scholarly journals pyCancerSig: subclassifying human cancer with comprehensive single nucleotide, structural and microsatellite mutational signature deconstruction from whole genome sequencing

2019 ◽  
Author(s):  
Jessada Thutkawkorapin ◽  
Jesper Eisfeldt ◽  
Emma Tham ◽  
Daniel Nilsson
Keyword(s):  
Human Cancer ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Learning Technology ◽  
Single Nucleotide ◽  
File Formats ◽  
Learning Technique ◽  
Cancer Genome Atlas ◽  
Python Package ◽  
Non Negative Matrix Factorization

AbstractBackgroundDNA damage accumulates over the course of cancer development. The often-substantial amount of somatic mutations in cancer poses a challenge to traditional methods to characterize tumors based on driver mutations. However, advances in machine learning technology can take advantage of this substantial amount of data.ResultsWe developed a command line interface python package, pyCancerSig, to perform sample profiling by integrating single nucleotide variation (SNV), structural variation (SV) and microsatellite instability (MSI) profiles into a unified profile. It also provides a command to decipher underlying cancer processes, employing an unsupervised learning technique, Non-negative Matrix Factorization, and a command to visualize the results. The package accepts common standard file formats (vcf, bam). The program was evaluated using a cohort of breast- and colorectal cancer from The Cancer Genome Atlas project (TCGA). The result showed that by integrating multiple mutations modes, the tool can correctly identify cases with known clear mutational signatures and can strengthen signatures in cases with unclear signal from an SNV-only profile.ConclusionspyCancerSig has demonstrated its capability in identifying known and unknown cancer processes, and at the same time, illuminates the association within and between the mutation modes.

scholarly journals pyCancerSig: subclassifying human cancer with comprehensive single nucleotide, structural and microsatellite mutational signature deconstruction from whole genome sequencing

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Jessada Thutkawkorapin ◽  
Jesper Eisfeldt ◽  
Emma Tham ◽  
Daniel Nilsson
Keyword(s):  
Human Cancer ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Learning Technology ◽  
Single Nucleotide ◽  
File Formats ◽  
Learning Technique ◽  
Cancer Genome Atlas ◽  
Python Package ◽  
Non Negative Matrix Factorization

Abstract Background DNA damage accumulates over the course of cancer development. The often-substantial amount of somatic mutations in cancer poses a challenge to traditional methods to characterize tumors based on driver mutations. However, advances in machine learning technology can take advantage of this substantial amount of data. Results We developed a command line interface python package, pyCancerSig, to perform sample profiling by integrating single nucleotide variation (SNV), structural variation (SV) and microsatellite instability (MSI) profiles into a unified profile. It also provides a command to decipher underlying cancer processes, employing an unsupervised learning technique, Non-negative Matrix Factorization, and a command to visualize the results. The package accepts common standard file formats (vcf, bam). The program was evaluated using a cohort of breast- and colorectal cancer from The Cancer Genome Atlas project (TCGA). The result showed that by integrating multiple mutations modes, the tool can correctly identify cases with known clear mutational signatures and can strengthen signatures in cases with unclear signal from an SNV-only profile. The software package is available at https://github.com/jessada/pyCancerSig. Conclusions pyCancerSig has demonstrated its capability in identifying known and unknown cancer processes, and at the same time, illuminates the association within and between the mutation modes.

scholarly journals Pathogenetic Analysis of Sinonasal Teratocarcinosarcomas Reveal Actionable β-catenin Overexpression and a β-catenin Mutation

2017 ◽  
Vol 78 (04) ◽  
pp. 346-352 ◽  
Author(s):  
Megan Yanik ◽  
Megan Scott ◽  
Carol Bradford ◽  
Jonathan McHugh ◽  
Scott McLean ◽  
...  
Keyword(s):  
Treatment Options ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Molecular Alterations ◽  
Online Databases ◽  
Activating Mutation ◽  
Cancer Mutations ◽  
Cancer Genome Atlas ◽  
Sinonasal Teratocarcinosarcoma ◽  
Index Tumor

Objective Sinonasal teratocarcinosarcomas are rare, aggressive tumors of the skull base. Treatment options are limited and outcomes are poor. Little is known in regard to the genetic factors regulating these tumors. Characterization of actionable molecular alterations in these tumors could provide potentially successful therapeutic options. Methods We performed targeted exome sequencing on an index sinonasal teratocarcinosarcoma specimen to identify potential driver mutations. We performed immunohistochemical stains for β-catenin on paraffin-embedded tissue on the index tumor and a subsequent teratocarcinosarcoma. Online databases of cancer mutations (Catalogue of Somatic Mutations in Cancer and The Cancer Genome Atlas) were accessed. Results We identified an activating p.S45F mutation in β-catenin in our index sinonasal teratocarcinosarcoma. This mutation results in constitutive signaling in the Wnt/β-catenin pathway. We confirmed β-catenin overexpression and nuclear localization via immunohistochemistry in the index tumor and a second patient. The p.S45F activating mutation was found in a variety of solid tumors, and accounts for 3.3 to 10.4% of all known β-catenin mutations. Conclusion We identified a potential driver mutation in β-catenin in a sinonasal teratocarcinosarcoma, resulting in β-catenin overexpression. These findings suggest a role for the Wnt/β-catenin pathway in sinonasal teratocarcinosarcoma tumorigenesis and a role for anti-β-catenin targeted therapy.

scholarly journals On the Reproducibility of TCGA Ovarian Cancer MicroRNA Profiles

2013 ◽  
Author(s):  
Ying-Wooi Wan ◽  
Claire Mach ◽  
Genevera I. Allen ◽  
Matthew Anderson ◽  
Zhandong Liu
Keyword(s):  
Ovarian Cancer ◽  
Cancer Survival ◽  
Human Cancer ◽  
The Cancer Genome Atlas ◽  
Detection Algorithms ◽  
Level 3 ◽  
Cancer Genome Atlas ◽  
Ovarian Cancer Survival ◽  
Generation Sequencing

Dysregulated microRNA (miRNA) expression is a well-established feature of human cancer. However, the role of specific miRNAs in determining cancer outcomes remains unclear. Using Level 3 expression data from the Cancer Genome Atlas (TCGA), we identified 61 miRNAs that are associated with overall survival in 469 ovarian cancers profiled by microarray (p<0.01). We also identified 12 miRNAs that are associated with survival when miRNAs were profiled in the same specimens using Next Generation Sequencing (miRNA-Seq) (p<0.01). Surprisingly, only 1 miRNA transcript is associated with ovarian cancer survival in both datasets. Our analyses indicate that this discrepancy is due to the fact that miRNA levels reported by the two platforms correlate poorly, even after correcting for potential issues inherent to signal detection algorithms. Further investigation is warranted.

scholarly journals Multi-omics analysis to identify driving factors in colorectal cancer

Epigenomics ◽  
2020 ◽  
Vol 12 (18) ◽  
pp. 1633-1650
Author(s):  
Xi Xu ◽  
Chaoju Gong ◽  
Yunfeng Wang ◽  
Yanyan Hu ◽  
Hong Liu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Molecular Mechanisms ◽  
Copy Number Variations ◽  
The Cancer Genome Atlas ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Omics Analysis ◽  
Competing Endogenous Rna Network ◽  
Cancer Genome Atlas

Aim: We aim to identify driving genes of colorectal cancer (CRC) through multi-omics analysis. Materials & methods: We downloaded multi-omics data of CRC from The Cancer Genome Atlas dataset. Integrative analysis of single-nucleotide variants, copy number variations, DNA methylation and differentially expressed genes identified candidate genes that carry CRC risk. Kernal genes were extracted from the weighted gene co-expression network analysis. A competing endogenous RNA network composed of CRC-related genes was constructed. Biological roles of genes were further investigated in vitro. Results: We identified LRRC26 and REP15 as novel prognosis-related driving genes for CRC. LRRC26 hindered tumorigenesis of CRC in vitro. Conclusion: Our study identified novel driving genes and may provide new insights into the molecular mechanisms of CRC.

scholarly journals The Study of the Expression of CGB1 and CGB2 in Human Cancer Tissues

Genes ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1082
Author(s):  
Piotr Białas ◽  
Aleksandra Śliwa ◽  
Anna Szczerba ◽  
Anna Jankowska
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Human Cancer ◽  
Germ Cell Tumors ◽  
Lung Squamous Cell Carcinoma ◽  
Cervical Squamous Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Cancer Tissues

Human chorionic gonadotropin (hCG) is a well-known hormone produced by the trophoblast during pregnancy as well as by both trophoblastic and non-trophoblastic tumors. hCG is built from two subunits: α (hCGα) and β (hCGβ). The hormone-specific β subunit is encoded by six allelic genes: CGB3, CGB5, CGB6, CGB7, CGB8, and CGB9, mapped to the 19q13.32 locus. This gene cluster also encompasses the CGB1 and CGB2 genes, which were originally considered to be pseudogenes, but as documented by several studies are transcriptionally active. Even though the protein products of these genes have not yet been identified, based on The Cancer Genome Atlas (TCGA) database analysis we showed that the mutual presence of CGB1 and CGB2 transcripts is a characteristic feature of cancers of different origin, including bladder urothelial carcinoma, cervical squamous cell carcinoma, esophageal carcinoma, head and neck squamous cell carcinoma, ovarian serous cystadenocarcinoma, lung squamous cell carcinoma, pancreatic adenocarcinoma, rectum adenocacinoma, testis germ cell tumors, thymoma, uterine corpus endometrial carcinoma and uterine carcinosarcoma.

Cutaneous melanoma mutation pathways and driver mutations as racially biased.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e22066-e22066
Author(s):  
Margaret I Sanchez ◽  
James Michael Grichnik
Keyword(s):  
Oxidative Damage ◽  
Racial Differences ◽  
Cutaneous Melanoma ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Racial Groups ◽  
Mutational Burden ◽  
Tumor Mutational Burden ◽  
Cancer Genome Atlas ◽  
Substitution Mutations

e22066 Background: Cutaneous melanoma (CM) demonstrates differences in its clinical prevalence in different racial groups. CM generally exhibits a high tumor mutational burden (TMB) and mutually exclusive driving mutations in NRAS, BRAF or KIT. TMB may be driven by different pathways including ultraviolet radiation (UVR), oxidation and deamination. UVR is the most common mutational signature found in CMs, but deamination and oxidation are also present. Methods: We analyzed 321 CMs exome data from The Cancer Genome Atlas network. BRAF, NRAS, KIT and those without (WT) were used to divide the melanomas. Germline SNPs with racial information (Caucasian, African and Asian) that were enriched in melanomas with a particular driving mutation were identified. Results: We compared the 3 racial groups across the 4 driving mutation types, Asian SNPs were significantly higher in KIT, African in WT and Caucasian in BRAF and NRAS. The melanomas were also evaluated by the type of substitution mutations including CC > TT for UV, G > T for oxidative damage and (G/A)C (G) > (G/A)T(G) for deamination. UV and deamination appeared inversely proportional, while oxidative damage appeared to be independent. UV signal was more prominent in BRAF and NRAS groups. KIT had a greater percentage of deamination while WT revealed more oxidative damage. We further compared UV and non-UV (CC > TT absence) KIT subgroups for racial differences. Asian SNPs were greatly increased in non-UV subgroup whereas Caucasian SNPs were in UV subgroup. Further, the non-UV KIT subgroup was divided into deamination and oxidative damage subgroups to compare racial differences. Deamination was significantly increased in Asians whereas oxidative damage was higher in Caucasians. In the case of the WT group, African SNPs were significantly higher in the non UV subgroup and were primarily correlated with oxidative damage. Conclusions: This study suggests that racial genetic background may predispose the distinctive mutational and genetic environments of melanoma development.

scholarly journals Pan-cancer pharmacogenetics: targeted sequencing panels or exome sequencing?

2020 ◽  
Vol 21 (15) ◽  
pp. 1073-1084
Author(s):  
Laurentijn Tilleman ◽  
Björn Heindryckx ◽  
Dieter Deforce ◽  
Filip Van Nieuwerburgh
Keyword(s):  
Drug Interactions ◽  
Exome Sequencing ◽  
Informed Choice ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Targeted Sequencing ◽  
Driver Mutations ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

Aim: This study provides clinicians and researchers with an informed choice between current commercially available targeted sequencing panels and exome sequencing panels in the context of pan-cancer pharmacogenetics. Materials & methods: Nine contemporary commercially available targeted pan-cancer panels and the xGen Exome Research Panel v2 were investigated to determine to what extent they cover the pharmacogenetic variant–drug interactions in five available cancer knowledgebases, and the driver mutations and fusion genes in the Cancer Genome Atlas. Results: xGen Exome Research Panel v2 and TrueSight Oncology 500 target 71.0 and 68.9% of the pharmacogenetic interactions in the available knowledgebases; and 93.7 and 86.0% of the driver mutations in the Cancer Genome Atlas, respectively. All other studied panels target lower percentages. Conclusion: Exome sequencing outperforms pan-cancer targeted sequencing panels in terms of covered cancer pharmacogenetic variant–drug interactions and pharmacogenetic cancer variants.

scholarly journals Big data visualization identifies the multidimensional molecular landscape of human gliomas

2016 ◽  
Vol 113 (19) ◽  
pp. 5394-5399 ◽  
Author(s):  
Hamid Bolouri ◽  
Lue Ping Zhao ◽  
Eric C. Holland
Keyword(s):  
Drug Targets ◽  
The Cancer Genome Atlas ◽  
Lower Grade ◽  
Single Nucleotide ◽  
Big Data Visualization ◽  
Genome Wide ◽  
Lower Grade Glioma ◽  
Distinct Sequence ◽  
Cancer Genome Atlas ◽  
Molecular Landscape

We show that visualizing large molecular and clinical datasets enables discovery of molecularly defined categories of highly similar patients. We generated a series of linked 2D sample similarity plots using genome-wide single nucleotide alterations (SNAs), copy number alterations (CNAs), DNA methylation, and RNA expression data. Applying this approach to the combined glioblastoma (GBM) and lower grade glioma (LGG) The Cancer Genome Atlas datasets, we find that combined CNA/SNA data divide gliomas into three highly distinct molecular groups. The mutations commonly used in clinical evaluation of these tumors are regionally distributed in these plots. One of the three groups is a mixture of GBM and LGG that shows similar methylation and survival characteristics to GBM. Altogether, our approach identifies eight molecularly defined glioma groups with distinct sequence/expression/methylation profiles. Importantly, we show that regionally clustered samples are enriched for specific drug targets.

scholarly journals In Silico Inference of Synthetic Cytotoxic Interactions from Paclitaxel Responses

2021 ◽  
Vol 22 (3) ◽  
pp. 1097
Author(s):  
Jeong Hoon Lee ◽  
Kye Hwa Lee ◽  
Ju Han Kim
Keyword(s):  
Cancer Survival ◽  
Human Cancer ◽  
Survival Rates ◽  
Cumulative Effect ◽  
Suppressor Gene ◽  
The Cancer Genome Atlas ◽  
Prognostic Effect ◽  
Ic50 Values ◽  
Cancer Genome Atlas ◽  
Tp53 Tumor Suppressor Gene

To exploit negatively interacting pairs of cancer somatic mutations in chemotherapy responses or synthetic cytotoxicity (SC), we systematically determined mutational pairs that had significantly lower paclitaxel half maximal inhibitory concentration (IC50) values. We evaluated 407 cell lines with somatic mutation profiles and estimated their copy number and drug-inhibitory concentrations in Genomics of Drug Sensitivity in Cancer (GDSC) database. The SC effect of 142 mutated gene pairs on response to paclitaxel was successfully cross-validated using human cancer datasets for urogenital cancers available in The Cancer Genome Atlas (TCGA) database. We further analyzed the cumulative effect of increasing SC pair numbers on the TP53 tumor suppressor gene. Patients with TCGA bladder and urogenital cancer exhibited improved cancer survival rates as the number of disrupted SC partners (i.e., SYNE2, SON, and/or PRY) of TP53 increased. The prognostic effect of SC burden on response to paclitaxel treatment could be differentiated from response to other cytotoxic drugs. Thus, the concept of pairwise SC may aid the identification of novel therapeutic and prognostic targets.

scholarly journals RNF125 Modulates Tumor Immunity by Promoting Ubiquitination of PD-L1

2021 ◽  
Author(s):  
Yumeng Peng ◽  
Huan Yang ◽  
Zihui Li ◽  
Huilong Li ◽  
Xiaolin Qiu ◽  
...  
Keyword(s):  
Tumor Immunity ◽  
Human Cancer ◽  
Ring Finger ◽  
Ring Domain ◽  
The Cancer Genome Atlas ◽  
Comprehensive Treatment ◽  
Cancer Genome Atlas ◽  
Using Data ◽  
The Stability ◽  
L1 Protein

Abstract In recent years, the incidence of tumors has been increasing, and the overall cure rate by traditional treatment methods does not exceed 20%. One of the most effective and promising strategies for comprehensive treatment of tumors is immunotherapy, such as treatment with the PD-1/PD-L1 antibody. Here, we showed that ring finger protein 125 (RNF125), an E3 ligase in the RING domain family, could interact with PD-L1 to reduce the stability of PD-L1 protein. In addition, RNF125 downregulated the expression of PD-L1 by promoting its ubiquitination at K48, whereas a mutation in the RING domain of RNF125 disrupted this function. A significant positive correlation between RNF125 and genes involved with tumor immunity was determined in cancer samples, as determined using data from The Cancer Genome Atlas (TCGA). Furthermore, we elucidated the effects of RNF125 on the occurrence and development of tumors in mice. Analyses of wild-type and RNF125 knockout mice transplanted with MC-38 cells revealed enhanced MC-38 tumor growth in KO mice. These data indicated that RNF125 could participate in tumor immunity by promoting the K48-linked ubiquitination of PD-L1 to affect the occurrence and development of tumors, providing a potential target for enhancing therapeutic efficacy for human cancer treatment.

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