Genomics Of Drug Sensitivity In Cancer Database Research Articles

CDK4/6 inhibition sensitizes MEK inhibition by inhibiting cell cycle and proliferation in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is not sensitive to most chemotherapy drugs, leading to poor chemotherapy efficacy. Recently, Trametinib and Palbociclib have promising prospects in the treatment of pancreatic cancer. This article aims to explore the effects of Trametinib on pancreatic cancer and address the underlying mechanism of resistance as well as its reversal strategies. The GDSC (Genomics of Drug Sensitivity in Cancer) and CTD2 (Cancer Target Discovery and Development) were utilized to screen the potential drug candidate in PDAC cell lines. The dose-increase method combined with the high-dose shock method was applied to induce the Trametinib-resistant PANC-1 and MIA PaCa-2 cell lines. The CCK8 proliferation assay, colony formation assay, flow cytometry, and western blot were conducted to verify the inhibitory effect of Trametinib and Palbociclib. RNA-seq was performed in resistant PDAC cell lines to find the differential expression genes related to drug resistance and predict pathways leading to the reversal of Trametinib resistance. The GDSC and CTD2 database screening revealed that Trametinib demonstrates a significant inhibitory effect on PDAC. We found that Trametinib has a lower IC50 than Gemcitabine in PDAC cell lines. Both Trametinib and Gemcitabine can decrease the proliferation capacity of pancreatic cells, induce cell cycle arrest, and increase apoptosis. Simultaneously, the phosphorylation of the AKT and ERK pathways were inhibited by the treatment of Trametinib. In addition, the RNA-seq of Trametinib-induced resistance PDAC cell lines reveals that the cyclin-dependent kinase (CDK)-RB-E2F regulatory axis and G2/M DNA damage checkpoint might lead the drug resistance. Besides, the combination of Trametinib with Palbociclib could inhibit the proliferation and cell cycle of both resistant cells lines and also restore the sensitivity of drug-resistant cells to Trametinib. Last but not least, the interferon-α and interferon-γ expression were upregulated in resistance cell lines, which might lead to the reversal of drug resistance. The study shows Trametinib has a critical inhibitory effect on PDAC. Besides, the combination of Trametinib with Palbociclib can inhibit the proliferation of PDAC-resistant cells.

Transcriptomic correlates of cell cycle checkpoints with distinct prognosis, molecular characteristics, immunological regulation, and therapeutic response in colorectal adenocarcinoma.

Colorectal adenocarcinoma (COAD), accounting for the most common subtype of colorectal cancer (CRC), is a kind of malignant digestive tumor. Some cell cycle checkpoints (CCCs) have been found to contribute to CRC progression, whereas the functional roles of a lot of CCCs, especially the integrated role of checkpoint mechanism in the cell cycle, remain unclear. The Genomic Data Commons (GDC) The Cancer Genome Atlas (TCGA) COAD cohort was retrieved as the training dataset, and GSE24551 and GSE29623 were downloaded from Gene Expression Omnibus (GEO) as the validation datasets. A total of 209 CCC-related genes were derived from the Gene Ontology Consortium and were subsequently enrolled in the univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, finally defining a CCC signature. Cell proliferation and Transwell assay analyses were utilized to evaluate the functional roles of signature-related CCCs. The underlying CCC signature, molecular characteristics, immune-related features, and therapeutic response were finally estimated. The Genomics of Drug Sensitivity in Cancer (GDSC) database was employed for the evaluation of chemotherapeutic responses. The aberrant gene expression of CCCs greatly contributed to COAD development and progression. Univariate Cox regression analysis identified 27 CCC-related genes significantly affecting the overall survival (OS) of COAD patients; subsequently, LASSO analysis determined a novel CCC signature. Noticeably, CDK5RAP2, MAD1L1, NBN, RGCC, and ZNF207 were first identified to be correlated with the prognosis of COAD, and it was proven that all of them were significantly correlated with the proliferation and invasion of HCT116 and SW480 cells. In TCGA COAD cohort, CCC signature robustly stratified COAD patients into high and low CCC score groups (median OS: 57.24 months vs. unreached, p< 0.0001), simultaneously, with the good AUC values for OS prediction at 1, 2, and 3 years were 0.74, 0.78, and 0.77. Furthermore, the prognostic capacity of the CCC signature was verified in the GSE24551 and GSE29623 datasets, and the CCC signature was independent of clinical features. Moreover, a higher CCC score always indicated worse OS, regardless of clinical features, histological subtypes, or molecular subgroups. Intriguingly, functional enrichment analysis confirmed the CCC score was markedly associated withextracellular, matrix and immune (chemokine)-related signaling, cell cycle-related signaling, and metabolisms. Impressively, a higher CCC score was positively correlated with a majority of chemokines, receptors, immunostimulators, and anticancer immunity, indicating a relatively immune-promoting microenvironment. In addition, GSE173839, GSE25066, GSE41998, and GSE194040 dataset analyses of the underlying CCC signature suggested that durvalumab with olaparib and paclitaxel, taxane-anthracycline chemotherapy, neoadjuvant cyclophosphamide/doxorubicin with ixabepilone or paclitaxel, and immunotherapeutic strategies might be suitable for COAD patients with higher CCC score. Eventually, the GDSC database analysis showed that lower CCC scores were likely to be more sensitive to 5-fluorouracil, bosutinib, gemcitabine, gefitinib, methotrexate, mitomycin C, and temozolomide, while patients with higher CCC score seemed to have a higher level of sensitivity to bortezomib and elesclomol. The novel CCC signature exhibited a good ability for prognosis prediction for COAD patients, and the CCC score was found to be highly correlated with molecular features, immune-related characteristics, and therapeutic responses, which would greatly promote clinical management and precision medicine for COAD.

Constructing a prognostic model for head and neck squamous cell carcinoma based on glucose metabolism related genes.

Glucose metabolism (GM) plays a crucial role in cancer cell proliferation, tumor growth, and survival. However, the identification of glucose metabolism-related genes (GMRGs) for effective prediction of prognosis in head and neck squamous cell carcinoma (HNSC) is still lacking. We conducted differential analysis between HNSC and Normal groups to identify differentially expressed genes (DEGs). Key module genes were obtained using weighted gene co-expression network analysis (WGCNA). Intersection analysis of DEGs, GMRGs, and key module genes identified GMRG-DEGs. Univariate and multivariate Cox regression analyses were performed to screen prognostic-associated genes. Independent prognostic analysis of clinical traits and risk scores was implemented using Cox regression. Gene set enrichment analysis (GSEA) was used to explore functional pathways and genes between high- and low-risk groups. Immune infiltration analysis compared immune cells between the two groups in HNSC samples. Drug prediction was performed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Quantitative real-time fluorescence PCR (qRT-PCR) validated the expression levels of prognosis-related genes in HNSC patients. We identified 4973 DEGs between HNSC and Normal samples. Key gene modules, represented by black and brown module genes, were identified. Intersection analysis revealed 76 GMRG-DEGs. Five prognosis-related genes (MTHFD2, CDKN2A, TPM2, MPZ, and DNMT1) were identified. A nomogram incorporating age, lymph node status (N), and risk score was constructed for survival prediction in HNSC patients. Immune infiltration analysis showed significant differences in five immune cell types (Macrophages M0, memory B cells, Monocytes, Macrophages M2, and Dendritic resting cells) between the high- and low-risk groups. GDSC database analysis identified 53 drugs with remarkable differences between the groups, including A.443654 and AG.014699. DNMT1 and MTHFD2 were up-regulated, while MPZ was down-regulated in HNSC. Our study highlights the significant association of five prognosis-related genes (MTHFD2, CDKN2A, TPM2, MPZ, and DNMT1) with HNSC. These findings provide further evidence of the crucial role of GMRGs in HNSC.

The identification of a two-gene prognostic model based on cisplatin resistance-related ceRNA network in small cell lung cancer

BackgroundSmall cell lung cancer (SCLC) is a very malignant tumor with rapid growth and early metastasis. Platinum-based chemo-resistance is the major issue for SCLC treatment failure. Identifying a new prognostic model will help to make an accurate treatment decision for SCLC patients.MethodsUsing the genomics of drug sensitivity in cancer (GDSC) database, we identified cisplatin resistance-related lncRNAs in SCLC cells. Based on the competing endogenous RNA (ceRNA) network, we identified the mRNAs correlated with the lncRNAs. Using Cox and LASSO regression analysis, a prognostic model was established. The survival prediction accuracy was evaluated by receiver operating characteristic (ROC) curve and Kaplan–Meier analysis. GSEA, GO, KEGG and CIBERSORT tools were used for functional enrichment and immune cells infiltration analysis.ResultsWe first screened out 10 differentially expressed lncRNAs between cisplatin resistant and sensitive SCLC cells from GDSC database. Based on ceRNA network, 31 mRNAs were identified with a correlation with the 10 lncRNAs. Furthermore, two genes (LIMK2 and PI4K2B) were identified by Cox and LASSO regression analysis to construct a prognostic model. Kaplan–Meier analysis indicated that the high-risk group had a poor overall survival compared with the low-risk group. The predicted area under the ROC curve (AUC) was 0.853 in the training set, and the AUC was 0.671 in the validation set. In the meanwhile, the low expression of LIMK2 or the high expression of PI4K2B in SCLC tumors was also significantly associated with poor overall survival in both training and validation sets. Functional enrichment analysis showed that the low-risk group was enriched in the apoptosis pathway and high immune infiltration of T cells. Finally, an apoptosis-related gene Cathepsin D (CTSD) was identified to be up-regulated in the low-risk group, and its higher expression correlated with better overall survival in SCLC.ConclusionWe established a prognostic model and potential biomarkers (LIMK2, PI4K2B and CTSD), which could help to improve the risk stratification of SCLC patients.

Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature

Precision medicine offers remarkable potential for the treatment of cancer, but is largely focused on tumors that harbor actionable mutations. Gene expression signatures can expand the scope of precision medicine by predicting response to traditional (cytotoxic) chemotherapy agents without relying on changes in mutational status. We present a new signature extraction method, inspired by the principle of convergent phenotypes, which states that tumors with disparate genetic backgrounds may evolve similar phenotypes independently. This evolutionary-informed method can be utilized to produce consensus signatures predictive of response to over 200 chemotherapeutic drugs found in the Genomics of Drug Sensitivity in Cancer (GDSC) Database. Here, we demonstrate its use by extracting the Cisplatin Response Signature (CisSig). We show that this signature can predict cisplatin response within carcinoma-based cell lines from the GDSC database, and expression of the signatures aligns with clinical trends seen in independent datasets of tumor samples from The Cancer Genome Atlas (TCGA) and Total Cancer Care (TCC) database. Finally, we demonstrate preliminary validation of CisSig for use in muscle-invasive bladder cancer, predicting overall survival in a small cohort of patients who undergo cisplatin-containing chemotherapy. This methodology can be used to produce robust signatures that, with further clinical validation, may be used for the prediction of traditional chemotherapeutic response, dramatically increasing the reach of personalized medicine in cancer.

Abstract 2773: Homologous recombination deficiency in the CCLE cell lines correlates with in vitro resistance to platinum agents and PARP inhibitors

Abstract Background: Since the advent of high-throughput sequencing technologies, multi-omics data along with large-scale drug sensitivity screening data on cancer cell lines from the NCI-60 panel and the CCLE (Cancer Cell Line Encyclopedia) project have been established and widely used for cancer research. Although we previously reported the utility of homologous recombination deficiency (HRD), commonly found in ovarian and breast cancer, as a clinical biomarker across cancer types (PMID:35613413), whether HRD status in cancer cell lines can be an indicator of drug sensitivity to platinum agents or PARP inhibitors has not been fully investigated. Methods: From CCLE publicly available datasets, BRCA1/2 gene mutations and their loss of heterozygosity (LOH) status, HRD score, mutational signature 3 (SBS3), and BRCA1 methylation silencing were analyzed. In vitro drug screening data were obtained from the GDSC (Genomics of Drug Sensitivity in Cancer), CTRP (Cancer Therapeutic Response Portal), and PRISM (Profiling Relative Inhibition Simultaneously in Mixtures) databases. We additionally analyzed anticancer drug response in cell line-derived xenograft models using the dataset of ROADMAPS (Responses to Oncology Agents and Dosing in Models to Aid Preclinical Studies). Results: A total of 1260 cell lines from the CCLE were analyzed. HRD score and SBS3 were positively correlated (ρ=0.36). These scores were higher in samples with BRCA1 methylation and BRCA1/2 mutations involving locus-specific LOH (referred to collectively as BRCA alteration), but not in samples with BRCA1/2 mutations lacking locus-specific LOH. Per-drug correlation analysis between cell line HRD scores and the areas under the drug response curve (AUCs) showed statistically significant (p&amp;lt;0.05) positive correlations in 60% (6/10) of platinum compounds and in 46% (6/13) of PARP inhibitors, but no negative correlations in any of these drugs. A similar trend was observed in the analysis using SBS3 and in the two-group comparison with and without BRCA alteration, indicating that cell lines with HRD were more resistant to platinum agents or PARP inhibitors. In contrast, drug response analysis in xenograft models showed that HRD score and SBS3 were non-significantly higher in cisplatin-high-sensitivity cell lines than those with low sensitivity (median HRD score 45 vs 36, median SBS3 11.7 vs 3.5). Conclusion: When comparing in vitro drug sensitivity among cancer cell lines, HRD correlates with resistance rather than with response to platinum or PARP inhibitors. Thus, the association between HRD status and drug sensitivity of cancer cells is substantially different between tumors in dish culture and in clinical settings. This study provides critical insights that should be considered when using cancer cell lines and underscores the need for experimental models that better reflect efficacy in clinical tumors. Citation Format: Shiro Takamatsu, Noriomi Matsumura. Homologous recombination deficiency in the CCLE cell lines correlates with in vitro resistance to platinum agents and PARP inhibitors [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 2773.

Identifying tumor antigens and immune subtypes of renal cell carcinoma for immunotherapy development.

Renal cell carcinoma (RCC) is one of the leading causes of death in men. Messenger ribonucleic acid (mRNA) vaccines may be an attractive means to achieve satisfactory results. Cancer immunotherapy is a promising cancer treatment strategy. However, immunotherapy is not widely used in renal cell carcinoma, as only a few patients show a positive response. The present study aimed to identify potential antigens associated with renal cell carcinoma to develop an anti-renal cell carcinoma mRNA vaccine. Moreover, the immune subtypes of renal cell carcinoma cells were determined. The Cancer Genome Atlas (TCGA) analysis revealed gene expression profiles and clinical information. Antigen-presenting cells infiltrated the immune system using Tumor Immune Estimation Resource (TIMER) tool (http://timer.cistrome.org/). GDSC (Genomics of Drug Sensitivity in Cancer) database were used to estimate drug sensitivity. The 13 immune-related genes discovery could be targets for immunotherapy in renal cell carcinoma patients, as they were associated with a better prognosis and a higher level of antigen-presenting cells. These immune subtypes have significant relationships with immunological checkpoints, immunogenic cell death regulators, and RCC prognostic variables. Furthermore, DBH-AS1 was identified as a potential antigen for developing an mRNA vaccine. The CCK8 assay demonstrated that the proliferative capacity of 786-O and Caki-1 cells overexpressing DBH-AS1 was higher than in the control group. In addition, transwell assay revealed that 786-O and Caki-1 cells overexpressing DBH-AS1 showed higher invasion capacity compared with control. This study provides a theoretical basis for the development of mRNA vaccines. Our findings suggest that DBH-AS1 could be potential antigens for developing RCC mRNA vaccines.

Identification and Validation of a DNA Damage Repair-Related Signature for Diffuse Large B-Cell Lymphoma.

Background Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin's lymphoma in adults, whose prognostic scoring system remains to be improved. Dysfunction of DNA repair genes is closely associated with the development and prognosis of diffuse large B-cell lymphoma. The aim of this study was to establish and validate a DNA repair-related gene signature associated with the prognosis of DLBCL and to investigate the clinical predictive value of this signature. Methods DLBCL cases were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. One hundred ninety-nine DNA repair-related gene sets were retrieved from the GeneCards database. The LASSO Cox regression was used to generate the DNA repair-related gene signature. Subsequently, the level of immune cell infiltration and the correlation between the gene signature and immune cells were analyzed using the CIBERSORT algorithm. Based on the Genomics of Drug Sensitivity in Cancer (GDSC) database, the relationship between the signature and drug sensitivity was analyzed, and together with the nomogram and gene set variation analysis (GSVA), the value of the signature for clinical application was evaluated. Results A total of 14 DNA repair genes were screened out and included in the final risk model. Subgroup analysis of the training and validation cohorts showed that the risk model accurately predicted overall survival of DLBCL patients, with patients in the high-risk group having a worse prognosis than patients in the low-risk group. Subsequently, the risk score was confirmed as an independent prognostic factor by multivariate analysis. Furthermore, by CIBERSORT analysis, we discovered that immune cells, such as regulatory T cells (Tregs), activated memory CD4+ T cells, and gamma delta T cells showed significant differences between the high- and low-risk groups. In addition, we found some interesting associations of our signature with immune checkpoint genes (CD96, TGFBR1, and TIGIT). By analyzing drug sensitivity data in the GDSC database, we were able to identify potential therapeutics for DLBCL patients stratified according to our signature. Conclusions Our study identified and validated a 14-DNA repair-related gene signature for stratification and prognostic prediction of DLBCL patients, which might guide clinical personalization of treatment.

Comprehensive FGFR3 alteration-related transcriptomic characterization is involved in immune infiltration and correlated with prognosis and immunotherapy response of bladder cancer.

BackgroundBladder cancer (BC) threatens the health of human beings worldwide because of its high recurrence rate and mortality. As an actionable biomarker, fibroblast growth factor receptor 3 (FGFR3) alterations have been revealed as a vital biomarker and associated with favorable outcomes in BC. However, the comprehensive relationship between the FGFR3 alteration associated gene expression profile and the prognosis of BC remains ambiguous.Materials and MethodsGenomic alteration profile, gene expression data, and related clinical information of BC patients were downloaded from The Cancer Genomics database (TCGA), as a training cohort. Subsequently, the Weighted Gene Co-expression Network Analysis (WGCNA) was conducted to identify the hub modules correlated with FGFR3 alteration. The univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to obtain an FGFR3 alteration-related gene (FARG) prognostic signature and FARG-based nomogram. The receiver operating characteristic (ROC) curve analysis was used for evaluation of the ability of prognosis prediction. The FARG signature was validated in four independent datasets, namely, GSE13507, GSE31684, GSE32548, and GSE48075, from Gene Expression Omnibus (GEO). Then, clinical feature association analysis, functional enrichment, genomic alteration enrichment, and tumor environment analysis were conducted to reveal differential clinical and molecular characterizations in different risk groups. Lastly, the treatment response was evaluated in the immunotherapy-related dataset of the IMvigor210 cohort and the frontline chemotherapy dataset of GSE48276, and the chemo-drug sensitivity was estimated via Genomics of Drug Sensitivity in Cancer (GDSC).ResultsThere were a total of eleven genes (CERCAM, TPST1, OSBPL10, EMP1, CYTH3, NCRNA00201, PCDH10, GAP43, COLQ, DGKB, and SETBP1) identified in the FARG signature, which divided BC patients from the TCGA cohort into high- and low-risk groups. The Kaplan–Meier curve analysis demonstrated that BC patients in the low-risk group have superior overall survival (OS) than those in the high-risk group (median OS: 27.06 months vs. 104.65 months, p < 0.0001). Moreover, the FARG signature not only showed a good performance in prognosis prediction, but also could distinguish patients with different neoplasm disease stages, notably whether patients presented with muscle invasive phenotype. Compared to clinicopathological features, the FARG signature was found to be the only independent prognostic factor, and subsequently, a FARG-based prognostic nomogram was constructed with better ability of prognosis prediction, indicated by area under ROC curve (AUC) values for 1-, 3-, and 5-year OS of 0.69, 0.71, and 0.79, respectively. Underlying the FARG signature, multiple kinds of metabolism- and immune-related signaling pathways were enriched. Genomic alteration enrichment further identified that FGFR3 alterations, especially c.746C>G (p.Ser249Cys), were more prevalent in the low-risk group. Additionally, FARG score was positively correlated with ESTIMATE and TIDE scores, and the low-risk group had abundant enrichment of plasma B cells, CD8+ T cells, CD4+ naive T cells, and helper follicular T cells, implying that patients in the low-risk group were likely to make significant responses to immunotherapy, which was further supported by the analysis in the IMvigor210 cohort as there was a significantly higher response rate among patients with lower FARG scores. The analysis of the GDSC database finally demonstrated that low-risk samples were more sensitive to methotrexate and tipifarnib, whereas those in the high-risk group had higher sensitivities in cisplatin, docetaxel, and paclitaxel, instead.ConclusionThe novel established FARG signature based on a comprehensive FGFR3 alteration-related transcriptomic profile performed well in prognosis prediction and was also correlated with immunotherapy and chemotherapy treatment responses, which had great potential in future clinical applications.

A novel m6A-based HCC prognosis signature and potential mechanisms in predicting immune and anti-angiogenic response.

e16174 Background: The combination of immune checkpoint inhibitors and anti-angiogenic drugs has been approved as the preferred first-line treatment for advanced hepatocellular carcinoma (HCC). However, only a small subset of HCC patients could benefit from this combination treatment. Methods: The m6A clusters were identified by consistent cluster analysis via R package “consense-cluster plus” based on the expression of 26 m6A regulators. The immune cells infiltration was estimation of by ssGSEA, MCPCOUNT and TIMER 2.0 algorithm. The m6A-related prognosis significant genes were screened by univariate Cox, lasso-Cox and multivariate Cox regression, and the prognosis signature was constructed by multivariate Cox regression. The IC50s of anti-angiogenic drugs in GDSC (Genomics of Drug Sensitivity in Cancer) database were calculated by “pRRophetic” package. The packages “Celldex” and “SingleR” were used to annotate cell types, and to present the expression landscapes of 5 hub genes in different sites and immune cell subpopulations of HCC patients. Results: We identified three m6A clusters with distinct immune and angiogenesis microenvironments. We further constructed a 5-gene prognosis signature (termed as m6Asig-Score) which could predict both immune and anti-angiogenic responses. We illustrated that high m6Asig-Score is associated with poor prognosis, advanced TNM stage, and high TP53 mutation frequency. Besides, the m6Asig-Score was negatively associated with immune checkpoint inhibitors and anti-angiogenic drugs response. We further found that two of the five m6Asig-Score inner genes, B2M and SMOX, were associated with immune cell infiltration, immune response and the sensitivity to sorafenib, which were validated in two independent immunotherapy cohorts and the GDSC database. Conclusions: The m6Asig-Score, B2M and SMOX might have a significant impact on guiding the combined strategy of immunotherapy and anti-angiogenic therapy in HCC.

Identification of HCC Subtypes With Different Prognosis and Metabolic Patterns Based on Mitophagy.

Background: Mitophagy is correlated with tumor initiation and development of malignancy. However, HCC heterogeneity with reference to mitophagy has yet not been systematically explored. Materials and Methods: Mitophagy-related, glycolysis-related, and cholesterol biosynthesis-related gene sets were obtained from the Reactome database. Mitophagy-related and metabolism-related subtypes were identified using the ConsensusClusterPlus algorithm. Univariate Cox regression was analysis was performed to identify prognosis-related mitophagy regulators. Principal component analysis (PCA) was used to create composite measures of the prognosis-related mitophagy regulators (mitophagyscore). Individuals with a mitophagyscore higher or lower than the median value were classified in high- or low-risk groups. Kaplan-Meier survival and ROC curve analyses were utilized to evaluate the prognostic value of the mitophagyscore. The nomogram and calibration curves were plotted using the“rms” R package. The package “limma” was used for differential gene expression analysis. Differentially expressed genes (DEGs) between high- and low-risk groups were used as queries in the CMap database. R package “pRRophetic” and Genomics of Drug Sensitivity in Cancer (GDSC) database were used to determine the sensitivity of 21 previously reported anti-HCC drugs. Results: Three distinct HCC subtypes with different mitophagic accumulation (low, high, and intermediate mitophagy subtypes) were identified. High mitophagy subtype had the worst outcome and highest glycolysis level. The lowest degree of hypoxia and highest cholesterol biosynthesis was observed in the low mitophagy subtype; oncogenic dedifferentiation level in the intermediate mitophagy subtype was the lowest. Mitophagyscore could serve as a novel prognostic indicator for HCC.High-risk patients had a poorer prognosis (log-rank test, p < 0.001). The area under the ROC curve for mitophagyscore in 1-year survival was 0.77 in the TCGA cohort and 0.75 in the ICGC cohort. Nine candidate small molecules which were potential drugs for HCC treatment were identified from the CMap database. A decline in the sensitivity towards 21 anti-HCC drugs was observed in low-risk patients by GDSC database. We also identified a novel key gene, SPP1, which was highly associated with different mitophagic subtypes. Conclusion: Based on bioinformatic analyses, we systematically examined the HCC heterogeneity with reference to mitophagy and observed three distinct HCC subtypes having different prognoses and metabolic patterns.

Establishment and validation of an individualized macrophage-related gene signature to predict overall survival in patients with triple negative breast cancer.

BackgroundRecently, researchers have classified highly heterogeneous triple negative breast cancer (TNBC) into different subtypes from different perspectives and investigated the characteristics of different subtypes to pursue individualized treatment. With the increase of immunotherapy and its preliminary application in TNBC treatment, the value of immune-related strategies in the treatment of TNBC has been initially reflected. Based thereon, this study plans to classify and further explore TNBC from the perspective of immune cell infiltration.MethodThe fractions of immune cells of TNBC patients were assessed by six immune component analysis methods in The Cancer Genome Atlas (TCGA) database. Hub genes significantly related to poor prognosis were verified by weighted gene co-expression network analysis (WGCNA) analysis, Lasso analysis, and univariate KM analysis. Two cohorts of TNBC patients with complete prognosis information were collected for validation analysis. Finally, the Genomics of Drug Sensitivity in Cancer (GDSC) database was adopted to ascertain the sensitivity differences of different populations based on hub-gene grouping to different chemotherapy drugs.ResultsFive hub genes (CD79A, CXCL13, IGLL5, LHFPL2, and PLEKHF1) of the key co-expression gene module could divide TNBC patients into two groups (Cluster A and Cluster B) based on consistency cluster analysis. The patients with Cluster A were responsible for significantly worse prognosis than the patients with Cluster B (P = 0.023). In addition, another classification method, PCoA, and two other datasets (GSE103091 and GSE76124), were used to obtain consistent results with previous findings, which verified the stability of the classification method and dataset in this study. The grouping criteria based on the previous results were developed and the accuracy of the cut-off values was validated. A prognosis model of TNBC patients was then constructed based on the grouping results of five hub genes and N staging as prognostic factors. The results of ROC and decision curve analyses showed that this model had high prediction accuracy and patients could benefit therefrom. Finally, GDSC database analysis proved that patients in Cluster A were more sensitive to Vinorelbine. Separate analysis of the sensitivity of patients in Cluster A to Gemcitabine and Vinorelbine showed that the patients in Cluster A exhibited higher sensitivity to Vinorelbine. We hypothesized that these five genes were related to gemcitabine resistance and they could serve as biomarkers for clinical drug decision-making after anthracene resistance and taxane resistance in patients with advanced TNBC.ConclusionThis study found five hub prognostic genes associated with macrophages, and a prognostic model was established to predict the survival of TNBC patients. Finally, these five genes were related to gemcitabine resistance in TNBC patients.

Applications of Machine Learning to Predict Cisplatin Resistance in Lung Cancer

PurposeLung cancer, mainly lung adenocarcinoma, lung squamous cell carcinoma and small cell lung cancer, has the highest incidence and cancer-related mortality worldwide. Platinum-based chemotherapy plays an important role in the treatment of various lung cancer subtypes, but not all patients benefit from this treatment regimen; thus, it is worth identifying lung cancer patients who are resistant or sensitive to platinum-based therapy.MethodsThe drug response and sequencing data of 170 lung cancer cell lines were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) database, and support vector machines (SVMs) and beam search were used to select an optimal gene panel that can predict the sensitivity of cell lines to cisplatin. Then, we used available cell line data to explore the potential mechanisms.ResultsIn this work, the drug response and sequencing data of 170 lung cancer cell lines were downloaded from the GDSC database, and SVMs and beam search were used to screen a panel of genes related to lung cancer cell line resistance to cisplatin. A final panel of nine genes (PLXNC1, KIAA0649, SPTBN4, SLC14A2, F13A1, COL5A1, SCN2A, PLEC, and ALMS1) was identified, and achieved an area under the curve (AUC) of 0.873 ± 0.004. The natural logarithm of the half maximal inhibitory concentration (lnIC50) values of the mutant-type (panel-MT) group was significantly higher than that of the wild-type (panel-WT) group, regardless of the lung cancer subtype. The differentially expressed pathways between the two groups may explain this difference.ConclusionIn this study, we found that a panel of nine genes can accurately predict sensitivity to cisplatin, which may provide individualized treatment recommendations to improve the prognosis of patients with lung cancer.

Downregulation of c-Myc expression confers sensitivity to CHK1 inhibitors in hematologic malignancies.

Checkpoint kinase 1 inhibitors (CHK1i) have shown impressive single-agent efficacy in treatment of certain tumors, as monotherapy or potentiators of chemotherapy in clinical trials, but the sensitive tumor types and downstream effectors to dictate the therapeutic responses to CHK1i remains unclear. In this study we first analyzed GDSC (Genomics of Drug Sensitivity in Cancer) and DepMap database and disclosed that hematologic malignancies (HMs) were relatively sensitive to CHK1i or CHK1 knockdown. This notion was confirmed by examining PY34, a new and potent in-house selective CHK1i, which exhibited potent anti-HM effect in vitro and in vivo, as single agent. We demonstrated that the downregulation of c-Myc and its signaling pathway was the common transcriptomic profiling response of sensitive HM cell lines to PY34, whereas overexpressing c-Myc could partially rescue the anticancer effect of PY34. Strikingly, we revealed the significant correlations between downregulation of c-Myc and cell sensitivity to PY34 in 17 HM cell lines and 39 patient-derived cell (PDC) samples. Thus, our results demonstrate that HMs are more sensitive to CHK1i than solid tumors, and c-Myc downregulation could represent the CHK1i efficacy in HMs.

Significance and Mechanisms Analyses of RB1 Mutation in Bladder Cancer Disease Progression and Drug Selection by Bioinformatics Analysis.

Bladder cancer is still a disease of significant morbidity and mortality. In bladder cancer, RB1 is one of the most common mutant genes. In this study, we explored the Genomics of Drug Sensitivity in Cancer (GDSC) database for drug sensitivity. The latest TCGA data were downloaded for analysis. To deal with functional enrichment analysis, GSEA, KEGG and GO were used. Prognostic analyses have been carried out using the GEPIA online tool. Results from the GDSC database showed that bladder cancer cells with RB1 mutation are more resistant to Dactolisib, MK-2206 and GNE-317. RB1 mutation was found in 25%bladder cancer patients. Patients with RB1 mutation often had lower RB1 mRNA expression level and higher histologic grade. In addition, we identified 999 differentially expressed genes in both groups. Functional enrichment analysis suggested that DEGs were primarily enriched in multiple metabolic progressions, cell proliferation and cancer related pathways. There were strong correlations between WT1, GPR37, CHRM2 and EZH2 expression levels and the prognosis. In all, the significance of RB1 mutation in disease progression and drug selection in bladder cancer was suggested by our results, and multiple genes and pathways related to such a program were identified.

Identification and validation of L Antigen Family Member 3 as an immune-related biomarker associated with the progression of papillary thyroid cancer

BackgroundPapillary thyroid cancer (PTC) is heterogeneous cancer with many different immune cells involved in its pathogenesis. L Antigen Family Member 3 (LAGE3) is an ESO/LAGE gene family member that has not been extensively studied in PTC. MethodsComprehensive bioinformatics analyses of LAGE3 were based on The Cancer Genome Atlas, Gene Expression Omnibus, and Genomics of Drug Sensitivity in Cancer (GDSC) databases. We also performed RNA-sequencing on 78 paired samples from local PTC patients. ResultsWe observed that LAGE3 was significantly up-regulated in most solid tumor types, including PTC compared with corresponding normal tissues. The high level of LAGE3 was also significantly associated with advanced malignancy. LAGE3 expression was significantly associated with cancer-related pathways, biochemical metabolism, and immune-related terms. Further, tumor microenvironment analysis indicated LAGE3 was positively correlated with different immune cells infiltrating levels and the activity of different steps of the cancer-immunity cycle. Analyses based on the GDSC database revealed that low levels of LAGE3 might be resistant to WZ3105, I-BET-762, and PHA-793887. In addition, the experimental results validated that knocking down LAGE3 could affect proliferation, migration, and invasion in the PTC cell lines. ConclusionThis study discloses that LAGE3 plays an oncogenic and cancer-immunological role, also providing novel PTC biological and clinical implications.

C8orf48 inhibits the tumorigenesis of colorectal cancer by regulating the MAPK signaling pathway

AimsColorectal cancer (CRC) is a leading cause of cancer-related death globally. Thus, in this study, we aimed to investigate chromosome 8 open reading frame 48 (C8orf48) as a biomarker for early detection of CRC. Main methodsRNA expression and methylation profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Cell proliferation, migration and invasion assays were performed to confirm the function of C8orf48 in CRC cells. Dual-luciferase reporter assay was used to identify that C8orf48 was the direct target of miR-556. Genomics of Drug Sensitivity in Cancer (GDSC) database, gene set enrichment analysis (GSEA) and western blot analysis were performed to explore the mechanism of C8orf48. Key findingswe found that C8orf48 is down-regulated in clinical samples of CRC tissues. Enrichment analysis showed that C8orf48 is associated with methylation biomarkers in CRC, and TCGA database confirmed that the methylation of C8orf48 is up-regulated in the early stage of CRC. We further revealed that the overexpression of C8orf48 decreased CRC cell proliferation, migration and invasion. Luciferase reporter indicated that C8orf48 was the direct target of the oncogene miR-556. Additionally, we used GDSC database, GSEA database and western blot analysis to demonstrate that C8orf48 plays a suppressor role in CRC by inhibiting MAPK signaling pathway. SignificanceC8orf48 was identified as a biomarker for early detection of CRC for the first time, and might provide novel information for CRC prediction and therapy.

The critical role of BAP1 mutation in the prognosis and treatment selection of kidney renal clear cell carcinoma.

BackgroundThe BAP1 mutation is commonly found kidney renal clear cell carcinoma (KIRC) and a potential biomarker of individualized therapy. We evaluated the clinical significance of BAP1 mutation in the prognosis and treatment therapies for KIRC. Potential key pathways and related genes associated with these mechanisms were also identified in this investigation.MethodsWe identified the relevant data of patients BAP1 mutated on the cBioPortal and the compounds with significant selectivity to BAP1 mutations on the Genomics of Drug Sensitivity in Cancer (GDSC). And then, we identified the differences in mRNA expression levels of biological function annotation and pathways between mutated and wild type BAP1 patients by GSEA analysis. Furthermore, we screened the differentially expressed genes (DEGs) between BAP1 mutated and wild typed in KIRC patients and performed the GO and KEGG analysis. Finally, we conducted a protein-protein interaction (PPI) network to investigate the interaction between proteins encoded by candidate DEGs.ResultsReview of the TCGA data revealed 41 patients (10%) with KIRC displayed the BAP1 mutation. Further analysis led to the identification of 730 DEGs, while 617 genes were shown to be down-regulated, with 113 genes displaying upregulation. GO and KEGG pathway analysis indicated DEGs as enriched in metabolism, drug metabolism-cytochrome P450, and Drug-metabolizing enzymes. Subsequently, the top 10 hub genes, ranked by the degree in the PPI network were identified. Furthermore, our findings verify that the BAP1 mutation was associated with the deterioration of prognosis in patients with KIRC. Additionally, analysis of the GDSC database revealed that KIRC patients with BPP1 mutation are more prone to responding to Linsitinib.ConclusionsOur investigation identified the main pathways and relevant genes related to the BAP1 mutation in KIRC, which can contribute to the development of targeted treatment strategies for enhanced prognostic predictions of KIRC.

Multiscale omic assessment of EWSR1-NFATc2 fusion positive sarcomas to identify conserved fusion breakpoint and activation of the mTOR pathway.

e23536 Background: EWSR1-NFATc2 ( E-N) fusion positive sarcomas are rare cancers historically categorized as Ewing sarcomas. Emerging evidence suggests unique molecular characteristics and chemotherapy sensitivities in these cancers. Here we present the largest cohort of genomically profiled E-N fusion positive sarcomas and evaluate their breakpoints and putative pathway alterations. Methods: Comprehensive genomic profiling of 1,024 EWSR1 fusion positive sarcomas, including 14 E-N fusions, was obtained through the Foundation Medicine research database (FMI). Additional data from the Gene Expression Omnibus, the Genomics of Drug Sensitivity in Cancer (GDSC), and The Cancer Genome Atlas (TCGA) datasets were used. Descriptive statistics, principle component analysis, differential expression, and pathway analysis were used as appropriate. Results: E-N fusions identified in the FMI database demonstrated consistent breakpoints located between exon 2 and 3 on NFATc2; consistent with previous reports suggesting that the primary transactivation domain and regulatory domains of NFATc2 are conserved in the EWRS1-NFATc2 fusion. Cluster analysis demonstrated separate E-N fusion positive sarcoma grouping than other EWRS1 sarcomas, while identifying an enrichment of mTOR pathway variants in E-N fusion positive samples (p = 0.05). Gene expression data from E-N identified a significant activation of the mTOR pathway in E-N positive sarcomas compared to either EWSR1-ETS (p = 0.002) or CIC-DUX4 positive sarcomas (p &lt; 0.001). In the GDSC database, NFATc2-High lines exhibited activation of the mTOR pathway (p &lt; 0.0001) and were more sensitive to the mTOR inhibitor rapamycin (p = 0.0006). Pan-cancer analysis of 33 non-overlapping TCGA datasets, elevated NFATc2 mRNA expression was significantly associated with activated RICTOR and mTOR signaling (p &lt; 0.0001). Elevated NFATc2 correlated with higher tumor grade and poorer progression-free survival and overall survival. Conclusions: Taken together, the data presented here suggests that E-N fusion positive sarcomas represent a distinct molecularly entity in comparison to traditional Ewing sarcomas. Conserved action of NFATc2 may correlate to mTOR over activation.

MP39-03 LOSS OF MLH1 CONFERS RESISTANCE TO PI3Kβ INHIBITORS IN RENAL CLEAR CELL CARCINOMA WITH SETD2 MUTATION

Renal clear cell carcinoma (ccRCC) is characterized by frequent mutation in SETD2, which has recently been shown to regulate mismatch repair (MMR). We aim to investigate the association between MMR machinery genes and SETD2 mutation in ccRCC. We exploited the Genomics of Drug Sensitivity in Cancer (GDSC) database to identify selective inhibitors for SETD2 mutant ccRCC cells. We also exploited the Cancer Genome Atlas (TCGA) database to study the association between SETD2 status and MMR-related genes. In vitro studies were performed to validate the in silico findings. Reproduction of the GDSC database revealed four compounds with significant selectivity for SETD2 mutant ccRCC cells, amongst which two compounds targeted PI3Kβ. Phosphorylation of AKT at both S473 and T308 was decreased following PI3Kβ inhibitor treatment in SETD2 mutant ccRCC cells, whereas the basal pAKT level was not changed between mutant and wild-type SETD2. Both decreased MLH1 and increased AKT levels induced lower PMS2, indicating that MMR was mediated by SETD2 via both AKT and MLH1 in ccRCC. Analysis of the TCGA database further revealed high tendency of homozygous co-deletion of SETD2 and MLH1. In the absence of MLH1, suppression of pAKT by PI3Kβ inhibitor was mitigated and inhibition in cell proliferation, invasiveness, migratory ability and tumourigenesis was partially restored. Besides the reported H3K36-trimethylation pathway, we found that SETD2 mutation also mediated MMR via AKT-induced PMS2 decrease and co-loss of MLH1 loss in ccRCC.