The Experts below are selected from a list of 26808 Experts worldwide ranked by ideXlab platform
Feixiong Cheng - One of the best experts on this subject based on the ideXlab platform.
-
systems pharmacology based discovery of natural products for precision oncology through targeting cancer Mutated Genes
CPT: pharmacometrics & systems pharmacology, 2017Co-Authors: Jiansong Fang, Qi Wang, Zhongming Zhao, Feixiong ChengAbstract:: Massive cancer genomics data have facilitated the rapid revolution of a novel oncology drug discovery paradigm through targeting clinically relevant driver Genes or mutations for the development of precision oncology. Natural products with polypharmacological profiles have been demonstrated as promising agents for the development of novel cancer therapies. In this study, we developed an integrated systems pharmacology framework that facilitated identifying potential natural products that target Mutated Genes across 15 cancer types or subtypes in the realm of precision medicine. High performance was achieved for our systems pharmacology framework. In case studies, we computationally identified novel anticancer indications for several US Food and Drug Administration-approved or clinically investigational natural products (e.g., resveratrol, quercetin, genistein, and fisetin) through targeting significantly Mutated Genes in multiple cancer types. In summary, this study provides a powerful tool for the development of molecularly targeted cancer therapies through targeting the clinically actionable alterations by exploiting the systems pharmacology of natural products.
-
advances in computational approaches for prioritizing driver mutations and significantly Mutated Genes in cancer genomes
Briefings in Bioinformatics, 2016Co-Authors: Feixiong Cheng, Junfei Zhao, Zhongming ZhaoAbstract:Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer Genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.
-
A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly Mutated Genes in the human cancer genomes.
Journal of the American Medical Informatics Association, 2016Co-Authors: Feixiong Cheng, Junfei Zhao, Michaela Fooksa, Zhongming ZhaoAbstract:Objective Development of computational approaches and tools to effectively integrate multidomain data is urgently needed for the development of newly targeted cancer therapeutics. Methods We proposed an integrative network-based infrastructure to identify new druggable targets and anticancer indications for existing drugs through targeting significantly Mutated Genes (SMGs) discovered in the human cancer genomes. The underlying assumption is that a drug would have a high potential for anticancer indication if its up-/down-regulated Genes from the Connectivity Map tended to be SMGs or their neighbors in the human protein interaction network. Results We assembled and curated 693 SMGs in 29 cancer types and found 121 proteins currently targeted by known anticancer or noncancer (repurposed) drugs. We found that the approved or experimental cancer drugs could potentially target these SMGs in 33.3% of the Mutated cancer samples, and this number increased to 68.0% by drug repositioning through surveying exome-sequencing data in approximately 5000 normal-tumor pairs from The Cancer Genome Atlas. Furthermore, we identified 284 potential new indications connecting 28 cancer types and 48 existing drugs (adjusted P
-
a network based drug repositioning infrastructure for precision cancer medicine through targeting significantly Mutated Genes in the human cancer genomes
Journal of the American Medical Informatics Association, 2016Co-Authors: Feixiong Cheng, Junfei Zhao, Michaela Fooksa, Zhongming ZhaoAbstract:Objective Development of computational approaches and tools to effectively integrate multidomain data is urgently needed for the development of newly targeted cancer therapeutics. Methods We proposed an integrative network-based infrastructure to identify new druggable targets and anticancer indications for existing drugs through targeting significantly Mutated Genes (SMGs) discovered in the human cancer genomes. The underlying assumption is that a drug would have a high potential for anticancer indication if its up-/down-regulated Genes from the Connectivity Map tended to be SMGs or their neighbors in the human protein interaction network. Results We assembled and curated 693 SMGs in 29 cancer types and found 121 proteins currently targeted by known anticancer or noncancer (repurposed) drugs. We found that the approved or experimental cancer drugs could potentially target these SMGs in 33.3% of the Mutated cancer samples, and this number increased to 68.0% by drug repositioning through surveying exome-sequencing data in approximately 5000 normal-tumor pairs from The Cancer Genome Atlas. Furthermore, we identified 284 potential new indications connecting 28 cancer types and 48 existing drugs (adjusted P < .05), with a 66.7% success rate validated by literature data. Several existing drugs (e.g., niclosamide, valproic acid, captopril, and resveratrol) were predicted to have potential indications for multiple cancer types. Finally, we used integrative analysis to showcase a potential mechanism-of-action for resveratrol in breast and lung cancer treatment whereby it targets several SMGs ( ARNTL , ASPM, CTTN, EIF4G1, FOXP1, and STIP1 ). Conclusions In summary, we demonstrated that our integrative network-based infrastructure is a promising strategy to identify potential druggable targets and uncover new indications for existing drugs to speed up molecularly targeted cancer therapeutics.
Zhongming Zhao - One of the best experts on this subject based on the ideXlab platform.
-
systems pharmacology based discovery of natural products for precision oncology through targeting cancer Mutated Genes
CPT: pharmacometrics & systems pharmacology, 2017Co-Authors: Jiansong Fang, Qi Wang, Zhongming Zhao, Feixiong ChengAbstract:: Massive cancer genomics data have facilitated the rapid revolution of a novel oncology drug discovery paradigm through targeting clinically relevant driver Genes or mutations for the development of precision oncology. Natural products with polypharmacological profiles have been demonstrated as promising agents for the development of novel cancer therapies. In this study, we developed an integrated systems pharmacology framework that facilitated identifying potential natural products that target Mutated Genes across 15 cancer types or subtypes in the realm of precision medicine. High performance was achieved for our systems pharmacology framework. In case studies, we computationally identified novel anticancer indications for several US Food and Drug Administration-approved or clinically investigational natural products (e.g., resveratrol, quercetin, genistein, and fisetin) through targeting significantly Mutated Genes in multiple cancer types. In summary, this study provides a powerful tool for the development of molecularly targeted cancer therapies through targeting the clinically actionable alterations by exploiting the systems pharmacology of natural products.
-
advances in computational approaches for prioritizing driver mutations and significantly Mutated Genes in cancer genomes
Briefings in Bioinformatics, 2016Co-Authors: Feixiong Cheng, Junfei Zhao, Zhongming ZhaoAbstract:Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer Genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.
-
A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly Mutated Genes in the human cancer genomes.
Journal of the American Medical Informatics Association, 2016Co-Authors: Feixiong Cheng, Junfei Zhao, Michaela Fooksa, Zhongming ZhaoAbstract:Objective Development of computational approaches and tools to effectively integrate multidomain data is urgently needed for the development of newly targeted cancer therapeutics. Methods We proposed an integrative network-based infrastructure to identify new druggable targets and anticancer indications for existing drugs through targeting significantly Mutated Genes (SMGs) discovered in the human cancer genomes. The underlying assumption is that a drug would have a high potential for anticancer indication if its up-/down-regulated Genes from the Connectivity Map tended to be SMGs or their neighbors in the human protein interaction network. Results We assembled and curated 693 SMGs in 29 cancer types and found 121 proteins currently targeted by known anticancer or noncancer (repurposed) drugs. We found that the approved or experimental cancer drugs could potentially target these SMGs in 33.3% of the Mutated cancer samples, and this number increased to 68.0% by drug repositioning through surveying exome-sequencing data in approximately 5000 normal-tumor pairs from The Cancer Genome Atlas. Furthermore, we identified 284 potential new indications connecting 28 cancer types and 48 existing drugs (adjusted P
-
a network based drug repositioning infrastructure for precision cancer medicine through targeting significantly Mutated Genes in the human cancer genomes
Journal of the American Medical Informatics Association, 2016Co-Authors: Feixiong Cheng, Junfei Zhao, Michaela Fooksa, Zhongming ZhaoAbstract:Objective Development of computational approaches and tools to effectively integrate multidomain data is urgently needed for the development of newly targeted cancer therapeutics. Methods We proposed an integrative network-based infrastructure to identify new druggable targets and anticancer indications for existing drugs through targeting significantly Mutated Genes (SMGs) discovered in the human cancer genomes. The underlying assumption is that a drug would have a high potential for anticancer indication if its up-/down-regulated Genes from the Connectivity Map tended to be SMGs or their neighbors in the human protein interaction network. Results We assembled and curated 693 SMGs in 29 cancer types and found 121 proteins currently targeted by known anticancer or noncancer (repurposed) drugs. We found that the approved or experimental cancer drugs could potentially target these SMGs in 33.3% of the Mutated cancer samples, and this number increased to 68.0% by drug repositioning through surveying exome-sequencing data in approximately 5000 normal-tumor pairs from The Cancer Genome Atlas. Furthermore, we identified 284 potential new indications connecting 28 cancer types and 48 existing drugs (adjusted P < .05), with a 66.7% success rate validated by literature data. Several existing drugs (e.g., niclosamide, valproic acid, captopril, and resveratrol) were predicted to have potential indications for multiple cancer types. Finally, we used integrative analysis to showcase a potential mechanism-of-action for resveratrol in breast and lung cancer treatment whereby it targets several SMGs ( ARNTL , ASPM, CTTN, EIF4G1, FOXP1, and STIP1 ). Conclusions In summary, we demonstrated that our integrative network-based infrastructure is a promising strategy to identify potential druggable targets and uncover new indications for existing drugs to speed up molecularly targeted cancer therapeutics.
Xifeng Wu - One of the best experts on this subject based on the ideXlab platform.
-
Germline genetic variants in somatically significantly Mutated Genes in tumors are associated with renal cell carcinoma risk and outcome.
Carcinogenesis, 2018Co-Authors: Jianchun Gu, Xifeng Wu, Maosheng Huang, Nizar M. Tannir, Surena F. Matin, Jose A. Karam, Christopher G. Wood, Yuanqing YeAbstract:: Genome-wide association studies (GWAS) have identified 13 susceptibility loci for renal cell carcinoma (RCC). Additional genetic loci of risk remain to be explored. Moreover, the role of germline genetic variants in predicting RCC recurrence and overall survival (OS) is less understood. In this study, we focused on 127 significantly Mutated Genes from The Cancer Genome Atlas (TCGA) Pan-Cancer Analysis across 12 major cancer sites to identify potential genetic variants predictive of RCC risk and clinical outcomes. In a three-phase design with a total of 2657 RCC cases and 5315 healthy controls, two single nucleotide polymorphisms (SNPs) that map to PIK3CG (rs6466135:A, ORmeta = 0.85, 95% CI = 0.77-0.94, Pmeta = 1.4 × 10-3) and ATM (rs611646:T, ORmeta = 1.17, 95% CI = 1.05-1.31, Pmeta = 3.5 × 10-3) were significantly associated with RCC risk. With respect to RCC recurrence and OS, two separate datasets with a total of 661 stages I-III RCC patients (discovery: 367; validation: 294) were analyzed. The most significant association was observed for rs10932384:C (ERBB4) with both outcomes (recurrence: HRmeta = 0.52, 95% CI = 0.39-0.68, Pmeta = 3.81 × 10-6; OS: HRmeta = 0.50, 95% CI = 0.37-0.67, Pmeta = 6.00 × 10-6). In addition, six SNPs were significantly associated with either RCC recurrence or OS but not both (Pmeta < 0.01). Rs10932384:C was significantly correlated with mutation frequency of ERBB4 in clear cell RCC (ccRCC) patients (P = 0.003, Fisher's exact test). Cis-eQTL was observed for several SNPs in blood/transformed fibroblasts but not in RCC tumor tissues. In summary, we identified promising genetic predictors of recurrence and OS among RCC patients with localized disease.
-
Genetic variations in cancer-related significantly Mutated Genes and lung cancer susceptibility
Annals of Oncology, 2017Co-Authors: Y. Zhang, Liren Zhang, R. Li, David W. Chang, Yuanqing Ye, John D. Minna, Jack A. Roth, Xifeng WuAbstract:Background: Cancer initiation and development are driven by key mutations in driver Genes. Applying high-throughput sequencing technologies and bioinformatic analyses, The Cancer Genome Atlas (TCGA) project has identified panels of somatic mutations that contributed to the etiology of various cancers. However, there are few studies investigating the germline genetic variations in these significantly Mutated Genes (SMGs) and lung cancer susceptibility. Patients and methods: We comprehensively evaluated 1655 tagged single nucleotide polymorphisms (SNPs) located in 127 SMGs identified by TCGA, and test their association with lung cancer risk in large-scale case-control study. Functional effect of the validated SNPs, gene mutation frequency and pathways were analyzed. Results: We found 11 SNPs in 8 Genes showed consistent association (P
-
genetic variations in cancer related significantly Mutated Genes and lung cancer susceptibility
Annals of Oncology, 2017Co-Authors: Y. Zhang, R. Li, David W. Chang, Yuanqing Ye, John D. Minna, Jack A. Roth, Liang Zhang, Xifeng WuAbstract:Background: Cancer initiation and development are driven by key mutations in driver Genes. Applying high-throughput sequencing technologies and bioinformatic analyses, The Cancer Genome Atlas (TCGA) project has identified panels of somatic mutations that contributed to the etiology of various cancers. However, there are few studies investigating the germline genetic variations in these significantly Mutated Genes (SMGs) and lung cancer susceptibility. Patients and methods: We comprehensively evaluated 1655 tagged single nucleotide polymorphisms (SNPs) located in 127 SMGs identified by TCGA, and test their association with lung cancer risk in large-scale case-control study. Functional effect of the validated SNPs, gene mutation frequency and pathways were analyzed. Results: We found 11 SNPs in 8 Genes showed consistent association (P < 0.1) and 8 SNPs significantly associated with lung cancer risk (P < 0.05) in both discovery and validation phases. The most significant association was rs10412613 in PPP2R1A, with the minor G allele associated with a decreased risk of lung cancer [odds ratio = 0.91, 95% confidence interval (CI): 0.87-0.96, P = 2.3 × 10-4]. Cumulative analysis of risk score built as a weight sum of the 11 SNPs showed consistently elevated risk with increasing risk score (P for trend = 9.5 × 10-9). In stratified analyses, the association of PPP2R1A:rs10412613 and lung cancer risk appeared stronger among population of younger age at diagnosis and never smokers. The expression quantitative trait loci analysis indicated that rs10412613, rs10804682, rs635469 and rs6742399 genotypes significantly correlated with the expression of PPP2R1A, ATR, SETBP1 and ERBB4, respectively. From TCGA data, expression of the identified Genes was significantly different in lung tumors compared with normal tissues, and the Genes' highest mutation frequency was found in lung cancers. Integrative pathway analysis indicated the identified Genes were mainly involved in AKT/NF-κB regulatory pathway suggesting the underlying biological processes. Conclusion: This study revealed novel genetic variants in SMGs associated with lung cancer risk, which might contribute to elucidating the biological network involved in lung cancer development.
Y. Zhang - One of the best experts on this subject based on the ideXlab platform.
-
Genetic variations in cancer-related significantly Mutated Genes and lung cancer susceptibility
Annals of Oncology, 2017Co-Authors: Y. Zhang, Liren Zhang, R. Li, David W. Chang, Yuanqing Ye, John D. Minna, Jack A. Roth, Xifeng WuAbstract:Background: Cancer initiation and development are driven by key mutations in driver Genes. Applying high-throughput sequencing technologies and bioinformatic analyses, The Cancer Genome Atlas (TCGA) project has identified panels of somatic mutations that contributed to the etiology of various cancers. However, there are few studies investigating the germline genetic variations in these significantly Mutated Genes (SMGs) and lung cancer susceptibility. Patients and methods: We comprehensively evaluated 1655 tagged single nucleotide polymorphisms (SNPs) located in 127 SMGs identified by TCGA, and test their association with lung cancer risk in large-scale case-control study. Functional effect of the validated SNPs, gene mutation frequency and pathways were analyzed. Results: We found 11 SNPs in 8 Genes showed consistent association (P
-
genetic variations in cancer related significantly Mutated Genes and lung cancer susceptibility
Annals of Oncology, 2017Co-Authors: Y. Zhang, R. Li, David W. Chang, Yuanqing Ye, John D. Minna, Jack A. Roth, Liang Zhang, Xifeng WuAbstract:Background: Cancer initiation and development are driven by key mutations in driver Genes. Applying high-throughput sequencing technologies and bioinformatic analyses, The Cancer Genome Atlas (TCGA) project has identified panels of somatic mutations that contributed to the etiology of various cancers. However, there are few studies investigating the germline genetic variations in these significantly Mutated Genes (SMGs) and lung cancer susceptibility. Patients and methods: We comprehensively evaluated 1655 tagged single nucleotide polymorphisms (SNPs) located in 127 SMGs identified by TCGA, and test their association with lung cancer risk in large-scale case-control study. Functional effect of the validated SNPs, gene mutation frequency and pathways were analyzed. Results: We found 11 SNPs in 8 Genes showed consistent association (P < 0.1) and 8 SNPs significantly associated with lung cancer risk (P < 0.05) in both discovery and validation phases. The most significant association was rs10412613 in PPP2R1A, with the minor G allele associated with a decreased risk of lung cancer [odds ratio = 0.91, 95% confidence interval (CI): 0.87-0.96, P = 2.3 × 10-4]. Cumulative analysis of risk score built as a weight sum of the 11 SNPs showed consistently elevated risk with increasing risk score (P for trend = 9.5 × 10-9). In stratified analyses, the association of PPP2R1A:rs10412613 and lung cancer risk appeared stronger among population of younger age at diagnosis and never smokers. The expression quantitative trait loci analysis indicated that rs10412613, rs10804682, rs635469 and rs6742399 genotypes significantly correlated with the expression of PPP2R1A, ATR, SETBP1 and ERBB4, respectively. From TCGA data, expression of the identified Genes was significantly different in lung tumors compared with normal tissues, and the Genes' highest mutation frequency was found in lung cancers. Integrative pathway analysis indicated the identified Genes were mainly involved in AKT/NF-κB regulatory pathway suggesting the underlying biological processes. Conclusion: This study revealed novel genetic variants in SMGs associated with lung cancer risk, which might contribute to elucidating the biological network involved in lung cancer development.
R. Li - One of the best experts on this subject based on the ideXlab platform.
-
Genetic variations in cancer-related significantly Mutated Genes and lung cancer susceptibility
Annals of Oncology, 2017Co-Authors: Y. Zhang, Liren Zhang, R. Li, David W. Chang, Yuanqing Ye, John D. Minna, Jack A. Roth, Xifeng WuAbstract:Background: Cancer initiation and development are driven by key mutations in driver Genes. Applying high-throughput sequencing technologies and bioinformatic analyses, The Cancer Genome Atlas (TCGA) project has identified panels of somatic mutations that contributed to the etiology of various cancers. However, there are few studies investigating the germline genetic variations in these significantly Mutated Genes (SMGs) and lung cancer susceptibility. Patients and methods: We comprehensively evaluated 1655 tagged single nucleotide polymorphisms (SNPs) located in 127 SMGs identified by TCGA, and test their association with lung cancer risk in large-scale case-control study. Functional effect of the validated SNPs, gene mutation frequency and pathways were analyzed. Results: We found 11 SNPs in 8 Genes showed consistent association (P
-
genetic variations in cancer related significantly Mutated Genes and lung cancer susceptibility
Annals of Oncology, 2017Co-Authors: Y. Zhang, R. Li, David W. Chang, Yuanqing Ye, John D. Minna, Jack A. Roth, Liang Zhang, Xifeng WuAbstract:Background: Cancer initiation and development are driven by key mutations in driver Genes. Applying high-throughput sequencing technologies and bioinformatic analyses, The Cancer Genome Atlas (TCGA) project has identified panels of somatic mutations that contributed to the etiology of various cancers. However, there are few studies investigating the germline genetic variations in these significantly Mutated Genes (SMGs) and lung cancer susceptibility. Patients and methods: We comprehensively evaluated 1655 tagged single nucleotide polymorphisms (SNPs) located in 127 SMGs identified by TCGA, and test their association with lung cancer risk in large-scale case-control study. Functional effect of the validated SNPs, gene mutation frequency and pathways were analyzed. Results: We found 11 SNPs in 8 Genes showed consistent association (P < 0.1) and 8 SNPs significantly associated with lung cancer risk (P < 0.05) in both discovery and validation phases. The most significant association was rs10412613 in PPP2R1A, with the minor G allele associated with a decreased risk of lung cancer [odds ratio = 0.91, 95% confidence interval (CI): 0.87-0.96, P = 2.3 × 10-4]. Cumulative analysis of risk score built as a weight sum of the 11 SNPs showed consistently elevated risk with increasing risk score (P for trend = 9.5 × 10-9). In stratified analyses, the association of PPP2R1A:rs10412613 and lung cancer risk appeared stronger among population of younger age at diagnosis and never smokers. The expression quantitative trait loci analysis indicated that rs10412613, rs10804682, rs635469 and rs6742399 genotypes significantly correlated with the expression of PPP2R1A, ATR, SETBP1 and ERBB4, respectively. From TCGA data, expression of the identified Genes was significantly different in lung tumors compared with normal tissues, and the Genes' highest mutation frequency was found in lung cancers. Integrative pathway analysis indicated the identified Genes were mainly involved in AKT/NF-κB regulatory pathway suggesting the underlying biological processes. Conclusion: This study revealed novel genetic variants in SMGs associated with lung cancer risk, which might contribute to elucidating the biological network involved in lung cancer development.
