Discipline: Technology and Engineering
Subcategory: Cancer Research
Camille Marrero - University of Puerto Rico- Mayaguez Campus
Co-Author(s): Clara Isaza, Ponce Health Sciences University, Ponce, PR; Mauricio Cabrera Ríos, University of Puerto Rico Mayagüez Campus, Mayagüez, PR
Breast cancer is the second leading cause of cancer death in women according to statistics from the American Cancer Society. Certain factors such as, the genetic factor, have a crucial role in increasing the risk of suffering breast cancer. By understanding the genetic basis of breast cancer, society can benefit through preventive testing and early detection, as well as –given the case- personalized treatment. This work aims to provide biological evidence of genes that are deemed important to the presence and development of this illness, as well as their coordinated behavior. The determination of these genes shall be determined by the application of multiple criteria optimization. The novelty in this study is the characterization of the signaling path through the Travelling Salesman Problem combinatorial optimization formulation. This analysis was done with the use of a microarray database related to breast cancer by finding coordinated behavior among the expression levels of different genes in various types of breast cancer metastasis. To carry out the database analysis we applied the Multiple Criteria Optimization technique (MCO); this is an optimization problem that finds a set of solutions corresponding to the best possible relation among two or more conflicting performance measures under study. The genetic expression in each type of breast cancer metastasis was quantified using the metrics of difference of means and difference of medians. For the analysis, the MCO solutions were the genes that had associated the greatest differences in the selected metrics. Potential biomarkers were selected with an intragroup comparison. By finding the genes with the most significant change in expression in each individual metastasis category we were able to compare them with the other potential biomarkers of the other types of metastatic breast cancer.The resulting structure is, indeed, a global optimum and, thus, should provide a very competitive manner to direct biological search and organize the evidence of the roles and dynamics of genes in breast cancer. The database with GEO identifier GDS4761 was used for this analysis. The database contains global gene expression for 51,562 genes from 91 samples, however only 88 samples were considered as significant data. The cancerous samples were divided as mentioned: 5 bone metastasis, 11 local in the breast metastasis, 16 liver metastasis, 39 lymph node metastasis and 17 skin metastasis. Samples were collected before commencement of treatment. A multiple criteria optimization analysis was used to obtain the significant genes of each type of metastasis, so that a comparison could be made between the significant genes of one type of metastases with another. 10 comparative pairs of breast cancer metastases were generated: bone-breast metastasis, bone-liver metastasis, bone-lymph metastasis, bone-skin metastasis, breast-liver metastasis, breast-lymph metastasis, breast-skin metastasis, liver-lymph metastasis, liver-skin metastasis, and lymph-skin metastasis. Since multiple expressed genes for all the comparisons of the different groups were observed, the genes that displayed the most frequency were selected. Preliminary results demonstrated patterns of gene expression in comparative pairs that shared one type of metastasis when compared with other types. This study aims to detect important genes in breast cancer, and offer a structure of maximal correlation among them to guide the search of biological evidence for the role and dynamics of these genes. Ultimately, the idea is to provide analysis objectivity and translational power to the study of high-throughput biological experiments similar to microarrays to contribute to better understand and to make better decisions with regards to breast cancer. In future studies a traveling salesman problem approach will be applied where linear correlation values between the expression of different genes can be used to construct a signaling pathway. Uniprot Database and the Database for Annotation, Visualization, and Integrated Discovery (DAVID) will be used to further understand the effect and function of the patterns observed in the preliminary results.Not Submitted
Funder Acknowledgement(s): Puerto Rico Stokes Alliance for Minority Participation(PR-LSAMP). USDA-NIFA Award 2015-38422-24064 sub award 1000000920. BE AWARE Project.
Faculty Advisor: Mauricio Cabrera, email@example.com
Role: As part of this research my responsibilities have been to obtain the indicated database all the methods my research group developed. Once I get to the mathematical results obtained from the analysis of the MCO and TSP, my primary task is to give a biological interpretation to mathematical results. In order to give a clear and concise interpretation, I read several articles related to breast cancer metastasis and cancer research in general. Through literature and analytical thinking, I can dissect my results and propose significant genes in the occurrence of breast cancer metastasis.