Tumour budding, poorly differentiated clusters, and T-cell response in colorectal cancer

Abstract

Background Tumour budding and poorly differentiated clusters (PDC) represent forms of tumour invasion. We hypothesised that T-cell densities (reflecting adaptive anti-tumour immunity) might be inversely associated with tumour budding and PDC in colorectal carcinoma. Methods Utilising 915 colon and rectal carcinomas in two U.S.-wide prospective cohort studies, and multiplex immunofluorescence combined with machine learning algorithms, we assessed CD3, CD4, CD8, CD45RO (PTPRC), and FOXP3 co-expression patterns in lymphocytes. Tumour budding and PDC at invasive fronts were quantified by digital pathology and image analysis using the International tumour Budding Consensus Conference criteria. Using covariate data of 4,420 incident colorectal cancer cases, inverse probability weighting (IPW) was integrated with multivariable logistic regression analysis that assessed the association of T-cell subset densities with tumour budding and PDC while adjusting for selection bias due to tissue availability and potential confounders, including microsatellite instability status. Findings Tumour budding counts were inversely associated with density of CD3 + CD8 + [lowest vs. highest: multivariable odds ratio (OR), 0.50; 95% confidence interval (CI), 0.35–0.70; P trend < 0.001] and CD3 + CD8 + CD45RO + cells (lowest vs. highest: multivariable OR, 0.44; 95% CI, 0.31–0.63; P trend < 0.001) in tumour epithelial region. Tumour budding levels were associated with higher colorectal cancer-specific mortality (multivariable hazard ratio, 2.13; 95% CI, 1.57–2.89; P trend < 0.001) in Cox regression analysis. There were no significant associations of PDC with T-cell subsets. Interpretation Tumour epithelial naïve and memory cytotoxic T cell densities are inversely associated with tumour budding at invasive fronts, suggesting that cytotoxic anti-tumour immunity suppresses tumour microinvasion.

Pancreatic Cancers with High Grade Tumor Budding Exhibit Hallmarks of Diminished Anti-Tumor Immunity

“…Finally, cell-type deconvolution and signature-based scoring revealed that PDAC cases from TCGA with high levels of the budding gene signature, recapitulated an “immune-escape” phenotype with diminished T cells and B cells. Our results are supported by the work of Fujiyoshi et al, who utilized multiplex immunofluorescence to show that tumor budding numbers were inversely associated with CD3 + CD8 + cytotoxic T cells [ 37 ]. Conversely, in hepatocellular carcinoma (HCC), patients with HG tumor budding were shown to have higher densities of CD8 + T cells and CD20 + B cells [ 38 ].…”

Association of Fusobacterium nucleatum with Specific T-cell Subsets in the Colorectal Carcinoma Microenvironment

“…We constructed tissue microarrays of colorectal cancer cases with sufficient tissue materials, including up to four tumor cores from each case in a tissue microarray block (26). As described previously (27,28), 4 mm sections from tissue microarray blocks were sequentially stained using the following antibody and fluorophore combinations, in order: anti-CD3 antibody (clone F7.2.38, Dako; Agilent Technologies)/Opal-520, anti-FOXP3 (clone 206D, BioLegend)/Opal-540, anti-CD45RO (one of PTPRC isoforms, clone UCHL1, Dako)/ Opal-650, anti-CD8 (clone C8/144B, Dako)/Opal-570, anti-CD4 (clone 4B12, Dako)/Opal-690, and anti-KRT (keratin, pan-cytokeratins; clone AE1/AE3, Dako and clone C11, Cell Signaling Technology)/Opal-620 (Supplementary Fig. S1; with standardized protein nomenclature recommended by a panel of experts; ref.…”

Computational augmentation of neoplastic endometrial glands in digital pathology displays

“…Another limitation is the restriction to epithelial data; based on previous findings in breast tissue [27], there is likely to be information in endometrial stromal nuclear distributions that could improve classification. Epithelial nuclear distributions do not directly measure architectural parameters related to gland shape, which have prognostic utility [28], nor do they account for the immune environment, which has been shown to have diagnostic and prognostic value in endometrial carcinoma and in other systems [29,30]. Given the diagnostic value of gland crowding [18], we also would like to account for gland‐to‐gland spatial distributions in automated classification, possibly by adjusting P EIN values based on the proximity and P EIN values of nearest‐neighbor glands; however, although the computational algorithm does not explicitly incorporate gland crowding in its predictions, this information is incorporated by the end user when visually integrating spatial cloud data to identify EIN foci.…”