ArticleThe American journal of pathology2003
Accurate molecular classification of human cancers based on gene expression using a simple classifier with a pathological tree-based framework.
Article in The American journal of pathology, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 77 citations in OpenAlex.
- Genomic signatures for predicting survival and adjuvant chemotherapy benefit in patients with non-small-cell lung cancer.BMC medical genomics · 2012Trial
- Dehydrogenase/reductase SDR family member 2 silencing sensitizes an oxaliplatin‑resistant cell line to oxaliplatin by inhibiting excision repair cross‑complementing group 1 protein expression.Oncology reports · 2019Article
- The Wide World of Molecular Profiling for Tumor Classification.Clinical chemistry · 2018Review
- RNA-Seq accurately identifies cancer biomarker signatures to distinguish tissue of origin.Neoplasia (New York, N.Y.) · 2014Article
- Gene expression profiling in patients with carcinoma of unknown primary site: from translational research to standard of care.Virchows Archiv : an international journal of pathology · 2014Review
- A microarray-based gene expression analysis to identify diagnostic biomarkers for unknown primary cancer.PloS one · 2013Article
- Classification of tumor samples from expression data using decision trunks.Cancer informatics · 2013Article
- Hybrid model integrating immunohistochemistry and expression profiling for the classification of carcinomas of unknown primary site.The Journal of molecular diagnostics : JMD · 2010Article
- Predictive and prognostic molecular markers for cancer medicine.Therapeutic advances in medical oncology · 2010Article
- Comparison of histopathological and gene expression-based typing of cancer of unknown primary.Virchows Archiv : an international journal of pathology · 2010Article
- [Comparing immunohistochemical diagnosis of cancer of unknown primary with gene expression-based tumor classification].Der Pathologe · 2009Article
- ISOLATE: a computational strategy for identifying the primary origin of cancers using high-throughput sequencing.Bioinformatics (Oxford, England) · 2009Article
- Molecular classification and prognostication of adrenocortical tumors by transcriptome profiling.Clinical cancer research : an official journal of the American Association for Cancer Research · 2009Article
- caBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data.BMC bioinformatics · 2008Article
- The emergent role of microRNAs in molecular diagnostics of cancer.The Journal of molecular diagnostics : JMD · 2008Article
- The modern histopathologist: in the changing face of time.Diagnostic pathology · 2008Article
- A ground truth based comparative study on clustering of gene expression data.Frontiers in bioscience : a journal and virtual library · 2008Review
- Gene expression profiling may improve diagnosis in patients with carcinoma of unknown primary.British journal of cancer · 2008Article
- Approaches to working in high-dimensional data spaces: gene expression microarrays.British journal of cancer · 2008Review
- SED, a normalization free method for DNA microarray data analysis.BMC bioinformatics · 2004Article
Corrections and comments
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Authors and funding
16 authors at 2 institutions in 2 countries.
Funding
Abstract
Recent studies suggest accurate prediction of tissue of origin for human cancers can be achieved by applying sophisticated statistical learning procedures to gene expression data obtained from DNA microarrays. We have pursued the hypothesis that a more straightforward and equally accurate strategy for classifying human tumors is to use a simple algorithm that considers gene expression levels within a tree-based framework that encodes limited information about pathology and tissue ontogeny. By considering gene expression data within this framework, we found only a small number of genes were required to achieve a relatively high accuracy level in tumor classification. Using as few as 45 genes we were able to classify 157 of 190 human malignant tumors correctly, which is comparable to previous results obtained with sophisticated classifiers using thousands of genes. Our simple classifier accurately predicted the origin of metastatic tumors even when the classifier was trained using only primary tumors, and the classifier produced accurate predictions when trained and tested on expression data from different labs, and from different microarray platforms. Our findings suggest that accurate and robust cancer diagnosis from gene expression profiles can be achieved by mimicking the classification strategies routinely used by surgical pathologists.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.