Identifying Mechanisms of Resistance by Circulating Tumor DNA in EVOLVE, a Phase II Trial of Cediranib Plus Olaparib for Ovarian Cancer at Time of PARP Inhibitor Progression
Bibliographic record
Abstract
PURPOSE: To evaluate the use of blood cell-free DNA (cfDNA) to identify emerging mechanisms of resistance to PARP inhibitors (PARPi) in high-grade serous ovarian cancer (HGSOC). EXPERIMENTAL DESIGN: We used targeted sequencing (TS) to analyze 78 longitudinal cfDNA samples collected from 30 patients with HGSOC enrolled in a phase II clinical trial evaluating cediranib (VEGF inhibitor) plus olaparib (PARPi) after progression on PARPi alone. cfDNA was collected at baseline, before treatment cycle 2, and at end of treatment. These were compared with whole-exome sequencing (WES) of baseline tumor tissues. RESULTS: At baseline (time of initial PARPi progression), cfDNA tumor fractions were 0.2% to 67% (median, 3.25%), and patients with high ctDNA levels (>15%) had a higher tumor burden (sum of target lesions; P = 0.043). Across all timepoints, cfDNA detected 74.4% of mutations known from prior tumor WES, including three of five expected BRCA1/2 reversion mutations. In addition, cfDNA identified 10 novel mutations not detected by WES, including seven TP53 mutations annotated as pathogenic by ClinVar. cfDNA fragmentation analysis attributed five of these novel TP53 mutations to clonal hematopoiesis of indeterminate potential (CHIP). At baseline, samples with significant differences in mutant fragment size distribution had shorter time to progression (P = 0.001). CONCLUSIONS: Longitudinal testing of cfDNA by TS provides a noninvasive tool for detection of tumor-derived mutations and mechanisms of PARPi resistance that may aid in directing patients to appropriate therapeutic strategies. With cfDNA fragmentation analyses, CHIP was identified in several patients and warrants further investigation.
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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 | 0.000 | 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 | 0.001 |
| 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".