Shaza Fadlalah poster

Shaza Fadlalah poster

ANALYSIS OF SOMATIC MUTATION IN SOUTH AFRICAN BREAST CANCER       PATIENTS USING GENE EXPRESSION

 

Shaza Fadlalla¹

1Centre for Bioinformatics and Computational Biology, Genomics Research Institute and Department of Biochemistry

 

 

Background

Breast cancer has a high prevalence in South Africa, being the second most common form of cancer in women. Screening and early diagnosis of breast cancer is critically important for the successful treatment of this disease. Two high penetrance breast cancer susceptibility genes, BRCA1 and BRCA2 are clinically the most important genes associated with hereditary breast cancer.

International studies investigating the BRCA and other markers for breast cancer in African populations is limited. Effective markers are urgently needed for screening and management, and also for further molecular understanding of breast cancer in southern African population.

Aim

Identification of driver genes in a patient’s tumour may assist in the understanding of cancer progression and the selection of suitable treatment protocols.

Methods

FFPE tuomer samples were analyzed in the study, gene Expression analysis was done on somatic samples using the NanoString PanCancer Pathway Panel (NanoString, USA).Expression data was normalized and differentiated using nanostringdiff, Pathway analysis for differentially-expressed genes in somatic samples was performed using reactomePA.

Results

NanoString counts in the FFPE-based somatic samples were generally much lower than in the normal breast tissue controls. Counts from samples up to 48% of counts from the 770 cancer-related nCounter genes were discarded as they approached background levels. For the remaining genes, differentially expressed genes were identified using nanostringdiff. Annotation was done for Reactome Pathways using reactomePA .Predominant pathways included cell-cycle pathways and signalling pathways.

Conclusion

Gene expression data showed possible driver mutations in POLR2B, LRPB1, SMAD1 and MAP3K4. Gene expression analysis showed a significant disruption of signal transduction and G1 phase effects.

 

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