Biology subjects

Knoesel, T.

Publications and source records attributed to Knoesel, T..

4 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↗

bioRxiv2021

GATA4 and GATA6 loss-of-expression is associated with extinction of the classical programme and poor outcome in pancreatic ductal adenocarcinoma

ObjectiveGATA6 is a master regulator of pancreatic differentiation and a key regulator of the classical phenotype in pancreatic ductal adenocarcinoma (PDAC). Low GATA6 expression is associated with poor patient outcome. GATA4 is the second most expressed GATA factor in the pancreas. The aim was to assess whether, and how, GATA4 contributes to PDAC phenotype and to analyze the association of expression with clinical outcome. DesignWe analyzed PDAC transcriptomic data, stratifying cases according to GATA4 and GATA6 expression, and identified differentially expressed genes and pathways. A multicenter TMA study to assess GATA4 and GATA6 expression in PDAC samples (n=745) from patients undergoing tumour resection was performed using immunohistochemistry with antibodies of validated specificity. GATA4 and GATA6 levels were dichotomized into high/low categorical variables; association with outcome was assessed using univariable and multivariable Cox regression models. ResultsSubtype classification using transcriptomic data revealed that GATA4 mRNA is enriched in classical, compared to basal-like tumours. We classified samples in 4 groups as high/low for GATA4 and GATA6. Reduced expression of GATA4 did not have a major transcriptional impact. However, concomitant low expression of GATA4 enhanced the transcriptomic effects of GATA6 low expression. Reduced expression of both proteins in tumours was associated with the worst patient survival. GATA4 and GATA6 expression significantly decreased in metastases and negatively correlated with basal markers. ConclusionsOur analyses uncover a cooperative interaction between GATA4 and GATA6 to maintain the classical PDAC phenotype and provide compelling clinical rationale for assessing their expression as biomarkers of poor prognosis. SUMMARY BOXO_ST_ABSWhat is already known about this subject?C_ST_ABS- Patients with classical-type PDAC have a better outcome - Retrospective analyses suggest that classical-type PDAC is more sensitive to 5-FU-based chemotherapy - GATA6 is a surrogate biomarker of classical tumours and its expression is associated with better survival - GATA4 and GATA6 have overlapping and unique functions during pancreatic and gastrointestinal development What are the new findings?- Tumours displaying only low GATA4 expression have a transcriptomic profile similar to those with preserved expression of both transcription factors - Combined low expression of GATA4 and GATA6 has the highest transcriptomic impact - In a large multicenter tissue microarray study, patients with tumours showing low expression of both GATA4 and GATA6 have the worst overall survival - Low expression of GATA4 and GATA6 is an independent predictor of survival in patients with resectable PDAC - GATA4 levels are down-regulated in liver metastases and are negatively correlated with basal markers such as KRT5/6, KRT14, and TP63 How might it impact on clinical practice in the foreseeable future?- The combined assessment of GATA4 and GATA6 expression may improve prognostic stratification of patients with PDAC - Prospective studies should confirm whether GATA4 and GATA6 expression is also predictive of response to chemotherapy in PDAC patients

cancer biology↗

bioRxiv2019

Expression patterns of PD-L1 and PD-1 provide rationales for immune checkpoint inhibition in soft tissue sarcomas

Soft tissue sarcomas (STS) are highly malignant cancers with mesenchymal origin. In many instances, clinical outcome is poor due to high rates of local recurrence and metastasis.\n\nThe programmed death receptor ligand 1 (PD-L1) is expressed in several cancers. PD-L1 interacts with its receptor, PD-1, on the surface of tumor infiltrating lymphocytes (TILs), thereby attenuating anti-cancer immune response. Immune checkpoint inhibitors targeting this interaction are promising new anti-cancer drugs. However, present studies on the PD-L1 and PD-1 expression status in STS are limited either by small sample size, analysis of single STS subtypes, or lack of combinatorial assessment of PD-L1, PD-1 and TILs.\n\nTo overcome these limitations, we evaluated the expression patterns of intratumoral PD-L1, the amount of TILs and their PD-1 expression status, as well as associations with clinicopathological parameters in a large and comprehensive cohort of 274 samples comprising more than six STS subtypes.\n\nWe found that nearly all STS subtypes showed partial PD-L1 expression, albeit with a broad range of PD-L1 positivity across subtypes (50% angiosarcomas, 23% UPS, 13% leiomyosarcomas, 12% dedifferentiated liposarcomas, 3% synovial sarcomas, 0 MPNST, and 18% mixed sarcomas). Co-expression and correlation analyses uncovered that expression of PD-L1 was associated with more PD-1 positive TILs (P < 0.001), higher tumor grading (P = 0.022) and worse patients 5-year overall survival (P = 0.016).\n\nIn sum, the substantial portion of STS showing PD-L1 expression, the simultaneous presence of PD-1 positive TILs, and the association of PD-L1 with unfavorable clinical outcome provide a rationale for immune checkpoint inhibition in patients with PD-L1-positive STS.

cancer biology↗