Analysis of cancer cell line and tissue RNA sequencing data reveals an essential and dark matrisome.

Extracellular matrix remodeling is a hallmark of tissue development, homeostasis, and disease. The processes that mediate remodeling, and the consequences of such, are the topic of extensive focus in biomedical research. Cell culture methods represent a crucial tool utilized by those interested in matrisome function, the easiest of which are implemented with immortalized/cancer cell lines. These cell lines often form the foundations of a research proposal, or serve as vehicles of validation for other model systems. For these reasons, it is important to understand the complement of matrisome genes that are expressed when identifying appropriate cell culture models for hypothesis testing. To this end, we harvested bulk RNA sequencing data from the Cancer Cell Line Encyclopedia (CCLE) to assess matrisome gene expression in 1019 human cell lines. Our examination reveals that a large proportion of the matrisome is poorly represented in human cancer cell lines, with approximately 10% not expressed above threshold in any of the cell lines assayed. Conversely, we identify clusters of essential/common matrisome genes that are abundantly expressed in cell lines. To validate these observations against tissue data, we compared our findings with bulk RNA sequencing data from the Genotype-Tissue Expression (GTEx) portal and The Cancer Genome Atlas (TCGA) program. This comparison demonstrates general agreement between the "essential/common" and "dark/uncommon" matrisome across the three datasets, albeit with discordance observed in 59 matrisome genes between cell lines and tissues. Notably, all of the discordant genes are essential/common in tissues yet minimally expressed in cell lines, underscoring critical considerations for matrix biology researchers employing immortalized cell lines for their investigations.

Identification of SHCBP1 as a potential biomarker involving diagnosis, prognosis, and tumor immune microenvironment across multiple cancers.

Shc SH2-domain binding protein 1 (SHCBP1), a protein specific binding to SH2 domain of Src homolog and collagen homolog (Shc), takes part in the regulation of various signal transduction pathways, which has been reported to be associated with tumorigenesis and progression. However, the pathological mechanisms are not completely investigated. Thus, this study aimed to comprehensively elucidate the potential functions of SHCBP1 in multiple cancer types. The comprehensive analyses for SHCBP1 in various tumors, including gene expression, diagnosis, prognosis, immune-related features, genetic alteration, and function enrichment, were conducted based on multiple databases and analysis tools. SHCBP1 was upregulated in most types of cancers. The results of qRT-PCR had confirmed that SHCBP1 mRNA was significantly upregulated in lung adenocarcinoma (LUAD) and liver hepatocellular carcinoma (LIHC) cell lines. Based on the receiver operating characteristic (ROC) and survival analysis, SHCBP1 was considered as a potential diagnostic and prognostic biomarker. Furthermore, SHCBP1 expression was linked with tumor immunity and immunosuppressive microenvironment according to the correlation analysis of SHCBP1 expression with immune cells infiltration, immune checkpoint genes, and immune-related genes (MHC genes, chemokines, and chemokines receptors). Moreover, SHCBP1 expression correlated with tumor mutational burden (TMB), microsatellite instability (MSI), and neoantigens. The feature of SHCBP1 mutational landscape in pan-cancer was identified. Finally, we focused on investigating the clinical significance and the potential biological role of SHCBP1 in LUAD. Our study comprehensively uncovered that SHCBP1 could be identified as an immune-related biomarker for cancer diagnosis and prognosis, and a potential therapeutic target for tumor immunotherapy.

A Method for Coexpression Analysis.

Gene coexpression network analysis is a commonly used approach in bioinformatics and biomedical research to construct coexpression networks and detect coexpressed genes. This type of analysis has proven valuable for gene function prediction and for disease biomarker discovery.Here, we introduce and guide researchers through a method of differential coexpression analysis focusing on key autophagy and metabolic genes. We utilized the open-source Cancer Cell Line Encyclopedia (CCLE ) project as this is one of the most comprehensive genomic and transcriptomic resources including more than 1000 cell lines of distinct origins. However, the coexpression analysis method described here can also be applied to any open-source dataset where the RNA expression are provided.We here provide detailed comprehensive practical instructions for investigators to successfully identify novel coexpression signatures.

High dimensionality reduction by matrix factorization for systems pharmacology.

The extraction of predictive features from the complex high-dimensional multi-omic data is necessary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of features in in vitro, in vivo and clinical data is essential to discover the evolution and mechanisms of the drug responses and drug resistance. In this paper, we have utilized the matrix factorization (MF) as a modality for high dimensionality reduction in systems pharmacology. In this respect, we have proposed three novel feature selection methods using the mathematical conception of a basis for features. We have applied these techniques as well as three other MF methods to analyze eight different gene expression datasets to investigate and compare their performance for feature selection. Our results show that these methods are capable of reducing the feature spaces and find predictive features in terms of phenotype determination. The three proposed techniques outperform the other methods used and can extract a 2-gene signature predictive of a tyrosine kinase inhibitor treatment response in the Cancer Cell Line Encyclopedia.

Discovery of tumor immune infiltration-related snoRNAs for predicting tumor immune microenvironment status and prognosis in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) has a high mortality rate and is difficult to diagnose and treat in its early stage. Previous studies have demonstrated that small nucleolar RNAs (snoRNAs) play a critical role in tumor immune infiltration and the development of a variety of solid tumors. However, there have been no studies on the correlation between tumor-infiltrating immune-related snoRNAs (TIISRs) and LUAD. In this study, we filtered six immune-related snoRNAs based on the tissue specificity index (TSI) and expression profile of all snoRNAs between all LUAD cell lines from the Cancer Cell Line Encyclopedia and 21 types of immune cells from the Gene Expression Omnibus database. Further, we performed real-time quantitative polymerase chain reaction (RT-qPCR) to validate the expression status of these snoRNAs on peripheral blood mononuclear cells (PBMCs) and lung cancer cell lines. Next, we developed a TIISR signature based on the expression profiles of snoRNAs from 479 LUAD patients filtered by the random survival forest algorithm. We then analyzed the value of this TIISR signature (TIISR risk score) for assessing tumor immune infiltration, immune checkpoint inhibitor (ICI) treatment response, and the prognosis of LUAD between groups with high and low TIISR risk score. Further, we found that the TIISR risk score groups showed significant differences in biological characteristics and that the risk score could be used to assess the level of tumor immune cell infiltration, thereby predicting prognosis and responsiveness to immunotherapy in LUAD patients.

Expression and Prognosis Analyses of Runt-Related Transcription Factor Family in Human Leukemia.

Despite advances in early diagnosis and treatment, cancer remains the major reason for mortality worldwide. The Runt-related transcription factor (RUNX) family has been reported to participate in diverse human diseases. However, little is known about their expression and prognostic values in human leukemia. Herein, we conducted a detailed cancer versus normal analysis. The mRNA expression levels of the RUNX family in various kinds of cancers, including leukemia, were analyzed via the ONCOMINE and GEPIA (Gene Expression Profiling Interactive Analysis) databases. We observed that the mRNA expression levels of RUNX1, RUNX2, and RUNX3 were all increased in most cancers compared with normal tissues, especially in leukemia. Moreover, the expression levels of RUNX1, RUNX2, and RUNX3 are also highly expressed in almost all cancer cell lines, particularly in acute myeloid leukemia (AML) cell lines, analyzed by Cancer Cell Line Encyclopedia (CCLE) and European Bioinformatics Institute (EMBL-EBI) databases. Further, the LinkedOmics and GEPIA databases were used to evaluate the prognostic values. In survival analyses based on LinkedOmics, higher expression of RUNX1 and RUNX2 indicated a better overall survival (OS), but with no significance, whereas increased RUNX3 revealed a poor OS in leukemia. In addition, the GEPIA dataset was also used to perform survival analyses, and results manifested that the expression of RUNX1 and RUNX2 had no remarkable correction with OS in leukemia, but it showed highly expressed RUNX3 was significantly related with poor OS in leukemia. In conclusion, the RUNX family showed significant expression differences between cancer and normal tissues, especially leukemia, and RUNX3 could be a promising prognostic biomarker for leukemia.

Predicting drug response of tumors from integrated genomic profiles by deep neural networks.

BACKGROUND: The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer cell lines to a wide collection of anti-cancer drugs and illuminated the link between cellular genotypes and vulnerability. However, due to essential differences between cell lines and tumors, to date the translation into predicting drug response in tumors remains challenging. Recently, advances in deep learning have revolutionized bioinformatics and introduced new techniques to the integration of genomic data. Its application on pharmacogenomics may fill the gap between genomics and drug response and improve the prediction of drug response in tumors. RESULTS: We proposed a deep learning model to predict drug response (DeepDR) based on mutation and expression profiles of a cancer cell or a tumor. The model contains three deep neural networks (DNNs), i) a mutation encoder pre-trained using a large pan-cancer dataset (The Cancer Genome Atlas; TCGA) to abstract core representations of high-dimension mutation data, ii) a pre-trained expression encoder, and iii) a drug response predictor network integrating the first two subnetworks. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods (linear regression and support vector machine) and four analog DNN models of DeepDR, including DNNs built without TCGA pre-training, partly replaced by principal components, and built on individual types of input data. We then applied the model to predict drug response of 9059 tumors of 33 cancer types. Using per-cancer and pan-cancer settings, the model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets, such as vinorelbine for TTN-mutated tumors. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. CONCLUSIONS: Here we present, as far as we know, the first DNN model to translate pharmacogenomics features identified from in vitro drug screening to predict the response of tumors. The results covered both well-studied and novel mechanisms of drug resistance and drug targets. Our model and findings improve the prediction of drug response and the identification of novel therapeutic options.

Expression and prognosis analyses of forkhead box A (FOXA) family in human lung cancer.

Despite advances in early diagnosis and treatment, cancer still remains the major reason of mortality worldwide. The forkhead box A (FOXA) family is reported to participate in diverse human diseases. However, little is known about their expression and prognostic values in human lung cancer. Herein, we conducted a detailed cancer vs. normal analysis. The mRNA expression levels of FOXA family in numerous kind of cancers, including lung cancer, were analyzed using the Oncomine and GEPIA database. We observed that the mRNA expression levels of FOXA1, and FOXA3 were all increased while FOXA2 were decreased in most cancers compared with normal tissues, especially in lung cancer. Moreover, the expression levels of FOXA1, and FOXA3 are also highly expressed, while FOXA2 were decreased in almost all cancer cell lines, particularly in lung cancer cell lines, analyzing by Cancer Cell Line Encyclopedia (CCLE) and EMBL-EBI databases. Furthermore, the LinkedOmics database was used to evaluate the prognostic values, indicating that higher expression of FOXA1, FOXA3 indicated a poor overall survival (OS), while increased FOXA2 revealed a better OS in lung cancer. To conclusion, FOXA family showed significant expression differences between cancer and normal tissues, especially lung cancer, and FOXA1, FOXA3 could be promising prognostic biomarkers for lung cancer.

Expression of ephA7 in small cell lung cancer and its clinical significance / 肿瘤

Objective: To explore the expression of Ephrin receptor A7 (EphA7) in the patients with small cell lung cancer (SCLC) and its clinical significance. Methods: The expression of EphA7 mRNA in SCLC cell lines and tissues was analyzed by the Cancer Cell Line Encyclopedia (CCLE) and Oncomine databases, respectively. Seventy-three paraffin-embedded lung cancer specimens and six adjacent normal lung tissue samples from SCLC patients who underwent lobectomy or pneumonectomy resection in Tianjin Medical University Affiliated Cancer Hospital and Institution from January 2011 to December 2014 were collected. The expression of EphA7 protein was assessed by immunohistochemistry. The relationship between the expression of EphA7 protein and other clinicopathological factors was analyzed by x2 test. Kaplan-Meier survival curve and COX proportional hazard model were used to analyze the relationship between these clinicopathological parameters (including EphA7 expression) and the prognosis of SCLC patients. Results: The expression of EphA7 mRNA in SCLC cell lines was the highest among the 1 457 cell lines included in CCLE database. Three datasets of EphA7 mRNA expression in SCLC tissues were obtained from the Oncomine database. Compared with the normal lung tissues and non-small cell lung cancer, the expression level of EphA7 mRNA was relatively higher in SCLC tissues. The positive expression rate of EphA7 protein reached 72.6% (53/73) in the 73 patients with SCLC. The expression of EphA7 protein was significantly associated with lymph node metastasis and TNM stage (both P < 0.05). After adjusting other factors, it was found that the positive expression of EphA7 protein was an independent prognostic factor for the overall survival (OS) of SCLC patients [hazard ratio (HR) = 2.369, 95% confidence interval (CI): 1.075-5.219, P < 0.05], while TNM stage was an independent prognostic factor for both OS (HR = 2.273, 95% CI: 1.252-4.124, P < 0.05) and progression-free survival (PFS) (HR = 1.898, 95% CI: 1.088-3.312, P < 0.05) of SCLC patients, respectively. Conclusion: EphA7 mRNA and protein are highly expressed in SCLC tissues. The expression of EphA7 protein and TNM stage may be independent factors for the prognosis of SCLC patients.

System analysis of the regulation of the immune response by CD147 and FOXC1 in cancer cell lines.

CD147, encoded by BSG, is a highly glycosylated transmembrane protein that belongs to the immunological superfamily and expressed on the surface of many types of cancer cells. While CD147 is best known as a potent inducer of extracellular matrix metalloproteinases, it can also function as a key mediator of inflammatory and immune responses. To systematically elucidate the function of CD147 in cancer cells, we performed an analysis of genome-wide profiling across the Cancer Cell Line Encyclopedia (CCLE). We showed that CD147 mRNA expression was much higher than that of most other genes in cancer cell lines. CD147 varied widely across these cell lines, with the highest levels in the ovary (COLO704) and stomach (SNU668), intermediate levels in the lung (RERFLCKJ, NCIH596 and NCIH1651) and lowest levels in hematopoietic and lymphoid tissue (UT7, HEL9217, HEL and MHHCALL3) and the kidney (A704 and SLR20). Genome-wide analyses showed that CD147 expression was significantly negatively correlated with immune-related genes. Our findings implicated CD147 as a novel regulator of immune-related genes and suggest its important role as a master regulator of immune-related responses in cancer cell lines. We also found a high correlation between the expression of CD147 and FOXC1, and proved that CD147 was a direct transcriptional target of FOXC1. Our findings demonstrate that FOXC1 is a novel regulator of CD147 and confirms its role as a master regulator of the immune response.

Bayesian Neural Networks for Selection of Drug Sensitive Genes.

Recent advances in high-throughput biotechnologies have provided an unprecedented opportunity for biomarker discovery, which, from a statistical point of view, can be cast as a variable selection problem. This problem is challenging due to the high-dimensional and non-linear nature of omics data and, in general, it suffers three difficulties: (i) an unknown functional form of the nonlinear system, (ii) variable selection consistency, and (iii) high-demanding computation. To circumvent the first difficulty, we employ a feed-forward neural network to approximate the unknown nonlinear function motivated by its universal approximation ability. To circumvent the second difficulty, we conduct structure selection for the neural network, which induces variable selection, by choosing appropriate prior distributions that lead to the consistency of variable selection. To circumvent the third difficulty, we implement the population stochastic approximation Monte Carlo algorithm, a parallel adaptive Markov Chain Monte Carlo (MCMC) algorithm, on the OpenMP platform which provides a linear speedup for the simulation with the number of cores of the computer. The numerical results indicate that the proposed method can work very well for identification of relevant variables for high-dimensional nonlinear systems. The proposed method is successfully applied to identification of the genes that are associated with anticancerdrug sensitivities based on the data collected in the cancer cell line encyclopedia (CCLE) study.

Current Trends in Drug Sensitivity Prediction.

Cancer cell line panels have proved useful disease models to, among others, identify genomic markers of drug sensitivity and to develop new anticancer drugs. The increasing availability of in vitro sensitivity and cell line profiling data sets raises the question of whether this information could be used, and to which extent, to predict the activity of drugs in cancer cell lines and, ultimately, in patients tumors. Drug sensitivity prediction embraces those approaches aiming at predicting in vitro drug activity on cancer cell lines by integrating genomic and/or chemical information using machine learning models. In this review, we summarize the cytotoxicity assays generally used to determine in vitro activity on cultured cell lines, and revisit the drug sensitivity prediction studies that have leveraged chemical and cell line profiling data from the NCI60, Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) projects. A section outlining current limitations and future perspectives in the field closes the review.

A catalog of HLA type, HLA expression, and neo-epitope candidates in human cancer cell lines.

Cancer cell lines are a tremendous resource for cancer biology and therapy development. These multipurpose tools are commonly used to examine the genetic origin of cancers, to identify potential novel tumor targets, such as tumor antigens for vaccine devel-opment, and utilized to screen potential therapies in preclinical studies. Mutations, gene expression, and drug sensitivity have been determined for many cell lines using next-generation sequencing (NGS). However, the human leukocyte antigen (HLA) type and HLA expression of tumor cell lines, characterizations necessary for the development of cancer vaccines, have remained largely incomplete and, such information, when available, has been distributed in many publications. Here, we determine the 4-digit HLA type and HLA expression of 167 cancer and 10 non-cancer cell lines from publically available RNA-Seq data. We use standard NGS RNA-Seq short reads from "whole transcriptome" sequencing, map reads to known HLA types, and statistically determine HLA type, heterozygosity, and expression. First, we present previously unreported HLA Class I and II genotypes. Second, we determine HLA expression levels in each cancer cell line, providing insights into HLA downregulation and loss in cancer. Third, using these results, we provide a fundamental cell line "barcode" to track samples and prevent sample annotation swaps and contamination. Fourth, we integrate the cancer cell-line specific HLA types and HLA expression with available cell-line specific mutation information and existing HLA binding prediction algorithms to make a catalog of predicted antigenic mutations in each cell line. The compilation of our results are a fundamental resource for all researchers selecting specific cancer cell lines based on the HLA type and HLA expression, as well as for the development of immunotherapeutic tools for novel cancer treatment modalities.