Illuminating the noncoding genome in cancer

A Functional Survey of the Regulatory Landscape of Estrogen Receptor-Positive Breast Cancer Evolution

Only a handful of somatic alterations have been linked to endocrine therapy resistance in hormone-dependent breast cancer, potentially explaining ∼40% of relapses. If other mechanisms underlie the evolution of hormone-dependent breast cancer under adjuvant therapy is currently unknown. In this work, we employ functional genomics to dissect the contribution of cis-regulatory elements (CRE) to cancer evolution by focusing on 12 megabases of noncoding DNA, including clonal enhancers, gene promoters, and boundaries of topologically associating domains. Parallel epigenetic perturbation (CRISPRi) in vitro reveals context-dependent roles for many of these CREs, with a specific impact on dormancy entrance and endocrine therapy resistance. Profiling of CRE somatic alterations in a unique, longitudinal cohort of patients treated with endocrine therapies identifies a limited set of noncoding changes potentially involved in therapy resistance. Overall, our data uncover how endocrine therapies trigger the emergence of transient features which could ultimately be exploited to hinder the adaptive process. Significance: This study shows that cells adapting to endocrine therapies undergo changes in the usage or regulatory regions. Dormant cells are less vulnerable to regulatory perturbation but gain transient dependencies which can be exploited to decrease the formation of dormant persisters.

MANCR lncRNA Modulates Cell-Cycle Progression and Metastasis by Cis-Regulation of Nuclear Rho-GEF

A significant number of the genetic alterations observed in cancer patients lie within nonprotein-coding segments of the genome, including regions coding for long noncoding RNAs (lncRNAs). LncRNAs display aberrant expression in breast cancer (BrCa), but the functional implications of this altered expression remain to be elucidated. By performing transcriptome screen in a triple negative BrCa (TNBC) isogenic 2D and 3D spheroid model, we observed aberrant expression of >1000 lncRNAs during BrCa progression. The chromatin-associated lncRNA MANCR shows elevated expression in metastatic TNBC. MANCR is upregulated in response to cellular stress and modulates DNA repair and cell proliferation. MANCR promotes metastasis as MANCR-depleted cells show reduced cell migration, invasion, and wound healing in vitro, and reduced metastatic lung colonization in xenograft experiments in vivo. Transcriptome analyses reveal that MANCR modulates expression and pre-mRNA splicing of genes, controlling DNA repair and checkpoint response. MANCR promotes the transcription of NET1A, a Rho-GEF that regulates DNA damage checkpoint and metastatic processes in cis, by differential promoter usage. Experiments suggest that MANCR regulates the expression of cancer-associated genes by modulating the association of various transcription factors and RNA-binding proteins. Our results identified the metastasis-promoting activities of MANCR in TNBC by cis-regulation of gene expression.

3D genomic analysis reveals novel enhancer-hijacking caused by complex structural alterations that drive oncogene overexpression

Cancer genomes are composed of many complex structural alterations on chromosomes and extrachromosomal DNA (ecDNA), making it difficult to identify non-coding enhancer regions that are hijacked to activate oncogene expression. Here, we describe a 3D genomics-based analysis called HAPI (Highly Active Promoter Interactions) to characterize enhancer hijacking. HAPI analysis of HiChIP data from 34 cancer cell lines identified enhancer hijacking events that activate both known and potentially novel oncogenes such as MYC, CCND1, ETV1, CRKL, and ID4. Furthermore, we found enhancer hijacking among multiple oncogenes from different chromosomes, often including MYC, on the same complex amplicons such as ecDNA. We characterized a MYC-ERBB2 chimeric ecDNA, in which ERBB2 heavily hijacks MYC's enhancers. Notably, CRISPRi of the MYC promoter led to increased interaction of ERBB2 with MYC enhancers and elevated ERBB2 expression. Our HAPI analysis tool provides a robust strategy to detect enhancer hijacking and reveals novel insights into oncogene activation.

3D genomic analysis reveals novel enhancer-hijacking caused by complex structural alterations that drive oncogene overexpression

Cancer genomes are composed of many complex structural alterations on chromosomes and extrachromosomal DNA (ecDNA), making it difficult to identify non-coding enhancer regions that are hijacked to activate oncogene expression. Here, we describe a 3D genomics-based analysis called HAPI (Highly Active Promoter Interactions) to characterize enhancer hijacking. HAPI analysis of HiChIP data from 34 cancer cell lines identified enhancer hijacking events that activate both known and potentially novel oncogenes such as MYC, CCND1 , ETV1 , CRKL , and ID4 . Furthermore, we found enhancer hijacking among multiple oncogenes from different chromosomes, often including MYC , on the same complex amplicons such as ecDNA. We characterized a MYC - ERBB2 chimeric ecDNA, in which ERBB2 heavily hijacks MYC 's enhancers. Notably, CRISPRi of the MYC promoter led to increased interaction of ERBB2 with MYC enhancers and elevated ERBB2 expression. Our HAPI analysis tool provides a robust strategy to detect enhancer hijacking and reveals novel insights into oncogene activation.

Hidden secrets of the cancer genome: unlocking the impact of non-coding mutations in gene regulatory elements

Discoveries in the field of genomics have revealed that non-coding genomic regions are not merely "junk DNA", but rather comprise critical elements involved in gene expression. These gene regulatory elements (GREs) include enhancers, insulators, silencers, and gene promoters. Notably, new evidence shows how mutations within these regions substantially influence gene expression programs, especially in the context of cancer. Advances in high-throughput sequencing technologies have accelerated the identification of somatic and germline single nucleotide mutations in non-coding genomic regions. This review provides an overview of somatic and germline non-coding single nucleotide alterations affecting transcription factor binding sites in GREs, specifically involved in cancer biology. It also summarizes the technologies available for exploring GREs and the challenges associated with studying and characterizing non-coding single nucleotide mutations. Understanding the role of GRE alterations in cancer is essential for improving diagnostic and prognostic capabilities in the precision medicine era, leading to enhanced patient-centered clinical outcomes.

Analyzing the functional effects of DNA variants with gene editing

Continual advancements in genomics have led to an ever-widening disparity between the rate of discovery of genetic variants and our current understanding of their functions and potential roles in disease. Systematic methods for phenotyping DNA variants are required to effectively translate genomics data into improved outcomes for patients with genetic diseases. To make the biggest impact, these approaches must be scalable and accurate, faithfully reflect disease biology, and define complex disease mechanisms. We compare current methods to analyze the function of variants in their endogenous DNA context using genome editing strategies, such as saturation genome editing, base editing and prime editing. We discuss how these technologies can be linked to high-content readouts to gain deep mechanistic insights into variant effects. Finally, we highlight key challenges that need to be addressed to bridge the genotype to phenotype gap, and ultimately improve the diagnosis and treatment of genetic diseases.

Serum tRF-33-RZYQHQ9M739P0J as a novel biomarker for auxiliary diagnosis and disease course monitoring of hepatocellular carcinoma

Objective

In most cases, patients with hepatocellular carcinoma (HCC) develop advanced disease when diagnosed. Finding new molecules to combine with traditional biomarkers is crucial for HCC early diagnosis. In cancer development, tRNA-derived small RNAs (tsRNA) play a crucial role. Here, we aimed to identify a novel biomarker among tsRNAs that can facilitate HCC diagnosis and monitor its prognosis.

Methods

We screened candidate tsRNAs in 3 pairs of HCC and adjacent tissues through high-throughput sequencing. tRF-33-RZYQQ9M739P0J was screened in tissues, sera, and cells through quantitative real-time polymerase chain reaction (qRT-PCR) for further analysis. tRF-33-RZYQHQ9M739P0J was characterized using agarose gel electrophoresis, Sanger sequencing, and nuclear and cytoplasmic RNA isolation. Experiments at room temperature and repeated freeze-thaw cycles were conducted to evaluate the detection performance of tRF-33-RZYQHQ9M739P0J. We measured the levels of differential expression of tRF-33-RZYQHQ9M739P0J in sera using qRT-PCR. We applied the chi-square test to evaluate the correlation between tRF-33-RZYQHQ9M739P0J expression levels and clinicopathological features, and assessed its prognostic value by plotting Kaplan-Meier curves. The diagnostic efficacy of tRF-33-RZYQHQ9M739P0J was evaluated using the receiver operating characteristic (ROC) curve. Finally, the downstream genes related to tRF-33-RZYQHQ9M739P0J were explored through bioinformatics prediction.

Results

tRF-33-RZYQHQ9M739P0J was highly expressed in HCC tissues and sera, and its expression was correlated with metastasis, TNM stage, BCLC stage, and vein invasion. Expression of tRF-33-RZYQHQ9M739P0J were decreased after surgery in patients with HCC. High serum tRF-33-RZYQHQ9M739P0J levels are associated with low survival rates, and they can predict survival times in patients with HCC according to the Kaplan-Meier analysis. Combining tRF-33-RZYQHQ9M739P0J with serum alpha-fetoprotein and prothrombin induced by vitamin K absence II can improve the diagnostic efficiency of HCC, suggesting its potential as a biomarker for HCC.

Conclusion

tRF-33-RZYQHQ9M739P0J may not only be a promising non-invasive marker for early diagnosis, but also a predictor of liver cancer progression.

Convergent epigenetic evolution drives relapse in acute myeloid leukemia

Relapse of acute myeloid leukemia (AML) is highly aggressive and often treatment refractory. We analyzed previously published AML relapse cohorts and found that 40% of relapses occur without changes in driver mutations, suggesting that non-genetic mechanisms drive relapse in a large proportion of cases. We therefore characterized epigenetic patterns of AML relapse using 26 matched diagnosis-relapse samples with ATAC-seq. This analysis identified a relapse-specific chromatin accessibility signature for mutationally stable AML, suggesting that AML undergoes epigenetic evolution at relapse independent of mutational changes. Analysis of leukemia stem cell (LSC) chromatin changes at relapse indicated that this leukemic compartment underwent significantly less epigenetic evolution than non-LSCs, while epigenetic changes in non-LSCs reflected overall evolution of the bulk leukemia. Finally, we used single-cell ATAC-seq paired with mitochondrial sequencing (mtscATAC) to map clones from diagnosis into relapse along with their epigenetic features. We found that distinct mitochondrially-defined clones exhibit more similar chromatin accessibility at relapse relative to diagnosis, demonstrating convergent epigenetic evolution in relapsed AML. These results demonstrate that epigenetic evolution is a feature of relapsed AML and that convergent epigenetic evolution can occur following treatment with induction chemotherapy.

Detecting and understanding meaningful cancerous mutations based on computational models of mRNA splicing

Cancer research has long relied on non-silent mutations. Yet, it has become overwhelmingly clear that silent mutations can affect gene expression and cancer cell fitness. One fundamental mechanism that apparently silent mutations can severely disrupt is alternative splicing. Here we introduce Oncosplice, a tool that scores mutations based on models of proteomes generated using aberrant splicing predictions. Oncosplice leverages a highly accurate neural network that predicts splice sites within arbitrary mRNA sequences, a greedy transcript constructor that considers alternate arrangements of splicing blueprints, and an algorithm that grades the functional divergence between proteins based on evolutionary conservation. By applying this tool to 12M somatic mutations we identify 8K deleterious variants that are significantly depleted within the healthy population; we demonstrate the tool's ability to identify clinically validated pathogenic variants with a positive predictive value of 94%; we show strong enrichment of predicted deleterious mutations across pan-cancer drivers. We also achieve improved patient survival estimation using a proposed set of novel cancer-involved genes. Ultimately, this pipeline enables accelerated insight-gathering of sequence-specific consequences for a class of understudied mutations and provides an efficient way of filtering through massive variant datasets - functionalities with immediate experimental and clinical applications.

CMC: Cancer miRNA Census - a list of cancer-related miRNA genes

A growing body of evidence indicates an important role of miRNAs in cancer; however, there is no definitive, convenient-to-use list of cancer-related miRNAs or miRNA genes that may serve as a reference for analyses of miRNAs in cancer. To this end, we created a list of 165 cancer-related miRNA genes called the Cancer miRNA Census (CMC). The list is based on a score, built on various types of functional and genetic evidence for the role of particular miRNAs in cancer, e.g. miRNA-cancer associations reported in databases, associations of miRNAs with cancer hallmarks, or signals of positive selection of genetic alterations in cancer. The presence of well-recognized cancer-related miRNA genes, such as MIR21, MIR155, MIR15A, MIR17 or MIRLET7s, at the top of the CMC ranking directly confirms the accuracy and robustness of the list. Additionally, to verify and indicate the reliability of CMC, we performed a validation of criteria used to build CMC, comparison of CMC with various cancer data (publications and databases), and enrichment analyses of biological pathways and processes such as Gene Ontology or DisGeNET. All validation steps showed a strong association of CMC with cancer/cancer-related processes confirming its usefulness as a reference list of miRNA genes associated with cancer.

Role of oncogenic long noncoding RNA KCNQ1OT1 in colon cancer

The role of lncRNA KCNQ1 opposite strand/antisense transcript 1 (KCNQ1OT1) in colon cancer involves various tumorigenic processes and has been studied widely. However, the mechanism by which it promotes colon cancer remains unclear. Retroviral vector pSEB61 was retrofitted in established HCT116-siKCN and SW480-siKCN cells to silence KCNQ1OT1. Cellular proliferation was measured using CCK8 assay, and flow cytometry (FCM) detected cell cycle changes. RNA sequencing (RNA-Seq) analysis showed differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to analyze enriched functions and signaling pathways. RT-qPCR, immunofluorescence, and western blotting were carried out to validate downstream gene expressions. The effects of tumorigenesis were evaluated in BALB/c nude mice by tumor xenografts. Our data revealed that the silencing of KCNQ1OT1 in HCT116 and SW480 cells slowed cell growth and decreased the number of cells in the G2/M phase. RNA-Seq analysis showed the data of DEGs enriched in various GO and KEGG pathways such as DNA replication and cell cycle. RT-qPCR, immunofluorescence, and western blotting confirmed downstream CCNE2 and PCNA gene expressions. HCT116-siKCN cells significantly suppressed tumorigenesis in BALB/c nude mice. Our study suggests that lncRNA KCNQ1OT1 may provide a promising therapeutic strategy for colon cancer.

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.

Dosage amplification dictates oncogenic regulation by the NKX2-1 lineage factor in lung adenocarcinoma

Amplified oncogene expression is a critical and widespread driver event in cancer, yet our understanding of how amplification-mediated elevated dosage mediates oncogenic regulation is limited. Here, we find that the most significant focal amplification event in lung adenocarcinoma (LUAD) targets a lineage super-enhancer near the NKX2-1 lineage transcription factor. The NKX2-1 super-enhancer is targeted by focal and co-amplification with NKX2-1, and activation or repression controls NKX2-1 expression. We find that NKX2-1 is a widespread dependency in LUAD cell lines, where NKX2-1 pioneers enhancer accessibility to drive a lineage addicted state in LUAD, and NKX2-1 confers persistence to EGFR inhibitors. Notably, we find that oncogenic NKX2-1 regulation requires expression above a minimum dosage threshold-NKX2-1 dosage below this threshold is insufficient for cell viability, enhancer remodeling, and TKI persistence. Our data suggest that copy-number amplification can be a gain-of-function alteration, wherein amplification elevates oncogene expression above a critical dosage required for oncogenic regulation and cancer cell survival.

Convergent Epigenetic Evolution Drives Relapse in Acute Myeloid Leukemia

Relapse of acute myeloid leukemia (AML) is highly aggressive and often treatment refractory. We analyzed previously published AML relapse cohorts and found that 40% of relapses occur without changes in driver mutations, suggesting that non-genetic mechanisms drive relapse in a large proportion of cases. We therefore characterized epigenetic patterns of AML relapse using 26 matched diagnosis-relapse samples with ATAC-seq. This analysis identified a relapse-specific chromatin accessibility signature for mutationally stable AML, suggesting that AML undergoes epigenetic evolution at relapse independent of mutational changes. Analysis of leukemia stem cell (LSC) chromatin changes at relapse indicated that this leukemic compartment underwent significantly less epigenetic evolution than non-LSCs, while epigenetic changes in non-LSCs reflected overall evolution of the bulk leukemia. Finally, we used single-cell ATAC-seq paired with mitochondrial sequencing (mtscATAC) to map clones from diagnosis into relapse along with their epigenetic features. We found that distinct mitochondrially-defined clones exhibit more similar chromatin accessibility at relapse relative to diagnosis, demonstrating convergent epigenetic evolution in relapsed AML. These results demonstrate that epigenetic evolution is a feature of relapsed AML and that convergent epigenetic evolution can occur following treatment with induction chemotherapy.

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs

In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs. In this review, we present a curated collection of the most recent computational resources, which have been categorized into distinct groups: databases and annotation, identification and classification, interaction prediction, and structure prediction. As the repertoire of lncRNAs and their analysis tools continues to expand over the years, standardizing the computational pipelines and improving the existing annotation of lncRNAs will be crucial to facilitate functional genomics studies.

Repetitive DNA sequence detection and its role in the human genome

Repetitive DNA sequences playing critical roles in driving evolution, inducing variation, and regulating gene expression. In this review, we summarized the definition, arrangement, and structural characteristics of repeats. Besides, we introduced diverse biological functions of repeats and reviewed existing methods for automatic repeat detection, classification, and masking. Finally, we analyzed the type, structure, and regulation of repeats in the human genome and their role in the induction of complex diseases. We believe that this review will facilitate a comprehensive understanding of repeats and provide guidance for repeat annotation and in-depth exploration of its association with human diseases.

Delineating highly transcribed noncoding elements landscape in breast cancer

Highly transcribed noncoding elements (HTNEs) are critical noncoding elements with high levels of transcriptional capacity in particular cohorts involved in multiple cellular biological processes. Investigation of HTNEs with persistent aberrant expression in abnormal tissues could be of benefit in exploring their roles in disease occurrence and progression. Breast cancer is a highly heterogeneous disease for which early screening and prognosis are exceedingly crucial. In this study, we developed a HTNE identification framework to systematically investigate HTNE landscapes in breast cancer patients and identified over ten thousand HTNEs. The robustness and rationality of our framework were demonstrated via public datasets. We revealed that HTNEs had significant chromatin characteristics of enhancers and long noncoding RNAs (lncRNAs) and were significantly enriched with RNA-binding proteins as well as targeted by miRNAs. Further, HTNE-associated genes were significantly overexpressed and exhibited strong correlations with breast cancer. Ultimately, we explored the subtype-specific transcriptional processes associated with HTNEs and uncovered the HTNE signatures that could classify breast cancer subtypes based on the properties of hormone receptors. Our results highlight that the identified HTNEs as well as their associated genes play crucial roles in breast cancer progression and correlate with subtype-specific transcriptional processes of breast cancer.

High-Throughput Identification, Modeling, and Analysis of Cancer Driver Genes In Vivo

The vast number of genomic and molecular alterations in cancer pose a substantial challenge to uncovering the mechanisms of tumorigenesis and identifying therapeutic targets. High-throughput functional genomic methods in genetically engineered mouse models allow for rapid and systematic investigation of cancer driver genes. In this review, we discuss the basic concepts and tools for multiplexed investigation of functionally important cancer genes in vivo using autochthonous cancer models. Furthermore, we highlight emerging technical advances in the field, potential opportunities for future investigation, and outline a vision for integrating multiplexed genetic perturbations with detailed molecular analyses to advance our understanding of the genetic and molecular basis of cancer.

Application of CRISPR screen in mechanistic studies of tumor development, tumor drug resistance, and tumor immunotherapy

Tumor is one of the biggest threats to human health. Though tumor therapy has been dramatically advanced by the progress of technology and research in recent decades, it is still far from expectations. Thus, it is of great significance to explore the mechanisms of tumor growth, metastasis, and resistance. Screen based on Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-CRISPR-associated protein (Cas) 9 gene editing technology are powerful tools for exploring the abovementioned facets. This review summarizes the recent screen performed in cancer cells and immune cells in the tumor microenvironment. The screens in cancer cells mainly focus on exploring the mechanisms underlying cancer cells' growth, metastasis, and how cancer cells escape from the FDA approved drugs or immunotherapy. And the studies in tumor-associated immune cells are primarily aimed at identifying signaling pathways that can enhance the anti-tumor function of cytotoxic T lymphocytes (CTLs), CAR-T cells, and macrophages. Moreover, we discuss the limitations, merits of the CRISPR screen, and further its future application in tumor studies. Importantly, recent advances in high throughput tumor related CRISPR screen have deeply contributed to new concepts and mechanisms underlying tumor development, tumor drug resistance, and tumor immune therapy, all of which will eventually potentiate the clinical therapy for tumor patients.

Non-coding genome in small cell lung cancer between theoretical view and clinical applications

Small cell lung cancer (SCLC) is a highly aggressive cancer of the neuroendocrine system, characterized by poor differentiation, rapid growth, and poor overall survival (OS) of patients. Despite the recent advances in the treatment of SCLC recently, the 2-year survival rate of patients with the cancer is only 14-15%, occasioned by the acquired resistance to drugs and serious off-target effects. In humans, the coding region is only 2% of the total genome, and 20% of that is associated with human diseases. Beyond the coding genome are RNAs, promoters, enhancers, and other intricate elements. The non-coding regulatory regions, mainly the non-coding RNAs (ncRNAs), regulate numerous biological activities including cell proliferation, metastasis, and drug resistance. As such, they are potential diagnostic or prognostic biomarkers, and also potential therapeutic targets for SCLC. Therefore, understanding how non-coding elements regulate SCLC development and progression holds significant clinical implications. Herein, we summarized the recent discoveries on the relationship between the non-coding elements including long non-coding RNAs (lncRNA), microRNAs (miRNAs), circular RNA (circRNA), enhancers as well as promotors, and the pathogenesis of SCLC and their potential clinical applications.

Genome-wide mapping of somatic mutation rates uncovers drivers of cancer

Identification of cancer driver mutations that confer a proliferative advantage is central to understanding cancer; however, searches have often been limited to protein-coding sequences and specific non-coding elements (for example, promoters) because of the challenge of modeling the highly variable somatic mutation rates observed across tumor genomes. Here we present Dig, a method to search for driver elements and mutations anywhere in the genome. We use deep neural networks to map cancer-specific mutation rates genome-wide at kilobase-scale resolution. These estimates are then refined to search for evidence of driver mutations under positive selection throughout the genome by comparing observed to expected mutation counts. We mapped mutation rates for 37 cancer types and applied these maps to identify putative drivers within intronic cryptic splice regions, 5' untranslated regions and infrequently mutated genes. Our high-resolution mutation rate maps, available for web-based exploration, are a resource to enable driver discovery genome-wide.

Unravelling the tumour genome: The evolutionary and clinical impacts of structural variants in tumourigenesis

Structural variants (SVs) represent a major source of aberration in tumour genomes. Given the diversity in the size and type of SVs present in tumours, the accurate detection and interpretation of SVs in tumours is challenging. New classes of complex structural events in tumours are discovered frequently, and the definitions of the genomic consequences of complex events are constantly being refined. Detailed analyses of short-read whole-genome sequencing (WGS) data from large tumour cohorts facilitate the interrogation of SVs at orders of magnitude greater scale and depth. However, the inherent technical limitations of short-read WGS prevent us from accurately detecting and investigating the impact of all the SVs present in tumours. The expanded use of long-read WGS will be critical for improving the accuracy of SV detection, and in fully resolving complex SV events, both of which are crucial for determining the impact of SVs on tumour progression and clinical outcome. Despite the present limitations, we demonstrate that SVs play an important role in tumourigenesis. In particular, SVs contribute significantly to late-stage tumour development and to intratumoural heterogeneity. The evolutionary trajectories of SVs represent a window into the clonal dynamics in tumours, a comprehensive understanding of which will be vital for influencing patient outcomes in the future. Recent findings have highlighted many clinical applications of SVs in cancer, from early detection to biomarkers for treatment response and prognosis. As the methods to detect and interpret SVs improve, elucidating the full breadth of the complex SV landscape and determining how these events modulate tumour evolution will improve our understanding of cancer biology and our ability to capitalise on the utility of SVs in the clinical management of cancer patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

CRISPR in cancer biology and therapy

Over the past decade, CRISPR has become as much a verb as it is an acronym, transforming biomedical research and providing entirely new approaches for dissecting all facets of cell biology. In cancer research, CRISPR and related tools have offered a window into previously intractable problems in our understanding of cancer genetics, the noncoding genome and tumour heterogeneity, and provided new insights into therapeutic vulnerabilities. Here, we review the progress made in the development of CRISPR systems as a tool to study cancer, and the emerging adaptation of these technologies to improve diagnosis and treatment.

Computational analysis of cancer genome sequencing data

Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.

STAG2 regulates interferon signaling in melanoma via enhancer loop reprogramming

The cohesin complex participates in the organization of 3D genome through generating and maintaining DNA loops. Stromal antigen 2 (STAG2), a core subunit of the cohesin complex, is frequently mutated in various cancers. However, the impact of STAG2 inactivation on 3D genome organization, especially the long-range enhancer-promoter contacts and subsequent gene expression control in cancer, remains poorly understood. Here we show that depletion of STAG2 in melanoma cells leads to expansion of topologically associating domains (TADs) and enhances the formation of acetylated histone H3 lysine 27 (H3K27ac)-associated DNA loops at sites where binding of STAG2 is switched to its paralog STAG1. We further identify Interferon Regulatory Factor 9 (IRF9) as a major direct target of STAG2 in melanoma cells via integrated RNA-seq, STAG2 ChIP-seq and H3K27ac HiChIP analyses. We demonstrate that loss of STAG2 activates IRF9 through modulating the 3D genome organization, which in turn enhances type I interferon signaling and increases the expression of PD-L1. Our findings not only establish a previously unknown role of the STAG2 to STAG1 switch in 3D genome organization, but also reveal a functional link between STAG2 and interferon signaling in cancer cells, which may enhance the immune evasion potential in STAG2-mutant cancer.

Integrating whole genome sequencing, methylation, gene expression, topological associated domain information in regulatory mutation prediction: a study of follicular lymphoma

A major challenge in human genetics is of the analysis of the interplay between genetic and epigenetic factors in a multifactorial disease like cancer. Here, a novel methodology is proposed to investigate genome-wide regulatory mechanisms in cancer, as studied with the example of follicular Lymphoma (FL). In a first phase, a new machine-learning method is designed to identify Differentially Methylated Regions (DMRs) by computing six attributes. In a second phase, an integrative data analysis method is developed to study regulatory mutations in FL, by considering differential methylation information together with DNA sequence variation, differential gene expression, 3D organization of genome (e.g., topologically associated domains), and enriched biological pathways. Resulting mutation block-gene pairs are further ranked to find out the significant ones. By this approach, BCL2 and BCL6 were identified as top-ranking FL-related genes with several mutation blocks and DMRs acting on their regulatory regions. Two additional genes, CDCA4 and CTSO, were also found in top rank with significant DNA sequence variation and differential methylation in neighboring areas, pointing towards their potential use as biomarkers for FL. This work combines both genomic and epigenomic information to investigate genome-wide gene regulatory mechanisms in cancer and contribute to devising novel treatment strategies.

Mutations in Noncoding Cis-Regulatory Elements Reveal Cancer Driver Cistromes in Luminal Breast Cancer

Whole-genome sequencing of primary breast tumors enabled the identification of cancer driver genes and noncoding cancer driver plexuses from somatic mutations. However, differentiating driver from passenger events among noncoding genetic variants remains a challenge. Herein, we reveal cancer-driver cis-regulatory elements linked to transcription factors previously shown to be involved in development of luminal breast cancers by defining a tumor-enriched catalogue of approximately 100,000 unique cis-regulatory elements from 26 primary luminal estrogen receptor (ER)⁺ progesterone receptor (PR)⁺ breast tumors. Integrating this catalog with somatic mutations from 350 publicly available breast tumor whole genomes, we uncovered cancer driver cistromes, defined as the sum of binding sites for a transcription factor, for ten transcription factors in luminal breast cancer such as FOXA1 and ER, nine of which are essential for growth in breast cancer with four exclusive to the luminal subtype. Collectively, we present a strategy to find cancer driver cistromes relying on quantifying the enrichment of noncoding mutations over cis-regulatory elements concatenated into a functional unit. IMPLICATIONS: Mapping the accessible chromatin of luminal breast cancer led to discovery of an accumulation of mutations within cistromes of transcription factors essential to luminal breast cancer. This demonstrates coopting of regulatory networks to drive cancer and provides a framework to derive insight into the noncoding space of cancer.

A predominant enhancer co-amplified with the SOX2 oncogene is necessary and sufficient for its expression in squamous cancer

Amplification and overexpression of the SOX2 oncogene represent a hallmark of squamous cancers originating from diverse tissue types. Here, we find that squamous cancers selectively amplify a 3' noncoding region together with SOX2, which harbors squamous cancer-specific chromatin accessible regions. We identify a single enhancer e1 that predominantly drives SOX2 expression. Repression of e1 in SOX2-high cells causes collapse of the surrounding enhancers, remarkable reduction in SOX2 expression, and a global transcriptional change reminiscent of SOX2 knockout. The e1 enhancer is driven by a combination of transcription factors including SOX2 itself and the AP-1 complex, which facilitates recruitment of the co-activator BRD4. CRISPR-mediated activation of e1 in SOX2-low cells is sufficient to rebuild the e1-SOX2 loop and activate SOX2 expression. Our study shows that squamous cancers selectively amplify a predominant enhancer to drive SOX2 overexpression, uncovering functional links among enhancer activation, chromatin looping, and lineage-specific copy number amplifications of oncogenes.

Mechanistic insights into genetic susceptibility to prostate cancer

Prostate cancer (PCa) is the second most common cancer in men and is a highly heritable disease that affects millions of individuals worldwide. Genome-wide association studies have to date discovered nearly 270 genetic loci harboring hundreds of single nucleotide polymorphisms (SNPs) that are associated with PCa susceptibility. In contrast, the functional characterization of the mechanisms underlying PCa risk association is still growing. Given that PCa risk-associated SNPs are highly enriched in noncoding cis-regulatory genomic regions, accumulating evidence suggests a widespread modulation of transcription factor chromatin binding and allelic enhancer activity by these noncoding SNPs, thereby dysregulating gene expression. Emerging studies have shown that a proportion of noncoding variants can modulate the formation of transcription factor complexes at enhancers and CTCF-mediated 3D genome architecture. Interestingly, DNA methylation-regulated CTCF binding could orchestrate a long-range chromatin interaction between PCa risk enhancer and causative genes. Additionally, one-causal-variant-two-risk genes or multiple-risk-variant-multiple-genes are prevalent in some PCa risk-associated loci. In this review, we will discuss the current understanding of the general principles of SNP-mediated gene regulation, experimental advances, and functional evidence supporting the mechanistic roles of several PCa genetic loci with potential clinical impact on disease prevention and treatment.

Estimating the predictive power of silent mutations on cancer classification and prognosis

In recent years it has been shown that silent mutations, in and out of the coding region, can affect gene expression and may be related to tumorigenesis and cancer cell fitness. However, the predictive ability of these mutations for cancer type diagnosis and prognosis has not been evaluated yet. In the current study, based on the analysis of 9,915 cancer genomes and approximately three million mutations, we provide a comprehensive quantitative evaluation of the predictive power of various types of silent and non-silent mutations over cancer classification and prognosis. The results indicate that silent-mutation models outperform the equivalent null models in classifying all examined cancer types and in estimating the probability of survival 10 years after the initial diagnosis. Additionally, combining both non-silent and silent mutations achieved the best classification results for 68% of the cancer types and the best survival estimation results for up to nine years after the diagnosis. Thus, silent mutations hold considerable predictive power over both cancer classification and prognosis, most likely due to their effect on gene expression. It is highly advised that silent mutations are integrated in cancer research in order to unravel the full genomic landscape of cancer and its ramifications on cancer fitness.

Non-coding driver mutations in human cancer

Tumour formation involves random mutagenic events and positive evolutionary selection acting on a subset of such events, referred to as driver mutations. A decade of careful surveying of tumour DNA using exome-based analyses has revealed a multitude of protein-coding somatic driver mutations, some of which are clinically actionable. Today, a transition towards whole-genome analysis is well under way, technically enabling the discovery of potential driver mutations occurring outside protein-coding sequences. Mutations are abundant in this vast non-coding space, which is more than 50 times larger than the coding exome, but reliable identification of selection signals in non-coding DNA remains a challenge. In this Review, we discuss recent findings in the field, where the emerging landscape is one in which non-coding driver mutations appear to be relatively infrequent. Nevertheless, we highlight several notable discoveries. We consider possible reasons for the relative absence of non-coding driver events, as well as the difficulties associated with detecting signals of positive selection in non-coding DNA.

Study of HOTAIR LncRNA in AML patients in context to FLT3-ITD and NPM1 mutations status

Background

Long non-coding RNAs (LncRNAs) have recently been considered promising biomarkers for oncogenesis due to their epigenetic regulatory effects. HOTAIR is one of the oncogenic LncRNAs that was previously studied in different non-hematological malignancies. The current study set out to detect the expression level of HOTAIR LncRNA in AML patients concerning their clinical characteristics, laboratory data, FLT3-ITD, and NPM1 mutations, as well as treatment outcome. This study included quantitative detection of HOTAIR gene expression in 47 cases of AML using quantitative reverse transcription polymerase chain reaction, as well as NPM1 and FLT3-ITD genotyping.

Results

The HOTAIR expression was significantly higher in AML patients 6.87 (0.001) than in normal controls 1.66 (0.004–6.82) (p 0.007). The HOTAIR expression level was affected by chemotherapy, and it was correlated to hemoglobin level (p 0.001), age, total leukocytic count (p 0.022), and NPM1 mutation (p 0.017). HOTAIR gene expression level showed a correlation to relapse-free survival in the study group (p 0.04).

Conclusion

HOTAIR is overexpressed in patients with acute myeloid leukemia (AML). HOTAIR pre-treatment and post-chemotherapy gene expression levels can predict chemosensitivity and relapse.

Next-Generation Kinase Inhibitors Targeting Specific Biomarkers in Non-Small Cell Lung Cancer (NSCLC): A Recent Overview

Lung cancer causes many deaths globally. Mutations in regulatory genes, irregularities in specific signal transduction events, or alterations of signalling pathways are observed in cases of non-small cell lung cancer (NSCLC). Over the past two decades, a few kinases have been identified, validated, and studied as biomarkers for NSCLC. Among them, EGFR, ALK, ROS1, MET, RET, NTRK, and BRAF are regarded as targetable biomarkers to cure and/or control the disease. In recent years, the US Food and Drug Administration (FDA) approved more than 15 kinase inhibitors targeting these NSCLC biomarkers. The kinase inhibitors significantly improved the progression-free survival (PFS) of NSCLC patients. Challenges still remain for metastatic diseases and advanced NSCLC cases. New discoveries of potent kinase inhibitors and rapid development of modern medical technologies will help to control NSCLC cases. This article provides an overview of the discoveries of various types of kinase inhibitors against NSCLC, along with medicinal chemistry aspects and related developments in next-generation kinase inhibitors that have been reported in recent years.

Cervical Cancer Stem-Like Cell Transcriptome Profiles Predict Response to Chemoradiotherapy

Cervical cancer (CC) represents a major global health issue, particularly impacting women from resource constrained regions worldwide. Treatment refractoriness to standard chemoradiotheraphy has identified cancer stem cells as critical coordinators behind the biological mechanisms of resistance, contributing to CC recurrence. In this work, we evaluated differential gene expression in cervical cancer stem-like cells (CCSC) as biomarkers related to intrinsic chemoradioresistance in CC. A total of 31 patients with locally advanced CC and referred to Mário Penna Institute (Belo Horizonte, Brazil) from August 2017 to May 2018 were recruited for the study. Fluorescence-activated cell sorting was used to enrich CD34+/CD45- CCSC from tumor biopsies. Transcriptome was performed using ultra-low input RNA sequencing and differentially expressed genes (DEGs) using Log2 fold differences and adjusted p-value < 0.05 were determined. The analysis returned 1050 DEGs when comparing the Non-Responder (NR) (n=10) and Responder (R) (n=21) groups to chemoradiotherapy. These included a wide-ranging pattern of underexpressed coding genes in the NR vs. R patients and a panel of lncRNAs and miRNAs with implications for CC tumorigenesis. A panel of biomarkers was selected using the rank-based AUC (Area Under the ROC Curve) and pAUC (partial AUC) measurements for diagnostic sensitivity and specificity. Genes overlapping between the 21 highest AUC and pAUC loci revealed seven genes with a strong capacity for identifying NR vs. R patients (ILF2, RBM22P2, ACO16722.1, AL360175.1 and AC092354.1), of which four also returned significant survival Hazard Ratios. This study identifies DEG signatures that provide potential biomarkers in CC prognosis and treatment outcome, as well as identifies potential alternative targets for cancer therapy.

Significant non-existence of sequences in genomes and proteomes

Minimal absent words (MAWs) are minimal-length oligomers absent from a genome or proteome. Although some artificially synthesized MAWs have deleterious effects, there is still a lack of a strategy for the classification of non-occurring sequences as potentially malicious or benign. In this work, by using Markovian models with multiple-testing correction, we reveal significant absent oligomers, which are statistically expected to exist. This suggests that their absence is due to negative selection. We survey genomes and proteomes covering the diversity of life and find thousands of significant absent sequences. Common significant MAWs are often mono- or dinucleotide tracts, or palindromic. Significant viral MAWs are often restriction sites and may indicate unknown restriction motifs. Surprisingly, significant mammal genome MAWs are often present, but rare, in other mammals, suggesting that they are suppressed but not completely forbidden. Significant human MAWs are frequently present in prokaryotes, suggesting immune function, but rarely present in human viruses, indicating viral mimicry of the host. More than one-fourth of human proteins are one substitution away from containing a significant MAW, with the majority of replacements being predicted harmful. We provide a web-based, interactive database of significant MAWs across genomes and proteomes.

Germline and Tumor Sequencing as a Diagnostic Tool To Resolve Suspected Lynch Syndrome

Patients in whom mismatch repair (MMR)-deficient cancer develops in the absence of pathogenic variants of germline MMR genes or somatic hypermethylation of the MLH1 gene promoter are classified as having suspected Lynch syndrome (SLS). Germline whole-genome sequencing (WGS) and targeted and genome-wide tumor sequencing were applied to identify the underlying cause of tumor MMR deficiency in SLS. Germline WGS was performed on samples from 14 cancer-affected patients with SLS, including two sets of first-degree relatives. MMR genes were assessed for germline pathogenic variants, including complex structural rearrangements and noncoding variants. Tumor tissue was assessed for somatic MMR gene mutations using targeted, whole-exome sequencing or WGS. Germline WGS identified pathogenic MMR variants in 3 of the 14 cases (21.4%), including a 9.5-megabase inversion disrupting MSH2 in a mother and daughter. Excluding these 3 MMR carriers, tumor sequencing identified at least two somatic MMR gene mutations in 8 of 11 tumors tested (72.7%). In a second mother-daughter pair, a somatic cause of tumor MMR deficiency was supported by the presence of double somatic MSH2 mutations in their respective tumors. More than 70% of SLS cases had double somatic MMR mutations in the absence of germline pathogenic variants in the MMR or other DNA repair-related genes on WGS, and, therefore, were confidently assigned a noninherited cause of tumor MMR deficiency.

Long-Read Sequencing Improves the Detection of Structural Variations Impacting Complex Non-Coding Elements of the Genome

The advent of long-read sequencing offers a new assessment method of detecting genomic structural variation (SV) in numerous rare genetic diseases. For autism spectrum disorders (ASD) cases where pathogenic variants fail to be found in the protein-coding genic regions along chromosomes, we proposed a scalable workflow to characterize the risk factor of SVs impacting non-coding elements of the genome. We applied whole-genome sequencing on an Emirati family having three children with ASD using long and short-read sequencing technology. A series of analytical pipelines were established to identify a set of SVs with high sensitivity and specificity. At 15-fold coverage, we observed that long-read sequencing technology (987 variants) detected a significantly higher number of SVs when compared to variants detected using short-read technology (509 variants) (p-value < 1.1020 × 10-57). Further comparison showed 97.9% of long-read sequencing variants were spanning within the 1-100 kb size range (p-value < 9.080 × 10-67) and impacting over 5000 genes. Moreover, long-read variants detected 604 non-coding RNAs (p-value < 9.02 × 10-9), comprising 58% microRNA, 31.9% lncRNA, and 9.1% snoRNA. Even at low coverage, long-read sequencing has shown to be a reliable technology in detecting SVs impacting complex elements of the genome.

The Immune Functions of Keratinocytes in Skin Wound Healing

As the most dominant cell type in the skin, keratinocytes play critical roles in wound repair not only as structural cells but also exerting important immune functions. This review focuses on the communications between keratinocytes and immune cells in wound healing, which are mediated by various cytokines, chemokines, and extracellular vesicles. Keratinocytes can also directly interact with T cells via antigen presentation. Moreover, keratinocytes produce antimicrobial peptides that can directly kill the invading pathogens and contribute to wound repair in many aspects. We also reviewed the epigenetic mechanisms known to regulate keratinocyte immune functions, including histone modifications, non-protein-coding RNAs (e.g., microRNAs, and long noncoding RNAs), and chromatin dynamics. Lastly, we summarized the current evidence on the dysregulated immune functions of keratinocytes in chronic nonhealing wounds. Based on their crucial immune functions in skin wound healing, we propose that keratinocytes significantly contribute to the pathogenesis of chronic wound inflammation. We hope this review will trigger an interest in investigating the immune roles of keratinocytes in chronic wound pathology, which may open up new avenues for developing innovative wound treatments.