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Abstract A1-06: Recurrent mutations of hormone-positive breast cancer and association with outcome

2015· article· en· W2567166227 on OpenAlexaff
Obi L. Griffith, Malachi Griffith, Nicholas C. Spies, Jingqin Luo, Jasreet Hundal, Christopher A. Miller, David E. Larson, Robert S. Fulton, Shuzhen Liu, Samuel Leung, Richard K. Wilson, Torsten O. Nielsen, Elaine R. Mardis, Matthew J. Ellis

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBreast cancerOncologyMassive parallel sequencingInternal medicineExomeExome sequencingMedicineCancerDNA sequencingBiologyGeneticsGeneMutation

Abstract

fetched live from OpenAlex

Abstract Background: Relationships between recurrent somatic mutations and outcome in estrogen receptor positive (ER+) breast cancer have not been extensively studied as the original discovery efforts were from either heterogeneously treated patients or follow up was too brief. Targeted massively parallel sequencing (MPS) analysis was therefore conducted on DNA extracted from archived formalin-fixed breast primaries from a cohort of over 600 patents from British Columbia treated with five years of adjuvant tamoxifen monotherapy and followed for over 10 years (Nielsen et al CCR 16:5222, 2010). Methods: Genes were selected for targeted sequencing by meta-analysis of five large-scale breast cancer sequencing studies and manual review of breast cancer literature. In total, 83 genes were identified and 3286 probes were designed to tile across all known exons. Minimum starting input DNA was 50ng (mean=189.1ng). Illumina sequencing libraries were constructed, indexed, pooled, and enriched for target sequences by hybrid-capture followed by paired-end 100bp reads. The Genome Modeling System was used to perform single-tumor somatic variant prediction. Variant calls were filtered to include only targeted regions and exclude variants with global minor allele frequencies greater than 0.1% in unmatched non-tumor samples from 1000 genomes, NHLBI exome, and TCGA datasets. Kaplan-Meier univariate and multivariate survival analyses (including mutation status, clinical features and intrinsic subtype by qPCR) were performed for breast-cancer-specific and relapse free survival. Results: A total of 625 samples met minimum quality controls of 80% targeted space covered at 20X or greater. On average, each sample had 332M aligned bases and a mean coverage of 134.3X. In total, 3,628 variants were identified including 2,066 missense, 188 nonsense, and 298 frame shift insertions or deletions. Novel hot spots for recurrent mutation were identified in several genes including a splice site mutation in CBFB. Results indicate significant associations between mutation status and improved survival for MAP3K1, ERBB3, ARID1B, PIK3CA and SMG1 or worse survival for DDR1, NF1, FOXC1 and TP53. Six Y537S/C, two E380Q and 5 potentially novel ligand-binding-domain mutations were identified in ESR1. Such mutations were recently reported to be associated with resistance to hormone therapy but were discovered here in as many as 2.1% of pre-treatment samples. Analysis will be presented regarding the use of relapse events to differentiate passenger from driver events. Conclusion: Multiple recurrently mutated genes have both positive and negative associations with prognosis in tamoxifen monotherapy treated breast cancer populations. Associations with poor outcome suggest that DDR1, NF1 and FOXC1 are high priorities for pharmacological interventions. Citation Format: Obi L. Griffith, Malachi Griffith, Nicholas C. Spies, Jingqin Luo, Jasreet Hundal, Christopher A. Miller, David E. Larson, Robert Fulton, Shuzhen Liu, Samuel Leung, Richard K. Wilson, Torsten O. Nielsen, Elaine R. Mardis, Matthew J. Ellis. Recurrent mutations of hormone-positive breast cancer and association with outcome. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-06.

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)0.003
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 score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.050
GPT teacher head0.384
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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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