Intratumor morphologic and transcriptomic heterogeneity in V600EBRAF-mutated metastatic colorectal adenocarcinomas
Bibliographic record
Abstract
BACKGROUND: Intratumor heterogeneity (ITH) is described as the presence of various clones within one tumor, each with their own unique features in terms of morphology, inflammation, genetics or transcriptomics. Heterogeneity provides the fuel for drug resistance; therefore, an accurate assessment of tumor heterogeneity is essential for the development of effective therapies. The purpose of this study was to dissect morphologic and molecular ITH in colorectal adenocarcinoma. MATERIALS AND METHODS: BRAFmt) consecutive metastatic colorectal adenocarcinomas was assessed for morphologic heterogeneity. The two heterogeneous components of each specimen underwent a histopathological, immunohistochemical and molecular characterization to evaluate: histologic variant, grading, tumor-infiltrating lymphocytes (TILs), mismatch repair proteins' expression, KRAS/BRAF/NRAS mutations, microsatellite instability (MSI) status and consensus molecular subtype (CMS). RESULTS: BRAFmt primary colorectal adenocarcinomas presented a heterogeneous morphology. Among these, eight cases had adequate material for molecular profiling. Five out of the eight (62.5%) cases resulted instable at MSI testing. The majority (62.5%) of the samples showed a CMS4 phenotype based on gene expression profiling. Heterogeneity in CMS classification was observed in four out of eight cases. One out of eight cases presented significant heterogeneity in the number of TILs between the two components of the tumor. CONCLUSIONS: BRAFmt adenocarcinoma cases in our small series and might contribute to variability in response to anticancer therapy and clinical outcomes. Assessment of morphological and molecular ITH is needed to improve colorectal cancer classification and to tailor anticancer treatments and should be included in the pathology report.
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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 | 0.000 |
| 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".