Predicting the site of origin of tumors by a gene expression signature derived from normal tissues

Abstract

Multiple expression signatures for the prediction of the site of origin of metastatic cancer of unknown primary origin (CUP) have been developed. Owing to their limited coverage of tumor types and suboptimal prediction accuracy on distinct tumors, there is still room for alternative CUP gene expression signatures. Whereas in past studies, CUP classifiers were trained solely on data from tumor samples, we now use expression patterns from normal tissues for classifier training. This approach potentially avoids pitfalls related to the representation of genetically heterogeneous tumor subtypes during classifier training. Two expression data sets of normal human tissues have been reanalyzed to derive an expression signature for liver, prostate, kidney, ovarian and lung tissues. In reciprocal validation, classifiers trained on either data set achieved overall accuracies greater than 97%. Classifiers trained on combined expression data from both normal tissue data sets were able to predict the site of origin in a cohort of 652 primary tumors with B90% accuracy. Prediction accuracies of primary cancer-based classifiers were in the same range, as determined by cross-validation on this cohort. For individual tumor types, normal tissue-based classifiers achieved sensitivities in the range of 64-99% and specificities in the range of 92-100%. Primary origins for 12 of 20 metastases were predicted correctly, with false predictions highlighting the need for accurate sample preparation to avoid contaminations by metastasessurrounding tissue. We conclude that gene expression patterns of normal tissues harbor phenotypic information that is retained in tumors and can be sufficient to recover the type of primary tumor from expression patterns alone.

A Study of High-Grade Serous Ovarian Cancer Origins Implicates the SOX18 Transcription Factor in Tumor Development

“…''bulk'' molecular profiling (Flynn et al, 2018;Moran et al, 2016;Staub et al, 2010;Søndergaard et al, 2017).…”

“…One approach to investigate the cellular origins of cancer is to quantify similarities and differences between molecular signatures of tumors and the proposed tissues of origin (Staub et al, 2010). This is based on the hypothesis that the molecular blueprint of normal precursor cells is maintained in developing tumors.…”

Development and validation of a microRNA based diagnostic assay for primary tumor site classification of liver core biopsies

“…Tumor samples contain varying amounts of malignant cells, stromal cells and surrounding (contaminating) normal tissue from the biopsy/resection site. The influence of surrounding normal tissue on molecular classification is not clear, although a potential systematic classification bias, caused by normal tissue, has been reported (Elloumi et al., 2011; Staub et al., 2010). Most previously developed diagnostic classifiers require a high tumor content (≥50% tumor) (Meiri et al., 2012; Pillai et al., 2011; Ferracin et al., 2011; Kerr et al., 2012; Rosenfeld et al., 2008; Rosenwald et al., 2010) and use microdissection for tumor cell enrichment, prior to gene expression analysis.…”

Classification of unknown primary tumors with a data-driven method based on a large microarray reference database

“…The origin of the metastasis needs to be identified as primary treatment regimes for cancer are typically based on the anatomical origin and histological type of the primary tumor. Studies by several groups [4-7,20] have shown that finding the tissue of origin of metastatic samples is possible based on gene expression data. Some of these tests are already commercially available and have been clinically applied [13-15,17].…”

“…[20] demonstrated that CUP prediction is possible using available microarray data from about 800 healthy samples and 600 cancer samples extracted from the Gene Expression Omnibus (GEO) [21] as a reference. They were able to construct a predictor using both cancer and healthy tissue samples.…”