Leveraging computational pathology, a recent study from the Centre for Evolution and Cancer, The Institute of Cancer Research in London, identified spatial cellular patterns in colorectal cancer that could provide new insights into patient outcomes.
The study, published in The Journal of Pathology, examined 458 pre-treatment tissue samples from 375 patients across three clinical cohorts. Using an artificial intelligence model to analyze the samples, the researchers highlighted the prognostic significance of immune and endothelial cell dynamics within the colorectal cancer (CRC) tumor microenvironment (TME).
CRC is characterized by diverse histological subtypes and variable clinical outcomes. Microsatellite instability (MSI) and microsatellite stability (MSS) play critical roles in determining prognosis and treatment response. While immune checkpoint inhibitors have transformed the treatment landscape for MSI-high CRC, the researchers noted that similar advancements have not been realized for MSS tumors. This disparity underscores the need for a deeper understanding of how immune cells and other components of the TME interact with tumors to influence disease progression.
Traditionally, studying the TME relied on manual histological assessment—a labor-intensive process prone to variability. Using high-throughput digital slide scanners and deep learning algorithms, researchers trained a classifier to identify and analyze individual cell types in digitized tissue sections. This automated approach enabled the analysis of eight key cell types, including lymphocytes, macrophages, and endothelial cells.
“Ultimately, features and metrics identified using computational pathology might aid progress towards a more comprehensive description of tumour biology and, in combination with clinically annotated cohorts, identify important patient subsets whose response to treatment differs,” the researchers wrote.
Consistent with prior studies, the density of tumor-infiltrating lymphocytes (TILs) emerged as a robust predictor of progression-free survival (PFS). Higher TIL counts correlated with improved outcomes, particularly in MSI-high cancers. However, macrophage infiltration showed mixed results, likely due to the inability to distinguish between pro-inflammatory (M1) and anti-inflammatory (M2) phenotypes within standard histological sections, the researchers noted.
A novel finding of this study is the prognostic importance of endothelial cells. Elevated endothelial cell density near tumor cells was significantly associated with worse PFS across multiple cohorts, suggesting a link to vascular invasion—a known marker of poor prognosis in CRC. These results were consistent even after adjusting for other clinical factors.
The researchers noted differences in the prognostic value of immune and endothelial cell metrics among the cohorts. For instance, high lymphocyte infiltration was associated with poorer outcomes in the MSS VALENTINO cohort but indicated better outcomes in the MSI-high MISSONI cohort. These findings highlight the complex interplay between tumor biology and the immune landscape.
The research team validated their AI-based findings using multiplex immunofluorescence (mIF), a technique that confirmed the accuracy of the computational models. Although mIF is resource-intensive, its integration with computational pathology could enable large-scale, cost-effective biomarker discovery in the future, they noted.
“While the process of clinical implementation does present many additional considerations and will ultimately require biomarkers to be tested within prospective clinical trials, we believe that an AI-driven investigation of the TME, evaluated within well-annotated clinical trial data, provides a strong basis for identifying the prognostic characteristics of tumour biology,” they wrote.
By identifying spatial patterns and cell densities that predict CRC outcomes, this study advances the potential for personalized medicine. AI-driven metrics could help stratify patients more effectively, guiding treatment decisions such as the escalation or de-escalation of therapies.
A conflict of interest statement from the researchers is included with the article.
