18239450

Source:http://linkedlifedata.com/resource/pubmed/id/18239450

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0006826, umls-concept:C0020792, umls-concept:C0684249, umls-concept:C1292724, umls-concept:C1519316, umls-concept:C1533148
pubmed:issue	6
pubmed:dateCreated	2008-4-21
pubmed:abstractText	Expression profiling analysis of human cancers is a promising approach to obtain precise molecular classification of cancers, to develop stratification tools for therapeutic regimens, and to predict the biological behavior of neoplasia. Direct profiling of human cancers (herein defined as "the unbiased approach") presents, however, intrinsic problems connected with the high genetic noise embedded in the system. This, in turn, leads to fitting of the noise in the data (the so-called "overtraining") with consequent instability of the identified signatures, when applied on different cohorts of patients. To circumvent these problems, "biased approaches" - which exploit the molecular knowledge of cancer obtained in model systems - are being developed. Biased approaches, however, are not problem-free, in that they provide information limited to single oncogenic events, thereby failing, at least in principle, to capture the complex repertoire of alterations of human cancers. In this review, we compare the two approaches and provide a test case, from our studies, of how "integrated" strategies, which combine biased and unbiased approaches, might lead to the identification of stable and reliable predictive signatures in cancer.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/101137841
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Mar
pubmed:issn	1551-4005
pubmed:author	pubmed-author:BianchiFabrizioF, pubmed-author:Di FiorePier PaoloPP, pubmed-author:NicassioFrancescoF
pubmed:issnType	Electronic
pubmed:day	15
pubmed:volume	7
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	729-34
pubmed:meshHeading	pubmed-meshheading:18239450-Data Interpretation, Statistical, pubmed-meshheading:18239450-Gene Expression Profiling, pubmed-meshheading:18239450-Humans, pubmed-meshheading:18239450-Lung Neoplasms, pubmed-meshheading:18239450-Models, Genetic
pubmed:year	2008
pubmed:articleTitle	Unbiased vs. biased approaches to the identification of cancer signatures: the case of lung cancer.
pubmed:affiliation	IFOM, Fondazione Istituto FIRC di Oncologia Molecolare, Milan, Italy.
pubmed:publicationType	Journal Article, Review, Research Support, Non-U.S. Gov't