Abstract 1122: Personalized oncogenomics in advanced stage breast cancer
Bibliographic record
Abstract
Abstract Background Breast cancer is a complex disease with clinical, pathological, and molecular heterogeneity. Recent studies have identified several subtypes of breast cancers driven by specific molecular pathways that can be inhibited by targeted drugs. We studied the feasibility of using molecular data from whole genome and transcriptome sequencing of breast cancers to guide treatment. Methods Patients were consented as part of the Personalized Onco-Genomics (POG) study at the British Columbia Cancer Agency. Fresh tumor, blood for normal DNA, and archival tumor were collected. Tissue and blood samples were sequenced using the Ion Torrent AmpliSeq panel, followed by comprehensive DNA and RNA sequencing. In-depth bioinformatic analyses were performed. Somatic mutations, copy number changes, structural variants and gene expression were characterized. Results from genomic analyses were reviewed in a multi-disciplinary team. Results From August 2012 to October 2014, tissue samples from 30 patients with advanced stage breast cancer were analyzed. The median age at diagnosis was 53 years (range 32-75). The median number of lines of cytotoxic therapy prior to sequencing was 2 (range 0-10). All cases were invasive ductal carcinoma; 63% were ER+ and HER2-; 27% triple negative; 7% ER+ and HER2+; and 3% ER- and HER2+. The most frequently mutated genes were TP53 (67%), PI3KCA (23%), ESR1 (20%), ATM (10%), ARID1A (10%), and BRCA1/2 (10%). Somatic mutations in other genes of interest including HER2, PARP1, NF1, BAP1, PTEN, NOTCH1, were also identified. Molecular data were informative for patient care and/or actionable to guide treatment in 57% (17/30) of cases. Conclusion The use of whole genome sequencing technology to identify valuable molecular information to guide personalized breast cancer treatment is feasible. Further studies are warranted to evaluate the usefulness of genome-wide sequencing of breast cancers in clinical practice. Citation Format: Sophie Sun, Karen A. Gelmon, Stephen Chia, Caroline Lohrisch, Tamara Shenkier, Diego Villa, Yaoqing Shen, Martin Jones, Erin Pleasance, Katayoon Kasaian, Peter Eirew, Sreeja Leelakumari, Yusanne Ma, Tony Ng, Stephen Yip, Steven JM Jones, Marco A. Marra, Janessa J. Laskin. Personalized oncogenomics in advanced stage breast cancer. [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 1122. doi:10.1158/1538-7445.AM2015-1122
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".