Single-Cell RNA-Seq Reveals Heterogeneous lncRNA Expression in Xenografted Triple-Negative Breast Cancer Cells

Targeting and engineering long non-coding RNAs for cancer therapy

RNA therapeutics (RNATx) aim to treat diseases, including cancer, by targeting or employing RNA molecules for therapeutic purposes. Amongst the most promising targets are long non-coding RNAs (lncRNAs), which regulate oncogenic molecular networks in a cell type-restricted manner. lncRNAs are distinct from protein-coding genes in important ways that increase their therapeutic potential yet also present hurdles to conventional clinical development. Advances in genome editing, oligonucleotide chemistry, multi-omics and RNA engineering are paving the way for efficient and cost-effective lncRNA-focused drug discovery pipelines. In this Review, we present the emerging field of lncRNA therapeutics for oncology, with emphasis on the unique strengths and challenges of lncRNAs within the broader RNATx framework. We outline the necessary steps for lncRNA therapeutics to deliver effective, durable, tolerable and personalized treatments for cancer.

Recent advances of transcriptomics and proteomics in triple-negative breast cancer prognosis assessment

Triple-negative breast cancer (TNBC), a heterogeneous tumour that lacks the expression of oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2), is often characterized by aggressiveness and tends to recur or metastasize. TNBC lacks therapeutic targets compared with other subtypes and is not sensitive to endocrine therapy or targeted therapy except chemotherapy. Therefore, identifying the prognostic characteristics and valid therapeutic targets of TNBC could facilitate early personalized treatment. Due to the rapid development of various technologies, researchers are increasingly focusing on integrating 'big data' and biological systems, which is referred to as 'omics', as a means of resolving it. Transcriptomics and proteomics analyses play an essential role in exploring prospective biomarkers and potential therapeutic targets for triple-negative breast cancers, which provides a powerful engine for TNBC's therapeutic discovery when combined with complementary information. Here, we review the recent progress of TNBC research in transcriptomics and proteomics to identify possible therapeutic goals and improve the survival of patients with triple-negative breast cancer. Also, researchers may benefit from this article to catalyse further analysis and investigation to decipher the global picture of TNBC cancer.

A Portrait of Intratumoral Genomic and Transcriptomic Heterogeneity at Single-Cell Level in Colorectal Cancer

In the study of cancer, omics technologies are supporting the transition from traditional clinical approaches to precision medicine. Intra-tumoral heterogeneity (ITH) is detectable within a single tumor in which cancer cell subpopulations with different genome features coexist in a patient in different tumor areas or may evolve/differ over time. Colorectal carcinoma (CRC) is characterized by heterogeneous features involving genomic, epigenomic, and transcriptomic alterations. The study of ITH is a promising new frontier to lay the foundation towards successful CRC diagnosis and treatment. Genome and transcriptome sequencing together with editing technologies are revolutionizing biomedical research, representing the most promising tools for overcoming unmet clinical and research challenges. Rapid advances in both bulk and single-cell next-generation sequencing (NGS) are identifying primary and metastatic intratumoral genomic and transcriptional heterogeneity. They provide critical insight in the origin and spatiotemporal evolution of genomic clones responsible for early and late therapeutic resistance and relapse. Single-cell technologies can be used to define subpopulations within a known cell type by searching for differential gene expression within the cell population of interest and/or effectively isolating signal from rare cell populations that would not be detectable by other methods. Each single-cell sequencing analysis is driven by clustering of cells based on their differentially expressed genes. Genes that drive clustering can be used as unique markers for a specific cell population. In this review we analyzed, starting from published data, the possible achievement of a transition from clinical CRC research to precision medicine with an emphasis on new single-cell based techniques; at the same time, we focused on all approaches and issues related to this promising technology. This transition might enable noninvasive screening for early diagnosis, individualized prediction of therapeutic response, and discovery of additional novel drug targets.