Integrative exploration of genomic profiles for triple negative breast cancer identifies potential drug targets

Abstract

Background: Triple negative breast cancer (TNBC) is high-risk due to its rapid drug resistance and recurrence, metastasis, and lack of targeted therapy. So far, no molecularly targeted therapeutic agents have been clinically approved for TNBC. It is imperative that we discover new targets for TNBC therapy.

Objectives: A large volume of cancer genomics data are emerging and advancing breast cancer research. We may integrate different types of TNBC genomic data to discover molecular targets for TNBC therapy.

Data sources: We used publicly available TNBC tumor tissue genomic data in the Cancer Genome Atlas database in this study.

Methods: We integratively explored genomic profiles (gene expression, copy number, methylation, microRNA [miRNA], and gene mutation) in TNBC and identified hyperactivated genes that have higher expression, more copy numbers, lower methylation level, or are targets of miRNAs with lower expression in TNBC than in normal samples. We ranked the hyperactivated genes into different levels based on all the genomic evidence and performed functional analyses of the sets of genes identified. More importantly, we proposed potential molecular targets for TNBC therapy based on the hyperactivated genes.

Results: Some of the genes we identified such as FGFR2, MAPK13, TP53, SRC family, MUC family, and BCL2 family have been suggested to be potential targets for TNBC treatment. Others such as CSF1R, EPHB3, TRIB1, and LAD1 could be promising new targets for TNBC treatment. By utilizing this integrative analysis of genomic profiles for TNBC, we hypothesized that some of the targeted treatment strategies for TNBC currently in development are more likely to be promising, such as poly (ADP-ribose) polymerase inhibitors, while the others are more likely to be discouraging, such as angiogenesis inhibitors.

Limitations: The findings in this study need to be experimentally validated in the future.

Conclusion: This is a systematic study that combined 5 different types of genomic data to molecularly characterize TNBC and identify potential targets for TNBC therapy. The integrative analysis of genomic profiles for TNBC could assist in identifying potential new therapeutic targets and predicting the effectiveness of a targeted treatment strategy for TNBC therapy.

Figure 1

Numbers of the genes identified in all the genomic analyses.

Figure 2

The Level 3 genes and their hyperactivated status in the different genomic analyses. The gray color indicates that the gene is hyperactivated in the analysis, and the white indicates that the gene isn’t hyperactivated in the analysis.

Figure 3

TP53-centered protein–protein interaction network identified based on the Level 2 gene set using Ingenuity Pathway Analysis.

Figure 4

Compare mutation frequency of the frequently-mutated genes in between TNBC and breast cancer in general. The Fisher exact test P values are presented.