Friedrich Feuerhake’s research while affiliated with Hannover Medical School and other places

Background Intestinal biopsies from inflammatory bowel disease (IBD) patients are routinely collected for histopathological examination. These samples offer a unique opportunity to study cellular heterogeneity and molecular pathways, and assist in selecting relevant features for disease prognosis in precision medicine. Single-cell transcriptomic approaches can yield insights into cellular and molecular signatures linked to disease progression and treatment response (1, 2). Nonetheless, the absence of a systematical evaluation of tissue processing, dissociation, and the availability of multiple technological methods remains a significant limitation that requires testing to maximize data quality and insights across varied biopsy profiles. Methods This study systematically tested tissue dissociation and fixation protocols, as well as droplet-based and plate-based single-cell processing platforms to evaluate cell yield, viability, and cell-type granularity. Furthermore, biopsies were collected from both ulcerative colitis (UC) and Crohn’s disease (CD) patients, on the least- and most-inflamed areas of the intestinal mucosa. We included intestinal biopsies cryopreserved for various time spans to assess limitations in single-cell transcriptomics processing. Finally, we compare the probe-base to total RNA-seq methods to understand the depth of the resulting profiles and how comparable these two approaches are. Results We capture 120,952 high-quality transcription profiles from 32 biopsies of 13 IBD patients (9 UC and 4 CD patients). Independent of biopsy characteristics, the dissociation and processing that included a priori fixation method was more easily applied due to the multiple stop points throughout the protocol and the capability of multiplex biopsies in a run. The plate-based method could capture more cell-types, including epithelium cells and could be particularly beneficial for biopsies with low cellular integrity. In contrast, droplet-based methods enabled higher cell throughput, flexibility of multiplexing samples, and additional immune profile information (CITE-seq). Furthermore, the time of sample collection did not affect the overall cell numbers. Both total and probe-based approaches were easily integrated after subsetting for the common features. Conclusion These findings underscore the importance of methodological standardization, stability and comparability of protocols, which are key in disease-informed single-cell analysis. Emphasizing the critical need to refine processing techniques for precision medicine applications in de-centralized single-cell base studies under clinical trial settings. References 1. Martin JC, Chang C, Boschetti G, et al. Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy. Cell. 2019;178(6):1493-1508.e20. doi:10.1016/j.cell.2019.08.008 2. Smillie CS, Biton M, Ordovas-Montanes J, et al. Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis. Cell. 2019;178(3):714-730.e22. doi:10.1016/j.cell.2019.06.029

Oncolytic viruses (OV) expressing bispecific T-cell engagers (BiTEs) are promising tools for tumor immunotherapy but the range of target tumors is limited. To facilitate effective T-cell stimulation with broad-range applicability, we established membrane-associated T-cell engagers (MATEs) harboring the protein transduction domain of the HIV-Tat protein to achieve non-selective binding to target cells. In vitro, MATEs effectively activated murine T cells and improved killing of MC38 colon carcinoma cells. Similarly, humanized MATEs activated T cells in PBMCs from human donors. In MC38-tumors in mice, MATE-expression by the oncolytic adenovirus Ad5/11 facilitated intratumoral T-cell activation, reduced tumor growth and prolonged survival accompanied by infiltration of tumor-directed CD8 ⁺ T cells and improved CD8/CD4 T-cell ratio. Absence of early T-cell activation in tumor draining lymph nodes suggests the safe applicability of this strategy. Furthermore, MATE-expression by Ad5/11 was capable of breaking resistance to αPD-1 checkpoint therapy thereby promoting T-cell/tumor cell proximity and clustering of CD8 ⁺ and CD4 ⁺ T cells. In summary, we demonstrated that MATE expressing OVs are powerful T-cell activating tools suitable for local immunotherapy of a broad range of tumors.

Predicting the biological behavior and time to recurrence (TTR) of high-grade diffuse gliomas (HGG) after maximum safe neurosurgical resection and combined radiation and chemotherapy plays a pivotal role in planning clinical follow-up, selecting potentially necessary second-line treatment and improving the quality of life for patients diagnosed with a malignant brain tumor. The current standard-of-care (SoC) for HGG includes follow-up neuroradiological imaging to detect recurrence as early as possible and relies on several clinical, neuropathological, and radiological prognostic factors, which have limited accuracy in predicting TTR. In this study, using an in-silico analysis, we aim to improve predictive power for TTR by considering the role of (i) prognostically relevant information available through diagnostics used in the current SoC, (ii) advanced image-based information not currently part of the standard diagnostic workup, such as tumor-normal tissue interface (edge) features and quantitative data specific to biopsy positions within the tumor, and (iii) information on tumor-associated macrophages. In particular, we introduced a state-of-the-art spatio-temporal model of tumor-immune interactions, emphasizing the interplay between macrophages and glioma cells. This model serves as a synthetic reality for assessing the predictive value of various features. We generated a cohort of virtual patients based on our mathematical model. Each patient's dataset includes simulated T1Gd and Fluid-attenuated inversion recovery (FLAIR) MRI volumes. T1-weighted imaging highlights anatomical structures with high contrast, providing clear detail on brain morphology, whereas FLAIR suppresses fluid signals, improving the visualization of lesions near fluid-filled spaces, which is particularly helpful for identifying peritumoral edema. Additionally, we simulated results on macrophage density and proliferative activity, either in a specified part of the tumor, namely the tumor core or edge ("localized"), or unspecified ("non-localized"). To enhance the realism of our synthetic data, we imposed different levels of noise. Our findings reveal that macrophage density at the tumor edge contributed to a high predictive value of feature importance for the selected regression model. Moreover, there are lower MSE values for the "localized" biopsy in prediction accuracy toward recurrence post-resection compared with "non-localized" specimens in the noisy data. In conclusion, the results show that localized biopsies provided more information about tumor behavior, especially at the interface of tumor and normal tissue (Edge). High-grade diffuse gliomas (HGG) include the most common types of primary malignant brain tumors in adults, namely glioblastoma multiforme (GBM), astrocytoma grade 3/grade 4 (A°4/A°3, and oligodendroglioma grade 3 (O°3) 1. Glioblastoma is associated with the most unfavorable prognosis and poor significant therapeutic advances in the past few decades 2 , and A°4/A°3 with better prognosis but a similar need for aggressive therapy and almost inevitable recurrence 3-6. HGG shares their potential for diffuse invasion of adjacent CNS structures and their pheno-typical plasticity. In contrast, phenotypic plasticity is recognized as one of the hallmarks of cancer 7 and can be associated with tissue invasion in many A full list of affiliations appears at the end of the paper.

In recent years, there has been remarkable progress in the field of digital pathology, driven by the ability to model complex tissue patterns using advanced deep-learning algorithms. However, the robustness of these models is often severely compromised in the presence of data shifts (e.g., different stains, organs, centers, etc.). Alternatively, continual learning (CL) techniques aim to reduce the forgetting of past data when learning new data with distributional shift conditions. Specifically, rehearsal-based CL techniques, which store some past data in a buffer and then replay it with new data, have proven effective in medical image analysis tasks. However, privacy concerns arise as these approaches store past data, prompting the development of our novel Generative Latent Replay-based CL (GLRCL) approach. GLRCL captures the previous distribution through Gaussian Mixture Models instead of storing past samples, which are then utilized to generate features and perform latent replay with new data. We systematically evaluate our proposed framework under different shift conditions in histopathology data, including stain and organ shift. Our approach significantly outperforms popular buffer-free CL approaches and performs similarly to rehearsal-based CL approaches that require large buffers causing serious privacy violations.

Due to the increasing workload of pathologists, the need for automation to support diagnostic tasks and quantitative biomarker evaluation is becoming more and more apparent. Foundation models have the potential to improve generalizability within and across centers and serve as starting points for data efficient development of specialized yet robust AI models. However, the training foundation models themselves is usually very expensive in terms of data, computation, and time. This paper proposes a supervised training method that drastically reduces these expenses. The proposed method is based on multi-task learning to train a joint encoder, by combining 16 different classification, segmentation, and detection tasks on a total of 912,000 patches. Since the encoder is capable of capturing the properties of the samples, we term it the Tissue Concepts encoder. To evaluate the performance and generalizability of the Tissue Concepts encoder across centers, classification of whole slide images from four of the most prevalent solid cancers - breast, colon, lung, and prostate - was used. The experiments show that the Tissue Concepts model achieve comparable performance to models trained with self-supervision, while requiring only 6% of the amount of training patches. Furthermore, the Tissue Concepts encoder outperforms an ImageNet pre-trained encoder on both in-domain and out-of-domain data.