Biology subjects

Reitsam, N. G.

Publications and source records attributed to Reitsam, N. G..

4 recordsLinked to original sources

bioRxiv2026

Transforming Histology into Virtual Multiplex Immunofluorescence to Decode Prognostic Spatial Immunity in Hepatocellular Carcinoma

The spatial organization of the tumor immune microenvironment (TIME) drives hepatocellular carcinoma (HCC) prognosis but remains unquantifiable on routine H&E slides. Here, we present HCCExplorer, a deep learning framework that translates H&E into virtual multiplex immunofluorescence (mIF) and uses multi-modal graph learning to decode spatial survival signals. Trained on 30 H&E-mIF slide pairs, HCCExplorer evaluated a 1,813-slide multi-center cohort. It achieved superior overall survival stratification over clinical indices and state-of-the-art pathology and protein foundation models, yielding a concordance index of 0.71 and a Hazard Ratio (HR) of 15.46 (P < 0.001), maintaining stability across three external cohorts. Beyond risk stratification, interpretation of model features identified M1 macrophage infiltration as a protective determinant (HR=0.40, P < 0.05). Furthermore, it uncovered a protective "Containment Niche" at the invasion frontier (HR=0.02, P < 0.01), featuring macrophages co-localizing with Foxp3+ Tregs and CD4+ T cells. Ultimately, HCCExplorer provides actionable, spatially-resolved biomarkers from conventional histology for precision HCC management.

pathology↗

bioRxiv2026

WNT-driven immune evasion promotes malignant transformation of BRAF-mutant colorectal cancer

BRAF-mutant colorectal cancer (CRC) constitutes a molecularly and clinically distinct subtype with poor prognosis and resistance to standard therapies, representing a major unmet clinical need. Arising from the serrated pathway of colorectal carcinogenesis rather than the classical adenoma-carcinoma sequence, this subgroup remains relatively understudied yet displays a more aggressive disease course. To investigate the progression of serrated CRC, we generated multiple genetically engineered mouse models (GEMMs) of BRAF-mutant, microsatellite-stable (MSS) CRC that closely recapitulate human disease. Our findings demonstrate that WNT-pathway activation via APC- or CTNNB1-mutations, but not RNF43-loss, initiates serrated CRC by suppressing immune-mediated tumor surveillance. Mechanistically, WNT-signaling drives distinct alterations in T cell phenotypes within the tumor microenvironment, enabling tumor progression. Together, these data indicate that WNT-signaling mediates immune escape during the malignant transformation of BRAF-mutant CRC.

cancer biology↗

bioRxiv2025

Spatial dissection of ADC/RPT targets defines therapeutic opportunities inrhabdoid tumors

Rhabdoid tumors (RT) are among the most aggressive pediatric malignancies, characterized by early onset, loss of SWI/SNF complex members (SMARCB1 or SMARCA4), and dismal outcomes despite multimodal therapy. Refractory and relapsing RT remain almost uniformly fatal, and targeted or immune-based approaches have yet to demonstrate clinical benefit. To explore novel therapeutic vulnerabilities, we systematically investigated the expression of clinically actionable surface proteins that could serve as targets for antibody-drug conjugates (ADCs), radiopharmaceutical therapy (RPT), or cellular immunotherapies. Based on large-scale transcriptomic analyses, we prioritized FAP, CXCR4, and IL13RA2 and performed comprehensive protein-level validation by immunohistochemistry in an unprecedented cohort of 60 rhabdoid tumors spanning all molecular subgroups (ATRT-TYR, ATRT-SHH, ATRT-MYC, and eMRT). Integrating these data with spatial and single-nucleus transcriptomic profiling, we identified subgroup- and cell-type-specific expression patterns, including heterogeneous FAP distribution between stromal and tumor compartments and a distinct IL13RA2-positive rhabdoid cell population with melanosomal and stem-like features. These findings define a set of biologically and clinically relevant surface targets in RT and provide a translational blueprint for rational ADC and RPT target discovery in pediatric cancer.

cancer biology↗

bioRxiv2024

Counterfactual Diffusion Models for Mechanistic Explainability of Artificial Intelligence Models in Pathology

Deep learning can extract predictive and prognostic biomarkers from histopathology whole slide images. However, explainable artificial intelligence approaches widely used in digital pathology, such as attention heatmaps and class activation mapping, offer only limited interpretability regarding the features captured by classifiers. Here, we present MoPaDi (Morphing histoPathology Diffusion), a framework for generating counterfactual explanations for histopathology images that reveal which morphological or style features drive classifier predictions. MoPaDi combines diffusion autoencoders with task-specific multiple instance learning classifiers to manipulate images and flip predictions by modifying relevant features. We evaluated the framework on multiple datasets spanning colorectal, breast, liver, and lung cancers, including tissue type, cancer subtype, and biomarker (microsatellite instability) classification tasks. We assessed counterfactual explanations through quantitative analyses, pathologists evaluations, and independent foundation model-based classifiers. We found that MoPaDi was able to generate realistic counterfactual histopathology images, enabling pathologists to identify morphological features associated with the change in model predictions. Unlike conventional reviews of highly attended regions typical in digital pathology, MoPaDi explanations enabled pathologists to directly identify morphological features driving the classifiers predictions from a limited number of top-contributing tiles. Consistent with the literature, our biomarker classifier associated high microsatellite instability with mucinous differentiation, glandular patterns, and lymphocytic infiltration. Furthermore, MoPaDi revealed that changes in classifier predictions were mainly driven by morphological alterations rather than staining differences. Overall, MoPaDi is a practical framework for counterfactual explanations in computational pathology that reveals model-specific drivers of classification and increases trust in deep learning models.

bioinformatics↗