Somatic Somatic Single Nucleotide Variants Research Articles

Low-level brain somatic mutations in exonic regions are collectively implicated in autism with germline mutations in autism risk genes.

Low-level somatic mutations in the human brain are implicated in various neurological disorders. The contribution of low-level brain somatic mutations to autism spectrum disorder (ASD), however, remains poorly understood. Here, we performed high-depth exome sequencing with an average read depth of 559.3x in 181 cortical, cerebellar, and peripheral tissue samples to identify brain somatic single nucleotide variants (SNVs) in 24 ASD subjects and 31 controls. We detected ~2.4 brain somatic SNVs per exome per single brain region, with a variant allele frequency (VAF) as low as 0.3%. The mutational profiles, including the number, signature, and type, were not significantly different between the ASD patients and controls. Intriguingly, when considering genes with low-level brain somatic SNVs and ASD risk genes with damaging germline SNVs together, the merged set of genes carrying either somatic or germline SNVs in ASD patients was significantly involved in ASD-associated pathophysiology, including dendrite spine morphogenesis (p = 0.025), mental retardation (p = 0.012), and intrauterine growth retardation (p = 0.012). Additionally, the merged gene set showed ASD-associated spatiotemporal expression in the early and mid-fetal cortex, striatum, and thalamus (all p < 0.05). Patients with damaging mutations in the merged gene set had a greater ASD risk than did controls (odds ratio = 3.92, p = 0.025, 95% confidence interval = 1.12-14.79). The findings of this study suggest that brain somatic SNVs and germline SNVs may collectively contribute to ASD-associated pathophysiology.

Abstract 2850: Noncoding regulatory mutations as driving event for the oncogenic core regulatory circuitries of neuroblastoma

Abstract Background and Aim: Neuroblastoma (NB) is the most aggressive solid tumor of infancy and arises from neural-crest-derived progenitor cells. It is composed by cell types reflecting developmental stages of the adrenergic lineage: mesenchymal (MES) and adrenergic (ADRN) cell types. These cell identities result from the activity of NB specific Core Regulatory Circuitries (CRCs), transcription factor (TF) sets that co-occupy regulatory regions together impacting the expression of their own genes and those of lineage-specific ones governing cell state transitions and cellular identities. Our hypothesis is that noncoding somatic single nucleotide variants (SNVs) located in NB CRCs TF binding sites (TFBSs) underlie NB CRCs activity, affecting cellular differentiation and promoting tumor onset. We aim to investigate such pattern of regulatory elements and identify putative driver SNVs, exploring their role and characterizing their action mechanism. Methods: Transcriptionally active TFBSs (aTFBSs) bound by ADRN CRC TFs (GATA3, HAND2, ISL1, TBX2, MYCN, PHOX2B, ASCL1 and LMO1) were identified integrating 27 ChIP-seq and 11 ATAC-seq experiments in 6 NB ADRN cell lines (SKNBE2C, Kelly, NGP, COGN415, LAN5 and NB1643). In particular, aTFBSs were defined as ChIP-seq peaks overlapping ATAC-seq signals in the same cell line. SNVs from 317 NB whole genomes were mapped on aTFBSs and the enrichment of mutations in core regions respect to flanking ones was tested using Fisher test. Observed mutation rate in aTFBSs was compared with background mutation rate due to local sequence context. SNVs in mutated aTFBSs were selected and their impact on TF binding affinity was evaluated with FABIAN-variant and motifbreakR tools. aTFBSs target-genes were identified through promoter capture HiC (CHiC) in ADRN NB cells (SKNBE2C and SHSY5Y). Results: We found a significant (FDR &amp;lt; 0.05) mutation enrichment in aTFBSs of ISL1, PHOX2B, TBX2, GATA3 and MYCN, suggesting a common somatic mutation burden for a subset of ADRN CRC TFBSs in ADRN NB cells. Starting from 839 SNVs falling in significantly mutated aTFBSs, we selected 269 mutations impacting the binding of ADRN CRC TFs. This mutations subset mapped in aTFBSs interacting with genes that significantly enriched pathways involved in neuronal development and migration (FDR &amp;lt; 0.05), thus suggesting their possible role in affecting cell identity definition. As an example, chr6:137753475:G&amp;gt;T mapped in aTFBSs interacting with the promoter of OLIG3, which takes part in neuronal differentiation. Interestingly, chr6:137753475:G&amp;gt;T was predicted to break the binding to ISL1 (FABIAN score: -0.24), a key regulator of ADRN CRC. Conclusions: These results demonstrate that somatic noncoding SNVs can act synergistically to promote NB onset, affecting CRCs activity and, in turn, the normal cell identity definition. Citation Format: Mario Capasso, Vincenzo Aievola, Vito Alessandro Lasorsa, Annalaura Montella, Ferdinando Bonfiglio, Marianna Avitabile, Teresa Maiorino, Matilde Tirelli, Giuseppe D'Alterio, Matthias Fischer, Frank Westermann, Achille Iolascon. Noncoding regulatory mutations as driving event for the oncogenic core regulatory circuitries of neuroblastoma [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 2850.

Genomic Panorama and Clonal Evolutionary Trajectory of Classical Hairy Cell Leukemia

Classical Hairy Cell Leukemia (HCLc) is a rare B-cell malignancy characterized by hairy-like cell membrane projections. The cell of origin and pathway of malignant transformation in HCLc remain unknown. Early work using whole exome sequencing identified BRAFV600E as a genetic hallmark in HCLc (Tiacci et al 2011 and Weston-Bell et al 2016). However, no other recurrent coding mutations appeared, marking it an uncommon monogenic cancer. As BRAFV600E appears insufficient to drive tumorigenesis in other cancers (Dankort et al 2009; Rad et al 2013), it suggested that other driver alterations may be implicated in HCLc pathogenesis, perhaps in the non-coding genome, overlooked in exome studies alone. To probe this, we carried out whole genome sequencing (WGS) in HCLc. We established the genomic panorama, and importantly leveraged data to derive tumor phylogenies to determine the relative order of early driver mutational events that associate with the most recent common ancestor (MRCA) of all tumor cells. WGS was performed in 10 typical HCLc cases each paired with germline in purified cell sorted populations of disaggregated splenocytes. Sequencing involved 150 bp paired ends on Illumina HiSeq4000. Data analyses included identification of somatic single-nucleotide variants (SNVs) and small indels using CaVEMan and Pindel, respectively; copy-number aberrations (CNA) with Battenberg; and structural variants (SVs) with BRASS. The fitting of COSMIC mutational signatures was performed using mmsig. Mutational SNVs and CNA drivers were used for the analysis of tumor phylogenesis of individual tumors. We mapped the whole genome landscape in HCLc. Somatic coding variants emerged as previously reported. BRAFV600E was present as a clonal mutation in all HCLc tumors. Missense mutations in KLF2 (p.S275N and p.S275I) were identified in 2 tumors. Two somatic SNVs were detected in PRSS3 (p.D95G and p.R94S) and ANKRD30B (p.K388E and p.N392K) in 2 different cases respectively. In the noncoding genome, BCL6 and ZFP36L1 promoters were recurrently mutated in 5/10 cases. Driver mutations in BCL6 promoters have been reported in other B-cell malignancies to disrupt negative autoregulatory circuits (Pasqualucci et al 2003). Analysis of CNA showed a relatively stable genome (ploidy ~2). A large CNA chr5 gain was observed in a single tumor. Two additional focal CNA were spotlighted at chr17q25 (Gain) and chr20q11 (LOH) in two individual tumors. A recurrent interchromosomal translocation at chr12q14.3 locus was identified in 5 individual tumors, and an identical translocation at 14q21.3 with 15q11.2 observed in 2 tumors. Interestingly, novel kataegis-like mutations were observed at TRB loci across all HCLc cohort samples. We further identified somatic hypermutation (SHM) imprints in non-Ig loci, notably in BCL6. The mutational rate of AID targets at TRB and non-Ig loci were comparable to SHM occurring in Ig IGV genes. Deciphering the mutational signatures in HCLc genomes highlighted SBS9, a mutational signature associated with SHM in B-cell leukemia as one of the most relevant mechanisms, allied to the clock-like signatures of SBS1 and SBS5. Next, we utilized estimates of the cancer cell fraction to define which mutations are clonal and associate with the MRCA, and which mutations occured subclonally to derive individual tumor phylogenetic trees using methodology we have reported previously (Rabbie et al 2020; Ansari-Pour 2021). Here, analyses of clonal trajectories revealed that BRAFV600E occurred prior to subclonal diversification in MRCA in all cases. Significantly however, 1 or 2 additional driver mutations were conjointly detected with BRAFV600E in the MRCA in 6/10 HCLc cases prior to any subclonal evolution. Our data provide a comprehensive map of the whole genome in HCLc for the first time. Clonal evolutionary trajectories reveal heterogeneous genetic origins in HCLc, in which BRAFV600E is the sole driver mutation in one subset of disease, but requires 1 or 2 additional mutational drivers in the other subset to promote malignant transformation. Thus, unlike other cancers, BRAFV600E alone appears to suffice to induce malignant transformation in some HCLc. Additionally, SHM in non-Ig loci including BCL6 may implicate early traffic of the tumor cell of origin through the germinal center and early exit prior to acquisition of CD27, or mutational events at ectopic sites.

Identification and Interpretation of Clinically Relevant Somatic Variants from Whole-Genome Sequencing Data

Whole-genome sequencing provides an opportunity for a comprehensive test enabling detection of clinically relevant genetic aberrations for patients with hematological malignancies. However in diagnostic settings, it is challenging to identify and interpret clinically relevant somatic single nucleotide variants (SNV), small insertions and deletions (INDEL), structural variants (SV) and copy-number variants (CNV) in a manner that retains the sensitivity of the analysis while at the same time enabling a rapid interpretation. SNV and INDEL are identified using the information from within the sequenced reads, and range in size smaller than 10 bp. Both SV and CNV are typically longer than 100 bp and are identified using different properties from aligned reads, which includes split reads, and orientation and distance between the read pairs. These aberrations are further divided into different categories depending upon their complexity and the method of identification, which include duplications (DUP), deletions (DEL), inversions (INV) and translocation breakpoints (BND). Although CNV is defined as an additional type of variant, the copy number gains and losses, identified using read-depth profile, can be categorized as DUP and DEL. All four types of SV arise due to complex rearrangements in the genome. Their inherent complexity combined with the methods used to identify them makes it difficult to obtain a comprehensive set of true biological events. Furthermore, identification of clinically relevant somatic variants requires further classification of all variants into germline and somatic using additional variant-type dependent processing and filtering. Finally, to enable rapid processing that meets the clinical diagnostic demands regarding turnaround times, the entire process needs to be automated with minimum human intervention from sample preparation and bioinformatic analysis all the way through clinical interpretation. To tackle these challenges, we implemented an ensemble variant calling approach in BALSAMIC. This involves combining computational methods and tools to identify SNV, INDEL and all four types of SV. These variants are then subjected to further bioinformatic processing and rigorous filtering to prepare them for clinical interpretation in our custom-developed variant interpretation software, Scout. The entire workflow is automated using in-house automation software, cg. BALSAMIC utilizes FASTQ files as input and first performs quality control and trimming using FastQC and fastp, respectively. Trimmed reads are mapped to the human reference genome using Sentieon-tools. Duplicated reads are marked and quality controlled using Picard-tools and the results are summarized by MultiQC. SNV and INDEL are called using Sentieon TNscope and TNhaplotyper. The variants are further processed using depth, quality and allele frequency based filters. The filtered SNV and INDEL from TNscope and Tnhaplotyper are merged using bcftools for tumor-only samples. Manta, Delly-SV, and TIDDIT are used to call SV while ascatNgs (tumor-normal) and Delly-CNV are used to call CNV. CNV calls are further converted from CNV to DEL and DUP to curate merged sets for each SV-type across the different SV-callers. The SV calls from Manta, Delly, TIDDIT, and ascatNgs (tumor-normal) are then merged using SVDB. Prior to annotation, SVs with low quality score in the tumor sample, high allelic frequency (possible germline) in the normal sample, or longer than 10Mb are removed. Next, SVs are annotated using Ensembl-VEP and the variants in the intragenic regions are removed. Finally, SVs are uploaded to Scout and those overlapping with clinically relevant genes (defined using an in silico gene list) constitute the final set of clinically relevant somatic SNV, INDEL, SV and CNV. This workflow has analyzed to date more than two hundred cases of different tumor types mainly including hematological malignancies. This approach identifies thousands of variants across all types, but with rigorous processing and filtering, the identification of clinically relevant somatic variants is done in a manner compatible with the clinical diagnostic demands. Software availability The bioinformatics software BALSAMIC, cg and Scout are available from https://github.com/Clinical-Genomics, and the details of the software and methods used in BALSAMIC are available from https://balsamic.readthedocs.io. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Computational Prediction of the Pathogenic Status of Cancer-Specific Somatic Variants.

In-silico classification of the pathogenic status of somatic variants is shown to be promising in promoting the clinical utilization of genetic tests. Majority of the available classification tools are designed based on the characteristics of germline variants or the combination of germline and somatic variants. Significance of somatic variants in cancer initiation and progression urges for development of classifiers specialized for classifying pathogenic status of cancer somatic variants based on the model trained on cancer somatic variants. We established a gold standard exclusively for cancer somatic single nucleotide variants (SNVs) collected from the catalogue of somatic mutations in cancer. We developed two support vector machine (SVM) classifiers based on genomic features of cancer somatic SNVs located in coding and non-coding regions of the genome, respectively. The SVM classifiers achieved the area under the ROC curve of 0.94 and 0.89 regarding the classification of the pathogenic status of coding and non-coding cancer somatic SNVs, respectively. Our models outperform two well-known classification tools including FATHMM-FX and CScape in classifying both coding and non-coding cancer somatic variants. Furthermore, we applied our models to predict the pathogenic status of somatic variants identified in young breast cancer patients from METABRIC and TCGA-BRCA studies. The results indicated that using the classification threshold of 0.8 our “coding” model predicted 1853 positive SNVs (out of 6,910) from the TCGA-BRCA dataset, and 500 positive SNVs (out of 1882) from the METABRIC dataset. Interestingly, through comparative survival analysis of the positive predictions from our models, we identified a young-specific pathogenic somatic variant with potential for the prognosis of early onset of breast cancer in young women.

Genomic landscape of extraordinary responses in metastatic breast cancer

Extreme responders to anticancer therapy are rare among advanced breast cancer patients. Researchers, however, have yet to investigate treatment responses therein on the whole exome level. We performed whole exome analysis to characterize the genomic landscape of extreme responders among metastatic breast cancer patients. Clinical samples were obtained from breast cancer patients who showed exceptional responses to anti-HER2 therapy or hormonal therapy and from those who did not. Matched breast tumor tissue (somatic DNA) and blood samples (germline DNA) were collected from a total of 30 responders and 15 non-responders. Whole exome sequencing using Illumina HiSeq2500 was performed for all 45 patients (90 samples). Somatic single nucleotide variants (SNVs), indels, and copy number variants (CNVs) were identified for the genomes of each patient. Group-specific somatic variants and mutational burden were statistically analyzed. Sequencing of cancer exomes for all patients revealed 1839 somatic SNVs (1661 missense, 120 nonsense, 43 splice-site, 15 start/stop-lost) and 368 insertions/deletions (273 frameshift, 95 in-frame), with a median of 0.7 mutations per megabase (range, 0.08 to 4.2 mutations per megabase). Responders harbored a significantly lower nonsynonymous mutational burden (median, 26 vs. 59, P = 0.02) and fewer CNVs (median 13.6 vs. 97.7, P = 0.05) than non-responders. Multivariate analyses of factors influencing progression-free survival showed that a high mutational burden and visceral metastases were significantly related with disease progression. Extreme responders to treatment for metastatic breast cancer are characterized by fewer nonsynonymous mutations and CNVs.

Comprehensive identification of somatic nucleotide variants in human brain tissue

BackgroundPost-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells.ResultsHere, the Brain Somatic Mosaicism Network conducts a coordinated, multi-institutional study to examine the ability of existing methods to detect simulated somatic single-nucleotide variants (SNVs) in DNA mixing experiments, generate multiple replicates of whole-genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual, devise strategies to discover somatic SNVs, and apply various approaches to validate somatic SNVs. These efforts lead to the identification of 43 bona fide somatic SNVs that range in variant allele fractions from ~ 0.005 to ~ 0.28. Guided by these results, we devise best practices for calling mosaic SNVs from 250× whole-genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrate that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees.ConclusionsThis study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.

Whole-exome mutational landscape of neuroendocrine carcinomas of the gallbladder

Neuroendocrine carcinoma (NEC) of the gallbladder (GB-NEC) is a rare but extremely malignant subtype of gallbladder cancer (GBC). The genetic and molecular signatures of GB-NEC are poorly understood; thus, molecular targeting is currently unavailable. In the present study, we applied whole-exome sequencing (WES) technology to detect gene mutations and predicted somatic single-nucleotide variants (SNVs) in 15 cases of GB-NEC and 22 cases of general GBC. In 15 GB-NECs, the C > T mutation was predominant among the 6 types of SNVs. TP53 showed the highest mutation frequency (73%, 11/15). Compared with neuroendocrine carcinomas of other organs, significantly mutated genes (SMGs) in GB-NECs were more similar to those in pulmonary large-cell neuroendocrine carcinomas (LCNECs), with driver roles for TP53 and RB1. In the COSMIC database of cancer-related genes, 211 genes were mutated. Strikingly, RB1 (4/15, 27%) and NAB2 (3/15, 20%) mutations were found specifically in GB-NECs; in contrast, mutations in 29 genes, including ERBB2 and ERBB3, were identified exclusively in GBC. Mutations in RB1 and NAB2 were significantly related to downregulation of the RB1 and NAB2 proteins, respectively, according to immunohistochemical (IHC) data (p values = 0.0453 and 0.0303). Clinically actionable genes indicated 23 mutated genes, including ALK, BRCA1, and BRCA2. In addition, potential somatic SNVs predicted by ISOWN and SomVarIUS constituted 6 primary COSMIC mutation signatures (1, 3, 30, 6, 7, and 13) in GB-NEC. Genes carrying somatic SNVs were enriched mainly in oncogenic signaling pathways involving the Notch, WNT, Hippo, and RTK-RAS pathways. In summary, we have systematically identified the mutation landscape of GB-NEC, and these findings may provide mechanistic insights into the specific pathogenesis of this deadly disease.

Duplex Sequencing with Patient-Specific Hybrid Capture Panels Reveals Ultra-Low Frequency Measurable Residual Disease in Pediatric Acute Myeloid Leukemia

Sensitive and specific detection of measurable residual disease (MRD) after treatment in pediatric acute myeloid leukemia (AML) is prognostic of relapse and is important for clinical decision making. Mutation-based methods are increasingly being used, but are hampered by the limited number of common driver gene mutations to target as clone markers. Additional targets would greatly increase MRD detection power. However, even in cases with many AML-defining mutations, it is the limited accuracy of current molecular methods which establishes the lower bounds of sensitivity. Here we describe an ultrasensitive approach for disease monitoring with personalized hybrid capture panels targeting hundreds of somatic mutations identified by whole genome sequencing (WGS), and using extremely accurate Duplex Sequencing (DS) in longitudinal samples. In a pilot cohort of 13 patients we demonstrate detection sensitivities several orders of magnitude beyond currently available single locus testing or less accurate sequencing. With multi-target panels, overall power for MRD detection is cumulative across sites. For example, if a patient has MRD at a true frequency of 1/30,000, sequencing a single mutant site to 10,000x molecular depth would be unlikely to detect MRD. However, sequencing 10 sites each to 10,000x would effectively total 100,000x informative site depth, increasing power to &amp;gt;95%. However, standard sequencing assays are insufficiently accurate to achieve this theoretical limit of detection (LOD). DS enables accurate detection of individual variants to &amp;lt;10-5 with an error rate &amp;lt;10-7 and, thus, can achieve MRD sensitivities below one-in-one-million. Marrow aspirates were collected from 13 uniformly treated pediatric AML patients at time of diagnosis (TOD), during treatment (end of induction, EOI), in remission (end of therapy, EOT), and at relapse. 9/13 patients relapsed. DNA from TOD was analyzed by WGS. Germline variants were excluded and somatic single nucleotide variants (SNVs) were targeted by a custom probe panel designed for each patient. An average of 170 SNVs were targeted per patient (range 53-200). More than 90% of the SNVs were noncoding. Longitudinal samples were then analyzed with DS, which compares sequences from both strands of each DNA molecule to eliminate technical noise and reveal biological mutation signal with extreme accuracy and sensitivity. A median of 82% of WGS SNVs were validated by DS in the TOD DNA, and the vast majority of those were also present at relapse. Relapsers had more SNVs at diagnosis than non-relapsers. EOT samples were sequenced to an average Duplex molecular depth of 29,400x, with a maximum of 61,283x. The figure below shows time course plots tracking SNVs at diagnosis, EOT and relapse for 2 patients. Among mutations validated in TOD samples, a median of only 8 (5%) were detected per EOT sample (range 0-66 mutations). MRD was detected in 8/9 relapsers. Targeting 1 or even 10 SNVs would therefore have missed MRD in the majority of these patients. Among relapsers, median EOT SNV VAF was 0.069%. The lowest single VAF detected per EOT sample ranged from 0.036% to 0.002%. The presence of an SNV at diagnosis and relapse implies that it must truly be present at EOT, whether or not it is detected. Therefore, if a small minority of leukemic mutations are detected at EOT, the true overall MRD frequency is much lower than the LOD at any single site. In the only relapser where MRD was not detected, targeted SNVs were present at diagnosis and relapse, so additional sequencing depth at EOT would eventually reveal ultra-low frequency mutations. Among patients that did not relapse by the end of the study, median VAF at EOI (the latest time point DNA available) was 0.0258%. Therefore, non-relapsers have a lower median VAF at EOI than relapsers do even later at EOT, potentially indicating very early on that treatment is more successful. This study shows excellent performance of DS-based assays for detecting MRD with patient-specific panels. We have demonstrated that among large panels of validated somatic SNVs present at time of diagnosis, a median of 5% are identified at EOT in eventual relapsers. DS detected MRD in 8/9 patients, and at a median VAF well below the limit of detection of any other sequencing technology. Comprehensive personalized hybrid selection panels coupled with DS represents a powerful option for MRD monitoring in pediatric AML and potentially other cancers. Figure Disclosures Higgins: TwinStrand Biosciences: Current Employment. Lo:TwinStrand Biosciences: Current Employment. Hipp:TwinStrand Biosciences: Current Employment. Valentine:TwinStrand Biosciences: Current Employment. Williams:TwinStrand Biosciences: Current Employment. Radich:TwinStrand Biosciences: Research Funding. Salk:TwinStrand Biosciences: Current Employment.

Frequent Germline and Somatic Single Nucleotide Variants in the Promoter Region of the Ribosomal RNA Gene in Japanese Lung Adenocarcinoma Patients.

Ribosomal RNA (rRNA), the most abundant non-coding RNA species, is a major component of the ribosome. Impaired ribosome biogenesis causes the dysfunction of protein synthesis and diseases called “ribosomopathies,” including genetic disorders with cancer risk. However, the potential role of rRNA gene (rDNA) alterations in cancer is unknown. We investigated germline and somatic single-nucleotide variants (SNVs) in the rDNA promoter region (positions −248 to +100, relative to the transcription start site) in 82 lung adenocarcinomas (LUAC). Twenty-nine tumors (35.4%) carried germline SNVs, and eight tumors (9.8%) harbored somatic SNVs. Interestingly, the presence of germline SNVs between positions +1 and +100 (n = 12; 14.6%) was associated with significantly shorter recurrence-free survival (RFS) and overall survival (OS) by univariate analysis (p < 0.05, respectively), and was an independent prognostic factor for RFS and OS by multivariate analysis. LUAC cell line PC9, carrying rDNA promoter SNV at position +49, showed significantly higher ribosome biogenesis than H1650 cells without SNV. Upon nucleolar stress induced by actinomycin D, PC9 retained significantly higher ribosome biogenesis than H1650. These results highlight the possible functional role of SNVs at specific sites of the rDNA promoter region in ribosome biogenesis, the progression of LUAC, and their potential prognostic value.

ABEMUS: platform-specific and data-informed detection of somatic SNVs in cfDNA.

The use of liquid biopsies for cancer patients enables the non-invasive tracking oftreatment response and tumor dynamics through single or serial blood drawn tests. Next-generation sequencing assays allow for the simultaneous interrogation of extended sets of somatic single-nucleotide variants (SNVs) in circulating cell-free DNA (cfDNA), a mixture of DNA molecules originating both from normal and tumor tissue cells. However, low circulating tumor DNA (ctDNA) fractions together with sequencing background noise and potential tumor heterogeneity challenge the ability to confidently call SNVs. We present a computational methodology, called Adaptive Base Error Model in Ultra-deep Sequencing data (ABEMUS), which combines platform-specific genetic knowledge and empirical signal to readily detect and quantify somatic SNVs in cfDNA. We tested the capability of our method to analyze data generated using different platforms with distinct sequencing error properties and we compared ABEMUS performances with other popular SNV callers on both synthetic and real cancer patients sequencing data. Results show that ABEMUS performs better in most of the tested conditions proving its reliability in calling low variant allele frequencies somatic SNVs in low ctDNA levels plasma samples. ABEMUS is cross-platform and can be installed as R package. The source code is maintained on Github at http://github.com/cibiobcg/abemus, and it is also available at CRAN official R repository. Supplementary data are available at Bioinformatics online.

Corrigendum: Genome aging: somatic mutation in the brain links age-related decline with disease and nominates pathogenic mechanisms.

Aging is a mysterious process, not only controlled genetically but also subject to random damage that can accumulate over time. While DNA damage and subsequent mutation in somatic cells were first proposed as drivers of aging more than 60 years ago, whether and to what degree these processes shape the neuronal genome in the human brain could not be tested until recent technological breakthroughs related to single-cell whole-genome sequencing. Indeed, somatic single-nucleotide variants (SNVs) increase with age in the human brain, in a somewhat stochastic process that may nonetheless be controlled by underlying genetic programs. Evidence from the literature suggests that in addition to demonstrated increases in somatic SNVs during aging in normal brains, somatic mutation may also play a role in late-onset, sporadic neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease. In this review, we will discuss somatic mutation in the human brain, mechanisms by which somatic mutations occur and can be controlled, and how this process can impact human health.

Cistrome Partitioning Reveals Convergence of Somatic Mutations and Risk Variants on Master Transcription Regulators in Primary Prostate Tumors

Thousands of noncoding somatic single-nucleotide variants (SNVs) of unknown function are reported in tumors. Partitioning the genome according to cistromes reveals the enrichment of somatic SNVs in prostate tumors as opposed to adjacent normal tissue cistromes of master transcription regulators, including AR, FOXA1, and HOXB13. This parallels enrichment of prostate cancer genetic predispositions over these transcription regulators' tumor cistromes, exemplified at the 8q24 locus harboring both risk variants and somatic SNVs in cis-regulatory elements upregulating MYC expression. However, Massively Parallel Reporter Assays reveal that few SNVs can alter the transactivation potential of individual cis-regulatory elements. Instead, similar to inherited risk variants, SNVs accumulate in cistromes of master transcription regulators required for prostate cancer development.

Meltos: multi-sample tumor phylogeny reconstruction for structural variants.

We propose Meltos, a novel computational framework to address the challenging problem of building tumor phylogeny trees using somatic structural variants (SVs) among multiple samples. Meltos leverages the tumor phylogeny tree built on somatic single nucleotide variants (SNVs) to identify high confidence SVs and produce a comprehensive tumor lineage tree, using a novel optimization formulation. While we do not assume the evolutionary progression of SVs is necessarily the same as SNVs, we show that a tumor phylogeny tree using high-quality somatic SNVs can act as a guide for calling and assigning somatic SVs on a tree. Meltos utilizes multiple genomic read signals for potential SV breakpoints in whole genome sequencing data and proposes a probabilistic formulation for estimating variant allele fractions (VAFs) of SV events. In order to assess the ability of Meltos to correctly refine SNV trees with SV information, we tested Meltos on two simulated datasets with five genomes in both. We also assessed Meltos on two real cancer datasets. We tested Meltos on multiple samples from a liposarcoma tumor and on a multi-sample breast cancer data (Yates et al., 2015), where the authors provide validated structural variation events together with deep, targeted sequencing for a collection of somatic SNVs. We show Meltos has the ability to place high confidence validated SV calls on a refined tumor phylogeny tree. We also showed the flexibility of Meltos to either estimate VAFs directly from genomic data or to use copy number corrected estimates. Meltos is available at https://github.com/ih-lab/Meltos. Supplementary data are available at Bioinformatics online.

Genome aging: somatic mutation in the brain links age-related decline with disease and nominates pathogenic mechanisms.

Aging is a mysterious process, not only controlled genetically but also subject to random damage that can accumulate over time. While DNA damage and subsequent mutation in somatic cells were first proposed as drivers of aging more than 60years ago, whether and to what degree these processes shape the neuronal genome in the human brain could not be tested until recent technological breakthroughs related to single-cell whole-genome sequencing. Indeed, somatic single-nucleotide variants (SNVs) increase with age in the human brain, in a somewhat stochastic process that may nonetheless be controlled by underlying genetic programs. Evidence from the literature suggests that in addition to demonstrated increases in somatic SNVs during aging in normal brains, somatic mutation may also play a role in late-onset, sporadic neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease. In this review, we will discuss somatic mutation in the human brain, mechanisms by which somatic mutations occur and can be controlled, and how this process can impact human health.

Whole-exome sequencing and immunohistochemistry findings in von Hippel-Lindau disease.

Backgroundvon Hippel–Lindau (VHL) disease has a hereditary, autosomal dominant pattern, and multiple tumors can develop in multiple organs of a single patient. However, the exact mechanisms of tumorigenesis are unclear, and further studies are needed to clarify whether the same signaling pathways are involved in different VHL‐related tumors.MethodsWhole‐exome sequencing (WES) of tumor and paired peripheral blood samples were performed for a VHL disease pedigree. A bioinformatics analysis was conducted to identify candidate somatic single‐nucleotide variants (SNVs) present in all tumor tissues. Sanger sequencing was then used to validate the SNVs identified using WES. Immunohistochemistry was performed to analyze components of the mTOR pathway, which was abnormally activated in tumor tissues.ResultsTwo hemangioblastomas and two renal cell carcinomas were sequenced. The bioinformatics analysis revealed a VHL somatic variant in all tumors; no other SNV was detected. Immunohistochemistry showed the abnormal expression of the phospho‐S6 ribosomal protein in the hemangioblastomas, but not in the renal clear cell carcinomas.ConclusionExcept for a SNV in the VHL gene, no other somatic SNVs were detected using WES. The phospho‐S6 ribosomal protein in the mTOR pathway is a potential target in VHL‐related cerebellum hemangioblastomas.

Abstract LB-300: The life history of breast cancer in Nigerian women: Evidence for ethnic differences in tumor evolution based on whole-genome sequencing

Abstract Breast cancer is a heterogeneous disease and the incidence to mortality ratio is highest for women of African ancestry. Paucity of data from non-European ancestry groups limits our understanding of the underlying etiological differences or alternative routes to progression that may explain differential outcomes. To examine the contribution of genomic differences, we performed high-depth whole-genome sequencing on 100 breast tumors (90X) and their paired normal samples (30X) from indigenous African women with breast cancer in Southwest Nigeria and performed comparative analysis with 84 TCGA whole genome sequences of breast tumor/normal pairs (46 White, 30 African American and 8 samples representing other ethnicities). High confidence somatic single nucleotide variants (SNVs) were obtained by using MuTect and Strelka. To analyse intra-tumoral heterogeneity (ITH), Battenberg and DPClust were used to call copy number aberrations (CNA) and cluster somatic SNVs based on cancer cell fraction (CCF) respectively. We find that in the HR-/HER2+ subtype, clonal losses of chromosome 14q are highly enriched in Nigerians (41%) but absent in the Whites even though the proportion of this subtype was comparable between the two groups (42% and 33% respectively). This is an interesting observation since not only 14q loss is known to be an event associated with breast cancer aggressiveness, but HR-/HER2+ has also been reported to be enriched within the relatively younger Nigerian patients with breast cancer. This may in part explain inter-ethnic disparity in survival. Also, somatic SNV clustering analysis showed that Nigerian cancers have a higher level of ITH than Whites, which may explain the pronounced aggressiveness of breast cancer in women of African ancestry. In contrast, early drivers (e.g. TP53 and PIK3CA) and whole genome duplication rates were mostly similar between the groups. Our observations suggest differences in the underlying evolutionary trajectories of breast cancer across ethnic backgrounds. These data underscore the need for larger and more in-depth studies of diverse cancer genomes and, if validated, may translate into clinical intervention opportunities tailored to women of African ancestry and accelerate progress in precision cancer care. Note: This abstract was not presented at the meeting. Citation Format: Naser Ansari-Pour, Jason J. Pitt, Stefan Dentro, Dominic Fitzgerald, Yonglan Zheng, Toshio F. Yoshimatsu, Padma Rajagopal, Andreas Gruber, Ayodele Sanni, Olayiwola Oluwasola, Mustapha Ajani, Abayomi Odetunde, Abiodun Popoola, Adeyinka Falusi, Temidayo Ogundiran, John Obafunwa, Oladosu Ojengbede, Nasiru Ibrahim, Kevin P. White, Dezheng Huo, Peter Van Loo, David C. Wedge, Olufunmilayo Olopade. The life history of breast cancer in Nigerian women: Evidence for ethnic differences in tumor evolution based on whole-genome sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-300.

Abstract 3517: Establishing reference samples for universal benchmarking study of NGS technologies

Abstract Regulatory authorities including the FDA are well aware of the crucial role Next Generation Sequencing (NGS) plays in precision medicine. However, the lack of thoroughly-characterized and community-validated reference samples creates a challenge for the development and review of NGS applications in precision oncology. Over 200 members from over 60 institutions have formed the Somatic Mutation Working Group within the FDA-led Sequencing Quality Control Phase II (SEQC-II) Consortium to address this challenge. We have developed strategies to establish a community standard reference samples. We used multiple orthogonal sequencing technologies, sequencing replicates, sequencing centers, and bioinformatics analysis pipelines for mutation detection, thus minimizing biaes related to any particular sequencing platform, assay, or informatics software. Combining with targeted sequencing and microarray technologies, we established a high-confidence mutation call set (Gold Set). Here, we present our initial reference samples: a human triple-negative breast cancer cell line (HCC1395), and matched B lymphocyte derived normal cell line (HCC1395BL). The first public release of the Gold Set includes the fully phased germline variants, high-confidence somatic single nucleotide variants (SNV), small insertion and deletions (InDel), copy number variations (CNV), and structural variations (SV). For variant discovery, we have performed whole genome sequencing (WGS) for each of the tumor-normal genomes to combined depths of 650X from 12 replicates on Illumina HiSeq sequencers, 380X from 9 replicates on Illumina NovaSeq sequencer, 1000X from 11 replicates on 10X Genomics platform, and 50X on PacBio sequencer. In addition, we have also performed Hi-C sequencing to 34X. For variant validation, we have performed WGS to 300X for seven different tumor purities in a tumor-normal titration series, whole exome sequencing (WES) to a total of 12000X from 14 replicates, AmpliSeq targeted sequencing to 1000X, RNASeq to 186 million reads, Affymetrix GeneChip array to 2.1 million probes, and Cytogenetic arrays to 4.3 million probes. HCC1395 has a highly rearranged near-triploid genome with 66 chromosomes. The confidence levels of somatic mutations in the Gold Set were obtained based on the cross-institution and cross-aligner reproducibility of each mutation call. There are approximately 40,000 and 2,000 high-confidence somatic SNVs and InDels, with variant allele frequencies (VAF) ranging between 2% and 100%. The VAFs of both somatic and germline variants are consistent with our CNV calls. We will continuously accept input from the community to advance accuracy and completeness of our Gold Set for the reference samples in order to serve as a community standard well into the future. Citation Format: Li Tai Fang, Wenming Xiao, Somatic Mutation Working Group of the SEQC-II Consortium. Establishing reference samples for universal benchmarking study of NGS technologies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3517.

Exome sequencing identifies predisposing and fusion gene in ganglioneuroma, ganglioneuroblastoma and neuroblastoma.

This study intended to gain new insight into the genetic basis underlying ganglioneuroma (GN), ganglioneuroblastoma (GNB), and neuroblastoma (NB). Three fresh-frozen surgically resected tumor tissues (GN1, GNB1, and NB1) and matched blood samples (GN2, GNB2, and NB2) were respectively obtained from three pediatric patients with GN, GNB, and NB. After exome sequencing, we predicted the somatic single nucleotide variants (SNV) and insertion and deletion (InDel), and screened the predisposing genes. Finally, we detected and filtered the fusion gene using Fusionmap. Exome sequencing identified 815, 985, and 884 somatic SNV, and 56, 43, and 34 InDel for GN, NB, and GNB respectively. Total 29, 19 and 37 predisposing genes were identified from GN, GNB and NB samples, such as PIK3CA (GN), MUC4 (GN), PML (NB), TFR2 (GNB), and MAX (GNB). Additionally, four common fusion genes, such as HOXD11-AGAP3 and SAMD1-CDC42EP5, were identified from three tumor samples. Moreover, SAMD1-CDC42EP5 was also a common fusion position in three blood samples. These previously unrecognized predisposing genes, such as PIK3CA, MUC4, PML, TFR2 and MAX, and fusion genes, like HOXD11-AGAP3, and SAMD1-CDC42EP5 may have the potential to impact the progression and development of neuroblastic tumors.

Characterization and validation of somatic mutation spectrum to reveal heterogeneity in gastric cancer by single cell sequencing

Gastric cancer (GC) is a highly heterogeneous disease with multiple cellular types and poor prognosis. However, the cellular evolution and molecular basis of GC at the individual intra-tumor level has not been well demonstrated. We performed single-cell whole exome sequencing to detect somatic single-nucleotide variants (SNVs) and significantly mutated genes (SMGs) among 34 tumor cells and 9 normal cells from a patient with GC. The Complete Prediction for Protein Conformation (CPPC) approach directly predicting the folding conformation of the protein 3D structure with Protein Folding Shape Code, combined with functional experiments were used to confirm the characterization of mutated SMGs in GC cells. We identified 201 somatic SNVs, including 117 non-synonymous mutations in GC cells. Further analysis identified 24 significant mutated genes (SMGs) in single cells, for which a single amino acid change might affect protein conformation. Among them, two genes (CDC27 and FLG) that were mutated only in single cells but not in the corresponding tumor tissue, were recurrently present in another GC tissue cohort, and may play a potential role to promote carcinogenesis, as confirmed by functional characterization. Our findings showed a mutational landscape of GC at intra-tumor level for the first time and provided opportunities for understanding the heterogeneity and individualized target therapy for this disease.