09-10, 14:55–14:58 (Europe/Berlin), Main conference room - MW 0350
Intra-tumoral heterogeneity contributes to low survival rates in pancreatic ductal adenocarcinoma (PDAC). Using in situ sequencing and an interdisciplinary analysis approach, we will map tumor subtypes, immune cells, and fibroblasts in a large cohort of PDAC patients to study cellular processes in primary tumors and metastases and decipher the impact of tumor architecture on treatment response.
Despite progress in clinical research leading to improved survival rates for many cancers, pancreatic ductal adenocarcinoma (PDAC) remains a challenging exception. PDAC is marked by intra-tumoral heterogeneity (ITH), which is one of the main causes of poor patient survival rates . While preclinical research has largely been restricted to primary tumors, most patients diagnosed are late stage and show metastatic disease . Thus, to increase the efficacy of treatments and improve the overall prognosis of PDAC patients, advancing the personalized molecular stratification for the late stage and metastatic disease is imperative.
Spatially resolved single-cell transcriptomics (scST) methodologies such as Xenium In Situ (XIS) map individual RNA molecules at subcellular resolution and thus determine the transcriptional state of single cells in a tissue section. Their application to clinical samples promises to improve the understanding of ITH, metastasis, and treatment responses.
In collaboration with an expert team, we created a panel of 477 genes to study central pathogenic processes of PDAC. The panel includes marker genes for all major immune cells, cancer-associated fibroblasts (CAFs), “classical” and “basal-like” tumor cells as well as other pathologically relevant processes like epithelial-to-mesenchymal transition, metastasis, and hypoxia.
We used this panel to acquire scST datasets of three tissue microarrays (TMAs) comprising a total of about 100 samples from both primary tumors and metastases of 51 PDAC patients. After XIS analysis, TMA sections were stained histologically and thoroughly annotated by pathologists. To analyze the data, we established a novel framework to process and visualize XIS data of TMAs , facilitating the efficient integration of pathological annotations with computational analysis. We identified differentially expressed genes between cells of the primary tumor site and the metastasis side, focusing especially on the cancer cells and cells of the tumor microenvironment, including CAFs and different immune cells. Further, we are planning to use spatially aware analysis algorithms to identify differences in the cellular interaction networks between primary tumors. Findings of these analyses, including spatial distribution patterns, will be validated on a protein level using IHC and assessed for their potential use as markers for patient stratification to improve the effectivity of future PDAC therapy.
No previous knowledge expected
Bachelor and Master studies in Chemical Biology at University Konstanz. PhD at Helmholtz Center Munich on the development of a novel spatial transcriptomics technology. Now postdoctoral researcher at the Institute of Pathology of the Technical University Munich combining in situ sequencing, pathology and computational analysis to dissect the intra-tumoral heterogeneity of different cancer entities.