Incorporating IL7 receptor alpha signaling in the endodomain of B7H3-targeting chimeric antigen receptor T cells mediates antitumor activity in glioblastoma.

CAR-T-cell therapy has shown promise in treating hematological malignancies but faces challenges in treating solid tumors due to impaired T-cell function in the tumor microenvironment. To provide optimal T-cell activation, we developed a B7 homolog 3 protein (B7H3)-targeting CAR construct consisting of three activation signals: CD3ζ (signal 1), 41BB (signal 2), and the interleukin 7 receptor alpha (IL7Rα) cytoplasmic domain (signal 3). We generated B7H3 CAR-T cells with different lengths of the IL7Rα cytoplasmic domain, including the full length (IL7R-L), intermediate length (IL7R-M), and short length (IL7R-S) domains, and evaluated their functionality in vitro and in vivo. All the B7H3-IL7Rα CAR-T cells exhibited a less differentiated phenotype and effectively eliminated B7H3-positive glioblastoma in vitro. Superiority was found in B7H3 CAR-T cells contained the short length of the IL7Rα cytoplasmic domain. Integration of the IL7R-S cytoplasmic domain maintained pSTAT5 activation and increased T-cell proliferation while reducing activation-induced cell death. Moreover, RNA-sequencing analysis of B7H3-IL7R-S CAR-T cells after coculture with a glioblastoma cell line revealed downregulation of proapoptotic genes and upregulation of genes associated with T-cell proliferation compared with those in 2nd generation B7H3 CAR-T cells. In animal models, compared with conventional CAR-T cells, B7H3-IL7R-S CAR-T cells suppressed tumor growth and prolonged overall survival. Our study demonstrated the therapeutic potential of IL7Rα-incorporating CAR-T cells for glioblastoma treatment, suggesting a promising strategy for augmenting the effectiveness of CAR-T cell therapy.

Peak Identification and Quantification by Proteomic Mass Spectrogram Decomposition.

Recent advances in the liquid chromatography/mass spectrometry (LC/MS) technology have improved the sensitivity, resolution, and speed of proteome analysis, resulting in increasing demand for more sophisticated algorithms to interpret complex mass spectrograms. Here, we propose a novel statistical method, proteomic mass spectrogram decomposition (ProtMSD), for joint identification and quantification of peptides and proteins. Given the proteomic mass spectrogram and the reference mass spectra of all possible peptide ions associated with proteins as a dictionary, ProtMSD estimates the chromatograms of those peptide ions under a group sparsity constraint without using the conventional careful preprocessing (e.g., thresholding and peak picking). We show that the method was significantly improved using protein-peptide hierarchical relationships, isotopic distribution profiles, reference retention times of peptide ions, and prelearned mass spectra of noise. We examined the concept of database search, library search, and match-between-runs. Our ProtMSD showed excellent agreements of 3277 peptide ions (94.79%) and 493 proteins (98.21%) with Mascot/Skyline for an Escherichia coli proteome sample and of 4460 peptide ions (103%) and 588 proteins (101%) with match-between-runs by MaxQuant for a yeast proteome sample. This is the first attempt to use a matrix decomposition technique as a tool for LC/MS-based proteome identification and quantification.