Allogeneic natural killer (NK) cell therapy has been effective in treating cancer. Many studies have tested NK cell therapy using human pluripotent stem cells (hPSCs). However, the impacts of the origin of PSC-NK cells on competence are unclear. In this study, several types of hPSCs, including human-induced PSCs (hiPSCs) generated from CD34+, CD3−CD56+, and CD56− cells in umbilical cord blood (UCB), three lines of human embryonic stem cells (hESCs, ES-1. ES-2 and ES-3) and MHC I knockout (B2M-KO)-ESCs were used to differentiate into NK cells and their capacities were analyzed. All PSC types could differentiate into NK cells. Among the iPSC-derived NK cells (iPSC-NKs) and ESC-derived NK cells (ES-NKs), 34+ iPSCs and ES-3 had a higher growth rate and cytotoxicity, respectively, ES-3 also showed better efficacy than 34+ iPSCs. B2M-KO was similar to the wild type. These results suggest that the screening for differentiation of PSCs into NK cells prior to selecting the PSC lines for the development of NK cell immunotherapy is an essential process for universal allotransplantation, including the chimeric antigen receptor (CAR).

Patient-derived xenograft (PDX) models are powerful tools in cancer research, offering an accurate platform for evaluating cancer treatment efficacy and predicting responsiveness. However, these models necessitate surgical techniques for tumor tissue transplantation and face challenges with non-uniform tumor growth among animals. To address these issues, we attempted to develop a new PDX modeling method using high-grade serous ovarian cancer (HGSC), a fatal disease with a 5-year survival rate of 29%, which requires personalized research due to its morphological, genetic, and molecular heterogeneities. In this study, we developed a new patient-derived cancer cell xenograft (PDCX) model with high engraftment efficiency (64%) that utilizes primary cancer cells instead of patient tissues. Primary cancer cells can be stably cryopreserved for extended periods (up to 485 days), and when transplanted into female NSGA mice, they maintain morphological and molecular characteristics without significant genetic differences compared to their original primary tumors. Furthermore, PDCX models can be easily produced using a syringe, allowing for uniform tumor sizes across multiple animals. Additionally, M2 PDCXs exhibited a significantly faster growth rate compared to M2 PDTXs. Consequently, our PDCX model offers a streamlined approach for evaluating personalized cancer treatments with minimal experimental variability.

Kim KY, et al. Immune Netw. 2020 Jul;20(4):e33. https://doi.org/10.4110/in.2020.20.e33

Patient-derived xenograft (PDX) models are powerful tool in cancer research, offering a more accurate platform for evaluating the cancer treatment efficacy and predicting responsiveness. However, these models necessitate surgical techniques for tumor tissue transplantation and face challenges with non-uniform tumor growth among animals. To address these issues, we attempted to develop a new PDX modeling method using high-grade serous ovarian cancer (HGSC), a fatal disease with a 5-year survival rate of 29%, that requires personalized research due to its morphological, genetic, and molecular heterogeneity. In this study, we developed a new patient-derived cancer cell xenograft (PDCX) model with high engraftment efficiency (64%) that utilizes primary cancer cells instead of patient tissues. Primary cancer cells can be stably cryopreserved for extended periods (up to 485 days), and when transplanted into animals, they maintain morphological and molecular characteristic without significant genetic differences compared to their original primary tumors. Furthermore, PDCX models can be easily produced using a syringe, allowing for uniform tumor sizes across multiple animals. Additionally, M2 PDCX exhibited significantly faster growth rate compared to M2 PDTX (5.04 vs. 1.85, ****p< 0.0001). Consequently, our PDCX model offers a streamlined approach for the efficacy evaluation of personalized cancer treatments with minimal experimental variability.

Jiyoung Jang, Eun Hye Cho, Youngkyung Cho, Binderya Ganzorig, Ki Yeon Kim, Moon Gyo Kim, and Chungho Kim. Mol. Cells 2022;45:564-74. https://doi.org/10.14348/molcells.2022.2004