Author
Michael D. Radmacher
Other affiliations: Duke University, Kenyon College, Durham University ...read more
Bio: Michael D. Radmacher is an academic researcher from Ohio State University. The author has contributed to research in topics: Leukemia & BAALC. The author has an hindex of 40, co-authored 77 publications receiving 12921 citations. Previous affiliations of Michael D. Radmacher include Duke University & Kenyon College.
Topics: Leukemia, BAALC, Myeloid leukemia, Gene expression profiling, CEBPA
Papers
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TL;DR: Many genes underlying the classification of this subset of melanomas are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas.
Abstract: 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.
2,058 citations
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TL;DR: Significantly different groups of genes are expressed by breast cancers with BRCA1 mutations and breast cancersWith BRCa2 mutations, the results suggest that a heritable mutation influences the gene-expression profile of the cancer.
Abstract: Background 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. Methods RNA from samples of primary tumors 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. Results 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 othe...
1,638 citations
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TL;DR: In this article, the authors address statistical issues that arise from the use of DNA microarrays for an important group of objectives that has been called "class prediction", which includes derivation of predictors of prognosis, response to therapy, or any phenotype or genotype defined independently of the gene expression profile.
Abstract: DNA microarrays have made it possible to estimate the level of expression of thousands of genes for a sample of cells. Although biomedical investigators have been quick to adopt this powerful new research tool, accurate analysis and interpretation of the data have provided unique challenges. Indeed, many investigators are not experienced in the analytical steps needed to convert tens of thousands of noisy data points into reliable and interpretable biologic information. Although some investigators recognize the importance of collaborating with experienced biostatisticians to analyze microarray data, the number and availability of experienced biostatisticians is inadequate. Consequently, investigators are using available software to analyze their data, many seemingly without knowledge of potential pitfalls. Because of serious problems associated with the analysis and reporting of some DNA microarray studies, there is great interest in guidance on valid and effective methods for analysis of DNA microarray data. The design and analysis strategy for a DNA microarray experiment should be determined in light of the overall objectives of the study. Because DNA microarrays are used for a wide variety of objectives, it is not feasible to address the entire range of design and analysis issues in this commentary. Here, we address statistical issues that arise from the use of DNA microarrays for an important group of objectives that has been called “class prediction” (1). Class prediction includes derivation of predictors of prognosis, response to therapy, or any phenotype or genotype defined independently of the gene expression profile.
989 citations
01 Jan 2003
TL;DR: Statistical issues that arise from the use of DNA microarrays for an important group of objectives that has been called “class prediction” are addressed, which includes derivation of predictors of prognosis, response to therapy, or any phenotype or genotype defined independently of the gene expression profile.
951 citations
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TL;DR: The R172 IDH2 mutations, previously unreported in AML, characterize a novel subset of CN-AML patients lacking other prognostic mutations and associate with unique gene- and microRNA-expression profiles that may lead to the discovery of novel, therapeutically targetable leukemogenic mechanisms.
Abstract: PURPOSE To analyze the frequency and associations with prognostic markers and outcome of mutations in IDH genes encoding isocitrate dehydrogenases in adult de novo cytogenetically normal acute myeloid leukemia (CN-AML). PATIENTS AND METHODS Diagnostic bone marrow or blood samples from 358 patients were analyzed for IDH1 and IDH2 mutations by DNA polymerase chain reaction amplification/sequencing. FLT3, NPM1, CEBPA, WT1, and MLL mutational analyses and gene- and microRNA-expression profiling were performed centrally. Results IDH mutations were found in 33% of the patients. IDH1 mutations were detected in 49 patients (14%; 47 with R132). IDH2 mutations, previously unreported in AML, were detected in 69 patients (19%; 13 with R172 and 56 with R140). R172 IDH2 mutations were mutually exclusive with all other prognostic mutations analyzed. Younger age (< 60 years), molecular low-risk (NPM1-mutated/FLT3-internal tandem duplication-negative) IDH1-mutated patients had shorter disease-free survival than molecular low-risk IDH1/IDH2-wild-type (wt) patients (P = .046). R172 IDH2-mutated patients had lower complete remission rates than IDH1/IDH2wt patients (P = .007). Distinctive microarray gene- and microRNA-expression profiles accurately predicted R172 IDH2 mutations. The highest expressed gene and microRNAs in R172 IDH2-mutated patients compared with the IDH1/IDH2wt patients were APP (previously associated with complex karyotype AML) and miR-1 and miR-133 (involved in embryonal stem-cell differentiation), respectively. CONCLUSION IDH1 and IDH2 mutations are recurrent in CN-AML and have an unfavorable impact on outcome. The R172 IDH2 mutations, previously unreported in AML, characterize a novel subset of CN-AML patients lacking other prognostic mutations and associate with unique gene- and microRNA-expression profiles that may lead to the discovery of novel, therapeutically targetable leukemogenic mechanisms.
713 citations
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。
18,940 citations
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29 Sep 2017
TL;DR: Thank you very much for reading who classification of tumours of haematopoietic and lymphoid tissues, and maybe you have knowledge that, people have look hundreds of times for their chosen readings like this, but end up in malicious downloads.
Abstract: WHO CLASSIFICATION OF TUMOURS OF HAEMATOPOIETIC AND LYMPHOID TISSUES , WHO CLASSIFICATION OF TUMOURS OF HAEMATOPOIETIC AND LYMPHOID TISSUES , کتابخانه مرکزی دانشگاه علوم پزشکی تهران
13,835 citations
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TL;DR: Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.
Abstract: The purpose of this study was to classify breast carcinomas based on variations in gene expression patterns derived from cDNA microarrays and to correlate tumor characteristics to clinical outcome. A total of 85 cDNA microarray experiments representing 78 cancers, three fibroadenomas, and four normal breast tissues were analyzed by hierarchical clustering. As reported previously, the cancers could be classified into a basal epithelial-like group, an ERBB2-overexpressing group and a normal breast-like group based on variations in gene expression. A novel finding was that the previously characterized luminal epithelial/estrogen receptor-positive group could be divided into at least two subgroups, each with a distinctive expression profile. These subtypes proved to be reasonably robust by clustering using two different gene sets: first, a set of 456 cDNA clones previously selected to reflect intrinsic properties of the tumors and, second, a gene set that highly correlated with patient outcome. Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.
10,791 citations
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TL;DR: DNA microarray analysis on primary breast tumours of 117 young patients is used and supervised classification is applied to identify a gene expression signature strongly predictive of a short interval to distant metastases (‘poor prognosis’ signature) in patients without tumour cells in local lymph nodes at diagnosis, providing a strategy to select patients who would benefit from adjuvant therapy.
Abstract: Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.
9,664 citations
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TL;DR: This work proposes an approach to measuring statistical significance in genomewide studies based on the concept of the false discovery rate, which offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted.
Abstract: With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.
9,239 citations