Biology subjects

Mock, A.

Publications and source records attributed to Mock, A..

8 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↗

bioRxiv2025

Digital Spatial Pathway Mapping Reveals Prognostic Tumor States in Head and Neck Cancer

Head and neck squamous cell carcinoma (HNSCC) is a morphologically and molecularly heterogeneous disease with limited effectiveness of genotype-informed therapies. Transcriptome-derived estimates of signaling pathway activity carry prognostic and therapeutic potential but remain inaccessible in routine diagnostics due to cost and tissue constraints. Here, we introduce Digital Spatial Pathway Mapping, an AI-based computational pathology framework that infers signaling pathway activities directly from routine hematoxylin and eosin (H&E) slides, enabling in-silico spatial molecular readouts from standard histology. Models trained on HPV-negative HNSCC from TCGA and externally validated on CPTAC robustly predicted transcriptome-derived activities in cancer-relevant signaling pathways. To achieve spatial interpretability, we applied layer-wise relevance propagation (LRP) to generate heatmaps that highlight positive versus negative evidence for pathway activation. These LRP heatmaps were technically validated by patch-flipping tests and biologically validated against pathway-relevant immunohistochemistry in an independent patient cohort. From these explanations, we derived a tumor area pathway activity score (TAPAS) quantifying the spatial fraction of activated tumor regions within a slide. Applied to a retrospective HNSCC cohort of 1,066 slides from 112 resection specimens, TAPAS captured intratumoral heterogeneity and revealed two biologically dis-tinct tumor states - an oncogenic growth phenotype with widespread pathway activation and a pathway quiescent phenotype associated with higher recurrence risk independent of clinicopathological variables. These findings establish Digital Spatial Pathway Mapping as a scalable, in-silico approach to recover systems-level molecular information from standard histopathology, enabling prognostic and mechanistically grounded patient stratification in head and neck cancer.

pathology↗

bioRxiv2025

Deep Learning for Biomarker Discovery in Cancer Genomes

BackgroundAccurate determination of genomic biomarkers from tumor sequencing is fundamental to precision oncology, informing disease classification and treatment decisions. In practice, biomarker inference relies on computational pipelines that often compress high-dimensional mutation data into predefined summaries such as mutational signatures or composite genomic features. While robust and widely adopted, these representations may not fully capture the complexity of cancer genomes. Deep learning (DL) offers an end-to-end alternative by learning features directly from raw genomic data. However, clinical translation remains challenging due to limited empirical validation of new DL models and a lack of systematic comparisons with established machine learning (ML) baselines, particularly when transitioning from information-rich genome or exome data to real-world targeted sequencing profiles. Here, we compare state-of-the-art DL architectures with classical ML models across variant-level, copy-number (CNV), and multimodal inputs, using microsatellite instability (MSI) and homologous recombination deficiency (HRD) prediction as oncologically relevant tasks. We aim to derive practical guidance on modelling strategies across different data modalities and clinical sequencing contexts. MethodsFor MSI and HRD prediction, we trained multiple DL models, including supervised and self-supervised encoders, alongside feature-based ML approaches using tumor mutation data, copy-number alterations, and their multimodal combinations. Analyses were conducted on 5,647 patients in The Cancer Genome Atlas (TCGA), the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and two targeted sequencing panel cohorts. Model performance was evaluated on both whole-exome and panel-based datasets, and explainability analysis were performed for both DL and ML models. ResultsFor MSI, DL demonstrated stronger generalization than ML on external validation data (F1 0.97 vs 0.76) and maintained comparatively high performance under pseudo-panels conditions, whereas ML performance dropped. In a real-world targeted panel cohort, DL again showed more robust generalization than ML, with performance partly affected by cross-assay variability. For HRD, incorporation of CNV data was the primary determinant of predictive performance. Once CNVs were included, DL and ML achieved similar accuracy on external datasets (F1 0.61 vs 0.58). In panel-based settings, DL retained an advantage over ML (F1 0.78 vs 0.62). Model interpretation analyses indicated that both DL and ML relied on mutation and chromosomal patterns consistent with established MSI and HRD biology. ConclusionOverall, predictive performance depended strongly on data availability and clinical sequencing context. When information-rich inputs were available, both DL and classical ML achieved robust biomarker prediction, with DL generally matching or exceeding ML performance. The most pronounced advantages of DL emerged in cross-assay evaluations and data-sparse settings, where generalization was more reliable. Notably, the best-performing DL models were lightweight and interpretable, supporting practical deployment. In clinical genomics workflows, such models may complement established pipelines by leveraging patient sequencing data to provide additional evidence for treatment-relevant biomarker assessment.

bioinformatics↗

bioRxiv2022

Shallow nanopore RNA sequencing enables transcriptome profiling for precision cancer medicine

Transcriptome profiling is a mainstay of translational cancer research and is increasingly finding its way into precision oncology. While bulk RNA sequencing (RNA-seq) is widely available, high costs and long data return time are limiting factors for clinical applications. We investigated a portable nanopore long-read sequencing device (MinION, Oxford Nanopore Technologies) for transcriptome profiling of tumors. In particular, we investigated the impact of lower coverage than that of larger sequencing devices by comparing shallow nanopore RNA-seq data with short-read RNA-seq data generated using reversible dye terminator technology (Illumina) for ten samples representing four cancer types. Coupled with ShaNTi (Shallow Nanopore Sequencing for Transcriptomics), a newly developed data processing pipeline, a turnaround time of five days was achieved. The correlation of normalized gene-level counts between nanopore and Illumina RNA-seq was high for MinION but not for very low-throughput Flongle flow cells (r = 0.89 and r = 0.24, respectively). A cost-saving approach based on multiplexing of four samples per MinION flow cell maintained a high correlation with Illumina data (r = 0.56 - 0.86). In addition, we compared the utility of nanopore and Illumina RNA-seq data for analysis tools commonly applied in translational oncology: (i) Shallow nanopore and Illumina RNA-seq were equally useful for inferring signaling pathway activities with PROGENy. (ii) Highly expressed genes encoding kinases targeted by clinically approved small-molecule inhibitors were reliably identified by shallow nanopore RNA-seq. (iii) In tumor microenvironment composition analysis, quanTIseq performed better than CIBERSORT, likely due to higher average expression of the gene set used for deconvolution. (iv) Shallow nanopore RNA-seq was successfully applied to validate known gene fusions by breakpoint analysis. These findings suggest that shallow nanopore RNA-seq enables rapid, cost-effective, and biologically meaningful transcriptome profiling of tumors and warrants further exploration in precision cancer medicine studies.

cancer biology↗

bioRxiv2021

MEK1/2 inhibition transiently alters the tumor immune microenvironment to enhance immunotherapy efficacy against head and neck cancer

Although the mitogen-activated protein kinases (MAPK) pathway is hyperactive in head and neck cancer (HNC), inhibition of MEK1/2 in HNC patients has not shown clinically meaningful activity. Using pre-clinical HNC models, we demonstrated that treatment with the MEK1/2 blocker trametinib delays HNC initiation and progression by reducing tumor cell proliferation and enhancing the anti-tumor immunity of CD8+ T cells. Further activation of CD8+ T cells by supplementation with anti-programmed death-1 (PD-1) antibody eliminated tumors and induced an immune memory in the cured mice. Mechanistically, an early response to trametinib treatment sensitized tumors to PD-1-supplementation by attenuating the expression of tumor-derived colony-stimulating factor-1 (CSF-1), which reduced the abundance of two CSF-1R+CD11c+ myeloid-derived suppressor cell (MDSC) populations in the tumor microenvironment (TME). In contrast, prolonged treatment with trametinib abolished the anti-tumor activity of PD-1, because tumor cells undergoing the epithelial to mesenchymal transition (EMT) in response to trametinib restored CSF-1 expression and re-created an immune-suppressive TME. These findings provide the rationale for testing the trametinib/PD-1 combination in HNC and highlight the importance of sensitizing tumors to immunotherapies by using targeted therapies to interfere with the host-tumor interaction. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/457244v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1c65286org.highwire.dtl.DTLVardef@1541becorg.highwire.dtl.DTLVardef@f557f3org.highwire.dtl.DTLVardef@16134dc_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

cancer biology↗

bioRxiv2020

Primary and recurrent glioma patient-derived orthotopic xenografts (PDOX) represent relevant patient avatars for precision medicine

Patient-derived cancer models are essential tools for studying tumor biology and preclinical interventions. Here, we show that glioma patient-derived orthotopic xenografts (PDOXs) enable long-term propagation of patient tumors and represent clinically relevant patient avatars. We created a large collection of PDOXs from primary and recurrent gliomas with and without mutations in IDH1, which retained histopathological, genetic, epigenetic and transcriptomic features of patient tumors with no mouse-specific clonal evolution. Longitudinal PDOX models recapitulate the limited genetic evolution of gliomas observed in patient tumors following treatment. PDOX-derived standardized tumor organoid cultures enabled assessment of drug responses, which were validated in mice. PDOXs showed clinically relevant responses to Temozolomide and to targeted treatments such as EGFR and CDK4/6 inhibitors in (epi)genetically defined groups, according to MGMT promoter and EGFR/CDK status respectively. Dianhydrogalactitol, a bifunctional alkylating agent, showed promising potential against glioblastoma. Our study underlines the clinical relevance of glioma PDOX models for translational research and personalized treatment studies.

cancer biology↗

bioRxiv2020

Integrative kinomics reveals kinase-driven subtypes of gastric adenocarcinomas with sunitinib as a potential common inhibitor

Molecular subtyping of tumors promises a personalized stratification into different treatment regimens. In gastric adenocarcinoma, the four molecularly defined subtypes chromosomal instable (CIN), microsatellite unstable (MSI), genomically stable (GS) and EBV-positive subtype have been proposed. Following an integrative kinomics approach this computational analysis aimed to predict the best kinase inhibitor for every molecular subtype of gastric adenocarcinomas using publicly available TCGA data (n=404). Intriguingly, using the regulatory network of gastric cancer to estimate protein activity, 43% of all samples could be identified to be kinase-driven. These samples were divided into three clusters with mutually exclusive kinase activities that were independent of the established molecular subtypes. Integrating the pattern of kinase overexpression with an unsupervised target landscape of 37 approved clinical kinase drugs revealed that sunitinib had the best target spectrum across the activated kinases in all three sample clusters. Future work is warranted to validate the kinase-driven subsets of gastric cancer and sunitinib as a potential common inhibitor.

cancer biology↗