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

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

PSM Peptides From Community-Associated Methicillin-Resistant Staphylococcus aureus Impair the Adaptive Immune Response via Modulation of Dendritic Cell Subsets in vivo

Dendritic cells (DCs) are key players of the immune system and thus a target for immune evasion by pathogens. We recently showed that the virulence factors phenol-soluble-modulins (PSMs) produced by community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) strains induce tolerogenic DCs upon Toll-like receptor activation via the p38-CREB-IL-10 pathway in vitro. Here, we addressed the hypothesis that S. aureus PSMs disturb the adaptive immune response via modulation of DC subsets in vivo. Using a systemic mouse infection model we found that S. aureus reduced the numbers of splenic DC subsets, mainly CD4+ and CD8+ DCs independently of PSM secretion. S. aureus infection induced upregulation of the C-C motif chemokine receptor 7 (CCR7) on the surface of all DC subsets, on CD4+ DCs in a PSM-dependent manner, together with increased expression of MHCII, CD86, CD80, CD40, and the co-inhibitory molecule PD-L2, with only minor effects of PSMs. Moreover, PSMs increased IL-10 production in the spleen and impaired TNF production by CD4+ DCs. Besides, S. aureus PSMs reduced the number of CD4+ T cells in the spleen, whereas CD4+CD25+Foxp3+ regulatory T cells (Tregs) were increased. In contrast, Th1 and Th17 priming and IFN-γ production by CD8+ T cells were impaired by S. aureus PSMs. Thus, PSMs from highly virulent S. aureus strains modulate the adaptive immune response in the direction of tolerance by affecting DC functions

Wheeler Maps

Motivated by challenges in pangenomic read alignment, we propose a generalization of Wheeler graphs that we call Wheeler maps. A Wheeler map stores a text T[1..n] and an assignment of tags to the characters of T such that we can preprocess a pattern P[1..m] and then, given i and j, quickly return all the distinct tags labeling the first characters of the occurrences of P[i..j] in T. For the applications that most interest us, characters with long common contexts are likely to have the same tag, so we consider the number t of runs in the list of tags sorted by their characters’ positions in the Burrows-Wheeler Transform (BWT) of T. We show how, given a straight-line program with g rules for T, we can build an O(g+r+t)-space Wheeler map, where r is the number of runs in the BWT of T, with which we can preprocess a pattern P[1..m] in O(mlogn) time and then return the k distinct tags for P[i..j] in optimal O(k) time for any given i and j