Longitudinal profiling identifies co-occurring BRCA1/2 reversions, TP53BP1, RIF1 and PAXIP1 mutations in PARP inhibitor-resistant advanced breast cancer
Bibliographic record
Abstract
BACKGROUND: Resistance to therapies that target homologous recombination deficiency (HRD) in breast cancer limits their overall effectiveness. Multiple, preclinically validated, mechanisms of resistance have been proposed, but their existence and relative frequency in clinical disease are unclear, as is how to target resistance. PATIENTS AND METHODS: Longitudinal mutation and methylation profiling of circulating tumour (ct)DNA was carried out in 47 patients with metastatic BRCA1-, BRCA2- or PALB2-mutant breast cancer treated with HRD-targeted therapy who developed progressive disease-18 patients had primary resistance and 29 exhibited response followed by resistance. ctDNA isolated at multiple time points in the patient treatment course (before, on-treatment and at progression) was sequenced using a novel >750-gene intron/exon targeted sequencing panel. Where available, matched tumour biopsies were whole exome and RNA sequenced and also used to assess nuclear RAD51. RESULTS: BRCA1/2 reversion mutations were present in 60% of patients and were the most prevalent form of resistance. In 10 cases, reversions were detected in ctDNA before clinical progression. Two new reversion-based mechanisms were identified: (i) intragenic BRCA1/2 deletions with intronic breakpoints; and (ii) intragenic BRCA1/2 secondary mutations that formed novel splice acceptor sites, the latter being confirmed by in vitro minigene reporter assays. When seen before commencing subsequent treatment, reversions were associated with significantly shorter time to progression. Tumours with reversions retained HRD mutational signatures but had functional homologous recombination based on RAD51 status. Although less frequent than reversions, nonreversion mechanisms [loss-of-function (LoF) mutations in TP53BP1, RIF1 or PAXIP1] were evident in patients with acquired resistance and occasionally coexisted with reversions, challenging the notion that singular resistance mechanisms emerge in each patient. CONCLUSIONS: These observations map the prevalence of candidate drivers of resistance across time in a clinical setting, information with implications for clinical management and trial design in HRD breast cancers.
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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 | 0.000 | 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".