Journal
FRONTIERS IN IMMUNOLOGY
Volume 13, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fimmu.2022.829634
Keywords
immune checkpoint inhibitors; durable responses; multi-feature model; genetic biomarkers; non-small cell lung cancer; cancer immunity and immunotherapy
Categories
Funding
- Natural Science Foundation of China [62131009, 82072597]
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A comprehensive predictive model is urgently needed to predict the response to immune checkpoint inhibitors (ICIs) in cancer patients. By studying 162 non-small-cell lung cancer (NSCLC) patients, three genomic biomarkers were identified and used to construct a durable clinical benefit (DCB) prediction model, which showed good predictive value.
Due to the complex mechanisms affecting anti-tumor immune response, a single biomarker is insufficient to identify patients who will benefit from immune checkpoint inhibitors (ICIs) treatment. Therefore, a comprehensive predictive model is urgently required to predict the response to ICIs. A total of 162 non-small-cell lung cancer (NSCLC) patients undergoing ICIs treatment from three independent cohorts were enrolled and used as training and test cohorts (training cohort = 69, test cohort1 = 72, test cohort2 = 21). Eight genomic markers were extracted or calculated for each patient. Ten machine learning classifiers, such as the gaussian process classifier, random forest, and support vector machine (SVM), were evaluated. Three genomic biomarkers, namely tumor mutation burden, intratumoral heterogeneity, and loss of heterozygosity in human leukocyte antigen were screened out, and the SVM_poly method was adopted to construct a durable clinical benefit (DCB) prediction model. Compared with a single biomarker, the DCB multi-feature model exhibits better predictive value with the area under the curve values equal to 0.77 and 0.78 for test cohort1 and cohort2, respectively. The patients predicted to have DCB showed improved median progression-free survival (mPFS) and median overall survival (mOS) than those predicted to have non-durable clinical benefit.
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