scholarly journals Multivariate association analysis with somatic mutation data

Biometrics ◽  
2017 ◽  
Vol 74 (1) ◽  
pp. 176-184
Author(s):  
Qianchuan He ◽  
Yang Liu ◽  
Ulrike Peters ◽  
Li Hsu
Keyword(s):  
Association Analysis ◽  
Somatic Mutation ◽  
Mutation Data

scholarly journals Analysis of multi-omics differences in left-side and right-side colon cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11433
Author(s):  
Yanyi Huang ◽  
Jinzhong Duanmu ◽  
Yushu Liu ◽  
Mengyun Yan ◽  
Taiyuan Li ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Gene Mutation ◽  
Somatic Mutation ◽  
Transcriptome Data ◽  
Characteristic Analysis ◽  
Immune Microenvironment ◽  
Mutation Data ◽  
Upregulated Genes ◽  
Testing Set

Background Colon cancer is one of the most common tumors in the digestive tract. Studies of left-side colon cancer (LCC) and right-side colon cancer (RCC) show that these two subtypes have different prognoses, outcomes, and clinical responses to chemotherapy. Therefore, a better understanding of the importance of the clinical classifications of the anatomic subtypes of colon cancer is needed. Methods We collected colon cancer patients’ transcriptome data, clinical information, and somatic mutation data from the Cancer Genome Atlas (TCGA) database portal. The transcriptome data were taken from 390 colon cancer patients (172 LCC samples and 218 RCC samples); the somatic mutation data included 142 LCC samples and 187 RCC samples. We compared the expression and prognostic differences of LCC and RCC by conducting a multi-omics analysis of each using the clinical characteristics, immune microenvironment, transcriptomic differences, and mutation differences. The prognostic signatures was validated using the internal testing set, complete set, and external testing set (GSE39582). We also verified the independent prognostic value of the signature. Results The results of our clinical characteristic analysis showed that RCC had a significantly worse prognosis than LCC. The analysis of the immune microenvironment showed that immune infiltration was more common in RCC than LCC. The results of differential gene analysis showed that there were 360 differentially expressed genes, with 142 upregulated genes in LCC and 218 upregulated genes in RCC. The mutation frequency of RCC was generally higher than that of LCC. BRAF and KRAS gene mutations were the dominant genes mutations in RCC, and they had a strong mutual exclusion with APC, while APC gene mutation was the dominant gene mutation in LCC. This suggests that the molecular mechanisms of RCC and LCC differed. The 4-mRNA and 6-mRNA in the prognostic signatures of LCC and RCC, respectively, were highly predictive and may be used as independent prognostic factors. Conclusion The clinical classification of the anatomic subtypes of colon cancer is of great significance for early diagnosis and prognostic risk assessment. Our study provides directions for individualized treatment of left and right colon cancer.

scholarly journals Predicting Cancer Tissue-of-Origin by a Machine Learning Method Using DNA Somatic Mutation Data

2020 ◽  
Vol 11 ◽  
Author(s):  
Xiaojun Liu ◽  
Lianxing Li ◽  
Lihong Peng ◽  
Bo Wang ◽  
Jidong Lang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Somatic Mutation ◽  
Cancer Tissue ◽  
Machine Learning Method ◽  
Learning Method ◽  
Tissue Of Origin ◽  
Mutation Data

scholarly journals Hierarchical Bayesian analysis of somatic mutation data in cancer

2013 ◽  
Vol 7 (2) ◽  
pp. 883-903 ◽  
Author(s):  
Jie Ding ◽  
Lorenzo Trippa ◽  
Xiaogang Zhong ◽  
Giovanni Parmigiani
Keyword(s):  
Bayesian Analysis ◽  
Somatic Mutation ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Analysis ◽  
Mutation Data

Analysis of multi-omics differences in left-side and right-side colon cancer

2020 ◽  
Author(s):  
Yanyi Huang ◽  
Jinzhong Duanmu ◽  
Yushu Liu ◽  
Mengyun Yan ◽  
Taiyuan Li ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Somatic Mutation ◽  
Clinical Characteristics ◽  
Transcriptome Data ◽  
Clinical Classification ◽  
Immune Microenvironment ◽  
Mutation Data ◽  
Testing Set

Abstract Background:Colon cancer is one of the common tumors of digestive tract. Studies of left-side colon cancer(LCC) and right-side colon cancer(RCC) show that these two subtypes had different prognosis, outcomes, and clinical response to chemotherapy. Therefore,it is necessary to explore the necessity of clinical classification of anatomic subtypes about colon cancer.Methods:We selected the transcriptome data, clinical information and somatic mutation data of colon cancer patients from the the Cancer Genome Atlas(TCGA )database portal.The transcriptome data included 390 colon cancer patients(172 LCC samples and 218 RCC samples),and the somatic mutation data included 142 LCC samples and 187 RCC samples.By conducting a multi-omics analysis of the LCC and RCC from the four aspects of clinical characteristics, immune microenvironment , transcriptomic differences and mutation differences, so as to compare the expression and prognosis difference of LCC and RCC.We are the first to construct prognostic signatures respectively for LCC and RCC respectively.The prognostic signatures is validated by internal testing set, complete set and external testing set(GSE39582).Additionally we also verified the independent prognostic value of the signature.Results:Clinical characteristics analysis results show that RCC had a significantly worse prognosis than LCC.Analysis the immune microenvironment analysis shows that RCC was more immune infiltration than LCC.The results of differential gene analysis showed that there were 360 differential expressed genes,with 142 up genes in LCC and 218 up genes in RCC.Correlation analysis of mutated genes showed that the expression of mutated genes in RCC was negatively correlated, while the expression of mutated genes in LCC was positively correlated, and the mutation frequency of RCC was generally higher than that of LCC.Meanwhile, our 4-mRNA LCC and 6-mRNA RCC prognostic signatures are highly predictive and can be used as independent prognostic factors.Conclusion:The clinical classification of anatomic subtypes of colon cancer is of great significance for its early diagnosis and prognostic risk assessment.Our study provides directions for individualized treatment of left and right colon cancer.

scholarly journals Inference of cancer progression from somatic mutation data

2015 ◽  
Vol 48 (28) ◽  
pp. 234-238
Author(s):  
Hao Wu ◽  
Lin Gao ◽  
Nikola Kasabov
Keyword(s):  
Somatic Mutation ◽  
Cancer Progression ◽  
Mutation Data

scholarly journals A pan-cancer somatic mutation embedding using autoencoders

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Martin Palazzo ◽  
Pierre Beauseroy ◽  
Patricio Yankilevich
Keyword(s):  
Somatic Mutation ◽  
Language Processing ◽  
Computational Modelling ◽  
Support Vector ◽  
Complex Data ◽  
Cancer Disease ◽  
Neural Network Learning ◽  
Mutation Data ◽  
Tumor Types ◽  
Pan Cancer

Abstract Background Next generation sequencing instruments are providing new opportunities for comprehensive analyses of cancer genomes. The increasing availability of tumor data allows to research the complexity of cancer disease with machine learning methods. The large available repositories of high dimensional tumor samples characterised with germline and somatic mutation data requires advance computational modelling for data interpretation. In this work, we propose to analyze this complex data with neural network learning, a methodology that made impressive advances in image and natural language processing. Results Here we present a tumor mutation profile analysis pipeline based on an autoencoder model, which is used to discover better representations of lower dimensionality from large somatic mutation data of 40 different tumor types and subtypes. Kernel learning with hierarchical cluster analysis are used to assess the quality of the learned somatic mutation embedding, on which support vector machine models are used to accurately classify tumor subtypes. Conclusions The learned latent space maps the original samples in a much lower dimension while keeping the biological signals from the original tumor samples. This pipeline and the resulting embedding allows an easier exploration of the heterogeneity within and across tumor types and to perform an accurate classification of tumor samples in the pan-cancer somatic mutation landscape.

scholarly journals A Prediction Model for Preoperative Risk Assessment in Endometrial Cancer Utilizing Clinical and Molecular Variables

2019 ◽  
Vol 20 (5) ◽  
pp. 1205 ◽  
Author(s):  
Erin Salinas ◽  
Marina Miller ◽  
Andreea Newtson ◽  
Deepti Sharma ◽  
Megan McDonald ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Prediction Model ◽  
Somatic Mutation ◽  
Mirna Expression ◽  
Prediction Models ◽  
Gene Copy Number ◽  
The Cancer Genome Atlas ◽  
Gene Copy ◽  
Mutation Data

The utility of comprehensive surgical staging in patients with low risk disease has been questioned. Thus, a reliable means of determining risk would be quite useful. The aim of our study was to create the best performing prediction model to classify endometrioid endometrial cancer (EEC) patients into low or high risk using a combination of molecular and clinical-pathological variables. We then validated these models with publicly available datasets. Analyses between low and high risk EEC were performed using clinical and pathological data, gene and miRNA expression data, gene copy number variation and somatic mutation data. Variables were selected to be included in the prediction model of risk using cross-validation analysis; prediction models were then constructed using these variables. Model performance was assessed by area under the curve (AUC). Prediction models were validated using appropriate datasets in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prediction model with only clinical variables performed at 88%. Integrating clinical and molecular data improved prediction performance up to 97%. The best prediction models included clinical, miRNA expression and/or somatic mutation data, and stratified pre-operative risk in EEC patients. Integrating molecular and clinical data improved the performance of prediction models to over 95%, resulting in potentially useful clinical tests.

scholarly journals Testing tumors from different anatomic sites for clonal relatedness using somatic mutation data

Biometrics ◽  
2020 ◽  
Author(s):  
Irina Ostrovnaya ◽  
Audrey Mauguen ◽  
Venkatraman E. Seshan ◽  
Colin B. Begg
Keyword(s):  
Somatic Mutation ◽  
Clonal Relatedness ◽  
Mutation Data
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