RESUMEN
Prussian blue nanoparticle-based photothermal therapy (PBNP-PTT) is an effective tumor treatment capable of eliciting an antitumor immune response. Motivated by the ability of PBNP-PTT to potentiate endogenous immune responses, we recently demonstrated that PBNP-PTT could be used ex vivo to generate tumor-specific T cells against glioblastoma (GBM) cell lines as an adoptive T cell therapy (ATCT). In this study, we further developed this promising T cell development platform. First, we assessed the phenotype and function of T cells generated using PBNP-PTT. We observed that PBNP-PTT facilitated CD8+ T cell expansion from healthy donor PBMCs that secreted IFNγ and TNFα and upregulated CD107a in response to engagement with target U87 cells, suggesting specific antitumor T cell activation and degranulation. Further, CD8+ effector and effector memory T cell populations significantly expanded after co-culture with U87 cells, consistent with tumor-specific effector responses. In orthotopically implanted U87 GBM tumors in vivo, PBNP-PTT-derived T cells effectively reduced U87 tumor growth and generated long-term survival in >80% of tumor-bearing mice by Day 100, compared to 0% of mice treated with PBS, non-specific T cells, or T cells expanded from lysed U87 cells, demonstrating an enhanced antitumor efficacy of this ATCT platform. Finally, we tested the generalizability of our approach by generating T cells targeting medulloblastoma (D556), breast cancer (MDA-MB-231), neuroblastoma (SH-SY5Y), and acute monocytic leukemia (THP-1) cell lines. The resulting T cells secreted IFNγ and exerted increased tumor-specific cytolytic function relative to controls, demonstrating the versatility of PBNP-PTT in generating tumor-specific T cells for ATCT.
RESUMEN
Vestibular schwannomas (VS) are the most common tumor of the skull base with available treatment options that carry a risk of iatrogenic injury to the facial nerve, which can significantly impact patients' quality of life. As facial nerve outcomes remain challenging to prognosticate, we endeavored to utilize machine learning to decipher predictive factors relevant to facial nerve outcomes following microsurgical resection of VS. A database of patient-, tumor- and surgery-specific features was constructed via retrospective chart review of 242 consecutive patients who underwent microsurgical resection of VS over a 7-year study period. This database was then used to train non-linear supervised machine learning classifiers to predict facial nerve preservation, defined as House-Brackmann (HB) I vs. facial nerve injury, defined as HB II-VI, as determined at 6-month outpatient follow-up. A random forest algorithm demonstrated 90.5% accuracy, 90% sensitivity and 90% specificity in facial nerve injury prognostication. A random variable (rv) was generated by randomly sampling a Gaussian distribution and used as a benchmark to compare the predictiveness of other features. This analysis revealed age, body mass index (BMI), case length and the tumor dimension representing tumor growth towards the brainstem as prognosticators of facial nerve injury. When validated via prospective assessment of facial nerve injury risk, this model demonstrated 84% accuracy. Here, we describe the development of a machine learning algorithm to predict the likelihood of facial nerve injury following microsurgical resection of VS. In addition to serving as a clinically applicable tool, this highlights the potential of machine learning to reveal non-linear relationships between variables which may have clinical value in prognostication of outcomes for high-risk surgical procedures.