Gene signature

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A gene signature or gene expression signature is a single or combined group of genes in a cell with a uniquely characteristic pattern of gene expression[1] that occurs as a result of an altered or unaltered biological process or pathogenic medical condition.[2] This is not to be confused with the concept of gene expression profiling. Activating pathways in a regular physiological process or a physiological response to a stimulus results in a cascade of signal transduction and interactions that elicit altered levels of gene expression, which is classified as the gene signature of that physiological process or response.[3] The clinical applications of gene signatures breakdown into prognostic, diagnostic[4][5] and predictive signatures. The phenotypes that may theoretically be defined by a gene expression signature range from those that predict the survival or prognosis of an individual with a disease, those that are used to differentiate between different subtypes of a disease, to those that predict activation of a particular pathway. Ideally, gene signatures can be used to select a group of patients[6] for whom a particular treatment will be effective.[7][8]

Timeline of Gene Signature Detection[edit]

In 1995, 2 studies conducted identified unique approaches to analyzing global gene expression of a genome which collectively promoted the value of identifying and analyzing gene signatures for physiological relevance. The first study reports a technique that improves expressed sequence tag (EST) analysis, known as Serial Analysis of Gene Expression (SAGE) that hinged on sequencing and quantifying mRNA samples which acquired levels of gene expression that eventually revealed characteristic gene expression patterns.[9]

The second study identified a technique that is now widely known as the microarray which quantifies complementary DNA (cDNA) hybridization on a glass slide to analyze the expression of many genes in parallel.[10] These studies drew greater attention to the wealth of information that analysis of gene signatures bear that may or may not be physiologically relevant.

Pressing forward, the latter technique has revolutionized research in genetics and DNA chip technology[11] as it is a widely adopted technique to profile gene expression signatures such that these physiological responses can be cataloged[12] in repositories such as NCBI Gene Expression Omnibus. This catalogue of prognostic, diagnostic and predictive gene expression signatures allow for predictions of onset of pathogenic diseases in patients,[13] tumour and cancer classification,[14] and enhanced therapeutic strategies that predict the optimal target candidates subjects and genes.[15]

Today, microarrays and other quantitative methods such as RNA-seq that encompass gene expression profiling, are moving towards promotion of re-analysis and integration of the large, publicly available database of gene expression signatures and profiles to uncover the full threshold of information these expression signatures hold.[16]

Types of Gene Signatures[edit]

Prognostic Gene Signature[edit]

Prognostic refers to predicting the likely outcome or course of a disease. Classifying a biological phenotype or medical condition based on a specific gene signature or multiple gene signatures, can serve as a prognostic biomarker for the associated phenotype or condition. This concept termed prognostic gene signature, serves to offer insight into the overall outcome of the condition regardless of therapeutic intervention.[17] Several studies have been conducted with focus on identifying prognostic gene signatures with the hopes of improving the diagnostic methods and therapeutic courses adopted in a clinical settings. It is important to note that prognostic gene signatures are not a target of therapy; they offer additional information to consider when discussing details such as duration or dosage or drug sensitivity etc. in therapeutic intervention. The criteria a gene signature must meet to be deemed a prognostic marker include demonstration of its association with the outcomes of the condition, reproducibility and validation of its association in an independent group of patients and lastly, the prognostic value must demonstrate independence from other standard factors in a multivariate analysis.[3] The applications of these prognostic signatures include prognostic assays for breast cancer,[18][19] hepatocellular carcinoma,[20] leukaemia[21] and are continually being developed for other types of cancers and disorders as well.

Diagnostic Gene Signatures[edit]

A diagnostic gene signature serves as a biomarker that distinguishes phenotypically similar medical conditions that have a threshold of severity consisting of mild, moderate or severe phenotypes.[5] Establishing verified methods of diagnosing clinically indolent and significant cases allows practitioners to provide more accurate care and therapeutic options that range from no therapy, preventative care to symptomatic relief. These diagnostic signatures also allow for a more accurate representation of test samples used in research.[6] Similar to the procedure of validation of prognostic gene signature, a criteria exists for classifying a gene signature as a biomarker for a disorder or diseases outlined by Chau et al.[22][23]

Predictive Gene Signatures[edit]

A predictive gene signature is similar to a predictive biomarker, where it predicts the effect of treatment in patients or study participants that exhibit a particular disease phenotype. A predictive gene signature unlike a prognostic gene signature can be a target for therapy.[17] The information predictive signatures provide are more rigorous than that of prognostic signatures as they are based off of treatment groups with therapeutic intervention on the likely benefit from treatment, completely independent of prognosis.[24] Predictive gene signatures addresses the paramount need for ways to personalize and tailor therapeutic intervention in diseases. These signatures have implications in facilitating personalized medicine through identification of more novel therapeutic targets and identifying the most qualified subjects for optimal benefit of specific treatments.[3][25]

See also[edit]

References[edit]

  1. ^ Itadani H, Mizuarai S, Kotani H. Can systems biology understand pathway activation? Gene expression signatures as surrogate markers for understanding the complexity of pathway activation. Curr Genomics. 2008 Aug;9(5):349-60.PMID 19517027
  2. ^ Liu J, Campen A, Huang S, Peng SB, Ye X, Palakal M, Dunker AK, Xia Y, Li S. "Identification of a gene signature in cell cycle pathway for breast cancer prognosis using gene expression profiling data. BMC Med Genomics 2008 Sep 11;1:39. PMID 18786252
  3. ^ a b c Chibon, Frederic (May 2013). "Cancer gene expression signatures - the rise and fall?". European Journal of Cancer (Oxford, England: 1990). 49 (8): 2000–2009. doi:10.1016/j.ejca.2013.02.021. ISSN 1879-0852. PMID 23498875. 
  4. ^ Warner, Digby F. "Defining a diagnostic gene signature for tuberculosis". The Lancet Respiratory Medicine. 4 (3): 170–171. doi:10.1016/s2213-2600(16)00063-1. 
  5. ^ a b Nguyen, Hao G.; Welty, Christopher J.; Cooperberg, Matthew R. "Diagnostic associations of gene expression signatures in prostate cancer tissue". Current Opinion in Urology. 25 (1): 65–70. doi:10.1097/mou.0000000000000131. 
  6. ^ a b Wouters BJ, Löwenberg B, Erpelinck-Verschueren CA, van Putten WL, Valk PJ, Delwel R. "Double CEBPA mutations, but not single CEBPA mutations, define a subgroup of acute myeloid leukemia with a distinctive gene expression profile that is uniquely associated with a favorable outcome. Blood 2009 Mar 26;113(13):3088-91. PMID 19171880
  7. ^ Hassane DC, Guzman ML, Corbett C, Li X, Abboud R, Young F, Liesveld JL, Carroll M, Jordan CT. "Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data. Blood 2008 Jun 15;111(12):5654-62. Epub 2008 Feb 27.PMID 18305216
  8. ^ Corsello SM, Roti G, Ross KN, Chow KT, Galinsky I, DeAngelo DJ, Stone RM, Kung AL, Golub TR, Stegmaier K. "Identification of AML1-ETO modulators by chemical genomics. Blood 2009 Jun 11;113(24):6193-205 PMID 19377049
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  10. ^ Schena, M.; Shalon, D.; Davis, R. W.; Brown, P. O. (1995-10-20). "Quantitative monitoring of gene expression patterns with a complementary DNA microarray". Science. 270 (5235): 467–470. ISSN 0036-8075. PMID 7569999. 
  11. ^ Kurian, K. M.; Watson, C. J.; Wyllie, A. H. (February 1999). "DNA chip technology". The Journal of Pathology. 187 (3): 267–271. doi:10.1002/(SICI)1096-9896(199902)187:33.0.CO;2-#. ISSN 0022-3417. PMID 10398077. 
  12. ^ Lamb, Justin; Crawford, Emily D.; Peck, David; Modell, Joshua W.; Blat, Irene C.; Wrobel, Matthew J.; Lerner, Jim; Brunet, Jean-Philippe; Subramanian, Aravind (2006-09-29). "The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease". Science. 313 (5795): 1929–1935. doi:10.1126/science.1132939. ISSN 1095-9203. PMID 17008526. 
  13. ^ Diagnostic, Prognostic and Therapeutic Value of Gene Signatures | SpringerLink. doi:10.1007/978-1-61779-358-5. 
  14. ^ Sørlie, T.; Perou, C. M.; Tibshirani, R.; Aas, T.; Geisler, S.; Johnsen, H.; Hastie, T.; Eisen, M. B.; van de Rijn, M. (2001-09-11). "Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications". Proceedings of the National Academy of Sciences of the United States of America. 98 (19): 10869–10874. doi:10.1073/pnas.191367098. ISSN 0027-8424. PMID 11553815. 
  15. ^ Scherf, U.; Ross, D. T.; Waltham, M.; Smith, L. H.; Lee, J. K.; Tanabe, L.; Kohn, K. W.; Reinhold, W. C.; Myers, T. G. (March 2000). "A gene expression database for the molecular pharmacology of cancer". Nature Genetics. 24 (3): 236–244. doi:10.1038/73439. ISSN 1061-4036. PMID 10700175. 
  16. ^ Wang, Zichen; Monteiro, Caroline D.; Jagodnik, Kathleen M.; Fernandez, Nicolas F.; Gundersen, Gregory W.; Rouillard, Andrew D.; Jenkins, Sherry L.; Feldmann, Axel S.; Hu, Kevin S. (2016-09-26). "Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd". Nature Communications. 7: 12846. doi:10.1038/ncomms12846. 
  17. ^ a b Oldenhuis, C. N. a. M.; Oosting, S. F.; Gietema, J. A.; de Vries, E. G. E. (May 2008). "Prognostic versus predictive value of biomarkers in oncology". European Journal of Cancer (Oxford, England: 1990). 44 (7): 946–953. doi:10.1016/j.ejca.2008.03.006. ISSN 0959-8049. PMID 18396036. 
  18. ^ Nielsen, Torsten; Wallden, Brett; Schaper, Carl; Ferree, Sean; Liu, Shuzhen; Gao, Dongxia; Barry, Garrett; Dowidar, Naeem; Maysuria, Malini (2014-03-13). "Analytical validation of the PAM50-based Prosigna Breast Cancer Prognostic Gene Signature Assay and nCounter Analysis System using formalin-fixed paraffin-embedded breast tumor specimens". BMC Cancer. 14: 177. doi:10.1186/1471-2407-14-177. ISSN 1471-2407. 
  19. ^ Liu, Rui; Wang, Xinhao; Chen, Grace Y.; Dalerba, Piero; Gurney, Austin; Hoey, Timothy; Sherlock, Gavin; Lewicki, John; Shedden, Kerby (2007-01-18). "The Prognostic Role of a Gene Signature from Tumorigenic Breast-Cancer Cells". New England Journal of Medicine. 356 (3): 217–226. doi:10.1056/nejmoa063994. ISSN 0028-4793. PMID 17229949. 
  20. ^ Hoshida, Yujin; Villanueva, Augusto; Sangiovanni, Angelo; Sole, Manel; Hur, Chin; Andersson, Karin L.; Chung, Raymond T.; Gould, Joshua; Kojima, Kensuke. "Prognostic Gene Expression Signature for Patients With Hepatitis C–Related Early-Stage Cirrhosis". Gastroenterology. 144 (5): 1024–1030. doi:10.1053/j.gastro.2013.01.021. 
  21. ^ Verhaak, Roel G. W.; Goudswaard, Chantal S.; Putten, Wim van; Bijl, Maarten A.; Sanders, Mathijs A.; Hugens, Wendy; Uitterlinden, André G.; Erpelinck, Claudia A. J.; Delwel, Ruud (2005-12-01). "Mutations in nucleophosmin (NPM1) in acute myeloid leukemia (AML): association with other gene abnormalities and previously established gene expression signatures and their favorable prognostic significance". Blood. 106 (12): 3747–3754. doi:10.1182/blood-2005-05-2168. ISSN 0006-4971. PMID 16109776. 
  22. ^ Chau, Cindy H.; Rixe, Olivier; McLeod, Howard; Figg, William D. (2008-10-01). "Validation of Analytic Methods for Biomarkers Used in Drug Development". Clinical Cancer Research. 14 (19): 5967–5976. doi:10.1158/1078-0432.ccr-07-4535. 
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  25. ^ Grob, Jean-Jacques; Mortier, Laurent; D’Hondt, Lionel; Grange, Florent; Baurain, Jean Francois; Dréno, Brigitte; Lebbe, Céleste; Robert, Caroline; Dompmartin, Anne (2017-11-01). "Safety and immunogenicity of MAGE-A3 cancer immunotherapeutic with dacarbazine in patients with MAGE-A3-positive metastatic cutaneous melanoma: an open phase I/II study with a first assessment of a predictive gene signature". ESMO Open. 2 (5): e000203. doi:10.1136/esmoopen-2017-000203. ISSN 2059-7029.