Background Improvements in detection and molecular characterization of leptomeningeal metastasis from lung cancer (LC-LM) coupled with cerebrospinal fluid (CSF)-penetrating targeted therapies have altered disease management. A barrier to formal study of these therapies in LM is quantification of disease burden. Also, outcomes of patients with targetable mutations in LC-LM are not well defined. This study employs molecular and radiographic measures of LM disease burden and correlates these with outcome. Methods We reviewed charts of 171 patients with LC-LM treated at Memorial Sloan Kettering. A subset had MRI and CSF studies available. Radiographic involvement (n = 76) was scored by number of gadolinium-enhancing sites in 8 locations. CSF studies included cytopathology, circulating tumor cell (CTC) quantification (n = 16), and cell-free DNA (cfDNA) analysis (n = 21). Clinical outcomes were compared with Kaplan–Meier log-rank test and Cox proportional hazards methodologies. Results Median overall survival was 4.2 months (95% CI: 3.6–4.9); 84 patients (49%) harbored targetable mutations. Among bevacizumab-naïve patients with MRI and CSF cytology at time of LC-LM diagnosis, extent of radiographic involvement correlated with risk of death (hazard ratio [HR]: 1.16; 95% CI: 1.02–1.33; P = 0.03), as did CSF CTC (HR: 3.39, 95% CI: 1.01–11.37; P = 0.048) and CSF cfDNA concentration (HR: 2.58; 95% CI: 0.94–7.05; P = 0.06). Those without a targetable mutation were almost 50% more likely to die (HR: 1.49; 95% CI: 1.06–2.11; P = 0.02). Conclusions Extent of radiographic involvement and quantification of CSF CTC and cfDNA show promise as prognostic indicators. These findings support molecular characterization and staging for clinical management, prognostication, and clinical trial stratification of LC-LM.

Tumor antigen heterogeneity, a severely immunosuppressive tumor microenvironment (TME) and lymphopenia resulting in inadequate immune intratumoral trafficking, have rendered glioblastoma (GBM) highly resistant to therapy. To address these obstacles, here we describe a unique, sophisticated combinatorial platform for GBM: a cooperative multifunctional immunotherapy based on genetically engineered human natural killer (NK) cells bearing multiple antitumor functions including local tumor responsiveness that addresses key drivers of GBM resistance to therapy: antigen escape, immunometabolic reprogramming of immune responses, and poor immune cell homing. We engineered dual-specific chimeric antigen receptor (CAR) NK cells to bear a third functional moiety that is activated in the GBM TME and addresses immunometabolic suppression of NK cell function: a tumor-specific, locally released antibody fragment which can inhibit the activity of CD73 independently of CAR signaling and decrease the local concentration of adenosine. The multifunctional human NK cells targeted patient-derived GBM xenografts, demonstrated local tumor site–specific activity in the tissue, and potently suppressed adenosine production. We also unveil a complex reorganization of the immunological profile of GBM induced by inhibiting autophagy. Pharmacologic impairment of the autophagic process not only sensitized GBM to antigenic targeting by NK cells but promoted a chemotactic profile favorable to NK infiltration. Taken together, our study demonstrates a promising NK cell–based combinatorial strategy that can target multiple clinically recognized mechanisms of GBM progression simultaneously.

BackgroundGlioblastoma (GBM) is the most common malignant central nervous system tumor, and MGMT promoter hypermethylation in this tumor has been shown to be associated with better prognosis. We evaluated the capacity of radiomics features to add complementary information to MGMT status, to improve the ability to predict prognosis.Methods159 patients with untreated GBM were included in this study and divided into training and independent test sets. 286 radiomics features were extracted from the magnetic resonance images acquired prior to any treatments. A least absolute shrinkage selection operator (LASSO) selection followed by Kaplan-Meier analysis was used to determine the prognostic value of radiomics features to predict overall survival (OS). The combination of MGMT status with radiomics was also investigated and all results were validated on the independent test set.ResultsLASSO analysis identified 8 out of the 286 radiomic features to be relevant which were then used for determining association to OS. One feature (edge descriptor) remained significant on the external validation cohort after multiple testing (p=0.04) and the combination with MGMT identified a group of patients with the best prognosis with a survival probability of 0.61 after 43 months (p=0.0005).ConclusionOur results suggest that combining radiomics with MGMT is more accurate in stratifying patients into groups of different survival risks when compared to with using these predictors in isolation. We identified two subgroups within patients who have methylated MGMT: one with a similar survival to unmethylated MGMT patients and the other with a significantly longer OS.