Abstract

GENOMICS ASSOCIATION RESOLUTE THE PROGRESSION OF BREAST CANCER

Dr. Ram Mohan Muthukannan*, Syed Abuthahir Hakkeem, Divakar Balamanikandan, Shanfiya Banu Syed Abuthahir, Surya Kannan, Kiruthiga Chidambaram, J. Karthikeyan, Vinothapooshan G. and Dr. Deepa Natarajan

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Abstract

Breast Cancer (BC) is one of the most prevalent cancer and the second most common cancer of death in women. Among various characteristics of breast cancer, progression, and aggression are currently evaluated using genomic markers. Ki67, TP53, GATA3, PIK3CA, AKT1, ERBB2 large scale genomic analyses have revealed and mutational impact for this diseases. Frequent somatic mutations occur and activating Pi3k – AKT signaling and inactivate the GATA3 JUN kinase pathway. The overexpression of ERBB2 causes by the genomic mutational impact for the development of breast cancer. The over expression of ERBB2 have a clear transcription profile leads to pathogenesis of Breast Cancer. These includes a number of clinically important alterations and mutations are inactivating SWI – SNF and JAK2 – STAT3 pathways. It can provide information on prognosis and predict response to treatment in the adjuvant and neoadjuvant settings (Leung et al., 2016; Urruticoechea et al., 2005; Schwab et al., 1982; Viale et al., 2008). High ki67 score is associated with poor prognosis (Azambuja et al., 2007). Ki67 is a nuclear protein found in a Hodgkin lymphoma cell line (Gerdes et al., 1983). The original aim and scope of TCGA was to genomically characterize primary, untreated tumors with a basic set of genetic alterations and transcript profiles. As the program is now completed, a future challenge is to expand these analyses to larger sample sets, additional data types, such as metabolite levels, a wider range of epigenetic states, posttranslational modifications of proteins, and to investigate metastatic disease and genomic alterations that arise in post-treatment samples, as well as analysing the role of a wider range of germline alterations and their interplay with somatic events. These new avenues of research will benefit from pathway-level analysis for which the templates and curation pipelines presented here constitute a promising starting point. Similarly, as the catalog of clinically actionable alterations continues to grow, understanding intra- and inter pathway dependencies, such as the ones considered here, will be crucial for the development of effective combination therapies that address or prevent resistance to initially successful single agent therapies.

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