- Browse by Author
Browsing by Author "Huo, Yang"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
Item DSCN: Double-target selection guided by CRISPR screening and network(Public Library of Science, 2022-08-19) Liu, Enze; Wu, Xue; Wang, Lei; Huo, Yang; Wu, Huanmei; Li, Lang; Cheng, Lijun; Medicine, School of MedicineCancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients. We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated a high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In DSCN algorithm, various scoring schemes were evaluated. The 'diffusion-path' method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon and VIPER, in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCN's computational speed is also at least ten times fast than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), DSCNi showed high correlation between target combinations predicted and real synergistic combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations.Item Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy(MDPI, 2020-09-07) Li, Jin; Huo, Yang; Wu, Xue; Liu, Enze; Zeng, Zhi; Tian, Zhen; Fan, Kunjie; Stover, Daniel; Cheng, Lijun; Li, Lang; Medicine, School of MedicineIn the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction.Item Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data(MDPI, 2019-09-25) Wang, Limei; Li, Jin; Liu, Enze; Kinnebrew, Garrett; Zhang, Xiaoli; Stover, Daniel; Huo, Yang; Zeng, Zhi; Jiang, Wanli; Cheng, Lijun; Feng, Weixing; Li, Lang; BioHealth Informatics, School of Informatics and ComputingAlternatively-activated pathways have been observed in biological experiments in cancer studies, but the concept had not been fully explored in computational cancer system biology. Therefore, an alternatively-activated pathway identification method was proposed and applied to primary breast cancer and breast cancer liver metastasis research using microarray data. Interestingly, the results show that cytokine-cytokine receptor interaction and calcium signaling were significantly enriched under both conditions. TGF beta signaling was found to be the hub in network topology analysis. In total, three types of alternatively-activated pathways were recognized. In the cytokine-cytokine receptor interaction pathway, four active alteration patterns in gene pairs were noticed. Thirteen cytokine-cytokine receptor pairs with inverse activity changes of both genes were verified by the literature. The second type was that some sub-pathways were active under only one condition. For the third type, nodes were significantly active in both conditions, but with different active genes. In the calcium signaling and TGF beta signaling pathways, node E2F5 and E2F4 were significantly active in primary breast cancer and metastasis, respectively. Overall, our study demonstrated the first time using microarray data to identify alternatively-activated pathways in breast cancer liver metastasis. The results showed that the proposed method was valid and effective, which could be helpful for future research for understanding the mechanism of breast cancer metastasis.Item Subpathway Analysis of Transcriptome Profiles Reveals New Molecular Mechanisms of Acquired Chemotherapy Resistance in Breast Cancer(MDPI, 2022-10-05) Huo, Yang; Shao, Shuai; Liu, Enze; Li, Jin; Tian, Zhen; Wu, Xue; Zhang, Shijun; Stover, Daniel; Wu, Huanmei; Cheng, Lijun; Li, Lang; Biostatistics and Health Data Science, School of MedicineChemoresistance has been a major challenge in the treatment of patients with breast cancer. The diverse omics platforms and small sample sizes reported in the current studies of chemoresistance in breast cancer limit the consensus regarding the underlying molecular mechanisms of chemoresistance and the applicability of these study findings. Therefore, we built two transcriptome datasets for patients with chemotherapy-resistant breast cancers—one comprising paired transcriptome samples from 40 patients before and after chemotherapy and the second including unpaired samples from 690 patients before and 45 patients after chemotherapy. Subsequent conventional pathway analysis and new subpathway analysis using these cohorts uncovered 56 overlapping upregulated genes (false discovery rate [FDR], 0.018) and 36 downregulated genes (FDR, 0.016). Pathway analysis revealed the activation of several pathways in the chemotherapy-resistant tumors, including those of drug metabolism, MAPK, ErbB, calcium, cGMP-PKG, sphingolipid, and PI3K-Akt, as well as those activated by Cushing’s syndrome, human papillomavirus (HPV) infection, and proteoglycans in cancers, and subpathway analysis identified the activation of several more, including fluid shear stress, Wnt, FoxO, ECM-receptor interaction, RAS signaling, Rap1, mTOR focal adhesion, and cellular senescence (FDR < 0.20). Among these pathways, those associated with Cushing’s syndrome, HPV infection, proteoglycans in cancer, fluid shear stress, and focal adhesion have not yet been reported in breast cancer chemoresistance. Pathway and subpathway analysis of a subset of triple-negative breast cancers from the two cohorts revealed activation of the identical chemoresistance pathways.