Analyses of non-coding somatic drivers in 2,658 cancer whole genomes

Developmental origins shape the paediatric cancer genome

In the past two decades, technological advances have brought unprecedented insights into the paediatric cancer genome revealing characteristics distinct from those of adult cancer. Originating from developing tissues, paediatric cancers generally have low mutation burden and are driven by variants that disrupt the transcriptional activity, chromatin state, non-coding cis-regulatory regions and other biological functions. Within each tumour, there are multiple populations of cells with varying states, and the lineages of some can be tracked to their fetal origins. Genome-wide genetic screening has identified vulnerabilities associated with both the cell of origin and transcription deregulation in paediatric cancer, which have become a valuable resource for designing new therapeutic approaches including those for small molecules, immunotherapy and targeted protein degradation. In this Review, we present recent findings on these facets of paediatric cancer from a pan-cancer perspective and provide an outlook on future investigations.

Multi-Omics Integration for Liver Cancer Using Regression Analysis

Genetic biomarkers have played a pivotal role in the classification, prognostication, and guidance of clinical cancer therapies. Large-scale and multi-dimensional analyses of entire cancer genomes, as exemplified by projects like The Cancer Genome Atlas (TCGA), have yielded an extensive repository of data that holds the potential to unveil the underlying biology of these malignancies. Mutations stand out as the principal catalysts of cellular transformation. Nonetheless, other global genomic processes, such as alterations in gene expression and chromosomal re-arrangements, also play crucial roles in conferring cellular immortality. The incorporation of multi-omics data specific to cancer has demonstrated the capacity to enhance our comprehension of the molecular mechanisms underpinning carcinogenesis. This report elucidates how the integration of comprehensive data on methylation, gene expression, and copy number variations can effectively facilitate the unsupervised clustering of cancer samples. We have identified regressors that can effectively classify tumor and normal samples with an optimal integration of RNA sequencing, DNA methylation, and copy number variation while also achieving significant p-values. Further, these regressors were trained using linear and logistic regression with k-means clustering. For comparison, we employed autoencoder- and stacking-based omics integration and computed silhouette scores to evaluate the clusters. The proof of concept is illustrated using liver cancer data. Our analysis serves to underscore the feasibility of unsupervised cancer classification by considering genetic markers beyond mutations, thereby emphasizing the clinical relevance of additional global cellular parameters that contribute to the transformative process in cells. This work is clinically relevant because changes in gene expression and genomic re-arrangements have been shown to be signatures of cellular transformation across cancers, as well as in liver cancers.

Massively parallel screen uncovers many rare 3' UTR variants regulating mRNA abundance of cancer driver genes

Understanding the function of rare non-coding variants represents a significant challenge. Using MapUTR, a screening method, we studied the function of rare 3' UTR variants affecting mRNA abundance post-transcriptionally. Among 17,301 rare gnomAD variants, an average of 24.5% were functional, with 70% in cancer-related genes, many in critical cancer pathways. This observation motivated an interrogation of 11,929 somatic mutations, uncovering 3928 (33%) functional mutations in 155 cancer driver genes. Functional MapUTR variants were enriched in microRNA- or protein-binding sites and may underlie outlier gene expression in tumors. Further, we introduce untranslated tumor mutational burden (uTMB), a metric reflecting the amount of somatic functional MapUTR variants of a tumor and show its potential in predicting patient survival. Through prime editing, we characterized three variants in cancer-relevant genes (MFN2, FOSL2, and IRAK1), demonstrating their cancer-driving potential. Our study elucidates the function of tens of thousands of non-coding variants, nominates non-coding cancer driver mutations, and demonstrates their potential contributions to cancer.

Tetrahedral DNA nanostructures enhance transcription isothermal amplification for multiplex detection of non-coding RNAs

This study introduces an innovative detection system for multiple cancer biomarkers, employing transcription isothermal amplification methods in conjunction with a tetrahedral DNA nanostructure (TDN). We demonstrate that TDN enhances various transcription isothermal amplification methods by placing DNA probes in proximity. Notably, the TDN-enhanced split T7 promoter-based isothermal transcription amplification with light-up RNA aptamer (STAR) system stands out for its optimal performance and operational simplicity, especially in identifying non-coding RNAs such as microRNAs and long non-coding RNAs (lncRNAs). Multiplex detection of lncRNAs was also achieved by generating distinct light-up RNA aptamers, each emitting unique fluorescence signals. The system effectively identified the target lncRNAs, demonstrating high sensitivity and selectivity in both cell lines and clinical samples. The system, utilizing the single enzyme T7 RNA polymerase, can be easily tailored for alternative targets by substituting target-specific sequences in DNA probes and seamlessly integrated with other isothermal amplification methods for greater sensitivity and accuracy in the detection of multiple cancer biomarkers.

Towards accurate indel calling for oncopanel sequencing through an international pipeline competition at precisionFDA

Accurately calling indels with next-generation sequencing (NGS) data is critical for clinical application. The precisionFDA team collaborated with the U.S. Food and Drug Administration's (FDA's) National Center for Toxicological Research (NCTR) and successfully completed the NCTR Indel Calling from Oncopanel Sequencing Data Challenge, to evaluate the performance of indel calling pipelines. Top performers were selected based on precision, recall, and F1-score. The performance of many other pipelines was close to the top performers, which produced a top cluster of performers. The performance was significantly higher in high confidence regions and coding regions, and significantly lower in low complexity regions. Oncopanel capture and other issues may have occurred that affected the recall rate. Indels with higher variant allele frequency (VAF) may generally be called with higher confidence. Many of the indel calling pipelines had good performance. Some of them performed generally well across all three oncopanels, while others were better for a specific oncopanel. The performance of indel calling can further be improved by restricting the calls within high confidence intervals (HCIs) and coding regions, and by excluding low complexity regions (LCR) regions. Certain VAF cut-offs could be applied according to the applications.

The intricate dance of tumor evolution: Exploring immune escape, tumor migration, drug resistance, and treatment strategies

Recent research has unveiled fascinating insights into the intricate mechanisms governing tumor evolution. These studies have illuminated how tumors adapt and proliferate by exploiting various factors, including immune evasion, resistance to therapeutic drugs, genetic mutations, and their ability to adapt to different environments. Furthermore, investigations into tumor heterogeneity and chromosomal aberrations have revealed the profound complexity that underlies the evolution of cancer. Emerging findings have also underscored the role of viral influences in the development and progression of cancer, introducing an additional layer of complexity to the field of oncology. Tumor evolution is a dynamic and complex process influenced by various factors, including immune evasion, drug resistance, tumor heterogeneity, and viral influences. Understanding these elements is indispensable for developing more effective treatments and advancing cancer therapies. A holistic approach to studying and addressing tumor evolution is crucial in the ongoing battle against cancer. The main goal of this comprehensive review is to explore the intricate relationship between tumor evolution and critical aspects of cancer biology. By delving into this complex interplay, we aim to provide a profound understanding of how tumors evolve, adapt, and respond to treatment strategies. This review underscores the pivotal importance of comprehending tumor evolution in shaping effective approaches to cancer treatment.

Germline structural variation globally impacts the cancer transcriptome including disease-relevant genes

Germline variation and somatic alterations contribute to the molecular profile of cancers. We combine RNA with whole genome sequencing across 1,218 cancer patients to determine the extent germline structural variants (SVs) impact expression of nearby genes. For hundreds of genes, recurrent and common germline SV breakpoints within 100 kb associate with increased or decreased expression in tumors spanning various tissues of origin. A significant fraction of germline SV expression associations involves duplication of intergenic enhancers or 3' UTR disruption. Genes altered by both somatic and germline SVs include ATRX and CEBPA. Genes essential in cancer cell lines include BARD1 and IRS2. Genes with both expression and germline SV breakpoint patterns associated with patient survival include GCLM. Our results capture a class of phenotypic variation at work in the disease setting, including genes with cancer roles. Specific germline SVs represent potential cancer risk variants for genetic testing, including those involving genes with targeting implications.

Genome-wide repeat landscapes in cancer and cell-free DNA

Genetic changes in repetitive sequences are a hallmark of cancer and other diseases, but characterizing these has been challenging using standard sequencing approaches. We developed a de novo kmer finding approach, called ARTEMIS (Analysis of RepeaT EleMents in dISease), to identify repeat elements from whole-genome sequencing. Using this method, we analyzed 1.2 billion kmers in 2837 tissue and plasma samples from 1975 patients, including those with lung, breast, colorectal, ovarian, liver, gastric, head and neck, bladder, cervical, thyroid, or prostate cancer. We identified tumor-specific changes in these patients in 1280 repeat element types from the LINE, SINE, LTR, transposable element, and human satellite families. These included changes to known repeats and 820 elements that were not previously known to be altered in human cancer. Repeat elements were enriched in regions of driver genes, and their representation was altered by structural changes and epigenetic states. Machine learning analyses of genome-wide repeat landscapes and fragmentation profiles in cfDNA detected patients with early-stage lung or liver cancer in cross-validated and externally validated cohorts. In addition, these repeat landscapes could be used to noninvasively identify the tissue of origin of tumors. These analyses reveal widespread changes in repeat landscapes of human cancers and provide an approach for their detection and characterization that could benefit early detection and disease monitoring of patients with cancer.

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.

Deep whole-genome analysis of 494 hepatocellular carcinomas

Over half of hepatocellular carcinoma (HCC) cases diagnosed worldwide are in China1-3. However, whole-genome analysis of hepatitis B virus (HBV)-associated HCC in Chinese individuals is limited4-8, with current analyses of HCC mainly from non-HBV-enriched populations9,10. Here we initiated the Chinese Liver Cancer Atlas (CLCA) project and performed deep whole-genome sequencing (average depth, 120×) of 494 HCC tumours. We identified 6 coding and 28 non-coding previously undescribed driver candidates. Five previously undescribed mutational signatures were found, including aristolochic-acid-associated indel and doublet base signatures, and a single-base-substitution signature that we termed SBS_H8. Pentanucleotide context analysis and experimental validation confirmed that SBS_H8 was distinct to the aristolochic-acid-associated SBS22. Notably, HBV integrations could take the form of extrachromosomal circular DNA, resulting in elevated copy numbers and gene expression. Our high-depth data also enabled us to characterize subclonal clustered alterations, including chromothripsis, chromoplexy and kataegis, suggesting that these catastrophic events could also occur in late stages of hepatocarcinogenesis. Pathway analysis of all classes of alterations further linked non-coding mutations to dysregulation of liver metabolism. Finally, we performed in vitro and in vivo assays to show that fibrinogen alpha chain (FGA), determined as both a candidate coding and non-coding driver, regulates HCC progression and metastasis. Our CLCA study depicts a detailed genomic landscape and evolutionary history of HCC in Chinese individuals, providing important clinical implications.

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.

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.

Optimizing sequence design strategies for perturbation MPRAs: a computational evaluation framework

The advent of perturbation-based massively parallel reporter assays (MPRAs) technique has facilitated the delineation of the roles of non-coding regulatory elements in orchestrating gene expression. However, computational efforts remain scant to evaluate and establish guidelines for sequence design strategies for perturbation MPRAs. In this study, we propose a framework for evaluating and comparing various perturbation strategies for MPRA experiments. Within this framework, we benchmark three different perturbation approaches from the perspectives of alteration in motif-based profiles, consistency of MPRA outputs, and robustness of models that predict the activities of putative regulatory motifs. While our analyses show very similar results across multiple benchmarking metrics, the predictive modeling for the approach involving random nucleotide shuffling shows significant robustness compared with the other two approaches. Thus, we recommend designing sequences by randomly shuffling the nucleotides of the perturbed site in perturbation-MPRA, followed by a coherence check to prevent the introduction of other variations of the target motifs. In summary, our evaluation framework and the benchmarking findings create a resource of computational pipelines and highlight the potential of perturbation-MPRA in predicting non-coding regulatory activities.

Distinct senescence mechanisms restrain progression of dysplastic nevi

Telomerase reverse transcriptase (TERT) promoter mutations (TPMs) are frequently found in different cancer types, including ∼70% of sun-exposed skin melanomas. In melanoma, TPMs are among the earliest mutations and can be present during the transition from nevus to melanoma. However, the specific factors that contribute to the selection of TPMs in certain nevi subsets are not well understood. To investigate this, we analyzed a group of dysplastic nevi (DN) by sequencing genes commonly mutated in melanocytic neoplasms. We examined the relationship between the identified mutations, patient age, telomere length, histological features, and the expression of p16. Our findings reveal that TPMs are more prevalent in DN from older patients and are associated with shorter telomeres. Importantly, these TPMs were not found in nevi with BRAF V600E mutations. Conversely, DN with BRAF V600E mutations were observed in younger patients, had longer telomeres and a higher proportion of p16-positive cells. This suggests that these nevi arrest growth independently of telomere shortening through a mechanism known as oncogene-induced senescence (OIS). These characteristics extend to melanoma-sequencing datasets, where melanomas with BRAF V600E mutations were more likely to have a CDKN2A inactivation, overriding OIS. In contrast, melanomas without BRAF V600E mutations showed a higher frequency of TPMs. Our data imply that TPMs are selected to bypass replicative senescence (RS) in cells that were not arrested by OIS. Overall, our results indicate that a subset of melanocytic neoplasms face constraints from RS, while others encounter OIS and RS. The order in which these barriers are overcome during progression to melanoma depends on the mutational context.

Genomic alterations in thyroid cancer: biological and clinical insights

Tumours can arise from thyroid follicular cells if they acquire driver mutations that constitutively activate the MAPK signalling pathway. In addition, a limited set of additional mutations in key genes drive tumour progression towards more aggressive and less differentiated disease. Unprecedented insights into thyroid tumour biology have come from the breadth of thyroid tumour sequencing data from patients and the wide range of mutation-specific mechanisms identified in experimental models, in combination with the genomic simplicity of thyroid cancers. This knowledge is gradually being translated into refined strategies to stratify, manage and treat patients with thyroid cancer. This Review summarizes the biological underpinnings of the genetic alterations involved in thyroid cancer initiation and progression. We also provide a rationale for and discuss specific examples of how to implement genomic information to inform both recommended and investigational approaches to improve thyroid cancer prognosis, redifferentiation strategies and targeted therapies.

A comprehensive analysis of clinical and polygenic germline influences on somatic mutational burden

Tumor mutational burden (TMB), the total number of somatic mutations in the tumor, and copy number burden (CNB), the corresponding measure of aneuploidy, are established fundamental somatic features and emerging biomarkers for immunotherapy. However, the genetic and non-genetic influences on TMB/CNB and, critically, the manner by which they influence patient outcomes remain poorly understood. Here, we present a large germline-somatic study of TMB/CNB with >23,000 individuals across 17 cancer types, of which 12,000 also have extensive clinical, treatment, and overall survival (OS) measurements available. We report dozens of clinical associations with TMB/CNB, observing older age and male sex to have a strong effect on TMB and weaker impact on CNB. We additionally identified significant germline influences on TMB/CNB, including fine-scale European ancestry and germline polygenic risk scores (PRSs) for smoking, tanning, white blood cell counts, and educational attainment. We quantify the causal effect of exposures on somatic mutational processes using Mendelian randomization. Many of the identified features associated with TMB/CNB were additionally associated with OS for individuals treated at a single tertiary cancer center. For individuals receiving immunotherapy, we observed a complex relationship between PRSs for educational attainment, self-reported college attainment, TMB, and survival, suggesting that the influence of this biomarker may be substantially modified by socioeconomic status. While the accumulation of somatic alterations is a stochastic process, our work demonstrates that it can be shaped by host characteristics including germline genetics.

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

Enhancer hijacking, caused by structural alterations on chromosomes as well as extrachromosomal DNA (ecDNA), is a common cancer driver event. The complexity and ubiquity of structural alterations in cancer genomes make it difficult to identify enhancer hijacking using genome sequencing alone. Here we describe a 3D genomics-based analysis called HAPI (Highly Active Promoter Interactions) to characterize enhancer hijacking caused by structural alterations. HAPI analysis of HiChIP data from 34 cancer cell lines identified novel enhancer hijacking events that involve chromosomal rearrangements and activate both known and potentially novel oncogenes such as MYC, CCND1, ETV1, CRKL, and ID4, which we validated using CRISPRi assays and RNA-seq analysis. Furthermore, we found that ecDNAs often contain multiple oncogenes from different chromosomes, which causes nested enhancer hijacking among them. We found that ecDNAs containing MYC often harbor additional oncogenes from other chromosomes such as CDX2, ERBB2, or CD44 that co-opt MYC's enhancers for their overexpression, which we validated using dual-color DNA FISH and CRISPRi assays. These enhancer hijacking events involving multiple oncogenes on ecDNAs have important implications for therapeutic strategies that either target the co-opting oncogenes or the hijacked enhancers. Our publicly available HAPI analysis tool provides a robust strategy to detect enhancer hijacking and reveals novel insights into oncogene activation caused by chromosomal and extrachromosomal structural alterations.

Selective Occupation by E2F and RB of Loci Expressed by RNA Polymerase III

In all cases tested, TFIIIB is responsible for recruiting pol III to its genetic templates. In mammalian cells, RB binds TFIIIB and prevents its interactions with both promoter DNA and pol III, thereby suppressing transcription. As TFIIIB is not recruited to its target genes when bound by RB, the mechanism predicts that pol III-dependent templates will not be occupied by RB; this contrasts with the situation at most genes controlled by RB, where it can be tethered by promoter-bound sequence-specific DNA-binding factors such as E2F. Contrary to this prediction, however, ChIP-seq data reveal the presence of RB in multiple cell types and the related protein p130 at many loci that rely on pol III for their expression, including RMRP, RN7SL, and a variety of tRNA genes. The sets of genes targeted varies according to cell type and growth state. In such cases, recruitment of RB and p130 can be explained by binding of E2F1, E2F4 and/or E2F5. Genes transcribed by pol III had not previously been identified as common targets of E2F family members. The data provide evidence that E2F may allow for the selective regulation of specific non-coding RNAs by RB, in addition to its influence on overall pol III output through its interaction with TFIIIB.

High UV damage and low repair, but not cytosine deamination, stimulate mutation hotspots at ETS binding sites in melanoma

Noncoding mutation hotspots have been identified in melanoma and many of them occur at the binding sites of E26 transformation-specific (ETS) proteins; however, their formation mechanism and functional impacts are not fully understood. Here, we used UV (Ultraviolet) damage sequencing data and analyzed cyclobutane pyrimidine dimer (CPD) formation, DNA repair, and CPD deamination in human cells at single-nucleotide resolution. Our data show prominent CPD hotspots immediately after UV irradiation at ETS binding sites, particularly at sites with a conserved TTCCGG motif, which correlate with mutation hotspots identified in cutaneous melanoma. Additionally, CPDs are repaired slower at ETS binding sites than in flanking DNA. Cytosine deamination in CPDs to uracil is suggested as an important step for UV mutagenesis. However, we found that CPD deamination is significantly suppressed at ETS binding sites, particularly for the CPD hotspot on the 5' side of the ETS motif, arguing against a role for CPD deamination in promoting ETS-associated UV mutations. Finally, we analyzed a subset of frequently mutated promoters, including the ribosomal protein genes RPL13A and RPS20, and found that mutations in the ETS motif can significantly reduce the promoter activity. Thus, our data identify high UV damage and low repair, but not CPD deamination, as the main mechanism for ETS-associated mutations in melanoma and uncover important roles of often-overlooked mutation hotspots in perturbing gene transcription.

Genomic hallmarks and therapeutic targets of ribosome biogenesis in cancer

Hyperactive ribosome biogenesis (RiboSis) fuels unrestricted cell proliferation, whereas genomic hallmarks and therapeutic targets of RiboSis in cancers remain elusive, and efficient approaches to quantify RiboSis activity are still limited. Here, we have established an in silico approach to conveniently score RiboSis activity based on individual transcriptome data. By employing this novel approach and RNA-seq data of 14 645 samples from TCGA/GTEx dataset and 917 294 single-cell expression profiles across 13 cancer types, we observed the elevated activity of RiboSis in malignant cells of various human cancers, and high risk of severe outcomes in patients with high RiboSis activity. Our mining of pan-cancer multi-omics data characterized numerous molecular alterations of RiboSis, and unveiled the predominant somatic alteration in RiboSis genes was copy number variation. A total of 128 RiboSis genes, including EXOSC4, BOP1, RPLP0P6 and UTP23, were identified as potential therapeutic targets. Interestingly, we observed that the activity of RiboSis was associated with TP53 mutations, and hyperactive RiboSis was associated with poor outcomes in lung cancer patients without TP53 mutations, highlighting the importance of considering TP53 mutations during therapy by impairing RiboSis. Moreover, we predicted 23 compounds, including methotrexate and CX-5461, associated with the expression signature of RiboSis genes. The current study generates a comprehensive blueprint of molecular alterations in RiboSis genes across cancers, which provides a valuable resource for RiboSis-based anti-tumor therapy.

Cancer Evolution: A Multifaceted Affair

Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery.

SIGNIFICANCE

Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.

Leveraging Tissue-Specific Enhancer-Target Gene Regulatory Networks Identifies Enhancer Somatic Mutations That Functionally Impact Lung Cancer

Enhancers are noncoding regulatory DNA regions that modulate the transcription of target genes, often over large distances along with the genomic sequence. Enhancer alterations have been associated with various pathological conditions, including cancer. However, the identification and characterization of somatic mutations in noncoding regulatory regions with a functional effect on tumorigenesis and prognosis remain a major challenge. Here, we present a strategy for detecting and characterizing enhancer mutations in a genome-wide analysis of patient cohorts, across three lung cancer subtypes. Lung tissue-specific enhancers were defined by integrating experimental data and public epigenomic profiles, and the genome-wide enhancer-target gene regulatory network of lung cells was constructed by integrating chromatin three-dimensional architecture data. Lung cancers possessed a similar mutation burden at tissue-specific enhancers and exons but with differences in their mutation signatures. Functionally relevant alterations were prioritized on the basis of the pathway-level integration of the effect of a mutation and the frequency of mutations on individual enhancers. The genes enriched for mutated enhancers converged on the regulation of key biological processes and pathways relevant to tumor biology. Recurrent mutations in individual enhancers also affected the expression of target genes, with potential relevance for patient prognosis. Together, these findings show that noncoding regulatory mutations have a potential relevance for cancer pathogenesis and can be exploited for patient classification.

SIGNIFICANCE

Mapping enhancer-target gene regulatory interactions and analyzing enhancer mutations at the level of their target genes and pathways reveal convergence of recurrent enhancer mutations on biological processes involved in tumorigenesis and prognosis.

Hotspot propensity across mutational processes

The sparsity of mutations observed across tumours hinders our ability to study mutation rate variability at nucleotide resolution. To circumvent this, here we investigated the propensity of mutational processes to form mutational hotspots as a readout of their mutation rate variability at single base resolution. Mutational signatures 1 and 17 have the highest hotspot propensity (5-78 times higher than other processes). After accounting for trinucleotide mutational probabilities, sequence composition and mutational heterogeneity at 10 Kbp, most (94-95%) signature 17 hotspots remain unexplained, suggesting a significant role of local genomic features. For signature 1, the inclusion of genome-wide distribution of methylated CpG sites into models can explain most (80-100%) of the hotspot propensity. There is an increased hotspot propensity of signature 1 in normal tissues and de novo germline mutations. We demonstrate that hotspot propensity is a useful readout to assess the accuracy of mutation rate models at nucleotide resolution. This new approach and the findings derived from it open up new avenues for a range of somatic and germline studies investigating and modelling mutagenesis.

miR-142: A Master Regulator in Hematological Malignancies and Therapeutic Opportunities

MicroRNAs (miRNAs) are a type of non-coding RNA whose dysregulation is frequently associated with the onset and progression of human cancers. miR-142, an ultra-conserved miRNA with both active -3p and -5p mature strands and wide-ranging physiological targets, has been the subject of countless studies over the years. Due to its preferential expression in hematopoietic cells, miR-142 has been found to be associated with numerous types of lymphomas and leukemias. This review elucidates the multifaceted role of miR-142 in human physiology, its influence on hematopoiesis and hematopoietic cells, and its intriguing involvement in exosome-mediated miR-142 transport. Moreover, we offer a comprehensive exploration of the genetic and molecular landscape of the miR-142 genomic locus, highlighting its mutations and dysregulation within hematological malignancies. Finally, we discuss potential avenues for harnessing the therapeutic potential of miR-142 in the context of hematological malignancies.

Genomic and epigenomic integrative subtypes of renal cell carcinoma in a Japanese cohort

Renal cell carcinoma (RCC) comprises several histological types characterised by different genomic and epigenomic aberrations; however, the molecular pathogenesis of each type still requires further exploration. We perform whole-genome sequencing of 128 Japanese RCC cases of different histology to elucidate the significant somatic alterations and mutagenesis processes. We also perform transcriptomic and epigenomic sequencing to identify distinguishing features, including assay for transposase-accessible chromatin sequencing (ATAC-seq) and methyl sequencing. Genomic analysis reveals that the mutational signature differs among the histological types, suggesting that different carcinogenic factors drive each histology. From the ATAC-seq results, master transcription factors are identified for each histology. Furthermore, clear cell RCC is classified into three epi-subtypes, one of which expresses highly immune checkpoint molecules with frequent loss of chromosome 14q. These genomic and epigenomic features may lead to the development of effective therapeutic strategies for RCC.

Incorporating mutational heterogeneity to identify genes that are enriched for synonymous mutations in cancer

BACKGROUND

Synonymous mutations, which change the DNA sequence but not the encoded protein sequence, can affect protein structure and function, mRNA maturation, and mRNA half-lives. The possibility that synonymous mutations might be enriched in cancer has been explored in several recent studies. However, none of these studies control for all three types of mutational heterogeneity (patient, histology, and gene) that are known to affect the accurate identification of non-synonymous cancer-associated genes. Our goal is to adopt the current standard for non-synonymous mutations in an investigation of synonymous mutations.

RESULTS

Here, we create an algorithm, MutSigCVsyn, an adaptation of MutSigCV, to identify cancer-associated genes that are enriched for synonymous mutations based on a non-coding background model that takes into account the mutational heterogeneity across these levels. Using MutSigCVsyn, we first analyzed 2572 cancer whole-genome samples from the Pan-cancer Analysis of Whole Genomes (PCAWG) to identify non-synonymous cancer drivers as a quality control. Indicative of the algorithm accuracy we find that 58.6% of these candidate genes were also found in Cancer Census Gene (CGC) list, and 66.2% were found within the PCAWG cancer driver list. We then applied it to identify 30 putative cancer-associated genes that are enriched for synonymous mutations within the same samples. One of the promising gene candidates is the B cell lymphoma 2 (BCL-2) gene. BCL-2 regulates apoptosis by antagonizing the action of proapoptotic BCL-2 family member proteins. The synonymous mutations in BCL2 are enriched in its anti-apoptotic domain and likely play a role in cancer cell proliferation.

CONCLUSION

Our study introduces MutSigCVsyn, an algorithm that accounts for mutational heterogeneity at patient, histology, and gene levels, to identify cancer-associated genes that are enriched for synonymous mutations using whole genome sequencing data. We identified 30 putative candidate genes that will benefit from future experimental studies on the role of synonymous mutations in cancer biology.

Genomic sequence context differs between germline and somatic structural variants allowing for their differentiation in tumor samples without paired normals

There is currently no method to distinguish between germline and somatic structural variants (SVs) in tumor samples that lack a matched normal sample. In this study, we analyzed several features of germline and somatic SVs from a cohort of 974 patients from The Cancer Genome Atlas (TCGA). We identified a total of 21 features that differed significantly between germline and somatic SVs. Several of the germline SV features were associated with each other, as were several of the somatic SV features. We also found that these associations differed between the germline and somatic classes, for example, we found that somatic inversions were more likely to be longer events than their germline counterparts. Using these features we trained a support vector machine (SVM) classifier on 555,849 TCGA SVs to computationally distinguish germline from somatic SVs in the absence of a matched normal. This classifier had an ROC curve AUC of 0.984 when tested on an independent test set of 277,925 TCGA SVs. In this dataset, we achieved a positive predictive value (PPV) of 0.81 for an SV called somatic by the classifier being truly somatic. We further tested the classifier on a separate set of 7,623 SVs from pediatric high-grade gliomas (pHGG). In this non-TCGA cohort, our classifier achieved a PPV of 0.828, showing robust performance across datasets.

Long non-coding RNA mitophagy and ALK-negative anaplastic lymphoma-associated transcript: a novel regulator of mitophagy in T-cell lymphoma

Long non-coding RNA (lncRNA) are emerging as powerful and versatile regulators of transcriptional programs and distinctive biomarkers of progression of T-cell lymphoma. Their role in the aggressive anaplastic lymphoma kinase-negative (ALK-) subtype of anaplastic large cell lymphoma (ALCL) has been elucidated only in part. Starting from our previously identified ALCL-associated lncRNA signature and performing digital gene expression profiling of a retrospective cohort of ALCL, we defined an 11 lncRNA signature able to discriminate among ALCL subtypes. We selected a not previously characterized lncRNA, MTAAT, with preferential expression in ALK- ALCL, for molecular and functional studies. We demonstrated that lncRNA MTAAT contributes to an aberrant mitochondrial turnover restraining mitophagy and promoting cellular proliferation. Functionally, lncRNA MTAAT acts as a repressor of a set of genes related to mitochondrial quality control via chromatin reorganization. Collectively, our work demonstrates the transcriptional role of lncRNA MTAAT in orchestrating a complex transcriptional program sustaining the progression of ALK- ALCL.

A Novel Type of IDH-wildtype Glioma Characterized by Gliomatosis Cerebri-like Growth Pattern, TERT Promoter Mutation, and Distinct Epigenetic Profile

Diffuse gliomas in adults encompass a heterogenous group of central nervous system neoplasms. In recent years, extensive (epi-)genomic profiling has identified several glioma subgroups characterized by distinct molecular characteristics, most importantly IDH1/2 and histone H3 mutations. A group of 16 diffuse gliomas classified as "adult-type diffuse high-grade glioma, IDH-wildtype, subtype F (HGG-F)" was identified by the DKFZ v12.5 Brain Tumor Classifier . Histopathologic characterization, exome sequencing, and review of clinical data was performed in all cases. Based on unsupervised t -distributed stochastic neighbor embedding and clustering analysis of genome-wide DNA methylation data, HGG-F shows distinct epigenetic profiles separate from established central nervous system tumors. Exome sequencing demonstrated frequent TERT promoter (12/15 cases), PIK3R1 (11/16), and TP53 mutations (5/16). Radiologic characteristics were reminiscent of gliomatosis cerebri in 9/14 cases (64%). Histopathologically, most cases were classified as diffuse gliomas (7/16, 44%) or were suspicious for the infiltration zone of a diffuse glioma (5/16, 31%). None of the cases demonstrated microvascular proliferation or necrosis. Outcome of 14 patients with follow-up data was better compared to IDH-wildtype glioblastomas with a median progression-free survival of 58 months and overall survival of 74 months (both P <0.0001). Our series represents a novel type of adult-type diffuse glioma with distinct molecular and clinical features. Importantly, we provide evidence that TERT promoter mutations in diffuse gliomas without further morphologic or molecular signs of high-grade glioma should be interpreted in the context of the clinicoradiologic presentation as well as epigenetic profile and may not be suitable as a standalone marker for glioblastoma, IDH-wildtype.

Methylation-directed regulatory networks determine enhancing and silencing of mutation disease driver genes and explain inter-patient expression variation

BACKGROUND

Common diseases manifest differentially between patients, but the genetic origin of this variation remains unclear. To explore possible involvement of gene transcriptional-variation, we produce a DNA methylation-oriented, driver-gene-wide dataset of regulatory elements in human glioblastomas and study their effect on inter-patient gene expression variation.

RESULTS

In 175 of 177 analyzed gene regulatory domains, transcriptional enhancers and silencers are intermixed. Under experimental conditions, DNA methylation induces enhancers to alter their enhancing effects or convert into silencers, while silencers are affected inversely. High-resolution mapping of the association between DNA methylation and gene expression in intact genomes reveals methylation-related regulatory units (average size = 915.1 base-pairs). Upon increased methylation of these units, their target-genes either increased or decreased in expression. Gene-enhancing and silencing units constitute cis-regulatory networks of genes. Mathematical modeling of the networks highlights indicative methylation sites, which signified the effect of key regulatory units, and add up to make the overall transcriptional effect of the network. Methylation variation in these sites effectively describe inter-patient expression variation and, compared with DNA sequence-alterations, appears as a major contributor of gene-expression variation among glioblastoma patients.

CONCLUSIONS

We describe complex cis-regulatory networks, which determine gene expression by summing the effects of positive and negative transcriptional inputs. In these networks, DNA methylation induces both enhancing and silencing effects, depending on the context. The revealed mechanism sheds light on the regulatory role of DNA methylation, explains inter-individual gene-expression variation, and opens the way for monitoring the driving forces behind deferential courses of cancer and other diseases.

PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants

Structural variants (SVs) represent a major source of genetic variation associated with phenotypic diversity and disease susceptibility. While long-read sequencing can discover over 20,000 SVs per human genome, interpreting their functional consequences remains challenging. Existing methods for identifying disease-related SVs focus on deletion/duplication only and cannot prioritize individual genes affected by SVs, especially for noncoding SVs. Here, we introduce PhenoSV, a phenotype-aware machine-learning model that interprets all major types of SVs and genes affected. PhenoSV segments and annotates SVs with diverse genomic features and employs a transformer-based architecture to predict their impacts under a multiple-instance learning framework. With phenotype information, PhenoSV further utilizes gene-phenotype associations to prioritize phenotype-related SVs. Evaluation on extensive human SV datasets covering all SV types demonstrates PhenoSV's superior performance over competing methods. Applications in diseases suggest that PhenoSV can determine disease-related genes from SVs. A web server and a command-line tool for PhenoSV are available at https://phenosv.wglab.org .

Finding Needles in the Haystack: Strategies for Uncovering Noncoding Regulatory Variants

Despite accumulating evidence implicating noncoding variants in human diseases, unraveling their functionality remains a significant challenge. Systematic annotations of the regulatory landscape and the growth of sequence variant data sets have fueled the development of tools and methods to identify causal noncoding variants and evaluate their regulatory effects. Here, we review the latest advances in the field and discuss potential future research avenues to gain a more in-depth understanding of noncoding regulatory variants.

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.

Targeting oncogenic TERT promoter variants by allele-specific epigenome editing

BACKGROUND

Activation of dominant oncogenes by small or structural genomic alterations is a common driver mechanism in many cancers. Silencing of such dominantly activated oncogenic alleles, thus, is a promising strategy to treat cancer. Recently, allele-specific epigenome editing (ASEE) has been described as a means to reduce transcription of genes in an allele-specific manner. In cancer, specificity to an oncogenic allele can be reached by either targeting directly a pathogenic single-nucleotide variant or a polymorphic single-nucleotide variant linked to the oncogenic allele. To investigate the potential of ASEE in cancer, we here explored this approach by targeting variants at the TERT promoter region. The TERT promoter region has been described as one of the most frequently mutated non-coding cancer drivers.

RESULTS

Sequencing of the TERT promoter in cancer cell lines showed 53% (41/77) to contain at least one heterozygous sequence variant allowing allele distinction. We chose the hepatoblastoma cell line Hep-G2 and the lung cancer cell line A-549 for this proof-of-principle study, as they contained two different kinds of variants, namely the activating mutation C228T in the TERT core promoter and the common SNP rs2853669 in the THOR region, respectively. These variants were targeted in an allele-specific manner using sgRNA-guided dCas9-DNMT3A-3L complexes. In both cell lines, we successfully introduced DNA methylation specifically to the on-target allele of the TERT promoter with limited background methylation on the off-target allele or an off-target locus (VEGFA), respectively. We observed a maximum CpG methylation gain of 39% and 76% on the target allele when targeting the activating mutation and the common SNP, respectively. The epigenome editing translated into reduced TERT RNA expression in Hep-G2.

CONCLUSIONS

We applied an ASEE-mediated approach to silence TERT allele specifically. Our results show that the concept of dominant oncogene inactivation by allele-specific epigenome editing can be successfully translated into cancer models. This new strategy may have important advantages in comparison with existing therapeutic approaches, e.g., targeting telomerase, especially with regard to reducing adverse side effects.

Enhancer regulatory networks globally connect non-coding breast cancer loci to cancer genes

Genetic studies have associated thousands of enhancers with breast cancer. However, the vast majority have not been functionally characterized. Thus, it remains unclear how variant-associated enhancers contribute to cancer. Here, we perform single-cell CRISPRi screens of 3,512 regulatory elements associated with breast cancer to measure the impact of these regions on transcriptional phenotypes. Analysis of >500,000 single-cell transcriptomes in two breast cancer cell lines shows that perturbation of variant-associated enhancers disrupts breast cancer gene programs. We observe variant-associated enhancers that directly or indirectly regulate the expression of cancer genes. We also find one-to-multiple and multiple-to-one network motifs where enhancers indirectly regulate cancer genes. Notably, multiple variant-associated enhancers indirectly regulate TP53. Comparative studies illustrate sub-type specific functions between enhancers in ER+ and ER- cells. Finally, we developed the pySpade package to facilitate analysis of single-cell enhancer screens. Overall, we demonstrate that enhancers form regulatory networks that link cancer genes in the genome, providing a more comprehensive understanding of the contribution of enhancers to breast cancer development.

Enhancers dysfunction in the 3D genome of cancer cells

Eukaryotic genomes are spatially organized inside the cell nucleus, forming a threedimensional (3D) architecture that allows for spatial separation of nuclear processes and for controlled expression of genes required for cell identity specification and tissue homeostasis. Hence, it is of no surprise that mis-regulation of genome architecture through rearrangements of the linear genome sequence or epigenetic perturbations are often linked to aberrant gene expression programs in tumor cells. Increasing research efforts have shed light into the causes and consequences of alterations of 3D genome organization. In this review, we summarize the current knowledge on how 3D genome architecture is dysregulated in cancer, with a focus on enhancer highjacking events and their contribution to tumorigenesis. Studying the functional effects of genome architecture perturbations on gene expression in cancer offers a unique opportunity for a deeper understanding of tumor biology and sets the basis for the discovery of novel therapeutic targets.

Bioinformatics analysis and experimental validation of tumorigenic role of PPIA in gastric cancer

Gastric cancer (GC) is a malignant tumor with high incidence rate and mortality. Due to the lack of effective diagnostic indicators, most patients are diagnosed in late stage and have a poor prognosis. An increasing number of studies have proved that Peptidylprolyl isomerase A (PPIA) can play an oncogene role in various cancer types. However, the precise mechanism of PPIA in GC is still unclear. Herein, we analyzed the mRNA levels of PPIA in pan-cancer. The prognostic value of PPIA on GC was also evaluated using multiple databases. Additionally, the relationship between PPIA expression and clinical factors in GC was also examined. We further confirmed that PPIA expression was not affected by genetic alteration and DNA methylation. Moreover, the upstream regulator miRNA and lncRNA of PPIA were identified, which suggested that LINC10232/miRNA-204-5p/PPIA axis might act as a potential biological pathway in GC. Finally, this study revealed that PPIA was negatively correlated with immune checkpoint expression, immune cell biomarkers, and immune cell infiltration in GC.

Noncoding RNA mutations in cancer

Cancer is driven by both germline and somatic genetic changes. Efforts have been devoted to characterizing essential genetic variations in cancer initiation and development. Most attention has been given to mutations in protein-coding genes and associated regulatory elements such as promoters and enhancers. The development of sequencing technologies and in silico and experimental methods has allowed further exploration of cancer predisposition variants and important somatic mutations in noncoding RNAs, mainly for long noncoding RNAs and microRNAs. Association studies including GWAS have revealed hereditary variations including SNPs and indels in lncRNA or miRNA genes and regulatory regions. These mutations altered RNA secondary structures, expression levels, and target recognition and then conferred cancer predisposition to carriers. Whole-exome/genome sequencing comparing cancer and normal tissues has revealed important somatic mutations in noncoding RNA genes. Mutation hotspots and somatic copy number alterations have been identified in various tumor-associated noncoding RNAs. Increasing focus and effort have been devoted to studying the noncoding region of the genome. The complex genetic network of cancer initiation is being unveiled. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Telomerase Upregulation Induces Progression of Mouse BrafV600E-Driven Thyroid Cancers and Triggers Nontelomeric Effects

Mutations in the promoter of the telomerase reverse transcriptase (TERT) gene are the paradigm of a cross-cancer alteration in a noncoding region. TERT promoter mutations (TPM) are biomarkers of poor prognosis in cancer, including thyroid tumors. TPMs enhance TERT transcription, which is otherwise silenced in adult tissues, thus reactivating a bona fide oncoprotein. To study TERT deregulation and its downstream consequences, we generated a Tert mutant promoter mouse model via CRISPR/Cas9 engineering of the murine equivalent locus (Tert-123C>T) and crossed it with thyroid-specific BrafV600E-mutant mice. We also employed an alternative model of Tert overexpression (K5-Tert). Whereas all BrafV600E animals developed well-differentiated papillary thyroid tumors, 29% and 36% of BrafV600E+Tert-123C>T and BrafV600E+K5-Tert mice progressed to poorly differentiated cancers at week 20, respectively. Tert-upregulated tumors showed increased mitosis and necrosis in areas of solid growth, and older animals displayed anaplastic-like features, that is, spindle cells and macrophage infiltration. Murine TPM increased Tert transcription in vitro and in vivo, but temporal and intratumoral heterogeneity was observed. RNA-sequencing of thyroid tumor cells showed that processes other than the canonical Tert-mediated telomere maintenance role operate in these specimens. Pathway analysis showed that MAPK and PI3K/AKT signaling, as well as processes not previously associated with this tumor etiology, involving cytokine, and chemokine signaling, were overactivated. These models constitute useful preclinical tools to understand the cell-autonomous and microenvironment-related consequences of Tert-mediated progression in advanced thyroid cancers and other aggressive tumors carrying TPMs.

IMPLICATIONS

Telomerase-driven cancer progression activates pathways that can be dissected and perhaps therapeutically exploited.

Landscape of enhancer disruption and functional screen in melanoma cells

BACKGROUND

The high mutation rate throughout the entire melanoma genome presents a major challenge in stratifying true driver events from the background mutations. Numerous recurrent non-coding alterations, such as those in enhancers, can shape tumor evolution, thereby emphasizing the importance in systematically deciphering enhancer disruptions in melanoma.

RESULTS

Here, we leveraged 297 melanoma whole-genome sequencing samples to prioritize highly recurrent regions. By performing a genome-scale CRISPR interference (CRISPRi) screen on highly recurrent region-associated enhancers in melanoma cells, we identified 66 significant hits which could have tumor-suppressive roles. These functional enhancers show unique mutational patterns independent of classical significantly mutated genes in melanoma. Target gene analysis for the essential enhancers reveal many known and hidden mechanisms underlying melanoma growth. Utilizing extensive functional validation experiments, we demonstrate that a super enhancer element could modulate melanoma cell proliferation by targeting MEF2A, and another distal enhancer is able to sustain PTEN tumor-suppressive potential via long-range interactions.

CONCLUSIONS

Our study establishes a catalogue of crucial enhancers and their target genes in melanoma growth and progression, and illuminates the identification of novel mechanisms of dysregulation for melanoma driver genes and new therapeutic targeting strategies.

MALAT1 expression is associated with aggressive behavior in indolent B-cell neoplasms

MALAT1 long non-coding RNA has oncogenic roles but has been poorly studied in indolent B-cell neoplasms. Here, MALAT1 expression was analyzed using RNA-seq, microarrays or qRT-PCR in primary samples from clinico-biological subtypes of chronic lymphocytic leukemia (CLL, n = 266), paired Richter transformation (RT, n = 6) and follicular lymphoma (FL, n = 61). In peripheral blood (PB) CLL samples, high MALAT1 expression was associated with a significantly shorter time to treatment independently from other known prognostic factors. Coding genes expressed in association with MALAT1 in CLL were predominantly related to oncogenic pathways stimulated in the lymph node (LN) microenvironment. In RT paired samples, MALAT1 levels were lower, concordant with their acquired increased independency of external signals. Moreover, MALAT1 levels in paired PB/LN CLLs were similar, suggesting that the prognostic value of MALAT1 expression in PB is mirroring expression differences already present in LN. Similarly, high MALAT1 expression in FL predicted for a shorter progression-free survival, in association with expression pathways promoting FL pathogenesis. In summary, MALAT1 expression is related to pathophysiology and more aggressive clinical behavior of indolent B-cell neoplasms. Particularly in CLL, its levels could be a surrogate marker of the microenvironment stimulation and may contribute to refine the clinical management of these patients.

Somatic gain-of-function mutations in BUD13 promote oncogenesis by disrupting Fbw7 function

Somatic mutations occurring on key enzymes are extensively studied and targeted therapies are developed with clinical promises. However, context-dependent enzyme function through distinct substrates complicated targeting a given enzyme. Here, we develop an algorithm to elucidate a new class of somatic mutations occurring on enzyme-recognizing motifs that cancer may hijack to facilitate tumorigenesis. We validate BUD13-R156C and -R230Q mutations evading RSK3-mediated phosphorylation with enhanced oncogenicity in promoting colon cancer growth. Further mechanistic studies reveal BUD13 as an endogenous Fbw7 inhibitor that stabilizes Fbw7 oncogenic substrates, while cancerous BUD13-R156C or -R230Q interferes with Fbw7Cul1 complex formation. We also find this BUD13 regulation plays a critical role in responding to mTOR inhibition, which can be used to guide therapy selections. We hope our studies reveal the landscape of enzyme-recognizing motif mutations with a publicly available resource and provide novel insights for somatic mutations cancer hijacks to promote tumorigenesis with the potential for patient stratification and cancer treatment.

Analysis of Several Common APOBEC-type Mutations in Bladder Tumors Suggests Links to Viral Infection

FGFR3 and PIK3CA are among the most frequently mutated genes in bladder tumors. We hypothesized that recurrent mutations in these genes might be caused by common carcinogenic exposures such as smoking and other factors. We analyzed 2,816 bladder tumors with available data on FGFR3 and/or PIK3CA mutations, focusing on the most recurrent mutations detected in ≥10% of tumors. Compared to tumors with other FGFR3/PIK3CA mutations, FGFR3-Y375C was more common in tumors from smokers than never-smokers (P = 0.009), while several APOBEC-type driver mutations were enriched in never-smokers: FGFR3-S249C (P = 0.013) and PIK3CA-E542K/PIK3CA-E545K (P = 0.009). To explore possible causes of these APOBEC-type mutations, we analyzed RNA sequencing (RNA-seq) data from 798 bladder tumors and detected several viruses, with BK polyomavirus (BKPyV) being the most common. We then performed IHC staining for polyomavirus (PyV) Large T-antigen (LTAg) in an independent set of 211 bladder tumors. Overall, by RNA-seq or IHC-LTAg, we detected PyV in 26 out of 1,010 bladder tumors with significantly higher detection (P = 4.4 × 10-5), 25 of 554 (4.5%) in non-muscle-invasive bladder cancers (NMIBC) versus 1 of 456 (0.2%) of muscle-invasive bladder cancers (MIBC). In the NMIBC subset, the FGFR3/PIK3CA APOBEC-type driver mutations were detected in 94.7% (18/19) of PyV-positive versus 68.3% (259/379) of PyV-negative tumors (P = 0.011). BKPyV tumor positivity in the NMIBC subset with FGFR3- or PIK3CA-mutated tumors was also associated with a higher risk of progression to MIBC (P = 0.019). In conclusion, our results support smoking and BKPyV infection as risk factors contributing to bladder tumorigenesis in the general patient population through distinct molecular mechanisms.

PREVENTION RELEVANCE

Tobacco smoking likely causes one of the most common mutations in bladder tumors (FGFR3-Y375C), while viral infections might contribute to three others (FGFR3-S249C, PIK3CA-E542K, and PIK3CA-E545K). Understanding the causes of these mutations may lead to new prevention and treatment strategies, such as viral screening and vaccination.

Precision medicine in the era of multi-omics: can the data tsunami guide rational treatment decision?

Precision medicine for cancer is rapidly moving to an approach that integrates multiple dimensions of the biology in order to model mechanisms of cancer progression in each patient. The discovery of multiple drivers per tumor challenges medical decision that faces several treatment options. Drug sensitivity depends on the actionability of the target, its clonal or subclonal origin and coexisting genomic alterations. Sequencing has revealed a large diversity of drivers emerging at treatment failure, which are potential targets for clinical trials or drug repurposing. To effectively prioritize therapies, it is essential to rank genomic alterations based on their proven actionability. Moving beyond primary drivers, the future of precision medicine necessitates acknowledging the intricate spatial and temporal heterogeneity inherent in cancer. The advent of abundant complex biological data will make artificial intelligence algorithms indispensable for thorough analysis. Here, we will discuss the advancements brought by the use of high-throughput genomics, the advantages and limitations of precision medicine studies and future perspectives in this field.

Best practices for perturbation MPRA-a computational evaluation framework of sequence design strategies

The advent of the perturbation-based massively parallel reporter assays (MPRAs) technique has enabled delineating of the roles of non-coding regulatory elements in orchestrating gene expression. However, computational efforts remain scant to evaluate and establish guidelines for sequence design strategies for perturbation MPRAs. Here, we propose a framework for evaluating and comparing various perturbation strategies for MPRA experiments. Under this framework, we benchmark three different perturbation approaches from the perspectives of alteration in motif-based profiles, consistency of MPRA outputs, and robustness of models that predict the activities of putative regulatory motifs. Although our analyses show similar while significant results in multiple metrics, the method of randomly shuffling nucleotides outperform the other two methods. Thus, we still recommend designing sequences by randomly shuffling the nucleotides of the perturbed site in perturbation-MPRA. The evaluation framework, together with the benchmarking findings in our work, creates a resource of computational pipelines and illustrates the promise of perturbation-MPRA for predicting non-coding regulatory activities.

Recapitulation of patient-specific 3D chromatin conformation using machine learning

Regulatory networks containing enhancer-gene edges define cellular states. Multiple efforts have revealed these networks for reference tissues and cell lines by integrating multi-omics data. However, the methods developed cannot be applied for large patient cohorts due to the infeasibility of chromatin immunoprecipitation sequencing (ChIP-seq) for limited biopsy material. We trained machine-learning models using chromatin interaction analysis with paired-end tag sequencing (ChIA-PET) and high-throughput chromosome conformation capture combined with chromatin immunoprecipitation (HiChIP) data that can predict connections using only assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA-seq data as input, which can be generated from biopsies. Our method overcomes limitations of correlation-based approaches that cannot distinguish between distinct target genes of given enhancers or between active vs. poised states in different samples, a hallmark of network rewiring in cancer. Application of our model on 371 samples across 22 cancer types revealed 1,780 enhancer-gene connections for 602 cancer genes. Using CRISPR interference (CRISPRi), we validated enhancers predicted to regulate ESR1 in estrogen receptor (ER)+ breast cancer and A1CF in liver hepatocellular carcinoma.

PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer

Identifying personalized cancer driver genes and further revealing their oncogenic mechanisms is critical for understanding the mechanisms of cell transformation and aiding clinical diagnosis. Almost all existing methods primarily focus on identifying driver genes at the cohort or individual level but fail to further uncover their underlying oncogenic mechanisms. To fill this gap, we present an interpretable framework, PhenoDriver, to identify personalized cancer driver genes, elucidate their roles in cancer development and uncover the association between driver genes and clinical phenotypic alterations. By analyzing 988 breast cancer patients, we demonstrate the outstanding performance of PhenoDriver in identifying breast cancer driver genes at the cohort level compared to other state-of-the-art methods. Otherwise, our PhenoDriver can also effectively identify driver genes with both recurrent and rare mutations in individual patients. We further explore and reveal the oncogenic mechanisms of some known and unknown breast cancer driver genes (e.g. TP53, MAP3K1, HTT, etc.) identified by PhenoDriver, and construct their subnetworks for regulating clinical abnormal phenotypes. Notably, most of our findings are consistent with existing biological knowledge. Based on the personalized driver profiles, we discover two existing and one unreported breast cancer subtypes and uncover their molecular mechanisms. These results intensify our understanding for breast cancer mechanisms, guide therapeutic decisions and assist in the development of targeted anticancer therapies.

Interactions between the DNA Damage Response and the Telomere Complex in Carcinogenesis: A Hypothesis

Contrary to what was once thought, direct cancer originating from normal stem cells seems to be extremely rare. This is consistent with a preneoplastic period of telomere length reduction/damage in committed cells that becomes stabilized in transformation. Multiple observations suggest that telomere damage is an obligatory step preceding its stabilization. During tissue turnover, the telomeres of cells undergoing differentiation can be damaged as a consequence of defective DNA repair caused by endogenous or exogenous agents. This may result in the emergence of new mechanism of telomere maintenance which is the final outcome of DNA damage and the initial signal that triggers malignant transformation. Instead, transformation of stem cells is directly induced by primary derangement of telomere maintenance mechanisms. The newly modified telomere complex may promote survival of cancer stem cells, independently of telomere maintenance. An inherent resistance of stem cells to transformation may be linked to specific, robust mechanisms that help maintain telomere integrity.

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.