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      Predicting the clinical status of human breast cancer by using gene expression profiles

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          Abstract

          Prognostic and predictive factors are indispensable tools in the treatment of patients with neoplastic disease. For the most part, such factors rely on a few specific cell surface, histological, or gross pathologic features. Gene expression assays have the potential to supplement what were previously a few distinct features with many thousands of features. We have developed Bayesian regression models that provide predictive capability based on gene expression data derived from DNA microarray analysis of a series of primary breast cancer samples. These patterns have the capacity to discriminate breast tumors on the basis of estrogen receptor status and also on the categorized lymph node status. Importantly, we assess the utility and validity of such models in predicting the status of tumors in crossvalidation determinations. The practical value of such approaches relies on the ability not only to assess relative probabilities of clinical outcomes for future samples but also to provide an honest assessment of the uncertainties associated with such predictive classifications on the basis of the selection of gene subsets for each validation analysis. This latter point is of critical importance in the ability to apply these methodologies to clinical assessment of tumor phenotype.

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          Most cited references 24

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          Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring

           T. Golub (1999)
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            Molecular classification of cutaneous malignant melanoma by gene expression profiling.

            The most common human cancers are malignant neoplasms of the skin. Incidence of cutaneous melanoma is rising especially steeply, with minimal progress in non-surgical treatment of advanced disease. Despite significant effort to identify independent predictors of melanoma outcome, no accepted histopathological, molecular or immunohistochemical marker defines subsets of this neoplasm. Accordingly, though melanoma is thought to present with different 'taxonomic' forms, these are considered part of a continuous spectrum rather than discrete entities. Here we report the discovery of a subset of melanomas identified by mathematical analysis of gene expression in a series of samples. Remarkably, many genes underlying the classification of this subset are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas. Global transcript analysis can identify unrecognized subtypes of cutaneous melanoma and predict experimentally verifiable phenotypic characteristics that may be of importance to disease progression.
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              Gene-expression profiles in hereditary breast cancer.

              Many cases of hereditary breast cancer are due to mutations in either the BRCA1 or the BRCA2 gene. The histopathological changes in these cancers are often characteristic of the mutant gene. We hypothesized that the genes expressed by these two types of tumors are also distinctive, perhaps allowing us to identify cases of hereditary breast cancer on the basis of gene-expression profiles. RNA from samples of primary tumor from seven carriers of the BRCA1 mutation, seven carriers of the BRCA2 mutation, and seven patients with sporadic cases of breast cancer was compared with a microarray of 6512 complementary DNA clones of 5361 genes. Statistical analyses were used to identify a set of genes that could distinguish the BRCA1 genotype from the BRCA2 genotype. Permutation analysis of multivariate classification functions established that the gene-expression profiles of tumors with BRCA1 mutations, tumors with BRCA2 mutations, and sporadic tumors differed significantly from each other. An analysis of variance between the levels of gene expression and the genotype of the samples identified 176 genes that were differentially expressed in tumors with BRCA1 mutations and tumors with BRCA2 mutations. Given the known properties of some of the genes in this panel, our findings indicate that there are functional differences between breast tumors with BRCA1 mutations and those with BRCA2 mutations. Significantly different groups of genes are expressed by breast cancers with BRCA1 mutations and breast cancers with BRCA2 mutations. Our results suggest that a heritable mutation influences the gene-expression profile of the cancer.
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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proceedings of the National Academy of Sciences
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                September 25 2001
                September 18 2001
                September 25 2001
                : 98
                : 20
                : 11462-11467
                Article
                10.1073/pnas.201162998
                58752
                11562467
                © 2001
                Product

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