High tumor mutation burden (TMB) in microsatellite stable (MSS) colorectal cancers: Diverse molecular associations point to variable pathophysiology
Bibliographic record
Abstract
BACKGROUND: Colorectal cancers with defects in the Mismatch Repair (MMR) system represent a minority of the disease. MMR defective cancers are characterized by high Tumor Mutation Burden (TMB) and are sensitive to immunotherapy with immune checkpoint inhibitors. In contrast, the majority of colorectal cancers are MMR proficient (Microsatellite Stable, MSS) and display a low TMB. However, a few of these MSS cancers have high TMB. METHODS: Published genomic studies of colorectal cancers were examined to identify cases profiled as MSS and having a TMB above 10 mutations / Mb. Data from four studies detailed in the cBioportal for cancer genomics site and providing data on MSI status were examined. RESULTS: In the MSK study of metastatic colorectal cancers, 7.5% of patients with MSS tumors had a high TMB of more than 10 mutations/ Mb. The MSK study of localized rectal cancers showed that 9.5% of patients with MSS tumors had a high TMB. The DFCI cohort included 10 patients with TMB above 10 mutations/ Mb characterized as MSS and not having MMR or proofreading polymerases mutations. Mutations in genes encoding for proteins of the KRAS pathways were more frequent in MSS tumors with high TMB than in counterparts with low TMB. Moreover, genes involved in DNA damage response and in epigenetic regulations were more frequently mutated in MSS colorectal cancers with high TMB. CONCLUSION: Alterations of the KRAS signal transduction pathways, DDR gene mutations and epigenetic modifier mutations may contribute to increase mutation burden in subsets of MSS colorectal cancers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".