Biology subjects

Kos, T.

Publications and source records attributed to Kos, T..

3 recordsLinked to original sources

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↗

bioRxiv2026

Cross-species single-cell atlases chart progression, therapy-driven remodelling and immune evasion in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is typically diagnosed at advanced stages, yet single-cell datasets that capture late-stage and treated disease remain sparse, hindering progress in understanding tumour heterogeneity and therapy resistance. Here, we have generated integrated single-cell transcriptomic atlases of human and mouse PDAC to define the cellular and molecular landscape of the disease, from early to advanced and metastatic stages, including post-treatment disease, and to enable direct cross-species comparison. Using scANVI to harmonize 16 human studies comprising 257 donors and representative mouse models (101 tumours), we compiled over 1.6 million cells and established a four-level hierarchical taxonomy of more than 60 distinct cell states spanning malignant, stromal, immune, endothelial, adipose, exocrine and endocrine compartments. We resolve ten malignant programmes linked to progression and uncover rare immune phenotypes, including CD4CD8 double-positive T cells that remain poorly characterized in PDAC. Notably, we show that radiotherapy (RT) exposure is associated with enrichment of an EMT-persistent malignant state and an immunosuppressive microenvironment characterized by expansion of tumour-associated endothelium, depletion of intratumoral T cells and heightened laminin-CD44 signalling, with RT-associated genes linked to adverse prognosis in independent cohorts. Cross-species mapping reveals that orthotopic syngeneic allografts more faithfully recapitulate the cellular diversity and EMT-enriched states of advanced human PDAC, underrepresented in autochthonous genetically engineered models, with differences driven primarily by cell-type composition rather than pathway divergence. Together, these atlases and pretrained models provide a broadly accessible reference for benchmarking PDAC model fidelity and for interrogating mechanisms of tumour progression, microenvironmental remodelling and therapy response and resistance.

cancer biology↗

bioRxiv2025

Single-chain permuted proteins for dimerization-based control of protein activity and cellular processes

Strategies for detecting and controlling protein interactions play a critical role in gaining insight into molecular mechanisms of biological processes and for the control of cellular processes. Conditional protein reconstitution allows control of the selected protein function based on the proximity, defined by the genetically fused domain pairs, which may be regulated by chemical or biological signals. This typically requires two protein components in a stoichiometric ratio, which increases the complexity and genetic footprint with split segments often being unstable and prone to aggregation. To overcome this limitation, we developed an approach based on a permuted protein reconstitution by conditional dimerization (PROPER). According to this strategy, the N- and C-terminal domains of selected proteins are swapped and a loop is replaced by a short linker that prevents the functionality of a monomeric protein, which reconstitutes only upon di- or oligomerization, controlled by a genetically fused domain that dimerizes by a chemical signal or senses a dimeric target. This design principle was demonstrated on three proteins with diverse functions: a protease, luciferase, and a cytokine. We demonstrate chemically and biologically inducible systems that enable controllable induction of cell death, virus detection, and immune cell stimulation. The PROPER platform expands the chemical biology toolbox, with the benefits of split proteins accomplished by a single component.

synthetic biology↗