Cancer Cell Line Encyclopedia Dataset Research Articles

Identification of biological markers in cancer disease using explainable artificial intelligence

AbstractThe research aims to improve the prediction of drug sensitivity on cancer cell lines using gene expression data and molecular fingerprints of drugs. The proposed study uses a deep learning model, BioMarkerX, trained on the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) datasets utilizing Particle Swarm Optimization technique to select specific genes as features. The model achieves high prediction accuracy with a Root Mean Square Error (RMSE) of 0.40 ± 0.02 and R2 of 0.83 ± 0.03 on the CCLE dataset, and an RMSE of 0.36 ± 0.05 and R2 of 0.83 ± 0.03 on the GDSC dataset. The approach also used an explainable artificial intelligence model to discover biological markers linked to cancer development. This can provide insights into targeted therapies for improving cancer treatment outcomes. Overall, the study presents an effective approach for identifying important biological markers relevant to cancer disease, aiding in the development of more efficient anticancer medications.

Epigenetic Silencing of MTAP in Hodgkin's Lymphoma Renders It Sensitive to a 2 nd Generation PRMT5 Inhibitor

Background: PRMT5 is type II arginine methyltransferase that catalyses symmetric dimethylation of arginine residues (SDMA) and plays an important role in cancer biology. By methylating a number of substrates, PRMT5 can regulate important processes such as DNA repair, RNA splicing, and cellular proliferation. In addition, PRMT5 is overexpressed in various cancer types and has been identified as a candidate for therapeutic intervention through the development of small molecules that inhibit PRMT5 methyltransferase activity. However, first generation PRMT5 inhibitors have shown limited clinical benefit mainly due to development of on-target toxicity, primarily in the bone marrow. AstraZeneca has developed a second generation PRMT5 inhibitor (AZ-PRMT5i), that selectively inhibits PRMT5 in MTAP deficient tumours while sparing MTAP proficient normal cells. MTAP (methylthioadenosine phosphorylase) is a metabolic enzyme involved in methionine salvage pathway. MTAP deficiency results in accumulation of the metabolite methylothioadenosine (MTA) in tumor cells that induces partial inhibition of PRMT5, rendering these tumors sensitive to PRMT5 inhibition. Homozygous deletion of the MTAP gene, that results in the loss of MTAP protein, has been found in approximately 15% of advanced solid tumors. Here, for the first time, we describe epigenetic silencing of the MTAP gene in Hodgkin's Lymphoma (HL) cell lines. This silencing results in the loss of MTAP protein expression thus increasing sensitivity to PRMT5 inhibition. Importantly, MTAP protein loss was also observed in primary cHL samples, opening a novel opportunity for the treatment of HL. Methods: Bioinformatic data mining of the Cancer Cell Line Encyclopedia (CCLE) dataset has been used to overlay levels of expression of MTAP mRNA with MTAP copy number and MTAP promoter DNA methylation. MTAP protein expression and activity of AZ-PRMT5i were assessed in 5 HL cell lines in vitro; efficacy of AZ-PRMT5i and levels of target engagement were tested in vivo in the L540 xenograft model. MTAP expression levels were determined by IHC analysis using 55 primary samples from cHL patients. Results: Using an unbiased analysis of the CCLE dataset, we have identified a subset of cell lines that in the absence of MTAP genetic loss were showing low levels of MTAP mRNA expression, equivalent to levels of MTAP mRNA in MTAP homozygous deleted cell lines. Interestingly, this was predominantly the case in HL cell lines where it was detected in 67% of cases (4 out of 6 cell lines). MTAP methylation profile analysis of HL cell lines revealed elevated MTAP DNA promoter methylation in cell lines with low MTAP mRNA expression. In addition, MTAP protein was not detected by Western Blot. Accordingly, lack of MTAP protein expression resulted in increased sensitivity of these cells to AZ-PRMT5i compared to a HL cell line that expressed MTAP protein. Utilizing RNA-seq for transcriptional profiling of AZ-PRMT5i on a MTAP-deficient HL cell line has identified robust changes in genes that regulate the cell cycle, DNA replication and TP53 pathways thus confirming on target activity. In addition, AZ-PRMT5i demonstrated strong dose dependent efficacy in a MTAP silenced L540 xenograft model where greater than 90% of tumour growth inhibition was detected, with no significant body weight loss. Corresponding dose-dependent inhibition of SDMA is observed in the treated tumours. Finally, IHC analysis of MTAP expression in 55 primary cHL samples has demonstrated that 46 of 55 cHL samples (84%) had absent nuclear MTAP expression. Conclusion: Here we are presenting a novel finding showing MTAP protein expression loss in the majority of tested primary cHL samples, which could provide a collateral vulnerability and novel therapeutic opportunity to 2 nd generation PRMT5 inhibitors as demonstrated in our pre-clinical models.

Tensor completion with noisy side information

We develop a new model for tensor completion which incorporates noisy side information available on the rows and columns of a 3-dimensional tensor. This method learns a low rank representation of the data along with regression coefficients for the observed noisy features. Given this model, we propose an efficient alternating minimization algorithm to find high-quality solutions that scales to large data sets. Through extensive computational experiments, we demonstrate that this method leads to significant gains in out-of-sample accuracy filling in missing values in both simulated and real-world data. We consider the problem of imputing drug response in three large-scale anti-cancer drug screening data sets: the Genomics of Drug Sensitivity in Cancer (GDSC), the Cancer Cell Line Encyclopedia (CCLE), and the Genentech Cell Line Screening Initiative (GCSI). On imputation tasks with 20% to 80% missing data, we show that the proposed method TensorGenomic matches or outperforms state-of-the-art methods including the original tensor model and a multilevel mixed effects model. With 80% missing data, TensorGenomic improves the R^2 from 0.404 to 0.552 in the GDSC data set, 0.407 to 0.524 in the CCLE data set, and 0.331 to 0.453 in the GCSI data set compared to the tensor model which does not take into account genomic side information.

NeuPD—A Neural Network-Based Approach to Predict Antineoplastic Drug Response

With the beginning of the high-throughput screening, in silico-based drug response analysis has opened lots of research avenues in the field of personalized medicine. For a decade, many different predicting techniques have been recommended for the antineoplastic (anti-cancer) drug response, but still, there is a need for improvements in drug sensitivity prediction. The intent of this research study is to propose a framework, namely NeuPD, to validate the potential anti-cancer drugs against a panel of cancer cell lines in publicly available datasets. The datasets used in this work are Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). As not all drugs are effective on cancer cell lines, we have worked on 10 essential drugs from the GDSC dataset that have achieved the best modeling results in previous studies. We also extracted 1610 essential oncogene expressions from 983 cell lines from the same dataset. Whereas, from the CCLE dataset, 16,383 gene expressions from 1037 cell lines and 24 drugs have been used in our experiments. For dimensionality reduction, Pearson correlation is applied to best fit the model. We integrate the genomic features of cell lines and drugs’ fingerprints to fit the neural network model. For evaluation of the proposed NeuPD framework, we have used repeated K-fold cross-validation with 5 times repeats where K = 10 to demonstrate the performance in terms of root mean square error (RMSE) and coefficient determination (R2). The results obtained on the GDSC dataset that were measured using these cost functions show that our proposed NeuPD framework has outperformed existing approaches with an RMSE of 0.490 and R2 of 0.929.

Abstract 5403: Predicting cancer cell response to TEAD auto-palmitoylation inhibitor using bulk RNA-seq data and a random-forest based algorithm

Abstract TEAD transcription factors are the major effectors of the Hippo-YAP/TAZ pathway essential in controlling organ size and maintaining tissue homeostasis. Upon coactivator YAP/TAZ binding, TEAD transcription factors are activated to induce expression of genes involved in cell proliferation and survival. It has been shown that auto-palmitoylation is required for TEAD interaction with YAP/TAZ and hence activation of transcriptional activity. Potent small molecule TEAD auto-palmitoylation inhibitors have been reported (Tang et al, 2021, Mol Cancer Ther.). These TEAD inhibitors disrupt YAP/TAZ-TEAD protein interaction, suppress TEAD transcriptional activity, and selectively block proliferation of NF2-deficient mesothelioma in vitro and inhibit NF2 mutant xenograft tumor growth in vivo. Although genetic alterations of pathway components (such as, NF2) leading to YAP/TAZ constitutive nuclear localization and TEAD activation have been reported in a variety of human malignancies, these alterations are infrequent in most cancers, and increased accumulation of nuclear YAP has been reported in cancers (e.g., hepatocellular carcinoma) that do not harbor pathway mutations. Utilizing in vitro efficacy data from cell line screens, gene expression (RNA-Seq) datasets, and a custom bioinformatics data-processing and normalization pipeline, we sought to elucidate gene expression patterns by using a random-forest based classifier. Our classifier, which aims to predict the response (efficacy) of treatment using normalized gene expression of selected genes, was originally trained on the public bulk RNA-seq pan-cancer in vitro dataset available in the Cancer Cell Line Encyclopedia (CCLE) from the Broad Institute, as well as a privately obtained TEADi response data for corresponding CCLE cell lines. Binary response labels (responder/non responder) for each cell line were obtained by thresholding efficacy measures. The classifier - evaluated using cross-validation - achieved an average AUC of 0.80. Then from our screen of 50 patient-derived Chinese liver cancer (CLC) cell models (Qiu et al, 2019, Cancer Cell), both as single agent and in combination with mTOR inhibitor everolimus, we found that TEAD inhibitors (TEADi) were efficacious in several of these liver cancer models. This allowed us to verify the classifier without any re-training on an independent dataset (CLC). In this independent validation scenario, the classifier achieved the AUC of 0.82. Having established efficacy of predictions on both in vitro datasets, we re-trained the classifier using both CCLE and CLC datasets as input to achieve maximum predictive performance, and we predicted, post-hoc, response status of 99 responders out of 2056 patient-derived xenograft models. The algorithm showed early promise by retrospectively classifying a model with in vivo efficacy as the most likely to respond. Citation Format: Adam Kurkiewicz, Sebastian Y. Müller, Oliver Gibson, Sara Castellano, Germán Stark, Pol A. Vecino, Shuirong Zhou, Tracy T. Tang. Predicting cancer cell response to TEAD auto-palmitoylation inhibitor using bulk RNA-seq data and a random-forest based algorithm. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5403.

Abstract 3559: Understanding Ewing sarcoma cell fate

Abstract Like several other translocation-positive sarcomas, Ewing sarcoma (ES) exists exclusively in a high-grade, undifferentiated state. We believe that the pathognomonic EWS-FLI1 fusion protein (FP) responsible for ES is intimately linked to cell fate, differentiation, and plasticity. Over the last two decades, an emerging body of research has begun the challenging task of deciphering how the FP - via its action as an aberrant transcription factor- blocks differentiation and promotes reprogramming toward a quasi-stem-like cell with features of neuroectodermal or mesenchymal stem cells. We compared ES transcriptome data to other cancers in an unsupervised comparison between the gene expression profile of cancer cells in the Cancer Cell Line Encyclopedia (CCLE) and tumor expression datasets. While ES is traditionally classified as a bone cancer, it does not cluster near other bone cancer subtypes (e.g., osteosarcomas) or soft-tissue sarcomas. To that end, we sought to better understand how targeting the FP affected ES differentiation and lineage. Using a CRISPR knockout (CRISPR-KO) model of A673, we targeted Exon 4 of the EWSR1 protein in the n-terminus of the FP and generated a clonal pool (EWSR1 CRISPR KO). We used single-cell RNA sequencing (scRNA-seq) to identify the transcriptional changes after FP knockout (FP KO). We identified five cell clusters in the data. We observed the expression of FP target genes (EWS-FLI1 On), PRKCB, and LIPI and saw robust expression in four of the clusters suggesting normal FP expression. Conversely, FP repressed genes (EWS-FLI1 Off), LOX and IGFBP3, were elevated in cluster 2, which contained only EWSR1 CRISPR KO cells. This suggests that the FP was successfully knocked out in cluster 2 (FP EWSR1 KO). Next, we asked how the FP KO changed the lineage/fate of A673 cells. We hypothesized that loss of the FP would induce mesenchymal/fibroblastic fate in ES cells. To answer this, we used SingleR, which compares the gene expression profile of query cells to the CCLE data set, to identify the nearest cell type. Interestingly, all clusters except the FP EWSR1 KO cluster identified closely with ES and related tumors. The FP EWSR1 KO cluster, which expressed EWS-FLI1 ‘off’ genes, did not resemble ES. Using a granular identification with cell lines, we confirmed that the highest score was A673 since these were A673 cell lines. However, the FP EWSR1 KO resulted in a phenotype unrelated to A673 and more closely resembled cell lines with fibroblastic phenotype. Our data suggest that targeting the FP significantly changed ES lineage. While the FP KO did not lead to cell death, our results suggest a novel gene regulatory network acts to sustain ES lineage. Further perturbations in this network may lead to a better understanding of ES treatment, particularly as novel EWS-FLI1-directed therapies emerge. Citation Format: Danh Truong, Sandhya Krishnan, Alexandar Lazar, David McCall, Joseph A. Ludwig. Understanding Ewing sarcoma cell fate. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3559.

DROEG: a method for cancer drug response prediction based on omics and essential genes integration.

Predicting therapeutic responses in cancer patients is a major challenge in the field of precision medicine due to high inter- and intra-tumor heterogeneity. Most drug response models need to be improved in terms of accuracy, and there is limited research to assess therapeutic responses of particular tumor types. Here, we developed a novel method DROEG (Drug Response based on Omics and Essential Genes) for prediction of drug response in tumor cell lines by integrating genomic, transcriptomic and methylomic data along with CRISPR essential genes, and revealed that the incorporation of tumor proliferation essential genes can improve drug sensitivity prediction. Concisely, DROEG integrates literature-based and statistics-based methods to select features and uses Support Vector Regression for model construction. We demonstrate that DROEG outperforms most state-of-the-art algorithms by both qualitative (prediction accuracy for drug-sensitive/resistant) and quantitative (Pearson correlation coefficient between the predicted and actual IC50) evaluation in Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia datasets. In addition, DROEG is further applied to the pan-gastrointestinal tumor with high prevalence and mortality as a case study at both cell line and clinical levels to evaluate the model efficacy and discover potential prognostic biomarkers in Cisplatin and Epirubicin treatment. Interestingly, the CRISPR essential gene information is found to be the most important contributor to enhance the accuracy of the DROEG model. To our knowledge, this is the first study to integrate essential genes with multi-omics data to improve cancer drug response prediction and provide insights into personalized precision treatment.

Pan-cancer analysis of LINC02535 as a potential biomarker and its oncogenic role in lung adenocarcinoma

BackgroundLINC02535 has gained much attention for its oncogenicity across several cancers, but the systematic pan-cancer analysis of LINC02535 has not been carried out before. MethodsHerein, we explored the expression level, prognostic value, and hallmark pathways of LINC02535 across multiple cancers using the Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia (CCLE) databases. Moreover, the expression and biological features of LINC02535 in lung adenocarcinoma (LUAD) were confirmed by qRT-PCR, in vitro and in vivo experiments. ResultsLINC02535 is differentially expressed in 10 of 17 human cancers and serves as a favorable or unfavorable biomarker in distinct cancer types. Gene set enrichment analysis (GSEA) indicated that key oncogenic pathways/phenotypes were remarkably activated in most cancers with intratumoral increased LINC02535, whereas these pathways/phenotypes were suppressed in other cancer types (colon adenocarcinoma, kidney renal clear cell carcinoma, rectal adenocarcinoma) with intratumoral decreased LINC02535. Of note, the epithelial-mesenchymal transition (EMT) phenotype was greatly enriched in LUAD patients with elevated LINC02535. Based on the TCGA and CCLE datasets, LINC02535 was positively correlated with the EMT-related gene CD73 (also named as NT5E, an immunosuppressive gene) in almost all cancer types (Spearman R > 0.5, P < 0.001) including LUAD. Most importantly, qRT-PCR confirmed that LINC02535 was upregulated in lung cancer cells or tissues as opposed to human bronchial epithelial cells or paratumor tissues. Knockdown of LINC02535 inhibited proliferation, migration of LUAD cells and retarded xenografted tumor growth. Moreover, silencing of LINC02535 induced apoptosis and cell cycle arrest at G1 phase. ConclusionsThe findings from our pan-cancer analysis provide a relatively comprehensive understanding of the potential value of LINC02535 across multiple cancers, and the oncogenic role of LINC02535 in LUAD has been confirmed.

Darinaparsin (ZIO-101) enhances the sensitivity of small-cell lung cancer to PARP inhibitors.

Small-cell lung cancer (SCLC) is an aggressive high-grade neuroendocrine carcinoma of the lung associated with early metastasis and an exceptionally poor prognosis. Little progress has been made in developing efficacious targeted therapy for this recalcitrant disease. Herein, we showed that H3.3, encoded by two genes (H3F3A and H3F3B), was prominently overexpressed in SCLC. Darinaparsin (ZIO-101), a derivative of arsenic trioxide, dose- and time-dependently inhibited the viability of SCLC cells in an H3.3-dependent manner. More importantly, ZIO-101 treatment resulted in substantial accumulation of H3.3 and PARP1 besides induction of G2/M cell cycle arrest and apoptosis in SCLC cells. Through integrative analysis of the RNA-seq data from Cancer Cell Line Encyclopedia dataset, JNCI and Genomics of Drug Sensitivity in Cancer 2 datasets, we found that H3F3A expression was negatively correlated with the IC50 values of PARP inhibitors (PARPi). Furthermore, co-targeting H3.3 and PARP1 by ZIO-101 and BMN673/olaparib achieved synergistic growth inhibition against SCLC in vitro and in vivo. In conclusion, it is feasible to target H3.3 by ZIO-101 to potentiate the response rate of PARPi in SCLC patients.

PRKRIP1, A Splicing Complex Factor, Is a Marker of Poor Prognosis in Colorectal Cancer.

Alternative splicing plays a vital role in cancer development and progression. The splicing C complex is involved in alternative splicing. However, the role of PRKR-interacting protein 1 (PRKRIP1), a component of the splicing C complex, in colorectal cancer (CRC) remains unclear. This study aimed to determine the clinicopathological, biological and prognostic significance of PRKRIP1 expression in CRC. We used a bioinformatics approach to screen for oncogenes using The Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia (CCLE) datasets and identified PRKRIP1 as a driver gene on chromosome 7q. The mRNA expression of PRKRIP1 was measured using reverse transcription-quantitative PCR in 165 surgically resected CRC samples in our hospital, and its localization was determined using immunohistochemical staining. Gene Set Enrichment Analysis (GSEA) was performed using TCGA dataset. High PRKRIP1 expression was significantly associated with poor prognosis in both the samples and TCGA dataset. A positive correlation was observed between copy number variation and PRKRIP1 expression in TCGA and CCLE datasets, and the frequency of PRKRIP1 mutations was less than 5%. Immunohistochemistry revealed that PRKRIP1 was located in the cytoplasm of tumor cells. GSEA revealed that PRKRIP1 expression was correlated with apoptosis-related gene sets. PRKRIP1 overexpression may be a poor prognostic biomarker for CRC. Although it is known that PRKRIP1, a spliceosome factor, is essential for splicing, we now revealed the way by which its expression accelerates CRC progression.

EBSRMF: Ensemble based similarity-regularized matrix factorization to predict anticancer drug responses

Drug sensitivity prediction to a panel of cancer cell lines using computational approaches has been a challenge for two decades. With the emergence of high-throughput screening technologies, thousands of compounds and cancer cell lines panels with drug sensitivity data are publicly available at various pharmacogenomics databases. Analyzing these data is crucial to improve cancer treatment and develop new anticancer drugs. In this work, we propose EBSRMF: Ensemble Based Similarity-Regularized Matrix Factorization, which is a bagging based framework to improve the drug sensitivity prediction on the Cancer Cell Line Encyclopedia (CCLE) data. Based on the fact that similar drugs and cell lines exhibit similar drug response, we have investigated cell line and drug similarity matrices based on gene expression profiles and chemical structure respectively. The drug sensitivity value is used as outcome values which are the half maximal inhibitory concentrations (IC50). In order to improve the generalization ability of the proposed model, a homogeneous ensemble based bagging learning approach is also investigated where multiple SRMF models are used to train N subsets of the input data. The outcome of each training algorithm is aggregated using the averaging method to predict the outcome. Experiments are conducted on two benchmark datasets: CCLE and GDSC. The proposed model is compared with state-of-the-art models using multiple evaluation metrics including Root Means Square Error (RMSE) and Pearson Correlation Coefficient (PCC). The proposed model is quite promising and achieves better performance on CCLE dataset when compared with the existing approaches.

Integrative analysis of expression, prognostic significance and immune infiltration of RFC family genes in human sarcoma.

Objective: To reveal the expression and prognostic value of replication factor C family genes (RFCs) in patients with sarcoma.Results: The results showed that the mRNA expression levels of RFC2, RFC3, RFC4, and RFC5 were increased in sarcoma tissues. In addition, Cancer Cell Line Encyclopedia (CCLE) dataset analysis indicated that RFC1, RFC2, RFC3, RFC4, and RFC5 were elevated expressed in sarcoma cell lines. Moreover, Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier Plotter showed that highly expressed RFC2-5 were associated with poor overall survival (OS) or relapse-free survival (RFS) in sarcoma patients. The results of the Tumor Immune Estimation Resource (TIMER) database indicated that the expression of RFCs was negatively correlated with the infiltration of CD4+ T cells and macrophages.Conclusions: There were significant differences in the expression of RFCs between normal tissue and sarcoma tissue, and RFC2, RFC3, RFC4, and RFC5 might be promising prognostic biomarkers for sarcoma.Methods: The expression of RFCs was analyzed using the ONCOMINE dataset and GEPIA dataset. CCLE dataset was used to assess the expression of RFCs in the cancer cell line. The prognostic value of RFCs was evaluated by GEPIA and Kaplan-Meier analysis. Furthermore, the association between RFCs and their co-expressed genes were explored via ONCOMINE and GEPIA datasets. We used the TIMER dataset to analyze the immune cell infiltration of RFCs in sarcoma.

Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines.

The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA).

Comprehensive analysis of expression and prognosis for LMNB family genes in human sarcoma.

Previous studies indicated that lamin proteins were thought to be related to gene expression, chromatin structure, and unclear stability. There are 2 types of vertebrate lamins, including A and B. The 2 B type proteins are encoded by lamin B1 (LMNB1) and lamin B2 (LMNB2). The LMNBs factor has been found to be associated with the development of multiple tumors, but its association with sarcoma has been barely mentioned.The transcription levels of LMNBs were analyzed via Oncomine database. Gene Expression Profiling Interactive Analysis (GEPIA) dataset was adopted to analyze the differential expression of LMNBs in sarcoma. Cancer Cell Line Encyclopedia dataset was used to explore the expression of LMNBs in sarcoma cell line. We analyzed the prognostic value of LMNBs in GEPIA and Kaplan-Meier Plotter. Oncomine and GEPIA datasets were also used to detect the relationship between LMNBs and their co-expressed genes. We used the Database for Annotation, Visualization and Integrated Discovery to conduct the Gene Ontology analysis of LMNBs and their co-expressed genes. Kyoto Encyclopedia of Genes and Genomes was also used to analyze the pathway of LMNBs.LMNB1 and LMNB2 were reported to be hyperexpressed in sarcoma. The expression of LMNBs was elevated in various sarcoma cell lines. According to the results, we observed that LMNBs were connected to the poor overall survival, recurrence-free survival, and disease-free survival of sarcoma patients.This study indicated that hyperexpression of LMNBs was significantly related to worse outcome of sarcoma, LMNB1 and LMNB2 were expected to become potential biomarkers for human.

Mutation Analysis of Radioresistant Early-Stage Cervical Cancer

Radiotherapy is a definitive treatment for early-stage cervical cancer; however, a subset of this disease recurs locally, necessitating establishment of predictive biomarkers and treatment strategies. To address this issue, we performed gene panel-based sequencing of 18 stage IB cervical cancers treated with definitive radiotherapy, including two cases of local recurrence, followed by in vitro and in silico analyses. Simultaneous mutations in KRAS and SMAD4 (KRASmt/SMAD4mt) were detected only in a local recurrence case, indicating potential association of this mutation signature with radioresistance. In isogenic cell-based experiments, a combination of activating KRAS mutation and SMAD4 deficiency led to X-ray resistance, whereas either of these factors alone did not. Analysis of genomic data from 55,308 cancers showed a significant trend toward co-occurrence of mutations in KRAS and SMAD4. Gene Set Enrichment Analysis of the Cancer Cell Line Encyclopedia dataset suggested upregulation of the pathways involved in epithelial mesenchymal transition and inflammatory responses in KRASmt/SMAD4mt cancer cells. Notably, irradiation with therapeutic carbon ions led to robust killing of X-ray-resistant KRASmt/SMAD4mt cancer cells. These data indicate that the KRASmt/SMAD4mt signature is a potential predictor of radioresistance, and that carbon ion radiotherapy is a potential option to treat early-stage cervical cancers with the KRASmt/SMAD4mt signature.

Fanconi Anemia Complementation Group E, a DNA Repair-Related Gene, Is a Potential Marker of Poor Prognosis in Hepatocellular Carcinoma

Introduction: Fanconi anemia complementation group E (FANCE) is a Fanconi anemia (FA) pathway gene that regulates DNA repair. We evaluated the clinical relevance of FANCE expression in hepatocellular carcinoma (HCC). Methods: First, the associations between the expression of FA pathway genes including FANCE and clinical outcomes in HCC patients were analyzed in 2 independent cohorts: The Cancer Genome Atlas (TCGA, n = 373) and our patient cohort (n = 53). Localization of FANCE expression in HCC tissues was observed by immunohistochemical staining. Gene set enrichment analysis (GSEA) and gene network analysis (SiGN_BN) were conducted using the TCGA dataset. Next, an in vitro proliferation assay was performed using FANCE-knockdown HCC cell lines (HuH7 and HepG2). The association between mRNA expression of FANCE and that of DNA damage response genes in HCC was analyzed using TCGA and Cancer Cell Line Encyclopedia datasets. Finally, the association between FANCE mRNA expression and overall survival (OS) in various digestive carcinomas was analyzed using TCGA data. Results: FANCE was highly expressed in HCC cells. Multivariate analysis indicated that high FANCE mRNA expression was an independent factor predicting poor OS. GSEA revealed a positive relationship between enhanced FANCE expression and E2F and MYC target gene expression in HCC tissues. FANCE knockdown attenuated the proliferation of HCC cells, as well as reduced cdc25A expression and elevated histone H3 pSer10 expression. SiGN_BN revealed that FANCE mRNA expression was positively correlated with DNA damage response genes (H2A histone family member X and checkpoint kinase 1) in HCC tissues. Significant effects of high FANCE expression on OS were observed in hepatobiliary pancreatic carcinomas, including HCC. Conclusions: FANCE may provide a potential therapeutic target and biomarker of poor prognosis in HCC, possibly by facilitating tumor proliferation, which is mediated partly by cell cycle signaling activation.

Oxytocin receptor is a promising therapeutic target of malignant mesothelioma.

Malignant mesothelioma (MM) is one of the most aggressive tumors. We conducted bioinformatics analysis using Cancer Cell Line Encyclopedia (CCLE) datasets to identify new molecular markers in MM. Overexpression of oxytocin receptor (OXTR), which is a G‐protein–coupled receptor for the hormone and neurotransmitter oxytocin, mRNA was distinctively identified in MM cell lines. Therefore, we assessed the role of OXTR and its clinical relevance in MM. Kaplan‐Meier and Cox regression analyses were applied to assess the association between overall survival and OXTR mRNA expression using The Cancer Genome Atlas (TCGA) datasets. The function of OXTR and the efficacy of its antagonists were investigated in vitro and in vivo using MM cell lines. Consistent with the findings from CCLE datasets analysis, OXTR mRNA expression was highly increased in MM tissues compared with other cancer types in the TCGA datasets, and MM cases with high OXTR expression showed poor overall survival. Moreover, OXTR knockdown dramatically decreased MM cell proliferation in cells with high OXTR expression via tumor cell cycle disturbance, whereas oxytocin treatment significantly increased MM cell growth. OXTR antagonists, which have high selectivity for OXTR, inhibited the growth of MM cell lines with high OXTR expression, and oral administration of the OXTR antagonist, cligosiban, significantly suppressed MM tumor progression in a xenograft model. Our findings suggest that OXTR plays a crucial role in MM cell proliferation and is a promising therapeutic target that may broaden potential therapeutic options and could be a prognostic biomarker of MM.

Comprehensive molecular and clinical characterization of SLC1A5 in human cancers

Although SLC1A5 has been reported to be closely associated with some cancer types, a comprehensive and systematic assessment of SLC1A5 across human cancers is lacking. Thus, Pan-cancer analysis of SLC1A5 was performed across 30 types of human cancers in this study. We examined mRNA expression, protein expression, copy number variation (CNV), DNA methylation, clinical relevance, cell functions, drug response and total immune infiltrates of SLC1A5 in more than 9000 patients across 30 human cancer types from The Cancer Genome Atlas (TCGA) dataset. Additionally, nine independent Gene Expression Omnibus datasets, more than 800 cancer cell lines from the Cancer Cell Line Encyclopedia dataset and the Project Achilles dataset were used to validate our findings in the TCGA dataset. Landscapes of SLC1A5 were established across multiple cancers. We showed that SLC1A5 is upregulated in multiple cancers, particularly in digestive and respiratory system cancers. SLC1A5 upregulation may be driven by CNV gain and DNA hypomethylation in human cancers. Furthermore, SLC1A5 overexpression is associated with tumor progression and poor survival in multiple cancers. Moreover, we systematically explored the potential effects of SLC1A5 expression on cell functions and drug response in human cancers. SLC1A5 knockdown showed significant proliferation-inhibiting effects in most human cancer types, especially in the digestive system and KRAS-mutant cancers. SLC1A5 expression is associated with proliferation activities of KRAS-mutant cancer cell lines and drug response of many anti-cancer drugs. Finally, we demonstrated that SLC1A5-realted tumor immune microenvironment characteristics showed strong heterogeneity in human cancers. Taken together, our findings highlight the important roles of SLC1A5 in tumorigenesis, progression, prognosis and therapy.

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

BackgroundCombined 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.ResultsHere, 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.ConclusionOur 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.

DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines.

High-throughput screening technologies have provided a large amount of drug sensitivity data for a panel of cancer cell lines and hundreds of compounds. Computational approaches to analyzing these data can benefit anticancer therapeutics by identifying molecular genomic determinants of drug sensitivity and developing new anticancer drugs. In this study, we have developed a deep learning architecture to improve the performance of drug sensitivity prediction based on these data. We integrated both genomic features of cell lines and chemical information of compounds to predict the half maximal inhibitory concentrations [Formula: see text] on the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC) datasets using a deep neural network, which we called DeepDSC. Specifically, we first applied a stacked deep autoencoder to extract genomic features of cell lines from gene expression data, and then combined the compounds' chemical features to these genomic features to produce final response data. We conducted 10-fold cross-validation to demonstrate the performance of our deep model in terms of root-mean-square error (RMSE) and coefficient of determination [Formula: see text]. We show that our model outperforms the previous approaches with RMSE of 0.23 and [Formula: see text] of 0.78 on CCLE dataset, and RMSE of 0.52 and [Formula: see text] of 0.78 on GDSC dataset, respectively. Moreover, to demonstrate the prediction ability of our models on novel cell lines or novel compounds, we left cell lines originating from the same tissue and each compound out as the test sets, respectively, and the rest as training sets. The performance was comparable to other methods.