Abstract 2787: Exon sequencing of candidate genes for early onset ER negative breast cancer risk reveals novel gene-level associations
Bibliographic record
Abstract
Abstract Purpose: To test whether genes located in GWAS-identified regions contain exonic variants that show novel gene-level associations with early-onset ER negative breast cancer risk. Background: Only a small portion of the expected genetic contribution to breast cancer risk has been identified by examining the effect of common variants using single-variant analyses. In contrast to single-variant analyses, sequencing studies combined with gene-based tests evaluate the collective effect of all variants in a gene. In regions identified as associated with disease in GWAS, sequencing combined with gene-based tests can potentially provide evidence whether exonic variation contributes to disease risk independently of the common variants already. However, these studies have not yet been widely implemented in breast cancer research. Methods: We selected 19 genes in 11 genomic regions in which previous GWA studies found associations between common variants and breast cancer. We sequenced the exons of each of these genes in 210 cases (women diagnosed with breast cancer before age 45 who did not carry a known BRCA-1 or BRCA-2 mutation and whose tumors were ER-), and 169 age-matched controls. We analyzed these exons using the variance-based SKAT-O test to determine if exonic variation in any of these 19 genes was associated with risk of developing breast cancer. To examine whether any association was driven by the known GWAS variant, we repeated the analysis, controlling for the common variant that single-variant association tests indicated was most strongly associated with breast cancer. To examine whether the association was driven by putative “functional” variants, we also repeated both analyses, including only non-synonymous variants that alter the amino acid sequence. Results: Exonic variants in three genes were collectively associated with breast cancer risk in our population of early-onset cases: ZMIZ1 (p = 2.07•10-4), FGF3 (p = 1.30•10-3), and ANKLE1 (p = 2.80•10-5). In all of these three genes, the associations persisted or even strengthened after adjusting for the top variant identified by GWAS in the region (ZMIZ1 p = 2.41•10-5; FGF3 p = 1.41•10-3; ANKLE1 p = 7.63•10-6). Functional variants in these three genes (restricted only to non-synonomous variants) also were collectively associated with risk (ZMIZ1 p = 2.65•10-5; FGF3 p = 4.52•10-5; ANKLE1 p = 6.52•10-5). These associations are similar after adjusting for the strongest single variant association identified by GWAS in the region (ZMIZ1 p = 5.10•10-4; FGF3 p = 7.74•10-5; ANKLE1 p = 9.42•10-5). Conclusions: Our results are consistent with the hypothesis that the exons of genomic regions identified through GWAS contain additional variants that contribute to ER- breast cancer risk. These results, that need to be independently validated in a larger study, contribute to our understanding of the genetic determinants that influence the risk of ER- early onset breast cancer. Citation Format: Molly Scannell Bryan, Muhammad G. Kibirya, Irene Andrulis, Jenny Chang-Claude, Habibul Ahsan, Brandon Pierce. Exon sequencing of candidate genes for early onset ER negative breast cancer risk reveals novel gene-level associations. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2787. doi:10.1158/1538-7445.AM2015-2787
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".