Faculty Opinions recommendation of RUNX1-mutated families show phenotype heterogeneity and a somatic mutation profile unique to germline predisposed AML.

Long noncoding RNAs (lncRNAs) exert an increasingly important effect on genome instability and the prognosis of cancer patients. The present research established a computational framework originating from the mutation assumption combining lncRNA expression profile and somatic mutation profile in the genome of renal cancer to assess the effect of lncRNAs on the gene instability of renal cancer. A total of 45 differentially expressed lncRNAs were evaluated to be genome-instability-associated from the high and low cumulative somatic mutations groups. Then we established a prognosis model based on three genome-instability-associated lncRNAs (AC156455.1, AC016405.3, and LINC01234)-GlncScore. The GlncScore was then verified in testing cohort and the total TCGA renal cancer cohort. The GlncScore was evaluated to have an accurate prediction for the survival of patients. Furthermore, GlncScore was associated with somatic mutation patterns, indicating its capacity of reflecting genome instability in renal cancer. In conclusion, this study evaluated the effect of lncRNAs on genome instability of renal cancer and provided new hidden cancer biomarkers related to genome instability in renal cancer.

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Somatic Mutation Profile of Atypical Fibroxanthoma and Cutaneous Undifferentiated Pleomorphic Sarcoma

Dermatologic Surgery ◽

10.1097/dss.0000000000002342 ◽

2020 ◽

Vol Publish Ahead of Print ◽

Cited By ~ 1

Author(s):

Young H. Lim ◽

Theodore D. Zaki ◽

Jonathan L. Levinsohn ◽

Anjela Galan ◽

Keith A. Choate ◽

...

Keyword(s):

Somatic Mutation ◽

Undifferentiated Pleomorphic Sarcoma ◽

Atypical Fibroxanthoma ◽

Pleomorphic Sarcoma ◽

Mutation Profile

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Heterogeneity of genomic profile in patients with HER2-positive breast cancer

Endocrine Related Cancer ◽

10.1530/erc-19-0414 ◽

2020 ◽

Vol 27 (3) ◽

pp. 153-162 ◽

Cited By ~ 5

Author(s):

Bo Chen ◽

Guochun Zhang ◽

Guangnan Wei ◽

Yulei Wang ◽

Liping Guo ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Somatic Mutation ◽

Breast Tumors ◽

Molecular Heterogeneity ◽

Breast Cancer Patients ◽

Her2 Positive ◽

Her2 Positive Breast Cancer ◽

Mutation Profile ◽

Positive Breast Cancer

HER2-positive breast cancer is a biologically and clinically heterogeneous disease. Based on the expression of hormone receptors (HR), breast tumors can be further categorized into HR positive and HR negative. Here, we elucidated the comprehensive somatic mutation profile of HR+ and HR− HER2-positive breast tumors to understand their molecular heterogeneity. In this study, 64 HR+/HER2+ and 43 HR-/HER2+ stage I-III breast cancer patients were included. Capture-based targeted sequencing was performed using a panel consisting of 520 cancer-related genes, spanning 1.64 megabases of the human genome. A total of 1119 mutations were detected among the 107 HER2-positive patients. TP53, CDK12 and PIK3CA were the most frequently mutated, with mutation rates of 76, 61 and 49, respectively. HR+/HER2+ tumors had more gene amplification, splice site and frameshift mutations and a smaller number of missense, nonsense and insertion-deletion mutations than HR-/HER2+ tumors. In KEGG analysis, HR+/HER2+ tumors had more mutations in genes involved in homologous recombination (P = 0.004), TGF-beta (P = 0.007) and WNT (P = 0.002) signaling pathways than HR-/HER2+ tumors. Moreover, comparative analysis of our cohort with datasets from The Cancer Genome Atlas and Molecular Taxonomy of Breast Cancer International Consortium revealed the distinct somatic mutation profile of Chinese HER2-positive breast cancer patients. Our study revealed the heterogeneity of somatic mutations between HR+/HER2+ and HR-/HER2+ in Chinese breast cancer patients. The distinct mutation profile and related pathways are potentially relevant in the development of optimal treatment strategies for this subset of patients.

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Identifying Cancer Subtypes based on Somatic Mutation Profile

Proceedings of the ACM 8th International Workshop on Data and Text Mining in Bioinformatics - DTMBIO '14 ◽

10.1145/2665970.2665980 ◽

2014 ◽

Cited By ~ 6

Author(s):

Sungchul Kim ◽

Lee Sael ◽

Hwanjo Yu

Keyword(s):

Somatic Mutation ◽

Cancer Subtypes ◽

Mutation Profile

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cancerAlign: Stratifying tumors by unsupervised alignment across cancer types

10.1101/2020.11.17.387860 ◽

2020 ◽

Author(s):

Bowen Gao ◽

Yunan Luo ◽

Jianzhu Ma ◽

Sheng Wang

Keyword(s):

Somatic Mutation ◽

Large Scale ◽

Cancer Genomics ◽

Population Level ◽

Substantial Improvement ◽

Cancer Type ◽

Consensus Clustering ◽

Learning To Learn ◽

Mutation Profile ◽

Cancer Types

ABSTRACTTumor stratification, which aims at clustering tumors into biologically meaningful subtypes, is the key step towards personalized treatment. Large-scale profiled cancer genomics data enables us to develop computational methods for tumor stratification. However, most of the existing approaches only considered tumors from an individual cancer type during clustering, leading to the overlook of common patterns across cancer types and the vulnerability to the noise within that cancer type. To address these challenges, we proposed cancerAlign to map tumors of the target cancer type into latent spaces of other source cancer types. These tumors were then clustered in each latent space rather than the original space in order to exploit shared patterns across cancer types. Due to the lack of aligned tumor samples across cancer types, cancerAlign used adversarial learning to learn the mapping at the population level. It then used consensus clustering to integrate cluster labels from different source cancer types. We evaluated cancerAlign on 7,134 tumors spanning 24 cancer types from TCGA and observed substantial improvement on tumor stratification and cancer gene prioritization. We further revealed the transferability across cancer types, which reflected the similarity among them based on the somatic mutation profile. cancerAlign is an unsupervised approach that provides deeper insights into the heterogeneous and rapidly accumulating somatic mutation profile and can be also applied to other genome-scale molecular information.Availabilityhttps://github.com/bowen-gao/cancerAlign

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Fully-Connected Neural Networks with Reduced Parameterization for Predicting Histological Types of Lung Cancer from Somatic Mutations

Biomolecules ◽

10.3390/biom10091249 ◽

2020 ◽

Vol 10 (9) ◽

pp. 1249

Author(s):

Kazuma Kobayashi ◽

Amina Bolatkan ◽

Shuichiro Shiina ◽

Ryuji Hamamoto

Keyword(s):

Lung Cancer ◽

Somatic Mutation ◽

Binary Classification ◽

Model Performance ◽

Data Matrix ◽

Training Dataset ◽

Mutation Profile ◽

Latent Representations ◽

Fully Connected ◽

The Relationship

Several challenges appear in the application of deep learning to genomic data. First, the dimensionality of input can be orders of magnitude greater than the number of samples, forcing the model to be prone to overfitting the training dataset. Second, each input variable’s contribution to the prediction is usually difficult to interpret, owing to multiple nonlinear operations. Third, genetic data features sometimes have no innate structure. To alleviate these problems, we propose a modification to Diet Networks by adding element-wise input scaling. The original Diet Networks concept can considerably reduce the number of parameters of the fully-connected layers by taking the transposed data matrix as an input to its auxiliary network. The efficacy of the proposed architecture was evaluated on a binary classification task for lung cancer histology, that is, adenocarcinoma or squamous cell carcinoma, from a somatic mutation profile. The dataset consisted of 950 cases, and 5-fold cross-validation was performed for evaluating the model performance. The model achieved a prediction accuracy of around 80% and showed that our modification markedly stabilized the learning process. Also, latent representations acquired inside the model allowed us to interpret the relationship between somatic mutation sites for the prediction.

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