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Ung MH, Wang GL, Varn FS, Cheng C. Application of pharmacologically induced transcriptomic profiles to interrogate PI3K-Akt-mTOR pathway activity associated with cancer patient prognosis. Oncotarget 2018; 7:84142-84154. [PMID: 27589846 PMCID: PMC5356650 DOI: 10.18632/oncotarget.11776] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Accepted: 08/25/2016] [Indexed: 02/06/2023] Open
Abstract
The PI3K-Akt-mTOR signaling pathway has been identified as a key driver of carcinogenesis in several cancer types. As such, a major area of focus in cancer biology is the development of genomic biomarkers that can measure the activity level of the PI3K-Akt-mTOR pathway. In this study, we systematically estimate PI3K-Akt-mTOR pathway activity in breast primary tumor samples using transcriptomic profiles derived from drug treatment in MCF7 cell lines. We demonstrate that gene expression profiles derived from chemically-induced protein inhibition allows us to measure PI3K-Akt-mTOR pathway activity in patient tumor samples. With this approach, we predict prognosis and response to chemotherapy in cancer patients, and screen for potential pharmacological modulators of PI3K-Akt-mTOR pathway inhibitors.
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Affiliation(s)
- Matthew H Ung
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, 03755 USA
| | - George L Wang
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, 03755 USA
| | - Frederick S Varn
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, 03755 USA
| | - Chao Cheng
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, 03755 USA.,Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, 03755 USA.,Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, 03766 USA
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Cheng F, Hong H, Yang S, Wei Y. Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era. Brief Bioinform 2017; 18:682-697. [PMID: 27296652 DOI: 10.1093/bib/bbw051] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Indexed: 12/12/2022] Open
Abstract
Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.
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Ung MH, Varn FS, Cheng C. In silico frameworks for systematic pre-clinical screening of potential anti-leukemia therapeutics. Expert Opin Drug Discov 2016; 11:1213-1222. [PMID: 27689915 DOI: 10.1080/17460441.2016.1243524] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Leukemia is a collection of highly heterogeneous cancers that arise from neoplastic transformation and clonal expansion of immature hematopoietic cells. Post-treatment recurrence is high, especially among elderly patients, thus necessitating more effective treatment modalities. Development of novel anti-leukemic compounds relies heavily on traditional in vitro screens which require extensive resources and time. Therefore, integration of in silico screens prior to experimental validation can improve the efficiency of pre-clinical drug development. Areas covered: This article reviews different methods and frameworks used to computationally screen for anti-leukemic agents. In particular, three approaches are discussed including molecular docking, transcriptomic integration, and network analysis. Expert opinion: Today's data deluge presents novel opportunities to develop computational tools and pipelines to screen for likely therapeutic candidates in the treatment of leukemia. Formal integration of these methodologies can accelerate and improve the efficiency of modern day anti-leukemic drug discovery and ease the economic and healthcare burden associated with it.
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Affiliation(s)
- Matthew H Ung
- a Department of Molecular and Systems Biology , Geisel School of Medicine at Dartmouth , Hanover , NH , USA
| | - Frederick S Varn
- a Department of Molecular and Systems Biology , Geisel School of Medicine at Dartmouth , Hanover , NH , USA
| | - Chao Cheng
- a Department of Molecular and Systems Biology , Geisel School of Medicine at Dartmouth , Hanover , NH , USA.,b Department of Biomedical Data Science , Geisel School of Medicine at Dartmouth , Lebanon , NH , USA.,c Norris Cotton Cancer Center , Lebanon , NH , USA
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Ung MH, Sun CH, Weng CW, Huang CC, Lin CC, Liu CC, Cheng C. Integrated Drug Expression Analysis for leukemia: an integrated in silico and in vivo approach to drug discovery. THE PHARMACOGENOMICS JOURNAL 2016; 17:351-359. [PMID: 26975228 DOI: 10.1038/tpj.2016.18] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Revised: 11/18/2015] [Accepted: 02/04/2016] [Indexed: 02/06/2023]
Abstract
Screening for drug compounds that exhibit therapeutic properties in the treatment of various diseases remains a challenge even after considerable advancements in biomedical research. Here, we introduce an integrated platform that exploits gene expression compendia generated from drug-treated cell lines and primary tumor tissue to identify therapeutic candidates that can be used in the treatment of acute myeloid leukemia (AML). Our framework combines these data with patient survival information to identify potential candidates that presumably have a significant impact on AML patient survival. We use a drug regulatory score (DRS) to measure the similarity between drug-induced cell line and patient tumor gene expression profiles, and show that these computed scores are highly correlated with in vitro metrics of pharmacological activity. Furthermore, we conducted several in vivo validation experiments of our potential candidate drugs in AML mouse models to demonstrate the accuracy of our in silico predictions.
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Affiliation(s)
- M H Ung
- Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.,Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - C-H Sun
- Institute of Biomedical Science, National Chung-Hsing University, Taichung, Taiwan
| | - C-W Weng
- Institute of Genomics and Bioinformatics, National Chung-Hsing University, Taichung, Taiwan
| | - C-C Huang
- Institute of Biomedical Science, National Chung-Hsing University, Taichung, Taiwan
| | - C-C Lin
- Institute of Biomedical Science, National Chung-Hsing University, Taichung, Taiwan
| | - C-C Liu
- Institute of Genomics and Bioinformatics, National Chung-Hsing University, Taichung, Taiwan
| | - C Cheng
- Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.,Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.,Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
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