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Groupe de distingués

Public·11 Distingués
Denis Gavrilov
Denis Gavrilov

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Download File 1107647.zip



Nonprofit Explorer has organizations claiming tax exemption in each of the 27 subsections of the 501(c) section of the tax code, and which have filed a Form 990, Form 990EZ or Form 990PF. Taxable trusts and private foundations that are required to file a form 990PF are also included. Small organizations filing a Form 990N "e-Postcard" are not included in this data.


We used spatial transcriptional profiling (GeoMx) to test whether immune activation in responding tumors involved the intraepithelial tumor compartment. We confirmed that spatial transcriptional profiling accurately captured immune cells by correlating gene-expression data and cell counts of CD3+ and CD8+ cells by multispectral immunofluorescence (mIF; Supplementary Fig. S5B). We acquired topologic transcriptional immune profiles of mIF-guided regions of interest (54) from responding and nonresponding tumors, profiling tumor islets, and stroma at baseline and following LDRT. Using single sample gene set enrichment analysis (ssGSEA) of such signatures (55), we observed distinct immune profiles associated with therapeutic outcome. Responding tumors exhibited an increase in Th1, CD8+, and TEM signatures located mainly in tumor islets following LDRT (Fig. 6E). Conversely, nonresponding tumors displayed an upregulation of M2 macrophage and neutrophil signatures following radiotherapy, which were detected mainly in tumor stroma (Fig. 6E).


NanoGeoMX and RNA-seq data were merged together in order to gain in statistical power and in the number of postirradiation versus preirradiation comparisons. To do so, NanoGeoMX data were first rebulked using the following methodology: The gene expression of ROIs from a same patient, treatment, and type (immune, tumor, or mixed) were first averaged. Then, we further averaged tumor, mixed, and immune-averaged profiles together per patient and treatment, which resulted in one expression profile per patient and per treatment (as for bulk RNA-seq). RNA-seq data were narrowed down to the genes represented in the NanoGeoMX platform (1,404 genes). We then merged RNA-seq and NanoGeoMX data in a single expression matrix and applied a batch correction algorithm using the ComBat function of the sva R package. Finally, we performed Pearson correlation tests between samples and found that they cluster primarily by patient origin, which indicated that our merging method was appropriate.


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