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Record W4399865048 · doi:10.1016/j.annonc.2024.06.009

Impact of hormone receptor status and tumor subtypes of breast cancer in young BRCA carriers

2024· article· en· W4399865048 on OpenAlexaff
Luca Arecco, Marco Bruzzone, R. Bas, Hyun Jin Kim, Antonio Di Meglio, Rinat Bernstein‐Molho, Florentine Hilbers, Katarzyna Pogoda, Estela Carrasco, Kevin Punie, Jyoti Bajpai, Elisa Agostinetto, Nerea Lopetegui‐Lia, A.H. Partridge, Kelly‐Anne Phillips, Angela Toss, Christine Rousset‐Jablonski, G. Curigliano, Thomas Renaud, Alberta Ferrari, Shani Paluch–Shimon, Robert Fruscio, Wanyuan Cui, Natalie Wong, Claudio Vernieri, Fergus J. Couch, Maria Vittoria Dieci, Alexios Matikas, Mariya Rozenblit, Laura De Marchis, Fabio Puglisi, Alessandra Fabi, Stephanie L. Graff, I. Witzel, Andrea Fontana, Romina Pesce, Renata Duchnowska, Helena Luna Pais, Valentina Sini, Emir Sokolović, Evandro de Azambuja, Marcello Ceppi, Eva Blondeaux, Matteo Lambertini

Bibliographic record

VenueAnnals of Oncology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsMcGill UniversityJewish General Hospital
FundersNational Health and Medical Research CouncilMedical Research CouncilEuropean Society for Medical OncologyNational Breast Cancer FoundationPeter MacCallum Cancer CentreAssociazione Italiana per la Ricerca sul CancroKorea Health Industry Development InstituteNational Institutes of HealthCancer Australia
KeywordsMedicineBreast cancerHormone receptorOncologyInternal medicineHormoneCancerReceptorCancer research

Abstract

fetched live from OpenAlex

BACKGROUND: Hormone receptor expression is a known positive prognostic and predictive factor in breast cancer; however, limited evidence exists on its prognostic impact on prognosis of young patients harboring a pathogenic variant (PV) in the BRCA1 and/or BRCA2 genes. PATIENTS AND METHODS: This international, multicenter, retrospective cohort study included young patients (aged ≤40 years) diagnosed with invasive breast cancer and harboring germline PVs in BRCA genes. We investigated the impact of hormone receptor status on clinical behavior and outcomes of breast cancer. Outcomes of interest [disease-free survival (DFS), breast cancer-specific survival (BCSS), and overall survival (OS)] were first investigated according to hormone receptor expression (positive versus negative), and then according to breast cancer subtype [luminal A-like versus luminal B-like versus triple-negative versus human epidermal growth factor receptor 2 (HER2)-positive breast cancer]. RESULTS: From 78 centers worldwide, 4709 BRCA carriers were included, of whom 2143 (45.5%) had hormone receptor-positive and 2566 (54.5%) hormone receptor-negative breast cancer. Median follow-up was 7.9 years. The rate of distant recurrences was higher in patients with hormone receptor-positive disease (13.1% versus 9.6%, P < 0.001), while the rate of second primary breast cancer was lower (9.1% versus 14.7%, P < 0.001) compared to patients with hormone receptor-negative disease. The 8-year DFS was 65.8% and 63.4% in patients with hormone receptor-positive and negative disease, respectively. The hazard ratio of hormone receptor-positive versus negative disease changed over time for DFS, BCSS, and OS (P < 0.05 for interaction of hormone receptor status and survival time). Patients with luminal A-like breast cancer had the worst long-term prognosis in terms of DFS compared to all the other subgroups (8-year DFS: 60.8% in luminal A-like versus 63.5% in triple-negative versus 65.5% in HER2-positive and 69.7% in luminal B-like subtype). CONCLUSIONS: In young BRCA carriers, differences in recurrence pattern and second primary breast cancer among hormone receptor-positive versus negative disease warrant consideration in counseling patients on treatment, follow-up, and risk-reducing surgery.

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 score0.004
Threshold uncertainty score0.008

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 head0.019
GPT teacher head0.361
Teacher spread0.342 · 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

Citations27
Published2024
Admission routes1
Has abstractyes

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