Purpose: We aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment. Methods: The clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What’s more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results. Results: At multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868–0.931), 0.854(0.819–0.899) and 0.831(0.813–0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values < 0.05). Conclusion: The combined model allowed for the prognostic prediction of ccRCC patients by incorporating the DLRS and significant clinicopathologic features. The radiomics features could reflect the tumor heterogeneity and microenvironment.

Chimeric antigen receptor (CAR)–T cell therapies have revolutionized the landscape of cancer treatment, in particular in the context of hematologic malignancies. However, for solid tumors that lack tumor-specific antigens, CAR-T cells can infiltrate and attack nonmalignant tissues expressing the CAR target antigen, leading to on-target, off-tumor toxicity. Severe on-target, off-tumor toxicities have been observed in clinical trials of CAR-T therapy for solid tumors, highlighting the need to address this issue. Here, we demonstrated that targeting the cell adhesion and migration molecules lymphocyte function–associated antigen 1 (LFA-1; CD11a/CD18) and very late activation antigen 4 (VLA-4; CD49d/CD29) with blocking antibodies reduced the on-target, off-tumor toxicity of CAR-T cells in mice. To translate this observation into improved CAR-T cell therapy, we either knocked out both CD11a and CD49d or knocked down CD11a and CD49d along with PSGL1 , another cell adhesion molecule, in CAR-T cells. We found that these modified CAR-T cells exhibited reduced on-target, off-tumor toxicity in vivo without affecting CAR-T cell efficacy. Furthermore, we showed that this approach promoted T cell memory formation and decreased tonic signaling. On the basis of these data, we engineered a human version of these low-toxicity CAR-T cells and further validated the feasibility of this approach in vitro and in vivo. Together, these results provide a potential solution to address the clinical challenge of on-target, off-tumor toxicity in CAR-T therapy.

Due to the difficulty in addressing multifactorial complex diseases such as chronic liver injury, we designed multilayer structured microneedles based on multiple pathogenic factors. This study addresses chronic liver injury characterized by high tissue fibrosis and hepatocyte necrosis by utilizing hepatocyte growth factor (HGF) and stem cell exosome solution (HGF@EV) to encapsulate a slow-release antifibrotic drug, nintedanib, within soluble microneedles (H@EV-H/G/N MNP). Applying the patch directly to the skin allows for continuous absorption and gradual degradation of nintedanib in vivo. In vitro experiments showed that nintedanib inhibits M2 polarization, reduces TGF-β secretion, and, in combination with microneedles, suppresses fibroblast proliferation and migration, thus hindering liver fibrosis progression. The regenerative effect of the HGF-loaded stem cell exosome solution led to significant hepatocyte proliferation. Under this dual action, the liver function and quality of life of the mice were effectively improved. By extension, different multilayer microneedles can be constructed to target the pathogenic characteristics of various diseases. This multimodal therapeutic system addresses complex refractory diseases characterized by multiple pathogenic factors.