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Verrijp, K.

Publications and source records attributed to Verrijp, K..

2 recordsLinked to original sources

Immunofilaments Provide a Nanoscale Platform for In Vivo T Cell Expansion and Cancer Immunotherapy

Adoptive T cell therapy has successfully been implemented for the treatment of cancer. Nevertheless, the precarious ex vivo expansion of T cells by artificial antigen-presenting cells (aAPCs) remains cumbersome and can compromise T cell functionality, thereby limiting their therapeutic potential. Here, we propose a radically different approach aiming at direct expansion of T cells in vivo, thereby omitting the need for large-scale ex vivo T cell production. We engineered nanosized immunofilaments (IFs), consisting of a soluble semiflexible polyisocyanopeptide polymer backbone that presents peptide-loaded major histocompatibility complexes and co-stimulatory molecules in a multivalent fashion. We demonstrate that IFs readily activate and expand antigen-specific T cells in a manner highly similar to natural APCs, as evidenced by transcriptomic analyses of T cells. Upon intravenous injection, IFs reach lymphoid organs including spleen and lymph nodes and induce antigen-specific T cell responses in vivo. Moreover, IFs display remarkable anti-tumor efficacy resulting in inhibition of melanoma metastases formation and reduction of primary tumor growth in synergy with immune checkpoint blockade. In conclusion, nanosized IFs represent a powerful new type of aAPC that provide a modular platform for direct activation and expansion of antigen-specific T cells in vivo, which can greatly contribute to cancer immunotherapy.

immunology↗

A Segmentation-Free Machine Learning Architecture for Immune Landscape Phenotyping in Solid Tumors by Multichannel Imaging

Tissue specimens taken from primary tumors or metastases contain important information for diagnosis and treat-ment of cancer patients. Multiplex imaging allows in situ visualization of heterogeneous cell populations, such as immune cells, in tissue samples. Most image processing pipelines first segment cell boundaries and then measure marker expression to assign cell phenotypes. In dense tissue environments, this segmentation-first approach can be inaccurate due to segmentation errors or overlapping cells. Here we introduce the machine learning pipeline "ImmuNet" that identifies positions and phenotypes of cells without segmenting them. ImmuNet is easy to train: human annotators only need to click on an immune cell and score its expression of each marker. This approach al-lowed us to annotate 34,458 cells. We show that ImmuNet consistently outperforms a state-of-the-art segmentation-based pipeline for multiplex immunohistochemistry analysis across tissue types, cell types and tissue densities, achieving error rates below 5-10% on challenging detection and phenotyping tasks. We externally validate Im-muNet results by comparing them to flow cytometric measurements from the same tissue. In summary, ImmuNet is an effective, simpler alternative to segmentation-based approaches when only cell positions and phenotypes, but not their shapes, are required for downstream analyses. Thus, ImmuNet helps researchers to analyze multiplex tissue images more easily and accurately.

immunology↗