Comparable CD8+ T-cell responses to SARS-CoV-2 vaccination in single-cell transcriptomics of recently allogeneic transplanted patients and healthy individuals.

Despite extensive research on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination responses in healthy individuals, there is comparatively little known beyond antibody titers and T-cell responses in the vulnerable cohort of patients after allogeneic hematopoietic stem cell transplantation (ASCT). In this study, we assessed the serological response and performed longitudinal multimodal analyses including T-cell functionality and single-cell RNA sequencing combined with T cell receptor (TCR)/B cell receptor (BCR) profiling in the context of BNT162b2 vaccination in ASCT patients. In addition, these data were compared to publicly available data sets of healthy vaccinees. Protective antibody titers were achieved in 40% of patients. We identified a distorted B- and T-cell distribution, a reduced TCR diversity, and increased levels of exhaustion marker expression as possible causes for the poorer vaccine response rates in ASCT patients. Immunoglobulin heavy chain gene rearrangement after vaccination proved to be highly variable in ASCT patients. Changes in TCRα and TCRß gene rearrangement after vaccination differed from patterns observed in healthy vaccinees. Crucially, ASCT patients elicited comparable proportions of SARS-CoV-2 vaccine-induced (VI) CD8+ T-cells, characterized by a distinct gene expression pattern that is associated with SARS-CoV-2 specificity in healthy individuals. Our study underlines the impaired immune system and thus the lower vaccine response rates in ASCT patients. However, since protective vaccine responses and VI CD8+ T-cells can be induced in part of ASCT patients, our data advocate early posttransplant vaccination due to the high risk of infection in this vulnerable group.

Simultaneous Genetic Ablation of PD-1, LAG-3, and TIM-3 in CD8 T Cells Delays Tumor Growth and Improves Survival Outcome.

Immune checkpoint inhibitors (ICI) represented a step forward in improving the outcome of patients with various refractory solid tumors and several therapeutic regimens incorporating ICI have already been approved for a variety of tumor entities. However, besides remarkable long-term responses, checkpoint inhibition can trigger severe immune-related adverse events in some patients. In order to improve safety of ICI as well as T cell therapy, we tested the feasibility of combining T cell-based immunotherapy with genetic disruption of checkpoint molecule expression. Therefore, we generated H-Y and ovalbumin antigen-specific CD8+ T cells with abolished PD-1, LAG-3, and TIM-3 expression through CRISPR/Cas9 technology. CD8+ T cells, subjected to PD-1, LAG-3, and TIM-3 genetic editing, showed a strong reduction in immune checkpoint molecule expression after in vitro activation, while no relevant reduction in responsiveness to in vitro stimulation was observed. At the same time, in B16-OVA tumor model, transferred genetically edited OT-1 CD8+ T cells promoted longer survival compared to control T cells and showed enhanced expansion without associated toxicity. Our study supports the notion that antigen-specific adoptive T cell therapy with concomitant genetic disruption of multiple checkpoint inhibitory receptors could represent an effective antitumor immunotherapy approach with improved tolerability profile.

GestaltMatcher facilitates rare disease matching using facial phenotype descriptors.

Many monogenic disorders cause a characteristic facial morphology. Artificial intelligence can support physicians in recognizing these patterns by associating facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this 'supervised' approach means that diagnoses are only possible if the disorder was part of the training set. To improve recognition of ultra-rare disorders, we developed GestaltMatcher, an encoder for portraits that is based on a deep convolutional neural network. Photographs of 17,560 patients with 1,115 rare disorders were used to define a Clinical Face Phenotype Space, in which distances between cases define syndromic similarity. Here we show that patients can be matched to others with the same molecular diagnosis even when the disorder was not included in the training set. Together with mutation data, GestaltMatcher could not only accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism but also enable the delineation of new phenotypes.

CADA: phenotype-driven gene prioritization based on a case-enriched knowledge graph.

Many rare syndromes can be well described and delineated from other disorders by a combination of characteristic symptoms. These phenotypic features are best documented with terms of the Human Phenotype Ontology (HPO), which are increasingly used in electronic health records (EHRs), too. Many algorithms that perform HPO-based gene prioritization have also been developed; however, the performance of many such tools suffers from an over-representation of atypical cases in the medical literature. This is certainly the case if the algorithm cannot handle features that occur with reduced frequency in a disorder. With Cada, we built a knowledge graph based on both case annotations and disorder annotations. Using network representation learning, we achieve gene prioritization by link prediction. Our results suggest that Cada exhibits superior performance particularly for patients that present with the pathognomonic findings of a disease. Additionally, information about the frequency of occurrence of a feature can readily be incorporated, when available. Crucial in the design of our approach is the use of the growing amount of phenotype-genotype information that diagnostic labs deposit in databases such as ClinVar. By this means, Cada is an ideal reference tool for differential diagnostics in rare disorders that can also be updated regularly.