Exploring the interplay between colorectal cancer subtypes genomic variants and cellular morphology: A deep-learning approach

Molecular subtypes of colorectal cancer (CRC) significantly influence treatment decisions. While convolutional neural networks (CNNs) have recently been introduced for automated CRC subtype identification using H&E stained histopathological images, the correlation between CRC subtype genomic variants and their corresponding cellular morphology expressed by their imaging phenotypes is yet to be fully explored. The goal of this study was to determine such correlations by incorporating genomic variants in CNN models for CRC subtype classification from H&E images. We utilized the publicly available TCGA-CRC-DX dataset, which comprises whole slide images from 360 CRC-diagnosed patients (260 for training and 100 for testing). This dataset also provides information on CRC subtype classifications and genomic variations. We trained CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology patterns. We assessed the interplay between CRC subtypes' genomic variations and cellular morphology patterns by evaluating the CRC subtype classification accuracy of the different models in a stratified 5-fold cross-validation experimental setup using the area under the ROC curve (AUROC) and average precision (AP) as the performance metrics. The CNN models that account for potential correlation between genomic variations within CRC subtypes and their cellular morphology pattern achieved superior accuracy compared to the baseline CNN classification model that does not account for genomic variations when using either single-nucleotide-polymorphism (SNP) molecular features (AUROC: 0.824 +/- 0.02 vs. 0.761 +/- 0.04, p<0.05, AP: 0.652 +/- 0.06 vs. 0.58 +/- 0.08) or CpG-Island methylation phenotype (CIMP) molecular features (AUROC: 0.834 +/- 0.01 vs. 0.787 +/- 0.03, p<0.05, AP: 0.687 +/- 0.02 vs. 0.64 +/- 0.05). Combining the CNN models account for variations in CIMP and SNP further improved classification accuracy (AUROC: 0.847 +/- 0.01 vs. 0.787 +/- 0.03, p = 0.01, AP: 0.68 +/- 0.02 vs. 0.64 +/- 0.05). The improved accuracy of CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology as expressed by H&E imaging phenotypes may elucidate the biological cues impacting cancer histopathological imaging phenotypes. Moreover, considering CRC subtypes genomic variations has the potential to improve the accuracy of deep-learning models in discerning cancer subtype from histopathological imaging data.