A conjoined universal helper epitope can unveil antitumor effects of a neoantigen vaccine targeting an MHC class I-restricted neoepitope.

Personalized cancer vaccines targeting neoantigens arising from somatic missense mutations are currently being evaluated for the treatment of various cancers due to their potential to elicit a multivalent, tumor-specific immune response. Several cancers express a low number of neoantigens; in these cases, ensuring the immunotherapeutic potential of each neoantigen-derived epitope (neoepitope) is crucial. In this study, we discovered that therapeutic vaccines targeting immunodominant major histocompatibility complex (MHC) I-restricted neoepitopes require a conjoined helper epitope in order to induce a cytotoxic, neoepitope-specific CD8+ T-cell response. Furthermore, we show that the universally immunogenic helper epitope P30 can fulfill this requisite helper function. Remarkably, conjoined P30 was able to unveil immune and antitumor responses to subdominant MHC I-restricted neoepitopes that were, otherwise, poorly immunogenic. Together, these data provide key insights into effective neoantigen vaccine design and demonstrate a translatable strategy using a universal helper epitope that can improve therapeutic responses to MHC I-restricted neoepitopes.

Effective effectors: How T cells access and infiltrate the central nervous system.

Several Phase II and III clinical trials have demonstrated that immunotherapy can induce objective responses in otherwise refractory malignancies in tumors outside the central nervous system. In large part, effector T cells mediate much of the antitumor efficacy in these trials, and potent antitumor T cells can be generated through vaccination, immune checkpoint blockade, adoptive transfer, and genetic manipulation. However, activated T cells must still traffic to, infiltrate, and persist within tumor in order to mediate tumor lysis. These requirements for efficacy pose unique challenges for brain tumor immunotherapy, due to specific anatomical barriers and populations of specialized immune cells within the central nervous system that function to constrain immunity. Both autoimmune and infectious diseases of the central nervous system provide a wealth of information on how T cells can successfully migrate to the central nervous system and then engender sustained immune responses. In this review, we will examine the commonalities in the efferent arm of immunity to the brain for autoimmunity, infection, and tumor immunotherapy to identify key factors underlying potent immune responses.

Rest tremor quantification based on fuzzy inference systems and wearable sensors.

BACKGROUND: Currently the most consistent, widely accepted and detailed instrument to rate Parkinson's disease (PD) is the Movement Disorder Society sponsored Unified Parkinson Disease Rating Scale (MDS-UPDRS). However, the motor examination is based upon subjective human interpretation trying to capture a snapshot of PD status. Wearable sensors and machine learning have been broadly used to analyze PD motor disorder, but still most ratings and examinations lay outside MDS-UPDRS standards. Moreover, logical connections between features and output ratings are not clear and complex to derive from the model, thus limiting the understanding of the structure in the data. METHODS: Fifty-seven PD patients underwent a full motor examination in accordance to the MDS-UPDRS on twelve different sessions, gathering 123 measurements. Overall, 446 different combinations of limb features correlated to rest tremors amplitude are extracted from gyroscopes, accelerometers, and magnetometers and feed into a fuzzy inference system to yield severity estimations. RESULTS: A method to perform rest tremor quantification fully adhered to the MDS-UPDRS based on wearable sensors and fuzzy inference system is proposed, which enables a reliable and repeatable assessment while still computing features suggested by clinicians in the scale. This quantification is straightforward and scalable allowing clinicians to improve inference by means of new linguistic statements. In addition, the method is immediately accessible to clinical environments and provides rest tremor amplitude data with respect to the timeline. A better resolution is also achieved in tremors rating by adding a continuous range.

Airport take-off noise assessment aimed at identify responsible aircraft classes.

Assessment of aircraft noise is an important task of nowadays airports in order to fight environmental noise pollution given the recent discoveries on the exposure negative effects on human health. Noise monitoring and estimation around airports mostly use aircraft noise signals only for computing statistical indicators and depends on additional data sources so as to determine required inputs such as the aircraft class responsible for noise pollution. In this sense, the noise monitoring and estimation systems have been tried to improve by creating methods for obtaining more information from aircraft noise signals, especially real-time aircraft class recognition. Consequently, this paper proposes a multilayer neural-fuzzy model for aircraft class recognition based on take-off noise signal segmentation. It uses a fuzzy inference system to build a final response for each class p based on the aggregation of K parallel neural networks outputs Op(k) with respect to Linear Predictive Coding (LPC) features extracted from K adjacent signal segments. Based on extensive experiments over two databases with real-time take-off noise measurements, the proposed model performs better than other methods in literature, particularly when aircraft classes are strongly correlated to each other. A new strictly cross-checked database is introduced including more complex classes and real-time take-off noise measurements from modern aircrafts. The new model is at least 5% more accurate with respect to previous database and successfully classifies 87% of measurements in the new database.

Myeloablative temozolomide enhances CD8⁺ T-cell responses to vaccine and is required for efficacy against brain tumors in mice.

Temozolomide (TMZ) is an alkylating agent shown to prolong survival in patients with high grade glioma and is routinely used to treat melanoma brain metastases. A prominent side effect of TMZ is induction of profound lymphopenia, which some suggest may be incompatible with immunotherapy. Conversely, it has been proposed that recovery from chemotherapy-induced lymphopenia may actually be exploited to potentiate T-cell responses. Here, we report the first demonstration of TMZ as an immune host-conditioning regimen in an experimental model of brain tumor and examine its impact on antitumor efficacy of a well-characterized peptide vaccine. Our results show that high-dose, myeloablative (MA) TMZ resulted in markedly reduced CD4(+), CD8(+) T-cell and CD4(+)Foxp3(+) TReg counts. Adoptive transfer of naïve CD8(+) T cells and vaccination in this setting led to an approximately 70-fold expansion of antigen-specific CD8(+) T cells over controls. Ex vivo analysis of effector functions revealed significantly enhanced levels of pro-inflammatory cytokine secretion from mice receiving MA TMZ when compared to those treated with a lower lymphodepletive, non-myeloablative (NMA) dose. Importantly, MA TMZ, but not NMA TMZ was uniquely associated with an elevation of endogenous IL-2 serum levels, which we also show was required for optimal T-cell expansion. Accordingly, in a murine model of established intracerebral tumor, vaccination-induced immunity in the setting of MA TMZ-but not lymphodepletive, NMA TMZ-led to significantly prolonged survival. Overall, these results may be used to leverage the side-effects of a clinically-approved chemotherapy and should be considered in future study design of immune-based treatments for brain tumors.