Whole-genome sequencing reveals complex mechanisms of intrinsic resistance to BRAF inhibition
Bibliographic record
Abstract
BACKGROUND: BRAF is mutated in ∼42% of human melanomas (COSMIC. http://www.sanger.ac.uk/genetics/CGP/cosmic/) and pharmacological BRAF inhibitors such as vemurafenib and dabrafenib achieve dramatic responses in patients whose tumours harbour BRAF(V600) mutations. Objective responses occur in ∼50% of patients and disease stabilisation in a further ∼30%, but ∼20% of patients present primary or innate resistance and do not respond. Here, we investigated the underlying cause of treatment failure in a patient with BRAF mutant melanoma who presented primary resistance. METHODS: We carried out whole-genome sequencing and single nucleotide polymorphism (SNP) array analysis of five metastatic tumours from the patient. We validated mechanisms of resistance in a cell line derived from the patient's tumour. RESULTS: We observed that the majority of the single-nucleotide variants identified were shared across all tumour sites, but also saw site-specific copy-number alterations in discrete cell populations at different sites. We found that two ubiquitous mutations mediated resistance to BRAF inhibition in these tumours. A mutation in GNAQ sustained mitogen-activated protein kinase (MAPK) signalling, whereas a mutation in PTEN activated the PI3 K/AKT pathway. Inhibition of both pathways synergised to block the growth of the cells. CONCLUSIONS: Our analyses show that the five metastases arose from a common progenitor and acquired additional alterations after disease dissemination. We demonstrate that a distinct combination of mutations mediated primary resistance to BRAF inhibition in this patient. These mutations were present in all five tumours and in a tumour sample taken before BRAF inhibitor treatment was administered. Inhibition of both pathways was required to block tumour cell growth, suggesting that combined targeting of these pathways could have been a valid therapeutic approach for this patient.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".