Association between somatic microsatellite instability, hypermutation status, and specific T cell subsets in colorectal cancer tumors
Bibliographic record
Abstract
Background Microsatellite instability-high (MSI-high) tumors comprise ~15% of sporadic colorectal cancers (CRC) and are associated with elevated T cell infiltration. However, the universality of this response across T cell subtypes with distinct functions is unknown. Methods Including 1,236 CRC tumors from three observational studies, we conducted in-situ T cell profiling using a customized 9-plex (CD3, CD4, CD8, CD45RA, CD45RO, FOXP3, KRT, MKI67, and DAPI) multispectral immunofluorescence assay. MSI status was assessed through polymerase chain reaction or immunohistochemical assays. We used multivariable ordinal logistic regression to estimate odds ratios (OR per increasing quantile) and 95% confidence intervals (CIs) for the association of MSI status with quantiles of T cell densities in either tumor epithelial or stromal tissue areas. Results Compared to microsatellite instability low or microsatellite stable (MSI-low/MSS) tumors, MSI-high status was associated with higher density for the majority of immune subsets (twelve out of eighteen) in both epithelial and stromal tissue areas. The strongest associations were for CD3+CD8+ T cells in epithelial areas [OR (95% CI) for naive, memory, and regulatory subsets = 3.49 (2.57, 4.75); 2.82 (2.10, 3.78); 3.04 (2.24, 4.13), respectively]. Conversely, stromal area CD3+CD4+ memory T cells were inversely associated [OR (95% CI) = 0.68 (0.51, 0.91)]. Discussion MSI-high status was strongly associated with higher densities of most T cell subsets in both epithelial and stromal tissue areas. Our investigation supports efforts to identify patients who may be more likely to respond to current immunotherapy treatments. Significance This study helps us better understand how a clinically relevant tumor phenotype, microsatellite instability status, is related to different functioning T cell densities in colorectal tumors, which may impact future immunotherapy strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".