1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
EARN as a precision oncology tool leads us to propose the targeted genes panel for metastatic breast cancer
Paper ID : 1187-ICB10
Authors:
Leila Mirsadeghi *1, Kaveh Kavousi2, Ali Mohammad Banaei-Moghaddam3
1مدرس مدعو
2University of Tehran
3بیوشیمی، موسسه بیوشیمی و بیوفیزیک، دانشگاه تهران، تهران، ایران
Abstract:
Today, there are a lot of bio-markers on the prognosis and diagnosis of complex diseases such as primary breast cancer. However, our understanding of the drivers that influence cancer aggression is limited. Present investigation studies somatic mutation data consisting of 450 metastatic breast tumor samples from cBio Cancer Genomics Portal. We use four software tools to extract features from this data. Then, an ensemble classifier learning algorithm called EARN (Ensemble of Artificial Neural Network, Random Forest, and non-linear Support Vector Machine) is proposed to evaluate plausible driver genes for metastatic breast cancer (MBCA). It is an attempt to focus on the findings in four aspects of MBCA prognosis. First, drivers and passengers predicted by SVM, ANN, RF, and EARN are introduced. Second, the performance of four learning methods is evaluated using statistical criteria. Third, the outputs of the biological inference based on gene set enrichment analysis (GSEA) and pathway enrichment analysis (PEA) are discussed. Finally, the PEA using ReactomeFIVIz tool (FDR<0.03) for the top 100 predicted genes by EARN leads us to propose a new gene set panel for MBCA, including HDAC3, ABAT, GRIN1, PLCB1, and KPNA2 as well as NCOR1, TBL1XR1, SIRT4, KRAS, CACNA1E, PRKCG, GPS2, SIN3A, ACTB, KDM6B, and PRMT1. Furthermore, we compare results for MBCA to other outputs regarding 983 primary breast invasive carcinoma (BRCA) tumor samples obtained from The Cancer Genome Atlas (TCGA). Meanwhile, the 16-gene panel proposed by EARN has been surveyed in the whole-exome sequence of an archived FFPE sample obtained from breast tissue of an anonymous Iranian female patient with invasive breast carcinoma. This research leverages both computational and experimental approaches to assist precision oncologists to design compact targeted panels that eliminate the need for whole-genome/exome sequencing.
Keywords:
Metastasis breast tumor; Mutation data; Ensemble classifier; Plausible driver genes; Targeted gene panel
Status : Paper Accepted (Oral Presentation)