Bauernhofer, T. | bioRxiv Science

bioRxiv2024

Circulating tumor cell characterization and classification by novel combinatorial dual-color (CoDuCo) in situ hybridization and supervised machine learning

Metastatic prostate cancer is a highly heterogeneous and dynamic disease and practicable tools for patient stratification and resistance monitoring are urgently needed. Liquid biopsy analysis of circulating tumor DNA and circulating tumor cells (CTCs) are promising, but due to the diversity of resistance mechanisms, comprehensive testing is essential. Previously, we demonstrated that CTCs can be characterized by mRNA-based in situ padlock probe hybridization. Now, we have developed a novel combinatorial dual-color (CoDuCo) approach with increased multiplex capacity of up to 15 distinct markers, complemented by semi-automated image analysis and machine learning-assisted CTC classification. Here, we present three exemplary cases of patient samples in which the CoDuCo assay visualized diverse resistance mechanisms (AR-V7, neuroendocrine differentiation (SYP, CHGA, NCAM1)), as well as druggable targets and predictive markers (PSMA, DLL3, SLFN11). The combination of high multiplex capacity and microscopy-based single-cell analysis is a unique and powerful feature of the CoDuCo in situ assay. This synergy enables the identification and characterization of CTCs with epithelial, epithelial-mesenchymal, and neuroendocrine phenotypes, the detection of CTC clusters, and the visualization of CTC heterogeneity. In conclusion, the assay is a promising tool for monitoring the dynamic molecular changes associated with drug response and resistance in prostate cancer. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/592946v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@46a186org.highwire.dtl.DTLVardef@116824dorg.highwire.dtl.DTLVardef@c4c842org.highwire.dtl.DTLVardef@1378b67_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2018

Inference of tumor cell-specific transcription factor binding from cell-free DNA

Alterations in transcription factors are important drivers of tumorigenesis, but non-invasive assays for assessing transcription factor activity are lacking. Here, we evaluated the feasibility of inferring transcription factor binding in solid tumors from their nucleosome footprint in circulating cell-free DNA. We developed a novel analysis pipeline to determine accessibility of transcription factor binding sites and applied it to 244 cell-free DNA samples from patients with prostate, breast and colon cancer. We observed patient-specific as well as tumor-specific patterns. Specifically, inferred binding patterns for the transcription factors AR, HOXB13, and NKX3-1 allowed us to classify patients by tumor type, including subtypes of prostate cancer, which has clinical implications for the management of patients. Our approach for mapping tumor-specific transcription factor binding in vivo based on blood samples makes a key part of the noncoding genome amenable for clinical analysis.

cancer biology↗