scholarly journals An Integrated Framework for Genome Analysis Reveals Numerous Previously Unrecognizable Structural Variants in Leukemia Patients’ Samples

2019 ◽  
Author(s):  
Jie Xu ◽  
Fan Song ◽  
Emily Schleicher ◽  
Christopher Pool ◽  
Darrin Bann ◽  
...  
Keyword(s):  
Genome Mapping ◽  
Large Fraction ◽  
Genomic Analysis ◽  
Structural Variants ◽  
Driver Genes ◽  
Pediatric Leukemia ◽  
Cis Acting ◽  
Cancer Driver ◽  
The Status ◽  
Intergenic Regions

AbstractWhile genomic analysis of tumors has stimulated major advances in cancer diagnosis, prognosis and treatment, current methods fail to identify a large fraction of somatic structural variants in tumors. We have applied a combination of whole genome sequencing and optical genome mapping to a number of adult and pediatric leukemia samples, which revealed in each of these samples a large number of structural variants not recognizable by current tools of genomic analyses. We developed computational methods to determine which of those variants likely arose as somatic mutations. The method identified 97% of the structural variants previously reported by karyotype analysis of these samples and revealed an additional fivefold more such somatic rearrangements. The method identified on average tens of previously unrecognizable inversions and duplications and hundreds of previously unrecognizable insertions and deletions. These structural variants recurrently affected a number of leukemia associated genes as well as cancer driver genes not previously associated with leukemia and genes not previously associated with cancer. A number of variants only affected intergenic regions but caused cis-acting alterations in expression of neighboring genes. Analysis of TCGA data indicates that the status of several of the recurrently mutated genes identified in this study significantly affect survival of AML patients. Our results suggest that current genomic analysis methods fail to identify a majority of structural variants in leukemia samples and this lacunae may hamper diagnostic and prognostic efforts.

scholarly journals Characterizations of Gene Alterations in Melanoma Patients from Chinese Population

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yi Luo ◽  
Zhenzhen Zhang ◽  
Jianfan Liu ◽  
Linqing Li ◽  
Xuezheng Xu ◽  
...  
Keyword(s):  
Braf Mutations ◽  
Driver Genes ◽  
Cancer Driver ◽  
Formalin Fixed Paraffin ◽  
Significant Difference ◽  
The Status ◽  
Ffpe Samples ◽  
Formalin Fixed Paraffin Embedded ◽  
Comprehensive Genomic Profiling ◽  
Gene Alterations

Melanoma is a human skin malignant tumor with high invasion and poor prognosis. The limited understanding of genomic alterations in melanomas in China impedes the diagnosis and therapeutic strategy selection. We conducted comprehensive genomic profiling of melanomas from 39 primary and metastatic formalin-fixed paraffin-embedded (FFPE) samples from 27 patients in China based on an NGS panel of 223 genes. No significant difference in gene alterations was found between primary and metastasis melanomas. The status of germline mutation, CNV, and somatic mutation in our cohort was quite different from that reported in Western populations. We further delineated the mutation patterns of 4 molecular subgroups (BRAF, RAS, NF1, and Triple-WT) of melanoma in our cohort. BRAF mutations were more frequently identified in melanomas without chromic sun-induced damage (non-CSD), while RAS mutations were more likely observed in acral melanomas. NF1 and Triple-WT subgroups were unbiased between melanomas arising in non-CSD and acral skin. BRAF, RAS, and NF1 mutations were significantly associated with lymph node metastasis or presence of ulceration, implying that these cancer driver genes were independent prognostic factors. In summary, our results suggest that mutational profiles of malignant melanomas in China are significantly different from Western countries, and both gene mutation and amplification play an important role in the development and progression of melanomas.

scholarly journals Bayesian inference of cancer driver genes using signatures of positive selection

2017 ◽  
Author(s):  
Luis Zapata ◽  
Hana Susak ◽  
Oliver Drechsel ◽  
Marc R. Friedländer ◽  
Xavier Estivill ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Large Fraction ◽  
Driver Gene ◽  
Tumor Type ◽  
Sequencing Data ◽  
Cancer Etiology ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Types ◽  
Cancer Cell Fraction

AbstractTumors are composed of an evolving population of cells subjected to tissue-specific selection, which fuels tumor heterogeneity and ultimately complicates cancer driver gene identification. Here, we integrate cancer cell fraction, population recurrence, and functional impact of somatic mutations as signatures of selection into a Bayesian inference model for driver prediction. In an in-depth benchmark, we demonstrate that our model, cDriver, outperforms competing methods when analyzing solid tumors, hematological malignancies, and pan-cancer datasets. Applying cDriver to exome sequencing data of 21 cancer types from 6,870 individuals revealed 98 unreported tumor type-driver gene connections. These novel connections are highly enriched for chromatin-modifying proteins, hinting at a universal role of chromatin regulation in cancer etiology. Although infrequently mutated as single genes, we show that chromatin modifiers are altered in a large fraction of cancer patients. In summary, we demonstrate that integration of evolutionary signatures is key for identifying mutational driver genes, thereby facilitating the discovery of novel therapeutic targets for cancer treatment.

scholarly journals Mutations in Cancer Driver Genes: An Insight into Prostate Cancer Progression

2019 ◽  
pp. 1-7
Author(s):  
Sahdeo Prasad ◽  
Sanjay K Srivastava
Keyword(s):  
Prostate Cancer ◽  
Cancer Progression ◽  
Drug Targets ◽  
Cellular Functions ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Initiation ◽  
The Status ◽  
Using Data ◽  
Insight Into

Prostate cancer is one of the most common uro-oncological disease in men and is globally leading cause of cancer related deaths in males. The somatic mutation has a strong link in the occurrence of cancer. Mutation in the oncogenes and tumor suppressor genes that alter key cellular functions can lead to prostate cancer initiation and progression. Whole genome sequencing has identified numerous genetic alternations and further provided a detail view of the mutations in genes that drive progression of prostate cancer. TP53, SPOP, PTEN, ATM, AR, CTNNB1, FOXA1, KMT2D, BRACA2 and APC were found as frequently mutated genes in prostate cancer. Using data from cBioPortal and PubMed, this review summarizes the status and possible impact of mutations in these driver genes on survival, progression, and metastasis of prostate cancer. This study will contribute a better understanding of biological basis for clinical variability in prostate cancer patients and may provide new genetic diagnostic markers and drug targets.

scholarly journals CancerGeneNet: linking driver genes to cancer hallmarks

2019 ◽  
Vol 48 (D1) ◽  
pp. D416-D421 ◽  
Author(s):  
Marta Iannuccelli ◽  
Elisa Micarelli ◽  
Prisca Lo Surdo ◽  
Alessandro Palma ◽  
Livia Perfetto ◽  
...  
Keyword(s):  
Large Fraction ◽  
Driver Mutations ◽  
Gene Products ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Hallmarks ◽  
Disease Phenotypes ◽  
Regulatory Circuits ◽  
Cancer Drivers ◽  
Cancer Phenotypes

Abstract CancerGeneNet (https://signor.uniroma2.it/CancerGeneNet/) is a resource that links genes that are frequently mutated in cancers to cancer phenotypes. The resource takes advantage of a curation effort aimed at embedding a large fraction of the gene products that are found altered in cancer cells into a network of causal protein relationships. Graph algorithms, in turn, allow to infer likely paths of causal interactions linking cancer associated genes to cancer phenotypes thus offering a rational framework for the design of strategies to revert disease phenotypes. CancerGeneNet bridges two interaction layers by connecting proteins whose activities are affected by cancer drivers to proteins that impact on the ‘hallmarks of cancer’. In addition, CancerGeneNet annotates curated pathways that are relevant to rationalize the pathological consequences of cancer driver mutations in selected common cancers and ‘MiniPathways’ illustrating regulatory circuits that are frequently altered in different cancers.

Utilizing patient information to identify subtype heterogeneity of cancer driver genes

2021 ◽  
pp. 096228022110558
Author(s):  
Ho-Hsiang Wu ◽  
Xing Hua ◽  
Jianxin Shi ◽  
Nilanjan Chatterjee ◽  
Bin Zhu
Keyword(s):  
Type I Error ◽  
Smoking Status ◽  
The Cancer Genome Atlas ◽  
Type I ◽  
Driver Genes ◽  
Cancer Subtypes ◽  
Cancer Driver ◽  
The Status ◽  
Cancer Genome Atlas ◽  
Cancer Driver Genes

Identifying cancer driver genes is essential for understanding the mechanisms of carcinogenesis and designing therapeutic strategies. Although driver genes have been identified for many cancer types, it is still not clear whether the selection pressure of driver genes is homogeneous across cancer subtypes. We propose a statistical framework MutScot to improve the identification of driver genes and to investigate the heterogeneity of driver genes across cancer subtypes. Through simulation studies, we show that MutScot properly controls the type I error in detecting driver genes. In addition, we demonstrate that MutScot can identify subtype heterogeneity of driver genes. Applications to three studies in The Cancer Genome Atlas (TCGA) project showcase that MutScot has a desirable sensitivity for detecting driver genes and that MutScot identifies subtype heterogeneity of driver genes in breast cancer and lung cancer with regards to the status of hormone receptor and to the smoking status, respectively.

scholarly journals Hepatitis B virus integrations promote local and distant oncogenic driver alterations in hepatocellular carcinoma

Gut ◽  
2021 ◽  
pp. gutjnl-2020-323153 ◽  
Author(s):  
Camille Péneau ◽  
Sandrine Imbeaud ◽  
Tiziana La Bella ◽  
Theo Z Hirsch ◽  
Stefano Caruso ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Genome Mapping ◽  
Clonal Selection ◽  
Young Patients ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Important Challenge ◽  
Set Up ◽  
Liver Tissues

ObjectiveInfection by HBV is the main risk factor for hepatocellular carcinoma (HCC) worldwide. HBV directly drives carcinogenesis through integrations in the human genome. This study aimed to precisely characterise HBV integrations, in relation with viral and host genomics and clinical features.DesignA novel pipeline was set up to perform viral capture on tumours and non-tumour liver tissues from a French cohort of 177 patients mainly of European and African origins. Clonality of each integration event was determined with the localisation, orientation and content of the integrated sequence. In three selected tumours, complex integrations were reconstructed using long-read sequencing or Bionano whole genome mapping.ResultsReplicating HBV DNA was more frequently detected in non-tumour tissues and associated with a higher number of non-clonal integrations. In HCC, clonal selection of HBV integrations was related to two different mechanisms involved in carcinogenesis. First, integration of viral enhancer nearby a cancer-driver gene may lead to a strong overexpression of oncogenes. Second, we identified frequent chromosome rearrangements at HBV integration sites leading to cancer-driver genes (TERT, TP53, MYC) alterations at distance. Moreover, HBV integrations have direct clinical implications as HCC with a high number of insertions develop in young patients and have a poor prognosis.ConclusionDeep characterisation of HBV integrations in liver tissues highlights new HBV-associated driver mechanisms involved in hepatocarcinogenesis. HBV integrations have multiple direct oncogenic consequences that remain an important challenge for the follow-up of HBV-infected patients.

scholarly journals Machine learning algorithm improved automated droplet classification of ddPCR for detection of BRAF V600E in paraffin-embedded samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gabriel A. Colozza-Gama ◽  
Fabiano Callegari ◽  
Nikola Bešič ◽  
Ana C. de J. Paviza ◽  
Janete M. Cerutti
Keyword(s):  
Machine Learning ◽  
Sanger Sequencing ◽  
Learning Algorithm ◽  
Absolute Quantification ◽  
Braf V600e Mutation ◽  
Braf V600e ◽  
Driver Genes ◽  
Quantitative Classification ◽  
Cancer Driver

AbstractSomatic mutations in cancer driver genes can help diagnosis, prognosis and treatment decisions. Formalin-fixed paraffin-embedded (FFPE) specimen is the main source of DNA for somatic mutation detection. To overcome constraints of DNA isolated from FFPE, we compared pyrosequencing and ddPCR analysis for absolute quantification of BRAF V600E mutation in the DNA extracted from FFPE specimens and compared the results to the qualitative detection information obtained by Sanger Sequencing. Sanger sequencing was able to detect BRAF V600E mutation only when it was present in more than 15% total alleles. Although the sensitivity of ddPCR is higher than that observed for Sanger, it was less consistent than pyrosequencing, likely due to droplet classification bias of FFPE-derived DNA. To address the droplet allocation bias in ddPCR analysis, we have compared different algorithms for automated droplet classification and next correlated these findings with those obtained from pyrosequencing. By examining the addition of non-classifiable droplets (rain) in ddPCR, it was possible to obtain better qualitative classification of droplets and better quantitative classification compared to no rain droplets, when considering pyrosequencing results. Notable, only the Machine learning k-NN algorithm was able to automatically classify the samples, surpassing manual classification based on no-template controls, which shows promise in clinical practice.

scholarly journals Ranking cancer drivers via betweenness-based outlier detection and random walks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Cesim Erten ◽  
Aissa Houdjedj ◽  
Hilal Kazan
Keyword(s):  
Cancer Genomics ◽  
Interaction Network ◽  
Molecular Data ◽  
Alternative Methods ◽  
Patient Specific ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Protein Protein Interaction ◽  
Genomic Studies

Abstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

scholarly journals nanotatoR: a tool for enhanced annotation of genomic structural variants

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Surajit Bhattacharya ◽  
Hayk Barseghyan ◽  
Emmanuèle C. Délot ◽  
Eric Vilain
Keyword(s):  
De Novo ◽  
Genome Mapping ◽  
Gene List ◽  
Sufficient Information ◽  
Rna Seq ◽  
Structural Variants ◽  
De Novo Genome Assembly ◽  
Pathogenic Variants ◽  
Increased Sensitivity

Abstract Background Whole genome sequencing is effective at identification of small variants, but because it is based on short reads, assessment of structural variants (SVs) is limited. The advent of Optical Genome Mapping (OGM), which utilizes long fluorescently labeled DNA molecules for de novo genome assembly and SV calling, has allowed for increased sensitivity and specificity in SV detection. However, compared to small variant annotation tools, OGM-based SV annotation software has seen little development, and currently available SV annotation tools do not provide sufficient information for determination of variant pathogenicity. Results We developed an R-based package, nanotatoR, which provides comprehensive annotation as a tool for SV classification. nanotatoR uses both external (DGV; DECIPHER; Bionano Genomics BNDB) and internal (user-defined) databases to estimate SV frequency. Human genome reference GRCh37/38-based BED files are used to annotate SVs with overlapping, upstream, and downstream genes. Overlap percentages and distances for nearest genes are calculated and can be used for filtration. A primary gene list is extracted from public databases based on the patient’s phenotype and used to filter genes overlapping SVs, providing the analyst with an easy way to prioritize variants. If available, expression of overlapping or nearby genes of interest is extracted (e.g. from an RNA-Seq dataset, allowing the user to assess the effects of SVs on the transcriptome). Most quality-control filtration parameters are customizable by the user. The output is given in an Excel file format, subdivided into multiple sheets based on SV type and inheritance pattern (INDELs, inversions, translocations, de novo, etc.). nanotatoR passed all quality and run time criteria of Bioconductor, where it was accepted in the April 2019 release. We evaluated nanotatoR’s annotation capabilities using publicly available reference datasets: the singleton sample NA12878, mapped with two types of enzyme labeling, and the NA24143 trio. nanotatoR was also able to accurately filter the known pathogenic variants in a cohort of patients with Duchenne Muscular Dystrophy for which we had previously demonstrated the diagnostic ability of OGM. Conclusions The extensive annotation enables users to rapidly identify potential pathogenic SVs, a critical step toward use of OGM in the clinical setting.

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