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Results for “Molecular Classification”

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At least 109 records · Page 6Linked to original sources
PubMed2003

Molecular classification of synovial sarcomas, leiomyosarcomas and malignant fibrous histiocytomas by gene expression profiling.

In this study, we have used genome-wide expression profiling to categorise synovial sarcomas, leiomyosarcomas and malignant fibrous histiocytomas (MFHs). Following hierarchical clustering analysis of the expression data, the best match between tumour clusters and conventional diagnosis was observed for synovial sarcomas. Eight of nine synovial sarcomas examined formed a cluster that was characterised by higher expression of a set of 48 genes. In contrast, sarcomas conventionally classified as leiomyosarcomas and MFHs did not match the clusters defined by hierarchical clustering analysis. One major cluster contained a mixture of both leiomyosarcomas and MFHs and was defined by the lower expression of a set of 202 genes. A cluster containing a subgroup of MFHs was also detected. These results may have implications for the classification of soft tissue sarcomas, and are consistent with the view that sarcomas conventionally defined as MFHs do not represent a separate diagnostic category.

Cluster Analysis↗
PubMed2005

Molecular classification of papillary thyroid carcinoma: distinct BRAF, RAS, and RET/PTC mutation-specific gene expression profiles discovered by DNA microarray analysis.

Thyroid cancer poses a significant clinical challenge, and our understanding of its pathogenesis is incomplete. To gain insight into the pathogenesis of papillary thyroid carcinoma, transcriptional profiles of four normal thyroids and 51 papillary carcinomas (PCs) were generated using DNA microarrays. The tumors were genotyped for their common activating mutations: BRAF V600E point mutation, RET/PTC1 and 3 rearrangement and point mutations of KRAS, HRAS and NRAS. Principal component analysis based on the entire expression data set separated the PCs into three groups that were found to reflect tumor morphology and mutational status. By combining expression profiles with mutational status, we defined distinct expression profiles for the BRAF, RET/PTC and RAS mutation groups. Using small numbers of genes, a simple classifier was able to classify correctly the mutational status of all 40 tumors with known mutations. One tumor without a detectable mutation was predicted by the classifier to have a RET/PTC rearrangement and was shown to contain one by fluorescence in situ hybridization analysis. Among the mutation-specific expression signatures were genes whose differential expression was a direct consequence of the mutation, as well as genes involved in a variety of biological processes including immune response and signal transduction. Expression of one mutation-specific differentially expressed gene, TPO, was validated at the protein level using immunohistochemistry and tissue arrays containing an independent set of tumors. The results demonstrate that mutational status is the primary determinant of gene expression variation within these tumors, a finding that may have clinical and diagnostic significance and predicts success for therapies designed to prevent the consequences of these mutations.

Base Sequence↗
PubMed2003

Molecular classification of familial non-BRCA1/BRCA2 breast cancer.

In the decade since their discovery, the two major breast cancer susceptibility genes BRCA1 and BRCA2, have been shown conclusively to be involved in a significant fraction of families segregating breast and ovarian cancer. However, it has become equally clear that a large proportion of families segregating breast cancer alone are not caused by mutations in BRCA1 or BRCA2. Unfortunately, despite intensive effort, the identification of additional breast cancer predisposition genes has so far been unsuccessful, presumably because of genetic heterogeneity, low penetrance, or recessive/polygenic mechanisms. These non-BRCA1/2 breast cancer families (termed BRCAx families) comprise a histopathologically heterogeneous group, further supporting their origin from multiple genetic events. Accordingly, the identification of a method to successfully subdivide BRCAx families into recognizable groups could be of considerable value to further genetic analysis. We have previously shown that global gene expression analysis can identify unique and distinct expression profiles in breast tumors from BRCA1 and BRCA2 mutation carriers. Here we show that gene expression profiling can discover novel classes among BRCAx tumors, and differentiate them from BRCA1 and BRCA2 tumors. Moreover, microarray-based comparative genomic hybridization (CGH) to cDNA arrays revealed specific somatic genetic alterations within the BRCAx subgroups. These findings illustrate that, when gene expression-based classifications are used, BRCAx families can be grouped into homogeneous subsets, thereby potentially increasing the power of conventional genetic analysis.

Adult↗
PubMed2001

Molecular classification of multiple tumor types.

Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. Previous works show several pairs of tumor types can be successfully distinguished by their gene expression patterns (Golub et al. 1999, Ben-Dor et al. 2000, Alizadeh et al. 2000). However, the simultaneous classification across a heterogeneous set of tumor types has not been well studied yet. We obtained 190 samples from 14 tumor classes and generated a combined expression dataset containing 16063 genes for each of those samples. We performed multi-class classification by combining the outputs of binary classifiers. Three binary classifiers (k-nearest neighbors, weighted voting, and support vector machines) were applied in conjunction with three combination scenarios (one-vs-all, all-pairs, hierarchical partitioning). We achieved the best cross validation error rate of 18.75% and the best test error rate of 21.74% by using the one-vs-all support vector machine algorithm. The results demonstrate the feasibility of performing clinically useful classification from samples of multiple tumor types.

Algorithms↗
PubMed2006

Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data.

MOTIVATION: Multilayer perceptrons (MLP) represent one of the widely used and effective machine learning methods currently applied to diagnostic classification based on high-dimensional genomic data. Since the dimensionalities of the existing genomic data often exceed the available sample sizes by orders of magnitude, the MLP performance may degrade owing to the curse of dimensionality and over-fitting, and may not provide acceptable prediction accuracy. RESULTS: Based on Fisher linear discriminant analysis, we designed and implemented an MLP optimization scheme for a two-layer MLP that effectively optimizes the initialization of MLP parameters and MLP architecture. The optimized MLP consistently demonstrated its ability in easing the curse of dimensionality in large microarray datasets. In comparison with a conventional MLP using random initialization, we obtained significant improvements in major performance measures including Bayes classification accuracy, convergence properties and area under the receiver operating characteristic curve (A(z)). SUPPLEMENTARY INFORMATION: The Supplementary information is available on http://www.cbil.ece.vt.edu/publications.htm

Biomarkers, Tumor↗
PubMed1999

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

Acute Disease↗
PubMed2003

Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray.

Diffuse large B-cell lymphoma (DLBCL) can be divided into prognostically important subgroups with germinal center B-cell-like (GCB), activated B-cell-like (ABC), and type 3 gene expression profiles using a cDNA microarray. Tissue microarray (TMA) blocks were created from 152 cases of DLBCL, 142 of which had been successfully evaluated by cDNA microarray (75 GCB, 41 ABC, and 26 type 3). Sections were stained with antibodies to CD10, bcl-6, MUM1, FOXP1, cyclin D2, and bcl-2. Expression of bcl-6 (P <.001) or CD10 (P =.019) was associated with better overall survival (OS), whereas expression of MUM1 (P =.009) or cyclin D2 (P <.001) was associated with worse OS. Cases were subclassified using CD10, bcl-6, and MUM1 expression, and 64 cases (42%) were considered GCB and 88 cases (58%) non-GCB. The 5-year OS for the GCB group was 76% compared with only 34% for the non-GCB group (P <.001), which is similar to that reported using the cDNA microarray. Bcl-2 and cyclin D2 were adverse predictors in the non-GCB group. In multivariate analysis, a high International Prognostic Index score (3-5) and the non-GCB phenotype were independent adverse predictors (P <.0001). In summary, immunostains can be used to determine the GCB and non-GCB subtypes of DLBCL and predict survival similar to the cDNA microarray.

Adolescent↗
PubMed2005

Molecular classification of tamoxifen-resistant breast carcinomas by gene expression profiling.

PURPOSE: To discover a set of markers predictive for the type of response to endocrine therapy with the antiestrogen tamoxifen using gene expression profiling. PATIENTS AND METHODS: The study was performed on 112 estrogen receptor-positive primary breast carcinomas from patients with advanced disease and clearly defined types of response (ie, 52 patients with objective response v 60 patients with progressive disease) from start of first-line treatment with tamoxifen. Main clinical end points are the effects of therapy on tumor size and time until tumor progression (progression-free survival [PFS]). RNA isolated from tumor samples was amplified and hybridized to 18,000 human cDNA microarrays. RESULTS: Using a training set of 46 breast tumors, 81 genes were found to be differentially expressed (P < or = .05) between tamoxifen-responsive and -resistant tumors. These genes were involved in estrogen action, apoptosis, extracellular matrix formation, and immune response. From the 81 genes, a predictive signature of 44 genes was extracted and validated on an independent set of 66 tumors. This 44-gene signature is significantly superior (odds ratio, 3.16; 95% CI, 1.10 to 9.11; P = .03) to traditional predictive factors in univariate analysis and also significantly related with a longer PFS in univariate (hazard ratio, 0.54; 95% CI, 0.31 to 0.94; P = .03) as well as in multivariate analyses (P = .03). CONCLUSION: Our data show that gene expression profiling can be used to discriminate between breast cancer patients with progressive disease and objective response to tamoxifen. Additional studies are needed to confirm if the predictive signature might allow identification of individual patients who could benefit from other (adjuvant) endocrine therapies.

Adult↗
PubMed2006

Molecular classification of human cancers using a 92-gene real-time quantitative polymerase chain reaction assay.

CONTEXT: Correct diagnosis of the tissue origin of a metastatic cancer is the first step in disease management, but it is frequently difficult using standard pathologic methods. Microarray-based gene expression profiling has shown great promise as a new tool to address this challenge. OBJECTIVE: Adoption of microarray technologies in the clinic remains limited. We aimed to bridge this technological gap by developing a real-time quantitative polymerase chain reaction (RT-PCR) assay. DESIGN: We constructed a microarray database of 466 frozen and 112 formalin-fixed, paraffin-embedded (FFPE) samples of both primary and metastatic tumors, measuring expression of 22,000 genes. From the microarray database, we used a genetic algorithm to search for gene combinations optimal for multitumor classification. A 92-gene RT-PCR assay was then designed and used to generate a database for 481 frozen and 119 FFPE tumor samples. RESULTS: The microarray-based K-nearest neighbor classifier demonstrated 84% accuracy in classifying 39 tumor types via cross-validation and 82% accuracy in predicting 112 independent FFPE samples. We successfully translated the microarray database to the RT-PCR platform, which allowed an overall success rate of 87% in classifying 32 different tumor classes in the validation set of 119 FFPE tumor samples. CONCLUSIONS: The RT-PCR-based expression assay involving 92 genes represents a powerful tool for accurately and objectively identifying the site of origin for metastatic tumors, especially in the cases of cancer of unknown primary. The assay uses RT-PCR and routine FFPE samples, making it suitable for rapid clinical adoption.

Algorithms↗
PubMed2002

Molecular classification of head and neck squamous cell carcinoma using cDNA microarrays.

Squamous cell carcinomas of the head and neck constitute an anatomically heterogeneous group of neoplasms that share in common a causal association with tobacco and alcohol exposure. The clinical course of these neoplasms is difficult to predict based on established prognostic clinicopathological criteria. Given the genetic complexity of head and neck cancers, it is not surprising that correlations with individual genetic abnormalities have also been disappointing. Several authors have suggested that global gene expression patterns can be used to subgroup patients with cancer. Here we report the use of cDNA microarrays containing 9216 clones to measure global patterns of gene expression in these neoplasms. We have used a statistical algorithm to identify 375 genes, which divide patients with head and neck tumors into two clinically distinct subgroups based on gene expression patterns. Our results demonstrate that gene expression profiling can be used as a predictor of outcome.

Adult↗
PubMed2003

Molecular classification of cancer: unsupervised self-organizing map analysis of gene expression microarray data.

An unsupervised self-organizing map-based clustering strategy has been developed to classify tissue samples from an oligonucleotide microarray patient database. Our method is based on the likelihood that a test data vector may have a gene expression fingerprint that is shared by more than one tumor class and as such can identify datasets that cannot be unequivocally assigned to a single tumor class. Our self-organizing map analysis completely separated the tumor from the normal expression datasets. Within the 14 different tumor types, classification accuracies on the order of approximately 80% correct were achieved. Nearly perfect classifications were found for leukemia, central nervous system, melanoma, uterine, and lymphoma tumor types, with very poor classifications found for colorectal, ovarian, breast, and lung tumors. Classification results were further analyzed to identify sets of differentially expressed genes between tumor and normal gene expressions and among each tumor class. Within the total pool of 1139 genes most differentially expressed in this dataset, subsets were found that could be vetted according to previously published literature sources to be specific tumor markers. Attempts to classify gene expression datasets from other sources found a wide range of classification accuracies. Discussions about the utility of this method and the quality of data needed for accurate tumor classifications are provided.

Algorithms↗
PubMed2002

PAI-1 and EGFR expression in adult glioma tumors: toward a molecular prognostic classification.

PURPOSE: Molecular classification of gliomas is a major challenge in the effort to improve therapeutic decisions. The plasminogen activator system, including plasminogen activator inhibitor type 1 (PAI-1), plays a key role in tumor invasion and neoangiogenesis. Epidermal growth factor receptor (EGFR) is involved in the control of proliferation. The contribution of PAI-1 and EGFR to the survival of gliomas was retrospectively investigated. METHODS AND MATERIALS: Fifty-nine adult gliomas treated by neurosurgery and conventional irradiation were analyzed, including 9 low-grade (2) and 50 high-grade (3-4) tumors (WHO classification). PAI-1 was measured on cytosols and EGFR on solubilized membranes using ELISA methods. RESULTS: High PAI-1 levels were strongly associated with high histologic grade (p < 0.001) and histologic necrosis (p < 0.001). PAI-1 also correlated positively with patient age (p = 0.05) and negatively with Karnofsky index (p = 0.01). By univariate analysis of the high-grade population, higher PAI-1 (p < 0.0001) and EGFR values (p = 0.02) were associated with shorter overall survival. Only PAI-1 was an independent factor in multivariate analysis. Grade 3 tumors with low PAI-1 (100% 3-year overall survival rate) presented the same clinical outcome as the low-grade tumors. CONCLUSIONS: In this prognostic study, PAI-1 and EGFR expression revealed similarities and differences between high-grade gliomas that were not apparent by traditional clinical criteria. These data strongly support that biologic factors should be included in glioma classification and the design of clinical trials to treat more homogeneous populations.

Adolescent↗