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Background: In the global effort to discover biomarkers for cancer prognosis, prediction tools have become essential resources. TCR (T cell receptor) repertoires contain important features that differentiate healthy controls from cancer patients or differentiate outcomes for patients being treated with different drugs. Considering, tools that can easily and quickly generate and identify important features out of TCR repertoire data and build accurate classifiers to predict future outcomes are essential. Results: This paper introduces GENTLE (GENerator of T cell receptor repertoire features for machine LEarning): an open-source, user-friendly web-application tool that allows TCR repertoire researchers to discover important features; to create classifier models and evaluate them with metrics; and to quickly generate visualizations for data interpretations. We performed a case study with repertoires of TRegs (regulatory T cells) and TConvs (conventional T cells) from healthy controls versus patients with breast cancer. We showed that diversity features were able to distinguish between the groups. Moreover, the classifiers built with these features could correctly classify samples ('Healthy' or 'Breast Cancer')from the TRegs repertoire when trained with the TConvs repertoire, and from the TConvs repertoire when trained with the TRegs repertoire. Conclusion: The paper walks through installing and using GENTLE and presents a case study and results to demonstrate the application's utility. GENTLE is geared towards any researcher working with TCR repertoire data and aims to discover predictive features from these data and build accurate classifiers. GENTLE is available on https://github.com/dhiego22/gentle and https://share.streamlit.io/dhiego22/gentle/main/gentle.py. [ABSTRACT FROM AUTHOR]

Background: Clinical studies have shown that the efficacy of programmed cell death receptor-1/programmed cell death ligand-1 (PD-1/PD-L1) inhibitors on glioblastoma (GBM) is much lower than what is expected because of the low immunogenicity of GBM. Ferroptosis of cancer cells can induce the maturation of dendritic cells (DC cells) and increase the activity of T cell. The activated T cells release IFN-γ, which subsequently induces the ferroptosis of cancer cells. Thus, the aim of this paper is to set up a new GBM-targeted drug delivery system (Fe3O4-siPD-L1@M-BV2) to boost ferroptosis for immunotherapy of drug-resistant GBM. Results: Fe3O4-siPD-L1@M-BV2 significantly increased the accumulation of siPD-L1 and Fe2+ in orthotopic drug-resistant GBM tissue in mice. Fe3O4-siPD-L1@M-BV2 markedly decreased the protein expression of PD-L1 and increased the ratio between effector T cells and regulatory T cells in orthotopic drug-resistant GBM tissue. Moreover, Fe3O4-siPD-L1@M-BV2 induced ferroptosis of GBM cells and maturation of DC cell, and it also increased the ratio between M1-type microglia and M2-type microglia in orthotopic drug-resistant GBM tissue. Finally, the growth of orthotopic drug-resistant GBM in mice was significantly inhibited by Fe3O4-siPD-L1@M-BV2. Conclusion: The mutual cascade amplification effect between ferroptosis and immune reactivation induced by Fe3O4-siPD-L1@M-BV2 significantly inhibited the growth of orthotopic drug-resistant GBM and prolonged the survival time of orthotopic drug-resistant GBM mice. [ABSTRACT FROM AUTHOR]