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Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Prognostic Mutational Signatures of NSCLC Patients treated with chemotherapy, immunotherapy and chemoimmunotherapy
Margaret R Smith1, Yuezhu Wang1, Ralph D'Agostino2
1Department of Cancer Biology, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA.
Abstract:
Different types of therapy are currently being used to treat non-small cell lung cancer (NSCLC) depending on the stage of tumor and the presence of potentially druggable mutations. However, few biomarkers are available to guide clinicians in selecting the most effective therapy for all patients with various genetic backgrounds. To examine whether patients' mutation profiles are associated with the response to a specific treatment, we collected comprehensive clinical characteristics and sequencing data from 524 patients with stage III and IV NSCLC treated at Atrium Health Wake Forest Baptist. Overall survival based Cox-proportional hazard regression models were applied to identify mutations that were "beneficial" (HR < 1) or "detrimental" (HR > 1) for patients treated with chemotherapy (chemo), immune checkpoint inhibitor (ICI) and chemo+ICI combination therapy (Chemo+ICI) followed by the generation of mutation composite scores (MCS) for each treatment. We also found that MCS is highly treatment specific that MCS derived from one treatment group failed to predict the response in others. Receiver operating characteristics (ROC) analyses showed a superior predictive power of MCS compared to TMB and PD-L1 status for immune therapy-treated patients. Mutation interaction analysis also identified novel co-occurring and mutually exclusive mutations in each treatment group. Our work highlights how patients' sequencing data facilitates the clinical selection of optimized treatment strategies.
Insights
Patient mutation profiles can predict non-small cell lung cancer (NSCLC) treatment response. Mutation composite scores (MCS) offer superior prediction for immune therapy compared to TMB and PD-L1 status, aiding personalized NSCLC treatment selection.
Area of Science:
- Oncology
- Genomics
- Translational Medicine
Background:
- Non-small cell lung cancer (NSCLC) treatment selection is complex, often lacking specific biomarkers for diverse patient genetic profiles.
- Current therapies include chemotherapy, immune checkpoint inhibitors (ICI), and combination treatments, necessitating better predictive tools.
Purpose of the Study:
- To investigate the association between patient mutation profiles and treatment response in advanced NSCLC.
- To develop and validate mutation composite scores (MCS) for predicting efficacy of different NSCLC therapies.
Main Methods:
- Collected clinical and sequencing data from 524 stage III/IV NSCLC patients.
- Utilized Cox-proportional hazard regression to identify beneficial/detrimental mutations for chemotherapy, ICI, and Chemo+ICI treatments.
- Generated treatment-specific MCS and compared predictive power with TMB and PD-L1 using ROC analysis.
Main Results:
- MCS demonstrated high specificity for predicting response to specific treatment modalities.
- MCS showed superior predictive ability over TMB and PD-L1 for patients receiving immune therapy.
- Identified novel mutation interactions (co-occurring and mutually exclusive) within each treatment group.
Conclusions:
- Patient sequencing data and derived MCS can significantly enhance the clinical selection of optimized NSCLC treatment strategies.
- Mutation profiling offers a promising avenue for personalized medicine in NSCLC, improving therapeutic outcomes.
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