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

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.

scholarly journals Comprehensive Analysis of the Unfolded Protein Response in Breast Cancer Subtypes

2017 ◽  
pp. 1-9 ◽  
Author(s):  
Dadi Jiang ◽  
Brandon Turner ◽  
Jie Song ◽  
Ruijiang Li ◽  
Maximilian Diehn ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Patient Survival ◽  
Gene Signature ◽  
Comprehensive Analysis ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Unfolded Protein ◽  
The Unfolded Protein Response ◽  
Protein Response

Purpose Triple-negative breast cancers (TNBCs) are associated with a worse prognosis and patients with TNBC have fewer therapeutic options than patients with non-TNBC. Recently, the IRE1α-XBP1 branch of the unfolded protein response (UPR) was implicated in TNBC prognosis on the basis of a relatively small patient population, suggesting the diagnostic and therapeutic value of this pathway in TNBCs. In addition, the IRE1α-XBP1 and hypoxia-induced factor 1 α (HIF1α) pathways have been identified as interacting partners in TNBC, suggesting a novel mechanism of regulation. To comprehensively evaluate and validate these findings, we investigated the relative activities and relevance to patient survival of the UPR and HIF1α pathways in different breast cancer subtypes in large populations of patients. Materials and Methods We performed a comprehensive analysis of gene expression and survival data from large cohorts of patients with breast cancer. The patients were stratified based on the average expression of the UPR or HIF1α gene signatures. Results We identified a strong positive association between the XBP1 gene signature and estrogen receptor–positive status or the HIF1α gene signature, as well as the predictive value of the XBP1 gene signature for survival of patients who are estrogen receptor negative, or have TNBC or HER2+. In contrast, another important UPR branch, the ATF4/CHOP pathway, lacks prognostic value in breast cancer in general. Activity of the HIF1α pathway is correlated with patient survival in all the subtypes evaluated. Conclusion These findings clarify the relevance of the UPR pathways in different breast cancer subtypes and underscore the potential therapeutic importance of the IRE1α-XBP1 branch in breast cancer treatment.

scholarly journals Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data

Genes ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 888
Author(s):  
Yuqi Lin ◽  
Wen Zhang ◽  
Huanshen Cao ◽  
Gaoyang Li ◽  
Wei Du
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Feature Selection ◽  
Deep Neural Networks ◽  
Selection Process ◽  
Breast Cancer Subtype ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Subtypes ◽  
Omics Data ◽  
Cancer Subtypes

With the high prevalence of breast cancer, it is urgent to find out the intrinsic difference between various subtypes, so as to infer the underlying mechanisms. Given the available multi-omics data, their proper integration can improve the accuracy of breast cancer subtype recognition. In this study, DeepMO, a model using deep neural networks based on multi-omics data, was employed for classifying breast cancer subtypes. Three types of omics data including mRNA data, DNA methylation data, and copy number variation (CNV) data were collected from The Cancer Genome Atlas (TCGA). After data preprocessing and feature selection, each type of omics data was input into the deep neural network, which consists of an encoding subnetwork and a classification subnetwork. The results of DeepMO based on multi-omics on binary classification are better than other methods in terms of accuracy and area under the curve (AUC). Moreover, compared with other methods using single omics data and multi-omics data, DeepMO also had a higher prediction accuracy on multi-classification. We also validated the effect of feature selection on DeepMO. Finally, we analyzed the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which were discovered during the feature selection process. We believe that the proposed model is useful for multi-omics data analysis.

scholarly journals Deep-Learning–Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data

2020 ◽  
pp. 480-490 ◽  
Author(s):  
Zixiao Lu ◽  
Siwen Xu ◽  
Wei Shao ◽  
Yi Wu ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Strong Association ◽  
Tumor Infiltrating Lymphocytes ◽  
Genetic Alterations ◽  
Image Features ◽  
Cancer Immunology ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Infiltrating Lymphocytes

PURPOSE Tumor-infiltrating lymphocytes (TILs) and their spatial characterizations on whole-slide images (WSIs) of histopathology sections have become crucial in diagnosis, prognosis, and treatment response prediction for different cancers. However, fully automatic assessment of TILs on WSIs currently remains a great challenge because of the heterogeneity and large size of WSIs. We present an automatic pipeline based on a cascade-training U-net to generate high-resolution TIL maps on WSIs. METHODS We present global cell-level TIL maps and 43 quantitative TIL spatial image features for 1,000 WSIs of The Cancer Genome Atlas patients with breast cancer. For more specific analysis, all the patients were divided into three subtypes, namely, estrogen receptor (ER)–positive, ER-negative, and triple-negative groups. The associations between TIL scores and gene expression and somatic mutation were examined separately in three breast cancer subtypes. Both univariate and multivariate survival analyses were performed on 43 TIL image features to examine the prognostic value of TIL spatial patterns in different breast cancer subtypes. RESULTS The TIL score was in strong association with immune response pathway and genes (eg, programmed death-1 and CLTA4). Different breast cancer subtypes showed TIL score in association with mutations from different genes suggesting that different genetic alterations may lead to similar phenotypes. Spatial TIL features that represent density and distribution of TIL clusters were important indicators of the patient outcomes. CONCLUSION Our pipeline can facilitate computational pathology-based discovery in cancer immunology and research on immunotherapy. Our analysis results are available for the research community to generate new hypotheses and insights on breast cancer immunology and development.

scholarly journals Uncovering the Subtype-Specific Molecular Characteristics of Breast Cancer by Multiomics Analysis of Prognosis-Associated Genes, Driver Genes, Signaling Pathways, and Immune Activity

2021 ◽  
Vol 9 ◽  
Author(s):  
Xinhui Li ◽  
Jian Zhou ◽  
Mingming Xiao ◽  
Lingyu Zhao ◽  
Yan Zhao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Signaling Pathways ◽  
Immune Cell ◽  
Copy Number Variations ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Subtypes ◽  
Molecular Characteristics ◽  
Driver Genes ◽  
Cancer Subtypes ◽  
Molecular Events

Breast cancer is a heterogeneous malignant disease with different prognoses and has been divided into four molecular subtypes. It is believed that molecular events occurring in breast stem/progenitor cells contribute to the carcinogenesis and development of different breast cancer subtypes. However, these subtype-specific molecular characteristics are largely unknown. In this study, we employed 1217 breast cancer samples from The Cancer Genome Atlas (TCGA) database for a multiomics analysis of the molecular characteristics of different breast cancer subtypes based on PAM50 algorithms. We detected the expression changes of subtype-specific genes and revealed that the expression of particular subtype-specific genes significantly affected prognosis. We also investigated the mutations and copy number variations (CNVs) of breast cancer driver genes and the representative genes of ten signaling pathways in different subtypes and revealed several subtype-specifically altered genes. Moreover, we detected the infiltration of various immune cells in different subtypes of breast cancer and showed that the infiltration levels of major immune cell types are different among these subtypes. Additionally, we investigated the factors affecting the immune infiltration level and the immune cytolytic activity in different breast cancer subtypes, namely, the mutation burden, genome instability and cancer-associated fibroblast (CAF) infiltration. This study may shed light on the molecular events contributing to carcinogenesis and development and provide potential markers and targets for the clinical diagnosis and treatment of different breast cancer subtypes.

Identification of new therapeutic leads for triple negative breast cancer subtypes

Planta Medica ◽  
2015 ◽  
Vol 81 (11) ◽  
Author(s):  
AJ Robles ◽  
L Du ◽  
S Cai ◽  
RH Cichewicz ◽  
SL Mooberry
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Therapeutic Leads

scholarly journals DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

2021 ◽  
Vol 28 ◽  
pp. 107327482098851
Author(s):  
Zeng-Hong Wu ◽  
Yun Tang ◽  
Yan Zhou
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Methylation Status ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Consensus Clustering ◽  
Breast Cancer Patients ◽  
Cancer Subtypes ◽  
Novel Biomarkers

Background: Epigenetic changes are tightly linked to tumorigenesis development and malignant transformation’ However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells. Methods: In this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA). Results: Seven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups. Conclusions: The model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.

scholarly journals Clinical Significance of Annexin A2 Expression in Breast Cancer Patients

Cancers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 2
Author(s):  
Lee D. Gibbs ◽  
Kelsey Mansheim ◽  
Sayantan Maji ◽  
Rajesh Nandy ◽  
Cheryl M. Lewis ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Clinical Significance ◽  
Cell Lines ◽  
Breast Cancer Subtypes ◽  
Enzyme Linked Immunosorbent Assay ◽  
Breast Cancer Patients ◽  
Serum Samples ◽  
Cancer Subtypes ◽  
Tumor Tissues

Increasing evidence suggests that AnxA2 contributes to invasion and metastasis of breast cancer. However, the clinical significance of AnxA2 expression in breast cancer has not been reported. The expression of AnxA2 in cell lines, tumor tissues, and serum samples of breast cancer patients were analyzed by immunoblotting, immunohistochemistry, and enzyme-linked immunosorbent assay, respectively. We found that AnxA2 was significantly upregulated in tumor tissues and serum samples of breast cancer patients compared with normal controls. The high expression of serum AnxA2 was significantly associated with tumor grades and poor survival of the breast cancer patients. Based on molecular subtypes, AnxA2 expression was significantly elevated in tumor tissues and serum samples of triple-negative breast cancer (TNBC) patients compared with other breast cancer subtypes. Our analyses on breast cancer cell lines demonstrated that secretion of AnxA2 is associated with its tyrosine 23 (Tyr23) phosphorylation in cells. The expression of non-phosphomimetic mutant of AnxA2 in HCC1395 cells inhibits its secretion from cells compared to wild-type AnxA2, which further suggest that Tyr23 phosphorylation is a critical step for AnxA2 secretion from TNBC cells. Our analysis of AnxA2 phosphorylation in clinical samples further confirmed that the phosphorylation of AnxA2 at Tyr23 was high in tumor tissues of TNBC patients compared to matched adjacent non-tumorigenic breast tissues. Furthermore, we observed that the diagnostic value of serum AnxA2 was significantly high in TNBC compared with other breast cancer subtypes. These findings suggest that serum AnxA2 concentration could be a potential diagnostic biomarker for TNBC patients.

Sign in / Sign up

Export Citation Format

Share Document