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Abstract P6-19-01: Evaluation of multiple transcriptomic gene risk signatures in male breast cancer

2019· article· en· W2944463051 on OpenAlexaff
Jane Bayani, Coralie Poncet, CQ Yao, Cheryl Crozier, Neven Anouk, Tammy Piper, C Cunningham, Maria Sobol, Stefan Aebi, Kim Benstead, Oliver Bögler, Lissandra Dal Lago, Judith Fraser, FH Hilbers, Ingrid Hedenfalk, Larissa A. Korde, Barbro Linderholm, John W.M. Martens, Lavinia P. Middleton, Melissa P. Murray, Catherine M. Kelly, Charlotte Juul Nilsson, M Nowaczyk, Stéphanie Peeters, Aleksandar Perić, Peggy L. Porter, Caroline P. Schröder, IT Rubio, KJ Ruddy, Christi J. van Asperen, D. Van den Weyngaert, C. van Deurzen, E van Leeuwen-Stok, Joanna Vermeij, Eric P. Winer, PC Boutros, SH Giordano, Fátima Cardoso, JM Bartlett

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
KeywordsMedicineOncologyBreast cancerInternal medicineProportional hazards modelHazard ratioCancerGene signatureRisk assessmentSubtypingGeneConfidence intervalGene expressionBiologyGenetics

Abstract

fetched live from OpenAlex

Abstract Introduction: Male breast cancer (MBC) is a rare disease accounting for less than 1% of all breast cancers (BC) and 1% of all cancers in males. The clinical management is largely extrapolated from female BC. Several multigene assays are increasingly used to guide clinical treatment decisions in female BC, however there is little data on the utility of these tests in MBC. Methods: Here we present the gene expression results of 380 M0, ER+ve, HER2-ve MBCs enrolled in the Part 1 (retrospective joint analysis) International Male Breast Cancer Program of 1483 patients diagnosed between 1990-2010 (Cardoso et al. Annals of Oncology, 2018). Using a custom Nanostring™ panel comprised of the genes from the commercial risk tests Prosigna®, OncotypeDx® and Mammaprint®, risk scores and intrinsic subtyping data were generated to recapitulate the commercial tests as described by Bayani and Yao et al (npjBreast Cancer, 2017). Survival outcomes by risk classification were analyzed using Cox models with time-dependent covariates when the proportional hazard assumption was not met and adjusted for clinical and treatment variables. Results: Prosigna-like risk scores identified 99 (26.1%) as low-risk, 159 (41.8%) as intermediate-risk, and 122 (32.1%) as high-risk. Using the TAILORx cut-off (25) for OncotypeDx-like risk of recurrence scoring, 158 (41.6%) were identified as low-risk, while 222 (58.4%) were identified as high-risk. MammaPrint-like results identified 175 (46.1%) as low-risk and 205 (53.9%) as high-risk. Overall, patients classified as high-risk had higher grade, more nodal involvement, larger tumors, and more frequently treated with chemotherapy than low-risk patients. Survival analyses demonstrated clear clinical utility for each test, showing patients at high-risk with poor relapse-free survival (RFS) as compared to patients classified as low-risk: Prosigna-like RFS at 3-years (HR=2.20, 95% CI, 1.28-3.80); Oncotype-like RFS at 3-years (HR=1.92, 95% CI, 1.17-3.17); MammaPrint-like RFS (HR=1.51, 95% CI, 1.00-2.27); with similar findings for distant relapse-free survival (DRFS) and overall survival (OS). Across outcomes and all gene signatures, patients with concordant Low/Low risk classification had better prognosis than those with concordant High/High risk classification. PAM50 intrinsic subtyping identified 147 (38.7%) as Luminal A, 57 (15.0%) as Luminal B, 80 (21.1%) as Her2-enriched and 96 (25.3%) as Basal-like; showing overall 34.5% concordance to clinic-pathological subtyping by central pathology (95% CI, 29.7%-39.5%). Comparison between the tests in the MBC cohort and a comparable cohort of female BC from the Tamoxifen Exemestane Adjuvant Multinational (TEAM) trial processed in the same way will be presented. Conclusion: Common transcriptomic assays designed to assess residual risk, validated in female BC, provide similar information in male BC patients. Not surprisingly, disagreement between test results at the individual patient level was observed. To our knowledge, this is the largest study of MBC assayed to generate risk scores of the current commercial BC tests to demonstrate their clinical utility and their differences and similarity to female BC. This work has been funded by the Breast Cancer Research Foundation (BCRF). Citation Format: Bayani J, Poncet C, Yao CQ, Crozier C, Anouk N, Piper T, Cunningham C, Sobol M, Aebi S, Benstead K, Bogler O, Dal Lago L, Fraser J, Hilbers FH, Hedenfalk I, Korde L, Linderholm B, Martens J, Middleton L, Murray M, Kelly C, Nilsson C, Nowaczyk M, Peeters S, Peric A, Porter P, Schröder C, Rubio IT, Ruddy KJ, van Asperen C, Van Den Weyngaert D, van Deurzen C, van Leeuwen-Stok E, Vermeij J, Winer E, Boutros PC, Giordano SH, Cardoso F, Bartlett JM. Evaluation of multiple transcriptomic gene risk signatures in male breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P6-19-01.

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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.052
GPT teacher head0.398
Teacher spread0.345 · 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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Citations0
Published2019
Admission routes1
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