Machine learning analysis of pathological images to predict 1-year progression-free survival of immunotherapy in patients with small-cell lung cancer

J Immunother Cancer. 2024 Feb 15;12(2):e007987. doi: 10.1136/jitc-2023-007987.

Abstract

Background: In small-cell lung cancer (SCLC), the tumor immune microenvironment (TIME) could be a promising biomarker for immunotherapy, but objectively evaluating TIME remains challenging. Hence, we aimed to develop a predictive biomarker of immunotherapy efficacy through a machine learning analysis of the TIME.

Methods: We conducted a biomarker analysis in a prospective study of patients with extensive-stage SCLC who received chemoimmunotherapy as the first-line treatment. We trained a model to predict 1-year progression-free survival (PFS) using pathological images (H&E, programmed cell death-ligand 1 (PD-L1), and double immunohistochemical assay (cluster of differentiation 8 (CD8) and forkhead box P3 (FoxP3)) and patient information. The primary outcome was the mean area under the curve (AUC) of machine learning models in predicting the 1-year PFS.

Results: We analyzed 100,544 patches of pathological images from 78 patients. The mean AUC values of patient information, pathological image, and combined models were 0.789 (range 0.571-0.982), 0.782 (range 0.750-0.911), and 0.868 (range 0.786-0.929), respectively. The PFS was longer in the high efficacy group than in the low efficacy group in all three models (patient information model, HR 0.468, 95% CI 0.287 to 0.762; pathological image model, HR 0.334, 95% CI 0.117 to 0.628; combined model, HR 0.353, 95% CI 0.195 to 0.637). The machine learning analysis of the TIME had better accuracy than the human count evaluations (AUC of human count, CD8-positive lymphocyte: 0.681, FoxP3-positive lymphocytes: 0.626, PD-L1 score: 0.567).

Conclusions: The spatial analysis of the TIME using machine learning predicted the immunotherapy efficacy in patients with SCLC, thus supporting its role as an immunotherapy biomarker.

Keywords: Computational Biology; Immune Checkpoint Inhibitors; Lung Neoplasms; Tumor Biomarkers; Tumor Microenvironment.

MeSH terms

  • B7-H1 Antigen
  • Biomarkers, Tumor / analysis
  • Carcinoma, Non-Small-Cell Lung* / pathology
  • Forkhead Transcription Factors
  • Humans
  • Immunotherapy / methods
  • Lung Neoplasms* / drug therapy
  • Machine Learning
  • Progression-Free Survival
  • Prospective Studies
  • Small Cell Lung Carcinoma* / therapy
  • Tumor Microenvironment

Substances

  • B7-H1 Antigen
  • Biomarkers, Tumor
  • Forkhead Transcription Factors