Genomics Of Drug Sensitivity In Cancer Research Articles

Identification of key genes involved in tumor immune cell infiltration and cetuximab resistance in colorectal cancer

BackgroundThe anti-epidermal growth factor receptor (EGFR) antibody introduces adaptable variations to the transcriptome and triggers tumor immune infiltration, resulting in colorectal cancer (CRC) treatment resistance. We intended to identify genes that play essential roles in cetuximab resistance and tumor immune cell infiltration.MethodsA cetuximab-resistant CACO2 cellular model was established, and its transcriptome variations were detected by microarray. Meanwhile, public data from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA) database were downloaded. Integrated bioinformatics analysis was applied to detect differentially expressed genes (DEGs) between the cetuximab-resistant and the cetuximab-sensitive groups. Then, we investigated correlations between DEGs and immune cell infiltration. The DEGs from bioinformatics analysis were further validated in vitro and in clinical samples.ResultsWe identified 732 upregulated and 1259 downregulated DEGs in the induced cellular model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, along with Gene Set Enrichment Analysis and Gene Set Variation Analysis, indicated the functions of the DEGs. Together with GSE59857 and GSE5841, 12 common DEGs (SATB-2, AKR1B10, ADH1A, ADH1C, MYB, ATP10B, CDX-2, FAR2, EPHB2, SLC26A3, ORP-1, VAV3) were identified and their predictive values of cetuximab treatment were validated in GSE56386. In online Genomics of Drug Sensitivity in Cancer (GDSC) database, nine of twelve DEGs were recognized in the protein-protein (PPI) network. Based on the transcriptome profiles of CRC samples in TCGA and using Tumor Immune Estimation Resource Version 2.0, we bioinformatically determined that SATB-2, ORP-1, MYB, and CDX-2 expressions were associated with intensive infiltration of B cell, CD4+ T cell, CD8+ T cell and macrophage, which was then validated the correlation in clinical samples by immunohistochemistry. We found that SATB-2, ORP-1, MYB, and CDX-2 were downregulated in vitro with cetuximab treatment. Clinically, patients with advanced CRC and high ORP-1 expression exhibited a longer progression-free survival time when they were treated with anti-EGFR therapy than those with low ORP-1 expression.ConclusionsSATB-2, ORP-1, MYB, and CDX-2 were related to cetuximab sensitivity as well as enhanced tumor immune cell infiltration in patients with CRC.

A Prognostic Signature Based on Immunogenomic Profiling Offers Guidance for Esophageal Squamous Cell Cancer Treatment.

Our study aimed to develop an immune prognostic signature that could provide accurate guidance for the treatment of esophageal squamous cell cancer (ESCC). By implementing Single-Sample Gene Set Enrichment Analysis (ssGSEA), we established two ESCC subtypes (Immunity High and Immunity Low) in GSE53625 based on immune-genomic profiling of twenty-nine immune signature. We verified the reliability and reproducibility of this classification in the TCGA database. Immunity High could respond optimally to immunotherapy due to higher expression of immune checkpoints, including PD1, PDL1, CTLA4, and CD80. We used WGCNA analysis to explore the underlying regulatory mechanism of the Immunity High group. We further identified differentially expressed immune-related genes (CCR5, TSPAN2) in GSE53625 and constructed an independent two-gene prognostic signature we internally validated through calibration plots. We established that high-risk ESCC patients had worse overall survival (P=0.002, HR=2.03). Besides, high-risk ESCC patients had elevated levels of infiltrating follicle-helper T cells, naïve B cells, and macrophages as well as had overexpressed levels of some immune checkpoints, including B3H7, CTLA4, CD83, OX40L, and GEM. Moreover, through analyzing the Genomics of Drug Sensitivity in Cancer (GDSC) database, the high-risk group demonstrated drug resistance to some chemotherapy and targeted drugs such as paclitaxel, gefitinib, erlotinib, and lapatinib. Furthermore, we established a robust nomogram model to predict the clinical outcome in ESCC patients. Altogether, our proposed immune prognostic signature constitutes a clinically potential biomarker that will aid in evaluating ESCC outcomes and promote personalized treatment.

Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model

BackgroundPredicting the response of cancer cell lines to specific drugs is an essential problem in personalized medicine. Since drug response is closely associated with genomic information in cancer cells, some large panels of several hundred human cancer cell lines are organized with genomic and pharmacogenomic data. Although several methods have been developed to predict the drug response, there are many challenges in achieving accurate predictions. This study proposes a novel feature selection-based method, named Auto-HMM-LMF, to predict cell line-drug associations accurately. Because of the vast dimensions of the feature space for predicting the drug response, Auto-HMM-LMF focuses on the feature selection issue for exploiting a subset of inputs with a significant contribution.ResultsThis research introduces a novel method for feature selection of mutation data based on signature assignments and hidden Markov models. Also, we use the autoencoder models for feature selection of gene expression and copy number variation data. After selecting features, the logistic matrix factorization model is applied to predict drug response values. Besides, by comparing to one of the most powerful feature selection methods, the ensemble feature selection method (EFS), we showed that the performance of the predictive model based on selected features introduced in this paper is much better for drug response prediction. Two datasets, the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are used to indicate the efficiency of the proposed method across unseen patient cell-line. Evaluation of the proposed model showed that Auto-HMM-LMF could improve the accuracy of the results of the state-of-the-art algorithms, and it can find useful features for the logistic matrix factorization method.ConclusionsWe depicted an application of Auto-HMM-LMF in exploring the new candidate drugs for head and neck cancer that showed the proposed method is useful in drug repositioning and personalized medicine. The source code of Auto-HMM-LMF method is available in https://github.com/emdadi/Auto-HMM-LMF.

A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response.

Cancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalized regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumorigenesis and DNA damage response.

In Silico Inference of Synthetic Cytotoxic Interactions from Paclitaxel Responses

To exploit negatively interacting pairs of cancer somatic mutations in chemotherapy responses or synthetic cytotoxicity (SC), we systematically determined mutational pairs that had significantly lower paclitaxel half maximal inhibitory concentration (IC50) values. We evaluated 407 cell lines with somatic mutation profiles and estimated their copy number and drug-inhibitory concentrations in Genomics of Drug Sensitivity in Cancer (GDSC) database. The SC effect of 142 mutated gene pairs on response to paclitaxel was successfully cross-validated using human cancer datasets for urogenital cancers available in The Cancer Genome Atlas (TCGA) database. We further analyzed the cumulative effect of increasing SC pair numbers on the TP53 tumor suppressor gene. Patients with TCGA bladder and urogenital cancer exhibited improved cancer survival rates as the number of disrupted SC partners (i.e., SYNE2, SON, and/or PRY) of TP53 increased. The prognostic effect of SC burden on response to paclitaxel treatment could be differentiated from response to other cytotoxic drugs. Thus, the concept of pairwise SC may aid the identification of novel therapeutic and prognostic targets.

Identification of immune-based prostate cancer subtypes using mRNA expression.

Immune infiltration in Prostate Cancer (PCa) was reported to be strongly associated with clinical outcomes. However, previous research could not elucidate the diversity of different immune cell types that contribute to the functioning of the immune response system. In the present study, the CIBERSORT method was employed to evaluate the relative proportions of immune cell profiling in PCa samples, adjacent tumor samples and normal samples. Three types of molecular classification were identified in tumor samples using the ‘CancerSubtypes’ package of the R software. Each subtype had specific molecular and clinical characteristics. In addition, functional enrichment was analyzed in each subtype. The submap and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were also used to predict clinical response to the immune checkpoint blockade. Moreover, the Genomics of Drug Sensitivity in Cancer (GDSC) database was employed to screen for potential chemotherapeutic targets for the treatment of PCa. The results showed that Cluster I was associated with advanced PCa and was more likely to respond to immunotherapy. The findings demonstrated that differences in immune responses may be important drivers of PCa progression and response to treatment. Therefore, this comprehensive assessment of the 22 immune cell types in the PCa Tumor Environment (TEM) provides insights on the mechanisms of tumor response to immunotherapy and may help clinicians explore the development of new drugs.

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.

Relevance of STK11 Mutations Regarding Immune Cell Infiltration, Drug Sensitivity, and Cellular Processes in Lung Adenocarcinoma.

Serine/threonine kinase 11 (STK11) is one member of the serine/threonine kinase family, which is involved in regulating cell polarity, apoptosis, and DNA damage repair. In lung adenocarcinoma (LUAD), it can play as one tumor suppressor and always be mutated. In this study, we aimed to assess the relevance of STK11 mutations in LUAD, in which we also studied the correlation among immune cell infiltration, drug sensitivity, and cellular processes. By performing the bioinformatics analysis of the Cancer Genome Atlas (TCGA) about LUAD patients, we found that the mutation efficiency of STK11 mutations is about 19%. Additionally, the differentially expressed gene analysis showed that there were 746 differentially expressed genes (DEGs) between LUAD patients with and without STK11 mutations. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis showed that the DEGs were enriched in various tumorigenesis signaling pathways and metabolic processes. Among these DEGs, the top ranking 21 genes were found that they were more frequently mutated in the STK11 mutation group than in the wild-type group (p-value<0.01). Finally, the LUAD patients with STK11 mutations suffered the worse immune cell infiltration levels than the LUAD patients with wild-type. The STK11 gene copy number was correlated with immune cell infiltration. Aiming to develop the therapeutic drugs, we performed Genomics of Drug Sensitivity in Cancer (GDSC) data to identify the potential therapeutic candidate and the results showed that Nutlin-3a(-) may be a sensitive drug for LUAD cases harboring STK11 mutations. The specific genes and pathways shown to be associated with LUAD cases involving STK11 mutations may serve as targets for individualized LUAD treatment.

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.

&lt;p&gt;Screening and Identifying Cisplatin-Related Gene Mutations in Lung Squamous Cell Carcinoma&lt;/p&gt;

IntroductionPlatinum-based chemotherapy is the cornerstone of treatment for patients with LUSC, but cisplatin resistance greatly restricts its clinical application. Therefore, it is particularly important to screen the predominant LUSC population using biomarkers.MethodsData for 15 LUSC cell lines were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) Project database to screen for mutations related to cisplatin susceptibility. We conducted whole-exome sequencing (WES) of tumors from 58 LUSC patients from Sichuan Provincial People’s Hospital of University of Electronic Science and Technology. Subsequently, the clinical prognostic value of these mutations was verified by using The Cancer Genome Atlas (TCGA)-LUSC cohort and our cohort (n=58).ResultsBased on the cisplatin sensitivity data of GDSC-LUSC and survival analysis of TCGA-LUSC and Local-LUSC cohorts, we found that only mutation of IGF2R was associated with cisplatin sensitivity, better overall survival [OS; P=0.04, HR (95% CI): 0.42 (0.23–0.78)] and progression-free survival [PFS; P =0.016, HR (95% CI): 0.26 (0.12–0.59)]. However, there were no significant differences in the frequencies of gene mutations between the IGF2R-mutant (IGF2R-MT) and IGF2R-wild-type (IGF2R-WT) groups. Gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) indicated enhanced intracellular detoxification and decreased abnormal signaling activity to reverse cisplatin tolerance in the IGF2R-MT group.ConclusionThe results suggest that IGF2R mutations are a potential biomarker for screening LUSC patients suitable for cisplatin treatment.

Identification of Mutations Related to Cisplatin-Resistance and Prognosis of Patients With Lung Adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is the most common histologic type of non-small cell lung cancer (NSCLC; approximately 60%), and platinum-based chemotherapy is the cornerstone of the treatment for patients with LUAD. However, a considerable number of patients experience tumor recurrence after developing cisplatin (cis-diamminedichloroplatinum(II) or CDDP) resistance. Therefore, it is particularly important to screen primary CDDP-resistant LUAD patient populations, which can maximize the clinical benefits for these patients. Methods: Data for 61 LUAD cell lines were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) database to screen for mutations related to CDDP susceptibility, and we conducted whole-exome sequencing (WES) of tumors from 45 LUAD patients from Zhujiang Hospital of Southern Medical University. Subsequently, the clinical prognostic value of these mutations was verified by using The Cancer Genome Atlas (TCGA)-LUAD cohort and our cohort (n = 45). Results: Based on drug sensitivity data for the GDSC-LUAD cell lines and survival analysis of the cohorts TCGA-LUAD and Local-LUAD, we found only one gene (GREB1) with mutations related to decreased CDDP sensitivity as well as worse overall survival (OS) and progression-free survival (PFS) [OS: log-rank p = 0.038, hazard ratio (HR; 95% confidence interval (95% CI)): 2.19 (0.73–6.55); PFS: log-rank p = 0.001; HR: 4.65, 95% CI: 1.18–18.37]. The GREB1-mutant (GREB1-MT) group had a higher frequency of gene mutations. Additionally, gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) suggested reduced accumulation of intracellular drugs in the GREB1-MT group, in addition to increased drug efflux and enhanced DNA damage repair and intracellular detoxification. Conclusion: This study found that GREB1 mutations may mediate the primary resistance and clinical prognosis of LUAD patients undergoing treatment with CDDP. Further functional analysis showed that GREB1 mutations are related to the known mechanism of CDDP resistance. These results suggest that GREB1 mutations are potential biomarkers for screening of CDDP resistance among LUAD patients.

Silencing KIF18B enhances radiosensitivity: identification of a promising therapeutic target in sarcoma.

BackgroundSarcomas are rare heterogeneous tumours, derived from primitive mesenchymal stem cells, with more than 100 distinct subtypes. Radioresistance remains a major clinical challenge for sarcomas, demanding urgent for effective biomarkers of radiosensitivity.MethodsThe radiosensitive gene Kinesin family member 18B (KIF18B) was mined through bioinformatics with integrating of 15 Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) database. We used radiotherapy-sh-KIF18B combination to observe the anti-tumour effect in sarcoma cells and subcutaneous or orthotopic xenograft models. The KIF18B-sensitive drug T0901317 (T09) was further mined to act as radiosensitizer using the Genomics of Drug Sensitivity in Cancer (GDSC) database.FindingsKIF18B mRNA was significantly up-regulated in most of the subtypes of bone and soft tissue sarcoma. Multivariate Cox regression analysis showed that KIF18B high expression was an independent risk factor for prognosis in sarcoma patients with radiotherapy. Silencing KIF18B or using T09 significantly improved the radiosensitivity of sarcoma cells, delayed tumour growth in subcutaneous and orthotopic xenograft model, and elongated mice survival time. Furthermore, we predicted that T09 might bind to the structural region of KIF18B to exert radiosensitization.InterpretationThese results indicated that sarcomas with low expression of KIF18B may benefit from radiotherapy. Moreover, the radiosensitivity of sarcomas with overexpressed KIF18B could be effectively improved by silencing KIF18B or using T09, which may provide promising strategies for radiotherapy treatment of sarcoma.FundingsA full list of funding can be found in the Funding Sources section.

Association between copy-number alteration of +20q, \u221214q and \u221218p and cross-sensitivity to tyrosine kinase inhibitors in clear-cell renal cell carcinoma

BackgroundWe aim to explore association between copy number alteration (CNA) and sensitivity to common tyrosine kinase inhibitors (TKIs) used in clear-cell renal cell carcinoma (ccRCC) treatment.MethodsCNA with related sensitivity profiles were extracted from the Genomics of Drug Sensitivity in Cancer (GDSC) dataset and was cross-referenced with common CNA in ccRCC in the Cancer Genome Atlas (TCGA) dataset. Functional annotation was profiled using GSEA and NET-GE. Target genes within cytobands of interest were screened in silico and validated in vitro using proliferation assays in A498 and 786-O ccRCC cells.ResultsFour TKIs (Sunitinib, Cabozantinib, Axitinib and Sorafenib) that were clinically used in ccRCC were selected. In silico analysis showed gain of 20q (+20q) occurred in ~ 23% of cases and was associated with resistance to all four TKIs; loss of 14q (−14q) occurred in ~ 39% of cases and was associated with resistance to Sunitinib and Sorafenib; loss of 18p (−18p) occurred in ~ 39% of cases and was associated with sensitivity to Sunitinib and Sorafenib. All 3 CNAs were associated with worsened prognosis, respectively. Candidate target genes included of RBL1 on 20q, KLHL33 on 14q and ARHGAP28 on18q. In vitro validation showed RBL1 overexpression induced resistance to Sunitinib and Cabozantinib; KLHL33 silencing induced resistance to Sunitinib; ARHGAP28 silencing induced sensitivity to Cabozantinib. Functional annotation indicated FoxO signaling, hypoxic response and Wnt pathway, and Rho-related cellular adhesion were mechanistically associated with +20q, −14q and −18p, respectively.ConclusionCommon CNAs in ccRCC are associated with cancer-intrinsic cross-sensitivity to common TKIs. Further validation and functional analyses are therefore needed.

BET, SRC, and BCL2 family inhibitors are synergistic drug combinations with PARP inhibitors in ovarian cancer.

BackgroundHomologous recombination deficiencies (HRD) are present in approximately half of epithelial ovarian cancers, for which PARP inhibitors (PARPi) are becoming a preferred treatment option. However, a considerable proportion of these carcinomas acquire resistance or harbour de novo resistance, posing a significant challenge to treatment.MethodsTo identify new combinatorial therapeutics to overcome resistance to PARPi, we employed high-throughput conditional RNAi and drug screening of patient-derived ovarian cancer cells. To prioritise clinically relevant drug combinations, we integrated empirical validation with analysis of The Cancer Genome Atlas (TCGA) and Genomics of Drug Sensitivity in Cancer (GDSC) datasets to nominate candidate targets and drugs, reaching three main findings.FindingsFirstly, we found that the PARPi rucaparib enhanced the effect of BET inhibitors (CPI-203 & CPI-0610) irrespective of clinical subtype or HRD status. Additional drug combination screens identified that dasatinib, a non-receptor tyrosine kinase inhibitor, augmented the effects of rucaparib and BET inhibitors, proposing a potential broadly applicable triple-drug combination for high-grade serous and clear cell ovarian carcinomas. Secondly, rucaparib synergised with the BCL2 family inhibitor navitoclax, with preferential activity in ovarian carcinomas that harbour alterations in BRCA1/2, BARD1, or MSH2/6. Thirdly, we identified potentially antagonistic drug combinations between the PARPi rucaparib and vinca alkaloids, anthracyclines, and antimetabolites, cautioning their use in the clinic.InterpretationThese findings propose therapeutic strategies to address PARP inhibitor resistance using agents that are already approved or are in clinical development, with the potential for rapid translation to benefit a broad population of ovarian cancer patients.

Comprehensive Analysis of Prognostic Alternative Splicing Signatures in Oral Squamous Cell Carcinoma.

BackgroundAlternative splicing (AS) plays an essential role in tumorigenesis and progression. This study aimed to develop a novel prognostic model based on the AS events to obtain more accurate survival prediction and search for potential therapeutic targets in oral squamous cell carcinoma (OSCC).MethodsSeven types of AS events in 326 OSCC patients with RNA-seq were obtained from the TCGA SpliceSeq tool and the TCGA database. Cox analysis, the least absolute shrinkage and selection operator Cox regression and random forest were employed to establish prognostic models. Genomics of Drug Sensitivity in Cancer (GDSC) was adopted to estimate the possible drug sensiticity. Prognostic splicing factor (SF)-AS network was constructed by Cytoscape.ResultsThe final model included 12 AS events, showing satisfactory performance. The area under the curve for 3- and 5-year survival in the training cohort was 0.83 and 0.82, respectively while that in internal validation was 0.83 and 0.82 accordingly. The calibration curve also indicated a satisfactory agreement between the observation and the predictive values. Low-risk patients stratified by the final model presented higher sensitivity to three chemo drugs. Besides, the prognostic SF-AS regulatory network contained five key SFs and 62 AS events.ConclusionsWe developed a powerful prognostic AS signature for OSCC and deepened the understanding of SF-AS network regulatory mechanisms. Low-risk patients tended to be more sensitive to the three chemo drugs while five key SFs including CELF2, TIA1, HNRNPC, HNRNPK, and SRSF9 were identified as potential prognostic biomarkers, which may offer new prospects for effective therapies of OSCC.

Abstract 1434: Loss of PTEN confers resistance to neddylation inhibition through TOP2A in glioblastoma

Abstract As a disease that has not seen lasting advances in its standard of care for decades, glioblastoma (GBM) calls for novel therapeutic approaches, and given GBM's heterogeneous and aggressive nature, precision medicine stands out as an appealing treatment modality for this tragic disease. Neddylation is a post-translational modification process that regulates protein function and turnover. MLN4924, or Pevonedistat, inhibits the E1 ligase Nedd8 activating enzyme (NAE) and, in doing so, the E1, E2, and E3 neddylation cascade. In its most researched role, NEDD-8 is conjugated to the cullin subunit of cullin-RING ligases (CRLs), a family of E3 ligases that facilitate the ubiquitination of different substrates. Thus, inhibition of neddylation decreases ubiquitination and degradation of particular substrates: e.g., p21, p27, wee-1, and other cell cycle regulatory and pro-apoptotic proteins. We and others have shown that neddylation stands out a promising therapeutic approach to cancer and GBM, given the vulnerability to MLN4924 in subset of different in vitro and in vivo models. In this work, we establish the selective vulnerability to MLN4924 in GBM and explore the biological mechanisms that underlie this differential response. Using the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC) databases, we found that mutations and deletions in phosphatase and tensin homolog (PTEN) are associated with resistance to MLN4924 in both low-grade gliomas and GBM. Our established models validated these findings, as our sensitive models – GB1 (IC50= 0.28 μM) and LN18 (IC50 = 0.19 μM), – expressed PTEN, while our resistant models – M059K (IC50 = 5.5 μM) and SNU1105 (IC50 = 20.9 μM) – did not. Knocking down PTEN with siRNA in our sensitive models increased their IC50 values three- to four-fold. This relationship was further validated in multiple isogenic systems. We sought to uncover whether this PTEN-status dependent resistance was contingent on its well-studied lipid phosphatase activity and PI3K/AKT signaling. While the resistant models had higher baseline levels of phosphorylated AKT (p-AKT), we found that MLN4924 treatment did not consistently alter p-AKT levels in our sensitive and/or resistant models. We also used a PTEN null isogenic glioma stem cell system (GSC23), in which wild-type PTEN and lipid phosphatase dead (G129A) PTEN was re-expressed, and found that both PTEN models sensitized GSC23 to MLN4924 treatment equally. While independent of the PI3K/AKT axis, we did find that there were significant increases in topoisomerase II alpha (TOP2A) levels in the post-treatment lysate of our resistant (PTEN-null) models. Targeting this increase in TOP2A expression, we found strong synergy with two different TOP2 poisons – etoposide and doxorubicin – and MLN4924 in a combination DDR. For the first time, we show that PTEN status not only serves as a novel biomarker for MLN4924 response but also reveals a vulnerability to TOP2A inhibitors when used in combination with MLN4924. Citation Format: Brett Taylor, Shayesteh Ferdosi, Nanyun Tang, Rita Bybee, George Reid, Lauren Hartman, Darren Finlay, Kristiina Vuori, Matthew Lee, Sen Peng, Frank Furnari, Harshil Dhruv, Michael Berens. Loss of PTEN confers resistance to neddylation inhibition through TOP2A in glioblastoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1434.

Prognosisof Lung Adenocarcinoma Patients With NTRK3 Mutations to Immune Checkpoint Inhibitors.

BackgroundImmune checkpoint inhibitors (ICIs) are an important treatment modality that must be considered for patients with lung adenocarcinoma (LUAD). However, ICIs are effective only in some of these patients. Therefore, identifying biomarkers that accurately predict the prognosis of patients with LUAD treated with ICIs can help maximize their therapeutic benefits. This study aimed to identify a new potential predictor to better select and optimally benefit LUAD patients.MethodsWe first collected and analyzed a discovery immunotherapy cohort comprising clinical and mutation data for LUAD patients. Then, we evaluated whether the specific mutated genes can act as predictive biomarkers in this discovery immunotherapy cohort and further validated the findings in The Cancer Genome Atlas (TCGA) project LUAD cohort. Gene set enrichment analysis (GSEA) was used to explore possible alterations in DNA damage response (DDR) pathways within the gene mutation. Moreover, we analyzed whole-exome sequencing (WES) and drug sensitivity response data for LUAD cell lines in the Genomics of Drug Sensitivity in Cancer (GDSC) database.ResultsAmong the mutated genes screened from both the ICI treatment and TCGA-LUAD cohorts, NTRK3 mutation (mutant-type NTRK3, NTRK3-MT) was strongly associated with immunotherapy. First, significant differences in overall survival (OS) were observed between patients with NTRK3-MT and those with NTRK3-WT in the ICI treatment cohort but not in the non-ICI-treated TCGA-LUAD cohort. We then analyzed the association of NTRK3-MT with clinical characteristics and found the tumor mutation burden (TMB) to be significantly higher in both NTRK3-MT cohorts. However, significant differences in neoantigen levels and smoking history were found only for NTRK3-MT in the LUAD cohort from TCGA. Furthermore, some immune-related genes and immune cell-related genes were significantly upregulated in patients with NTRK3-MT compared to those with NTRK3-WT. In addition, NTRK3 mutation affected the deregulation of some signaling pathways and the DDR pathway.ConclusionsOur findings suggest that NTRK3-MT can predict the prognosis of patients with LUAD treated by ICIs and that it may have clinical significance for immunotherapy.

A prognostic signature based on immune-related genes for cervical squamous cell carcinoma and endocervical adenocarcinoma

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is the fourth commonest female malignancy worldwide. CESC progresses in immune-microenvironment mainly composed of infiltrating immune and stromal cells. Here, we performed an integrated analysis incorporating the expression profiles from the Cancer Genome Atlas (TCGA) database and scores of immune and stromal cells calculated by Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm. A two-gene signature (CD1C and CD6 genes) was established to predict the prognosis of CESC. Based on this signature, patients were divided into the high- and low-risk groups, and this signature showed good prognostic performance according to the results of Kaplan-Meier analysis and receiver operating characteristic (ROC) analysis in train set and two validation sets. A nomogram was built for evaluating the clinical applicability of this signature. In addition, based on Tumor Immune Estimation Resource (TIMER) database, 2 hub genes showed negative correlations with tumor purity and positive correlations with infiltrating levels of immune filtrating cells. What’s more, we propose new treatment strategies for the two prognostic subtypes. Low- risk patients were found presenting with a higher level of immune checkpoint molecules and showing higher immunogenicity in immunophenoscore (IPS) analysis, which indicated a better response for immunotherapy. Meanwhile, estimated by Genomics of Drug Sensitivity in Cancer (GDSC) database, the high-risk patients showed sensitive responses to five chemotherapy drugs. Finally, 10 candidate small-molecule drugs for CESC were defined. In summary, the CD1C-CD6 signature can accurately predict the prognosis of CESC.

Computational Prediction of Drug Responses in Cancer Cell Lines From Cancer Omics and Detection of Drug Effectiveness Related Methylation Sites

Accurately predicting the response of a cancer patient to a therapeutic agent remains an important challenge in precision medicine. With the rise of data science, researchers have applied computational models to study the drug inhibition effects on cancers based on cancer genomics and transcriptomics. Moreover, a common epigenetic modification, DNA methylation, has been related to the occurrence and development of cancer, as well as drug effectiveness. Therefore, it is helpful for improvement of drug response prediction through exploring the relationship between DNA methylation and drug effectiveness. Here, we proposed a computational model to predict drug responses in cancers through integration of cancer genomics, transcriptomics, epigenomics, and compound chemical properties. Meanwhile, we applied a regularized regression model (Least Absolute Shrinkage and Selection Operator, lasso) to detect the methylation sites that were closely related to drug effectiveness. The prediction models were trained on a well-known pharmacogenomics data resource, Genomics of Drug Sensitivity in Cancer (GDSC). The cross-validation indicates that the performance of the prediction model using DNA methylation is comparable to that of using other cancer omics, including oncogene mutation and gene expression data. It indicates the important role of DNA methylation in prediction of drug responses. Encyclopedia of DNA Elements (ENCODE) and Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST2) database analyses suggest that the methylation sites associated with drug effectiveness are mainly located in the transcription factor (TF) binding region. Therefore, we hypothesized that the sensitivity of cancer cells to drugs could be regulated by changing the methylation modification of TF binding region. In conclusion, we confirmed the important role of DNA methylation in prediction of drug responses, and provided some methylation sites that closely related to the drug effectiveness, which may be a great regulatory target for improvement of drug treatment effects on cancer patients.