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Bonstingl, L.

Publications and source records attributed to Bonstingl, L..

3 recordsLinked to original sources

Tissue architecture and immune niches govern ctDNA release in colorectal cancer

Circulating tumor DNA (ctDNA) is central to liquid biopsy-based cancer detection, yet its release into the bloodstream varies widely and remains poorly understood. To define the tissue-level determinants of ctDNA shedding in colorectal cancer (CRC), we integrated tumor-informed plasma sequencing with detailed histopathology, immunophenotyping, spatial transcriptomics, and in situ mutation detection in resectable stages (I-III). ctDNA detectability increased with tumor burden, and high ctDNA shedders exhibited a distinct architectural and microenvironmental phenotype characterized by expanded necrotic pseudolumina, frequent epithelial barrier disruption, and dense myeloid infiltration. Spatial profiling revealed stress-associated malignant programs and a myeloid-rich immune-luminal niche. In situ sequencing confirmed plasma-detected mutations within pseudoluminal debris, identifying these structures as focal reservoirs of shed DNA. These findings provide a mechanistic framework linking tissue architecture, immune remodelling, and spatially organized cell death to ctDNA release with implications for refining liquid biopsy applications.

cancer biology↗

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↗

Spatial tumour gene signature discriminates neoplastic from non-neoplastic compartments in colon cancer: unravelling predictive biomarkers for relapse

BackgroundTherapeutic management of stage II colon cancer remains difficult regarding the decision whether adjuvant chemotherapy should be administered or not. Low rates of recurrence are opposed to chemotherapy induced toxicity and current clinical features are limited in predicting disease relapse. Predictive biomarkers are urgently needed and we hypothesise that the spatial tissue composition of relapsed and non-relapsed colon cancer stage II patients reveals relevant biomarkers. MethodsThe spatial tissue composition of stage II colon cancer patients was examined by in situ sequencing technology with sub-cellular resolution. A panel of 175 genes was designed investigating specific cancer-associated processes and components of the tumour microenvironment. We identified a tumour gene signature to subclassify tissue into neoplastic and non-neoplastic tissue compartments based on spatial expression patterns generated by in situ sequencing (GTC-tool - Genes-To-Count). ResultsThe GTC-tool automatically identified tissue compartments that were used to quantify gene expression of biological processes upregulated within the neoplastic tissue in comparison to non-neoplastic tissue and within relapsed versus non-relapsed stage II colon patients. Three differentially expressed genes (FGFR2, MMP11 and OTOP2) in the neoplastic tissue compartments of relapsed patients in comparison to non-relapsed patients were identified predicting recurrence in stage II colon cancer. ConclusionsIn depth spatial in situ sequencing revealed novel potential predictive biomarkers for disease relapse in colon cancer stage II patients. Our developed open-access GTC-tool allows to accurately capture the tumour compartment and quantify spatial gene expression in colon cancer tissue.

cancer biology↗