Quantitative Proteomics of the Cancer Cell Line Encyclopedia.

Proteins are essential agents of biological processes. To date, large-scale profiling of cell line collections including the Cancer Cell Line Encyclopedia (CCLE) has focused primarily on genetic information whereas deep interrogation of the proteome has remained out of reach. Here, we expand the CCLE through quantitative profiling of thousands of proteins by mass spectrometry across 375 cell lines from diverse lineages to reveal information undiscovered by DNA and RNA methods. We observe unexpected correlations within and between pathways that are largely absent from RNA. An analysis of microsatellite instable (MSI) cell lines reveals the dysregulation of specific protein complexes associated with surveillance of mutation and translation. These and other protein complexes were associated with sensitivity to knockdown of several different genes. These data in conjunction with the wider CCLE are a broad resource to explore cellular behavior and facilitate cancer research.

Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening.

Elucidation of the mutational landscape of human cancer has progressed rapidly and been accompanied by the development of therapeutics targeting mutant oncogenes. However, a comprehensive mapping of cancer dependencies has lagged behind and the discovery of therapeutic targets for counteracting tumor suppressor gene loss is needed. To identify vulnerabilities relevant to specific cancer subtypes, we conducted a large-scale RNAi screen in which viability effects of mRNA knockdown were assessed for 7,837 genes using an average of 20 shRNAs per gene in 398 cancer cell lines. We describe findings of this screen, outlining the classes of cancer dependency genes and their relationships to genetic, expression, and lineage features. In addition, we describe robust gene-interaction networks recapitulating both protein complexes and functional cooperation among complexes and pathways. This dataset along with a web portal is provided to the community to assist in the discovery and translation of new therapeutic approaches for cancer.

Proteome-wide copy-number estimation from transcriptomics.

Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein's median copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26-29% of luminal tumors. By adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics.

MCAM abnormal expression and clinical outcome associations are highly cancer dependent as revealed through pan-cancer analysis.

MCAM (CD146) is a cell surface adhesion molecule that has been reported to promote cancer development, progression and metastasis and is considered as a potential tumor biomarker and therapeutic target. However, inconsistent reports exist, and its clinical value is yet to be confirmed. Here we took advantage of several large genomic data collections (Genotype-Tissue Expression, The Cancer Genome Atlas and Cancer Cell Line Encyclopedia) and comprehensively analyzed MCAM expression in thousands of normal and cancer samples and cell lines along with their clinical phenotypes and drug response information. Our results show that MCAM is very highly expressed in large vessel tissues while majority of tissues have low or minimal expression. Its expression is dramatically increased in a few tumors but significantly decreased in most other tumors relative to their pairing normal tissues. Increased MCAM expression is associated with a higher tumor stage and worse patient survival for some less common tumors but not for major ones. Higher MCAM expression in primary tumors may be complicated by tumor-associated or normal stromal blood vessels yet its significance may differ from the one from cancer cells. MCAM expression is weakly associated with the response to a few small molecular drugs and the association with targeted anti-BRAF agents suggests its involvement in that pathway which warrants further investigation.

Expression of CD44 Isoforms in Tumor Samples and Cell Lines of Human Colorectal Cancer.

Detection of colorectal cancer biomarkers (CRC) remains an urgent task for the diagnosis and prediction of the disease course. A promising approach is the study of cancer stem cell markers. The cell surface glycoprotein CD44 is very important for CRC and its stem cells. Alternative splicing of 9 variable exons of CD44 mRNA leads to the formation of various isoforms of the protein with different roles in the progression of cancer. Studies of the functions of CD44 isoforms require adequate models considering the distribution of CD44 isoforms in real tumor samples. In the present study, the expression profile of CD44 isoforms in CRC was assessed based on the publicly available mRNA sequencing data of patient tumors from the TCGA-COAD database. It was shown that normal tissues predominantly expressed isoforms 3 and 4 at nearly equal levels, whereas tumors mainly expressed isoforms 2, 3, and 4; isoform 3 was expressed at the highest level. Further, the most relevant cell lines for studying the role of CD44 in CRC were identified based on the analysis of mRNA sequencing data of 55 CRC cell lines form CCLE database.

SVExpress: identifying gene features altered recurrently in expression with nearby structural variant breakpoints.

BACKGROUND: Combined whole-genome sequencing (WGS) and RNA sequencing of cancers offer the opportunity to identify genes with altered expression due to genomic rearrangements. Somatic structural variants (SVs), as identified by WGS, can involve altered gene cis-regulation, gene fusions, copy number alterations, or gene disruption. The absence of computational tools to streamline integrative analysis steps may represent a barrier in identifying genes recurrently altered by genomic rearrangement. RESULTS: Here, we introduce SVExpress, a set of tools for carrying out integrative analysis of SV and gene expression data. SVExpress enables systematic cataloging of genes that consistently show increased or decreased expression in conjunction with the presence of nearby SV breakpoints. SVExpress can evaluate breakpoints in proximity to genes for potential enhancer translocation events or disruption of topologically associated domains, two mechanisms by which SVs may deregulate genes. The output from any commonly used SV calling algorithm may be easily adapted for use with SVExpress. SVExpress can readily analyze genomic datasets involving hundreds of cancer sample profiles. Here, we used SVExpress to analyze SV and expression data across 327 cancer cell lines with combined SV and expression data in the Cancer Cell Line Encyclopedia (CCLE). In the CCLE dataset, hundreds of genes showed altered gene expression in relation to nearby SV breakpoints. Altered genes involved TAD disruption, enhancer hijacking, and gene fusions. When comparing the top set of SV-altered genes from cancer cell lines with the top SV-altered genes previously reported for human tumors from The Cancer Genome Atlas and the Pan-Cancer Analysis of Whole Genomes datasets, a significant number of genes overlapped in the same direction for both cell lines and tumors, while some genes were significant for cell lines but not for human tumors and vice versa. CONCLUSION: Our SVExpress tools allow computational biologists with a working knowledge of R to integrate gene expression with SV breakpoint data to identify recurrently altered genes. SVExpress is freely available for academic or commercial use at https://github.com/chadcreighton/SVExpress . SVExpress is implemented as a set of Excel macros and R code. All source code (R and Visual Basic for Applications) is available.

Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines.

BACKGROUND: Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients' care. Tremendous progress has been made. RESULTS: In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data ( https://manticore.niehs.nih.gov/cancerRxTissue ). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. CONCLUSIONS: We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

Iterative sure independent ranking and screening for drug response prediction.

BACKGROUND: Prediction of drug response based on multi-omics data is a crucial task in the research of personalized cancer therapy. RESULTS: We proposed an iterative sure independent ranking and screening (ISIRS) scheme to select drug response-associated features and applied it to the Cancer Cell Line Encyclopedia (CCLE) dataset. For each drug in CCLE, we incorporated multi-omics data including copy number alterations, mutation and gene expression and selected up to 50 features using ISIRS. Then a linear regression model based on the selected features was exploited to predict the drug response. Cross validation test shows that our prediction accuracies are higher than existing methods for most drugs. CONCLUSIONS: Our study indicates that the features selected by the marginal utility measure, which measures the conditional probability of drug responses given the feature, are helpful for drug response prediction.

Application of transfer learning for cancer drug sensitivity prediction.

BACKGROUND: In precision medicine, scarcity of suitable biological data often hinders the design of an appropriate predictive model. In this regard, large scale pharmacogenomics studies, like CCLE and GDSC hold the promise to mitigate the issue. However, one cannot directly employ data from multiple sources together due to the existing distribution shift in data. One way to solve this problem is to utilize the transfer learning methodologies tailored to fit in this specific context. RESULTS: In this paper, we present two novel approaches for incorporating information from a secondary database for improving the prediction in a target database. The first approach is based on latent variable cost optimization and the second approach considers polynomial mapping between the two databases. Utilizing CCLE and GDSC databases, we illustrate that the proposed approaches accomplish a better prediction of drug sensitivities for different scenarios as compared to the existing approaches. CONCLUSION: We have compared the performance of the proposed predictive models with database-specific individual models as well as existing transfer learning approaches. We note that our proposed approaches exhibit superior performance compared to the abovementioned alternative techniques for predicting sensitivity for different anti-cancer compounds, particularly the nonlinear mapping model shows the best overall performance.

Integrative analysis of the cancer genome atlas and cancer cell lines encyclopedia large-scale genomic databases: MUC4/MUC16/MUC20 signature is associated with poor survival in human carcinomas.

BACKGROUND: MUC4 is a membrane-bound mucin that promotes carcinogenetic progression and is often proposed as a promising biomarker for various carcinomas. In this manuscript, we analyzed large scale genomic datasets in order to evaluate MUC4 expression, identify genes that are correlated with MUC4 and propose new signatures as a prognostic marker of epithelial cancers. METHODS: Using cBioportal or SurvExpress tools, we studied MUC4 expression in large-scale genomic public datasets of human cancer (the cancer genome atlas, TCGA) and cancer cell line encyclopedia (CCLE). RESULTS: We identified 187 co-expressed genes for which the expression is correlated with MUC4 expression. Gene ontology analysis showed they are notably involved in cell adhesion, cell-cell junctions, glycosylation and cell signaling. In addition, we showed that MUC4 expression is correlated with MUC16 and MUC20, two other membrane-bound mucins. We showed that MUC4 expression is associated with a poorer overall survival in TCGA cancers with different localizations including pancreatic cancer, bladder cancer, colon cancer, lung adenocarcinoma, lung squamous adenocarcinoma, skin cancer and stomach cancer. We showed that the combination of MUC4, MUC16 and MUC20 signature is associated with statistically significant reduced overall survival and increased hazard ratio in pancreatic, colon and stomach cancer. CONCLUSIONS: Altogether, this study provides the link between (i) MUC4 expression and clinical outcome in cancer and (ii) MUC4 expression and correlated genes involved in cell adhesion, cell-cell junctions, glycosylation and cell signaling. We propose the MUC4/MUC16/MUC20high signature as a marker of poor prognostic for pancreatic, colon and stomach cancers.

Relating pancreatic ductal adenocarcinoma tumor samples and cell lines using gene expression data in translational research.

Cancer cell lines are useful tools to study cancer biology. Choosing proper cell lines based on experimental design for different experiments is vital. Relating tumors and cell lines, and recognizing their similarities and differences are thus very important for translational research. Abundant online databases with genomic and expression profile are suitable resources for conducting the assessment. Pancreatic ductal adenocarcinoma (PDAC) is a severe cancer with grim prognosis. Current effective treatments of PDAC remain limited. In this study, we compared the gene expression profile of 178 PDAC tumor samples from The Cancer Genome Atlas and 44 pancreatic cancer cell lines from Cancer Cell Line Encyclopedia. We showed that all pancreatic cancer cell lines resemble PDAC tumors but the correlation is different. Our study will be used to guide the selection of PDAC cell lines.

Detection of homozygous deletions in tumor-suppressor genes ranging from dozen to hundreds nucleotides in cancer models.

Tumor-suppressor genes can be inactivated by several mechanisms and, in a majority of cases, both alleles need to be affected. One of the mechanisms of inactivation is due to deletions ranging from dozen to hundreds of nucleotides; such deletions are often missed by variant callers. HomDelDetect is a method to detect such homozygous deletions in cancer models, such as cancer cell lines and potentially patient tumor-derived xenografts. This method can be applied to partial exome, whole-exome sequencing, whole-genome sequencing, and RNA-seq data. We applied our method across a panel of CCLE cancer cell lines and observed good concordance with SNP array-based analysis and also detected deletions that have been missed by variant callers and by SNP arrays, demonstrating the ability of HomDelDetect to improve the annotations of tumor-suppressor genes in cancer models.

Enhanced Missing Proteins Detection in NCI60 Cell Lines Using an Integrative Search Engine Approach.

The Human Proteome Project (HPP) aims deciphering the complete map of the human proteome. In the past few years, significant efforts of the HPP teams have been dedicated to the experimental detection of the missing proteins, which lack reliable mass spectrometry evidence of their existence. In this endeavor, an in depth analysis of shotgun experiments might represent a valuable resource to select a biological matrix in design validation experiments. In this work, we used all the proteomic experiments from the NCI60 cell lines and applied an integrative approach based on the results obtained from Comet, Mascot, OMSSA, and X!Tandem. This workflow benefits from the complementarity of these search engines to increase the proteome coverage. Five missing proteins C-HPP guidelines compliant were identified, although further validation is needed. Moreover, 165 missing proteins were detected with only one unique peptide, and their functional analysis supported their participation in cellular pathways as was also proposed in other studies. Finally, we performed a combined analysis of the gene expression levels and the proteomic identifications from the common cell lines between the NCI60 and the CCLE project to suggest alternatives for further validation of missing protein observations.

Trigger factors of cutaneous lupus erythematosus: a review of current literature.

It is currently believed that autoimmune conditions are triggered and aggravated by a variety of environmental factors such as cigarette smoking, infections, ultraviolet light or chemicals, as well as certain medications and vaccines in genetically susceptible individuals. Recent scientific data have suggested a relevant role of these factors not only in systemic lupus erythematosus, but also in cutaneous lupus erythematosus (CLE). A variety of environmental factors have been proposed as initiators and exacerbators of this disease. In this review we focused on those with the most convincing evidence, emphasizing the role of drugs in CLE. Using a combined search strategy of the MEDLINE and CINAHL databases the following trigger factors and/or exacerbators of CLE have been identified and described: drugs, smoking, neoplasms, ultraviolet radiation and radiotherapy. In order to give a practical insight we emphasized the role of drugs from various groups and classes in CLE. We also aimed to present a short clinical profile of patients with lesions induced by various drug classes.

Treatment of cutaneous lupus erythematosus: current practice variations.

The treatment of cutaneous lupus erythematous (CLE) remains a challenge. Most of the therapeutic options used in CLE have not been tested in randomized controlled studies and to date no agent has been approved. Therefore, CLE treatment is mostly based on personal experience. To better characterize therapeutic habits among physicians treating CLE patients, a questionnaire-based study about various aspects of topical and systemic treatment for CLE has been performed. The questionnaire was distributed among CLE experts, mostly from Japan, the USA, and Europe. A total of 82 completed questionnaires were assessed. High-potent and potent corticosteroids as well as calcineurin inhibitors were the most often recommended topical treatment for all CLE subtypes. The most relevant factors for initiation of systemic therapy were severity of skin lesions, concomitant involvement of internal organs, CLE subtype and lack of response to topical therapies. Corticosteroids and antimalarials were considered as the most suitable and effective systemic drugs for CLE patients. However, significant differences were observed between various CLE subtypes and between different countries regarding the assessment of various topical and systemic treatment options. In conclusion, great variability of obtained answers underlines the need of development of CLE treatment guidelines suitable for different disease subtypes.

Cutaneous sarcoidosis masquerading as chronic cutaneous lupus erythematosus - case report.

BACKGROUND: Sarcoidosis is a multisystemic granulomatous disease of unknown origin. Chronic cutaneous lupus erythematosus (CCLE) is an autoimmune disease that is associated with autoantibody production and T-cell dysfunction. Cutaneous manifestations of sarcoidosis may mimic CCLE and vice versa making it difficult to reach a diagnosis clinically. CASE PRESENTATION: We present a case of a 57-year-old woman with long-standing sarcoidosis who presented to clinic with diffuse painful plaques that were very distinct and suggestive of CCLE. She had a family history of both sarcoidosis and CCLE. The patient was immediately started on topical corticosteroids and oral hydroxychloroquine. Skin biopsy and the absence of direct immunofluorescence confirmed a skin manifestation of her previously diagnosed sarcoidosis, despite the clinical morphology favoring classic CCLE. CONCLUSION: Sarcoidosis may have diverse manifestations and may mimic other disease processes. A detailed history along with a low threshold for biopsy is important for determining a diagnosis.

Tumid Lupus Erythematosus (TLE): A Review of a Rare Variant of Chronic Cutaneous Lupus Erythematosus (cCLE) with Emphasis on Differential Diagnosis.

Tumid lupus erythematosus (TLE) has been the subject of heated debate regarding its correct nosographic classification. The definition of TLE has changed over time, varying according to the different studies performed. In this review, we address the initial definition of TLE, the changes that have taken place in the understanding of TLE, and its placement within the classification of cutaneous lupus erythematosus (CLE), with a focus on clinical, histopathological, immunophenotypical, and differential diagnosis aspects.

Gene Expression and Drug Sensitivity Analysis of Mitochondrial Chaperones Reveals That HSPD1 and TRAP1 Expression Correlates with Sensitivity to Inhibitors of DNA Replication and Mitosis.

Mitochondria-critical metabolic hubs in eukaryotic cells-are involved in a wide range of cellular functions, including differentiation, proliferation, and death. Mitochondria import most of their proteins from the cytosol in a linear form, after which they are folded by mitochondrial chaperones. However, despite extensive research, the extent to which the function of particular chaperones is essential for maintaining specific mitochondrial and cellular functions remains unknown. In particular, it is not known whether mitochondrial chaperones influence the sensitivity to drugs used in the treatment of cancers. By mining gene expression and drug sensitivity data for cancer cell lines from publicly available databases, we identified mitochondrial chaperones whose expression is associated with sensitivity to oncology drugs targeting particular cellular pathways in a cancer-type-dependent manner. Importantly, we found the expression of TRAP1 and HSPD1 to be associated with sensitivity to inhibitors of DNA replication and mitosis. We confirmed experimentally that the expression of HSPD1 is associated with an increased sensitivity of ovarian cancer cells to drugs targeting mitosis and a reduced sensitivity to drugs promoting apoptosis. Taken together, our results support a model in which particular mitochondrial pathways hinge upon specific mitochondrial chaperones and provide the basis for understanding selectivity in mitochondrial chaperone-substrate specificity.

Prediction of CCLE Dataset Based on Integrated SVM Method.

In this paper, we focus on the prediction and analysis of biogenetic data with high complexity by building integrated SVM models. Considering the complexity and high dimension of data set, we adopt the integration method based on sample segmentation to build the model. The results of the CCLE data analysis show that the model we used has better prediction results and smaller prediction variance than the generalized linear model, the integrated generalized linear model, and the original SVM model.

Proteome-wide copy-number estimation from transcriptomics.

Protein copy numbers constrain systems-level properties of regulatory networks, but absolute proteomic data remain scarce compared to transcriptomics obtained by RNA sequencing. We addressed this persistent gap by relating mRNA to protein statistically using best-available data from quantitative proteomics-transcriptomics for 4366 genes in 369 cell lines. The approach starts with a central estimate of protein copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model that links mRNAs to protein. For dozens of independent cell lines and primary prostate samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, and empirical protein-to-mRNA ratios. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein interaction complexes, suggesting mechanistic relationships are embedded. We use the method to estimate viral-receptor abundances of CD55-CXADR from human heart transcriptomes and build 1489 systems-biology models of coxsackievirus B3 infection susceptibility. When applied to 796 RNA sequencing profiles of breast cancer from The Cancer Genome Atlas, inferred copy-number estimates collectively reclassify 26% of Luminal A and 29% of Luminal B tumors. Protein-based reassignments strongly involve a pharmacologic target for luminal breast cancer (CDK4) and an α-catenin that is often undetectable at the mRNA level (CTTNA2). Thus, by adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility limits of contemporary proteomics. The collection of gene-specific models is assembled as a web tool for users seeking mRNA-guided predictions of absolute protein abundance (http://janeslab.shinyapps.io/Pinferna).