SUMMARY The discovery of long non-coding RNA (lncRNA) has dramatically altered our understanding of cancer. Here, we describe a comprehensive analysis of lncRNA alterations at transcriptional, genomic, and epigenetic levels in 5,037 human tumor specimens across 13 cancer types from the Cancer Genome Atlas (TCGA). Our results suggest that the expression and dysregulation of lncRNAs are highly cancer-type specific compared to protein-coding genes. Using the integrative data generated by this analysis, we present a clinically guided small interfering RNA screening strategy and a co-expression analysis approach to identify cancer driver lncRNAs and predict their functions. This provides a resource for investigating lncRNAs in cancer and lays the groundwork for the development of new diagnostics and treatments.

NONCODE (http://www.bioinfo.org/noncode/) is an integrated knowledge database dedicated to non-coding RNAs (excluding tRNAs and rRNAs). Non-coding RNAs (ncRNAs) have been implied in diseases and identified to play important roles in various biological processes. Since NONCODE version 3.0 was released 2 years ago, discovery of novel ncRNAs has been promoted by high-throughput RNA sequencing (RNA-Seq). In this update of NONCODE, we expand the ncRNA data set by collection of newly identified ncRNAs from literature published in the last 2 years and integration of the latest version of RefSeq and Ensembl. Particularly, the number of long non-coding RNA (lncRNA) has increased sharply from 73 327 to 210 831. Owing to similar alternative splicing pattern to mRNAs, the concept of lncRNA genes was put forward to help systematic understanding of lncRNAs. The 56 018 and 46 475 lncRNA genes were generated from 95 135 and 67 628 lncRNAs for human and mouse, respectively. Additionally, we present expression profile of lncRNA genes by graphs based on public RNA-seq data for human and mouse, as well as predict functions of these lncRNA genes. The improvements brought to the database also include an incorporation of an ID conversion tool from RefSeq or Ensembl ID to NONCODE ID and a service of lncRNA identification. NONCODE is also accessible through http://www.noncode.org/.

Long noncoding RNAs (lncRNAs), which are transcripts that are larger than 200 nucleotides but do not appear to have protein-coding potential, play critical roles during tumorigenesis by functioning as scaffolds to regulate protein-protein, protein-DNA or protein-RNA interactions. Using a clinically guided genetic screening approach, we identified (lncRNA in Non-homologous end joining [NHEJ] pathway 1) as a lncRNA that is overexpressed in human triple-negative breast cancer. We found that LINP1 enhances double-strand DNA break repair by serving as a scaffold that links Ku80 and DNA-PKcs, thereby coordinating the NHEJ pathway. Importantly, blocking LINP1, which is regulated by the p53 and epidermal growth factor receptor (EGFR) signaling, increases sensitivity of tumor cell response to radiotherapy in breast cancer.

Strategies to enhance response to poly(adenosine diphosphate–ribose) polymerase inhibitor (PARPi) in primary and acquired homologous recombination (HR)–proficient tumors would be a major advance in cancer care. We used a drug synergy screen that combined a PARPi, olaparib, with 20 well-characterized epigenetic drugs and identified bromodomain and extraterminal domain inhibitors (BETis; JQ1, I-BET762, and OTX015) as drugs that acted synergistically with olaparib in HR-proficient cancer cells. Functional assays demonstrated that repressed BET activity reduces HR and thus enhances PARPi-induced DNA damage in cancer cells. We also found that inhibition or depletion of BET proteins impairs transcription of BRCA1 and RAD51, two genes essential for HR. Moreover, BETi treatment sensitized tumors to PARP inhibition in preclinical animal models of HR-proficient breast and ovarian cancers. Finally, we showed that the BRD4 gene was focally amplified across 20 types of common cancers. Combination with BETi could greatly expand the utility of PARP inhibition to patients with HR-proficient cancer.

SUMMARY Disparities in cancer care have been a long-standing challenge. We estimated the genetic ancestry of The Cancer Genome Atlas patients, and performed a pan-cancer analysis on the influence of genetic ancestry on genomic alterations. Compared with European Americans, African Americans (AA) with breast, head and neck, and endometrial cancers exhibit a higher level of chromosomal instability, while a lower level of chromosomal instability was observed in AAs with kidney cancers. The frequencies of TP53 mutations and amplification of CCNE1 were increased in AAs in the cancer types showing higher levels of chromosomal instability. We observed lower frequencies of genomic alterations affecting genes in the PI3K pathway in AA patients across cancers. Our result provides insight into genomic contribution to cancer disparities.

NPInter (http://www.bioinfo.org/NPInter) is a database that integrates experimentally verified functional interactions between noncoding RNAs (excluding tRNAs and rRNAs) and other biomolecules (proteins, RNAs and genomic DNAs). Extensive studies on ncRNA interactions have shown that ncRNAs could act as part of enzymatic or structural complexes, gene regulators or other functional elements. With the development of high-throughput biotechnology, such as cross-linking immunoprecipitation and high-throughput sequencing (CLIP-seq), the number of known ncRNA interactions, especially those formed by protein binding, has grown rapidly in recent years. In this work, we updated NPInter to version 2.0 by collecting ncRNA interactions from recent literature and related databases, expanding the number of entries to 201 107 covering 18 species. In addition, NPInter v2.0 incorporated a service for the BLAST alignment search as well as visualization of interactions.

Men of predominantly African Ancestry (AA) have higher prostate cancer (CaP) incidence and worse survival than men of predominantly European Ancestry (EA). While socioeconomic factors drive this disparity, genomic factors may also contribute to differences in the incidence and mortality rates. To compare the prevalence of prostate tumor genomic alterations and transcriptomic profiles by patient genetic ancestry, we evaluated genomic profiles from The Cancer Genome Atlas (TCGA) CaP cohort (n = 498). Patient global and local genetic ancestry were estimated by computational algorithms using genotyping data; 414 (83.1%) were EA, 61 (12.2%) were AA, 11 (2.2%) were East Asian Ancestry (EAA), 10 (2.0%) were Native American (NA), and 2 (0.4%) were other ancestry. Genetic ancestry was highly concordant with self-identified race/ethnicity. Subsequent analyses were limited to 61 AA and 414 EA cases. Significant differences were observed by ancestry in the frequency of SPOP mutations (20.3% AA vs. 10.0% EA; p = 5.6×10 −03 ), TMPRSS2-ERG fusions (29.3% AA vs. 39.6% EA; p = 4.4×10 −02 ), and PTEN deletions/losses (11.5% AA vs. 30.2% EA; p = 3.5×10 −03 ). Differentially expressed genes (DEGs) between AAs and EAs showed significant enrichment for prostate eQTL target genes (p = 8.09×10 −48 ). Enrichment of highly expressed DEGs for immune-related pathways was observed in AAs, and for PTEN/PI3K signaling in EAs. Nearly one-third of DEGs (31.3%) were long non-coding RNAs (DE-lncRNAs). The proportion of DE-lncRNAs with higher expression in AAs greatly exceeded that with lower expression in AAs (p = 1.2×10 −125

Despite the fact that a large quantity of noncoding RNAs (ncRNAs) have been identified, their functions remain unclear. To enable researchers to have a better understanding of ncRNAs’ functions, we updated the NPInter database to version 3.0, which contains experimentally verified interactions between ncRNAs (excluding tRNAs and rRNAs), especially long noncoding RNAs (lncRNAs) and other biomolecules (proteins, mRNAs, miRNAs and genomic DNAs). In NPInter v3.0, interactions pertaining to ncRNAs are not only manually curated from scientific literature but also curated from high-throughput technologies. In addition, we also curated lncRNA–miRNA interactions from in silico predictions supported by AGO CLIP-seq data. When compared with NPInter v2.0, the interactions are more informative (with additional information on tissues or cell lines, binding sites, conservation, co-expression values and other features) and more organized (with divisions on data sets by data sources, tissues or cell lines, experiments and other criteria). NPInter v3.0 expands the data set to 491,416 interactions in 188 tissues (or cell lines) from 68 kinds of experimental technologies. NPInter v3.0 also improves the user interface and adds new web services, including a local UCSC Genome Browser to visualize binding sites. Additionally, NPInter v3.0 defined a high-confidence set of interactions and predicted the functions of lncRNAs in human and mouse based on the interactions curated in the database. NPInter v3.0 is available at http://www.bioinfo.org/NPInter/.Database URL: http://www.bioinfo.org/NPInter/