Systematic lncRNA mapping to genome-wide co-essential modules uncovers cancer dependency on uncharacterized lncRNAs

POCALI: Prediction and Insight on CAncer LncRNAs by Integrating Multi-Omics Data with Machine Learning

Long non-coding RNAs (lncRNAs) are receiving increasing attention as biomarkers for cancer diagnosis and therapy. Although there are many computational methods to identify cancer lncRNAs, they do not comprehensively integrate multi-omics features for predictions or systematically evaluate the contribution of each omics to the multifaceted landscape of cancer lncRNAs. In this study, an algorithm, POCALI, is developed to identify cancer lncRNAs by integrating 44 omics features across six categories. The contributions of different omics are explored to identifying cancer lncRNAs and, more specifically, how each feature contributes to a single prediction. The model is evaluated and benchmarked POCALI with existing methods. Finally, the cancer phenotype and genomics characteristics of the predicted novel cancer lncRNAs are validated. POCALI identifies secondary structure and gene expression-related features as strong predictors of cancer lncRNAs, and epigenomic features as moderate predictors. POCALI performed better than other methods, especially in terms of sensitivity, and predicted more candidates. Novel POCALI-predicted cancer lncRNAs have strong relationships with cancer phenotypes, similar to known cancer lncRNAs. Overall, this study facilitates the identification of previously undetected cancer lncRNAs and the comprehensive exploration of the multifaceted feature contributions to cancer lncRNA prediction.

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.

Systematic Characterization of p53-Regulated Long Noncoding RNAs across Human Cancers Reveals Remarkable Heterogeneity among Different Tumor Types

The p53 tumor suppressor protein, a sequence-specific DNA binding transcription factor, regulates the expression of a large number of genes, in response to various forms of cellular stress. Although the protein coding target genes of p53 have been well studied, less is known about its role in regulating long noncoding genes and their functional relevance to cancer. Here we report the genome-wide identification of a large set (>1,000) of long noncoding RNAs (lncRNA), which are putative p53 targets in a colon cancer cell line and in human patient datasets from five different common types of cancer. These lncRNAs have not been annotated by other studies of normal unstressed systems. In the colon cancer cell line, a high proportion of these lncRNAs are uniquely induced by different chemotherapeutic agents that activate p53, whereas others are induced by more than one agent tested. Further, subsets of these lncRNAs independently predict overall and disease-free survival of patients across the five different common cancer types. Interestingly, both genetic alterations and patient survival associated with different lncRNAs are unique to each cancer tested, indicating extraordinary tissue-specific variability in the p53 noncoding response. The newly identified noncoding p53 target genes have allowed us to construct a classifier for tumor diagnosis and prognosis.

IMPLICATIONS

Our results not only identify myriad p53-regulated long noncoding (lncRNA), they also reveal marked drug-induced, as well as tissue- and tumor-specific heterogeneity in these putative p53 targets and our findings have enabled the construction of robust classifiers for diagnosis and prognosis.

Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer

In this review, we provide a general overview of the current panorama of mining strategies for multi-omics data to investigate lncRNAs with an actual or potential role as biological markers in cancer. Several multi-omics studies focusing on lncRNAs have been performed in the past with varying scopes. Nevertheless, many questions remain regarding the pragmatic application of different molecular technologies and bioinformatics algorithms for mining multi-omics data. Here, we attempt to address some of the less discussed aspects of the practical applications using different study designs for incorporating bioinformatics and statistical analyses of multi-omics data. Finally, we discuss the potential improvements and new paradigms aimed at unraveling the role and utility of lncRNAs in cancer and their potential use as molecular markers for cancer diagnosis and outcome prediction.

lncRNAs-EZH2 interaction as promising therapeutic target in cutaneous melanoma

Melanoma is the most lethal skin cancer with increasing incidence worldwide. Despite a great improvement of diagnostics and treatment of melanoma patients, this disease is still a serious clinical problem. Therefore, novel druggable targets are in focus of research. EZH2 is a component of the PRC2 protein complex that mediates epigenetic silencing of target genes. Several mutations activating EZH2 have been identified in melanoma, which contributes to aberrant gene silencing during tumor progression. Emerging evidence indicates that long non-coding RNAs (lncRNAs) are molecular "address codes" for EZH2 silencing specificity, and targeting lncRNAs-EZH2 interaction may slow down the progression of many solid cancers, including melanoma. This review summarizes current knowledge regarding the involvement of lncRNAs in EZH2-mediated gene silencing in melanoma. The possibility of blocking lncRNAs-EZH2 interaction in melanoma as a novel therapeutic option and plausible controversies and drawbacks of this approach are also briefly discussed.

Targeted MDM2 Degradation Reveals a New Vulnerability for p53-Inactivated Triple-Negative Breast Cancer

Triple-negative breast cancers (TNBC) frequently inactivate p53, increasing their aggressiveness and therapy resistance. We identified an unexpected protein vulnerability in p53-inactivated TNBC and designed a new PROteolysis TArgeting Chimera (PROTAC) to target it. Our PROTAC selectively targets MDM2 for proteasome-mediated degradation with high-affinity binding and VHL recruitment. MDM2 loss in p53 mutant/deleted TNBC cells in two-dimensional/three-dimensional culture and TNBC patient explants, including relapsed tumors, causes apoptosis while sparing normal cells. Our MDM2-PROTAC is stable in vivo, and treatment of TNBC xenograft-bearing mice demonstrates tumor on-target efficacy with no toxicity to normal cells, significantly extending survival. Transcriptomic analyses revealed upregulation of p53 family target genes. Investigations showed activation and a required role for TAp73 to mediate MDM2-PROTAC-induced apoptosis. Our data, challenging the current MDM2/p53 paradigm, show MDM2 is required for p53-inactivated TNBC cell survival, and PROTAC-targeted MDM2 degradation is an innovative potential therapeutic strategy for TNBC and superior to existing MDM2 inhibitors.

SIGNIFICANCE

p53-inactivated TNBC is an aggressive, therapy-resistant, and lethal breast cancer subtype. We designed a new compound targeting an unexpected vulnerability we identified in TNBC. Our MDM2-targeted degrader kills p53-inactivated TNBC cells, highlighting the requirement for MDM2 in TNBC cell survival and as a new therapeutic target for this disease. See related commentary by Peuget and Selivanova, p. 1043. This article is highlighted in the In This Issue feature, p. 1027.

Decoding the lncRNAome Across Diverse Cellular Stresses Reveals Core p53-effector Pan-cancer Suppressive lncRNAs

Despite long non-coding RNAs (lncRNAs) emerging as key contributors to malignancies, their transcriptional regulation, tissue-type expression under different conditions, and functions remain largely unknown. Developing a combined computational and experimental framework, which integrates pan-cancer RNAi/CRISPR screens, and genomic, epigenetic, and expression profiles (including single-cell RNA sequencing), we report across multiple cancers, core p53-transcriptionally regulated lncRNAs, which were thought to be primarily cell/tissue-specific. These lncRNAs were consistently directly transactivated by p53 with different cellular stresses in multiple cell types and associated with pan-cancer cell survival/growth suppression and patient survival. Our prediction results were verified through independent validation datasets, our own patient cohort, and cancer cell experiments. Moreover, a top predicted p53-effector tumor-suppressive lncRNA (we termed PTSL) inhibited cell proliferation and colony formation by modulating the G₂ regulatory network, causing G₂ cell-cycle arrest. Therefore, our results elucidated previously unreported, high-confidence core p53-targeted lncRNAs that suppress tumorigenesis across cell types and stresses.

Significance

Identification of pan-cancer suppressive lncRNAs transcriptionally regulated by p53 across different cellular stresses by integrating multilayered high-throughput molecular profiles. This study provides critical new insights into the p53 tumor suppressor by revealing the lncRNAs in the p53 cell-cycle regulatory network and their impact on cancer cell growth and patient survival.

The landscape of human p53-regulated long non-coding RNAs reveals critical host gene co-regulation

The role of long non-coding RNAs (lncRNAs) in p53-mediated tumor suppression has become increasingly appreciated in the past decade. Thus, the identification of p53-regulated lncRNAs can be a promising starting point to select and prioritize lncRNAs for functional analyses. By integrating transcriptome and transcription factor-binding data, we identified 379 lncRNAs that are recurrently differentially regulated by p53. Dissecting the mechanisms by which p53 regulates many of them, we identified sets of lncRNAs regulated either directly by p53 or indirectly through the p53-RFX7 and p53-p21-DREAM/RB:E2F pathways. Importantly, we identified multiple p53-responsive lncRNAs that are co-regulated with their protein-coding host genes, revealing an important mechanism by which p53 may regulate lncRNAs. Further analysis of transcriptome data and clinical data from cancer patients showed that recurrently p53-regulated lncRNAs are associated with patient survival. Together, the integrative analysis of the landscape of p53-regulated lncRNAs provides a powerful resource facilitating the identification of lncRNA function and displays the mechanisms of p53-dependent regulation that could be exploited for developing anticancer approaches.