Abstract A11: NGS-based tumor genomic profiling to identify ovarian cancer patients who benefit from the PARP inhibitor rucaparib.
Bibliographic record
Abstract
Abstract Background: PARP inhibitors (PARPi) are synthetically lethal to tumor cells with homologous recombination deficiency (HRD). HRD can result from deleterious BRCA1/2 mutations (BRCAmut) or other mechanisms that have not been fully elucidated. Regardless of mechanism, HRD leads to a common phenotype of genome-wide loss of heterozygosity (LOH). It has been hypothesized that this genomic phenotype can be used to identify BRCA wild-type (BRCAwt) HRD tumors likely sensitive to PARPi. Using comprehensive next generation sequencing (NGS)-based tumor genomic profiling, we developed an HRD assay for potential use as a companion diagnostic for rucaparib in high-grade ovarian cancer (HGOC) by combining tumor BRCA1/2 status and quantification of genomic LOH. Methods: In the phase 2 study ARIEL2 Part 1 (NCT01891344), pre-treatment screening biopsies and archival formalin-fixed paraffin embedded tumor specimens were profiled using Foundation Medicine's NGS-based HRD assay, which detects all classes of genomic alterations, including base substitutions, insertions/deletions, and homozygous deletions in BRCA1/2. Genomic LOH was assessed by sequencing >3,500 evenly-distributed single nucleotide polymorphisms across the genome and quantifying the extent of genomic LOH. A pre-specified genomic LOH cutoff was determined using publicly available SNP array data of ovarian tumors to predict platinum sensitivity as a surrogate marker for PARPi sensitivity. Response was assessed by RECIST v1.1 and GCIG CA-125 response criteria. Results: As of July 1 2015, 195 archival tumor and 152 screening biopsy samples (142 matched pairs) from 206 HGOC patients enrolled (204 patients treated) in ARIEL2 Part 1 were successfully profiled using the NGS-based HRD assay. Some screening biopsies were not suitable for successful NGS-based HRD assessment primarily because of insufficient tumor nuclei or inadequate tumor volume. Most matched pairs of archival and pre-trial screening samples exhibited similar genomic LOH profiles (r=0.86); however, 14% of screening samples had higher genomic LOH compared with archival samples collected more than one year earlier. All BRCA1/2 germline and somatic mutated tumors had high genomic LOH in the screening samples. Receiver operating characteristic analysis of genomic LOH showed utility in identifying RECIST/CA-125 responders to rucaparib (AUC=0.72, p<1e-4), with slightly better predictive utility using screening samples compared to archival samples (AUC=0.72 vs 0.69). Using the pre-specified genomic LOH cutoff, high genomic LOH tumors were detected in 54% of evaluable BRCAwt patients; significantly different overall response rates were found in patients with high vs low genomic LOH tumors (48% vs 26%; chi-square p=0.0074). Conclusions: We developed an NGS-based HRD assay that assesses tumor BRCA1/2 and genomic LOH to prospectively identify HGOC patients who may benefit from rucaparib treatment. The optimized NGS-based HRD assay will be prospectively tested in the ongoing portion of the phase 2 study (ARIEL2 Part 2, NCT01891344) and a phase 3 maintenance study (ARIEL3, NCT01968213) that will investigate rucaparib in HGOC. Citation Format: Iain A. McNeish, Kevin K. Lin, James X. Sun, Sandra Goble, Amit Oza, Robert L. Coleman, Clare L. Scott, Gottfried Konecny, Anna V. Tinker, David M. O'Malley, Rebecca Kristeleit, Ling Ma, James D. Brenton, Katherine Bell-McGuinn, Ana Oaknin, Alexandra Leary, Elaina Mann, Heidi Giordano, Roman Yelensky, Mitch Raponi, Elizabeth Swisher. NGS-based tumor genomic profiling to identify ovarian cancer patients who benefit from the PARP inhibitor rucaparib. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; Oct 17-20, 2015; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(2 Suppl):Abstract nr A11.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".