Histology‐based molecular profiling improves mutation detection for advanced thyroid cancer
Bibliographic record
Abstract
Advanced cancers frequently show histologic and molecular intratumoral heterogeneity. Therefore, we comprehensively characterized advanced, metastatic, radioiodine-resistant (RAIR) thyroid carcinomas at the molecular level in the context of histologic heterogeneity with the aim to identify potentially actionable mutations that may guide the use of specific tyrosine kinase inhibitor (TKI) treatment. Whole exome sequencing (WES) was applied to 29 macrodissected tissue samples of histologically heterogeneous and homogeneous areas, lymph node and lung metastases from six clinically and histologically well-characterized metastatic RAIR thyroid cancer patients with structural incomplete response to treatment. WES data were analyzed to identify potential driver mutations in oncogenic pathways, copy number alterations, microsatellite instability, mutant-allele tumor heterogeneity, and the relevance of histologic heterogeneity to molecular profiling. In addition to known driver mutations in BRAF, NRAS, EIF1AX, NCOA4-RET, and TERT, further potentially actionable drivers were identified in AKT1, ATM, E2F1, HTR2A, and MLH3. The analysis of the evolutionary history of the mutations and the reconstruction of the molecular phylogeny of the cancers show a remarkable association between histologic and molecular heterogeneity. A comprehensive molecular analysis of the primary tumor guided by histologic analysis may help to better stratify patients for precision medicine approaches. Given the association between the molecular and the histologic heterogeneity, the selection of tumor samples for molecular analysis should be based on meticulous histologic evaluation of the entire tumor.
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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".