Intra- and Inter-Tumor Heterogeneity of BRAFV600EMutations in Primary and Metastatic Melanoma
Bibliographic record
Abstract
The rationale for using small molecule inhibitors of oncogenic proteins as cancer therapies depends, at least in part, on the assumption that metastatic tumors are primarily clonal with respect to mutant oncogene. With the emergence of BRAF(V600E) as a therapeutic target, we investigated intra- and inter-tumor heterogeneity in melanoma using detection of the BRAF(V600E) mutation as a marker of clonality. BRAF mutant-specific PCR (MS-PCR) and conventional sequencing were performed on 112 tumors from 73 patients, including patients with matched primary and metastatic specimens (n = 18). Nineteen patients had tissues available from multiple metastatic sites. Mutations were detected in 36/112 (32%) melanomas using conventional sequencing, and 85/112 (76%) using MS-PCR. The better sensitivity of the MS-PCR to detect the mutant BRAF(V600E) allele was not due to the presence of contaminating normal tissue, suggesting that the tumor was comprised of subclones of differing BRAF genotypes. To determine if tumor subclones were present in individual primary melanomas, we performed laser microdissection and mutation detection via sequencing and BRAF(V600E)-specific SNaPshot analysis in 9 cases. Six of these cases demonstrated differing proportions of BRAF(V600E)and BRAF(wild-type) cells in distinct microdissected regions within individual tumors. Additional analyses of multiple metastatic samples from individual patients using the highly sensitive MS-PCR without microdissection revealed that 5/19 (26%) patients had metastases that were discordant for the BRAF(V600E) mutation. In conclusion, we used highly sensitive BRAF mutation detection methods and observed substantial evidence for heterogeneity of the BRAF(V600E) mutation within individual melanoma tumor specimens, and among multiple specimens from individual patients. Given the varied clinical responses of patients to BRAF inhibitor therapy, these data suggest that additional studies to determine possible associations between clinical outcomes and intra- and inter-tumor heterogeneity could prove fruitful.
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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 | 0.000 | 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".