Abstract 1944: Integrated analysis of copy number and miRNA profiling in triple negative breast cancer of Latina women
Bibliographic record
Abstract
Abstract Triple negative breast cancer (TNBC) is a clinically and molecularly heterogeneous disease, which incidence and outcome vary among the different ethnic and racial groups. These tumors are more commonly seen in younger women of African and Latinas/Hispanic descents, usually diagnosed at more advanced stages, with non-localized disease. Molecular studies have shown differences in the biology of these tumors in Latinas as key contributors for high mortality. The main objective of our study was to identify a miRNA signature associated with TNBC of Latina patients by integrating miRNA and array-CGH data. Archived paraffin samples of 32 TNBC and 25 non-TNBC cases from Latina patients, obtained from the Clinical Hospital (UFPR) in Brazil, were profiled for miRNA and array-CGH using the Nanostring and the Agilent SurePrint G3 Human array-CGH platforms, respectively. The miRNA and array-CGH data, obtained in the same samples, was directly integrated and combinatorial target predicted algorithms in conjunction with functional and pathway annotation enrichment systems were performed to identify the most relevant miRNAs and their corresponding gene targets. Eight-nine miRNAs were observed differentially expressed between the TNBC and non-TNBC lesions. Using miRBase and MiRDB target prediction databases 3,378 miRNAs targets were identified. After integration with array-CGH, a number of 15 miRNAs presented concomitant DNA copy number and miRNA alterations, reducing the number of targets to 1,242. Ingenuity pathway analysis identified the most affected canonical pathways, including IL-8, TGF-beta, PI3K-AKT and HER2 signaling pathways. Using our integration approach we were able to identify a robust miRNA signature associated with TNBC of Latina patients and to identify the potential molecular mechanism (s) that underline the observed miRNA deregulation in these patients. This signature revealed miRNAs and corresponding targets biologically relevant, involved in critical cancer signaling pathways and gene networks. Once this signature is functionally validated, its direct role in the aggressive clinical phenotype of these tumors will be determined. In summary, the findings of our study contributes to the identification of race/ethnic specific molecular targets that can set the basis for new TNBC treatments and for the future design of the most appropriate clinical trials and cancer control interventions for Latina women. Funding:This project was supported by the Georgetown University Center of Excellence in Regulatory Science and Innovation (CERSI; U01FD004319), a collaborative effort between the university and the U.S. Food and Drug Administration to promote regulatory science through innovative research and education. This research does not necessarily reflect the views of the FDA. Citation Format: Bruna M. Sugita, Mandeep Gill, Silma R. Pereira, Yara R. Zabala, Aline S. Fonseca, Selene E. Esposito, Catalin Marian, Iglenir J. Cavalli, Enilze MF Ribeiro, Yuriy Gusev, Luciane R. Cavalli. Integrated analysis of copy number and miRNA profiling in triple negative breast cancer of Latina women. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1944.
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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 | 0.000 |
| 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) | 0.002 | 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".