15 research outputs found

    Multiomics surface receptor profiling of the NCI-60 tumor cell panel uncovers novel theranostics for cancer immunotherapy.

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    BACKGROUND Immunotherapy with immune checkpoint inhibitors (ICI) has revolutionized cancer therapy. However, therapeutic targeting of inhibitory T cell receptors such as PD-1 not only initiates a broad immune response against tumors, but also causes severe adverse effects. An ideal future stratified immunotherapy would interfere with cancer-specific cell surface receptors only. METHODS To identify such candidates, we profiled the surface receptors of the NCI-60 tumor cell panel via flow cytometry. The resulting surface receptor expression data were integrated into proteomic and transcriptomic NCI-60 datasets applying a sophisticated multiomics multiple co-inertia analysis (MCIA). This allowed us to identify surface profiles for skin, brain, colon, kidney, and bone marrow derived cell lines and cancer entity-specific cell surface receptor biomarkers for colon and renal cancer. RESULTS For colon cancer, identified biomarkers are CD15, CD104, CD324, CD326, CD49f, and for renal cancer, CD24, CD26, CD106 (VCAM1), EGFR, SSEA-3 (B3GALT5), SSEA-4 (TMCC1), TIM1 (HAVCR1), and TRA-1-60R (PODXL). Further data mining revealed that CD106 (VCAM1) in particular is a promising novel immunotherapeutic target for the treatment of renal cancer. CONCLUSION Altogether, our innovative multiomics analysis of the NCI-60 panel represents a highly valuable resource for uncovering surface receptors that could be further exploited for diagnostic and therapeutic purposes in the context of cancer immunotherapy

    Efficient dynamic variation graphs

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    Pangenome Graphs

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    Efficient dynamic variation graphs.

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    MOTIVATION: Pangenomics is a growing field within computational genomics. Many pangenomic analyses use bidirected sequence graphs as their core data model. However, implementing and correctly using this data model can be difficult, and the scale of pangenomic datasets can be challenging to work at. These challenges have impeded progress in this field. RESULTS: Here, we present a stack of two C++ libraries, libbdsg and libhandlegraph, which use a simple, field-proven interface, designed to expose elementary features of these graphs while preventing common graph manipulation mistakes. The libraries also provide a Python binding. Using a diverse collection of pangenome graphs, we demonstrate that these tools allow for efficient construction and manipulation of large genome graphs with dense variation. For instance, the speed and memory usage are up to an order of magnitude better than the prior graph implementation in the VG toolkit, which has now transitioned to using libbdsgs implementations. AVAILABILITY AND IMPLEMENTATION: libhandlegraph and libbdsg are available under an MIT License from https://github.com/vgteam/libhandlegraph and https://github.com/vgteam/libbdsg
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