Molecular portraits of colorectal cancer morphological regions

Abstract

Heterogeneity of colorectal carcinoma (CRC) represents a major hurdle towards personalized medicine. Efforts based on whole tumor profiling demonstrated that the CRC molecular subtypes were associated with specific tumor morphological patterns representing tumor subregions. We hypothesize that molecular descriptors depend on morphological heterogeneity of the tumor, a dependency that negatively impacts the efficiency of current molecular predictors. We investigated intra-tumor heterogeneity by morphology-guided transcriptomics to better understand the links between gene expression and tumor morphology. The morphological patterns (morphotypes) considered were complex tubular, desmoplastic, mucinous, papillary, serrated, and solid/trabecular. Whole-transcriptomic profiling by microarrays of 202 tumor regions (representing the six morphotypes, tumor-adjacent normal tissue, supportive stroma, and matched whole tumors) from 111 stage II-IV CRCs identified morphotype-specific gene expression profiles and molecular programs and differences in their cellular buildup. The proportion of cell types (fibroblasts, epithelial and immune cells) and differentiation of epithelial cells were the main drivers of the observed disparities with activation of EMT and TNF-a signaling in contrast to MYC and E2F targets signaling, defining major gradients of changes at molecular level. Several types of gene expression-based (including single-cell) classifiers, prognostic and predictive signatures were examined to study their behavior across morphotypes. Most exhibited important morphotype-dependent variability within same tumor sections, with regional predictions often contradicting the whole-tumor classification. The results of our study show that morphotype-based tumor sampling allows for detection of molecular features that would otherwise be distilled in whole tumor profile, while maintaining histopathology context for their interpretation. This represents a practical approach at improving the reproducibility of expression profiling and, by consequence, of gene-based classifiers.