Biology subjects

Sallinger, K.

Publications and source records attributed to Sallinger, K..

3 recordsLinked to original sources

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↗

bioRxiv2022

Disturbed trophoblast transition links preeclampsia progression from placenta to the maternal syndrome

Pre-eclampsia (PE) is a syndrome that affects multiple organ systems and is the most severe hypertensive disorder in pregnancy. It frequently leads to preterm delivery, maternal and fetal morbidity and mortality and life-long complications1. We currently lack efficient screening tools2, 3 and early therapies4, 5 to address PE. To investigate the early stages of early onset PE, and identify candidate markers and pathways, we performed spatio-temporal multi-omics profiling of human PE placentae and healthy controls and validated targets in early gestation in a longitudinal clinical cohort. We used a single-nuclei RNA-seq approach combined with spatial proteo- and transcriptomics and mechanistic in vitro signalling analyses to bridge the gap from late pregnancy disease to early pregnancy pathomechanisms. We discovered a key disruption in villous trophoblast differentiation, which is driven by the increase of transcriptional coactivator p300, that ultimately ends with a senescence-associated secretory phenotype (SASP) of trophoblasts. We found a significant increase in the senescence marker activin A in preeclamptic maternal serum in early gestation, before the development of clinical symptoms, indicating a translation of the placental syndrome to the maternal side. Our work describes a new disease progression, starting with a disturbed transition in villous trophoblast differentiation. Our study identifies potential pathophysiology-relevant biomarkers for the early diagnosis of the disease as well as possible targets for interventions, which would be crucial steps toward protecting the mother and child from gestational mortality and morbidity and an increased risk of cardiovascular disease later in life.

systems biology↗

bioRxiv2022

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↗