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Record W4255743860 · doi:10.1158/1538-7445.am2016-134

Abstract 134: Mutational landscape of breast cancers from PALB2 germline mutation carriers

2016· article· en· W4255743860 on OpenAlexaff
Salvatore Piscuoglio, Charlotte K.Y. Ng, Y Hannah Wen, Paolo Peterlongo, Carlo Tondini, Markéta Janatová, Teo Soo Hwang, Pei-Sze Ng, Lai‐Meng Looi, William D. Foulkes, Georgia Chenevix‐Trench, Britta Weigelt, Melissa C. Southey, Marc Tischkowitz, Jorge S. Reis‐Filho

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsConcordia UniversityMcGill University
Fundersnot available
KeywordsPALB2Germline mutationBreast cancerGermlineGeneticsBiologyExomeLoss of heterozygosityCancer researchMutationBRCA2 ProteinCancerExome sequencingAlleleGene

Abstract

fetched live from OpenAlex

Abstract Introduction: The PALB2 gene encodes the partner and localizer of BRCA2 protein, which interacts with BRCA1/2 and is involved in homologous recombination DNA repair. Germline mutations in PALB2 are associated with an increased risk of breast cancer, with a cumulative risk of 35% by age 70 in female PALB2 mutation carriers. Whether the PALB2 wild-type allele is lost in the development of PALB2 breast cancers has yet to be defined. Further, the repertoire of somatic genetic alterations in these tumors is currently unknown. In this study we sought to characterize the genomic landscape of PALB2 breast cancers and to define the differences in the repertoire of somatic genetic alterations and mutational signatures between PALB2 and BRCA1 and BRCA2 breast cancers. Material and Methods: Representative samples from nine breast cancers from patients with known PALB2 germline mutations were microdissected. DNA samples from microdissected tumors and matched normal counterparts were subjected to whole exome sequencing on an Illumina HiSeq2000. Somatic mutations were defined using MuTect and insertions and deletions using Strelka and Varscan2. Driver mutations were defined by state-of-the-art bioinformatics methods. Mutational signatures were defined using non-negative matrix factorization. Copy number alterations (CNAs) and regions with loss of heterozygosity were determined using FACETS. The mutational frequency of breast cancers from PALB2 germline mutation carriers was compared to that of breast cancers from BRCA1 (n = 11) and BRCA2 (n = 10) germline mutation carriers from The Cancer Genome Atlas study. Results: Three patients harbored germline frame-shift PALB2 mutations (2 S1169fs, 1 T841fs), five displayed truncating mutations (3 W1038* and 2 Q775*) and 1 harbored a missense mutation (W1140G, of uncertain significance). Somatic loss of the PALB2 wild-type allele was found in 3 cases, in 2 of which the loss was caused by CNAs and in 1 case it was caused by a somatic PALB2 Q479* mutation. A median of 65 somatic mutations (range 45-223) and a median of 1 driver mutation (range 0-3) were identified per tumor. Cancer genes mutated in PALB2 breast cancers included TP53 (n = 2), PIK3CA (n = 2), NF1 (n = 1) and NCOR1 (n = 1). Six cases displayed mutational signatures consistent with the aging process; the BRCA signature was not found in any of the cases analyzed. Breast cancers from PALB2 mutation carriers had fewer somatic TP53 mutations than BRCA1 breast cancers (2/9, 22% vs 9/11, 82%, p = 0.02). No difference in the repertoire of somatic mutations between PALB2 and BRCA2 breast cancers was observed. Conclusion: Unlike breast cancers from BRCA1 and BRCA2 mutation carriers, the majority of breast cancers from PALB2 mutation carriers lacked somatic loss of the wild-type allele and none displayed a BRCA mutational signature. No highly recurrently mutated gene was identified, but pathogenic mutations in driver genes (TP53, PIK3CA, NF1 and NCOR1) were found. Citation Format: Salvatore Piscuoglio, Charlotte KY Ng, Y Hannah Wen, Arto Mannermaa, Paolo Peterlongo, Carlo Tondini, Marketa Janatova, Teo Soo Hwang, Pei-Sze Ng, Lai-Meng Looi, William Foulkes, Georgia Chenevix-Trench, Britta Weigelt, Melissa C. Southey, Marc Tischkowitz, Jorge S. Reis-Filho, PALB2 Interest Group. Mutational landscape of breast cancers from PALB2 germline mutation carriers. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 134.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.032
GPT teacher head0.365
Teacher spread0.334 · 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
Published2016
Admission routes1
Has abstractyes

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