Genome-wide copy number aberrations and HER2 and FGFR1 alterations in primary breast cancer by molecular inversion probe microarray
Bibliographic record
Abstract
// Hui Chen 1 , Rajesh R. Singh 2 , Xinyan Lu 2 , Lei Huo 1 , Hui Yao 3 , Kenneth Aldape 1,5 , Ronald Abraham 2 , Shumaila Virani 2 , Meenakshi Mehrotra 2 , Bal Mukund Mishra 2 , Alex Bousamra 1,4 , Constance Albarracin 1 , Yun Wu 1 , Sinchita Roy-Chowdhuri 1 , Rashmi Kanagal Shamanna 2 , Mark J. Routbort 2 , L. Jeffrey Medeiros 2 , Keyur P. Patel 2 , Russell Broaddus 1 , Aysegul Sahin 1 and Rajyalakshmi Luthra 2 1 Departments of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA 2 Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA 3 Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA 4 Allegheny Health Network, Pittsburgh, PA, USA 5 Department of Anatomic Pathology, Laboratory Medicine Program, University Health Network, Toronto, Canada Correspondence to: Rajyalakshmi Luthra, email: // Hui Chen, email: // Keywords : breast cancer, SNP microarray, molecular inversion probe microarray, chromothripsis, HER2, Pathology Section Received : September 16, 2016 Accepted : January 10, 2017 Published : January 24, 2017 Abstract Breast cancer remains the second leading cause of cancer-related death in women despite stratification based on standard hormonal receptor (HR) and HER2 testing. Additional prognostic markers are needed to improve breast cancer treatment. Chromothripsis, a catastrophic genome rearrangement, has been described recently in various cancer genomes and affects cancer progression and prognosis. However, little is known about chromothripsis in breast cancer. To identify novel prognostic biomarkers in breast cancer, we used molecular inversion probe (MIP) microarray to explore genome-wide copy number aberrations (CNA) and breast cancer-related gene alterations in DNA extracted from formalin-fixed paraffin-embedded tissue. We examined 42 primary breast cancers with known HR and HER2 status assessed via immunohistochemistry and FISH and analyzed MIP microarray results for correlation with standard tests and survival outcomes. Global genome-wide CNA ranged from 0.2% to 65.7%. Chromothripsis-like patterns were observed in 23/38 (61%) cases and were more prevalent in cases with ≥10% CNA (20/26, 77%) than in cases with <10% CNA (3/12, 25%; p <0.01). Most frequently involved chromosomal segment was 17q12-q21, the HER2 locus. Chromothripsis-like patterns involving 17q12 were observed in 8/19 (42%) of HER2- amplified tumors but not in any of the tumors without HER2 amplification (0/19; p <0.01). HER2 amplification detected by MIP microarray was 95% concordant with conventional testing (39/41). Interestingly, 21% of patients (9/42) had fibroblast growth factor receptor 1 ( FGFR1 )amplification and had a 460% higher risk for mortality than those without FGFR1 amplification ( p <0.01). In summary, MIP microarray provided a robust assessment of genomic CNA of breast cancer.
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 | 0.000 |
| 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".