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Exploring the classification of cancer cell lines from multiple omic views.

ABSTRACT: Background:Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial. Methods:In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, miRNA data, copy number variation data, DNA methylation data and reverse-phase protein array data were taken into the analysis. TumorMap web tool was used to illustrate the landscape of molecular classification.The molecular classification of patient samples was compared with cancer cell lines. Results:Eighteen molecular clusters were identified using integrated multiple omics clustering. Three pan-cancer clusters were found in integrated multiple omics clustering. By comparing with single omics clustering, we found that integrated clustering could capture both shared and complementary information from each omics data. Omics contribution analysis for clustering indicated that, although all the five omics data were of value, mRNA and proteomics data were particular important. While the classifications were generally consistent, samples from cancer patients were more diverse than cancer cell lines. Conclusions:The clustering analysis based on integrated omics data provides a novel multi-dimensional map of cancer cell lines that can reflect the extent to pan-cancer cell lines represent primary tumors, and an approach to evaluate the importance of omic features in cancer classification.

SUBMITTER: Yang X

PROVIDER: S-EPMC7441922 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Exploring the classification of cancer cell lines from multiple omic views.

Yang Xiaoxi X Wen Yuqi Y Song Xinyu X He Song S Bo Xiaochen X

PeerJ 20200818

<h4>Background</h4>Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial.<h4>Methods</h4>In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics ...[more]

PMID: 32874774

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Project description:BackgroundBreast cancer cell lines are frequently used as model systems to study the cellular properties and biology of breast cancer. Our objective was to characterize a large, commonly employed panel of breast cancer cell lines obtained from the American Type Culture Collection (ATCC 30-4500 K) to enable researchers to make more informed decisions in selecting cell lines for specific studies. Information about these cell lines was obtained from a wide variety of sources. In addition, new information about cellular pathways that are activated within each cell line was generated.MethodsWe determined key protein expression data using immunoblot analyses. In addition, two analyses on serum-starved cells were carried out to identify cellular proteins and pathways that are activated in these cells. These analyses were performed using a commercial PathScan array and a novel and more extensive phosphopeptide-based kinome analysis that queries 1290 phosphorylation events in major signaling pathways. Data about this panel of breast cancer cell lines was also accessed from several online sources, compiled and summarized for the following areas: molecular classification, mRNA expression, mutational status of key proteins and other possible cancer-associated mutations, and the tumorigenic and metastatic capacity in mouse xenograft models of breast cancer.ResultsThe cell lines that were characterized included 10 estrogen receptor (ER)-positive, 12 human epidermal growth factor receptor 2 (HER2)-amplified and 18 triple negative breast cancer cell lines, in addition to 4 non-tumorigenic breast cell lines. Within each subtype, there was significant genetic heterogeneity that could impact both the selection of model cell lines and the interpretation of the results obtained. To capture the net activation of key signaling pathways as a result of these mutational combinations, profiled pathway activation status was examined. This provided further clarity for which cell lines were particularly deregulated in common or unique ways.ConclusionsThese two new kinase or "Kin-OMIC" analyses add another dimension of important data about these frequently used breast cancer cell lines. This will assist researchers in selecting the most appropriate cell lines to use for breast cancer studies and provide context for the interpretation of the emerging results.

Project description:BackgroundHigh-throughput (omic) data have become more widespread in both quantity and frequency of use, thanks to technological advances, lower costs and higher precision. Consequently, computational scientists are confronted by two parallel challenges: on one side, the design of efficient methods to interpret each of these data in their own right (gene expression signatures, protein markers, etc.) and, on the other side, realization of a novel, pressing request from the biological field to design methodologies that allow for these data to be interpreted as a whole, i.e. not only as the union of relevant molecules in each of these layers, but as a complex molecular signature containing proteins, mRNAs and miRNAs, all of which must be directly associated in the results of analyses that are able to capture inter-layers connections and complexity.ResultsWe address the latter of these two challenges by testing an integrated approach on a known cancer benchmark: the NCI-60 cell panel. Here, high-throughput screens for mRNA, miRNA and proteins are jointly analyzed using factor analysis, combined with linear discriminant analysis, to identify the molecular characteristics of cancer. Comparisons with separate (non-joint) analyses show that the proposed integrated approach can uncover deeper and more precise biological information. In particular, the integrated approach gives a more complete picture of the set of miRNAs identified and the Wnt pathway, which represents an important surrogate marker of melanoma progression. We further test the approach on a more challenging patient-dataset, for which we are able to identify clinically relevant markers.ConclusionsThe integration of multiple layers of omics can bring more information than analysis of single layers alone. Using and expanding the proposed integrated framework to integrate omic data from other molecular levels will allow researchers to uncover further systemic information. The application of this approach to a clinically challenging dataset shows its promising potential.

Project description:BACKGROUND:Glucose regulated protein 78 (GRP78) is a resident chaperone of the endoplasmic reticulum and a master regulator of the unfolded protein response under physiological and pathological cell stress conditions. GRP78 is overexpressed in many cancers, regulating a variety of signaling pathways associated with tumor initiation, proliferation, adhesion and invasion which contributes to metastatic spread. GRP78 can also regulate cell survival and apoptotic pathways to alter responsiveness to anticancer drugs. Tumors that reside in or metastasize to the bone and bone marrow (BM) space can develop pro-survival signals through their direct adhesive interactions with stromal elements of this niche thereby resisting the cytotoxic effects of drug treatment. In this study, we report a direct correlation between GRP78 and the adhesion molecule N-cadherin (N-cad), known to play a critical role in the adhesive interactions of multiple myeloma and metastatic prostate cancer with the bone microenvironment. METHODS:N-cad expression levels (transcription and protein) were evaluated upon siRNA mediated silencing of GRP78 in the MM.1S multiple myeloma and the PC3 metastatic prostate cancer cell lines. Furthermore, we evaluated the effects of GRP78 knockdown (KD) on epithelial-mesenchymal (EMT) transition markers, morphological changes and adhesion of PC3 cells. RESULTS:GRP78 KD led to concomitant downregulation of N-cad in both tumors types. In PC3 cells, GRP78 KD significantly decreased E-cadherin (E-cad) expression likely associated with the induction in TGF-?1 expression. Furthermore, GRP78 KD also triggered drastic changes in PC3 cells morphology and decreased their adhesion to osteoblasts (OSB) dependent, in part, to the reduced N-cad expression. CONCLUSION:This work implicates GRP78 as a modulator of cell adhesion markers in MM and PCa. Our results may have clinical implications underscoring GRP78 as a potential therapeutic target to reduce the adhesive nature of metastatic tumors to the bone niche.

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Exploring the classification of cancer cell lines from multiple omic views.

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Exploring the classification of cancer cell lines from multiple omic views.

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