Comprehensive baseline ctDNA characterization in two biomarker-selected Phase 1/2 studies using genomic and methylation profiling
Bibliographic record
Abstract
ABSTRACT The development of DNA damage response (DDR)-directed therapies is a major area of clinical investigation, yet to date Poly (ADP-ribose) polymerase (PARP) inhibitors remain the only approved therapy in this space. Major challenges to DDR-targeted therapies in the post-PARPi era are the context dependency of DDR alterations and the presence of pre-existing resistance in this heavily pre-treated population. To that end, we used a contemporary platform to analyze pre-treatment circulating tumor DNA (ctDNA) samples from 173 patients enrolled onto two Phase 1/2 trials harboring pathogenic variants (PVs) in DDR genes. Baseline ctDNA analysis revealed a wealth of insights, including circulating tumor fraction estimation, impact of clonal hematopoiesis, PV allelic status, homologous recombination deficiency (HRD) signatures and presence of pre-existing resistance. HRD reversions were detected in 44% of evaluable patients and included large genomic rearrangements leading to deletion of whole or partial exons. We also discovered reversion of ATM in two patients previously treated with platinum chemotherapy, which has not previously been described. This study showcases the genomic complexity of DDR-altered tumors, revealed through baseline ctDNA profiling, an understanding of which is crucial for the future clinical development of novel DDR-directed therapies and combinations.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".