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Review
. 2018 Apr;16(2):108-119.
doi: 10.1016/j.gpb.2018.03.002. Epub 2018 May 9.

Applications of RNA Indexes for Precision Oncology in Breast Cancer

Liming Ma  1 , Zirui Liang  1 , Hui Zhou  1 , Lianghu Qu  2
Affiliations
Review

Applications of RNA Indexes for Precision Oncology in Breast Cancer

Liming Ma et al. Genomics Proteomics Bioinformatics. 2018 Apr.

Abstract

Precision oncology aims to offer the most appropriate treatments to cancer patients mainly based on their individual genetic information. Genomics has provided numerous valuable data on driver mutations and risk loci; however, it remains a formidable challenge to transform these data into therapeutic agents. Transcriptomics describes the multifarious expression patterns of both mRNAs and non-coding RNAs (ncRNAs), which facilitates the deciphering of genomic codes. In this review, we take breast cancer as an example to demonstrate the applications of these rich RNA resources in precision medicine exploration. These include the use of mRNA profiles in triple-negative breast cancer (TNBC) subtyping to inform corresponding candidate targeted therapies; current advancements and achievements of high-throughput RNA interference (RNAi) screening technologies in breast cancer; and microRNAs as functional signatures for defining cell identities and regulating the biological activities of breast cancer cells. We summarize the benefits of transcriptomic analyses in breast cancer management and propose that unscrambling the core signaling networks of cancer may be an important task of multiple-omic data integration for precision oncology.

Keywords: Breast cancer; Precision oncology; RNA interference; Transcriptomics; microRNA.

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Figures

Figure 1
Figure 1
Schema of the clinical classifications of breast cancers and the corresponding targeted therapies approved Breast cancers are clinically classified into three types: ER+, HER2+, and TNBC, according to the expression status of ER, PR, and HER2, which can be further divided into several subtypes as illustrated. TNBC is associated with the worst prognosis and is more aggressive than the other two types, and currently there are no targeted agents approved for TNBC yet. ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; TNBC, triple-negative breast cancer; SERM, selective estrogen receptor modulator; AI, aromatase inhibitor; BL, basal-like; IM, immunomodulatory; M, mesenchymal; MSL, mesenchymal stem-like; LAR, luminal androgen receptor; UNS, unstable.
Figure 2
Figure 2
A general flowchart for high-throughput RNAi screening High-throughput RNAi screening usually comprises three phases. In phase I, the screening strategies, including gene sets, RNAi libraries, and screening scales, are determined mainly depending on the researchers’ purposes. The results of phenotypic assays are evaluated and normalized for the selection of effective hits. In phase II, the primary hits are validated by a second round of screening to confirm the “driver” genes, uncover the hidden synthetic lethal relationships, and disclose the critical signaling pathways. In phase III, targeted agents are tested both in vitro and in vivo, alone or in combination with other approved therapies. DE, differentially expressed; siRNA, small interfering RNA; shRNA, short hairpin RNA.
Figure 3
Figure 3
Functional miRNAs involved in TNBC Many miRNAs are differentially expressed in TNBC. They may act as tumor promoters (oncomiRs) or tumor suppressors (anti-oncomiRs) to regulate the capacities of proliferation and/or invasion of TNBC by suppressing their corresponding targets. DDR, DNA damage response.
Figure 4
Figure 4
Applications of mRNA and miRNA indexes in breast cancer Transcriptomic analyses reveal the expression patterns of both mRNAs and miRNAs. TNBC has been classified into different subtypes according to the cluster analysis of the distinct mRNA expression profiles. Additionally, high-throughput RNAi screening is widely applied to authenticate the “driver” inactivated genes and identify the hidden synthetic lethal relationships. Transcriptomic analyses also facilitate the discovery and validation of breast cancer-associated miRNAs. Therapeutic strategies, based on the inhibition and restoration of deregulated miRNAs, are now undergoing trials and hold great promise for breast cancer treatment. ASO, antisense oligonucleotide; LNA, locked nucleic acid; TuD, tough decoy.
Figure 5
Figure 5
Integration of multi-omic data to converge at the core signaling transduction pathways in cancer management Comprehensive analyses of genome, transcriptome, proteome, epigenome, metabolome, and tumor microenvironment delineate the diverse aspects of cancer biology. Integration of these multi-omic data into the core intracellular signaling transduction pathways helps to provide accurate guidelines for cancer diagnosis and treatment.
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