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Breast cancer prognosis by combinatorial analysis of gene expression data

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TitleInfo
Title
Breast cancer prognosis by combinatorial analysis of gene expression data
Name (type = personal)
NamePart (type = family)
Axelrod
NamePart (type = given)
David E.
Affiliation
Genetics, Rutgers University
Role
RoleTerm (authority = marcrt); (type = text)
author
Name (type = personal)
NamePart (type = family)
Alexe
NamePart (type = given)
Gabriela
Affiliation
Center for Operations Research, Rutgers University
Role
RoleTerm (authority = marcrt); (type = text)
author
Name (type = personal)
NamePart (type = family)
Alexe
NamePart (type = given)
Sorin
Affiliation
Center for Operations Research, Rutgers University
Role
RoleTerm (authority = marcrt); (type = text)
author
Name (type = personal)
NamePart (type = family)
Bonates
NamePart (type = given)
Tiberius
Affiliation
Center for Operations Research, Rutgers University
Role
RoleTerm (authority = marcrt); (type = text)
author
Name (type = personal)
NamePart (type = family)
Lozina
NamePart (type = given)
Irina
Affiliation
Center for Operations Research, Rutgers University
Role
RoleTerm (authority = marcrt); (type = text)
author
Name (type = personal)
NamePart (type = family)
Reiss
NamePart (type = given)
Michael
Affiliation
Cancer Institute of New Jersey (CINJ), Rutgers University
Role
RoleTerm (authority = marcrt); (type = text)
author
Name (type = personal)
NamePart (type = family)
Hammer
NamePart (type = given)
Peter L.
Affiliation
Center for Operations Research, Rutgers University
Role
RoleTerm (authority = marcrt); (type = text)
author
Name (authority = RutgersOrg-Department); (type = corporate)
NamePart
Genetics
Name (authority = RutgersOrg-School); (type = corporate)
NamePart
School of Arts and Sciences (SAS) (New Brunswick)
TypeOfResource
Text
Genre (authority = RULIB-FS)
Article, Refereed
Genre (authority = NISO JAV)
Version of Record (VoR)
OriginInfo
DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2006
Publisher
BioMed Central
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
PhysicalDescription
Form (authority = RULIB)
InternetMediaType
application/pdf
Extent
20 pages
Abstract (type = abstract)
Introduction The potential of applying data analysis tools to microarray data for diagnosis and prognosis is illustrated on the recent breast cancer dataset of van 't Veer and coworkers. We re-examine that dataset using the novel technique of logical analysis of data (LAD), with the double objective of discovering patterns characteristic for cases with good or poor outcome, using them for accurate and justifiable predictions; and deriving novel information about the role of genes, the existence of special classes of cases, and other factors. Method Data were analyzed using the combinatorics and optimization-based method of LAD, recently shown to provide highly accurate diagnostic and prognostic systems in cardiology, cancer proteomics, hematology, pulmonology, and other disciplines. Results LAD identified a subset of 17 of the 25,000 genes, capable of fully distinguishing between patients with poor, respectively good prognoses. An extensive list of 'patterns' or 'combinatorial biomarkers' (that is, combinations of genes and limitations on their expression levels) was generated, and 40 patterns were used to create a prognostic system, shown to have 100% and 92.9% weighted accuracy on the training and test sets, respectively. The prognostic system uses fewer genes than other methods, and has similar or better accuracy than those reported in other studies. Out of the 17 genes identified by LAD, three (respectively, five) were shown to play a significant role in determining poor (respectively, good) prognosis. Two new classes of patients (described by similar sets of covering patterns, gene expression ranges, and clinical features) were discovered. As a by-product of the study, it is shown that the training and the test sets of van 't Veer have differing characteristics. Conclusion The study shows that LAD provides an accurate and fully explanatory prognostic system for breast cancer using genomic data (that is, a system that, in addition to predicting good or poor prognosis, provides an individualized explanation of the reasons for that prognosis for each patient). Moreover, the LAD model provides valuable insights into the roles of individual and combinatorial biomarkers, allows the discovery of new classes of patients, and generates a vast library of biomedical research hypotheses.
Note (type = publisherStatement)
The published version of this article is available at: http://dx.doi.org/10.1186/bcr1512
Subject (authority = LOCAL)
Topic
Breast cancer
Subject (authority = LOCAL)
Topic
Gene expression
Subject (authority = LOCAL)
Topic
Logical analysis of data (LAD)
Subject (authority = LCSH)
Topic
Breast--Cancer
Subject (authority = LCSH)
Topic
Gene expression
RelatedItem (type = host)
TitleInfo
Title
Axelrod, David
Identifier (type = local)
rucore30017800001
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore30017800001.Article.17039
Identifier (type = doi)
doi:10.7282/T31G0JNC
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Extension
DescriptiveEvent
Type
Citation
DateTime (encoding = w3cdtf)
2006
AssociatedObject
Type
Journal
Relationship
Has part
Name
Breast Cancer Research
Identifier (type = volume and issue)
8
Reference (type = url)
http://dx.doi.org/10.1186/bcr1512
Extension
DescriptiveEvent
Type
Grant award
AssociatedEntity
Role
Funder
Name
New Jersey Commission on Cancer Research
AssociatedEntity
Role
Originator
Name
Alexe, Gabriela
AssociatedObject
Type
Grant number
Name
Fellowship # 703054
Extension
DescriptiveEvent
Type
Grant award
AssociatedEntity
Role
Funder
Name
Institute for Advanced Study
Detail
Through The David and Lucile Packard Foundation, and
The Shelby White and Leon Levy Initiative Fund
AssociatedEntity
Role
Originator
Name
Alexe, Gabriela
Extension
DescriptiveEvent
Type
Grant award
AssociatedEntity
Role
Funder
Name
New Jersey Commission on Cancer Research
AssociatedEntity
Role
Originator
Name
Axelrod, David E.
AssociatedObject
Type
Grant number
Name
03- 1076-CCR-S-0
Extension
DescriptiveEvent
Type
Grant award
AssociatedEntity
Role
Funder
Name
National Science Foundation
AssociatedEntity
Role
Originator
Name
Axelrod, David E.
AssociatedObject
Type
Grant number
Name
IIS-0312953
Extension
DescriptiveEvent
Type
Grant award
AssociatedEntity
Role
Funder
Name
National Institutes of Health
AssociatedEntity
Role
Originator
Name
Axelrod, David E.
AssociatedObject
Type
Grant number
Name
CA113004
Extension
DescriptiveEvent
Type
Grant award
AssociatedEntity
Role
Funder
Name
National Institutes of Health
AssociatedEntity
Role
Originator
Name
Hammer, Peter L.
AssociatedObject
Type
Grant number
Name
HL-072771-01
AssociatedObject
Type
Grant number
Name
NIH-002748-001
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RightsDeclaration (AUTHORITY = FS); (ID = rulibRdec0004)
Copyright for scholarly resources published in RUcore is retained by the copyright holder. By virtue of its appearance in this open access medium, you are free to use this resource, with proper attribution, in educational and other non-commercial settings. Other uses, such as reproduction or republication, may require the permission of the copyright holder.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
RightsHolder (ID = 1)
Name (TYPE = personal)
FamilyName
Axelrod
GivenName
David
RightsHolder (ID = 2)
Name (TYPE = personal)
FamilyName
Alexe
GivenName
Gabriela
RightsHolder (ID = 3)
Name (TYPE = personal)
FamilyName
Alexe
GivenName
Sorin
RightsHolder (ID = 4)
Name (TYPE = personal)
FamilyName
Bonates
GivenName
Tiberius
RightsHolder (ID = 5)
Name (TYPE = personal)
FamilyName
Lozina
GivenName
Irina
RightsHolder (ID = 6)
Name (TYPE = personal)
FamilyName
Reiss
GivenName
Michael
RightsHolder (ID = 7)
Name (TYPE = personal)
FamilyName
Hammer
GivenName
Peter
RightsEvent (AUTHORITY = rulib); (ID = 1)
Type
Permission or license
AssociatedEntity (AUTHORITY = rulib); (ID = 1)
Name
Axelrod David
Role
Copyright holder
Affiliation
SAS - DLS - Genetics, Rutgers University
AssociatedEntity (AUTHORITY = rulib); (ID = 2)
Name
Alexe Gabriela
Role
Copyright holder
Affiliation
SAS - DLS - Genetics, Rutgers University
AssociatedEntity (AUTHORITY = rulib); (ID = 3)
Name
Alexe Sorin
Role
Copyright holder
Affiliation
SAS - DLS - Genetics, Rutgers University
AssociatedEntity (AUTHORITY = rulib); (ID = 4)
Name
Bonates Tiberius
Role
Copyright holder
Affiliation
SAS - DLS - Genetics, Rutgers University
AssociatedEntity (AUTHORITY = rulib); (ID = 5)
Name
Lozina Irina
Role
Copyright holder
Affiliation
SAS - DLS - Genetics, Rutgers University
AssociatedEntity (AUTHORITY = rulib); (ID = 6)
Name
Reiss Michael
Role
Copyright holder
Affiliation
The Cancer Institute of New Jersey
AssociatedEntity (AUTHORITY = rulib); (ID = 7)
Name
Hammer Peter
Role
Copyright holder
Affiliation
SAS - DLS - Genetics, Rutgers University
AssociatedObject (AUTHORITY = rulib); (ID = 1)
Type
License
Name
Multiple author license v. 1
Detail
I hereby grant to Rutgers, The State University of New Jersey (Rutgers) the non-exclusive right to retain, reproduce, and distribute the deposited work (Work) in whole or in part, in and from its electronic format, without fee. This agreement does not represent a transfer of copyright to Rutgers. Rutgers may make and keep more than one copy of the Work for purposes of security, backup, preservation, and access and may migrate the Work to any medium or format for the purpose of preservation and access in the future. Rutgers will not make any alteration, other than as allowed by this agreement, to the Work. I represent and warrant to Rutgers that the Work is my original work. I also represent that the Work does not, to the best of my knowledge, infringe or violate any rights of others. I further represent and warrant that I have obtained all necessary rights to permit Rutgers to reproduce and distribute the Work and that any third-party owned content is clearly identified and acknowledged within the Work. By granting this license, I acknowledge that I have read and agreed to the terms of this agreement and all related RUcore and Rutgers policies.
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