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Abstract P4-10-03: The genomic landscape of male breast cancers using the oncomine comprehensive assay for actionable mutations

2020· article· en· W3010523936 on OpenAlexaff
Jane Bayani, Coralie Poncet, Cheryl Crozier, Quang M. Trinh, Megan Hopkins, Aimé Lambert Uwimana, Tammy Piper, Carrie Cunningham, Monika Sobol, Stefan Aebi, Kim Benstead, Oliver Bögler, Lissandra Dal Lago, Florentine Hilbers, Ingrid Hedenfalk, Larissa A. Korde, Barbro Linderholm, John W.M. Martens, Lavinia P. Middleton, Melissa P. Murray, Catherine M. Kelly, Cecilia Nilsson, Monika Nowaczyk, Stéphanie Peeters, Aleksandra Peric, Peggy L. Porter, Carolien P. Schröder, Isabel T. Rubio, Kathryn J. Ruddy, Christi J. van Asperen, Daniëlle Van den Weyngaert, Elise van Leeuwen-Stok, Joanna Vermeij, Eric P. Winer, Lincoln Stein, Sharon H. Giordano, Fátima Cardoso, John M.S. Bartlett

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
KeywordsBreast cancerContext (archaeology)OncologyCancerMedicineInternal medicineTargeted therapyClinical trialBioinformaticsBiology

Abstract

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Abstract Introduction: Male breast cancer (BCa) 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 BCa. Few studies have examined the genomic landscape of male BCas, with six male BCas included in The Cancer Genome Atlas (TCGA). Familial studies of male BCas have shown genomic changes similar to female BCa, while a larger targeted sequencing study of 59 male breast cancers identified recurrent mutations affecting PIK3CA and GATA3. To date, there is still limited information regarding the genomic landscape of male BCas; particularly in the context of identifying targeted treatments. To reveal genomic changes that characterize male BCas in the context of known cancer driver genes linked to prognosis and targeted agents, we performed a targeted sequencing study on 248 male BCas from the International Male Breast Cancer Program. Methods: 248 primary M0, ER+ve, HER2-ve male BCas 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) were processed for nucleic acid extraction from formalin-fixed paraffin embedded (FFPE) tissues. Using the Thermo Fisher Scientific Oncomine Comprehensive Assay v3 (OCAv3), a validated targeted sequencing panel currently used in the NCI-MATCH trial (NCT02465060), we evaluated mutational and copy number variations (CNVs) of genes that are prognostic or predictive to targeted therapies currently in use in the clinic or late-stage clinical trials. The OCAv3 DNA pan-cancer panel assays 115 genes for determining mutational status (48 full coding and 67 hotspot) as well as copy-number assessment in 43 genes. The OCAv3 uses Ampliseq-based technology linked to the Oncomine NGS workflow to identify actionable mutations and CNVs Results: Of the 248 samples assayed, 216 passed strict quality control parameters (87.1%). Using the Oncomine NGS workflow, actionable mutations at ≥5% variant allele frequency (VAF) were most frequently identified in PIK3CA (29.2%), BRCA2 (11.1%), NF1 (11.6%), indels in TP53 (10.6%), ATR (5.6%), ATRX (5.1%), indel BRCA2 (5.1%), TP53 point mutations (4.6%), MET (4.6%), ATM (4.6%), NOTCH2 (4.6%), CHEK1 (4.2%), FANCI (4.2%), PTEN (3.2%) with a number of additional genes identified at lower frequencies. Gene amplifications were most frequently detected in MYC (24.5%), FGFR1 (14.8%), CCND1 (12%), FGF3 (9.7%), FGF19 (9.7%), MDM2 (6.5%), CDK4 (1.4%), FGFR3, MDM4, ERBB2 (0.9% each), and FLT3, AR, MYCL, CDK6, IGF1R, FGFR4, KRAS, AKT3 and ESR1 (0.5% each). Although the results here describe the mutations and copy-number changes deemed to be actionable, further analysis of all non-actionable somatic mutations and CNVs will be presented and compared to female BCas previously assayed using the same panel. Conclusion: In this targeted sequencing study of the largest series of male BCas to our knowledge, we have revealed that PIK3CA continues to be a frequently altered gene in both male and female BCas. However, there is an enrichment of mutations in genes related to DNA repair in male BCs. Interestingly, while MYC is commonly amplified in female BCa, a higher frequency of amplified cases were seen in male BCas, in contrast to female BCas. Together with our previously generated transcriptional profiling data in this data set, we believe that both common and unique biological processes comprising male and female BCas will ultimately improve their clinical management and move towards the goal of precision medicine. This work has been funded by the Breast Cancer Research Foundation (BCRF). Citation Format: Jane Bayani, Coralie Poncet, Cheryl Crozier, Quang M Trinh, Megan Hopkins, Aime Lambert Uwimana, Tammy Piper, Carrie Cunningham, Monika Sobol, Stefan Aebi, Kim Benstead, Oliver Bogler, Lissandra Dal Lago, Florentine Hilbers, Ingrid Hedenfalk, Larissa Korde, Barbro Linderholm, John Martens, Lavinia Middleton, Melissa Murray, Catherine Kelly, Cecilia Nilsson, Monika Nowaczyk, Stephanie Peeters, Aleksandra Peric, Peggy Porter, Carolien Schröder, Isabel T Rubio, Kathryn J Ruddy, Christi van Asperen, Danielle Van Den Weyngaert, Carolien HM van Deurzen, Elise van Leeuwen-Stok, Joanna Vermeij, Eric Winer, Lincoln D Stein, Sharon H Giordano, Fatima Cardoso, John MS Bartlett. The genomic landscape of male breast cancers using the oncomine comprehensive assay for actionable mutations [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-10-03.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.096
GPT teacher head0.397
Teacher spread0.301 · 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
Published2020
Admission routes1
Has abstractyes

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