AI and Cancer Research ── Colorectal ── 2026-11-05
Colorectal cancer: screening, treatment, trials and AI
- Abstract
Purpose: Colorectal cancer (CRC) prognosis is influenced by the tumor microenvironment (TME), including spatial immune cell organization and spatial immune aggregates (SIAs). However, the prognostic relevance of spatially resolved immune subsets, particularly B-cell-related phenotypes, remains incompletely defined and requires careful evaluation against established clinicopathological predictors.
Methods: A cohort of 786 CRC patients was analyzed using multiplex fluorescence immunohistochemistry (mIHC) targeting 10 immune phenotypes, coupled with deep learning-based single-cell segmentation and spatial statistics. A biologically informed density-based spatial clustering of applications with noise (DBSCAN) algorithm was used for automated SIA detection. Prognostic modeling was restricted to 636 patients with complete survival information and was evaluated using a leakage-free patient-level training/validation split. Benchmark models included TNM-based, clinicopathological, spatial-only, and integrated XGBoost models. Spatial heterogeneity, immune subset distributions, region-specific associations, and prognostic performance were systematically evaluated.
Results: Non-proliferating CD3 + CD8−FOXP3 − T cells dominated the TME (64.9%), and immune composition varied with disease stage and metastatic status. Automated SIA detection showed high concordance with pathologist-reviewed annotations and enabled region-specific profiling of immune phenotypes. SIA-region proliferating CD8 + T cells were associated with favorable outcome, whereas non-proliferating CD21 + B cells in non-SIA regions were associated with poorer outcome. Spatial proximity analysis identified metastasis-associated distance patterns, including shortened distances between CD21 − B cells and CD3 + CD8−FOXP3 − T cells. In prognostic benchmarking, clinicopathological variables remained the strongest predictors in the current cohort, whereas spatial features alone showed limited standalone discrimination. An integrated XGBoost model incorporating spatial and clinicopathological variables provided complementary prognostic information but did not materially outperform the clinicopathological benchmark.
Conclusion: Our study provides a high-resolution spatial immune atlas of CRC and identifies spatially resolved immune features associated with patient prognosis. The automated SIA detection framework and prognostic benchmarking analyses offer an exploratory basis for future validation of spatial biomarkers, while the current data support a complementary rather than dominant role for spatial features alongside established clinicopathological variables.
Journal IF-equivalent: 4.5 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: Lai Y, Wang J, Yong X, Wu Z, Guo G, Sun Y, et al. Prognostic architecture of spatially resolved immune ecosystems in colorectal cancer: a multiplex immunohistochemistry and machine learning study. Cell Oncol. 2026 Nov 2 [Epub ahead of print]. doi:10.1007/s13402-026-01309-z.Checked: Abstract only - Abstract
BACKGROUND: Tumor-infiltrating lymphocyte (TIL) density provides prognostic information in colon adenocarcinoma (COAD), but the additional value of TIL spatial organization remains unclear. METHODS: We developed CoATIL, a computational pathology framework quantifying TIL spatial architecture from routine H&E-stained whole-slide images. The study included 461 patients with microsatellite-stable or MSI-low COAD. The model was trained using 140 patients from University Hospitals Cleveland Medical Center, validated internally in 52 patients, and externally in The Cancer Genome Atlas (n = 130), and Guangdong Provincial People's Hospital (n = 139). Prognostic performance was assessed using Kaplan-Meier analysis and multivariable Cox regression. CoATIL was compared with a clinical model and eight foundation-model-based multiple-instance learning approaches. RESULTS: CoATIL was associated with progression-free survival (HR per standard-deviation increase, 1.32; 95% CI 1.13-1.55; p < 0.001; C-index=0.66) and remained prognostic after adjustment for clinicopathologic factors in each validation cohort (adjusted HRs, 2.00-4.40; all p < 0.05). High-versus-low-risk stratification was retained within stage II (HR 3.07; 95% CI 1.83-5.15; p < 0.001) and stage III disease (HR 3.24; 95% CI 1.28-8.19; p = 0.013). The combined clinical-CoATIL model achieved a higher C-index than the clinical model alone (0.69 versus 0.61; paired bootstrap difference, 0.08; 95% CI 0.03-0.14). Exploratory genomic analyses associated AMER1 and FBXW7 mutations with lower and higher CoATIL risk scores, respectively. CONCLUSIONS: Spatial immune architecture extracted from routine histology provides complementary prognostic information in COAD. Prospective validation is warranted.
Journal IF-equivalent: 3.1 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: Song B, Maurya H, Khalighi S, Aqeel A, Corredor G, Hammouda K, et al. Computational image features of immune architecture reveal molecular patterns associated with outcome and risk of recurrence in microsatellite-stable or MSI-low colon cancers. Eur J Cancer. 2026 Dec;249:117810. doi:10.1016/j.ejca.2026.117810. PMID: 42966031.Checked: Abstract only