Recurrent copy number alterations in young women with breast cancer
Bibliographic record
Abstract
// Chen Chi 1, 2 , Leigh C. Murphy 1, 3 and Pingzhao Hu 1, 2, 4 1 Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada 2 The George and Fay Yee Centre for Healthcare Innovation, University of Manitoba, Winnipeg, Manitoba, Canada 3 Research Institute of Oncology and Hematology, Cancer Care Manitoba, Winnipeg, Manitoba, Canada 4 Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada Correspondence to: Pingzhao Hu, email: pingzhao.hu@umanitoba.ca Keywords: recurrent copy number alterations; breast cancer; young women; risk genes; graph algorithm Received: September 07, 2017 Accepted: January 24, 2018 Published: January 29, 2018 ABSTRACT Breast cancer diagnosis in young women has emerged as an independent prognostic factor with higher recurrence risk and death than their older counterparts. We aim to find recurrent somatic copy number alteration (CNA) regions identified from breast cancer microarray data and associate the CNA status of the genes harbored in the regions to the survival of young women with breast cancer. By using the interval graph-based algorithm we developed, and the CNA data consisting of a Discovery set with 130 young women and a Validation set with 125 young women, we identified 38 validated recurrent CNAs containing 39 protein encoding genes. CNA gain regions encompassing genes CAPN2 , CDC73 and ASB13 are the top 3 with the highest occurring frequencies in both the Discovery and Validation dataset, while gene SGCZ ranked top for the recurrent CNA loss regions. The mutation status of 9 of the 39 genes shows significant associations with breast cancer specific survival. Interestingly, the expression level of 2 of the 9 genes, ASB13 and SGCZ , shows significant association with survival outcome. Patients with CNA mutations in both of these genes had a worse survival outcome when compared to patients without the gene mutations. The mutated CNA status in gene ASB13 was associated with a higher gene expression, which predicted patient survival outcome. Together, identification of the CNA events with prognostic significance in young women with breast cancer may be used in genomic-guided treatment.
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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) | 0.001 | 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".