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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<ref>{{cite journal | vauthors = Itadani H, Mizuarai S, Kotani H | date = Aug 2008 | title = Can systems biology understand pathway activation? Gene expression signatures as surrogate markers for understanding the complexity of pathway activation | journal = Curr Genomics | volume = 9 | issue = 5| pages = 349–60 | pmid = 19517027 | doi = 10.2174/138920208785133235 | pmc = 2694555 }}</ref> that occurs as a result of an altered or unaltered biological process or pathogenic medical condition.<ref>{{cite journal | vauthors = Liu J, Campen A, Huang S, Peng SB, Ye X, Palakal M, Dunker AK, Xia Y, Li S | date = Sep 2008 | title = Identification of a gene signature in cell cycle pathway for breast cancer prognosis using gene expression profiling data | journal = BMC Med. Genom. | volume = 1 | page = 39 | pmid = 18786252 | doi=10.1186/1755-8794-1-39 | pmc=2551605}}</ref> 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 (physiology)|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.<ref name=":0">{{cite journal | vauthors = Chibon F | title = Cancer gene expression signatures - the rise and fall? | journal = European Journal of Cancer | volume = 49 | issue = 8 | pages = 2000–9 | date = May 2013 | pmid = 23498875 | doi = 10.1016/j.ejca.2013.02.021 }}</ref> The clinical applications of gene signatures breakdown into prognostic, diagnostic<ref>{{cite journal | vauthors = Warner DF | title = Defining a diagnostic gene signature for tuberculosis | journal = The Lancet. Respiratory Medicine | volume = 4 | issue = 3 | pages = 170–1 | date = March 2016 | pmid = 26907219 | doi = 10.1016/s2213-2600(16)00063-1 }}</ref><ref name="Nguyen 65–70">{{cite journal | vauthors = Nguyen HG, Welty CJ, Cooperberg MR | title = Diagnostic associations of gene expression signatures in prostate cancer tissue | journal = Current Opinion in Urology | volume = 25 | issue = 1 | pages = 65–70 | date = January 2015 | pmid = 25405934 | doi = 10.1097/mou.0000000000000131 | url = https://cloudfront.escholarship.org/dist/prd/content/qt2px56845/qt2px56845.pdf }}</ref> 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 [[Biological pathway|pathway]]. Ideally, gene signatures can be used to select a group of patients<ref name=":1">{{cite journal | vauthors = Wouters BJ, Löwenberg B, Erpelinck-Verschueren CA, van Putten WL, Valk PJ, Delwel R | date = Mar 2009 | title = 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 | journal = Blood | volume = 113 | issue = 13| pages = 3088–91 | pmid = 19171880 | doi=10.1182/blood-2008-09-179895 | pmc=2662648}}</ref> for whom a particular treatment will be effective.<ref>{{cite journal | vauthors = Hassane DC, Guzman ML, Corbett C, Li X, Abboud R, Young F, Liesveld JL, Carroll M, Jordan CT | date = Jun 2008 | title = Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data | journal = Blood | volume = 111 | issue = 12| pages = 5654–62 | pmid = 18305216 | doi = 10.1182/blood-2007-11-126003 | pmc = 2424160 }}</ref><ref>{{cite journal | vauthors = Corsello SM, Roti G, Ross KN, Chow KT, Galinsky I, DeAngelo DJ, Stone RM, Kung AL, Golub TR, Stegmaier K | date = Jun 2009 | title = Identification of AML1-ETO modulators by chemical genomics. | journal = Blood | volume = 113 | issue = 24| pages = 6193–205 | pmid = 19377049 | doi=10.1182/blood-2008-07-166090 | pmc=2699238}}</ref>
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<ref>{{cite journal | vauthors = Itadani H, Mizuarai S, Kotani H | date = Aug 2008 | title = Can systems biology understand pathway activation? Gene expression signatures as surrogate markers for understanding the complexity of pathway activation | journal = Curr Genomics | volume = 9 | issue = 5| pages = 349–60 | pmid = 19517027 | doi = 10.2174/138920208785133235 | pmc = 2694555 }}</ref> that occurs as a result of an altered or unaltered biological process or pathogenic medical condition.<ref>{{cite journal | vauthors = Liu J, Campen A, Huang S, Peng SB, Ye X, Palakal M, Dunker AK, Xia Y, Li S | date = Sep 2008 | title = Identification of a gene signature in cell cycle pathway for breast cancer prognosis using gene expression profiling data | journal = BMC Med. Genom. | volume = 1 | page = 39 | pmid = 18786252 | doi=10.1186/1755-8794-1-39 | pmc=2551605}}</ref> 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 (physiology)|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.<ref name=":0">{{cite journal | vauthors = Chibon F | title = Cancer gene expression signatures - the rise and fall? | journal = European Journal of Cancer | volume = 49 | issue = 8 | pages = 2000–9 | date = May 2013 | pmid = 23498875 | doi = 10.1016/j.ejca.2013.02.021 }}</ref> The clinical applications of gene signatures breakdown into prognostic, diagnostic<ref>{{cite journal | vauthors = Warner DF | title = Defining a diagnostic gene signature for tuberculosis | journal = The Lancet. Respiratory Medicine | volume = 4 | issue = 3 | pages = 170–1 | date = March 2016 | pmid = 26907219 | doi = 10.1016/s2213-2600(16)00063-1 }}</ref><ref name="Nguyen 65–70">{{cite journal | vauthors = Nguyen HG, Welty CJ, Cooperberg MR | title = Diagnostic associations of gene expression signatures in prostate cancer tissue | journal = Current Opinion in Urology | volume = 25 | issue = 1 | pages = 65–70 | date = January 2015 | pmid = 25405934 | doi = 10.1097/mou.0000000000000131 | s2cid = 29746661 | url = https://cloudfront.escholarship.org/dist/prd/content/qt2px56845/qt2px56845.pdf }}</ref> 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 [[Biological pathway|pathway]]. Ideally, gene signatures can be used to select a group of patients<ref name=":1">{{cite journal | vauthors = Wouters BJ, Löwenberg B, Erpelinck-Verschueren CA, van Putten WL, Valk PJ, Delwel R | date = Mar 2009 | title = 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 | journal = Blood | volume = 113 | issue = 13| pages = 3088–91 | pmid = 19171880 | doi=10.1182/blood-2008-09-179895 | pmc=2662648}}</ref> for whom a particular treatment will be effective.<ref>{{cite journal | vauthors = Hassane DC, Guzman ML, Corbett C, Li X, Abboud R, Young F, Liesveld JL, Carroll M, Jordan CT | date = Jun 2008 | title = Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data | journal = Blood | volume = 111 | issue = 12| pages = 5654–62 | pmid = 18305216 | doi = 10.1182/blood-2007-11-126003 | pmc = 2424160 }}</ref><ref>{{cite journal | vauthors = Corsello SM, Roti G, Ross KN, Chow KT, Galinsky I, DeAngelo DJ, Stone RM, Kung AL, Golub TR, Stegmaier K | date = Jun 2009 | title = Identification of AML1-ETO modulators by chemical genomics. | journal = Blood | volume = 113 | issue = 24| pages = 6193–205 | pmid = 19377049 | doi=10.1182/blood-2008-07-166090 | pmc=2699238}}</ref>


== Timeline of gene signature detection ==
== Timeline of gene signature detection ==
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|expressed sequence tag (EST)]] analysis, known as [[Serial analysis of gene expression|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.<ref>{{cite journal | vauthors = Velculescu VE, Zhang L, Vogelstein B, Kinzler KW | title = Serial analysis of gene expression | journal = Science | volume = 270 | issue = 5235 | pages = 484–7 | date = October 1995 | pmid = 7570003 | doi=10.1126/science.270.5235.484}}</ref>
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|expressed sequence tag (EST)]] analysis, known as [[Serial analysis of gene expression|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.<ref>{{cite journal | vauthors = Velculescu VE, Zhang L, Vogelstein B, Kinzler KW | title = Serial analysis of gene expression | journal = Science | volume = 270 | issue = 5235 | pages = 484–7 | date = October 1995 | pmid = 7570003 | doi=10.1126/science.270.5235.484| s2cid = 16281846 }}</ref>


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.<ref>{{cite journal | vauthors = Schena M, Shalon D, Davis RW, Brown PO | title = Quantitative monitoring of gene expression patterns with a complementary DNA microarray | journal = Science | volume = 270 | issue = 5235 | pages = 467–70 | date = October 1995 | pmid = 7569999 | doi=10.1126/science.270.5235.467}}</ref> These studies drew greater attention to the wealth of information that analysis of gene signatures bear that may or may not be physiologically relevant.
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.<ref>{{cite journal | vauthors = Schena M, Shalon D, Davis RW, Brown PO | title = Quantitative monitoring of gene expression patterns with a complementary DNA microarray | journal = Science | volume = 270 | issue = 5235 | pages = 467–70 | date = October 1995 | pmid = 7569999 | doi=10.1126/science.270.5235.467| s2cid = 6720459 }}</ref> 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<ref>{{cite journal | vauthors = Kurian KM, Watson CJ, Wyllie AH | title = DNA chip technology | journal = The Journal of Pathology | volume = 187 | issue = 3 | pages = 267–71 | date = February 1999 | pmid = 10398077 | doi = 10.1002/(SICI)1096-9896(199902)187:3<267::AID-PATH275>3.0.CO;2-# }}</ref> as it is a widely adopted technique to profile gene expression signatures such that these physiological responses can be cataloged<ref>{{cite journal | vauthors = Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ, Clemons PA, Wei R, Carr SA, Lander ES, Golub TR | title = The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease | journal = Science | volume = 313 | issue = 5795 | pages = 1929–35 | date = September 2006 | pmid = 17008526 | doi = 10.1126/science.1132939 }}</ref> in repositories such as [https://www.ncbi.nlm.nih.gov/geo/ NCBI Gene Expression Omnibus]. This catalogue of prognostic, diagnostic and predictive gene expression signatures allow for predictions of onset of pathogenic diseases in patients,<ref>{{Cite book|title=Diagnostic, Prognostic and Therapeutic Value of Gene Signatures {{!}} SpringerLink|doi=10.1007/978-1-61779-358-5|year = 2012|isbn = 978-1-61779-357-8|last1 = Russo|first1 = Antonio|last2=Iacobelli|first2=Stefano|last3=Iovanna|first3=Juan|hdl=10447/110836}}</ref> tumour and cancer classification,<ref>{{cite journal | vauthors = Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Lønning PE, Børresen-Dale AL | title = Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 98 | issue = 19 | pages = 10869–74 | date = September 2001 | pmid = 11553815 | doi = 10.1073/pnas.191367098 | pmc=58566}}</ref> and enhanced therapeutic strategies that predict the optimal target candidates subjects and genes.<ref>{{cite journal | vauthors = Scherf U, Ross DT, Waltham M, Smith LH, Lee JK, Tanabe L, Kohn KW, Reinhold WC, Myers TG, Andrews DT, Scudiero DA, Eisen MB, Sausville EA, Pommier Y, Botstein D, Brown PO, Weinstein JN | title = A gene expression database for the molecular pharmacology of cancer | journal = Nature Genetics | volume = 24 | issue = 3 | pages = 236–44 | date = March 2000 | pmid = 10700175 | doi = 10.1038/73439 }}</ref>
Pressing forward, the latter technique has revolutionized research in genetics and DNA chip technology<ref>{{cite journal | vauthors = Kurian KM, Watson CJ, Wyllie AH | title = DNA chip technology | journal = The Journal of Pathology | volume = 187 | issue = 3 | pages = 267–71 | date = February 1999 | pmid = 10398077 | doi = 10.1002/(SICI)1096-9896(199902)187:3<267::AID-PATH275>3.0.CO;2-# }}</ref> as it is a widely adopted technique to profile gene expression signatures such that these physiological responses can be cataloged<ref>{{cite journal | vauthors = Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ, Clemons PA, Wei R, Carr SA, Lander ES, Golub TR | title = The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease | journal = Science | volume = 313 | issue = 5795 | pages = 1929–35 | date = September 2006 | pmid = 17008526 | doi = 10.1126/science.1132939 | s2cid = 8728079 }}</ref> in repositories such as [https://www.ncbi.nlm.nih.gov/geo/ NCBI Gene Expression Omnibus]. This catalogue of prognostic, diagnostic and predictive gene expression signatures allow for predictions of onset of pathogenic diseases in patients,<ref>{{Cite book|title=Diagnostic, Prognostic and Therapeutic Value of Gene Signatures {{!}} SpringerLink|doi=10.1007/978-1-61779-358-5|year = 2012|isbn = 978-1-61779-357-8|last1 = Russo|first1 = Antonio|last2=Iacobelli|first2=Stefano|last3=Iovanna|first3=Juan|hdl=10447/110836}}</ref> tumour and cancer classification,<ref>{{cite journal | vauthors = Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Lønning PE, Børresen-Dale AL | title = Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 98 | issue = 19 | pages = 10869–74 | date = September 2001 | pmid = 11553815 | doi = 10.1073/pnas.191367098 | pmc=58566| doi-access = free }}</ref> and enhanced therapeutic strategies that predict the optimal target candidates subjects and genes.<ref>{{cite journal | vauthors = Scherf U, Ross DT, Waltham M, Smith LH, Lee JK, Tanabe L, Kohn KW, Reinhold WC, Myers TG, Andrews DT, Scudiero DA, Eisen MB, Sausville EA, Pommier Y, Botstein D, Brown PO, Weinstein JN | title = A gene expression database for the molecular pharmacology of cancer | journal = Nature Genetics | volume = 24 | issue = 3 | pages = 236–44 | date = March 2000 | pmid = 10700175 | doi = 10.1038/73439 | s2cid = 1494000 }}</ref>


Today, microarrays and other quantitative methods such as [[RNA-Seq|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.<ref>{{cite journal | vauthors = Wang Z, Monteiro CD, Jagodnik KM, Fernandez NF, Gundersen GW, Rouillard AD, Jenkins SL, Feldmann AS, Hu KS, McDermott MG, Duan Q, Clark NR, Jones MR, Kou Y, Goff T, Woodland H, Amaral FM, Szeto GL, Fuchs O, Schüssler-Fiorenza Rose SM, Sharma S, Schwartz U, Bausela XB, Szymkiewicz M, Maroulis V, Salykin A, Barra CM, Kruth CD, Bongio NJ, Mathur V, Todoric RD, Rubin UE, Malatras A, Fulp CT, Galindo JA, Motiejunaite R, Jüschke C, Dishuck PC, Lahl K, Jafari M, Aibar S, Zaravinos A, Steenhuizen LH, Allison LR, Gamallo P, de Andres Segura F, Dae Devlin T, Pérez-García V, Ma'ayan A | display-authors = 6 | title = Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd | journal = Nature Communications | volume = 7 | pages = 12846 | date = September 2016 | pmid = 27667448 | doi = 10.1038/ncomms12846 | pmc=5052684}}</ref>
Today, microarrays and other quantitative methods such as [[RNA-Seq|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.<ref>{{cite journal | vauthors = Wang Z, Monteiro CD, Jagodnik KM, Fernandez NF, Gundersen GW, Rouillard AD, Jenkins SL, Feldmann AS, Hu KS, McDermott MG, Duan Q, Clark NR, Jones MR, Kou Y, Goff T, Woodland H, Amaral FM, Szeto GL, Fuchs O, Schüssler-Fiorenza Rose SM, Sharma S, Schwartz U, Bausela XB, Szymkiewicz M, Maroulis V, Salykin A, Barra CM, Kruth CD, Bongio NJ, Mathur V, Todoric RD, Rubin UE, Malatras A, Fulp CT, Galindo JA, Motiejunaite R, Jüschke C, Dishuck PC, Lahl K, Jafari M, Aibar S, Zaravinos A, Steenhuizen LH, Allison LR, Gamallo P, de Andres Segura F, Dae Devlin T, Pérez-García V, Ma'ayan A | display-authors = 6 | title = Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd | journal = Nature Communications | volume = 7 | pages = 12846 | date = September 2016 | pmid = 27667448 | doi = 10.1038/ncomms12846 | pmc=5052684}}</ref>

Revision as of 13:51, 30 August 2021

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

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

Prognostic gene signature

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

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 criterion exists for classifying a gene signature as a biomarker for a disorder or diseases outlined by Chau et al.[22][23]

Predictive gene signatures

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 on 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

References

  1. ^ Itadani H, Mizuarai S, Kotani H (Aug 2008). "Can systems biology understand pathway activation? Gene expression signatures as surrogate markers for understanding the complexity of pathway activation". Curr Genomics. 9 (5): 349–60. doi:10.2174/138920208785133235. PMC 2694555. PMID 19517027.
  2. ^ Liu J, Campen A, Huang S, Peng SB, Ye X, Palakal M, Dunker AK, Xia Y, Li S (Sep 2008). "Identification of a gene signature in cell cycle pathway for breast cancer prognosis using gene expression profiling data". BMC Med. Genom. 1: 39. doi:10.1186/1755-8794-1-39. PMC 2551605. PMID 18786252.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  3. ^ a b c Chibon F (May 2013). "Cancer gene expression signatures - the rise and fall?". European Journal of Cancer. 49 (8): 2000–9. doi:10.1016/j.ejca.2013.02.021. PMID 23498875.
  4. ^ Warner DF (March 2016). "Defining a diagnostic gene signature for tuberculosis". The Lancet. Respiratory Medicine. 4 (3): 170–1. doi:10.1016/s2213-2600(16)00063-1. PMID 26907219.
  5. ^ a b Nguyen HG, Welty CJ, Cooperberg MR (January 2015). "Diagnostic associations of gene expression signatures in prostate cancer tissue" (PDF). Current Opinion in Urology. 25 (1): 65–70. doi:10.1097/mou.0000000000000131. PMID 25405934. S2CID 29746661.
  6. ^ a b Wouters BJ, Löwenberg B, Erpelinck-Verschueren CA, van Putten WL, Valk PJ, Delwel R (Mar 2009). "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. 113 (13): 3088–91. doi:10.1182/blood-2008-09-179895. PMC 2662648. PMID 19171880.
  7. ^ Hassane DC, Guzman ML, Corbett C, Li X, Abboud R, Young F, Liesveld JL, Carroll M, Jordan CT (Jun 2008). "Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data". Blood. 111 (12): 5654–62. doi:10.1182/blood-2007-11-126003. PMC 2424160. PMID 18305216.
  8. ^ Corsello SM, Roti G, Ross KN, Chow KT, Galinsky I, DeAngelo DJ, Stone RM, Kung AL, Golub TR, Stegmaier K (Jun 2009). "Identification of AML1-ETO modulators by chemical genomics". Blood. 113 (24): 6193–205. doi:10.1182/blood-2008-07-166090. PMC 2699238. PMID 19377049.
  9. ^ Velculescu VE, Zhang L, Vogelstein B, Kinzler KW (October 1995). "Serial analysis of gene expression". Science. 270 (5235): 484–7. doi:10.1126/science.270.5235.484. PMID 7570003. S2CID 16281846.
  10. ^ Schena M, Shalon D, Davis RW, Brown PO (October 1995). "Quantitative monitoring of gene expression patterns with a complementary DNA microarray". Science. 270 (5235): 467–70. doi:10.1126/science.270.5235.467. PMID 7569999. S2CID 6720459.
  11. ^ Kurian KM, Watson CJ, Wyllie AH (February 1999). "DNA chip technology". The Journal of Pathology. 187 (3): 267–71. doi:10.1002/(SICI)1096-9896(199902)187:3<267::AID-PATH275>3.0.CO;2-#. PMID 10398077.
  12. ^ Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ, Clemons PA, Wei R, Carr SA, Lander ES, Golub TR (September 2006). "The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease". Science. 313 (5795): 1929–35. doi:10.1126/science.1132939. PMID 17008526. S2CID 8728079.
  13. ^ Russo, Antonio; Iacobelli, Stefano; Iovanna, Juan (2012). Diagnostic, Prognostic and Therapeutic Value of Gene Signatures | SpringerLink. doi:10.1007/978-1-61779-358-5. hdl:10447/110836. ISBN 978-1-61779-357-8.
  14. ^ Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Lønning PE, Børresen-Dale AL (September 2001). "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): 10869–74. doi:10.1073/pnas.191367098. PMC 58566. PMID 11553815.
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