Integrated network analyses for functional genomic studies in cancer
RNA-interference (RNAi) studies hold great promise for functional investigation of the significance of genetic variations and mutations, as well as potential synthetic lethalities, for understanding and treatment of cancer, yet technical and conceptual issues currently diminish the potential power o...
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| Format: | Article |
| Language: | en_US |
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Elsevier
2014
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| Online Access: | http://hdl.handle.net/1721.1/90297 https://orcid.org/0000-0003-4188-0414 https://orcid.org/0000-0001-9249-8181 |
| _version_ | 1811079116033622016 |
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| author | Fraenkel, Ernest Wilson, Jennifer Lynn Hemann, Michael Lauffenburger, Douglas A |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Fraenkel, Ernest Wilson, Jennifer Lynn Hemann, Michael Lauffenburger, Douglas A |
| author_sort | Fraenkel, Ernest |
| collection | MIT |
| description | RNA-interference (RNAi) studies hold great promise for functional investigation of the significance of genetic variations and mutations, as well as potential synthetic lethalities, for understanding and treatment of cancer, yet technical and conceptual issues currently diminish the potential power of this approach. While numerous research groups are usefully employing this kind of functional genomic methodology to identify molecular mediators of disease severity, response, and resistance to treatment, findings are generally confounded by “off-target” effects. These effects arise from a variety of issues beyond non-specific reagent behavior, such as biological cross-talk and feedback processes so thus can occur even with specific perturbation. Interpreting RNAi results in a network framework instead of merely as individual “hits” or “targets” leverages contributions from all hit/target contributions to pathways via their relationships with other network nodes. This interpretation can ameliorate dependence upon individual reagent performance and increase confidence in biological validation. Here we provide background on RNAi studies in cancer applications, review key challenges with functional genomics, and motivate the use of network models grounded in pathway analyses. |
| first_indexed | 2024-09-23T11:10:17Z |
| format | Article |
| id | mit-1721.1/90297 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:10:17Z |
| publishDate | 2014 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/902972022-10-01T01:47:11Z Integrated network analyses for functional genomic studies in cancer Fraenkel, Ernest Wilson, Jennifer Lynn Hemann, Michael Lauffenburger, Douglas A Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Department of Biology Wilson, Jennifer Lynn Hemann, Michael Fraenkel, Ernest Lauffenburger, Douglas A. RNA-interference (RNAi) studies hold great promise for functional investigation of the significance of genetic variations and mutations, as well as potential synthetic lethalities, for understanding and treatment of cancer, yet technical and conceptual issues currently diminish the potential power of this approach. While numerous research groups are usefully employing this kind of functional genomic methodology to identify molecular mediators of disease severity, response, and resistance to treatment, findings are generally confounded by “off-target” effects. These effects arise from a variety of issues beyond non-specific reagent behavior, such as biological cross-talk and feedback processes so thus can occur even with specific perturbation. Interpreting RNAi results in a network framework instead of merely as individual “hits” or “targets” leverages contributions from all hit/target contributions to pathways via their relationships with other network nodes. This interpretation can ameliorate dependence upon individual reagent performance and increase confidence in biological validation. Here we provide background on RNAi studies in cancer applications, review key challenges with functional genomics, and motivate the use of network models grounded in pathway analyses. National Science Foundation (U.S.). Graduate Research Fellowship National Cancer Institute (U.S.). Integrative Cancer Biology Program (Grant U54-CA112967) National Cancer Institute (U.S.) (Grant U01-CA155758) 2014-09-24T13:45:34Z 2014-09-24T13:45:34Z 2013-08 Article http://purl.org/eprint/type/JournalArticle 1044579X 1096-3650 http://hdl.handle.net/1721.1/90297 Wilson, Jennifer L., Michael T. Hemann, Ernest Fraenkel, and Douglas A. Lauffenburger. “Integrated Network Analyses for Functional Genomic Studies in Cancer.” Seminars in Cancer Biology 23, no. 4 (August 2013): 213–218. https://orcid.org/0000-0003-4188-0414 https://orcid.org/0000-0001-9249-8181 en_US http://dx.doi.org/10.1016/j.semcancer.2013.06.004 Seminars in Cancer Biology Creative Commons Attribution http://creativecommons.org/licenses/by/3.0/ application/pdf Elsevier Elsevier |
| spellingShingle | Fraenkel, Ernest Wilson, Jennifer Lynn Hemann, Michael Lauffenburger, Douglas A Integrated network analyses for functional genomic studies in cancer |
| title | Integrated network analyses for functional genomic studies in cancer |
| title_full | Integrated network analyses for functional genomic studies in cancer |
| title_fullStr | Integrated network analyses for functional genomic studies in cancer |
| title_full_unstemmed | Integrated network analyses for functional genomic studies in cancer |
| title_short | Integrated network analyses for functional genomic studies in cancer |
| title_sort | integrated network analyses for functional genomic studies in cancer |
| url | http://hdl.handle.net/1721.1/90297 https://orcid.org/0000-0003-4188-0414 https://orcid.org/0000-0001-9249-8181 |
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