Biology subjects

Jurmeister, P.

Publications and source records attributed to Jurmeister, P..

6 recordsLinked to original sources

bioRxiv2026

Matched primary-metastasis single-nucleus and spatial profiling of leiomyosarcoma identifies antigen-presenting fibroblasts at the tumor-organ interface

Leiomyosarcoma is an aggressive soft tissue sarcoma that metastasizes preferentially to lung and liver. Because matched primary and metastatic specimens are rarely available, the cellular changes that accompany metastatic progression remain poorly defined. Here we combined single-nucleus RNA sequencing of archival FFPE tissue from four matched primary-metastasis pairs (two lung, two liver metastases; 48,941 nuclei) with imaging-based spatial transcriptomics of an extended tissue microarray cohort of 21 patients (69 cores, 313,218 cells). Malignant cells, not assignable from transcriptomic references alone, were defined by inferred copy number profiles that were concordant with matched DNA methylation arrays and confirmed the clonal origin of each metastasis. Malignant metaprograms were organ-associated, with a stress- and hypoxia-associated program enriched in lung metastases and a proliferative, matrix-remodeling program enriched in liver metastases. Antigen-presenting cancer-associated fibroblasts (apCAFs) were enriched in metastases relative to matched primaries. This shift was reproduced in the spatial cohort (p = 0.001), where apCAFs formed a discrete band at the tumor-organ interface between malignant cells and adjacent parenchyma, whereas they were sparse and randomly distributed in primary tumors. Spatial transcriptomics further resolved the malignant compartment into three histomolecular phenotypes: (i) smooth-muscle-like with myxoid features, (ii) GPNMB with anaplastic features, and (iii) PDGFRB with vessel-rich stroma, each with a concordant immunohistochemical (-SMA, GPNMB, PDGFRB) and morphological correlate. Together, our data provide a matched primary-metastasis reference for leiomyosarcoma, identify apCAFs as a spatially organized, metastasis-associated stromal population, and delineate histomolecular phenotypes recognizable by routine immunohistochemistry, including a GPNMB phenotype potentially amenable to GPNMB-directed therapies.

cancer biology↗

bioRxiv2026

Pan-cancer virtual spatial transcriptomics from routine histology with Phoenix

Spatial transcriptomics links gene expression to tissue architecture, providing a mechanistic view of cellular organization. Yet existing datasets cover few donors and miss the complexity of human disease. Experimental costs remain prohibitive, and large-scale profiling is impractically slow for population-level studies. Accurate computational methods are urgently needed. Predicting gene expression from standard histology, however, remains an open problem, as current approaches transfer poorly to unseen cohorts and diseases. Here, we present Phoenix, a (latent) flow matching generative model that infers pan-cancer spatially resolved single-cell gene expression with high accuracy. Phoenix analyzes treatment response in silico: Applied to 763 head and neck cancer patients, it identified three new spatial biomarkers that we validated across two cancers (breast cancer, n = 84; ovarian cancer, n = 157) and treatment regimens (platinum, trastuzumab). Phoenix generalizes beyond carcinomas: In a large sarcoma cohort (802 tissue microarray cores), it accurately predicted cell-type-specific signatures in held-out samples and captured chemotherapy-induced immune remodeling. Phoenix also extends across species: In a mouse model, it accurately predicted the expression of pancreatic cancer lineage markers and the mutant mKras^G12D allele in silico. In total, we evaluated Phoenix on over 10,000 patients. Our results establish virtual spatial transcriptomics as a scalable framework for studying tissue organization, therapeutic response, and disease mechanisms.

bioinformatics↗

bioRxiv2024

A cost-effective and scalable approach for DNA extraction from FFPE tissues

Genomic profiling of cancer plays an increasingly vital role for diagnosis and therapy planning. In addition, research of novel diagnostic applications such as DNA methylation profiling requires large training and validation cohorts. Currently, most diagnostic cases processed in pathology departments are stored as formalin-fixed and paraffin embedded tissue blocks (FFPE). Consequently, there is a growing demand for high-throughput extraction of nucleic acids from FFPE tissue samples. While proprietary kits are available, they are expensive and offer little flexibility. Here, we present ht-HiTE, a high-throughput implementation of a recently published and highly efficient DNA extraction protocol. This approach enables manual and automated processing of 96-well plates with a liquid handler, offers two options for purification and utilizes off-the-shelf reagents. Finally, we show that methylation profiles obtained from DNA processed with ht-HiTE are of equivalent quality as compared to a manual, kit-based approach.

molecular biology↗

bioRxiv2023

High-resolution molecular atlas of a lung tumor in 3D

Cells live and interact in three-dimensional (3D) cellular neighborhoods. However, histology and spatial omics methods mostly focus on 2D tissue sections. Here we present a 3D spatial atlas of a routine clinical sample, an aggressive human lung carcinoma, by combining in situ quantification of 960 cancer-related genes across [~]340,000 cells with measurements of tissue-mechanical components. 3D cellular neighborhoods subdivided the tumor microenvironment into tumor, stromal, and immune multicellular niches. Interestingly, pseudotime analysis suggested that pro-invasive epithelial-to-mesenchymal transition (EMT), detected in stroma-infiltrating tumor cells, already occurred in one region at the tumor surface. There, myofibroblasts and macrophages specifically co-localized with pre-invasive tumor cells and their multicellular molecular signature identified patients with shorter survival. Moreover, cytotoxic T-cells did not infiltrate this niche but colocalized with inhibitory dendritic and regulatory T cells. Importantly, systematic scoring of cell-cell interactions in 3D neighborhoods highlighted niche-specific signaling networks accompanying tumor invasion and immune escape. Compared to 2D, 3D neighborhoods improved the characterization of immune niches by identifying dendritic niches, capturing the 3D extension of T-cell niches and boosting the quantification of niche-specific cell-cell interactions, including druggable immune checkpoints. We believe that 3D communication analyses can improve the design of clinical studies investigating personalized, combination immuno-oncology therapies.

systems biology↗

bioRxiv2021

The single-cell transcriptional landscape of lung carcinoid tumors

Lung carcinoid tumors, also referred to as pulmonary neuroendocrine tumors or lung carcinoids, are rare neoplasms of the lung with a more favorable prognosis than other subtypes of lung cancer. Still, some patients suffer from relapsed disease and metastatic spread while no consensus treatment exists for metastasized carcinoids. Several recent single-cell studies have provided detailed insights into the cellular heterogeneity of more common lung cancers, such as adeno- and squamous cell carcinoma. However, the characteristics of lung carcinoids on the single-cell level are yet completely unknown. To study the cellular composition and single-cell gene expression profiles in lung carcinoids, we applied single-cell RNA sequencing to three lung carcinoid tumor samples and normal lung tissue. The single-cell transcriptomes of carcinoid tumor cells reflected intertumoral heterogeneity associated with clinicopathological features, such as tumor necrosis and proliferation index. The immune microenvironment was specifically enriched in noninflammatory monocyte-derived myeloid cells. Tumor-associated endothelial cells were characterized by distinct gene expression profiles. A spectrum of vascular smooth muscle cells and pericytes predominated the stromal microenvironment. We found a small proportion of myofibroblasts exhibiting features reminiscent of cancer-associated fibroblasts. Stromal and immune cells exhibited potential paracrine interactions which may shape the microenvironment via NOTCH, VEGF, TGF{beta} and JAK/STAT signaling. Moreover, single-cell gene signatures of pericytes and myofibroblasts demonstrated prognostic value in bulk gene expression data. Here, we provide first comprehensive insights into the cellular composition and single-cell gene expression profiles in lung carcinoids, demonstrating the non-inflammatory and vessel-rich nature of their tumor microenvironment, and outlining relevant intercellular interactions which could serve as future therapeutic targets.

cancer biology↗

bioRxiv2020

Single-cell RNA sequencing reveals distinct tumor microenvironmental patterns in lung adenocarcinoma

Recent developments in immuno-oncology demonstrate that not only cancer cells, but also features of the tumor microenvironment guide precision medicine. Still, the relationship between tumor and microenvironment remains poorly understood. To overcome this limitation and identify clinically relevant microenvironmental and cancer features, we applied single-cell RNA sequencing to lung adenocarcinomas. While the highly heterogeneous carcinoma cell transcriptomes reflected histological grade and activity of relevant oncogenic pathways, our analysis revealed two distinct microenvironmental patterns. We identified a prognostically unfavorable group of tumors with a microenvironment composed of cancer-associated myofibroblasts, exhausted CD8+ T cells, proinflammatory monocyte-derived macrophages and plasmacytoid dendritic cells (CEP2 pattern) and a prognostically favorable group characterized by myeloid dendritic cells, anti-inflammatory monocyte-derived macrophages, normal-like myofibroblasts, NK cells and conventional T cells (MAN2C pattern). Our results show that single-cell gene expression profiling allows to identify patient subgroups based on the tumor microenvironment beyond cancer cell-centric profiling.

cancer biology↗