Sussman, R. | bioRxiv Science

bioRxiv2024

HLA-E and NKG2A Mediate Resistance to M. bovis BCG Immunotherapy in Non-Muscle-Invasive Bladder Cancer

BackgroundBacillus Calmette-Guerin (BCG) is the standard of care treatment for high-risk non-muscle-invasive bladder cancer (NMIBC), yet many patients develop recurrent disease despite evidence of ongoing immune activation. We investigated mechanisms of immune escape in BCG-unresponsive tumors and evaluated the therapeutic potential of targeting the HLA-E/NKG2A axis. MethodsSingle-cell RNA sequencing, spatial immunophenotyping, proteomic profiling, and functional ex vivo assays were performed using tumors and urine samples from patients with BCG-naive and BCG-unresponsive NMIBC. ResultsBCG-unresponsive tumors were enriched for HLA-E-expressing malignant cells compared with BCG-naive tumors. Increased HLA-E expression was associated with enhanced IFN-{gamma} signaling and was induced by IFN-{gamma} stimulation in primary tumor cells and bladder cancer tumor lines. Spatial analyses demonstrated accumulation of NKG2A+ NK and CD8 T cells in proximity to HLA-Ehigh tumor cells, with increased NKG2A:HLA-E interactions in BCG-unresponsive tumors. Despite high expression of cytotoxic mediators, NKG2A+ effector cells displayed impaired degranulation. Blockade of NKG2A with monalizumab restored degranulation of and cytotoxicity by tumor-infiltrating lymphocytes in autologous tumor co-cultures. ConclusionsBCG-unresponsive NMIBC tumors are enriched for HLA-E-expressing tumor cells and NKG2A+ effector lymphocytes, with increased engagement of the HLA-E/NKG2A axis within the tumor microenvironment. These findings identify the HLA-E/NKG2A axis as a therapeutic vulnerability and provide a rationale for clinical evaluation of NKG2A blockade as a bladder-sparing strategy for patients with BCG-unresponsive disease.

cancer biology↗

bioRxiv2022

The Radiogenomic and Spatiogenomic Landscapes of Glioblastoma, and Their Relationship to Oncogenic Drivers

Glioblastoma (GBM) is well-known for its molecular and spatial heterogeneity, which poses a challenge for precision therapies and clinical trial stratification. Here, in a comprehensive radiogenomics study of 358 GBMs, we investigated the associations between the imaging and spatial characteristics of the tumors with their cancer gene mutation status, as well as with the cross-sectionally inferred likely order of mutational events. We show that cross-validated machine learning analysis of multi-parametric MRI scans results in distinctive in vivo imaging signatures of several mutations, which are relatively more distinctive in homogeneous tumors which harbor only one of these mutations. These imaging signatures offer mechanistic insights into how various mutations influence the phenotype of the tumor and its surrounding infiltrated brain tissue via neovascularization and vascular leakage, increased cell density, invasion and migration, and other characteristics captured by respective imaging features. Furthermore, we found that spatial location and tumor distribution vary, depending on the GBMs molecular characteristics. Finally, distinct imaging and spatial characteristics were associated with cross-sectionally estimated evolutionary trajectories of the tumors. Collectively, our study establishes a panel of in vivo and clinically accessible imaging-AI biomarkers of GBM that reflect their molecular composition and oncogenic drivers.

bioengineering↗