Consensus molecular subtypes in colorectal cancer differ by geographic region.
Bibliographic record
Abstract
4061 Background: The consensus molecular subtypes (CMS) have emerged as a novel classification in colorectal cancer (CRC). However, these subtypes, were mostly derived from a US/European population, and have scant data in other ethnic groups. This study aimed to demonstrate molecular subtypes of CRC across geographic regions. Methods: Formalin fixed paraffin embedded (FFPE) tissue from untreated patients with stage II-III colon cancer from Brazil, Canada, Mexico, Thailand, and the US were evaluated. Gene expression profiling was performed at the University of Texas MD Anderson Cancer Center using NanoString’s nCounter technology and an optimized classifier for FFPE. Results: A total of 366 samples were included in this study, evenly distributed between the 5 international sites. While the US population matched previously reported distributions, the distribution of CMS subtypes varied substantially by region (P < 0.0001). While CMS1 was still associated with right-sided tumors (P < 0.001) and deficient mismatch repair (dMMR) (P < 0.001), the prevalence varied between 8% in Brazil to 30% in Mexico. CMS2 was found vary from 14% in Mexico to 47% in Brazil. The metabolic CMS3 subtype was present in only 3% in Thailand, but as high as 19% in Brazil. CMS4 was confirmed to be associated with higher stage (P = 0.047), and the prevalence was lowest in Brazil (14%) compared to 44% and 49% in US and Mexico, respectively. Expansion of study cohort is ongoing. Conclusions: CMS subtype prevalence differs substantially by geographic region in CRC. These variations suggest that transcriptomic-defined disease biology in international populations may be more heterogeneous than previously appreciated. Further studies in global populations are required to validate and extend these findings, which may have important impact for novel therapeutic development.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".