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Explosive advances in next‐generation sequencer (NGS) and computational analyses have enabled exploration of somatic protein‐altered mutations in most cancer types, with coding mutation data intensively accumulated. However, there is limited information on somatic mutations in non‐coding regions, including introns, regulatory elements and non‐coding RNA. Structural variants and pathogen in cancer genomes remain widely unexplored. Whole genome sequencing (WGS) approaches can be used to comprehensively explore all types of genomic alterations in cancer and help us to better understand the whole landscape of driver mutations and mutational signatures in cancer genomes and elucidate the functional or clinical implications of these unexplored genomic regions and mutational signatures. This review describes recently developed technical approaches for cancer WGS and the future direction of cancer WGS, and discusses its utility and limitations as an analysis platform and for mutation interpretation for cancer genomics and cancer precision medicine. Taking into account the diversity of cancer genomes and phenotypes, interpretation of abundant mutation information from WGS, especially non‐coding and structure variants, requires the analysis of large‐scale WGS data integrated with RNA‐Seq, epigenomics, immuno‐genomic and clinic‐pathological information. A representative Circos plot of cancer genome structure from whole genome sequencing analysis, which indicates SVs and CNAs in all of human chromosomes (1‐22+XY). Chromothripsis in chromosome 1 and 14 was observed.

DNA methylation plays a critical role in gene regulation, affecting cellular differentiation and disease progression, particularly in non‐coding regions. However, predicting the epigenetic consequences of non‐coding mutations at single‐cell resolution remains a challenge. Existing tools have limited prediction capacity and struggle to capture dynamic, cell‐type‐specific regulatory changes that are crucial for understanding disease mechanisms. Here, Methven, a deep learning framework designed is presented to predict the effects of non‐coding mutations on DNA methylation at single‐cell resolution. Methven integrates DNA sequence with single‐cell ATAC‐seq data and models SNP‐CpG interactions over 100 kbp genomic distances. By using a divide‐and‐conquer approach, Methven accurately predicts both short‐ and long‐range regulatory interactions and leverages the pre‐trained DNA language model for enhanced precision in classification and regression tasks. Methven outperforms existing methods and demonstrates robust generalizability to monocyte datasets. Importantly, it identifies CpG sites associated with rheumatoid arthritis, revealing key pathways involved in immune regulation and disease progression. Methven's ability to detect progressive epigenetic changes provides crucial insights into gene regulation in complex diseases. These findings demonstrate Methven's potential as a powerful tool for basic research and clinical applications, advancing this understanding of non‐coding mutations and their role in disease, while offering new opportunities for personalized medicine. Methven is a deep‐learning framework that predicts the impact of non‐coding SNPs on DNA methylation at single‐cell resolution. By integrating pre‐trained DNA language model with single‐cell ATAC‐seq data, Methven models SNP‐CpG interactions across genomic distances up to 100 kbp. It supports both classification and regression outputs, enabling dynamic predictions of the direction and magnitude of methylation changes, and uncovers cell‐type‐specific mechanisms linked to diseases such as rheumatoid arthritis.

Background Breast cancer cell lines (BCCLs) and patient-derived xenografts (PDXs) are the most frequently used models in breast cancer research. Despite their widespread usage, genome sequencing of these models is incomplete, with previous studies only focusing on targeted gene panels, whole exome or shallow whole genome sequencing. Deep whole genome sequencing is the most sensitive and accurate method to detect single nucleotide variants and indels, gene copy number and structural events such as gene fusions. Results Here we describe deep whole genome sequencing (WGS) of commonly used BCCL and PDX models using the Illumina X10 platform with an average ~ 60 × coverage. We identify novel genomic alterations, including point mutations and genomic rearrangements at base-pair resolution, compared to previously available sequencing data. Through integrative analysis with publicly available functional screening data, we annotate new genomic features likely to be of biological significance. CSMD1 , previously identified as a tumor suppressor gene in various cancer types, including head and neck, lung and breast cancers, has been identified with deletion in 50% of our PDX models, suggesting an important role in aggressive breast cancers. Conclusions Our WGS data provides a comprehensive genome sequencing resource of these models.

Background Androgen receptor (AR) is critical to the initiation, growth, and progression of prostate cancer. Once activated, the AR binds to cis-regulatory enhancer elements on DNA that drive gene expression. Yet, there are 10–100× more binding sites than differentially expressed genes. It is unclear how or if these excess binding sites impact gene transcription. Results To characterize the regulatory logic of AR-mediated transcription, we generated a locus-specific map of enhancer activity by functionally testing all common clinical AR binding sites with Self-Transcribing Active Regulatory Regions sequencing (STARRseq). Only 7% of AR binding sites displayed androgen-dependent enhancer activity. Instead, the vast majority of AR binding sites were either inactive or constitutively active enhancers. These annotations strongly correlated with enhancer-associated features of both in vitro cell lines and clinical prostate cancer samples. Evaluating the effect of each enhancer class on transcription, we found that AR-regulated enhancers frequently interact with promoters and form central chromosomal loops that are required for transcription. Somatic mutations of these critical AR-regulated enhancers often impact enhancer activity. Conclusions Using a functional map of AR enhancer activity, we demonstrated that AR-regulated enhancers act as a regulatory hub that increases interactions with other AR binding sites and gene promoters.

Non-coding variants have been shown to be related to disease by alteration of 3D genome structures. We propose a deep learning method, DeepMILO, to predict the effects of variants on CTCF/cohesin-mediated insulator loops. Application of DeepMILO on variants from whole-genome sequences of 1834 patients of twelve cancer types revealed 672 insulator loops disrupted in at least 10% of patients. Our results show mutations at loop anchors are associated with upregulation of the cancer driver genes BCL2 and MYC in malignant lymphoma thus pointing to a possible new mechanism for their dysregulation via alteration of insulator loops.

Non-coding mutations may drive cancer development. Statistical detection of non-coding driver regions is challenged by a varying mutation rate and uncertainty of functional impact. Here, we develop a statistically founded non-coding driver-detection method, ncdDetect, which includes sample-specific mutational signatures, long-range mutation rate variation, and position-specific impact measures. Using ncdDetect, we screened non-coding regulatory regions of protein-coding genes across a pan-cancer set of whole-genomes (n = 505), which top-ranked known drivers and identified new candidates. For individual candidates, presence of non-coding mutations associates with altered expression or decreased patient survival across an independent pan-cancer sample set (n = 5454). This includes an antigen-presenting gene (CD1A), where 5’UTR mutations correlate significantly with decreased survival in melanoma. Additionally, mutations in a base-excision-repair gene (SMUG1) correlate with a C-to-T mutational-signature. Overall, we find that a rich model of mutational heterogeneity facilitates non-coding driver identification and integrative analysis points to candidates of potential clinical relevance. Cancers are diseases caused by changes in DNA sequences. Some changes occur in the protein-coding part of the DNA sequence, in other words, in the stretches of DNA that include the instructions to make a given protein. Other changes occur in the remaining parts of the DNA that do not code for proteins, which accounts for about 98% of the human genome. Modern technologies allow us to identify these DNA changes, but, up until recently, this has only been possible for the protein-coding part of the DNA. Many studies have thus analyzed DNA changes in the protein-coding part of the human genome, while the larger, non-coding part remains rather unexplored. Advances in technology means that large datasets are becoming available where changes in DNA sequences are identified across the entire genomes of a collection of cancer patients. However, it is not clear which of these DNA changes play a role in the development of cancer and which are neutral with no effect on cancer. Now, Juul et al. have developed a new method, named 'ncdDetect', to search the human genome and identify stretches of DNA that when changed give cancer cells an advantage and allow them to grow. Juul et al. refer to these DNA stretches as 'driver elements', and, after analyzing the genomes from 505 patients with cancer, they identified some known driver elements and some potentially new ones. For example, possible driver elements were found in non-coding parts of the DNA that regulate genes called SMUG1 and CD1A. Both of these genes encode proteins that had been linked to cancer in the past, but driver elements had not previously been described in the nearby non-coding regions. Juul et al. also found a number of possible driver elements that might be important to consider in the treatment of cancers. Importantly, not all the candidate driver elements identified with ncdDetect are true drivers. The changes in DNA vary greatly between different types of cancer and even between different cases of a single type of cancer. Understanding and describing this variation continues to be a challenge in identifying driver elements, and so Juul et al. plan to keep improving the method to make sure that the driver elements it identifies are all trustworthy.

Super-enhancers are large, densely concentrated swaths of enhancers that regulate genes critical for cell identity. Tumorigenesis is accompanied by changes in the super-enhancer landscape. These aberrant super-enhancers commonly form to activate proto-oncogenes, or other genes upon which cancer cells depend, that initiate tumorigenesis, promote tumor proliferation, and increase the fitness of cancer cells to survive in the tumor microenvironment. These include well-recognized master regulators of proliferation in the setting of cancer, such as the transcription factor MYC which is under the control of numerous super-enhancers gained in cancer compared to normal tissues. This Review will cover the expanding cell-intrinsic and cell-extrinsic etiology of these super-enhancer changes in cancer, including somatic mutations, copy number variation, fusion events, extrachromosomal DNA, and 3D chromatin architecture, as well as those activated by inflammation, extra-cellular signaling, and the tumor microenvironment.

Background The identification of mutations that play a causal role in tumour development, so called “driver” mutations, is of critical importance for understanding how cancers form and how they might be treated. Several large cancer sequencing projects have identified genes that are recurrently mutated in cancer patients, suggesting a role in tumourigenesis. While the landscape of coding drivers has been extensively studied and many of the most prominent driver genes are well characterised, comparatively less is known about the role of mutations in the non-coding regions of the genome in cancer development. The continuing fall in genome sequencing costs has resulted in a concomitant increase in the number of cancer whole genome sequences being produced, facilitating systematic interrogation of both the coding and non-coding regions of cancer genomes. Results To examine the mutational landscapes of tumour genomes we have developed a novel method to identify mutational hotspots in tumour genomes using both mutational data and information on evolutionary conservation. We have applied our methodology to over 1300 whole cancer genomes and show that it identifies prominent coding and non-coding regions that are known or highly suspected to play a role in cancer. Importantly, we applied our method to the entire genome, rather than relying on predefined annotations ( e.g. promoter regions) and we highlight recurrently mutated regions that may have resulted from increased exposure to mutational processes rather than selection, some of which have been identified previously as targets of selection. Finally, we implicate several pan-cancer and cancer-specific candidate non-coding regions, which could be involved in tumourigenesis. Conclusions We have developed a framework to identify mutational hotspots in cancer genomes, which is applicable to the entire genome. This framework identifies known and novel coding and non-coding mutional hotspots and can be used to differentiate candidate driver regions from likely passenger regions susceptible to somatic mutation.

Structural variants (SVs) affecting non-coding -regulatory elements are a common cause of congenital limb malformation. Yet, the functional interpretation of these non-coding variants remains challenging. The human Liebenberg syndrome is characterised by a partial transformation of the arms into legs and has been shown to be caused by SVs at the locus leading to its misregulation in the forelimb by its native enhancer element Pen. This study aims to elucidate the genetic cause of an unsolved family with a mild form of Liebenberg syndrome and investigate the role of promoters in long-range gene regulation. Here, we identify SVs by whole genome sequencing (WGS) and use CRISPR-Cas9 genome editing in transgenic mice to assign pathogenicity to the SVs. In this study, we used WGS in a family with three mildly affected individuals with Liebenberg syndrome and identified the smallest deletion described so far including the first non-coding exon of . To functionally characterise the variant, we re-engineered the 8.5 kb deletion using CRISPR-Cas9 technology in the mouse and showed that the promoter of the housekeeping gene insulates the Pen enhancer from in forelimbs; its loss leads to misexpression of by the pan-limb activity of the Pen enhancer causing Liebenberg syndrome. Our data indicate that housekeeping promoters may titrate promiscuous enhancer activity to ensure normal morphogenesis. The deletion of the promoter as a cause of Liebenberg syndrome highlights this new mutational mechanism and its role in congenital disease.

There is an increasing implication of non-coding regions in pathological processes of genetic origin. This is partly due to the emergence of sophisticated techniques that have transformed research into gene expression by allowing a more global understanding of the genome, both at the genomic, epigenomic and chromatin levels. Here, we implemented the analysis of PAX6, whose coding loss-of-function variants are mainly implied in aniridia, by studying its non-coding regions (untranslated regions, introns and cis-regulatory sequences). In particular, we have taken advantage of the development of high-throughput approaches to screen the upstream and downstream regulatory regions of PAX6 in 47 aniridia patients without identified mutation in the coding sequence. This was made possible through the use of custom targeted resequencing and/or CGH array to analyze the entire PAX6 locus on 11p13. We found candidate variants in 30 of the 47 patients. 9/30 correspond to the well-known described 3′ deletions encompassing SIMO and other enhancer elements. In addition, we identified numerous different variants in various non-coding regions, in particular untranslated regions. Among these latter, most of them demonstrated an in vitro functional effect using a minigene strategy, and 12/21 are thus considered as causative mutations or very likely to explain the phenotypes. This new analysis strategy brings molecular diagnosis to more than 90% of our aniridia patients. This study revealed an outstanding mutation pattern in non-coding PAX6 regions confirming that PAX6 remains the major gene for aniridia.