ArticleBioinformatics (Oxford, England)2009
ISOLATE: a computational strategy for identifying the primary origin of cancers using high-throughput sequencing.
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed.
- NNICE: a deep quantile neural network algorithm for expression deconvolution.Scientific reports · 2024Article
- Tumor microenvironment: barrier or opportunity towards effective cancer therapy.Journal of biomedical science · 2022Review
- FastMix: a versatile data integration pipeline for cell type-specific biomarker inference.Bioinformatics (Oxford, England) · 2022Article
- Development of a pOJournal of medical devices · 2022Article
- Microdroplet-based one-step RT-PCR for ultrahigh throughput single-cell multiplex gene expression analysis and rare cell detection.Scientific reports · 2021Article
- CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data.PLoS computational biology · 2019Article
- Automated deconvolution of structured mixtures from heterogeneous tumor genomic data.PLoS computational biology · 2017Article
- Complex Sources of Variation in Tissue Expression Data: Analysis of the GTEx Lung Transcriptome.American journal of human genetics · 2016Article
- Topic model-based mass spectrometric data analysis in cancer biomarker discovery studies.BMC genomics · 2016Article
- Medoidshift clustering applied to genomic bulk tumor data.BMC genomics · 2016Article
- Gene Expression Deconvolution for Uncovering Molecular Signatures in Response to Therapy in Juvenile Idiopathic Arthritis.PloS one · 2016Article
- Using mixtures of biological samples as process controls for RNA-sequencing experiments.BMC genomics · 2015Article
- ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles.BMC bioinformatics · 2015Article
- An assessment of computational methods for estimating purity and clonality using genomic data derived from heterogeneous tumor tissue samples.Briefings in bioinformatics · 2015Review
- RNA-Seq accurately identifies cancer biomarker signatures to distinguish tissue of origin.Neoplasia (New York, N.Y.) · 2014Article
- Personalized medicine: from genotypes and molecular phenotypes towards therapy- session introduction.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2014Article
- TrAp: a tree approach for fingerprinting subclonal tumor composition.Nucleic acids research · 2013Article
- THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data.Genome biology · 2013Article
- Deconvolution of gene expression from cell populations across the C. elegans lineage.BMC bioinformatics · 2013Article
- Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction.Genome medicine · 2013Article
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2 authors.
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Abstract
motivationOne of the most deadly cancer diagnoses is the carcinoma of unknown primary origin. Without the knowledge of the site of origin, treatment regimens are limited in their specificity and result in high mortality rates. Though supervised classification methods have been developed to predict the site of origin based on gene expression data, they require large numbers of previously classified tumors for training, in part because they do not account for sample heterogeneity, which limits their application to well-studied cancers.
resultsWe present ISOLATE, a new statistical method that simultaneously predicts the primary site of origin of cancers and addresses sample heterogeneity, while taking advantage of new high-throughput sequencing technology that promises to bring higher accuracy and reproducibility to gene expression profiling experiments. ISOLATE makes predictions de novo, without having seen any training expression profiles of cancers with identified origin. Compared with previous methods, ISOLATE is able to predict the primary site of origin, de-convolve and remove the effect of sample heterogeneity and identify differentially expressed genes with higher accuracy, across both synthetic and clinical datasets. Methods such as ISOLATE are invaluable tools for clinicians faced with carcinomas of unknown primary origin. AVAILABILITY: ISOLATE is available for download at: http://morrislab.med.utoronto.ca/software CONTACT: gerald.quon@utoronto.ca; quaid.morris@utoronto.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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