Whole genome and RNA sequencing reveal the distinct genomic landscape of acral melanoma.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 9589-9589 ◽  
Author(s):  
Yan Kong ◽  
Zhihong Chi ◽  
Lu Si ◽  
Xinan Sheng ◽  
Chuanliang Cui ◽  
...  

9589 Background: Acral melanoma is a common subtype of melanoma in Asians with extremely poor prognosis, and therapy strategy has not been clearly established for acral melanoma. The aim of this study is to perform genomic and RNA profiling of acral melanoma to obtain the comprehensive genomic view of this subtype of melanoma. Methods: Genomic DNA was extracted with Qiagen DNeasy Blood or Tissue Kit . DNA libraries were prepared using a CancerPROTM-P88 BOX library Prep kit and sequencing was performed using the Illumina HiSeq X10. RNA was isolated with Qiagen RNeasy mini-spin column. cDNA was synthesized from total RNA using the SuperScript III first-strand synthesis system . RNA libraries were prepared using the NEBNext Ultra II Directional RNA Library Prep Kit and sequencing was performed using the Illumina HiSeq X10. Results: To obtain a comprehensive genomic and functional genomic view of acral melanoma, we sequenced the genomes of 14 acral melanoma and transcriptomes of 11 of these melanoma samples. We found a new mutation in the V28 codon of BRAF in three patients. Furthermore, we identified recurrent non-synonymous single nucleotide variants in previously described oncogene NRAS, as well as in genes encoding keratin associated proteins (KRTAP4-7, KRTAP4-5) and mucin (MUC2, MUC21). Notably, a recurrent noncoding hotspot mutation was discovered in 4 of 11 cases. By analyzing the RNA sequencing data, we found significant divergence on transcriptome between patients with or without ulcer. In total, we identified 201 genes were significantly up-regulated and 386 genes significantly down-regulated in acral melanoma patients with ulcer. Differentially expressed genes were enriched in pathways associated with cancers, melanogenesis, cell death signaling, skin diseases, etc. Conclusions: Our study reveals potentially different driver mutations and distinct transcriptome in acral melanoma patients compared with cutaneous melanoma patients and sheds lights to the further personalized medicine for acral melanoma patients.

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi111-vi111
Author(s):  
Matija Snuderl ◽  
Kasthuri Kannan ◽  
Jean-Pierre Gagner ◽  
Elad Mashiach ◽  
Matthias Karajannis ◽  
...  

Abstract BACKGROUND While most hemangioblastomas (~70%) are sporadic and occur predominantly in the cerebellum, they may present as well as familial form associated with von Hippel-Lindau (VHL) syndrome, an autosomal dominant disorder caused by germline mutations of the VHL gene that trigger nuclear translocation of hypoxia-inducible factor (HIF)-1α and angiogenesis. Although inactivation of VHL, a tumor suppressor gene, has been observed in hemangioblastomas, the underlying pathogenic mechanisms responsible for familial and sporadic hemangioblastomas remain incompletely understood. METHODS Whole exome sequencing of cerebellar hemangioblastoma tumors and matched blood leukocytes from 24 patients, age 24–63, was performed. After preparation and amplification of barcoded libraries, exomes were captured using Kapa Biosystems methodology and paired-end sequenced on Illumina HiSeq 2500 to an average 100-fold coverage. Following read alignment to hg19 genome, tumor and germline (leukocyte) sequences were compared, and pathogenic single nucleotide variants (SNVs) identified and validated by re-sequencing followed by pathway analysis. Additionally, tumor RNA isolated using Maxwell Promega was sequenced on Illumina instrument and the expression counts determined and normalized. RESULTS We found 314 pathogenic and/or highly deleterious mutations (both germline and somatic) with a median of 13 mutations per patient. Five patients had VHL syndrome (germline VHL mutation) and 4 carried somatic VHL mutations. Among the VHL tumors, 82 mutations were identified, including HNF1B, NOTCH1 and TCF7L1, suggesting a potential contribution of altered RNA metabolism based upon pathway analysis. Among all hemangioblastomas, germline growth factor receptor variants (FGFR4 p.G388R (14/23 (61%) patients), IGF1R, PDGFRA and TYK2) known to activate STAT3 signaling and induce HIF-1α and angiogenesis, were identified. Non-hierarchical clustering of RNA sequencing data revealed two transcriptionally-distinct subtypes of hemangioblastomas. CONCLUSIONS Our findings indicate that hemangioblastomas can also occur by germline mutations known to activate STAT3 signaling, which may have significant implication in genetic testing and counseling of patients with hemangioblastomas.


2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e14300-e14300 ◽  
Author(s):  
Xianling Guo ◽  
Song Gao ◽  
Li Yang ◽  
Juemin Fang ◽  
Guochao Wei ◽  
...  

e14300 Background: Acral and mucosal melanoma are rare subtypes accounting for about 3% of all melanoma cases. The cutaneous melanoma genomic landscape is well defined; however, little is known about the acral and mucosal melanoma mutational spectrum. In this pilot study, we evaluated the genomic and neo-antigen profiles and tumor mutational burden (TMB) from acral and mucosal melanoma patients with the aim of designing personalized vaccines and longitudinally tracking patients’ clinical courses. Methods: Tumor whole exome sequencing and neo-antigen profiling of 5 acral and 3 mucosal melanoma patients at Shanghai Tenth Peoples Hospital, Tongji University, China between April 2018 and January 2019 was performed using YuceBio’s proprietary analytics platform. Watsonä for Genomics, an artificial intelligence decision-support system, was used for variant interpretation and annotation. A comparative analysis was performed on Chinese acral melanoma data with the published Caucasian acral cohort from the Translational Genomics Research Institute (TGen) and The Cancer Genome Atlas (TCGA) predominantly Caucasian cutaneous melanoma data set. Results: TMB in our acral/mucosal melanoma cohort was 2.26/Megabase (Mb) compared to over 20/Mb in published cutaneous melanoma studies. Tumor neo-antigen burden (TNAB) in our group was 1.03 neo-epitopes/Mb. Low TNAB levels were associated with low TMB levels in all tumors. Incidence of BRAF and NRAS mutant cases in our cohort was 0% (0/8) and 13% (1/8) respectively compared to 19% (5/27) and 7% (2/27) of the Caucasian acral population in the TGen dataset. Incidence of BRAF and NRAS mutations in the TCGA cutaneous melanoma dataset was 54% (237/440) and 28% (125/440), respectively. Conclusions: TMB was significantly lower in acral/mucosal than in cutaneous melanoma and may be a surrogate for TNAB. Detection of BRAF and NRAS mutations, the two most prevalent driver mutations in cutaneous melanoma, were significantly lower frequencies in both Chinese and Caucasian acral melanoma patients in this study, suggesting alternate cancer drivers may exist in this subtype. Strategies to address challenges of low TNAB in vaccine development are being explored.


2019 ◽  
Vol 28 (21) ◽  
pp. 3569-3583 ◽  
Author(s):  
Patricia M Schnepp ◽  
Mengjie Chen ◽  
Evan T Keller ◽  
Xiang Zhou

Abstract Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type-specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single-cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analyzed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doors for novel ways to leverage the use of scRNA-seq for the future investigation of SNV function.


2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e13657-e13657
Author(s):  
Ruifang Mao ◽  
Shanshan Xiao ◽  
Rui Lin ◽  
Yuchen Wang ◽  
Tao Wang

e13657 Background: Identification of a broad spectrum of somatic mutations is crucial to guide targeted therapy such as for non-small cell lung cancer (NSCLC) patients. In the clinical environment, it requires well validated NGS workflow both for the wet-lab and dry-lab procedures. Here we describe a high sensitivity target NGS assay to accurately capture single nucleotide variants (SNVs), short insertions and deletions (indels), copy number alterations and gene rearrangements for formalin-fixed paraffin-embedded (FFPE) NSCLC patient samples. Extensive analytical validation was performed following the checklists of College of American Pathologists. Methods: Next generation sequencing (NGS) libraries were prepared using extracted DNA from FFPE tissue NSCLC patient samples. The protocol for library generation was optimized in several steps and incorporated 10bp unique molecular identifiers (UMIs). The libraries were sequenced on Illumina HiSeq X-Ten platform. The sequence data was analyzed by an in-house bioinformatics pipeline to call somatic mutations at an average depth of 4000X. Results: We tested the accuracy of 68 clinical tumor samples that were also validated by conventional or alternative methods in the third party CAP accredited labs. We observed 100% sensitivity and 100% specificity compared with the other lab¡¯s validation results. To define the limit of detection (LOD) for different mutation types, clinical DNA samples containing different variants were diluted with normal DNA. The LODs for SNV (as in EGFR L858R) and indel (as in EGFR 19del) were 0.5% and 1%, respectively. Addressing the LOD of fusion and copy number alteration is usually challenging. Our NGS assay was able to achieve 2% LOD for gene rearrangement (fusion) and 3.5 copies for copy number amplification. The high reproducibility was also achieved by inter- and intra- replicate experiments. Our NGS assay showed better performance than other widely used commercial NGS assay panels. Conclusions: We have validated an NGS based approach with UMI technology that is able to achieve high accuracy and sensitivity as low as 0.5% for detection of somatic mutations, which will improve the clinical testing performance for NSCLC FFPE samples with low allele frequencies of driver mutations.


2019 ◽  
Vol 3 (1) ◽  
pp. 21-32 ◽  
Author(s):  
Alex J. Cornish ◽  
Phuc H. Hoang ◽  
Sara E. Dobbins ◽  
Philip J. Law ◽  
Daniel Chubb ◽  
...  

Abstract The identification of driver mutations is fundamental to understanding oncogenesis. Although genes frequently mutated in B-cell lymphoma have been identified, the search for driver mutations has largely focused on the coding genome. Here we report an analysis of the noncoding genome using whole-genome sequencing data from 117 patients with B-cell lymphoma. Using promoter capture Hi-C data in naive B cells, we define cis-regulatory elements, which represent an enriched subset of the noncoding genome in which to search for driver mutations. Regulatory regions were identified whose mutation significantly alters gene expression, including copy number variation at cis-regulatory elements targeting CD69, IGLL5, and MMP14, and single nucleotide variants in a cis-regulatory element for TPRG1. We also show the commonality of pathways targeted by coding and noncoding mutations, exemplified by MMP14, which regulates Notch signaling, a pathway important in lymphomagenesis and whose expression is associated with patient survival. This study provides an enhanced understanding of lymphomagenesis and describes the advantages of using chromosome conformation capture to decipher noncoding mutations relevant to cancer biology.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 2876-2876
Author(s):  
TaeHyung Kim ◽  
Jae-Sook Ahn ◽  
Marc S Tyndel ◽  
Hyeoung-Joon Kim ◽  
Yeo-Kyeoung Kim ◽  
...  

Abstract Introduction Acute myeloid leukemia (AML) is a genetically heterogeneous disease. A recent study (NEJM, 2016) classified 1540 patients into 14 subgroups using mutation information from targeted next generation sequencing data as well as cytogenetic information [1]. The classification criteria of 7 of these subgroups rely solely on mutation information. NK-AML is characterized by its lack of cytogenetic abnormalities. In this study, we attempted to replicate the prognostic stratification in an independent set of NK-AML patients using the NEJM study's genomic classification criteria. Patients and Methods This study included a total of 393 patients who met the following eligibility criteria: 1) age ≥ 15 years; 2) a diagnosis of NK-AML confirmed by conventional cytogenetic analysis; 3) treatment with induction chemotherapy using a standard protocol (a 3-day course of anthracycline with a 7-day course of cytosine arabinoside). The median follow-up duration was 55.1 months (range, 0.7-182.9). Analysis of genetic mutations were performed using targeted sequencing by Illumina Hiseq 2000 (Agilent custom probe set targeting entire exon regions of a myeloid panel consisting of 94 genes). Results We identified driver mutations across 28 genes or genomic regions, with 2 or more driver mutations identified in 15/393 patients (3.8%). Based on the genomic classification criteria, the patients were classified as follows: 136 patients (34.6%) with NPM1 mutations, 42 patients (10.7%) with mutated chromatin modifiers and/or RNA-splicing genes, 6 patients (1.5%) with TP53 mutations, 40 patients (10.2%) with biallelic CEBPA mutations, 8 patients (2.0%) with IDH2-R172 mutations and no other class-defining lesions, 108 patients (27.5%) with driver mutations but no detected class-defining lesions, 38 patients (9.7%) with no detected driver mutations, and 15 patients (3.8%) who met the criteria of more than one genomic subgroup. Of the 393 patients, 325 patients (82.7%) achieved complete remission (CR). CR rates vary depending on the genomic subgroup (75.9%-97.4%). The CR rate for each subgroup was as follows: 86.8% (118/136) of patients with NPM1 mutations61.9% (26/42) of patients with mutated chromatin and/or RNA-splicing genes83.3% (5/6) of patients with TP53 mutations97.5% (38/40) of patients with biallelic CEBPA mutations87.5% (7/8) of patients with IDH2-R172 mutations and no other class-defining lesions75.9% (82/108) of patients with driver mutations but no detected class-defining lesions97.3% (37/38) of patients with no detected driver mutations80.0% (12/15) of patients meeting criteria of more than one subgroup 5-year OS and 5-year relapse incidence (RI) for each subgroup was as follows: 49.3% (95% CI, 40.1-58.5) and 39.8% (95% CI, 30.1-49.2) of patients with NPM1 mutations11.6% (95% CI, 1.4-21.8) and 71.4% (95% CI, 45.7-86.5) of patients with mutated chromatin and/or RNA-splicing genes50.0% (95% CI, 10.0-90.0) and 20.0% (95% CI, 0.4-61.2) of patients with TP53 mutations68.3% (95% CI, 53.4-83.2) and 19.7% (95% CI, 8.5-34.4) of patients with biallelic CEBPA mutations56.3% (95% CI, 17.3-95.3) and 21.4% (95% CI, 0.3-67.3) of patients with IDH2-R172 mutations and no other class-defining lesions26.6% (95% CI, 17.4-35.8) and 53.2% (95% CI, 40.7-64.3) of patients with driver mutations but no detected class-defining lesions29.1% (95% CI, 14.2-44.0) and 43.8% (95% CI, 27.1-59.3) of patients with no detected driver mutations40.0% (95% CI, 15.3-64.7) and 33.3% (95% CI, 9.2-60.3) of patients that meet the criteria of more than one subgroup. The CR rates of the subgroup with mutated chromatin and/or RNA-splicing genes was significantly lower than the rest of the cohort (61.9% vs. 85.2%, p=0.00016). The 5-year OS and 5-year RI of the subgroup were also poorer than the others [61.9% vs. 85.2% in OS (p=0.00016), 71.4% vs. 40.1% in RI (p < 0.0001)]. Conclusion Our NK-AML cohort showed similar survival patterns to the cohort in Papaemmanuil et al (NEJM 2016). The subgroup in AML with mutated chromatin and/or RNA-Splicing genes had the poorest prognosis with respect to CR rate and overall survival. This analysis replicates the result of recently published genomic classification and supports its use for categorizing NK-AML patients. Reference [1] Genomic Classification and Prognosis in Acute Myeloid Leukemia. Papaemmanuil E et al. N Engl J Med, 2016 vol. 374 (23) pp. 2209-2221. Figure Figure. Disclosures Jang: Kyowa Hakko Kirin Co., Ltd.: Research Funding.


2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fenglin Liu ◽  
Yuanyuan Zhang ◽  
Lei Zhang ◽  
Ziyi Li ◽  
Qiao Fang ◽  
...  

Abstract Background Systematic interrogation of single-nucleotide variants (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single-cell level. While SNV detection from abundant single-cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed. Results Here, we perform a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2, and VarScan2, using both simulation and scRNA-seq datasets, and identify multiple elements influencing their performance. While the specificities are generally high, with sensitivities exceeding 90% for most tools when calling homozygous SNVs in high-confident coding regions with sufficient read depths, such sensitivities dramatically decrease when calling SNVs with low read depths, low variant allele frequencies, or in specific genomic contexts. SAMtools shows the highest sensitivity in most cases especially with low supporting reads, despite the relatively low specificity in introns or high-identity regions. Strelka2 shows consistently good performance when sufficient supporting reads are provided, while FreeBayes shows good performance in the cases of high variant allele frequencies. Conclusions We recommend SAMtools, Strelka2, FreeBayes, or CTAT, depending on the specific conditions of usage. Our study provides the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.


2018 ◽  
Author(s):  
Liam Spurr ◽  
Nawaf Alomran ◽  
Piotr Słowiński ◽  
Muzi Li ◽  
Pavlos Bousounis ◽  
...  

MotivationBy testing for association of DNA genotypes with gene expression levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding how thousands of single nucleotide variants (SNVs) may affect gene expression. As compared to DNA genotypes, RNA genetic variation represents a phenotypic trait that reflects the actual allele content of the studied system. RNA genetic variation can be measured at expressed genome regions, and differs from the DNA genotype in sites subjected to regulatory forces. Therefore, assessment of correlation between RNA genetic variation and gene expression can reveal regulatory genomic relationships in addition to eQTLs.ResultsWe introduce ReQTL, an eQTL modification which substitutes the DNA allele count for the variant allele frequency (VAF) at expressed SNV loci in the transcriptome. We exemplify the method on sets of RNA-sequencing data from human tissues obtained though the Genotype-Tissue Expression Project (GTEx) and demonstrate that ReQTL analyses show consistently high performance and sufficient power to identify both previously known and novel molecular associations. The majority of the SNVs implicated in significant cis-ReQTLs identified by our analysis were previously reported as significant cis-eQTL loci. Notably, trans ReQTL loci in our data were substantially enriched in RNA-editing sites. In summary, ReQTL analyses are computationally feasible and do not require matched DNA data, hence they have a high potential to facilitate the discovery of novel molecular interactions through exploration of the increasingly accessible RNA-sequencing datasets.Availability and implementationSample scripts used in our ReQTL analyses are available with the Supplementary Material (ReQTL_sample_code)[email protected] or [email protected] InformationRe_QTL_Supplementary_Data.zip


Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 5327-5327
Author(s):  
Andrew R Carson ◽  
Bradley A Patay ◽  
Suzanne M Graham ◽  
Andrew Ross Cubbon ◽  
Timothy T Stenzel ◽  
...  

Abstract Introduction: Acute Myeloid Leukemia (AML) is the most common acute leukemia in adults, with roughly 19,000 new diagnoses expected yearly in the United States. Mutations in fms-related tyrosine kinase 3 (FLT3) and nucleophosmin (NPM1) are observed in over one third of all AML patients. These mutations include internal tandem duplications within the juxtamembrane domain (FLT3-ITD; 15-30% of patients) and substitutions within the tyrosine kinase domain (FLT3-TKD; 5-10% of patients) of FLT3, along with 4bp insertions (15-30% of patients) within the C-terminal domain of NPM1. These mutations have significant impacts on prognosis; patients with FLT3-ITDs have poor prognosis while patients with NPM1 mutations without an associated FLT3-ITD mutation have better long-term outcomes. Since characterization of these mutations is critical for accurate therapeutic decisions, assays have been developed to accurately identify these mutations in AML patients. However, these assays lack greater context because they do not identify coexisting mutations in other AML associated genes. As such, they fail to characterize additional prognostic markers that may more fully predict and stratify AML patients’ disease progression. To investigate the limitations of AML individual mutations assays, we identified coexisting mutations in 22 AML patients with known FLT3 and NPM1 mutations using the MyAML™ targeted sequencing panel. Methods: Isolated DNA from 22 AML samples with known FLT3-ITD, FLT3-TKD and NPM1 mutation status was sheared then hybridized to MyAML oligonucleotide baits comprised of exons (coding and non-coding) and breakpoint hotspots from 194 genes known or predicted to be involved in AML pathogenesis. Targeted loci were sequenced on an Illumina MiSeq utilizing v3 chemistry with the 600-cycle kit. By indexing two samples per flowcell, we were able to sequence 12.6 to 32.9 M unique reads per sample, providing an average depth of 985x across the ~3.5Mb target. Using a custom bioinformatics pipeline, we performed mutation detection analyses to identify single nucleotide variants, indels, and structural variant breakpoints. We also calculated variant allelic frequencies to investigate potential aneuploidy, loss of heterozygosity and clonality. Results: Using individual PCR with capillary electrophoresis (PCR/CE) assays, 14 FLT3-ITD, 7 FLT-TKD and 10 NPM1 mutations were initially detected in the 22 AML patient samples. These mutations were 100% concordant with results from the MyAML sequencing data. However, while the individual PCR/CE assays were limited to detecting specific mutations in two genes, the MyAML panel detected 4,172 protein altering variants in 155 of the 192 additionally targeted genes. These include 35 potential mutations in five key AML genes: 13 in DNMT3A, 5 in IDH1, 8 in IDH2, 4 in KIT and 5 in CEBPA. Eight of the 22 patients contained at least two potentially pathogenic mutations in these five genes, with one patient containing mutations in four of the genes. Interestingly, while some co-existing mutations appear to have the same mutant to wild-type allelic ratio as the main FLT3 or NPM1 mutations, others have distinct ratios that may suggest the presence of subclonal cellular populations. For example, we identified an NPM1 Mutation A (c.859_860insTCTG; p.W288fs*>9; COSM158604) in 40.0% of a patient’s sequencing reads, while a co-existing CEPBA missense mutation (c.961A>G; p.N321D; COSM96570) was only present in 4.3% of the sequencing reads. Conclusions: While individual assays for mutations in FLT3, NPM1 and other common AML genes are useful for patient stratification and prognosis, it is crucial to understand these mutations in a greater genomic context. As more AML-related mutations are detected, such as resistance mutations to treatments with novel tyrosine kinase inhibitors, it becomes increasing important to fully characterize a patient’s tumor genome in order to successfully classify and treat their disease. In addition, as more AML patient samples are sequenced, relationships between mutations and their clonal populations can be elucidated, potentially leading to more effective combination therapies. MyAML targeted gene sequencing is the most comprehensive AML specific assay for the identification of somatic and germline driver mutations in their clonal context for the prediction of recurrence and response to various treatment regimens. Disclosures Patay: Genection, Inc.: Consultancy. Cubbon:LabPMM LLC: Employment. Stenzel:Invivoscribe, Inc.: Employment. Miller:Invivoscribe, Inc.: Employment, Equity Ownership.


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