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Feasibility of monitoring response to the PARP inhibitor rucaparib with targeted deep sequencing of circulating tumor DNA (ctDNA) in women with high-grade serous carcinoma on the ARIEL2 trial.

2016· article· en· W2523570479 on OpenAlexaff
Anna Piskorz, Kevin Lin, James Anthony Morris, Elaina Mann, Amit M. Oza, Robert L. Coleman, David M. O’Malley, Michael Friedländer, Janiel M. Cragun, Ling Ma, Heidi Giordano, Mitch Raponi, Iain A. McNeish, Elizabeth M. Swisher, James D. Brenton

Bibliographic record

VenueJournal of Clinical Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineInternal medicineOncologyPARP inhibitorResponse Evaluation Criteria in Solid TumorsSerous fluidCancerCancer researchProgressive diseaseGeneChemotherapyGeneticsBiologyPoly ADP ribose polymerase

Abstract

fetched live from OpenAlex

5549 Background: TP53 mutations are present in > 97% cases of high-grade serous ovarian cancer (HGSOC). Detection of TP53 mutations in ctDNA extracted from plasma has the potential to monitor disease course and treatment response. We have developed targeted amplicon deep sequencing (TADS) to detect low frequency mutations throughout the TP53 gene in ctDNA. Rucaparib is a potent PARP inhibitor in development for treatment of tumors with HR pathway deficiency. We used TADS to assess TP53 mutant allele fraction (MAF) in ctDNA from patients in ARIEL2, a phase II study of rucaparib for treatment of relapsed HGSOC (NCT01891344). Methods: Plasma samples (n = 65) were collected from 18 patients during screening, on day 1 of each cycle, and at the end of rucaparib treatment. DNA extracted from plasma underwent TADS of TP53 (median depth 6916×). FFPE tumor specimens were profiled using an NGS-based assay with a targeted gene panel including TP53. Response was assessed by RECISTv1.1 and GCIG CA-125 criteria. Results: Concordant TP53 mutations were detected in tumor and ctDNA from plasma of all 18 patients. Median TP53 MAF at screening and cycle 1 day 1 was 5.1% (IQR 1.1–17.5, n = 16) and 3.8% (IQR 0.68–10.3, n = 16), respectively. 14 patients were evaluable for response between cycle 1 and 2 (missing sample n = 2; TP53 MAF < 0.5% n = 2). 7/9 patients with > 50% reduction of TP53 MAF in ctDNA at cycle 2 achieved a RECIST PR (see Table); this included 5/6 patients with either a germline or somatic mutation in BRCA1/BRCA2. No patients with < 50% reduction at cycle 2 (n = 5) achieved RECIST response. Conclusions: Noninvasive detection of TP53 mutations by TADS is feasible, using plasma samples collected from women with relapsed platinum-sensitive HGSOC participating in an international multicenter trial. ctDNA is a promising biomarker for monitoring response to the PARP inhibitor rucaparib and will now be explored in all ARIEL2 subjects. Clinical trial information: NCT01891344.% TP53 MAF change C1-C2 Mutation status* RECIST Response −100 gBRCA1 PR −100 WT PR −96 gBRCA2 PR −88 sBRCA2 PR −80 gBRCA1 PR −74 WT PR −74 WT SD −66 WT SD −54 gBRCA1 PR −39 WT PD −18 WT SD −11 WT PD +86 WT PD +188 sBRCA1 SD *g, germline; s, somatic

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.155
GPT teacher head0.421
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations14
Published2016
Admission routes1
Has abstractyes

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