TCR-engineered iNKT cells induce robust antitumor response by dual targeting cancer and suppressive myeloid cells.

Adoptive immunotherapy with T cells engineered with tumor-specific T cell receptors (TCRs) holds promise for cancer treatment. However, suppressive cues generated in the tumor microenvironment (TME) can hinder the efficacy of these therapies, prompting the search for strategies to overcome these detrimental conditions and improve cellular therapeutic approaches. CD1d-restricted invariant natural killer T (iNKT) cells actively participate in tumor immunosurveillance by restricting suppressive myeloid populations in the TME. Here, we showed that harnessing iNKT cells with a second TCR specific for a tumor-associated peptide generated bispecific effectors for CD1d- and major histocompatibility complex (MHC)-restricted antigens in vitro. Upon in vivo transfer, TCR-engineered iNKT (TCR-iNKT) cells showed the highest efficacy in restraining the progression of multiple tumors that expressed the cognate antigen compared with nontransduced iNKT cells or CD8+ T cells engineered with the same TCR. TCR-iNKT cells achieved robust cancer control by simultaneously modulating intratumoral suppressive myeloid populations and killing malignant cells. This dual antitumor function was further enhanced when the iNKT cell agonist α-galactosyl ceramide (α-GalCer) was administered as a therapeutic booster through a platform that ensured controlled delivery at the tumor site, named multistage vector (MSV). These preclinical results support the combination of tumor-redirected TCR-iNKT cells and local α-GalCer boosting as a potential therapy for patients with cancer.

The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning Approach.

The study aims to create a preoperative model from baseline demographic and health-related quality of life scores (HRQOL) to predict a good to excellent early clinical outcome using a machine learning (ML) approach. A single spine surgery center retrospective review of prospectively collected data from January 2016 to December 2020 from the institutional registry (SpineREG) was performed. The inclusion criteria were age ≥ 18 years, both sexes, lumbar arthrodesis procedure, a complete follow up assessment (Oswestry Disability Index-ODI, SF-36 and COMI back) and the capability to read and understand the Italian language. A delta of improvement of the ODI higher than 12.7/100 was considered a "good early outcome". A combined target model of ODI (Δ ≥ 12.7/100), SF-36 PCS (Δ ≥ 6/100) and COMI back (Δ ≥ 2.2/10) was considered an "excellent early outcome". The performance of the ML models was evaluated in terms of sensitivity, i.e., True Positive Rate (TPR), specificity, i.e., True Negative Rate (TNR), accuracy and area under the receiver operating characteristic curve (AUC ROC). A total of 1243 patients were included in this study. The model for predicting ODI at 6 months' follow up showed a good balance between sensitivity (74.3%) and specificity (79.4%), while providing a good accuracy (75.8%) with ROC AUC = 0.842. The combined target model showed a sensitivity of 74.2% and specificity of 71.8%, with an accuracy of 72.8%, and an ROC AUC = 0.808. The results of our study suggest that a machine learning approach showed high performance in predicting early good to excellent clinical results.

Artificial Intelligence in Thyroid Field-A Comprehensive Review.

Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.

Invariant NKT cells contribute to chronic lymphocytic leukemia surveillance and prognosis.

Chronic lymphocytic leukemia (CLL) is characterized by the expansion of malignant CD5+ B lymphocytes in blood, bone marrow, and lymphoid organs. CD1d-restricted invariant natural killer T (iNKT) cells are innate-like T lymphocytes strongly implicated in tumor surveillance. We investigated the impact of iNKT cells in the natural history of the disease in the Eµ-Tcl1 (Tcl1) CLL mouse model and 68 CLL patients. We found that Tcl1-CLL cells express CD1d and that iNKT cells critically delay disease onset but become functionally impaired upon disease progression. In patients, disease progression correlates with high CD1d expression on CLL cells and impaired iNKT cells. Conversely, disease stability correlates with negative or low CD1d expression on CLL cells and normal iNKT cells, suggesting indirect leukemia control. iNKT cells indeed hinder CLL survival in vitro by restraining CD1d-expressing nurse-like cells, a relevant proleukemia macrophage population. Multivariable analysis identified iNKT cell frequency as an independent predictor of disease progression. Together, these results support the contribution of iNKT cells to CLL immune surveillance and highlight iNKT cell frequency as a prognostic marker for disease progression.

Invariant NKT cell reconstitution in pediatric leukemia patients given HLA-haploidentical stem cell transplantation defines distinct CD4+ and CD4- subset dynamics and correlates with remission state.

Immune reconstitution plays a crucial role on the outcome of patients given T cell-depleted HLA-haploidentical hematopoietic stem cell transplantation (hHSCT) for hematological malignancies. CD1d-restricted invariant NKT (iNKT) cells are innate-like, lipid-reactive T lymphocytes controlling infections, cancer, and autoimmunity. Adult mature iNKT cells are divided in two functionally distinct CD4(+) and CD4(-) subsets that express the NK receptor CD161 and derive from thymic CD4(+)CD161(-) precursors. We investigated iNKT cell reconstitution dynamics in 33 pediatric patients given hHSCT for hematological malignancies, with a follow-up reaching 6 y posttransplantation, and correlated their emergence with disease relapse. iNKT cells fully reconstitute and rapidly convert into IFN-γ-expressing effectors in the 25 patients maintaining remission. CD4(+) cells emerge earlier than the CD4(-) ones, both displaying CD161(-) immature phenotypes. CD4(-) cells expand more slowly than CD4(+) cells, though they mature with significantly faster kinetics, reaching full maturation by 18 mo post-hHSCT. Between 4 and 6 y post-hHSCT, mature CD4(-) iNKT cells undergo a substantial expansion burst, resulting in a CD4(+)