scholarly journals TSNAD: an integrated software for cancer somatic mutation and tumour-specific neoantigen detection

2017 ◽  
Vol 4 (4) ◽  
pp. 170050 ◽  
Author(s):  
Zhan Zhou ◽  
Xingzheng Lyu ◽  
Jingcheng Wu ◽  
Xiaoyue Yang ◽  
Shanshan Wu ◽  
...  
Keyword(s):  
Best Practices ◽  
Somatic Mutations ◽  
Cancer Genomics ◽  
International Cancer Genome Consortium ◽  
Complex Molecules ◽  
Histocompatibility Complex ◽  
Genome Analysis Toolkit ◽  
Tumour Antigens ◽  
Genome Consortium ◽  
Integrated Software

Tumour antigens have attracted much attention because of their importance to cancer diagnosis, prognosis and targeted therapy. With the development of cancer genomics, the identification of tumour-specific neoantigens became possible, which is a crucial step for cancer immunotherapy. In this study, we developed software called the tumour-specific neoantigen detector for detecting cancer somatic mutations following the best practices of the genome analysis toolkit and predicting potential tumour-specific neoantigens, which could be either extracellular mutations of membrane proteins or mutated peptides presented by class I major histocompatibility complex molecules. This pipeline was beneficial to the biologist with little programmatic background. We also applied the software to the somatic mutations from the International Cancer Genome Consortium database to predict numerous potential tumour-specific neoantigens. This software is freely available from https://github.com/jiujiezz/tsnad.

scholarly journals International Cancer Genome Consortium Data Portal--a one-stop shop for cancer genomics data

Database ◽  
2011 ◽  
Vol 2011 (0) ◽  
pp. bar026-bar026 ◽  
Author(s):  
J. Zhang ◽  
J. Baran ◽  
A. Cros ◽  
J. M. Guberman ◽  
S. Haider ◽  
...  
Keyword(s):  
Cancer Genomics ◽  
Cancer Genome ◽  
International Cancer Genome Consortium ◽  
One Stop ◽  
Genome Consortium ◽  
Data Portal

scholarly journals The Role of Peptides in T Cell Alloreactivity Is Determined by Self–Major Histocompatibility Complex Molecules

2000 ◽  
Vol 191 (5) ◽  
pp. 805-812 ◽  
Author(s):  
Reinhard Obst ◽  
Nikolai Netuschil ◽  
Karsten Klopfer ◽  
Stefan Stevanović ◽  
Hans-Georg Rammensee
Keyword(s):  
T Cells ◽  
Major Histocompatibility Complex ◽  
T Cell ◽  
Target Cells ◽  
Major Histocompatibility ◽  
Complex Molecules ◽  
Mhc Molecules ◽  
Histocompatibility Complex ◽  
Alloreactive T Cells

By analyzing T cell responses against foreign major histocompatibility complex (MHC) molecules loaded with peptide libraries and defined self- and viral peptides, we demonstrate a profound influence of self-MHC molecules on the repertoire of alloreactive T cells: the closer the foreign MHC molecule is related to the T cell's MHC, the higher is the proportion of peptide-specific, alloreactive (“allorestricted”) T cells versus T cells recognizing the foreign MHC molecule without regard to the peptide in the groove. Thus, the peptide repertoire of alloreactive T cells must be influenced by self-MHC molecules during positive or negative thymic selection or peripheral survival, much like the repertoire of the self-restricted T cells. In consequence, allorestricted, peptide-specific T cells (that are of interest for clinical applications) are easier to obtain if T cells and target cells express related MHC molecules.

scholarly journals Human Herpesvirus 7 U21 Tetramerizes To Associate with Class I Major Histocompatibility Complex Molecules

2014 ◽  
Vol 88 (6) ◽  
pp. 3298-3308 ◽  
Author(s):  
N. A. May ◽  
Q. Wang ◽  
A. Balbo ◽  
S. L. Konrad ◽  
R. Buchli ◽  
...  
Keyword(s):  
Major Histocompatibility Complex ◽  
Human Herpesvirus ◽  
Class I ◽  
Major Histocompatibility ◽  
Complex Molecules ◽  
Histocompatibility Complex

scholarly journals A Statistical Framework for Evolutionary Analysis of Recurrent Somatic Mutations in Cancers

2020 ◽  
Author(s):  
Xun Gu
Keyword(s):  
Somatic Mutations ◽  
Cancer Genomics ◽  
Computational Procedure ◽  
The Cancer Genome Atlas ◽  
Component Model ◽  
Evolutionary Analysis ◽  
Cancer Genes ◽  
Component Mixture ◽  
Two Component ◽  
Empirical Bayesian Method

AbstractCurrent cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored, faciltating enormous high throuput analyses to explore the underlying mechanisms that may contribute to malignant initiation or progression. In the context of over-dominant passenger mutations (unrelated to cancers), the challenge is to identify somatic mutations that are cancer-driving. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model that enables to accomplish the following analyses. (i) We formulated a quasi-likelihood approach to test whether the two-component model is significantly better than a single-component model, which can be used for new cancer gene predicting. (ii) We implemented an empirical Bayesian method to calculate the posterior probabilities of a site to be cancer-driving for all sites of a gene, which can be used for new driving site predicting. (iii) We developed a computational procedure to calculate the somatic selection intensity at driver sites and passenger sites, respectively, as well as site-specific profiles for all sites. Using these newly-developed methods, we comprehensively analyzed 294 known cancer genes based on The Cancer Genome Atlas (TCGA) database.

scholarly journals Discovering the drivers of clonal hematopoiesis

2020 ◽  
Author(s):  
Oriol Pich ◽  
Iker Reyes-Salazar ◽  
Abel Gonzalez-Perez ◽  
Nuria Lopez-Bigas
Keyword(s):  
Positive Selection ◽  
Molecular Mechanisms ◽  
Somatic Mutations ◽  
Cancer Genomics ◽  
Variant Calling ◽  
Selective Advantage ◽  
Cancer Genes ◽  
Driver Genes ◽  
Hematopoietic Stem ◽  
Clonal Hematopoiesis

AbstractMutations in genes that confer a selective advantage to hematopoietic stem cells (HSCs) in certain conditions drive clonal hematopoiesis (CH). While some CH drivers have been identified experimentally or through epidemiological studies, the compendium of all genes able to drive CH upon mutations in HSCs is far from complete. We propose that identifying signals of positive selection in blood somatic mutations may be an effective way to identify CH driver genes, similarly as done to identify cancer genes. Using a reverse somatic variant calling approach, we repurposed whole-genome and whole-exome blood/tumor paired samples of more than 12,000 donors from two large cancer genomics cohorts to identify blood somatic mutations. The application of IntOGen, a robust driver discovery pipeline, to blood somatic mutations across both cohorts, and more than 24,000 targeted sequenced samples yielded a list of close to 70 genes with signals of positive selection in CH, available at http://www.intogen.org/ch. This approach recovers all known CH genes, and discovers novel candidates. Generating this compendium is an essential step to understand the molecular mechanisms of CH and to accurately detect individuals with CH to ascertain their risk to develop related diseases.

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