Discordant prognosis of mismatch repair deficiency in colorectal and endometrial cancer reflects variation in antitumour immune response and immune escape
Bibliographic record
Abstract
Abstract Defective DNA mismatch repair (dMMR) causes elevated tumour mutational burden (TMB) and microsatellite instability (MSI) in multiple cancer types. dMMR/MSI colorectal cancers (CRCs) have enhanced T‐cell infiltrate and favourable outcome; however, this association has not been reliably detected in other tumour types, including endometrial cancer (EC). We sought to confirm this and explore the underpinning mechanisms. We first meta‐analysed CRC and EC trials that have examined the prognostic value of dMMR/MSI and confirmed that dMMR/MSI predicts better prognosis in CRC, but not EC, with statistically significant variation between cancers (hazard ratio [HR] = 0.63, 95% confidence interval [CI] = 0.54–0.73 versus HR = 1.15, 95% CI = 0.72–1.58; P INT = 0.02). Next, we studied intratumoural immune infiltrate in CRCs and ECs of defined MMR status and found that while dMMR was associated with increased density of tumour‐infiltrating CD3 + and CD8 + T‐cells in both cancer types, the increases were substantially greater in CRC and significant only in this group ( P INT = 4.3e‐04 and 7.3e‐03, respectively). Analysis of CRC and EC from the independent Cancer Genome Atlas (TCGA) series revealed similar variation and significant interactions in proportions of tumour‐infiltrating lymphocytes, CD8 + , CD4 + , NK cells and immune checkpoint expression, confirming a more vigorous immune response to dMMR/MSI in CRC than EC. Agnostic analysis identified the IFNγ pathway activity as strongly upregulated by dMMR/MSI in CRC, but downregulated in EC by frequent JAK1 mutations, the impact of which on IFNγ response was confirmed by functional analyses. Collectively, our results confirm the discordant prognosis of dMMR/MSI in CRC and EC and suggest that this relates to differences in intratumoural immune infiltrate and tumour genome. Our study underscores the need for tissue‐specific analysis of cancer biomarkers and may help inform immunotherapy use. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".