Histopathological characteristics and artificial intelligence for predicting tumor mutational burden-high colorectal cancer

J Gastroenterol. 2021 Jun;56(6):547-559. doi: 10.1007/s00535-021-01789-w. Epub 2021 Apr 28.


Background: Tumor mutational burden-high (TMB-H), which is detected with gene panel testing, is a promising biomarker for immune checkpoint inhibitors (ICIs) in colorectal cancer (CRC). However, in clinical practice, not every patient is tested for TMB-H using gene panel testing. We aimed to identify the histopathological characteristics of TMB-H CRC for efficient selection of patients who should undergo gene panel testing. Moreover, we attempted to develop a convolutional neural network (CNN)-based algorithm to predict TMB-H CRC directly from hematoxylin and eosin (H&E) slides.

Methods: We used two CRC cohorts tested for TMB-H, and whole-slide H&E digital images were obtained from the cohorts. The Japanese CRC (JP-CRC) cohort (N = 201) was evaluated to detect the histopathological characteristics of TMB-H using H&E slides. The JP-CRC cohort and The Cancer Genome Atlas (TCGA) CRC cohort (N = 77) were used to develop a CNN-based TMB-H prediction model from the H&E digital images.

Results: Tumor-infiltrating lymphocytes (TILs) were significantly associated with TMB-H CRC (P < 0.001). The area under the curve (AUC) for predicting TMB-H CRC was 0.910. We developed a CNN-based TMB-H prediction model. Validation tests were conducted 10 times using randomly selected slides, and the average AUC for predicting TMB-H slides was 0.934.

Conclusions: TILs, a histopathological characteristic detected with H&E slides, are associated with TMB-H CRC. Our CNN-based model has the potential to predict TMB-H CRC directly from H&E slides, thereby reducing the burden on pathologists. These approaches will provide clinicians with important information about the applications of ICIs at low cost.

Keywords: Convolutional neural networks; Digital pathology; Gene panel testing; Immune checkpoint inhibitors.

MeSH terms

  • Artificial Intelligence*
  • Colorectal Neoplasms / epidemiology
  • Colorectal Neoplasms / genetics*
  • DNA Mutational Analysis / methods
  • DNA Mutational Analysis / statistics & numerical data
  • Humans
  • Japan
  • Mutation
  • Pathology / methods
  • Pathology / statistics & numerical data