Identifying Cancer Subtypes based on Somatic Mutation Profile

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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Computational Disease Subtyping based on Joint Analysis of Clinical and Genomic Data

10.1101/307751 ◽

2018 ◽

Author(s):

Diana Diaz ◽

Aliccia Bollig-Fischer ◽

Alexander Kotov

Keyword(s):

Breast Cancer ◽

Somatic Mutation ◽

Patient Survival ◽

Cox Model ◽

Genomic Data ◽

Cox Proportional Hazards Model ◽

The Cancer Genome Atlas ◽

Breast Cancer Subtypes ◽

Joint Analysis ◽

Cancer Subtypes

ABSTRACTObjectiveTo investigate application of non-negative tensor decomposition for disease subtype discovery based on joint analysis of clinical and genomic data.Data and MethodsSomatic mutation profiles including 11,996 genes of 503 breast cancer patients from the Cancer Genome Atlas (TCGA) along with 11 clinical variables and markers of these patients were used to construct a binary third-order tensor. CANDECOMP/PARAFAC method was applied to decompose the constructed tensor into rank-one component tensors. Definitions of breast cancer verotypes were constructed from the patient, gene and clinical vectors corresponding to each component tensor. Patient membership proportions in the identified verotypes were utilized in a Cox proportional hazards model to predict their survival.ResultsQualitative evaluation of the verotypes obtained by tensor factorization indicates that they correspond to clinically meaningful breast cancer subtypes. While some components correspond to the known HER2- or ER-positive breast cancer subtypes, other components correspond to a variant of triple negative subtype and a cohort of patients with high mutation load of tumor suppressor genes. Quantitative evaluation indicates that the Cox model utilizing computationally discovered breast cancer verotypes is more accurate (AUC=0.5796) at predicting patient survival than the Cox models utilizing random patient membership proportions in cancer subtypes (AUC=0.4056) as well as patient membership proportions in genotypes (AUC=0.4731) and phenotypes (AUC=0.5047) obtained by non-negative factorization of the somatic mutation and clinical matrices.ConclusionNon-negative factorization of a binary tensor constructed from clinical and genomic data enables high-throughput discovery of breast cancer verotypes that are effective at predicting patient survival.

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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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Cancer subtype identification using somatic mutation data

10.1101/228031 ◽

2017 ◽

Cited By ~ 1

Author(s):

Marieke L. Kuijjer ◽

Joseph N. Paulson ◽

Peter Salzman ◽

Wei Ding ◽

John Quackenbush

Keyword(s):

Somatic Mutation ◽

Treatment Options ◽

Biological Pathways ◽

The Cancer Genome Atlas ◽

Phenotypic Data ◽

Primary Tumors ◽

Cancer Subtypes ◽

Cancer Types ◽

Mutation Data ◽

Pan Cancer

BACKGROUNDWith the onset of next generation sequencing technologies, we have made great progress in identifying recurrent mutational drivers of cancer. As cancer tissues are now frequently screened for specific sets of mutations, a large amount of samples has become available for analysis. Classification of patients with similar mutation profiles may help identifying subgroups of patients who might benefit from specific types of treatment. However, classification based on somatic mutations is challenging due to the sparseness and heterogeneity of the data.METHODSHere, we describe a new method to de-sparsify somatic mutation data using biological pathways. We applied this method to 23 cancer types from The Cancer Genome Atlas, including samples from 5, 805 primary tumors.RESULTSWe show that, for most cancer types, de-sparsified mutation data associates with phenotypic data. We identify poor prognostic subtypes in three cancer types, which are associated with mutations in signal transduction pathways for which targeted treatment options are available. We identify subtype-drug associations for 14 additional subtypes. Finally, we perform a pan-cancer subtyping analysis and identify nine pan-cancer subtypes, which associate with mutations in four overarching sets of biological pathways.CONCLUSIONSThis study is an important step towards understanding mutational patterns in cancer.

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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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