Hotspot Mutation Panel Testing Reveals Clonal Evolution in a Study of 265 Paired Primary and Metastatic Tumors
Bibliographic record
Abstract
PURPOSE: We used a clinical next-generation sequencing (NGS) hotspot mutation panel to investigate clonal evolution in paired primary and metastatic tumors. EXPERIMENTAL DESIGN: A total of 265 primary and metastatic tumor pairs were sequenced using a 46-gene cancer mutation panel capable of detecting one or more single-nucleotide variants as well as small insertions/deletions. Mutations were tabulated together with tumor type and percentage, mutational variant frequency, time interval between onset of primary tumor and metastasis, and neoadjuvant therapy status. RESULTS: Of note, 227 of 265 (85.7%) tumor metastasis pairs showed identical mutation calls. Of the tumor pairs with identical mutation calls, 160 (60.4%) possessed defining somatic mutation signatures and 67 (25.3%) did not exhibit any somatic mutations. There were 38 (14.3%) cases that showed at least one novel mutation call between the primary and metastasis. Metastases were almost two times more likely to show novel mutations (n = 20, 7.5%) than primary tumors (n = 12, 4.5%). TP53 was the most common additionally mutated gene in metastatic lesions, followed by PIK3CA and SMAD4. PIK3CA mutations were more often associated with metastasis in colon carcinoma samples. CONCLUSIONS: Clinical NGS hotspot panels can be useful in analyzing clonal evolution within tumors as well as in determining subclonal mutations that can expand in future metastases. PIK3CA, SMAD4, and TP53 are most often involved in clonal divergence, providing potential targets that may help guide the clinical management of tumor progression or metastases.
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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 | 0.001 | 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".