Background: Tumor-derived organoid, namely tumoroid, can realistically retain the clinicopathologic features of original tumors even after long-term in vitro expansion. Here we develop this production methodology derived from hepatocellular carcinoma primary samples and generate a platform to evaluate the tumoricidal efficacy of autologous adoptive cell transfer including tumor infiltrating lymphocytes and peripheral blood lymphocytes.Methods: Haematoxylin and eosin together with immunohistochemistry staining were employed to ascertain the morphologic and histological features of tumoroids and original tumors. Tumor killing ability of T cells was detected by lactate dehydrogenase assay and propidium iodide staining. In tumoroid xenograft mouse model, tumor volumes were measured and T cell functions were examined by flow cytometry technique.Results: Four tumoroids with characteristics of poor differentiation and mild fibrosis were successfully established from fourteen hepatocellular carcinoma samples. More robust antitumor potential and hyperfunctional phenotype of all four tumor infiltrating lymphocytes were observed compared to matched peripheral blood lymphocytes in coculture system. In tumoroid xenograft mouse models, however, only one patient-derived tumor infiltrating lymphocytes with the highest antitumor activity can bestow efficient tumor eradication.Conclusions: Hepatocellular carcinoma tumoroid-based models could represent invaluable resources for evaluating the tumoricidal efficacy of autologous adoptive cell transfer. Tumor infiltrating lymphocytes should be a promising and yet-to-be-developed regimen to treat hepatocellular carcinoma.

Breast cancer (BRCA) remains a serious threat to women’s health, with the rapidly increasing morbidity and mortality being possibly due to a lack of a sophisticated classification system. To date, no reliable biomarker is available to predict prognosis. Cuproptosis has been recently identified as a new form of programmed cell death, characterized by the accumulation of copper in cells. However, little is known about the role of cuproptosis in breast cancer. In this study, a cuproptosis-related genes (CRGs) risk model was constructed, based on transcriptomic data with corresponding clinical information relating to breast cancer obtained from both the TCGA and GEO databases, to assess the prognosis of breast cancer by comprehensive bioinformatics analyses. The CRGs risk model was constructed and validated based on the expression of four genes (NLRP3, LIPT1, PDHA1 and DLST). BRCA patients were then divided into two subtypes according to the CRGs risk model. Furthermore, our analyses revealed that the application of this risk model was significantly associated with clinical outcome, immune infiltrates and tumor mutation burden (TMB) in breast cancer patients. Additionally, a new clinical nomogram model based on risk score was established and showed great performance in overall survival (OS) prediction, confirming the potential clinical significance of the CRGs risk model. Collectively, our findings revealed that the CRGs risk model can be a useful tool to stratify subtypes and that the cuproptosis-related signature plays an important role in predicting prognosis in BRCA patients.