Genomic and Epigenomic ctDNA Profiling in Liquid Biopsies from Heavily Pretreated Patients with DNA Damage Response–Deficient Tumors
Bibliographic record
Abstract
PURPOSE: The development of DNA damage response (DDR)-directed therapies is a major area of clinical investigation; however, to date, PARP inhibitors (PARPi) remain the only approved therapy in this space. Major challenges to DDR-targeted therapies in the post-PARPi therapy era are the context dependency of DDR alterations and the presence of preexisting resistance in this heavily pretreated population. Blood samples from patients with tumors harboring defects in DDR genes were used to evaluate the feasibility of a liquid biopsy platform to detect complex genomic events such as BRCA1/2 reversions, homologous recombination deficiency (HRD) signatures, pathogenic variant allele status, and differentially methylated regions for accurate quantitation of tumor fraction. EXPERIMENTAL DESIGN: Pretreatment ctDNA samples from 173 patients enrolled in two phase 1/2 clinical trials (TRESR; NCT04497116 and ATTACC; NCT04972110) were selected for analysis. RESULTS: In a phase I heavily pretreated patient population with DDR defects, complex genomic alterations (HRD, biallelic loss, and complex reversions) that historically require tumor tissue biopsies could be detected in ctDNA. Within the cohort of BRCA-associated tumor types previously treated with PARPi or platinum therapy, HRD reversions were detected in 44% of evaluable patients and included large genomic rearrangements leading to deletion of whole or partial exons which have been underrepresented in the literature because of technological limitations. CONCLUSIONS: This study showcases the genomic complexity of DDR-altered tumors as 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 | 0.000 | 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) | 0.002 | 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".