Dataset Information

Predicting Durable Responses to Immune Checkpoint Inhibitors in Non-Small-Cell Lung Cancer Using a Multi-Feature Model.

ABSTRACT: 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.

SUBMITTER: Wang L

PROVIDER: S-EPMC9072668 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Predicting Durable Responses to Immune Checkpoint Inhibitors in Non-Small-Cell Lung Cancer Using a Multi-Feature Model.

Wang Lei L Zhang Hongbing H Pan Chaohu C Yi Jian J Cui Xiaoli X Li Na N Wang Jiaqian J Gao Zhibo Z Wu Dongfang D Chen Jun J Jiang Jizong J Chu Qian Q

Frontiers in immunology 20220422

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, ...[more]

PMID: 35529874

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Project description:BackgroundAlthough predictive value of immune-related adverse events (irAEs) induced by immune checkpoint inhibitors (ICIs) have been suggested by several studies, their assessments were insufficient because patients were categorized only by the occurrence of irAEs. It has not been elucidated whether irAEs also play a significant role even in responders.Materials and methodsBetween December 2015 and September 2018, 106 patients with advanced non-small cell lung cancer treated with ICIs were enrolled in our prospective biomarker study. Twenty-three of these were responders, defined as those with complete or partial response. We investigated the proportion of irAEs among overall and responders. For responders, progression-free survival (PFS) and overall survival of ICIs were compared between those with and without irAEs. As an exploratory analysis, we measured 41 proteins from peripheral blood before and after ICI treatment.ResultsThe proportion of irAEs was significantly higher in responders than nonresponders (65.2% vs. 19.3%, p < .01). Among responders, clinical characteristics did not differ regardless of the occurrence of irAEs. However, there was a significant difference in PFS among responders (irAE group 19.1 months vs. non-irAE group 5.6 months; hazard ratio: 0.30 [95% confidence interval: 0.10-0.85]; p = .02). Of 41 protein analyses, fibroblast growth factor-2 at baseline and monocyte chemoattractant protein fold change showed significant differences between them (p < .04).ConclusionAlthough this is a small sample-sized study, irAE might be a predictive factor of durable efficacy, even in patients who responded to ICIs. Investigation into the significance of irAEs in responders will contribute to the establishment of optimal administration of ICI.Implications for practiceAlthough the predictive value of immune-related adverse events (irAEs) induced by immune checkpoint inhibitors (ICIs) has been suggested by several studies, it has not been elucidated whether irAEs also play a significant role even in responders. This study showed that more than 60% of responders had irAEs. It demonstrated the strong correlation between irAEs and efficacy even in responders. Investigation into the significance of irAEs in responders will contribute to the establishment of optimal administration of ICI.

Dataset Information

Predicting Durable Responses to Immune Checkpoint Inhibitors in Non-Small-Cell Lung Cancer Using a Multi-Feature Model.

Publications

Predicting Durable Responses to Immune Checkpoint Inhibitors in Non-Small-Cell Lung Cancer Using a Multi-Feature Model.

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