1
|
Rojas-Díaz D, Puerta-Yepes ME, Medina-Gaspar D, Botero JA, Rodríguez A, Rojas N. Mathematical Modeling for the Assessment of Public Policies in the Cancer Health-Care System Implemented for the Colombian Case. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6740. [PMID: 37754600 PMCID: PMC10531264 DOI: 10.3390/ijerph20186740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/28/2023] [Accepted: 07/20/2023] [Indexed: 09/28/2023]
Abstract
The incidence of cancer has been constantly growing worldwide, placing pressure on health systems and increasing the costs associated with the treatment of cancer. In particular, low- and middle-income countries are expected to face serious challenges related to caring for the majority of the world's new cancer cases in the next 10 years. In this study, we propose a mathematical model that allows for the simulation of different strategies focused on public policies by combining spending and epidemiological indicators. In this way, strategies aimed at efficient spending management with better epidemiological indicators can be determined. For validation and calibration of the model, we use data from Colombia-which, according to the World Bank, is an upper-middle-income country. The results of the simulations using the proposed model, calibrated and validated for Colombia, indicate that the most effective strategy for reducing mortality and financial burden consists of a combination of early detection and greater efficiency of treatment in the early stages of cancer. This approach is found to present a 38% reduction in mortality rate and a 20% reduction in costs (% GDP) when compared to the baseline scenario. Hence, Colombia should prioritize comprehensive care models that focus on patient-centered care, prevention, and early detection.
Collapse
Affiliation(s)
- Daniel Rojas-Díaz
- Area of Fundamental Sciences, School of Applied Sciences and Engineering, Universidad EAFIT, Medellin 050022, Colombia
| | - María Eugenia Puerta-Yepes
- Area of Fundamental Sciences, School of Applied Sciences and Engineering, Universidad EAFIT, Medellin 050022, Colombia
| | - Daniel Medina-Gaspar
- School of Finance, Economics, and Government, Universidad EAFIT, Medellin 050022, Colombia
| | - Jesús Alonso Botero
- School of Finance, Economics, and Government, Universidad EAFIT, Medellin 050022, Colombia
| | - Anwar Rodríguez
- Center for Economic Studies, National Association of Financial Institutions (ANIF), Bogota 110231, Colombia
| | - Norberto Rojas
- Center for Economic Studies, National Association of Financial Institutions (ANIF), Bogota 110231, Colombia
| |
Collapse
|
2
|
Liu Y, Song F, Li Z, Chen L, Xu Y, Sun H, Chang Y. A comprehensive tool for tumor precision medicine with pharmaco-omics data analysis. Front Pharmacol 2023; 14:1085765. [PMID: 36713829 PMCID: PMC9878337 DOI: 10.3389/fphar.2023.1085765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/04/2023] [Indexed: 01/14/2023] Open
Abstract
Background: Cancer precision medicine is an effective strategy to fight cancers by bridging genomics and drug discovery to provide specific treatment for patients with different genetic characteristics. Although some public databases and modelling frameworks have been developed through studies on drug response, most of them only considered the ramifications of the drug on the cell line and the effects on the patient still require a huge amount of work to integrate data from various databases and calculations, especially concerning precision treatment. Furthermore, not only efficacy but also the adverse effects of drugs on patients should be taken into account during cancer treatment. However, the adverse effects as essential indicators of drug safety assessment are always neglected. Method: A holistic estimation explores various drugs' efficacy levels by calculating their potency both in reversing and enhancing cancer-associated gene expression change. And a method for bridging the gap between cell culture and living tissue estimates the effectiveness of a drug on individual patients through the mappings of various cell lines to each person according to their genetic mutation similarities. Result: We predicted the efficacy of FDA-recommended drugs, taking into account both efficacy and toxicity, and obtained consistent results. We also provided an intuitive and easy-to-use web server called DBPOM (http://www.dbpom.net/, a comprehensive database of pharmaco-omics for cancer precision medicine), which not only integrates the above methods but also provides calculation results on more than 10,000 small molecule compounds and drugs. As a one-stop web server, clinicians and drug researchers can also analyze the overall effect of a drug or a drug combination on cancer patients as well as the biological functions that they target. DBPOM is now public, free to use with no login requirement, and contains all the data and code. Conclusion: Both the positive and negative effects of drugs during precision treatment are essential for practical application of drugs. DBPOM based on the two effects will become a vital resource and analysis platform for drug development, drug mechanism studies and the discovery of new therapies.
Collapse
Affiliation(s)
- Yijun Liu
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Fuhu Song
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Zhi Li
- Medical Oncology Department, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Liang Chen
- Department of Computer Science, College of Engineering, Shantou University, Shantou, China,Key Laboratory of Intelligent Manufacturing Technology of Ministry of Education, Shantou University, Shantou, China
| | - Ying Xu
- Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, The University of Georgia, Athens, GA, United States
| | - Huiyan Sun
- School of Artificial Intelligence, Jilin University, Changchun, China,International Center of Future Science, Jilin University, Changchun, China,*Correspondence: Huiyan Sun, ; Yi Chang,
| | - Yi Chang
- School of Artificial Intelligence, Jilin University, Changchun, China,International Center of Future Science, Jilin University, Changchun, China,*Correspondence: Huiyan Sun, ; Yi Chang,
| |
Collapse
|
3
|
Khan F, Akhtar S, Kamal MA. Nanoinformatics and Personalized Medicine: An Advanced Cumulative Approach for Cancer Management. Curr Med Chem 2023; 30:271-285. [PMID: 35692148 DOI: 10.2174/0929867329666220610090405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 02/10/2022] [Accepted: 03/15/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Even though the battle against one of the deadliest diseases, cancer, has advanced remarkably in the last few decades and the survival rate has improved significantly; the search for an ultimate cure remains a utopia. Nanoinformatics, which is bioinformatics coupled with nanotechnology, endows many novel research opportunities in the preclinical and clinical development of personalized nanosized drug carriers in cancer therapy. Personalized nanomedicines serve as a promising treatment option for cancer owing to their noninvasiveness and their novel approach. Explicitly, the field of personalized medicine is expected to have an enormous impact soon because of its many advantages, namely its versatility to adapt a drug to a cohort of patients. OBJECTIVE The current review explains the application of this newly emerging field called nanoinformatics to the field of precision medicine. This review also recapitulates how nanoinformatics could hasten the development of personalized nanomedicine for cancer, which is undoubtedly the need of the hour. CONCLUSION This approach has been facilitated by a humongous impending field named Nanoinformatics. These breakthroughs and advances have provided insight into the future of personalized medicine. Imperatively, they have been enabling landmark research to merge all advances, creating nanosized particles that contain drugs targeting cell surface receptors and other potent molecules designed to kill cancerous cells. Nanoparticle- based medicine has been developing and has become a center of attention in recent years, focusing primely on proficient delivery systems for various chemotherapy drugs.
Collapse
Affiliation(s)
- Fariya Khan
- Department of Bioengineering, Faculty of Engineering, Integral University, Lucknow - 226026, UP, India
| | - Salman Akhtar
- Department of Bioengineering, Faculty of Engineering, Integral University, Lucknow - 226026, UP, India.,Novel Global Community Educational Foundation, Hebersham, NSW2770, Australia
| | - Mohammad Amjad Kamal
- Institutes for Systems Genetics, Frontier Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.,King Fahad Medical Research Center, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.,Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh.,Enzymoics, 7, Peterlee Place, Hebersham, NSW 2770; Novel Global Community Educational Foundation, Australia
| |
Collapse
|
4
|
Solis-Vasquez L, Tillack AF, Santos-Martins D, Koch A, LeGrand S, Forli S. Benchmarking the Performance of Irregular Computations in AutoDock-GPU Molecular Docking. PARALLEL COMPUTING 2022; 109:102861. [PMID: 34898769 PMCID: PMC8654209 DOI: 10.1016/j.parco.2021.102861] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Irregular applications can be found in different scientific fields. In computer-aided drug design, molecular docking simulations play an important role in finding promising drug candidates. AutoDock is a software application widely used for predicting molecular interactions at close distances. It is characterized by irregular computations and long execution runtimes. In recent years, a hardware-accelerated version of AutoDock, called AutoDock-GPU, has been under active development. This work benchmarks the recent code and algorithmic enhancements incorporated into AutoDock-GPU. Particularly, we analyze the impact on execution runtime of techniques based on early termination. These enable AutoDock-GPU to explore the molecular space as necessary, while safely avoiding redundant computations. Our results indicate that it is possible to achieve average runtime reductions of 50% by using these techniques. Furthermore, a comprehensive literature review is also provided, where our work is compared to relevant approaches leveraging hardware acceleration for molecular docking.
Collapse
Affiliation(s)
- Leonardo Solis-Vasquez
- Embedded Systems and Applications Group. Technical University of Darmstadt, Darmstadt, Germany
- Hochschulstr. 10, D-64289, Darmstadt, Germany
| | - Andreas F. Tillack
- Department of Integrative Structural and Computational Biology. The Scripps Research Institute, La Jolla, CA, United States
| | - Diogo Santos-Martins
- Department of Integrative Structural and Computational Biology. The Scripps Research Institute, La Jolla, CA, United States
| | - Andreas Koch
- Embedded Systems and Applications Group. Technical University of Darmstadt, Darmstadt, Germany
| | | | - Stefano Forli
- Department of Integrative Structural and Computational Biology. The Scripps Research Institute, La Jolla, CA, United States
| |
Collapse
|
5
|
Chatterjee A, Paul S, Bisht B, Bhattacharya S, Sivasubramaniam S, Paul MK. Advances in targeting the WNT/β-catenin signaling pathway in cancer. Drug Discov Today 2021; 27:82-101. [PMID: 34252612 DOI: 10.1016/j.drudis.2021.07.007] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 05/27/2021] [Accepted: 07/06/2021] [Indexed: 01/05/2023]
Abstract
WNT/β-catenin signaling orchestrates various physiological processes, including embryonic development, growth, tissue homeostasis, and regeneration. Abnormal WNT/β-catenin signaling is associated with various cancers and its inhibition has shown effective antitumor responses. In this review, we discuss the pathway, potential targets for the development of WNT/β-catenin inhibitors, available inhibitors, and their specific molecular interactions with the target proteins. We also discuss inhibitors that are in clinical trials and describe potential new avenues for therapeutically targeting the WNT/β-catenin pathway. Furthermore, we introduce emerging strategies, including artificial intelligence (AI)-assisted tools and technology-based actionable approaches, to translate WNT/β-catenin inhibitors to the clinic for cancer therapy.
Collapse
Affiliation(s)
- Avradip Chatterjee
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Sayan Paul
- Department of Biotechnology, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu 627012, India; Centre for Cardiovascular Biology and Disease, Institute for Stem Cell Science and Regenerative Medicine (inStem), Bangalore 560065, India
| | - Bharti Bisht
- Department of Thoracic Surgery, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
| | - Shelley Bhattacharya
- Environmental Toxicology Laboratory, Department of Zoology (Centre for Advanced Studies), Visva Bharati (A Central University), Santiniketan 731235, India
| | - Sudhakar Sivasubramaniam
- Department of Biotechnology, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu 627012, India
| | - Manash K Paul
- Department of Pulmonary and Critical Care Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA.
| |
Collapse
|
6
|
Grbčić P, Fučkar Čupić D, Gamberi T, Kraljević Pavelić S, Sedić M. Proteomic Profiling of BRAFV600E Mutant Colon Cancer Cells Reveals the Involvement of Nucleophosmin/c-Myc Axis in Modulating the Response and Resistance to BRAF Inhibition by Vemurafenib. Int J Mol Sci 2021; 22:ijms22126174. [PMID: 34201061 PMCID: PMC8228139 DOI: 10.3390/ijms22126174] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/31/2021] [Accepted: 06/03/2021] [Indexed: 12/18/2022] Open
Abstract
BRAFV600E mutations are found in approximately 10% of colorectal cancer patients and are associated with worse prognosis and poor outcomes with systemic therapies. The aim of this study was to identify novel druggable features of BRAFV600E-mutated colon cancer (CC) cells associated with the response and resistance to BRAFV600E inhibitor vemurafenib. Towards this aim, we carried out global proteomic profiling of BRAFV600E mutant vs. KRAS mutant/BRAF wild-type and double wild-type KRAS/BRAF CC cells followed by bioinformatics analyses. Validation of selected proteomic features was performed by immunohistochemistry and in silico using the TCGA database. We reveal an increased abundance and activity of nucleophosmin (NPM1) in BRAFV600E-mutated CC in vitro, in silico and in tumor tissues from colon adenocarcinoma patients and demonstrate the roles of NPM1 and its interaction partner c-Myc in conveying the resistance to vemurafenib. Pharmacological inhibition of NPM1 effectively restored the sensitivity of vemurafenib-resistant BRAF-mutated CC cells by down-regulating c-Myc expression and activity and consequently suppressing its transcriptional targets RanBP1 and phosphoserine phosphatase that regulate centrosome duplication and serine biosynthesis, respectively. Altogether, findings from this study suggest that the NPM1/c-Myc axis could represent a promising therapeutic target to thwart resistance to vemurafenib in BRAF-mutated CC.
Collapse
Affiliation(s)
- Petra Grbčić
- Department of Biotechnology, University of Rijeka, Radmile Matejčić 2, 51000 Rijeka, Croatia;
| | - Dora Fučkar Čupić
- Faculty of Medicine, University of Rijeka, Ul. Braće Branchetta 20/1, 51000 Rijeka, Croatia;
| | - Tania Gamberi
- Dipartimento di Scienze Biomediche, Sperimentali e Cliniche Mario Serio, University of Florence, Viale Morgagni 50, 50134 Florence, Italy;
| | | | - Mirela Sedić
- Department of Biotechnology, University of Rijeka, Radmile Matejčić 2, 51000 Rijeka, Croatia;
- Correspondence: ; Tel.: +385-51-584-574
| |
Collapse
|
7
|
Li K, Du Y, Li L, Wei DQ. Bioinformatics Approaches for Anti-cancer Drug Discovery. Curr Drug Targets 2021; 21:3-17. [PMID: 31549592 DOI: 10.2174/1389450120666190923162203] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 07/17/2019] [Accepted: 07/26/2019] [Indexed: 12/23/2022]
Abstract
Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.
Collapse
Affiliation(s)
- Kening Li
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuxin Du
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lu Li
- Department of Bioinformatics, Nanjing Medical University, Nanjing 211166, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| |
Collapse
|
8
|
Craig M, Jenner AL, Namgung B, Lee LP, Goldman A. Engineering in Medicine To Address the Challenge of Cancer Drug Resistance: From Micro- and Nanotechnologies to Computational and Mathematical Modeling. Chem Rev 2020; 121:3352-3389. [PMID: 33152247 DOI: 10.1021/acs.chemrev.0c00356] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Drug resistance has profoundly limited the success of cancer treatment, driving relapse, metastasis, and mortality. Nearly all anticancer drugs and even novel immunotherapies, which recalibrate the immune system for tumor recognition and destruction, have succumbed to resistance development. Engineers have emerged across mechanical, physical, chemical, mathematical, and biological disciplines to address the challenge of drug resistance using a combination of interdisciplinary tools and skill sets. This review explores the developing, complex, and under-recognized role of engineering in medicine to address the multitude of challenges in cancer drug resistance. Looking through the "lens" of intrinsic, extrinsic, and drug-induced resistance (also referred to as "tolerance"), we will discuss three specific areas where active innovation is driving novel treatment paradigms: (1) nanotechnology, which has revolutionized drug delivery in desmoplastic tissues, harnessing physiochemical characteristics to destroy tumors through photothermal therapy and rationally designed nanostructures to circumvent cancer immunotherapy failures, (2) bioengineered tumor models, which have benefitted from microfluidics and mechanical engineering, creating a paradigm shift in physiologically relevant environments to predict clinical refractoriness and enabling platforms for screening drug combinations to thwart resistance at the individual patient level, and (3) computational and mathematical modeling, which blends in silico simulations with molecular and evolutionary principles to map mutational patterns and model interactions between cells that promote resistance. On the basis that engineering in medicine has resulted in discoveries in resistance biology and successfully translated to clinical strategies that improve outcomes, we suggest the proliferation of multidisciplinary science that embraces engineering.
Collapse
Affiliation(s)
- Morgan Craig
- Department of Mathematics and Statistics, University of Montreal, Montreal, Quebec H3C 3J7, Canada.,Sainte-Justine University Hospital Research Centre, Montreal, Quebec H3S 2G4, Canada
| | - Adrianne L Jenner
- Department of Mathematics and Statistics, University of Montreal, Montreal, Quebec H3C 3J7, Canada.,Sainte-Justine University Hospital Research Centre, Montreal, Quebec H3S 2G4, Canada
| | - Bumseok Namgung
- Division of Engineering in Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, United States.,Department of Medicine, Harvard Medical School, Boston, Massachusetts 02139, United States
| | - Luke P Lee
- Division of Engineering in Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, United States.,Department of Medicine, Harvard Medical School, Boston, Massachusetts 02139, United States
| | - Aaron Goldman
- Division of Engineering in Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, United States.,Department of Medicine, Harvard Medical School, Boston, Massachusetts 02139, United States
| |
Collapse
|
9
|
Villar-Prados A, Wu SY, Court KA, Ma S, LaFargue C, Chowdhury MA, Engelhardt MI, Ivan C, Ram PT, Wang Y, Baggerly K, Rodriguez-Aguayo C, Lopez-Berestein G, Ming-Yang S, Maloney DJ, Yoshioka M, Strovel JW, Roszik J, Sood AK. Predicting Novel Therapies and Targets: Regulation of Notch3 by the Bromodomain Protein BRD4. Mol Cancer Ther 2019; 18:421-436. [PMID: 30420565 PMCID: PMC6363833 DOI: 10.1158/1535-7163.mct-18-0365] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 08/24/2018] [Accepted: 11/06/2018] [Indexed: 11/16/2022]
Abstract
Systematic approaches for accurate repurposing of targeted therapies are needed. We developed and aimed to biologically validate our therapy predicting tool (TPT) for the repurposing of targeted therapies for specific tumor types by testing the role of Bromodomain and Extra-Terminal motif inhibitors (BETi) in inhibiting BRD4 function and downregulating Notch3 signaling in ovarian cancer.Utilizing established ovarian cancer preclinical models, we carried out in vitro and in vivo studies with clinically relevant BETis to determine their therapeutic effect and impact on Notch3 signaling.Treatment with BETis or siRNA-mediated BRD4 knockdown resulted in decreased cell viability, reduced cell proliferation, and increased cell apoptosis in vitro. In vivo studies with orthotopic mouse models demonstrated that treatment with BETi decreased tumor growth. In addition, knockdown of BRD4 with doxycycline-inducible shRNA increased survival up to 50% (P < 0.001). Treatment with either BETis or BRD4 siRNA decreased Notch3 expression both in vitro and in vivo BRD4 inhibition also decreased the expression of NOTCH3 targets, including HES1 Chromatin immunoprecipitation revealed that BRD4 was present at the NOTCH3 promoter.Our findings provide biological validation for the TPT by demonstrating that BETis can be an effective therapeutic agent for ovarian cancer by downregulating Notch3 expression.The TPT could rapidly identify candidate drugs for ovarian or other cancers along with novel companion biomarkers.
Collapse
Affiliation(s)
- Alejandro Villar-Prados
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
- School of Medicine, Medical Sciences Campus, University of Puerto Rico, San Juan, Puerto Rico
| | - Sherry Y Wu
- School of Biomedical Sciences, University of Queensland, Queensland, Australia
| | - Karem A Court
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Shaolin Ma
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Christopher LaFargue
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Mamur A Chowdhury
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Margaret I Engelhardt
- John P. and Kathrine G. McGovern Medical School, The University of Texas, Houston, Texas
| | - Cristina Ivan
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Prahlad T Ram
- Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Ying Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Keith Baggerly
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Cristian Rodriguez-Aguayo
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Gabriel Lopez-Berestein
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Shyh Ming-Yang
- National Center for Advancing Translational Sciences, NIH, Rockville, Maryland
| | - David J Maloney
- National Center for Advancing Translational Sciences, NIH, Rockville, Maryland
| | | | | | - Jason Roszik
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Anil K Sood
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.
- Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, Texas
| |
Collapse
|
10
|
Xu S, Liu R, Da Y. Comparison of tumor related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma. Thorac Cancer 2018; 9:974-988. [PMID: 29870138 PMCID: PMC6068465 DOI: 10.1111/1759-7714.12773] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Accepted: 05/02/2018] [Indexed: 12/14/2022] Open
Abstract
Background This study compared tumor‐related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma (LUAD) treatment. Methods Kyoto Encyclopedia of Genes and Genomes signaling pathway analyses were performed based on LUAD differentially expressed genes from The Cancer Genome Atlas (TCGA) project and genotype‐tissue expression controls. These results were compared to various known compounds using the Connectivity Mapping dataset. The clinical significance of the hub genes identified by overlapping pathway enrichment analysis was further investigated using data mining from multiple sources. A drug‐pathway network for LUAD was constructed, and molecular docking was carried out. Results After the integration of 57 LUAD‐related pathways and 35 pathways affected by small molecules, five overlapping pathways were revealed. Among these five pathways, the p53 signaling pathway was the most significant, with CCNB1, CCNB2, CDK1, CDKN2A, and CHEK1 being identified as hub genes. The p53 signaling pathway is implicated as a risk factor for LUAD tumorigenesis and survival. A total of 88 molecules significantly inhibiting the five LUAD‐related oncogenic pathways were involved in the LUAD drug‐pathway network. Daunorubicin, mycophenolic acid, and pyrvinium could potentially target the hub gene CHEK1 directly. Conclusion Our study highlights the critical pathways that should be targeted in the search for potential LUAD treatments, most importantly, the p53 signaling pathway. Some compounds, such as ciclopirox and AG‐028671, may have potential roles for LUAD treatment but require further experimental verification.
Collapse
Affiliation(s)
- Song Xu
- Department of Lung Cancer Surgery, Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Renwang Liu
- Department of Lung Cancer Surgery, Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Yurong Da
- Key Laboratory of Cellular and Molecular Immunology in Tianjin, Key Laboratory of Immune Microenvironment and Disease of the Ministry of Education, Department of Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| |
Collapse
|
11
|
Peng S, Yang S, Bo X, Li F. paraGSEA: a scalable approach for large-scale gene expression profiling. Nucleic Acids Res 2017; 45:e155. [PMID: 28973463 PMCID: PMC5737394 DOI: 10.1093/nar/gkx679] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2017] [Accepted: 07/27/2017] [Indexed: 12/28/2022] Open
Abstract
More studies have been conducted using gene expression similarity to identify functional connections among genes, diseases and drugs. Gene Set Enrichment Analysis (GSEA) is a powerful analytical method for interpreting gene expression data. However, due to its enormous computational overhead in the estimation of significance level step and multiple hypothesis testing step, the computation scalability and efficiency are poor on large-scale datasets. We proposed paraGSEA for efficient large-scale transcriptome data analysis. By optimization, the overall time complexity of paraGSEA is reduced from O(mn) to O(m+n), where m is the length of the gene sets and n is the length of the gene expression profiles, which contributes more than 100-fold increase in performance compared with other popular GSEA implementations such as GSEA-P, SAM-GS and GSEA2. By further parallelization, a near-linear speed-up is gained on both workstations and clusters in an efficient manner with high scalability and performance on large-scale datasets. The analysis time of whole LINCS phase I dataset (GSE92742) was reduced to nearly half hour on a 1000 node cluster on Tianhe-2, or within 120 hours on a 96-core workstation. The source code of paraGSEA is licensed under the GPLv3 and available at http://github.com/ysycloud/paraGSEA.
Collapse
Affiliation(s)
- Shaoliang Peng
- College of Computer Science and Electronic Engineering & National Supercomputer Centre in Changsha, Hunan University, Changsha 410082, China.,School of Computer Science, National University of Defense Technology, Changsha 410073, China
| | - Shunyun Yang
- School of Computer Science, National University of Defense Technology, Changsha 410073, China
| | - Xiaochen Bo
- Department of biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Fei Li
- Department of biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| |
Collapse
|