Identification of signaling pathways related to drug efficacy in hepatocellular carcinoma via integration of phosphoproteomic, genomic and clinical data
Hepatocellular Carcinoma (HCC) is one of the leading causes of death worldwide, with only a handful of treatments effective in unresectable HCC. Most of the clinical trials for HCC using new generation interventions (drug-targeted therapies) have poor efficacy whereas just a few of them show some pr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2015
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| Online Access: | http://hdl.handle.net/1721.1/99495 |
| _version_ | 1826209546523115520 |
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| author | Melas, Ioannis N. Lauffenburger, Douglas A. Alexopoulos, Leonidas G. |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Melas, Ioannis N. Lauffenburger, Douglas A. Alexopoulos, Leonidas G. |
| author_sort | Melas, Ioannis N. |
| collection | MIT |
| description | Hepatocellular Carcinoma (HCC) is one of the leading causes of death worldwide, with only a handful of treatments effective in unresectable HCC. Most of the clinical trials for HCC using new generation interventions (drug-targeted therapies) have poor efficacy whereas just a few of them show some promising clinical outcomes [1]. This is amongst the first studies where the mode of action of some of the compounds extensively used in clinical trials is interrogated on the phosphoproteomic level, in an attempt to build predictive models for clinical efficacy. Signaling data are combined with previously published gene expression and clinical data within a consistent framework that identifies drug effects on the phosphoproteomic level and translates them to the gene expression level. The interrogated drugs are then correlated with genes differentially expressed in normal versus tumor tissue, and genes predictive of patient survival. Although the number of clinical trial results considered is small, our approach shows potential for discerning signaling activities that may help predict drug efficacy for HCC. |
| first_indexed | 2024-09-23T14:24:11Z |
| format | Article |
| id | mit-1721.1/99495 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:24:11Z |
| publishDate | 2015 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/994952022-10-01T21:07:01Z Identification of signaling pathways related to drug efficacy in hepatocellular carcinoma via integration of phosphoproteomic, genomic and clinical data Melas, Ioannis N. Lauffenburger, Douglas A. Alexopoulos, Leonidas G. Massachusetts Institute of Technology. Department of Biological Engineering Lauffenburger, Douglas A. Hepatocellular Carcinoma (HCC) is one of the leading causes of death worldwide, with only a handful of treatments effective in unresectable HCC. Most of the clinical trials for HCC using new generation interventions (drug-targeted therapies) have poor efficacy whereas just a few of them show some promising clinical outcomes [1]. This is amongst the first studies where the mode of action of some of the compounds extensively used in clinical trials is interrogated on the phosphoproteomic level, in an attempt to build predictive models for clinical efficacy. Signaling data are combined with previously published gene expression and clinical data within a consistent framework that identifies drug effects on the phosphoproteomic level and translates them to the gene expression level. The interrogated drugs are then correlated with genes differentially expressed in normal versus tumor tissue, and genes predictive of patient survival. Although the number of clinical trial results considered is small, our approach shows potential for discerning signaling activities that may help predict drug efficacy for HCC. National Institutes of Health (U.S.) (Grant U54-CA119267) National Institutes of Health (U.S.) (Grant R01-CA96504) 2015-10-29T13:44:38Z 2015-10-29T13:44:38Z 2013-11 Article http://purl.org/eprint/type/ConferencePaper 978-1-4799-3163-7 http://hdl.handle.net/1721.1/99495 Melas, Ioannis N., Douglas A. Lauffenburger, and Leonidas G. Alexopoulos. “Identification of Signaling Pathways Related to Drug Efficacy in Hepatocellular Carcinoma via Integration of Phosphoproteomic, Genomic and Clinical Data.” 13th IEEE International Conference on BioInformatics and BioEngineering (November 2013). en_US http://dx.doi.org/10.1109/BIBE.2013.6701683 Proceedings of the 13th IEEE International Conference on BioInformatics and BioEngineering Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) PMC |
| spellingShingle | Melas, Ioannis N. Lauffenburger, Douglas A. Alexopoulos, Leonidas G. Identification of signaling pathways related to drug efficacy in hepatocellular carcinoma via integration of phosphoproteomic, genomic and clinical data |
| title | Identification of signaling pathways related to drug efficacy in hepatocellular carcinoma via integration of phosphoproteomic, genomic and clinical data |
| title_full | Identification of signaling pathways related to drug efficacy in hepatocellular carcinoma via integration of phosphoproteomic, genomic and clinical data |
| title_fullStr | Identification of signaling pathways related to drug efficacy in hepatocellular carcinoma via integration of phosphoproteomic, genomic and clinical data |
| title_full_unstemmed | Identification of signaling pathways related to drug efficacy in hepatocellular carcinoma via integration of phosphoproteomic, genomic and clinical data |
| title_short | Identification of signaling pathways related to drug efficacy in hepatocellular carcinoma via integration of phosphoproteomic, genomic and clinical data |
| title_sort | identification of signaling pathways related to drug efficacy in hepatocellular carcinoma via integration of phosphoproteomic genomic and clinical data |
| url | http://hdl.handle.net/1721.1/99495 |
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