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Machine learning for prediction of immunotherapeutic outcome in non-small-cell lung cancer based on circulating cytokine signatures. | LitMetric

AI Article Synopsis

  • Immune checkpoint inhibitors (ICIs) have improved survival for patients with non-small-cell lung cancer (NSCLC), but their effectiveness can vary; hence, a new machine learning tool called the Cytokine-based ICI Response Index (CIRI) was developed to enhance prediction of patient responses based on blood cytokine profiles.
  • The study involved 222 NSCLC patients, analyzing the levels of 93 cytokines before and after treatment, and utilized ensemble learning methods to identify key cytokines that could forecast overall survival (OS) for those undergoing ICI therapy.
  • The CIRI models identified specific cytokines that correlated with worse OS, achieving accurate prediction rates in independent cohorts; further advancements incorporating additional clinical data improved prediction capabilities,

Article Abstract

Background: Immune checkpoint inhibitor (ICI) therapy has substantially improved the overall survival (OS) in patients with non-small-cell lung cancer (NSCLC); however, its response rate is still modest. In this study, we developed a machine learning-based platform, namely the Cytokine-based ICI Response Index (CIRI), to predict the ICI response of patients with NSCLC based on the peripheral blood cytokine profiles.

Methods: We enrolled 123 and 99 patients with NSCLC who received anti-PD-1/PD-L1 monotherapy or combined chemotherapy in the training and validation cohorts, respectively. The plasma concentrations of 93 cytokines were examined in the peripheral blood obtained from patients at baseline (pre) and 6 weeks after treatment (early during treatment: edt). Ensemble learning random survival forest classifiers were developed to select feature cytokines and predict the OS of patients undergoing ICI therapy.

Results: Fourteen and 19 cytokines at baseline and on treatment, respectively, were selected to generate CIRI models (namely preCIRI14 and edtCIRI19), both of which successfully identified patients with worse OS in two completely independent cohorts. At the population level, the prediction accuracies of preCIRI14 and edtCIRI19, as indicated by the concordance indices (C-indices), were 0.700 and 0.751 in the validation cohort, respectively. At the individual level, patients with higher CIRI scores demonstrated worse OS [hazard ratio (HR): 0.274 and 0.163, and p<0.0001 and p=0.0044 in preCIRI14 and edtCIRI19, respectively]. By including other circulating and clinical features, improved prediction efficacy was observed in advanced models (preCIRI21 and edtCIRI27). The C-indices in the validation cohort were 0.764 and 0.757, respectively, whereas the HRs of preCIRI21 and edtCIRI27 were 0.141 (p<0.0001) and 0.158 (p=0.038), respectively.

Conclusions: The CIRI model is highly accurate and reproducible in determining the patients with NSCLC who would benefit from anti-PD-1/PD-L1 therapy with prolonged OS and may aid in clinical decision-making before and/or at the early stage of treatment.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347453PMC
http://dx.doi.org/10.1136/jitc-2023-006788DOI Listing

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