Non-coding Driver Research Articles

Melanoma-specific mutation hotspots in distal, non-coding, promoter-interacting regions implicate novel candidate driver genes

BackgroundTo develop targeted treatments, it is crucial to identify the full spectrum of genetic drivers in melanoma, including those in non-coding regions. However, recent efforts to explore non-coding regions have primarily focused on gene-adjacent elements such as promoters and non-coding RNAs, leaving intergenic distal regulatory elements largely unexplored.MethodsWe used Hi-C chromatin contact data from melanoma cells to map distal, non-coding, promoter-interacting regulatory elements genome-wide in melanoma. Using this “promoter-interaction network”, alongside whole-genome sequence and gene expression data from the Pan Cancer Analysis of Whole Genomes, we developed multivariate linear regression models to identify distal somatic mutation hotspots that affect promoter activity.ResultsWe identified eight recurrently mutated hotspots that are novel, melanoma-specific, located in promoter-interacting distal regulatory elements, alter transcription factor binding motifs, and affect the expression of genes (e.g., HSPB7, CLDN1, ADCY9 and FDXR) previously implicated as tumour suppressors/oncogenes in various cancers.ConclusionsOur study suggests additional non-coding drivers beyond the well-characterised TERT promoter in melanoma, offering new insights into the disruption of complex regulatory networks by non-coding mutations that may contribute to melanoma development. Furthermore, our study provides a framework for integrating multiple levels of biological data to uncover cancer-specific non-coding drivers.

Chromatin interaction maps identify oncogenic targets of enhancer duplications in cancer.

As a major type of structural variants, tandem duplication plays a critical role in tumorigenesis by increasing oncogene dosage. Recent work has revealed that noncoding enhancers are also affected by duplications leading to the activation of oncogenes that are inside or outside of the duplicated regions. However, the prevalence of enhancer duplication and the identity of their target genes remains largely unknown in the cancer genome. Here, by analyzing whole-genome sequencing data in a non-gene-centric manner, we identify 881 duplication hotspots in 13 major cancer types, most of which do not contain protein-coding genes. We show that the hotspots are enriched with distal enhancer elements and are highly lineage-specific. We develop a HiChIP-based methodology that navigates enhancer-promoter contact maps to prioritize the target genes for the duplication hotspots harboring enhancer elements. The methodology identifies many novel enhancer duplication events activating oncogenes such as ESR1, FOXA1, GATA3, GATA6, TP63, and VEGFA, as well as potentially novel oncogenes such as GRHL2, IRF2BP2, and CREB3L1 In particular, we identify a duplication hotspot on Chromosome 10p15 harboring a cluster of enhancers, which skips over two genes, through a long-range chromatin interaction, to activate an oncogenic isoform of the NET1 gene to promote migration of gastric cancer cells. Focusing on tandem duplications, our study substantially extends the catalog of noncoding driver alterations in multiple cancer types, revealing attractive targets for functional characterization and therapeutic intervention.

Large-scale multi-omic analysis identifies noncoding somatic driver mutations and nominates ZFP36L2 as a driver gene for pancreatic ductal adenocarcinoma.

Identification of somatic driver mutations in the noncoding genome remains challenging. To comprehensively characterize noncoding driver mutations for pancreatic ductal adenocarcinoma (PDAC), we first created genome-scale maps of accessible chromatin regions (ACRs) and histone modification marks (HMMs) in pancreatic cell lines and purified pancreatic acinar and duct cells. Integration with whole-genome mutation calls from 506 PDACs revealed 314 ACRs/HMMs significantly enriched with 3,614 noncoding somatic mutations (NCSMs). Functional assessment using massively parallel reporter assays (MPRA) identified 178 NCSMs impacting reporter activity (19.45% of those tested). Focused luciferase validation confirmed negative effects on gene regulatory activity for NCSMs near CDKN2A and ZFP36L2 . For the latter, CRISPR interference (CRISPRi) further identified ZFP36L2 as a target gene (16.0 - 24.0% reduced expression, P = 0.023-0.0047) with disrupted KLF9 binding likely mediating the effect. Our integrative approach provides a catalog of potentially functional noncoding driver mutations and nominates ZFP36L2 as a PDAC driver gene.

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.

Abstract 1686: Dissecting the 3D enhancer-promoter hubs in prostate tumorigenesis

Abstract The transcription process is controlled by regulatory elements that are often located in non-coding regions. Among regulatory elements, activities of enhancers that are bound by cell-type specific transcription factors (TFs) are tightly linked to cell identity. Enhancers loop to interact with the promoters of target genes at a distance to drive different cell fates. Evidently, transcriptional dysregulation linked to enhancers that are differentially activated in cancer is an underlying factor in tumorigenesis. To identify enhancers linked to prostate tumorigenesis, we analyzed hundreds of in-house and public H3K27ac ChIP-seq data generated from normal prostate and prostate cancer cell lines and tissues. We identified thousands of prostate cancer-specific enhancers (PSEs) that were activated exclusively in cancer conditions. To further identify the target genes of PSEs, we performed 3C-derived assays, such as Hi-C and Micro-C, and mapped genome-wide interactions. However, they were limited by the resolution to detect enhancer-promoter interactions. The recent development of Region Capture Micro-C (RCMC), a hybridization-based capture Micro-C method, allowed us to deeply map the highly nested interactions between regulatory elements. By utilizing RCMC, we generated high-resolution, focal contact maps that revealed comprehensive enhancer-promoter interactions near PSEs in prostate cancer cells. Interestingly, we found that PSEs loop to other PSEs clustered in the proximal genomic region, in addition to promoters of significantly upregulated genes in cancer compared to normal cells. We termed these regions as multi-connected "3D enhancer-promoter hubs”. To mechanistically dissect these hubs, we deleted 5+ PSEs one by one using the CRISPR/Cas9 system and performed multi-omics analyses, including RNA-seq and ChIP-seq. We found that single deletions of a subset of PSEs led to the downregulation of multiple genes looped to the deleted PSE as well as to other PSEs. Moreover, we found that deletion reduced the activity of other connected PSEs. When we further evaluated the chromatin contacts of regulatory elements in the PSE-deleted cells, we observed that many chromatin contacts remained unaffected. Finally, we identified TFs that bind to PSEs, and we are currently in the process of investigating enhancer-promoter hub connectivity by performing knockdown experiments on these TFs. Furthermore, we are using molecular assays to identify PSEs that could exert a phenotypic shift in cancer cell identity to drive prostate tumorigenesis. Overall, our study not only provides mechanistic insights into the spatial regulation of 3D enhancer-promoter hubs but also identifies new non-coding drivers in prostate tumorigenesis. Citation Format: Huan Cao, Zexun Wu, Leonardo Gonzalez-Smith, Seolyn Yang, Suhn K. Rhie. Dissecting the 3D enhancer-promoter hubs in prostate tumorigenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1686.

Abstract 3057: The TARZN complex binds to de novo enhancer mutations and promotes oncogenic expression in T-ALL

Abstract TAL1 is overexpressed in 40-60% of T-cell acute lymphoblastic leukaemia (T-ALL) cases and forms an oncogenic core regulatory circuit (CRC) with other transcription factors such as LMO1, LMO2 and GATA3. In 5% of T-ALL cases an insertion of a consensus GT dinucleotide is observed upstream of the TAL1 gene, termed the “Mutation of TAL1 enhancer” (MuTE), driving TAL1 overexpression. Using an in vitro reconstitution DNA pull-down assay combined with quantitative mass spectrometry, we identified nine candidate proteins that preferentially bound to the MuTE sequence compared to the WT sequence. Among the candidates, we demonstrate by reciprocal immunoprecipitation experiments coupled to quantitative mass spectrometry that the RNA methyltransferase TARBP1 and the zinc finger proteins ZBTB2, ZBTB25 and ZNF639 formed a complex that we term TARZN. Interestingly, the TARZN complex also bound to de novo super enhancer sites upstream of the LMO1 and LMO2 genes in T-ALL cells, indicating a putative common mechanism between these different non-coding driver mutations. Furthermore, knock-down of all TARZN members resulted in lower TAL1 protein expression in MuTE-positive but not in MuTE-negative T-ALL cells. Overall, these data suggest that the TARZN complex cooperatively promotes oncogenic expression in T-ALL. Citation Format: Nurkaiyisah Zaal Anuar, Chai Yeen Goh, Boon Haow Chua, Shi Hao Tan, Joana R. Costa, Marc R. Mansour, Takaomi Sanda, Dennis Kappei. The TARZN complex binds to de novo enhancer mutations and promotes oncogenic expression in T-ALL [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3057.

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 undescribedmutational 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.

Whole genome and RNA sequencing analyses for 254 Taiwanese hepatocellular carcinomas

BackgroundComprehensive and integrative analysis of hepatocellular carcinoma (HCC) is important. In this study, we explored Taiwanese HCCs using multi-omics analyses.MethodsWe analyzed 254 HCCs by whole genome sequencing and total RNA sequencing, and then used bioinformatic tools to analyze genomic and transcriptomic alterations in coding and non-coding sequences to explore the clinical importance of each sequence.ResultsThe frequencies of the five most commonly mutated cancer-related genes were TERT, TP53, CTNNB1, RB1, and ARID1A. Genetic alteration frequencies influenced the etiology of HCC; some alterations were also correlated with clinicopathological conditions. Many cancer-related genes had copy number alterations (CNAs) and structure variants (SVs) that changed according to etiology and exhibited potential associations with survival. We also identified several alterations in histone-related genes, HCC-related long non-coding RNAs, and non-coding driver genes that may contribute to the onset and progression of HCC. Transcriptomic analysis revealed that 229 differentially expressed and 148 novel alternative splicing (AS) genes, as well as the presence of fusion genes, were associated with patient survival. Moreover, somatic mutations, CNAs, and SVs were associated with immune checkpoint gene expression and tumor microenvironment. Finally, we identified relationships among AS, immune checkpoint gene expression and tumor microenvironment.ConclusionsThis study shows that genomic alterations are associated with survival, including DNA-based and RNA-based data. Moreover, genomic alterations and their associations with immune checkpoint genes and the tumor microenvironment may provide novel insights for the diagnosis and treatment of HCC.

Dr.Nod: computational framework for discovery of regulatory non-coding drivers in tissue-matched distal regulatory elements.

The discovery of cancer driver mutations is a fundamental goal in cancer research. While many cancer driver mutations have been discovered in the protein-coding genome, research into potential cancer drivers in the non-coding regions showed limited success so far. Here, we present a novel comprehensive framework Dr.Nod for detection of non-coding cis-regulatory candidate driver mutations that are associated with dysregulated gene expression using tissue-matched enhancer-gene annotations. Applying the framework to data from over 1500 tumours across eight tissues revealed a 4.4-fold enrichment of candidate driver mutations in regulatory regions of known cancer driver genes. An overarching conclusion that emerges is that the non-coding driver mutations contribute to cancer by significantly altering transcription factor binding sites, leading to upregulation of tissue-matched oncogenes and down-regulation of tumour-suppressor genes. Interestingly, more than half of the detected cancer-promoting non-coding regulatory driver mutations are over 20 kb distant from the cancer-associated genes they regulate. Our results show the importance of tissue-matched enhancer-gene maps, functional impact of mutations, and complex background mutagenesis model for the prediction of non-coding regulatory drivers. In conclusion, our study demonstrates that non-coding mutations in enhancers play a previously underappreciated role in cancer and dysregulation of clinically relevant target genes.

Revana: a comprehensive tool for regulatory variant analysis and visualization of cancer genomes.

As non-coding driver mutations move more into the focus of cancer research, a comprehensive and easy-to-use software solution for regulatory variant analysis and data visualization is highly relevant. The interpretation of regulatory variants in large tumor genome cohorts requires specialized analysis and visualization of multiple layers of data, including for example breakpoints of structural variants, enhancer elements and additional available gene locus annotation, in the context of changes in gene expression. We introduce a user-friendly tool, Revana (REgulatory Variant ANAlysis), that can aggregate and visually represent regulatory variants from cancer genomes in a gene-centric manner. It requires whole-genome and RNA sequencing data of a cohort of tumor samples and creates interactive HTML reports summarizing the most important regulatory events. Revana is implemented in R and JavaScript. It is available for download as an R package under <https://github.com/KiTZ-Heidelberg/revana>. Sample results can be viewed under <https://github.com/KiTZ-Heidelberg/revana-demo-report> and a short walkthrough is available under <https://github.com/KiTZ-Heidelberg/revana-demo-data>. Supplementary data are available at Bioinformatics online.

A network-based method for identifying cancer driver genes based on node control centrality.

Cancer is one of the major contributors to human mortality and has a serious influence on human survival and health. In biomedical research, the identification of cancer driver genes (cancer drivers for short) is an important task; cancer drivers can promote the progression and generation of cancer. To identify cancer drivers, many methods have been developed. These computational models only identify coding cancer drivers; however, non-coding drivers likewise play significant roles in the progression of cancer. Hence, we propose a Network-based Method for identifying cancer Driver Genes based on node Control Centrality (NMDGCC), which can identify coding and non-coding cancer driver genes. The process of NMDGCC for identifying driver genes mainly includes the following two steps. In the first step, we construct a gene interaction network by using mRNAs and miRNAs expression data in the cancer state. In the second step, the control centrality of the node is used to identify cancer drivers in the constructed network. We use the breast cancer dataset from The Cancer Genome Atlas (TCGA) to verify the effectiveness of NMDGCC. Compared with the existing methods of cancer driver genes identification, NMDGCC has a better performance. NMDGCC also identifies 295 miRNAs as non-coding cancer drivers, of which 158 are related to tumorigenesis of BRCA. We also apply NMDGCC to identify driver genes related to the different breast cancer subtypes. The result shows that NMDGCC detects many cancer drivers of specific cancer subtypes.

Retrospective Lineage Tracing of Pediatric Acute Myeloid Leukemia Using Single-Cell Whole Genome Sequencing

Background Acute myeloid leukemia (AML) accounts for 15-20% of all pediatric acute leukemias and is a very heterogeneous disease, both genetically and phenotypically. Although survival has increased gradually during the last decades, relapse-rates remain around 20-30%, and children who relapse have a relatively poor outcome. Still, the exact cause of pediatric AML (pAML) remains unclear. Previously, we found an increased clonal mutation burden in leukemic blasts of a subset of pAML cases, including the ones characterized by RUNX1-RUNX1T1 translocations. Here, we study the mutation accumulation and clonal evolution of pAML to elucidate the timing of driver accumulation and mutational processes underlying leukemogenesis in children. Methods All pAML bone marrow samples were acquired via the biobank of the Princess Máxima Center for pediatric oncology after ethical approval was obtained. We used primary template-directed amplification (PTA) to accurately analyze the complete genomes of single cells at nucleotide resolution. For PTA-mediated whole genome sequencing (WGS), we sorted single leukemic blasts, hematopoietic stem cells (HSCs), multipotent progenitor cells (MPPs) and several differentiated cell types. Subsequently, WGS data was used to study mutation accumulation and to perform phylogenetic lineage tracing of different cell types of the same patient. Results Thus far, we have analyzed the single-cell WGS data of 18 cells from two AML samples characterized by NUP98-NSD1 and RUNX1-RUNX1T1 fusions. We identified multiple cells that immunophenotypically resembled HSCs, MPPs and more mature cell types from different lineages, that shared the fusion gene with the AML blasts (Figure 1). Moreover, a subset of these cells shared one or more additional driver mutations with the immunophenotypically defined bulk leukemic blasts. Subsequent mutation analysis showed that cells with the oncogenic fusion had an increased somatic mutation burden compared to cells without the fusion. Accordingly, we studied mutational signatures to identify the processes underlying this mutation burden. Interestingly, besides the expected mutational signatures, we found that the increased mutation burden was mainly characterized by SBS18, a signature that is associated with damage induced by oxidative stress. This process was active throughout the leukemic lineage, whereas unrelated HSCs and MPPs of the same patients did not display this signature. This finding indicates that the process was specific for the leukemic lineage and therefore likely cell intrinsic. Next, we used driver and passenger mutations to construct phylogenetic lineage trees and found that the accumulation of drivers occurred stepwise. We show that the oncogenic fusion was the first driving event in different patients, followed by accumulation of additional driver mutations. For example, in one patient, based on the clock-like mutation burdens in the separate branches, we could estimate that the first drivers were already present when the patient was between 7.5 to 9 years old (Figure 1, branch A), while this patient was diagnosed at 15.2 years. We have previously identified similar patterns in multiple MLL-rearranged therapy-related AML (t-AML) patients. Both in the t-AML and pAML cases, some leukemic blasts did not have any additional genetic drivers compared to the phenotypical stem and progenitor cells. Conclusions We used single-cell WGS to identify pre-leukemic cells in bone marrow samples of pAML patients, which shared both the oncogenic rearrangement and additional driver mutations with the AML blasts. These pre-leukemic cells showed an increased mutation load, caused by distinct mutational processes. In addition, these cells were immunophenotypically and functionally stem-cell like, contributing to hematopoiesis. Our analyses show that, despite their aberrations, these cells still have differentiation potential. Interestingly, the first driver aberrations of pAML originated years before diagnosis. These data suggest that genetic driving aberrations may not be the only underlying mechanism of AML development in children. Noncoding drivers, epigenetic changes or environmental influences may play an important role in the final steps towards leukemogenesis. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

The Clinico-Genomic Landscape of Induction Failure in Childhood and Young Adult T-Cell Acute Lymphoblastic Leukemia (T-ALL)

Background T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive malignancy comprising 10-15% of childhood ALL. Previously, we found that Induction Failure (IF) occurs in approximately 10% of T-ALL cases, 3-fold more than in B-ALL, with only 50% achieving long-term survival (O'Connor et al, JCO 2017). Despite the preponderance of IF in T-ALL, little is known about the factors that predict treatment resistance and influence outcome, or how best to treat these patients. To address this, we present the largest cohort of T-ALL IF reported to date, comprising all IF cases on two large multi-national consecutive randomized trials, UKALL2003 and UKALL2011. Methods The UKALL2003 and UKALL2011 trials recruited 711 patients with T-ALL between 2004-2019; 688 of these patients were assessable at the end of induction (EOI). We supplemented existing trial data with new clinical data, and combined whole genome sequencing (WGS) and RNA-sequencing (RNAseq), to comprehensively characterize the clinical and genetic landscape of T-ALL IF. Results Of 688 T-ALL patients, 10.3% experienced IF. IF was significantly associated with increasing age occurring in 5.6% of patients below 10 years, 12.9% of those aged 10-16 years and 20% of patients over 16 years (p<0.001). Survival was significantly worse in IF (5yr Event-free survival (EFS) 47.9% (95%CI: 35.8 - 59.1) vs. 84.0% (95%CI: 80.7 - 86.8); HR:5.14 (95% CI: 2.82-6.07), p<0.001; 5yr Overall survival (OS) 52.1% (95%CI: 39.7 - 63.1) vs. 90.2% (95%CI: 87.4 - 92.5); HR: 6.38 (95% CI: 4.17-9.76), p<0.001). There was a clear trend towards increasing use of nelarabine over the study period; IF patients treated on UKALL2003 commonly remained on standard trial chemotherapy (74.2%; 26/35), while IF patients treated on the later UKALL2011 trial were more frequently treated with nelarabine and HSCT (68.6%; 24/35, p=0.0007). However, this did not translate into an improved outcome for IF patients, with no clear benefit of either HSCT or Nelarabine therapy. WGS revealed a picture of remarkable genetic heterogeneity, with 25 different initiating lesions converging on 10 subtype-defining T-ALL genes, rather than a single unifying driver of IF (Figure 1). Allocation of cases to conventional T-ALL subtypes found almost half were of the HOXA subtype, significantly more than in responsive T-ALL (p<0.001). This was consistent with immunophenotypic characterisation showing a significant enrichment of the Early T-cell precursor (ETP) subtype, present in 48% of IF cases vs. 10% of non-IF cases (p<0.001), indicating a less differentiated, more stem cell-like leukemia. Although the overall proportion of TAL1 cases was lower in IF, there was an unexpected dominance of non-coding TAL1 enhancer mutations, accounting for 87.5% of TAL1 cases. Importantly, TAL1 cases had a particularly dismal outcome with an OS of 12.5% (0.6-42.3). Notably, none of the 7 patients with a TAL1 non-coding driver lesion survived, and 2 never achieved remission, identifying this as a very high-risk genetic lesion in the context of IF. Mutational profiling found a reduction in mutations in several highly recurrent T-ALL genes (NOTCH1, FBXW7, CDKN2A, LEF1) coupled with an increase in mutations in WT1 and MED12, both of which have been associated with disease resistance in other leukemias. In addition, 2 genes not previously reported in T-ALL were recurrently mutated in IF with mutations in CHD4 in 5 cases and KAT6A in 4 cases, suggesting they may be novel drivers of refractory disease. Survival analyses also showed a significantly poorer survival in patients with mutations in MYCN and NRAS. WGS of samples collected at the end of induction showed no new lesions demonstrating that disease at presentation is representative of true refractory leukemia and that IF does not occur as a result of chemotherapy-induced mutagenesis. Conclusions Our results confirm the poor prognosis of IF in T-ALL and highlight a lack of progress in treatment of this high-risk leukemia, with no clear improvement in outcome despite increased use of nelarabine and HSCT. Genomic analyses identified a marked enrichment for non-coding TAL1 enhancer mutations, characterizing an ultra-high-risk group of patients in the context of IF, in which there were no survivors. The lack of a readily targetable pathway of primary chemoresistance supports alternative strategies, such as those employing immunotherapeutic agents. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Chromatin accessibility of primary human cancers ties regional mutational processes and signatures with tissues of origin.

Somatic mutations in cancer genomes are associated with DNA replication timing (RT) and chromatin accessibility (CA), however these observations are based on normal tissues and cell lines while primary cancer epigenomes remain uncharacterised. Here we use machine learning to model megabase-scale mutation burden in 2,500 whole cancer genomes and 17 cancer types via a compendium of 900 CA and RT profiles covering primary cancers, normal tissues, and cell lines. CA profiles of primary cancers, rather than those of normal tissues, are most predictive of regional mutagenesis in most cancer types. Feature prioritisation shows that the epigenomes of matching cancer types and organ systems are often the strongest predictors of regional mutation burden, highlighting disease-specific associations of mutational processes. The genomic distributions of mutational signatures are also shaped by the epigenomes of matched cancer and tissue types, with SBS5/40, carcinogenic and unknown signatures most accurately predicted by our models. In contrast, fewer associations of RT and regional mutagenesis are found. Lastly, the models highlight genomic regions with overrepresented mutations that dramatically exceed epigenome-derived expectations and show a pan-cancer convergence to genes and pathways involved in development and oncogenesis, indicating the potential of this approach for coding and non-coding driver discovery. The association of regional mutational processes with the epigenomes of primary cancers suggests that the landscape of passenger mutations is predominantly shaped by the epigenomes of cancer cells after oncogenic transformation.

Whole genome analysis reveals the genomic complexity in metastatic cutaneous squamous cell carcinoma.

Metastatic cutaneous squamous cell carcinoma (CSCC) is a highly morbid disease requiring radical surgery and adjuvant therapy, which is associated with a poor prognosis. Yet, compared to other advanced malignancies, relatively little is known of the genomic landscape of metastatic CSCC. We have previously reported the mutational signatures and mutational patterns of CCCTC-binding factor (CTCF) regions in metastatic CSCC. However, many other genomic components (indel signatures, non-coding drivers, and structural variants) of metastatic CSCC have not been reported. To this end, we performed whole genome sequencing on lymph node metastases and blood DNA from 25 CSCC patients with regional metastases of the head and neck. We designed a multifaceted computational analysis at the whole genome level to provide a more comprehensive perspective of the genomic landscape of metastatic CSCC. In the non-coding genome, 3′ untranslated region (3′UTR) regions of EVC (48% of specimens), PPP1R1A (48% of specimens), and ABCA4 (20% of specimens) along with the tumor-suppressing long non-coding RNA (lncRNA) LINC01003 (64% of specimens) were significantly functionally altered (Q-value < 0.05) and represent potential non-coding biomarkers of CSCC. Recurrent copy number loss in the tumor suppressor gene PTPRD was observed. Gene amplification was much less frequent, and few genes were recurrently amplified. Single nucleotide variants driver analyses from three tools confirmed TP53 and CDKN2A as recurrently mutated genes but also identified C9 as a potential novel driver in this disease. Furthermore, indel signature analysis highlighted the dominance of ID signature 13 (ID13) followed by ID8 and ID9. ID9 has previously been shown to have no association with skin melanoma, unlike ID13 and ID8, suggesting a novel pattern of indel variation in metastatic CSCC. The enrichment analysis of various genetically altered candidates shows enrichment of “TGF-beta regulation of extracellular matrix” and “cell cycle G1 to S check points.” These enriched terms are associated with genetic instability, cell proliferation, and migration as mechanisms of genomic drivers of metastatic CSCC.

Cancer carrier screening in the general population using whole-genome sequencing.

Cancer is a major cause of death, and its early identification and intervention have potential for clinical actionability and benefits for human health. The studies using whole-genome sequencing (WGS) and large samples analysis of cancer-related genes have been rarely done. We performed WGS to explore germline mutations in coding and non-coding areas of cancer-related genes and non-coding driver genes and regulatory areas. Structural variants (SVs) was also analyzed. We used several tools and a subgrouping method to analyze the variants in 1491 healthy participants. Moreover, 275 cancer-related genes sequencing was carried out in 125 cancer patients. The incidence of familial cancer in the Taiwanese general population is 8.79% (131/1491). Cancer carrier rate of cancer-related genes is about 7.04% (105/1491) for pathogenic/likely pathogenic variants (P/LP) on ClinVar database only, and 28.24% (421/1491) for P/LP and loss of function variants. The carrier frequencies of cancer-related genes P/LP on ClinVar database were as follows: 8.40% (11/131), 7.11% (28/394), and 6.83% (66/966) in FC, 1MC, and nMC, respectively. The SVs and non-coding driver gene variants are uncommon. There are 1.54% (23/1491) of actionable cancer genes in American College of Medical Genetics and Genomics (ACMG), and the germline mutation rate of 275 cancer-related genes is 7.2% (9/125) in cancer patients including 4.0% (5/125) of actionable cancer genes in ACMG. After analyzing the frequencies of P/LP variants on GJB2 and SLC25A13 genes, we suggest that these two genes may not be cancer-related genes and need be re-evaluated. WGS analysis can completely detect germline mutations in cancer carriers. This study use subgrouping approach for samples provides a strategy to study whether a gene or variant is a cancer-related gene or variant in the future studies.

Abstract 2170: Automated analysis of significant noncoding mutations in somatic whole cancer genomes

Abstract Precision cancer medicine and tailoring therapies to specific molecular targets are based mainly on driver mutations in protein-coding regions of the genome. Complex computational tools were developed to characterize coding driver mutations in 30+ cancer types. However, genomic and functional characterization of noncoding drivers outside coding regions (~98% of the genome) and a systematic understanding of how these events contribute to tumor development is still lacking. While driver mutations in coding regions directly change protein functions (e.g., tonic activation of a receptor), drivers in the noncoding genome can activate genes not expressed in normal tissue, thereby recruiting them for oncogenesis. This suggests that the biology of drivers in coding and noncoding regions differs substantially, and that specific tools will be needed for the accurate interpretation of noncoding mutations in whole-genome sequencing data. Here, we developed a new statistical test that accounts for the biology of noncoding regions, including epigenetic structure, fluctuations of mutation rates, and positional clustering, for the genome-wide analysis of somatic noncoding mutations in more than 3,500 whole cancer genomes across 19 human cancer types. On average, we identified ~5 novel noncoding events per cancer type, including many of the noncoding findings from previous studies (e.g., PCAWG consortium) along with novel observations. Many significant results fell into two categories: (i) highly localized mutagenic processes not observed in the rest of the genome and (ii) significantly mutated promoter and enhancer regions of genes associated with tumor signaling. We observed that findings associated with localized processes translated into distinct transcriptional states in bulk RNA-seq data and single-cell expression profiles. Moreover, they enabled the prediction of tumor cells-of-origin. Many significantly mutated regulatory regions exhibited differential expression of their associated target gene. We validated a subset of these findings, including noncoding mutations altering the activity of the estrogen pathway in breast cancer, by performing CRISPR-interference and luciferase reporter experiments in cancer cell lines. Mutated and non-mutated samples further harbored differential epigenomic profiles, suggesting noncoding mutations directly affected the regulatory activity of their target regions. Many significantly mutated regulatory regions involved known cancer genes, while others targeted genes that had been functionally linked to cancer before but were not considered canonical cancer genes. Broadly, our work reveals that interpreting whole cancer genomes involves challenges specific to noncoding regions. Our extensive catalog of testable hypotheses provides a blueprint for prospective experimental and computational follow-up studies that build on the concepts of our work. Citation Format: Felix Dietlein, Alex B. Wang, Christian Fagre, Anran Tang, Nicolle Besselink, Edwin Cuppen, Chunliang Li, Shamil R. Sunyaev, James T. Neal, Eliezer M. Van Allen. Automated analysis of significant noncoding mutations in somatic whole cancer genomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2170.

Abstract 1508: Chromatin accessibility of primary human cancers ties regional mutational processes with tissues of origin

Abstract Cancer genomes are shaped by mutational processes with complex spatial variation at multiple scales. However, the underlying mechanisms of this mutagenesis and its functional and genetic determinants remain poorly understood. Somatic regional mutagenesis is known to correlate with DNA replication timing and chromatin accessibility; however, these studies have used epigenetic information from common cell lines while the epigenomes of primary human cancers remain uncharacterized in this context. Here we model megabase-scale mutation frequencies of single nucleotide variants (SNVs) in thousands of whole cancer genomes using hundreds of genome-wide profiles of chromatin accessibility and replication timing spanning primary cancer samples, normal tissues, and cell lines. Using a machine learning framework, we show that CA profiles of primary cancers, rather than those of normal cells, predict regional mutagenesis and single base substitution (SBS) signatures in most cancer types. Thus the majority of passenger mutations follow the epigenetic landscapes of transformed cells of matching cancer sites and tissues. Mutational signatures associated with carcinogen exposure, as well as signatures of unknown origin, show the strongest associations with epigenomes while associations with endogenous signatures are weaker. In most cases, overall mutation burden and SBS signatures inversely correlate with chromatin accessibility, however certain signatures are instead enriched in chromatin-accessible elements. Lastly, the genomic regions with excess mutations unexplained by the epigenetic profiles in our models likely represent a combination of localized mutagenesis and positive selection of functional oncogenic mutations. Our computational models highlight an enrichment of known driver mutations, point out novel intergenic regions with putative non-coding drivers, and indicate an over-representation of developmental and lineage-specific genes in these frequently mutated genomic regions. These results characterize the complex interplay of mutational processes, genome function, and somatic evolution in cancer and its tissues of origin, and also outline strategies for distinguishing driver and passenger mutations. Citation Format: Jüri Reimand, Oliver Ocsenas. Chromatin accessibility of primary human cancers ties regional mutational processes with tissues of origin [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1508.

HWVoteRank: A Network-Based Voting Approach for Identifying Coding and Non-Coding Cancer Drivers

Cancer drivers play an important role in regulating cell growth, cell cycles, and DNA replication. Identifying these cancer drivers provides cancer researchers with indispensable knowledge that has important implications for clinical decision making. Some methods have been recently proposed to identify coding and non-coding cancer drivers through controllability analysis in network and eigenvector centrality based on community detection. However, the performance of these methods is not satisfactory. In this work, we focus on the strategy of selecting a set of critical nodes in cancer-special network as cancer drivers, and propose a novel approach for identifying coding and non-coding drives via a network-based voting mechanism. We name our approach HWVoteRank. Compared with two recent methods to identify cancer drivers, CBNA and NIBNA, and three algorithms for identifying key nodes on BRCA dataset, our method can achieve the best efficiency. By analyzing the results, it is found that our approach has better ability in identifying miRNA cancer drivers. We also applied our approach to identification of drivers of miRNA during Epithelial–Mesenchymal transition and drivers for cancer subtype. Through literature research, we found that those drivers explored by our approach are of biological significance.

Genomic instability is enriched in localized prostate cancers from men of African ancestry.

270 Background: Whole genome sequencing (WGS) of prostate cancers (PC) from American men of African ancestry (AA) is limited despite AA men having twice the incidence of and mortality from PC as compared to their European ancestry (EA) men. Herein we describe analysis of AA PC WGS to identify genomic contributions to incidence and outcome disparities. Methods: WGS data from AA localized hormone naïve (HN) PCs (n = 23) from our institution were combined with publicly available WGS HNPC datasets (n = 5); quantitative predominant genome-wide ancestry was approximated via RFMix. A comparable EA cohort (n = 224) was similarly assembled. Ancestry groups were compared via regression models correcting for Gleason grade, PSA, and pathologic stage. Results: Analysis of known HNPC driver genes revealed lower frequency of PTEN (2/28 v 70/224, 7% v 31%, p = 0.006) and higher frequency of MYC (5/28 v 13/224, 18% v 5%, p = 0.014) and FOXA1 (7/28 v 24/224, 25% v 11%, p = 0.018) alterations in AA tumors relative to EA. An unbiased search for coding and noncoding drivers uncovered recurrent FOXA1 promoter (n = 8, p = 1.1e-8, RR = 3.92) and gene body protein-coding (n = 5, p = 1.2e-6, RR = 7.83) mutations, as targets of somatic selection and affecting nearly half of AA cases. Despite comparable tumor mutational burdens in each group, analysis of genome-wide mutational signatures revealed an AA-specific enrichment of SNVs in trinucleotide contexts associated with mismatch repair deficiency (SBS6, p = 1.68e-2, RR = 49.1). AA tumors also had significantly more small deletions (sig. ID2) relative to EA samples (p = 2.25e-31, RR = 9.47), implying replication slippage at lagging strands. Finally, AA PC genomes had consistently higher MSI scores relative to EA (median [IQR]: 4.03 [3.8-4.53] v 1.52 [1.21-1.85], p = 2.95e-44), yet lower than MSI-H colon cancers. These associations were independent of tissue preservation method or source, suggesting they reflect an ancestry-specific mutational process. Comparison of germline and somatic variants between AA and EA uncovered candidate DNA damage response genes (DDR) for further functional validation. Conclusions: Analysis of the largest AA cohort to date of WGS HNPC has revealed an ancestry-specific somatic mutational processes resulting in elevated rate of MMR-linked SNVs, replication-slippage associated small deletions, and MSI, relative to EA. The uniformity of these data suggest that AAs may harbor an inherited factor contributing to increased somatic genomic instability in the HNPC context. These results are compatible with published analyses demonstrating lower expression of DDR genes in AA versus EA PCs. Greater MSI and indels (via neoantigen formation) may explain the higher response proportions in AA PC patients treated with immune- and radiotherapies. Studies are ongoing to define mechanisms via associations between germline predisposition, somatic modification, and transcriptional outcome.