Intratumoral immunosuppression profiles in 11q-deleted neuroblastomas provide new potential therapeutic targets

High‐risk neuroblastoma (NB) patients with 11q deletion frequently undergo late but consecutive relapse cycles with fatal outcome. To date, no actionable targets to improve current multi‐modal treatment have been identified. We analyzed immune microenvironment and genetic profiles of high‐risk NB correlating with 11q immune status. We show in two independent cohorts that 11q‐deleted NB exhibit various immune‐inhibitory mechanisms, including increased CD4+ resting T cells and M2 macrophages, higher expression of programmed death‐ligand 1, interleukin‐10, transforming growth factor‐beta‐1 and indoleamine 2,3‐dioxygenase 1 (P<0.05), and also higher chromosomal breakages (P≤0.02) and hemizygosity of immunosuppressive miRNAs than MYCN‐amplified and other 11q‐non‐deleted high‐risk NB. We also analyzed benefits of maintenance treatment in 83 high‐risk stage M NB patients focusing on 11q status, either with standard anti‐GD2 immunotherapy (n=50) or previous retinoic acid‐based therapy alone (n=33). Immunotherapy associated with higher EFS (50 vs. 30, P=0.028) and OS (72 vs. 52, P=0.047) at 3 years in the overall population. Despite benefits from standard anti‐GD2 immunotherapy in high‐risk NB patients, those with 11q deletion still face poor outcome. This NB subgroup displays intratumoral immune suppression profiles, revealing a potential therapeutic strategy with combination immunotherapy to circumvent this immune checkpoint blockade

Tumour growth: An approach to calibrate parameters of a multiphase porous media model based on in vitro observations of Neuroblastoma spheroid growth in a hydrogel microenvironment

To unravel processes that lead to the growth of solid tumours, it is necessary to link knowledge of cancer biology with the physical properties of the tumour and its interaction with the surrounding microenvironment. Our understanding of the underlying mechanisms is however still imprecise. We therefore developed computational physics-based models, which incorporate the interaction of the tumour with its surroundings based on the theory of porous media. However, the experimental validation of such models represents a challenge to its clinical use as a prognostic tool. This study combines a physics-based model with in vitro experiments based on microfluidic devices used to mimic a three-dimensional tumour microenvironment. By conducting a global sensitivity analysis, we identify the most influential input parameters and infer their posterior distribution based on Bayesian calibration. The resulting probability density is in agreement with the scattering of the experimental data and thus validates the proposed workflow. This study demonstrates the huge challenges associated with determining precise parameters with usually only limited data for such complex processes and models, but also demonstrates in general how to indirectly characterise the mechanical properties of neuroblastoma spheroids that cannot feasibly be measured experimentally