Single Nucleotide Variant Data Research Articles

Comprehensive scRNA-seq Analysis and Identification of CD8_+T Cell Related Gene Markers for Predicting Prognosis and Drug Resistance of Hepatocellular Carcinoma.

Tumor heterogeneity of immune infiltration of cells plays a decisive role in hepatocellular carcinoma (HCC) therapy response and prognosis. This study investigated the effect of different subtypes of CD8+T cells on the HCC tumor microenvironment about its prognosis. Single-cell RNA sequencing, transcriptome, and single-nucleotide variant data from LUAD patients were obtained based on the GEO, TCGA, and HCCD18 databases. CD8+ T cells-associated subtypes were identified by consensus clustering analysis, and genes with the highest correlation with prognostic CD8+ T cell subtypes were identified using WGCNA. The ssGSEA and ESTIMATE algorithms were used to calculate pathway enrichment scores and immune cell infiltration levels between different subtypes. Finally, the TIDE algorithm, CYT score, and tumor responsiveness score were utilized to predict patient response to immunotherapy. We defined 3 CD8+T cell clusters (CD8_0, CD8_1, CD8_2) based on the scRNA- seq dataset (GSE149614). Among, CD8_2 was prognosis-related risk factor with HCC. We screened 30 prognosis genes from CD8_2, and identified 3 molecular subtypes (clust1, clust2, clust3). Clust1 had better survival outcomes, higher gene mutation, and enhanced immune infiltration. Furthermore, we identified a 12 genes signature (including CYP7A1, SPP1, MSC, CXCL8, CXCL1, GCNT3, TMEM45A, SPP2, ME1, TSPAN13, S100A9, and NQO1) with excellent prediction performance for HCC prognosis. In addition, High-score patients with higher immune infiltration benefited less from immunotherapy. The sensitivity of low-score patients to multiple drugs including Parthenolide and Shikonin was significantly higher than that of high-score patients. Moreover, high-score patients had increased oxidative stress pathways scores, and the RiskScore was closely associated with oxidative stress pathways scores. And the nomogram had good clinical utility. To predict the survival outcome and immunotherapy response for HCC, we developed a 12-gene signature based on the heterogeneity of the CD8+ T cells.

Cancercelllines.org-a novel resource for genomic variants in cancer cell lines.

Cancer cell lines are an important component in biological and medical research, enabling studies of cellular mechanisms as well as the development and testing of pharmaceuticals. Genomic alterations in cancer cell lines are widely studied as models for oncogenetic events and are represented in a wide range of primary resources. We have created a comprehensive, curated knowledge resource-cancercelllines.org-with the aim to enable easy access to genomic profiling data in cancer cell lines, curated from a variety of resources and integrating both copy number and single nucleotide variants data. We have gathered over 5600 copy number profiles as well as single nucleotide variant annotations for 16 000 cell lines and provide these data with mappings to the GRCh38 reference genome. Both genomic variations and associated curated metadata can be queried through the GA4GH Beacon v2 Application Programming Interface (API) and a graphical user interface with extensive data retrieval enabled using GA4GH data schemas under a permissive licensing scheme. Database URL: https://cancercelllines.org.

Integrative Multi-Omics Analysis of Tumor-Immune Microenvironment in Multiple Myeloma: Novel Insights and Therapeutic Implications

Despite notable therapeutic advances that have improved the survival of multiple myeloma (MM) patients, development of drug resistance remains a major problem. Transcriptomic analysis provides an opportunity to dissect the complexity of tumors, including the surrounding microenvironment, which has a significant impact on MM tumor progression and patients' response to treatment, as demonstrated by the effectiveness of immunomodulatory therapies. To improve the tailoring of targeted and immune based therapeutic strategies, it is crucial to decipher the tumor-immune microenvironment profile in MM patients. We used a multi-omics data integration approach, including RNAseq-based gene expression for MM tumor cells (MMCs) (n=196) and for the corresponding purified tumor microenvironment (TME) (n=124), single nucleotide variant data from whole exome sequencing of MMCs (n=100), deconvolution of TME immune subtypes (n=124), and relevant clinical metadata. This allowed us to thoroughly characterize both the tumor and its TME. Applying Multiomics Factor Analysis (MOFA) identified 13 principal factors capturing the diversity of the disease, encompassing both MM malignant and microenvironment components. Factors 1 and 2 specifically represented the heterogeneity in the TME, incorporating gene expression profiles and deconvolution-based immune cell subtypes. On the other hand, Factors 6, 9, 11, and 12 were specific to variations in the gene expression profile of the malignant component. Importantly, the analysis of the variables linked to these main factors uncovers not only genes with established functions in myeloma but also novel candidates with potential importance. Based on the top-weight in the gene expression and deconvolution data, Factor 1 showed a positive association with MMP8 expression by TME cells and a negative correlation with the presence of CD8 positive T cells in the TME. Factor 2, on the other hand, exhibited positive alignments with the expression of BHLHA15, TNFRSF17 (BCMA), KLF15, and IGF1 by TME cells. Regarding Factor 6 and Factor 11, which capture tumor fraction heterogeneity, Factor 6 showed alignment with the expression of cancer testis antigens ( MAGEA6, MAGEA3), CXCL12, and KLF4. Meanwhile, Factor 11 was associated with the expression of genes such as SULF2, as well as CRP levels, percentage of CD138+ cells, and the percentage of cells in cell cycle phase S. Next, we investigated the predictive potential of the latent factors inferred by MOFA in models of clinical outcomes. Interestingly, 6 out of the 13 factors identified by MOFA showed significant associations with patients' overall survival (cox regression, p <= 0.05). These factors include Factor 1 and 2, which are related to the composition of the TME, and Factor 6 and 9, which are associated with myeloma progression status, osteolysis, t(11;14), t(4;14), and MM molecular classification. Finally, performing unsupervised clustering using the latent MOFA factors, we identified two main groups with distinct TME subtypes. These groups can be further divided into nine subgroups, each exhibiting unique TME characteristics. Notably, the TME subtypes showed correlations with essential gene expression signatures related to immune response regulation, T cell activation, and natural killer mediated immunity. Additionally, the significant presence of deconvolution-based immune cell types, including T cell CD8+, T cell regulatory, T cell CD4+ (non-regulatory), NK cells and Monocytes was observed. Moreover, the TME subtypes were associated with patients' overall survival and response to daratumumab. Patients presenting TME enriched in immune cells were associated with a significant better outcome ( p = 0.0026). These findings highlight the importance of TME analysis in predicting treatment response and MM patients' outcome in the context of immune-based therapies. Our integrative multi-omics analysis revealed comprehensive MM heterogeneity and distinct immune subtypes that could be of therapeutic interest for personalized therapeutic approaches.

Plasma Circulating Tumor DNA (ctDNA) as an Alternative to Tissue DNA for Genotyping of DLBCL: Results from the POLARIX Study

Introduction: In the POLARIX study (NCT03274492), polatuzumab vedotin in combination with rituximab plus cyclophosphamide, doxorubicin, and prednisone (Pola-R-CHP) demonstrated prolonged progression-free survival (PFS) versus rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) in patients with previously untreated diffuse large B-cell lymphoma (DLBCL; Tilly et al. N Eng J Med 2022); the PFS benefit was maintained with longer follow-up (Herrera et al. Blood 2022). We previously validated the prognostic value of ctDNA at baseline, on treatment, and at the end of treatment in POLARIX (Herrera et al. Blood 2022). Here, we assess and compare the mutation landscape and molecular subtyping of DLBCL by plasma ctDNA versus tissue DNA using the LymphGen classifier. Methods: Baseline plasma ctDNA single nucleotide variants (SNVs) were identified using the AVENIO ctDNA NHL assay (Research Use Only) with an allelic frequency (AF) of 0.5% (Stokowski et al. Blood 2022). Tissue SNVs at baseline were determined by whole exome sequencing (WES) with an AF cutoff of 5%. Plasma-depleted whole blood at baseline was used as a source of germline DNA to filter non-tumor-specific variants for both ctDNA and WES assays. Mutation landscapes in tissue DNA and plasma ctDNA were compared, focusing on genomic regions covered by both assays and variants within the coding regions. LymphGen uses genetic mutations, copy number data, and BCL2/BCL6 rearrangements to classify samples into at least one of seven genetic subtypes (Wright et al. Cancer Cell 2020). Genetic subtypes were defined by the LymphGen classifier using tissue and ctDNA SNVs, per the following: A53 ( TP53 and TP53BP1 mutations), BN2 ( BCL6 translocations and NOTCH2 mutations), EZB ( EZH2 mutations and BCL2 translocations), MCD ( MYD88L265P and CD79B mutations), N1 ( NOTCH1 mutations), and ST2 ( SGK1 and TET2 mutations). Hazard ratios (HR) were adjusted for International Prognostic Index score (2 vs 3-5) and age (≤60 vs >60 years). Results: At baseline, 443 patients had both WES and ctDNA data. When comparing mutation landscapes between tissue DNA and ctDNA, a median 82% (lower quartile: 0.5, upper quartile: 1.0) of tissue SNVs per patient were also found in their matched ctDNA samples. In total, tissue and ctDNA SNVs were found in 163 and 202 genes, respectively. Among these genes, 154 had SNVs in both tissue and ctDNA samples, representing 96% and 78% of mutated genes identified in tissue and ctDNA, respectively. Eleven genes showed statistically significant differences in distribution between tissue and ctDNA (Fisher's exact test, false discovery rate<0.01; Benjamini and Hochberg. J R Stat Soc 1995), including MEF2BNB-MEF2B, MEF2B, BCL10, BNC2, TNFAIP3, RIMS2, CDH19, KIF2B, BCL6, KMT2D and MYC; all but one ( MEF2B) showed higher mutation frequencies in ctDNA than in tissue. The distribution of molecular subtypes is listed in Table 1; due to a lack of information on copy number alterations, the A53 subtype was not included. Based on the tissue SNV data, LymphGen was able to determine the molecular subtypes of 442 patients. Using LymphGen results from tissue SNVs as a reference, 333 (75.3%) patients had the same subtype designation by tissue and ctDNA SNVs, and 109 (24.7%) had different designations ( Table 2). Among the 109 patients, 9 (8.2%) were assigned to a different molecular subtype by ctDNA, 50 (45.9%) patients with tissue SNV-defined molecular subtype were assigned to the undetermined subtype by ctDNA SNVs, and 50 (45.9%) patients with undetermined status by tissue SNVs were classified into distinctive genetic subtypes by ctDNA variants. In a pooled analysis, the 2-year PFS estimates of individual subtypes by tissue versus ctDNA SNVs were consistent ( Table 1). Conclusion: Our analyses demonstrated that the mutation landscape of ctDNA in DLBCL characterized by the AVENIO ctDNA NHL assay resembles that of tumor tissue determined by WES. Patients with molecular subtypes defined by WES or ctDNA had similar PFS outcomes. Overall, these findings support the use of plasma ctDNA as an alternative to tumor tissue for the genotyping of DLBCL.

A prognostic model for STAD based on hypoxia- and immune-related genes

IntroductionFeatured with vast heterogeneity, gastric cancer (GC) is one of the leading causes of cancer-related deaths. A specific prognostic model is necessary for the improvement of clinical treatment strategies. Hypoxia is a common feature in the tumor microenvironment that promotes tumor progression. However, the current evaluation of hypoxic tumor immune microenvironment in GC is still inadequate.Material and methodsWith sequence data and single nucleotide variants data obtained from The Cancer Genome Atlas-STAD dataset as well as hypoxia- and immune-related genes acquired from MsigDB and ImmPort, a hypoxia-immune-based gene signature of gastric adenocarcinoma (STAD) was built by Cox regression analysis. Riskscore could be used as an independent prognostic factor.ResultsReceiver operating characteristic curve and survival curve showed the accuracy of the model. Pearson correlation analysis showed that DUSP1, one of the hypoxia- and immune-related feature genes, was positively correlated with immune cell scores and immune-related function scores. In addition, low-risk group peers were found to be in higher immune infiltration status and had higher immunophenoscore as demonstrated by single-sample GSEA, indicating better response to immune checkpoint inhibitors (ICI) treatment among the low-risk group. q-PCR results showed that DUSP1, IGFBP1, CGB5, GPC3 and EGF were significantly highly expressed in STAD cells, while FAM3D and FGF8 were significantly down-regulated.ConclusionsOverall, our study not only paves the way for future studies focusing on hypoxia and immune microenvironment but also improves STAD patient’s prognosis and their response to immunotherapy.

Establishment of a N1-methyladenosine-related risk signature for breast carcinoma by bioinformatics analysis and experimental validation

ObjectivesBreast carcinoma (BRCA) has resulted in a huge health burden globally. N1-methyladenosine (m1A) RNA methylation has been proven to play key roles in tumorigenesis. Nevertheless, the function of m1A RNA methylation-related genes in BRCA is indistinct.MethodsThe RNA sequencing (RNA-seq), copy-number variation (CNV), single-nucleotide variant (SNV), and clinical data of BRCA were acquired via The Cancer Genome Atlas (TCGA) database. In addition, the GSE20685 dataset, the external validation set, was acquired from the Gene Expression Omnibus (GEO) database. 10 m1A RNA methylation regulators were obtained from the previous literature, and further analyzed through differential expression analysis by rank-sum test, mutation by SNV data, and mutual correlation by Pearson Correlation Analysis. Furthermore, the differentially expressed m1A-related genes were selected through overlapping m1A-related module genes obtained by weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) in BRCA and DEGs between high- and low- m1A score subgroups. The m1A-related model genes in the risk signature were derived by univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. In addition, a nomogram was built through univariate and multivariate Cox analyses. After that, the immune infiltration between the high- and low-risk groups was investigated through ESTIMATE and CIBERSORT. Finally, the expression trends of model genes in clinical BRCA samples were further confirmed by quantitative real-time PCR (RT‒qPCR).ResultsEighty-five differentially expressed m1A-related genes were obtained. Among them, six genes were selected as prognostic biomarkers to build the risk model. The validation results of the risk model showed that its prediction was reliable. In addition, Cox independent prognosis analysis revealed that age, risk score, and stage were independent prognostic factors for BRCA. Moreover, 13 types of immune cells were different between the high- and low-risk groups and the immune checkpoint molecules TIGIT, IDO1, LAG3, ICOS, PDCD1LG2, PDCD1, CD27, and CD274 were significantly different between the two risk groups. Ultimately, RT-qPCR results confirmed that the model genes MEOX1, COL17A1, FREM1, TNN, and SLIT3 were significantly up-regulated in BRCA tissues versus normal tissues.ConclusionsAn m1A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA.

DualGCN: a dual graph convolutional network model to predict cancer drug response

BackgroundDrug resistance is a critical obstacle in cancer therapy. Discovering cancer drug response is important to improve anti-cancer drug treatment and guide anti-cancer drug design. Abundant genomic and drug response resources of cancer cell lines provide unprecedented opportunities for such study. However, cancer cell lines cannot fully reflect heterogeneous tumor microenvironments. Transferring knowledge studied from in vitro cell lines to single-cell and clinical data will be a promising direction to better understand drug resistance. Most current studies include single nucleotide variants (SNV) as features and focus on improving predictive ability of cancer drug response on cell lines. However, obtaining accurate SNVs from clinical tumor samples and single-cell data is not reliable. This makes it difficult to generalize such SNV-based models to clinical tumor data or single-cell level studies in the future.ResultsWe present a new method, DualGCN, a unified Dual Graph Convolutional Network model to predict cancer drug response. DualGCN encodes both chemical structures of drugs and omics data of biological samples using graph convolutional networks. Then the two embeddings are fed into a multilayer perceptron to predict drug response. DualGCN incorporates prior knowledge on cancer-related genes and protein–protein interactions, and outperforms most state-of-the-art methods while avoiding using large-scale SNV data.ConclusionsThe proposed method outperforms most state-of-the-art methods in predicting cancer drug response without the use of large-scale SNV data. These favorable results indicate its potential to be extended to clinical and single-cell tumor samples and advancements in precision medicine.

Methodology for clinical genotyping of CYP2D6 and CYP2C19

Many antidepressants, atomoxetine, and several antipsychotics are metabolized by the cytochrome P450 enzymes CYP2D6 and CYP2C19, and guidelines for prescribers based on genetic variants exist. Although some laboratories offer such testing, there is no consensus regarding validated methodology for clinical genotyping of CYP2D6 and CYP2C19. The aim of this paper was to cross-validate multiple technologies for genotyping CYP2D6 and CYP2C19 against each other, and to contribute to feasibility for clinical implementation by providing an enhanced range of assay options, customizable automated translation of data into haplotypes, and a workflow algorithm. AmpliChip CYP450 and some TaqMan single nucleotide variant (SNV) and copy number variant (CNV) data in the Genome-based therapeutic drugs for depression (GENDEP) study were used to select 95 samples (out of 853) to represent as broad a range of CYP2D6 and CYP2C19 genotypes as possible. These 95 included a larger range of CYP2D6 hybrid configurations than have previously been reported using inter-technology data. Genotyping techniques employed were: further TaqMan CNV and SNV assays, xTAGv3 Luminex CYP2D6 and CYP2C19, PharmacoScan, the Ion AmpliSeq Pharmacogenomics Panel, and, for samples with CYP2D6 hybrid configurations, long-range polymerase chain reactions (L-PCRs) with Sanger sequencing and Luminex. Agena MassARRAY was also used for CYP2C19. This study has led to the development of a broader range of TaqMan SNV assays, haplotype phasing methodology with TaqMan adaptable for other technologies, a multiplex genotyping method for efficient identification of some hybrid haplotypes, a customizable automated translation of SNV and CNV data into haplotypes, and a clinical workflow algorithm.

SUMOylation Regulator-Related Molecules Can Be Used as Prognostic Biomarkers for Glioblastoma

IntroductionSUMOylation is one of the post-translational modifications. The relationship between the expression of SUMOylation regulators and the prognosis of glioblastoma is not quite clear.Materials and MethodsThe single nucleotide variant data, the transcriptome data, and survival information were acquired from The Cancer Genome Atlas, Gene Expression Omnibus, and cBioportal database. Wilcoxon test was used to analyze differentially expressed genes between glioblastoma and normal brain tissues. Gene set enrichment analysis was conducted to find the possible functions. One risk scoring model was built by the least absolute shrinkage and selection operator Cox regression. Kaplain–Meier survival curves and receiver operating characteristic curves were applied to evaluate the effectiveness of the model in predicting the prognosis of glioblastoma.ResultsSingle-nucleotide variant mutations were found in SENP7, SENP3, SENP5, PIAS3, RANBP2, USPL1, SENP1, PIAS2, SENP2, and PIAS1. Moreover, UBE2I, UBA2, PIAS3, and SENP1 were highly expressed in glioblastoma, whereas PIAS1, RANBP2, SENP5, and SENP2 were downregulated in glioblastoma. Functional enrichment analysis showed that the SUMOylation regulators of glioblastoma might involve cell cycle, DNA replication, and other functions. A prognostic model of glioblastoma was constructed based on SUMOylation regulator-related molecules (ATF7IP, CCNB1IP1, and LBH). Kaplain–Meier survival curves and receiver operating characteristic curves showed that the model had a strong ability to predict the overall survival of glioblastoma.ConclusionThis study analyzed the expression of 15 SUMOylation regulators in glioblastoma. The risk assessment model was constructed based on the SUMOylation regulator-related genes, which had a strong predictive ability for the overall survival of patients with glioblastoma. It might provide targets for the study of the relationship between SUMOylation and glioblastoma.

Evaluation of Patient Demographics in Clinical Cancer Genomic Testing.

Inequitable use of next-generation sequencing (NGS) testing for cancer risk and treatment can contribute to heath disparity. Consequently, it is important to assess the population receiving this testing. In this article, we characterize the population receiving both germline and somatic NGS testing for cancer predisposition and precision oncology at the Genetics and Solid Tumors Laboratory of the University of Washington Medical Center. The general demographics, including ancestry, of patients receiving somatic testing to identify genes related to cancer treatment or prognosis, diagnosis, or germline testing for heritable cancer risk from January 2015 to July 2017 were characterized. Ancestry was determined using single nucleotide variant data and documented pedigree. The demographics of the patient population receiving testing were compared with a reference population comprising patients receiving care from the University of Washington Medical Center with a diagnosis of malignant neoplasm of breast, ovary, colon, rectum, or prostate between January 2015 and May 2018. A total of 2210 unique patients were included in this study. Women composed 66% of our total tested population. Patients of European ancestry composed 78% of the tested cohort. The percentages of American Indian/Alaskan Native and Native Hawaiian/Other Pacific Islander in the cohort receiving NGS testing were significantly different than their respective distributions in the reference cohort. Characterizing the demographics of patients receiving NGS testing for cancer predisposition and precision oncology using single nucleotide variant data and documented pedigree may help identify potential health disparities.

SimRVSequences: an R package to simulate genetic sequence data for pedigrees.

We present the R package SimRVSequences to simulate sequence data for pedigrees. SimRVSequences allows for simulations of large numbers of single-nucleotide variants (SNVs) and scales well with increasing numbers of pedigrees. Users provide a sample of pedigrees and SNV data from a sample of unrelated individuals. SimRVSequences is publicly-available on CRAN https://cran.r-project.org/web/packages/SimRVSequences/. Supplementary data are available at Bioinformatics online.

6. PGT-A METHOD ALLOWING COMBINED PGT-M FOR DETECTION OF HETEROPLASMY AND ESTIMATION OF MTDNA MUTATION LOAD IN EMBRYO BIOPSIES

Introduction A large number of pathogenic mitochondrial DNA (mtDNA) mutations have been identified and are implicated in a variety of disorders. As mtDNA is maternally inherited, women with pathogenic mtDNA mutations are likely to have affected children, the severity of the phenotype depending on the heteroplasmy proportion. A systematic meta-analysis showed that there is a ≥ 95% chance of being unaffected at a mutant level of ≤ 18% (Hellebrekers et al, 2012). PGT-M can be used to identify embryos with mutation loads below this phenotypic threshold. The PG-Seq™ kit yields superior coverage of mtDNA and may allow combined PGT-A and PGT-M for mtDNA diseases from single biopsies. Material and methods Two cell lines, a reference and “mutant”, with mitochondrial single nucleotide variants (SNV) were selected. Six 5-cell samples from each cell line were whole genome amplified (WGA). The replicates were pooled before the cell lines were mixed in proportions ranging from 0% to 100% (mutant), in increments of 10%. Libraries were prepared for each of the pools prior to sequencing using the MiSeq instrument (Illumina). SNV sites with Results The average (± sd) mitochondrial read count for cell line samples was 5037 (± 1083). After filtering, the average (± sd) depth of coverage for the cell line samples at the SNV sites was 25.8x (± 12.5). Combining SNV data in silico to achieve a read depth typical of a standard PG-Seq™ kit 48 sample run using a single embryo biopsy (500,000 reads total), the observed SNV proportion was within 25% of the expected proportion in all cases. Combining SNV data to achieve a read depth of 5x a standard PG-Seq™ kit 48 sample run, the observed SNV proportion was within 11% of the expected proportion in all cases. This means that in any case where a heteroplasmy is detected at 7% or lower, including no detection, the mutant load should be below the phenotype threshold. Conclusions The superior amplification of mtDNA achieved by the PG-Seq™ kit may allow combined PGT-A and PGT-M for mtDNA analysis. Combining PGT-A and PGT-M in this novel way would provide a streamlined workflow over currently available workflows. While accuracy is dependent on read depth, read depth can be increased by modulating sequencing throughput or by alternate means such as using PerkinElmer's Target Sequence Enrichment protocol. Due to variable but consistent depth of coverage across the mtDNA, some sites will be more suited for analysis by this method than others.

Reflection of neuroblastoma intratumor heterogeneity in the new OHC-NB1 disease model.

Accurate modeling of intratumor heterogeneity presents a bottleneck against drug testing. Flexibility in a preclinical platform is also desirable to support assessment of different endpoints. We established the model system, OHC-NB1, from a bone marrow metastasis from a patient diagnosed with MYCN-amplified neuroblastoma and performed whole-exome sequencing on the source metastasis and the different models and passages during model development (monolayer cell line, 3D spheroid culture and subcutaneous xenograft tumors propagated in mice). OHC-NB1 harbors a MYCN amplification in double minutes, 1p deletion, 17q gain and diploid karyotype, which persisted in all models. A total of 80-540 single-nucleotide variants (SNVs) was detected in each sample, and comparisons between the source metastasis and models identified 34 of 80 somatic SNVs to be propagated in the models. Clonal reconstruction using the combined copy number and SNV data revealed marked clonal heterogeneity in the originating metastasis, with four clones being reflected in the model systems. The set of OHC-NB1 models represents 43% of somatic SNVs and 23% of the cellularity in the originating metastasis with varying clonal compositions, indicating that heterogeneity is partially preserved in our model system.

Evolution and mutations predisposing to daptomycin resistance in vancomycin-resistant Enterococcus faecium ST736 strains.

We recently identified a novel vancomycin-resistant Enterococcus faecium (VREfm) clone ST736 with reduced daptomycin susceptibility. The objectives of this study were to assess the population dynamics of local VREfm strains and genetic alterations predisposing to daptomycin resistance in VREfm ST736 strains. Multilocus sequence typing and single nucleotide variant data were derived from whole-genome sequencing of 250 E. faecium isolates from 1994–1995 (n = 43), 2009–2012 (n = 115) and 2013 (n = 92). A remarkable change was noticed in the clonality and antimicrobial resistance profiles of E. faecium strains between 1994–1995 and 2013. VREfm sequence type 17 (ST17), the prototype strain of clade A1, was the dominant clone (76.7%) recognized in 1994–1995. By contrast, clone ST736 accounted for 46.7% of VREfm isolates, followed by ST18 (26.1%) and ST412 (20.7%) in 2013. Bayesian evolutionary analysis suggested that clone ST736 emerged between 1996 and 2009. Co-mutations (liaR.W73C and liaS.T120A) of the liaFSR system were identified in all ST736 isolates (n = 111, 100%) examined. Thirty-eight (34.2%) ST736 isolates exhibited daptomycin-resistant phenotype, of which 13 isolates had mutations in both the liaFSR and cardiolipin synthase (cls) genes and showed high level of resistance with a daptomycin MIC50 of 32 μg/mL. The emergence of ST736 strains with mutations predisposing to daptomycin resistance and subsequent clonal spread among inpatients contributed to the observed high occurrence of daptomycin resistance in VREfm at our institution. The expanding geographic distribution of ST736 strains in other states and countries raises concerns about its global dissemination.

Implicating or exonerating the gut microbiome in blood-borne infection.

StrainSifter is a tool that is able to identify the relatedness of a blood-borne infection to the gut microbiome using single-nucleotide variant data.

Identification of an Ultra High-Risk and Targetable Molecular Signature in Relapsed Pediatric T-ALL

▪Relapsed T-cell acute lymphoblastic leukemia (T-ALL) represents one of the major challenges of pediatric oncology as it is often resistant to treatment and fatal. In the search for genes that define critical steps of relapse and can serve as prognostic markers we compared 67 relapses (REL) and 147 samples collected at initial diagnosis (INI) by targeted sequencing of 313 leukemia-related genes. In addition to the analysis of single nucleotide variants (SNV) and small insertions and deletions (InDels), we made use of the available coverage data for profiling of copy number alterations (CNA).Of the 147 INI patients, 31 were treated according to the ALL-BFM 2000 and 116 according to the AIEOP-BFM ALL 2009 protocol. All REL patients were recruited from the ALL-REZ BFM 2002 trial. We analyzed bone marrow DNA by targeted capture of 313 genes (5964 exons) using the Haloplex Target Enrichment Kit (Agilent). We used Varscan to detect both SNV and InDels. In the absence of available remission samples, we subtracted known SNPs (dbSNP, 1000 gp) and variants present in at least one of 20 non-leukemic samples that we sequenced in parallel to the patients’ samples. Only mutations with an AF >10% were considered. Copy number analysis based on read-depth data was validated by MLPA analysis of 14 genes showing a sensitivity of 99% and by low coverage WGS. In total, we have SNV data on all 147 INI and 67 REL patients and CNA data on 144 and 58 patients, respectively.Altogether, we identified relapse specific genetic events in 32 of 67 RELs. Our results confirm that NT5C2 mutations are highly enriched in relapse (REL: 17/67 vs. INI: 1/147; p=0.0001). Although activation of NT5C2 was associated with the occurrence of early first relapse (p=0.02), it did not correlate with induction failure and therefore has no prognostic impact. Similarly, amplifications of chromosome 17 q11.2-24.3, a region that contains genes of the STAT and ABCA families were significantly more frequent in REL than in INI (REL 7/58 vs. INI 3/144, p=0.0068), but also had no prognostic implications. By contrast, TP53 mutations were highly predictive of a second event: all 8 patients who carried a total of 9 TP53 mutations and deletions died within 9 months after first relapse (p=0.002), whereas 17 of the other 58 (29%) survived. Inactivation of TP53 was significantly correlated with higher mutation rates in other genes compared to those with wild-type TP53 (ttest= p<0.0001). TP53 alterations combined with other recurrent mutations in genes responsible for surveillance of DNA integrity (MSH6 and USP7) recognize 15 of the 49 patients with a fatal post-relapse outcome. This group appeared to be particularly resistant to induction therapy as only 2/14 (no data available for 1 pt) patients compared to 17/52 without TP53/MSH6/USP7 mutations achieved second remission and could receive stem cell transplantation (p=0.0006). Activating mutations of KRAS or NRAS thus activating the Ras/Raf/MEK/ERK pathway were found in 8 of these 49 REL patients, 6 of whom died within 3 months, indicating that these mutations cause treatment resistance (p<0.05). Further, likely inactivating mutations of CNOT3 and known activating insertions of IL7R have been exclusively found in 7 REL patients who failed induction. Four of these 7 RELs also carried TP53 or RAS mutations. This combination of mutations predicts an exquisitely poor outcome and results in an ultra-high risk prognostic signature that identifies 24/49 REL patients, who failed induction treatment and died.In conclusion, targeted sequencing of relapsed T-ALL identified a molecular signature predicting an exquisitely poor outcome in 50% of relapses, who failed salvage treatment. This group of patients does not benefit from current treatment strategies thus identifying a subgroup with a dire clinical need for experimental therapy. Notably, approx. 25% REL patients who failed induction carried RAS and IL7R mutations. These patients may be considered for personalized treatment with either MEK- or JAK/STAT-inhibitors. Another 4 patients carry TP53 missense mutations and may benefit from treatment with p53 refolding compounds. DisclosuresNo relevant conflicts of interest to declare.

Abstract 3099: Longitudinal genomic and transcriptomic analysis of mantle cell lymphoma in a targeted combination trial of a selective CDK4/6 inhibitor

Abstract We used whole exome (WES) and whole transcriptome sequencing (WTS) to study progression of Mantle Cell Lymphoma (MCL) in the context of a clinical trial of targeted combination therapy of a CDK4 inhibitor PD0332991 (palbociclib) and a proteasome inhibitor bortezomib. Our longitudinal study design coupled with an integrative analysis approach enabled detailed molecular characterization of each subject's disease. This includes multiple time points for three therapy Responders (Rs) and two Non-Responders (NRs); a primary goal was to understand genetic reasons for differential response. To that end, we identified copy number variants (CNVs) and single nucleotide variants (SNVs) specific to NRs that are supported by both WES and WTS data and implicate pathways relevant to mechanisms of drug action. CNVs were identified using multiple orthogonal methods that use read depth and genome-wide allele frequencies. These revealed a 29Mb chr9 hemizygous deletion unique to NRs. This region includes 15 interferon genes of the type 1 interferon signaling pathway (enrichment: p&amp;lt;10-9), which is involved in B cell differentiation and proliferation, and T cell and natural killer cell activation. Interferon signaling is implicated in bortezomib function and loss of interferon genes may contribute to resistance in non-responders. Additional key genes in deleted regions are JAK2, RPS6, and CDKN2A, a CDK2 inhibitor, loss of which may antagonize palbociclib-induced G1 arrest NRs also shared a 24Mb chr3 duplication resulting in gain of PIK3CA and PIK3CB. Amplification of these genes is common in cancers, including MCL (classical analysis) and DLBCL, and often associated with poor prognosis. But, this is the only longitudinal study of PI3K CNV in a targeted therapy. In context of PD+bortezomib treatment, higher levels of PI3K may affect sensitivity to proteasome inhibition activated autophagy since bortezomib's inhibitory activity involves suppression of the PI3K/AKT/mTOR pathway. PIK3CA is amplified in a responder as well, suggesting additional determinants for clinical response. Other CNVs differentiating Rs and NRs are a 24Mb chr8 deletion found only in Rs and a 4Mb chr17 deletion found only in NRs, which includes p53 in a subject who also has a p53 mutation, resulting in 100% expression of the mutant allele. The described CNVs result in detectable shifts in expression of a large proportion of affected genes. Coupled with SNV data, where we identified 21 SNVs shared by NRs and absent in Rs, these results offer molecular clues to mechanisms of resistance in NRs, which we are currently pursuing with functional studies. Overall, we performed a thorough genomic and transcriptomic analysis of a longitudinal investigation of MCL patients during a targeted combination therapy phase I clinical trial and identified potential biomarkers of response spanning CNVs, SNVs, and differentially expressed genes. Citation Format: Priyanka Vijay, Pedro Blecua, Maurizio Di Liberto, David Chiron, Xiangao Huang, Olivier Elemento, Peter Martin, John P. Leonard, Christopher E. Mason, Selina Chen-Kiang. Longitudinal genomic and transcriptomic analysis of mantle cell lymphoma in a targeted combination trial of a selective CDK4/6 inhibitor. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3099. doi:10.1158/1538-7445.AM2015-3099

Identification of constrained cancer driver genes based on mutation timing.

Cancer drivers are genomic alterations that provide cells containing them with a selective advantage over their local competitors, whereas neutral passengers do not change the somatic fitness of cells. Cancer-driving mutations are usually discriminated from passenger mutations by their higher degree of recurrence in tumor samples. However, there is increasing evidence that many additional driver mutations may exist that occur at very low frequencies among tumors. This observation has prompted alternative methods for driver detection, including finding groups of mutually exclusive mutations and incorporating prior biological knowledge about gene function or network structure. Dependencies among drivers due to epistatic interactions can also result in low mutation frequencies, but this effect has been ignored in driver detection so far. Here, we present a new computational approach for identifying genomic alterations that occur at low frequencies because they depend on other events. Unlike passengers, these constrained mutations display punctuated patterns of occurrence in time. We test this driver–passenger discrimination approach based on mutation timing in extensive simulation studies, and we apply it to cross-sectional copy number alteration (CNA) data from ovarian cancer, CNA and single-nucleotide variant (SNV) data from breast tumors and SNV data from colorectal cancer. Among the top ranked predicted drivers, we find low-frequency genes that have already been shown to be involved in carcinogenesis, as well as many new candidate drivers. The mutation timing approach is orthogonal and complementary to existing driver prediction methods. It will help identifying from cancer genome data the alterations that drive tumor progression.

VCF2Networks: applying genotype networks to single-nucleotide variants data.

A wealth of large-scale genome sequencing projects opens the doors to new approaches to study the relationship between genotype and phenotype. One such opportunity is the possibility to apply genotype networks analysis to population genetics data. Genotype networks are a representation of the set of genotypes associated with a single phenotype, and they allow one to estimate properties such as the robustness of the phenotype to mutations, and the ability of its associated genotypes to evolve new adaptations. So far, though, genotype networks analysis has rarely been applied to population genetics data. To help fill this gap, here we present VCF2Networks, a tool to determine and study genotype network structure from single-nucleotide variant data. VCF2Networks is available at https://bitbucket.org/dalloliogm/vcf2networks. giovanni.dallolio@kcl.ac.uk Supplementary data are available at Bioinformatics online.

GrabBlur--a framework to facilitate the secure exchange of whole-exome and -genome SNV data using VCF files.

BackgroundNext Generation Sequencing (NGS) of whole exomes or genomes is increasingly being used in human genetic research and diagnostics. Sharing NGS data with third parties can help physicians and researchers to identify causative or predisposing mutations for a specific sample of interest more efficiently. In many cases, however, the exchange of such data may collide with data privacy regulations. GrabBlur is a newly developed tool to aggregate and share NGS-derived single nucleotide variant (SNV) data in a public database, keeping individual samples unidentifiable. In contrast to other currently existing SNV databases, GrabBlur includes phenotypic information and contact details of the submitter of a given database entry. By means of GrabBlur human geneticists can securely and easily share SNV data from resequencing projects. GrabBlur can ease the interpretation of SNV data by offering basic annotations, genotype frequencies and in particular phenotypic information - given that this information was shared - for the SNV of interest.Tool descriptionGrabBlur facilitates the combination of phenotypic and NGS data (VCF files) via a local interface or command line operations. Data submissions may include HPO (Human Phenotype Ontology) terms, other trait descriptions, NGS technology information and the identity of the submitter. Most of this information is optional and its provision at the discretion of the submitter. Upon initial intake, GrabBlur merges and aggregates all sample-specific data. If a certain SNV is rare, the sample-specific information is replaced with the submitter identity. Generally, all data in GrabBlur are highly aggregated so that they can be shared with others while ensuring maximum privacy. Thus, it is impossible to reconstruct complete exomes or genomes from the database or to re-identify single individuals. After the individual information has been sufficiently "blurred", the data can be uploaded into a publicly accessible domain where aggregated genotypes are provided alongside phenotypic information. A web interface allows querying the database and the extraction of gene-wise SNV information. If an interesting SNV is found, the interrogator can get in contact with the submitter to exchange further information on the carrier and clarify, for example, whether the latter's phenotype matches with phenotype of their own patient.