Potential use of γδ T cell-based vaccines in cancer immunotherapy

Immunotherapy is a fast advancing methodology involving one of two approaches: (1) compounds targeting immune checkpoints and (2) cellular immunomodulators. The latter approach is still largely experimental and features in vitro generated, live immune effector cells, or antigen-presenting cells. γδ T cells are known for their efficient in vitro tumor killing activities. Consequently, many laboratories worldwide are currently testing the tumor killing function of γδ T cells in clinical trials. Reported benefits are modest; however, these studies have demonstrated that large γδ T-cell infusions were well tolerated. Here, we discuss the potential of using human γδ T cells not as effector cells but as a novel cellular vaccine for treatment of cancer patients. Antigen-presenting γδ T cells do not require to home to tumor tissues but, instead, need to interact with endogenous, tumor-specific αβ T cells in secondary lymphoid tissues. Newly mobilized effector αβ T cells are then thought to overcome the immune blockade by creating proinflammatory conditions fit for effector T-cell homing to and killing of tumor cells. Immunotherapy may include tumor antigen-loaded γδ T cells alone or in combination with immune checkpoint inhibitors

A Chlorophyll-Derived Phylloxanthobilin Is a Potent Antioxidant That Modulates Immunometabolism in Human PBMC

Phyllobilins are natural products derived from the degradation of chlorophyll, which proceeds via a common and strictly controlled pathway in higher plants. The resulting tetrapyrrolic catabolites-the phyllobilins-are ubiquitous in nature;despite their high abundance, there is still a lack of knowledge about their physiological properties. Phyllobilins are part of human nutrition and were shown to be potent antioxidants accounting with interesting physiological properties. Three different naturally occurring types of phyllobilins-a phylloleucobilin, a dioxobilin-type phylloleucobilin and a phylloxanthobilin (PxB)-were compared regarding potential antioxidative properties in a cell-free and in a cell-based antioxidant activity test system, demonstrating the strongest effect for the PxB. Moreover, the PxB was investigated for its capacity to interfere with immunoregulatory metabolic pathways of tryptophan breakdown in human blood peripheral mononuclear cells. A dose-dependent inhibition of tryptophan catabolism to kynurenine was observed, suggesting a suppressive effect on pathways of cellular immune activation. Although the exact mechanisms of immunomodulatory effects are yet unknown, these prominent bioactivities point towards health-relevant effects, which warrant further mechanistic investigations and the assessment of the in vivo extrapolatability of results. Thus, phyllobilins are a still surprisingly unexplored family of natural products that merit further investigation

Rethinking data augmentation for adversarial robustness

Recent work has proposed novel data augmentation methods to improve the adversarial robustness of deep neural networks. In this paper, we re-evaluate such methods through the lens of different metrics that characterize the augmented manifold, finding contradictory evidence. Our extensive empirical analysis involving 5 data augmentation methods, all tested with an increasing probability of augmentation, shows that: (i) novel data augmentation methods proposed to improve adversarial robustness only improve it when combined with classical augmentations (like image flipping and rotation), and even worsen adversarial robustness if used in isolation; and (ii) adversarial robustness is significantly affected by the augmentation probability, conversely to what is claimed in recent work. We conclude by discussing how to rethink the development and evaluation of novel data augmentation methods for adversarial robustness. Our open-source code is available at https://github.com/eghbalz/rethink_da_for_a

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning

The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative of the data that will be encountered at test time. This assumption is challenged by the threat of poisoning, an attack that manipulates the training data to compromise the model's performance at test time. Although poisoning has been acknowledged as a relevant threat in industry applications, and a variety of different attacks and defenses have been proposed so far, a complete systematization and critical review of the field is still missing. In this survey, we provide a comprehensive systematization of poisoning attacks and defenses in machine learning, reviewing more than 100 papers published in the field in the last 15 years. We start by categorizing the current threat models and attacks, and then organize existing defenses accordingly. While we focus mostly on computer-vision applications, we argue that our systematization also encompasses state-of-the-art attacks and defenses for other data modalities. Finally, we discuss existing resources for research in poisoning, and shed light on the current limitations and open research questions in this research field

Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation

We study the problem of choosing algorithm hyper-parameters in unsupervised domain adaptation, i.e., with labeled data in a source domain and unlabeled data in a target domain, drawn from a different input distribution. We follow the strategy to compute several models using different hyper-parameters, and, to subsequently compute a linear aggregation of the models. While several heuristics exist that follow this strategy, methods are still missing that rely on thorough theories for bounding the target error. In this turn, we propose a method that extends weighted least squares to vector-valued functions, e.g., deep neural networks. We show that the target error of the proposed algorithm is asymptotically not worse than twice the error of the unknown optimal aggregation. We also perform a large scale empirical comparative study on several datasets, including text, images, electroencephalogram, body sensor signals and signals from mobile phones. Our method outperforms deep embedded validation (DEV) and importance weighted validation (IWV) on all datasets, setting a new state-of-the-art performance for solving parameter choice issues in unsupervised domain adaptation with theoretical error guarantees. We further study several competitive heuristics, all outperforming IWV and DEV on at least five datasets. However, our method outperforms each heuristic on at least five of seven datasets.Comment: Oral talk (notable-top-5%) at International Conference On Learning Representations (ICLR), 202

Expanded Human Blood-Derived γδT Cells Display Potent Antigen-Presentation Functions

Cell-based immunotherapy strategies target tumors directly (via cytolytic effector cells) or aim at mobilizing endogenous anti-tumor immunity. The latter approach includes dendritic cells (DC) most frequently in the form of in vitro cultured peripheral blood monocytes-derived DC. Human blood γδT cells are selective for a single class of non-peptide agonists (“phosphoantigens”) and develop into potent antigen-presenting cells (APC), termed γδT-APC within 1–3 days of in vitro culture. Availability of large numbers of γδT-APC would be advantageous for use as a novel cellular vaccine. We here report optimal γδT cell expansion (>10(7) cells/ml blood) when peripheral blood mononuclear cells (PBMC) from healthy individuals and melanoma patients were stimulated with zoledronate and then cultured for 14 days in the presence of IL-2 and IL-15, yielding γδT cell cultures of variable purity (77 ± 21 and 56 ± 26%, respectively). They resembled effector memory αβT (T(EM)) cells and retained full functionality as assessed by in vitro tumor cell killing as well as secretion of pro-inflammatory cytokines (IFNγ, TNFα) and cell proliferation in response to stimulation with phosphoantigens. Importantly, day 14 γδT cells expressed numerous APC-related cell surface markers and, in agreement, displayed potent in vitro APC functions. Day 14 γδT cells from PBMC of patients with cancer were equally effective as their counterparts derived from blood of healthy individuals and triggered potent CD8(+) αβT cell responses following processing and cross-presentation of simple (influenza M1) and complex (tuberculin purified protein derivative) protein antigens. Of note, and in clear contrast to peripheral blood γδT cells, the ability of day 14 γδT cells to trigger antigen-specific αβT cell responses did not depend on re-stimulation. We conclude that day 14 γδT cell cultures provide a convenient source of autologous APC for use in immunotherapy of patients with various cancers