Mutational Profiles Reveal an Aberrant TGF-β-CEA Regulated Pathway in Colon Adenomas
Bibliographic record
Abstract
Mutational processes and signatures that drive early tumorigenesis are centrally important for early cancer prevention. Yet, to date, biomarkers and risk factors for polyps (adenomas) that inordinately and rapidly develop into colon cancer remain poorly defined. Here, we describe surprisingly high mutational profiles through whole-genome sequence (WGS) analysis in 2 of 4 pairs of benign colorectal adenoma tissue samples. Unsupervised hierarchical clustered transcriptomic analysis of a further 7 pairs of adenomas reveals distinct mutational signatures regardless of adenoma size. Transitional single nucleotide substitutions of C:G>T:A predominate in the adenoma mutational spectrum. Strikingly, we observe mutations in the TGF-β pathway and CEA-associated genes in 4 out of 11 adenomas, overlapping with the Wnt pathway. Immunohistochemical labeling reveals a nearly 5-fold increase in CEA levels in 23% of adenoma samples with a concomitant loss of TGF-β signaling. We also define a functional role by which the CEA B3 domain interacts with TGFBR1, potentially inactivating the tumor suppressor function of TGF-β signaling. Our study uncovers diverse mutational processes underlying the transition from early adenoma to cancer. This has broad implications for biomarker-driven targeting of CEA/TGF-β in high-risk adenomas and may lead to early detection of aggressive adenoma to CRC progression.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".