Biology subjects

Bergmayr, L.

Publications and source records attributed to Bergmayr, L..

3 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↗

bioRxiv2023

The type of DNA damage response after Decitabine treatment depends on the level of DNMT activity

Decitabine and Azacytidine are considered as epigenetic drugs that induce DNA- methyltransferase (DNMT)-DNA crosslinks, resulting in DNA-hypomethylation and -damage. Although they are applied against myeloid cancers, important aspects of their mode of action remain unknown, which highly limits their clinical potential. Using a combinatorial approach, we reveal that the efficacy profile of both compounds primarily depends on the level of induced DNA-damage. Under low DNMT-activity, only Decitabine has a substantial impact. Conversely, when DNMT-activity is high, toxicity and cellular response to both compounds are dramatically increased, but do not primarily depend on DNA-hypomethylation or RNA-associated processes, contradicting an RNA-dependent effect of Azacytidine. By applying spatial proteomics, we show that Decitabine induces a strictly DNMT-dependent multifaceted DNA- damage response based on chromatin-recruitment of various repair-associated proteins. The choice of DNA-repair pathway herby depends on the severity of Decitabine-induced DNA- lesions. While mismatch (MMR) and base-excision DNA repair (BER) as well as RAD50- dependent DNA double-strand break repair are always activated in response to Decitabine, Fanconi anemia-dependent DNA-repair combined with homologous recombination is only activated when DNMT-activity is moderate. In contrast, high DNMT-activity and therefore immense replication stress, induce DNA repair by non-homologous and alternative end-joining.

biochemistry↗