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Record W4390607200 · doi:10.1038/s41523-023-00607-1

Circulating tumour mutation detection in triple-negative breast cancer as an adjunct to tissue response assessment

2024· article· en· W4390607200 on OpenAlexafffund
Elena Zaikova, Brian Yu Chieh Cheng, Viviana Cerda, Esther Kong, Daniel Lai, Amy Lum, Cherie Bates, Wendie Den Brok, Takako Kono, Sylvie Bourque, Angela Chan, Xioalan Feng, David Fenton, Anagha Gurjal, Nathalie LeVasseur, Caroline Lohrisch, Sarah Roberts, Tamara Shenkier, Christine Simmons, Diego Villa, Ruth R. Miller, Rosalía Aguirre-Hernández, Samuel Aparício, Karen A. Gelmon

Bibliographic record

Venuenpj Breast Cancer · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsBC Cancer AgencySpinal Cord Injury BC
FundersBC Cancer FoundationCanadian Institutes of Health ResearchSusan G. KomenGovernment of CanadaBreast Cancer Research Foundation
KeywordsMedicineBreast cancerTriple-negative breast cancerLiquid biopsyOncologyBiopsyInternal medicineNeoadjuvant therapyCirculating tumor DNAMinimal residual diseasePathologyCancer

Abstract

fetched live from OpenAlex

Circulating tumour DNA (ctDNA) detection via liquid biopsy is an emerging alternative to tissue biopsy, but its potential in treatment response monitoring and prognosis in triple negative breast cancer (TNBC) is not yet well understood. Here we determined the prevalence of actionable mutations detectable in ctDNA using a clinically validated cancer gene panel assay in patients with TNBC, without recurrence at the time of study entry. Sequencing of plasma DNA and validation of variants from 130 TNBC patients collected within 7 months of primary treatment completion revealed that 7.7% had detectable residual disease with a hotspot panel. Among neoadjuvant treated patients, we observed a trend where patients with incomplete pathologic response and positive ctDNA within 7 months of treatment completion were at much higher risk of reduced progression free survival. We propose that a high risk subset of early TNBC patients treated in neoadjuvant therapy protocols may be identifiable by combining tissue response and sensitive ctDNA detection.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.319
Teacher spread0.310 · 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

Citations22
Published2024
Admission routes2
Has abstractyes

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