Colorectal adenosquamous carcinoma: genomic profiling of a rare histotype of colorectal cancer
Bibliographic record
Abstract
Abstract Colorectal adenosquamous carcinoma (ASC) is exceedingly rare, comprising less than 0.1% of all colorectal malignancies, and is characterized by an aggressive disease course, with a higher metastatic rate and worse outcome than conventional colorectal adenocarcinoma. A comprehensive molecular profile of this group of neoplasms is still lacking. A total of 22 cases of colorectal ASCs (with 22 primary lesions and 7 metastases matched with 4 primaries) were subject to NGS targeting 67 cancer-related genes (VariantPlex solid tumor; Archer). Mismatch repair (MMR), p53, and V600EBRAF status were also investigated by immunohistochemistry. In 28 of 29 (96.6%) ASC samples, at least one single-nucleotide variant (SNV) or copy number variation (CNV) was detected. Among the 22 primary tumors, the most frequently mutated genes were TP53 (59.1%), APC (40.9%), KRAS (27.3%), BRAF (13.6%), and GNAS (9.1%). Only 1/22 (4.5%) primary ASC was MMR-deficient (MMRd) and harbored a BRAF mutation. Limited differences in SNVs were observed between primary and metastatic diseases. This study sheds light on the molecular landscape of colorectal ASCs. According to our data, the genomic profile of colorectal ASC is similar to that of conventional colorectal carcinoma, with significant druggable genetic alterations. Further studies are required to understand the more aggressive clinical behavior of this neoplasm.
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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".