Correlation analysis connects cancer subtypes.
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ABSTRACT:
SUBMITTER: Lin P
PROVIDER: S-EPMC3704535 | biostudies-literature | 2013
REPOSITORIES: biostudies-literature
Publications
We provided a cross-tissue comparative analysis of between-subtype molecular commonality for ovarian cancer, breast cancer, hepatocellular carcinoma, glioma, lung squamous carcinoma and nasopharyngeal carcinoma. Our analysis showed that molecular subtypes with similar phenotype or similar clinical outcome could be correlated by their transcriptional profile and pathway profile. Pathway dysregulation across multiple cancer subtypes was also revealed by Gene Set Enrichment Analysis. Dysregulation ...[more]
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Project description:Pancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. Therefore, there is a need to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset, including proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis based on the proteomics data, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes. In addition, we explore the clinical characteristics of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways previously associated with pancreatic cancer are altered among both subtypes of pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes that can be leveraged to devise more effective treatments.
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Project description:MotivationMolecular subtyping by integrative modeling of multi-omics and clinical data can help the identification of robust and clinically actionable disease subgroups; an essential step in developing precision medicine approaches.ResultsWe developed a novel outcome-guided molecular subgrouping framework, called Deep Multi-Omics Integrative Subtyping by Maximizing Correlation (DeepMOIS-MC), for integrative learning from multi-omics data by maximizing correlation between all input -omics views. DeepMOIS-MC consists of two parts: clustering and classification. In the clustering part, the preprocessed high-dimensional multi-omics views are input into two-layer fully connected neural networks. The outputs of individual networks are subjected to Generalized Canonical Correlation Analysis loss to learn the shared representation. Next, the learned representation is filtered by a regression model to select features that are related to a covariate clinical variable, for example, a survival/outcome. The filtered features are used for clustering to determine the optimal cluster assignments. In the classification stage, the original feature matrix of one of the -omics view is scaled and discretized based on equal frequency binning, and then subjected to feature selection using RandomForest. Using these selected features, classification models (for example, XGBoost model) are built to predict the molecular subgroups that were identified at clustering stage. We applied DeepMOIS-MC on lung and liver cancers, using TCGA datasets. In comparative analysis, we found that DeepMOIS-MC outperformed traditional approaches in patient stratification. Finally, we validated the robustness and generalizability of the classification models on independent datasets. We anticipate that the DeepMOIS-MC can be adopted to many multi-omics integrative analyses tasks.Availability and implementationSource codes for PyTorch implementation of DGCCA and other DeepMOIS-MC modules are available at GitHub (https://github.com/duttaprat/DeepMOIS-MC).Supplementary informationSupplementary data are available at Bioinformatics Advances online.
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Project description:IntroductionSmall cell lung cancer (SCLC) has recently been characterized as heterogeneous tumors due to consensus nomenclature for distinct molecular subtypes on the basis of differential expression of four transcription markers (ASCL1, NEUROD1, POU2F3, and YAP1). It is necessary to validate molecular subtype classification in primary SCLC tumors by immunohistochemical (IHC) staining and investigate its relevance to survival outcomes.MethodsUsing a large number of surgically resected primary SCLC tumors, we assessed the mRNA and protein levels of the four subtype markers (ASCL1, NEUROD1, POU2F3 and YAP1) in two independent cohorts, respectively. Next, molecular subtypes defined by the four subtype markers was conducted to identify the association with clinicopathologic characteristics, survival outcomes, the expression of classic neuroendocrine markers, and molecules related to tumor immune microenvironment.ResultsSamples were categorized into four subtypes based on the relative expression levels of the four subtype markers, yielding to ASCL1, NEUROD1, POU2F3 and YAP1 subtypes, respectively. The combined neuroendocrine differentiation features were more prevalent in either ASCL1 or NEUROD1 subtypes. Kaplan-Meier analyses found that patients with tumors of the YAP1 subtype and ASCL1 subtype obtained the best and worst prognosis on both mRNA and IHC levels, respectively. Based on multivariate Cox proportional-hazards regression model, molecular subtype classification determined by IHC was identified as an independent indicator for survival outcomes in primary SCLC tumors. Correlation analyses indicated that the four subtype markers in SCLC cancer cells were interacted with its tumor immune microenvironment. Specifically, tumors positive for YAP1 was associated with fewer CTLA4+ T cell infiltration, while more immune-inhibitory receptors (FoxP3,PD1, and CTLA4) and fewer immune-promoting receptor (CD8) were found in tumors positive for ASCL1.ConclusionsWe validated the new molecular subtype classification and clinical relevance on both mRNA and protein levels from primary SCLC tumors. The molecular subtypes determined by IHC could be a pre-selected effective biomarker significantly influenced on prognosis in patients with SCLC, which warrants further studies to provide better preventative and therapeutic options for distinct molecular subtypes.
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Project description:BACKGROUND: Optimizing treatment through microarray-based molecular subtyping is a promising method to address the problem of heterogeneity in breast cancer; however, current application is restricted to prediction of distant recurrence risk. This study investigated whether breast cancer molecular subtyping according to its global intrinsic biology could be used for treatment customization. METHODS: Gene expression profiling was conducted on fresh frozen breast cancer tissue collected from 327 patients in conjunction with thoroughly documented clinical data. A method of molecular subtyping based on 783 probe-sets was established and validated. Statistical analysis was performed to correlate molecular subtypes with survival outcome and adjuvant chemotherapy regimens. Heterogeneity of molecular subtypes within groups sharing the same distant recurrence risk predicted by genes of the Oncotype and MammaPrint predictors was studied. RESULTS: We identified six molecular subtypes of breast cancer demonstrating distinctive molecular and clinical characteristics. These six subtypes showed similarities and significant differences from the Perou-Sørlie intrinsic types. Subtype I breast cancer was in concordance with chemosensitive basal-like intrinsic type. Adjuvant chemotherapy of lower intensity with CMF yielded survival outcome similar to those of CAF in this subtype. Subtype IV breast cancer was positive for ER with a full-range expression of HER2, responding poorly to CMF; however, this subtype showed excellent survival when treated with CAF. Reduced expression of a gene associated with methotrexate sensitivity in subtype IV was the likely reason for poor response to methotrexate. All subtype V breast cancer was positive for ER and had excellent long-term survival with hormonal therapy alone following surgery and/or radiation therapy. Adjuvant chemotherapy did not provide any survival benefit in early stages of subtype V patients. Subtype V was consistent with a unique subset of luminal A intrinsic type. When molecular subtypes were correlated with recurrence risk predicted by genes of Oncotype and MammaPrint predictors, a significant degree of heterogeneity within the same risk group was noted. This heterogeneity was distributed over several subtypes, suggesting that patients in the same risk groups require different treatment approaches. CONCLUSIONS: Our results indicate that the molecular subtypes established in this study can be utilized for customization of breast cancer treatment.
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