Machine learning-based insights into circulating autoantibody dynamics and treatment outcomes in patients with NSCLC
Feifei Wei1,2, Hiroyuki Takeda3, Koichi Azuma4
1Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Kanagawa, Japan.
Introduction:
Immune checkpoint inhibitors (ICIs) targeting the programmed death-1/ligand-1 (PD-1/PD-L1) axis have significantly improved treatment outcomes in non-small cell lung cancer (NSCLC); however, challenges remain owing to the limited durability of therapeutic responses and the occurrence of immune-related adverse events (irAEs). This study aimed to characterize dynamic changes in the circulating autoantibody (CAAB) profile during ICI treatment and explore their association with treatment outcomes in patients with NSCLC.
Methods:
A panel of 59 CAABs showing substantial treatment-related changes was initially identified using AlphaScreen assays in a primary screening of five patients who developed ir-pneumonitis. These CAABs were subsequently profiled in paired pre-and post-treatment plasma samples obtained from 179 patients with NSCLC treated with anti-PD-1/PD-L1 therapy at two Japanese centers. Associations between CAAB dynamics and clinical parameters-including baseline characteristics, treatment regimens, and treatment outcomes (irAEs, ir-pneumonitis, response, progression-free survival [PFS], and overall survival [OS])-were evaluated using permutational multivariate analysis of variance and univariate binary logistic and Cox regression, elastic net regularization regression, and random forest regression.
Results:
Using permutational multivariate analysis of variance and univariate binary logistic/Cox regression, we comprehensively assessed the global associations between CAAB dynamics and eight clinical parameters, including background factors (PD-L1 expression and treatment line), treatment regimens (chemotherapy exposure), and treatment outcomes (irAE occurrence, ir-pneumonitis development, RECIST-assessed response, PFS, and OS), indicating that chemotherapy exposure was the only significant and strong factor influencing CAAB dynamics. In patients receiving ICI monotherapy, univariate logistic or Cox regression analyses were performed to identify individual CAABs significantly associated with each outcome, highlighting both shared and distinct immunological features underlying different clinical endpoints. Through machine learning-based evaluation of the predictive potential of CAAB dynamics for five treatment outcomes across the overall cohort and six subgroups defined by three stratification variables, four optimized CAAB signatures with robust predictive performance for ICI treatment outcomes were established.
Conclusions:
These findings suggest the involvement of distinct immune pathways in therapeutic benefits and toxicity. Collectively, our results provide mechanistic insights into ICI-induced humoral immune regulation, highlight the potential utility of CAABs as biomarkers to enhance benefit-to-risk assessment, and guide the development of personalized immunotherapy strategies for NSCLC.
Insights
Circulating autoantibody (CAAB) profiles dynamically change during immune checkpoint inhibitor (ICI) therapy for non-small cell lung cancer (NSCLC). These CAAB dynamics, particularly influenced by chemotherapy, can predict treatment outcomes and guide personalized immunotherapy strategies.
Area of Science:
- Immunology
- Oncology
- Biomarker Discovery
Background:
- Immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 axis improve non-small cell lung cancer (NSCLC) outcomes but face challenges with response durability and immune-related adverse events (irAEs).
- Understanding dynamic changes in circulating autoantibody (CAAB) profiles during ICI therapy is crucial for predicting treatment responses and toxicities.
Purpose of the Study:
- To characterize dynamic changes in CAAB profiles during ICI treatment in NSCLC patients.
- To explore the association of CAAB dynamics with treatment outcomes, including irAEs, response, progression-free survival (PFS), and overall survival (OS).
Main Methods:
- A panel of 59 CAABs was identified and profiled in paired pre- and post-treatment plasma samples from 179 NSCLC patients treated with anti-PD-1/PD-L1 therapy.
- Statistical analyses included permutational multivariate analysis of variance, logistic regression, Cox regression, and random forest modeling to assess associations between CAAB dynamics and clinical parameters.
- Machine learning was employed to develop predictive CAAB signatures for ICI treatment outcomes.
Main Results:
- Chemotherapy exposure was the primary factor influencing CAAB dynamics during ICI treatment.
- Individual CAABs were significantly associated with specific clinical endpoints (irAEs, ir-pneumonitis, response, PFS, OS) in patients receiving ICI monotherapy.
- Four optimized CAAB signatures demonstrated robust predictive performance for ICI treatment outcomes across various patient subgroups.
Conclusions:
- CAAB dynamics provide mechanistic insights into ICI-induced humoral immune regulation, suggesting distinct immune pathways for therapeutic benefits and toxicity.
- CAABs hold potential as biomarkers for enhancing benefit-to-risk assessment in ICI therapy.
- These findings can guide the development of personalized immunotherapy strategies for NSCLC.
