Dataset Information

Prostate-specific markers to identify rare prostate cancer cells in liquid biopsies.

ABSTRACT: Despite improvements in early detection and advances in treatment, patients with prostate cancer continue to die from their disease. Minimal residual disease after primary definitive treatment can lead to relapse and distant metastases, and increasing evidence suggests that circulating tumour cells (CTCs) and bone marrow-derived disseminated tumour cells (BM-DTCs) can offer clinically relevant biological insights into prostate cancer dissemination and metastasis. Using epithelial markers to accurately detect CTCs and BM-DTCs is associated with difficulties, and prostate-specific markers are needed for the detection of these cells using rare cell assays. Putative prostate-specific markers have been identified, and an optimized strategy for staining rare cancer cells from liquid biopsies using these markers is required. The ideal prostate-specific marker will be expressed on every CTC or BM-DTC throughout disease progression (giving high sensitivity) and will not be expressed on non-prostate-cancer cells in the sample (giving high specificity). Some markers might not be specific enough to the prostate to be used as individual markers of prostate cancer cells, whereas others could be truly prostate-specific and would make ideal markers for use in rare cell assays. The goal of future studies is to use sensitive and specific prostate markers to consistently and reliably identify rare cancer cells.

SUBMITTER: van der Toom EE

PROVIDER: S-EPMC6324967 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Prostate-specific markers to identify rare prostate cancer cells in liquid biopsies.

van der Toom Emma E EE Axelrod Haley D HD de la Rosette Jean J JJ de Reijke Theo M TM Pienta Kenneth J KJ Valkenburg Kenneth C KC

Nature reviews. Urology 20190101 1

Despite improvements in early detection and advances in treatment, patients with prostate cancer continue to die from their disease. Minimal residual disease after primary definitive treatment can lead to relapse and distant metastases, and increasing evidence suggests that circulating tumour cells (CTCs) and bone marrow-derived disseminated tumour cells (BM-DTCs) can offer clinically relevant biological insights into prostate cancer dissemination and metastasis. Using epithelial markers to accu ...[more]

PMID: 30479377

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Project description:The analysis of tumours using biomarkers in blood is transforming cancer diagnosis and therapy. Cancers are characterised by evolving genetic alterations, making it difficult to develop reliable and broadly applicable DNA-based biomarkers for liquid biopsy. In contrast to the variability in gene mutations, the methylation pattern remains generally constant during carcinogenesis. Thus, methylation more than mutation analysis may be exploited to recognise tumour features in the blood of patients. In this work, we investigated the possibility of using global CpG (CpG means a CG motif in the context of methylation. The p represents the phosphate. This is used to distinguish CG sites meant for methylation from other CG motifs or from mentions of CG content) island methylation profiles as a basis for the prediction of cancer state of patients utilising liquid biopsy samples. We retrieved existing GEO methylation datasets on hepatocellular carcinoma (HCC) and cell-free DNA (cfDNA) from HCC patients and healthy donors, as well as healthy whole blood and purified peripheral blood mononuclear cell (PBMC) samples, and used a random forest classifier as a predictor. Additionally, we tested three different feature selection techniques in combination. When using cfDNA samples together with solid tumour samples and healthy blood samples of different origin, we could achieve an average accuracy of 0.98 in a 10-fold cross-validation. In this setting, all the feature selection methods we tested in this work showed promising results. We could also show that it is possible to use solid tumour samples and purified PBMCs as a training set and correctly predict a cfDNA sample as cancerous or healthy. In contrast to the complete set of samples, the feature selections led to varying results of the respective random forests. ANOVA feature selection worked well with this training set, and the selected features allowed the random forest to predict all cfDNA samples correctly. Feature selection based on mutual information could also lead to better than random results, but LASSO feature selection would not lead to a confident prediction. Our results show the relevance of CpG islands as tumour markers in blood.

Dataset Information

Prostate-specific markers to identify rare prostate cancer cells in liquid biopsies.

Publications

Prostate-specific markers to identify rare prostate cancer cells in liquid biopsies.

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