MétaCan
Menu
← Back to cohort

Abstract PS06-04: The prognostic and predictive impact of circulating tumour DNA (ctDNA) dynamics in patients with metastatic Triple Negative Breast Cancer (TNBC) on olaparib based therapy: Results from Cohort E of the PlasmaMATCH trial

2024· article· en· W4396591725 on OpenAlexaff
Iseult Browne, Javier Pascual, Rosalind Cutts, Belinda Kingston, Sarah Hrebien, Lucy Kilburn, Alex Pearson, Laura Moretti, Andrew Wardley, Iain R. Macpherson, Richard D. Baird, Rebecca Roylance, Iris Faull, Kimberly C. Banks, Isaac García-Murillas, Judith M. Bliss, Alistair Ring, Nicholas C. Turner

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsOlaparibMedicineTriple-negative breast cancerOncologyBreast cancerInternal medicineCancerMetastatic breast cancerCohortDNABiologyPoly ADP ribose polymerase

Abstract

fetched live from OpenAlex

Abstract Background: Early changes in ctDNA levels, ctDNA dynamics, may help identify which patients are responding to therapy earlier than imaging. Few studies have assessed ctDNA dynamics during PARP inhibitor therapy. Here we report paired baseline and early on treatment ctDNA analysis from cohort E of plasmaMATCH, that recruited patients with TNBC to treatment with olaparib (PARP inhibitor) plus ceralasertib (ATR inhibitor). Methods: The plasmaMATCH trial assessed the ability of ctDNA testing to allocate patients to mutation matched treatment cohorts (A-D). Patients with TNBC, and without mutations matching cohorts B-D, were enrolled on cohort E. Samples were collected for ctDNA analysis pre-treatment at cycle 1-day1 (C1D1) and cycle 2-day 1 (C2D1). A minimum of 14 days of treatment in the first cycle was required for inclusion in this analysis. Samples were sequenced using error-corrected gene targeted panels (Guardant360, or GuardantOMNI, Guardant Health). Circulating DNA ratio (CDR) was calculated as the ratio of C2D1 ctDNA level to C1D1, pre-specified using the weighted mean of variant allele fractions (AF) of clonal mutations at C1D1, excluding variants with AF < 0.3%, and variants in genes frequently mutated in clonal haematopoesis (GNAS, JAK2, IDH1, IDH2 and ATM). The optimal cut-point for predicting progression free survival (PFS) was assessed as the cut-point with the highest Harrell’s C-index. Results: Of the 75 patients that were enrolled into cohort E, 53 patients had samples sent for paired C1D1-C2D1 ctDNA sequencing, 2 failed sequencing, and all 51 (68%) patients had detectable ctDNA at C1D1. The ctDNA analysis set was representative of the overall enrolled population. The optimal ctDNA dynamics C-index cut-point for predicting PFS was 0 (undetectable ctDNA at C2D1). Median PFS with ctDNA CDR >0 (detectable ctDNA at C2D1) was 4.3 months (95% CI 2.4-5.8), and with undetectable ctDNA was 12 months (95% CI 8-NA) (HR 4.02, 95% CI 1.22-13.23, p=0.01). Splitting patients by median CDR was not predictive (HR=0.98; 95%CI: 0.52-1.82, p=0.94). Confirmed objective response rate was 85.7% (42.1-99.6) in patients with undetectable ctDNA at C2D1, and with detectable ctDNA was 11.4% (3.8-24.6) (OR 4.02, 95% CI 1.22-13.23, p=0.01). Of the 7 patients with undetectable ctDNA at C2D1, one had a BRCA2 germline mutation, and all other patients were wildtype for BRCA1/2 mutations in tumour and germline. All patients with undetectable ctDNA and BRCA1/2 wildtype had a confirmed response.In cohorts A-D (mutation targeted therapies, in predominantly ER positive cancer), the optimal ctDNA dynamics C-index CDR cut-point was 0.165 (HR 3.44, 95%CI: 2.06-5.75, p< 0.001), with median CDR cut-point also highly predictive (HR=2.14, 95%CI:1.36-3.36, p=0.001). Undetectable ctDNA was also strongly predictive (HR=4.41; 95%CI: 1.97-9.87, p< 0.001) in cohort A-D.In cohort E, a significant association was found between baseline ctDNA and PFS, with an optimal C-index cutpoint of 6.81% (HR=3.02, 95% CI: 1.39-6.56, p=0.001). Median PFS for baseline ctDNA ≤ 6.81% was 10.2 months (95% CI 3.7-17.2), and for baseline ctDNA >6.81% was 4.4 months (95% CI 2.2-5.5). Conclusions: ‘Clearance’ of ctDNA to become undetectable at C2D1 identified sporadic TNBC patients who benefited from olaparib and ceralasertib. Although ‘clearance’ of ctDNA was associated with good outcome on olaparib plus ceralasertib, median CDR was not predictive of treatment benefit. This contrasts the results of ctDNA dynamic assessment of cohort A-D, where median CDR was highly predictive of treatment benefit. ctDNA dynamic assessment may differ between mutation targeted therapies (cohorts A-D) that induce cell-cycle arrest, and PARP inhibitors (cohort E) that inhibit DNA repair mechanisms. Implementing ctDNA dynamics into clinical trials and care may require distinct analysis for different therapies. Citation Format: Iseult Browne, Javier Pascual, Rosalind Cutts, Belinda Kingston, Sarah Hrebien, Lucy Kilburn, Alex Pearson, Laura Moretti, Andrew Wardley, Iain Macpherson, Richard Baird, Rebecca Roylance, Iris Faull, Kimberly C Banks, Isaac Garcia-Murillas, Judith Bliss, Alistair Ring, Nicholas Turner. The prognostic and predictive impact of circulating tumour DNA (ctDNA) dynamics in patients with metastatic Triple Negative Breast Cancer (TNBC) on olaparib based therapy: Results from Cohort E of the PlasmaMATCH trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS06-04.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.024
GPT teacher head0.326
Teacher spread0.302 · 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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCancer Research→Same topicCancer Genomics and Diagnostics→French-language works237,207→