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Borst, J.

Publications and source records attributed to Borst, J..

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Radiotherapy and cisplatin increase immunotherapy efficacy by enabling local and systemic intratumoral T-cell activity

To increase cancer immunotherapy success, PD-1 blockade must be combined with rationally selected treatments. Here, we examined in a poorly immunogenic mouse breast cancer model the potential of antibody-based immunomodulation and conventional anti-cancer treatments to collaborate with anti-PD-1 treatment. One important requirement to improve anti-PD-1-mediated tumor control was to promote tumor-specific cytotoxic T cell (CTL) priming, which was achieved by stimulating the CD137 costimulatory receptor. A second requirement was to overrule PD-1-unrelated mechanisms of CTL suppression in the tumor micro-environment (TME). This was achieved by radiotherapy and cisplatin treatment. In the context of CD137/PD-1-targeting immunotherapy, radiotherapy allowed for tumor elimination by altering the TME, rather than intrinsic CTL functionality. Combining this radioimmunotherapy regimen with low-dose cisplatin improved CTL-dependent regression of a contralateral tumor outside the radiation field. Thus, systemic tumor control may be achieved by combining immunotherapy protocols that promote T cell priming with (chemo)radiation protocols that permit CTL activity in both the irradiated tumor and (occult) metastases.\n\nSummary statementThis study reveals that radiotherapy and cisplatin can be re-purposed to improve antibody-based immunotherapy success in poorly immunogenic breast cancer by overruling PD-1 unrelated mechanisms of T cell suppression in the tumor micro-environment.

cancer biology

Inter-Subject Alignment of MEG Datasets at the Neural Representational Space

Pooling neural imaging data across subjects requires aligning recordings from different subjects. In magnetoencephalography (MEG) recordings, sensors across subjects are poorly correlated both because of differences in the exact location of the sensors, and structural and functional differences in the brains. It is possible to achieve alignment by assuming that the same regions of different brains correspond across subjects. However, this relies on both the assumption that brain anatomy and function are well correlated, and the strong assumptions that go into solving the underdetermined inverse problem given the high dimensional source space. In this paper, we investigated an alternative method that bypasses source-localization. Instead, it analyzes the sensor recordings themselves and aligns their temporal signatures across subjects. We used a multivariate approach, multi-set canonical correlation analysis (M-CCA), to transform individual subject data to a low dimensional common representational space. We evaluated the robustness of this approach over a synthetic dataset, by examining the effect of different factors that add to the noise and individual differences in the data. On a MEG dataset, we demonstrated that M-CCA performs better than a method that assumes perfect sensor correspondence and a method that applies source localization. Lastly, we described how the standard M-CCA algorithm could be further improved with a regularization term that incorporates spatial sensor information.

neuroscience