by comutations, which may confer differential benefit to PD-(L)1 immunotherapy. In this study, we leveraged computational biological modeling (CBM) of tumor genomic data to identify PD-(L)1 immunotherapy sensitivity among KRASMUT NSCLC molecular subgroups.
Materials And Methods:
In this multicohort retrospective analysis, the genotype clustering frequency ranked method was used for molecular clustering of tumor genomic data from 776 patients with KRASMUT NSCLC. These genomic data were input into the CBM, in which customized protein networks were characterized for each tumor. The CBM evaluated sensitivity to PD-(L)1 immunotherapy using three metrics: programmed death-ligand 1 expression, dendritic cell infiltration index (nine chemokine markers), and immunosuppressive biomarker expression index (14 markers).
Results:
Genotype clustering identified eight molecular subgroups and the CBM characterized their shared cancer pathway characteristics: KRAS/TP53, KRAS/CDKN2A/B/C, KRAS/STK11, KRAS/KEAP1, KRAS/STK11/KEAP1, KRAS/PIK3CA, KRAS /ATM, and KRAS without comutation. CBM identified PD-(L)1 immunotherapy sensitivity in the KRAS/TP53, KRAS/PIK3CA, and KRAS alone subgroups and resistance in the KEAP1 containing subgroups. There was insufficient genomic information to elucidate PD-(L)1 immunotherapy sensitivity by the CBM in the KRAS/CDKN2A/B/C, KRAS/STK11, and KRAS/ATM subgroups. In an exploratory clinical cohort of 34 patients with advanced KRASMUT NSCLC treated with PD-(L)1 immunotherapy, the CBM-assessed overall survival correlated well with actual overall survival (r = 0.80, P < .001).
Conclusion:
CBM identified distinct PD-(L)1 immunotherapy sensitivity among molecular subgroups of KRASMUT NSCLC, in line with previous literature. These data provide proof-of-concept that computational modeling of tumor genomics could be used to expand on hypotheses from clinical observations of patients receiving PD-(L)1 immunotherapy and suggest mechanisms that underlie PD-(L)1 immunotherapy sensitivity.
Insights
Computational biological modeling identified distinct PD-(L)1 immunotherapy sensitivity in KRAS-mutated non-small-cell lung cancer subgroups. This approach shows promise for predicting patient response to immunotherapy based on tumor genomics.
Area of Science:
- Oncology
- Computational Biology
- Genomics
Background:
- KRAS-mutated non-small-cell lung cancer (NSCLC) is a heterogeneous disease.
- Comutations in KRAS-mutated NSCLC may influence response to PD-(L)1 immunotherapy.
- Identifying specific molecular subgroups is crucial for predicting immunotherapy efficacy.
Purpose of the Study:
- To leverage computational biological modeling (CBM) to analyze tumor genomic data.
- To identify PD-(L)1 immunotherapy sensitivity across different molecular subgroups of KRAS-mutated NSCLC.
Main Methods:
- Retrospective analysis of genomic data from 776 patients with KRAS-mutated NSCLC.
- Molecular clustering using the genotype clustering frequency ranked method.
- CBM evaluation of PD-(L)1 immunotherapy sensitivity based on PD-L1 expression, dendritic cell infiltration, and immunosuppressive biomarkers.
Main Results:
- Eight molecular subgroups of KRAS-mutated NSCLC were identified.
- CBM predicted PD-(L)1 immunotherapy sensitivity in KRAS/TP53, KRAS/PIK3CA, and KRAS-alone subgroups.
- Resistance was predicted in KEAP1-containing subgroups; CBM-assessed survival correlated with actual survival (r=0.80, P<.001) in an exploratory cohort.
Conclusions:
- CBM successfully identified differential PD-(L)1 immunotherapy sensitivity among KRAS-mutated NSCLC molecular subgroups.
- These findings provide proof-of-concept for using computational modeling of tumor genomics to predict immunotherapy response.
- The study suggests potential mechanisms underlying PD-(L)1 immunotherapy sensitivity in NSCLC.
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