A Machine Learning–Based Scoring System to Identify High Immunoactivity Microsatellite Stability Tumors by Quantifying Similarity to Microsatellite Instability-High Tumors in Colorectal Cancers: Development and Quantitative Study
Bibliographic record
Abstract
Background: Microsatellite stability (MSS) colorectal cancers (CRCs) have a limited response to immune checkpoint inhibitors (ICIs) compared to microsatellite instability-high (MSI-H) CRCs. Nevertheless, previous studies have shown that some MSS CRCs are sensitive to ICIs, although established criteria for treatment justification are still lacking. Objective: This study aimed to test the tumor-infiltrating lymphocyte (TIL) features of MSS and develop a novel computational tool for the similarity prediction between MSS and MSI-H status in patients with CRC based on multiple factors. Methods: We collected and analyzed data from 188 patients with CRC, including MSI status, immune cell distributions, clinical features, and gene mutations, using statistical methods and Cox regression. An ensemble machine learning-based MSI-H score was developed using stacked extreme gradient boosting classifiers to quantify the similarity of patient data to MSI-H data based on immune cell distributions, clinical features, and gene mutations. The model was robust and could address missing input data for immune cell distributions and gene mutations. Results: The scorer performed well (mean Cohen κ of 0.40, SD 0.05, over 10 random seeds) in identifying MSI-H-like MSS samples with TIL distributions similar to genuine MSI-H CRCs. No significant difference was observed between the TIL features of MSI-H-like MSS CRCs and MSI-H CRCs. The disparity between MSI-H-like MSS CRCs and MSS CRCs potentially lies in the T regulatory cells (P=.09) and macrophage (P=.16) populations within the tumor stromal region. Conclusions: Some patients with MSS CRC presented similar immune cell distributions with high immunoactivity compared to patients with MSI-H CRC. The MSI-H score serves as a metric to quantify the similarity of MSS CRCs to MSI-H CRCs and presents a promising avenue for more personalized and effective cancer immunotherapy treatment, offering a clinical reference for potential ICI targets in MSS CRCs.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".