Abstract GMM-048: CTDNA RESPONSE TO THE PARP INHIBITOR RUCAPARIB PREDICTS PROGRESSION-FREE SURVIVAL AND BEST OVERALL RESPONSE ON THE ARIEL2 TRIAL
Bibliographic record
Abstract
Abstract BACKGROUND: High grade serous ovarian carcinoma (HGSOC) is characterized by ubiquitous TP53 mutation and has the highest genomic complexity of all epithelial neoplasms. Sensitivity to PARP inhibitor therapy is strongly associated with homologous recombination deficiency (HRD). Genomic biomarkers of HRD such as genome-wide loss of heterozygosity (LOH) scores predict HRD and response to rucaparib. We hypothesized that functional testing of response during PARP inhibitor treatment using changes in circulating tumour DNA (ctDNA) could improve prediction of patient outcome. We tested whether the change in ctDNA TP53 mutant allele fraction (MAF) after one cycle of rucaparib treatment was predictive of progression free survival (PFS) and response in patients from the phase 2 ARIEL2 trial in women with platinum-sensitive recurrent high grade ovarian cancer (NCT01891344). METHODS: We analyzed serial plasma samples (n = 636) from 142 HGSOC patients during screening, on day 1 of each treatment cycle, and at the end of rucaparib treatment. Targeted amplicon deep sequencing (TADS) of TP53 was performed on DNA extracted from plasma (median depth 6916×). Somatic TP53 mutation and loss of heterozygosity score (LOH) were available from archival and biopsy specimens. Statistical analyses were pre-specified and ctDNA analysis was carried out blinded to visit and response data. TP53 MAF changes after one cycle of treatment were compared with PFS and best overall response assessed by RECIST v1.1 and GCIG CA-125 criteria. Optimal cut points for ctDNA response were determined using a cross-validation analysis. In cases with >1 TP53 mutation, response assessment was performed using the mutation with highest MAF. RESULTS: We detected TP53 mutations in plasma from 134 patients; all cases were concordant between tumour and plasma except for one patient (present in plasma but not tumour). In 18 patients (13%), 2 or more TP53 mutations were detected in ctDNA. The median TP53 MAF prior to cycle 1 was 2.6% (IQR 0.3–8.6). Reduction of >70% of TP53 MAF in ctDNA between cycle 1 and 2 was significantly predictive of improved PFS (n = 97; HR = 0.53, 95% CI 0.34-0.85, p = 0.008, median 273 vs 158 days, sensitivity 76%, specificity 62%) and best overall response (n = 97; OR = 7.04, 95% CI 2.69–21.06, p < 0.001). Combining ctDNA and LOH scores did not improve prediction of response. CONCLUSIONS Response measured by >70% fall in TP53 ctDNA between pre-cycle 1 and pre-cycle 2 of rucaparib therapy was significantly associated with best overall response and improved PFS. Similar findings were observed in a retrospective study of recurrent HGSOC treated with standard of care chemotherapy. The pathological or genomic factors causing multiple TP53 mutations in ctDNA are unknown. The association between early fall in ctDNA and validated RECIST and CA-125 response markers provides strong evidence that ctDNA may have utility for detecting early response to targeted therapy. Further analyses in randomized studies should be performed to confirm that ctDNA response has strong predictive value. Citation Format: Anna Piskorz, David Robertson, Kevin K. Lin, James Morris, Elaina Mann, Amit Oza, Robert L. Coleman, David M. O'Malley, Michael Friedlander, Janiel M. Cragun, Ling Ma, Heidi Giordano, Iain A. McNeish, Elizabeth Swisher, James Wason, James D. Brenton. CTDNA RESPONSE TO THE PARP INHIBITOR RUCAPARIB PREDICTS PROGRESSION-FREE SURVIVAL AND BEST OVERALL RESPONSE ON THE ARIEL2 TRIAL [abstract]. In: Proceedings of the 12th Biennial Ovarian Cancer Research Symposium; Sep 13-15, 2018; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2019;25(22 Suppl):Abstract nr GMM-048.
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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) | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".