Cancer immunotherapy with autologous chimeric antigen receptor (CAR) T cells faces challenges in manufacturing and patient selection that could be avoided by using ‘off-the-shelf’ products, such as allogeneic CAR natural killer T (^AlloCAR-NKT) cells. Previously, we reported a system for differentiating human hematopoietic stem and progenitor cells into ^AlloCAR-NKT cells, but the use of three-dimensional culture and xenogeneic feeders precluded its clinical application. Here we describe a clinically guided method to differentiate and expand IL-15-enhanced ^AlloCAR-NKT cells with high yield and purity. We generated ^AlloCAR-NKT cells targeting seven cancers and, in a multiple myeloma model, demonstrated their antitumor efficacy, expansion and persistence. The cells also selectively depleted immunosuppressive cells in the tumor microenviroment and antagonized tumor immune evasion via triple targeting of CAR, TCR and NK receptors. They exhibited a stable hypoimmunogenic phenotype associated with epigenetic and signaling regulation and did not induce detectable graft versus host disease or cytokine release syndrome. These properties of ^AlloCAR-NKT cells support their potential for clinical translation.

Cell-based cancer immunotherapy, such as chimeric antigen receptor (CAR) engineered T and natural killer (NK) cell therapies, has become a revolutionary new pillar in cancer treatment. Interleukin 15 (IL-15), a potent immunostimulatory cytokine that potentiates T and NK cell immune responses, has demonstrated the reliability and potency to potentially improve the therapeutic efficacy of current cell therapy. Structurally similar to interleukin 2 (IL-2), IL-15 supports the persistence of CD8⁺ memory T cells while inhibiting IL-2-induced T cell death that better maintains long-term anti-tumor immunity. In this review, we describe the biology of IL-15, studies on administrating IL-15 and/or its derivatives as immunotherapeutic agents, and IL-15-armored immune cells in adoptive cell therapy. We also discuss the advantages and challenges of incorporating IL-15 in cell-based immunotherapy and provide directions for future investigation.

Cell-based immunotherapy, such as chimeric antigen receptor (CAR) T cell therapy, has revolutionized the treatment of hematological malignancies, especially in patients who are refractory to other therapies. However, there are critical obstacles that hinder the widespread clinical applications of current autologous therapies, such as high cost, challenging large-scale manufacturing, and inaccessibility to the therapy for lymphopenia patients. Therefore, it is in great demand to generate the universal off-the-shelf cell products with significant scalability. Human induced pluripotent stem cells (iPSCs) provide an “unlimited supply” for cell therapy because of their unique self-renewal properties and the capacity to be genetically engineered. iPSCs can be differentiated into different immune cells, such as T cells, natural killer (NK) cells, invariant natural killer T (iNKT) cells, gamma delta T (γδ T), mucosal-associated invariant T (MAIT) cells, and macrophages (Mφs). In this review, we describe iPSC-based allogeneic cell therapy, the different culture methods of generating iPSC-derived immune cells (e.g., iPSC-T, iPSC-NK, iPSC-iNKT, iPSC-γδT, iPSC-MAIT and iPSC-Mφ), as well as the recent advances in iPSC-T and iPSC-NK cell therapies, particularly in combinations with CAR-engineering. We also discuss the current challenges and the future perspectives in this field towards the foreseeable applications of iPSC-based immune therapy.

Advancements in dermoscopy techniques have elucidated identifiable characteristics of melanoma which revolve around the asymmetrical constitution of melanocytic lesions consequent of unfettered proliferative growth as a malignant lesion. This study explores the applications of hierarchical density-based spatial clustering of applications with noise (HDBSCAN) in terms of the direct diagnostic implications of applying agglomerative clustering in the spectroscopic analysis of malignant melanocytic lesions and benign dermatologic spots. 100 images of benign (n = 50) and malignant moles (n = 50) were sampled from the International Skin Imaging Collaboration Archive and processed through two separate Python algorithms. The first of which deconvolutes the three-digit tupled integer identifiers of pixel color in image composition into three separate matrices corresponding to the red, green and blue color channel. Statistical characterization of integer variance was utilized to determine the optimal channel for comparative analysis between malignant and benign image groups. The second applies HDBSCAN to the matrices, identifying agglomerative clustering in the dataset. The results indicate the potential diagnostic applications of HDBSCAN analysis in fast-processing dermoscopy, as optimization of clustering parameters according to a binary search strategy produced an accuracy of 85% in the classification of malignant and benign melanocytic lesions.

Objective: Identify predictors of patient no‐show at an urban safety‐net otolaryngology outpatient clinic.Methods: Retrospective cohort study including all scheduled patients and appointments in 2023. Predictor variables included sociodemographic factors, primary diagnosis, insurance, and the neighborhood deprivation index (NDI) based on census tract information. The outcome was analyzed as a binary variable using univariate and multivariate mixed‐effects logistic regression models.Results: Among 2339 patients and 4641 scheduled appointments, 1639 patients completed all scheduled visits and 700 (29.9%) missed at least 1 visit. Among all appointments, 18.4% were missed. The prior no‐show rate was 9% (IQR 4%–18%), and days from scheduling to appointment was 42 days (IQR 19–75). Univariate analysis demonstrated significant sociodemographic factors associated with higher odds of missing an appointment, including NDI (OR 1.03, p = 0.001), male gender (OR 1.35, p = 0.004), Black/African American race (OR 1.49, p = 0.022), unemployment and disability status (OR 1.45, p = 0.007 and OR 2.12, p < 0.001 respectively), unstable/unknown housing (OR 3.66, p < 0.001), and sexual orientation as lesbian or gay (OR 1.93, p = 0.003). NDI remained a significant factor in multivariate analysis (OR 1.03, p = 0.001). Patient portal inactivation and lead time were significant intervenable factors in multivariate analysis (OR 1.23, p = 0.049 and OR 1.26, p < 0.001, respectively).Conclusion: NDI, patient portal activation, and time from appointment scheduling to visit are significant predictors of patient no‐show. This study offers insights into potential interventions addressing specific barriers to improving patient no‐show rates for an urban, safety‐net outpatient population.Level of Evidence: 3 (retrospective cohort study).