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Ferraro, G.

Publications and source records attributed to Ferraro, G..

2 recordsLinked to original sources

Combined blockade of VEGF, Angiopoietin-2, and PD1 reprograms glioblastoma endothelial cells into quasi-antigen-presenting cells

Glioblastoma (GBM) remains a highly aggressive and uniformly fatal tumor, with a 5-year survival of <10%. Moreover, all randomized clinical trials with immune-checkpoint blockers have failed to date. Here we report that tumor endothelial cell (EC) dysfunction confers resistance to immunotherapy in preclinical GBM models. Anti-VEGF-therapy-induced vascular normalization is insufficient to fully restore the EC function and alleviate inflammatory edema induced by blocking programmed cell death protein 1 (PD1). Strikingly, concomitant blockade of angiopoietin 2 (Ang2), vascular endothelial growth factor (VEGF), and programmed cell death protein 1 (PD1) reprograms dysfunctional ECs to quasi-antigen presenting cells and upregulates receptors required for cytotoxic T lymphocyte entry into the tumor. Blocking VEGF, Ang2, and PD1 induces durable anti-tumor response while controlling edema. Upregulation of the transcription factor T-bet is necessary for generating resident memory T cells elicited by this combination therapy. Moreover, a human GBM organoid model formed from human cancer cells, ECs, CD14 and CD8 cells recapitulated these findings. Our study reveals the role of Ang2 in resistance to VEGF and/or PD1-blockade and provides a compelling rationale for clinical evaluation of this combination in GBM patients.

cancer biology↗

Dual Passive Reactive Brain Computer Interface: a Novel Approach to Human-Machine Symbiosis

The present study proposes a novel concept of neuroadaptive technology, namely a dual passive-reactive Brain-Computer Interface (BCI), that enables bi-directional interaction between humans and machines. We have implemented such a system in a realistic flight simulator using the NextMind classification algorithms and framework to decode pilots intention (reactive BCI) and to infer their level of attention (passive BCI). Twelve pilots used the reactive BCI to perform checklists along with an anti-collision radar monitoring task that was supervised by the passive BCI. The latter simulated an automatic avoidance maneuver when it detected that pilots missed an incoming collision. The reactive BCI reached 100% classification accuracy with a mean reaction time of 1.6s when exclusively performing the checklist task. Accuracy was up to 98.5% with a mean reaction time of 2.5s when pilots also had to fly the aircraft and monitor the anti-collision radar. The passive BCI achieved a F1 - score of 0.94. This first demonstration shows the potential of a dual BCI to improve human-machine teaming which could be applied to a variety of applications.

neuroscience↗