Novel genome editing and transient gene therapies have been developed the past ten years, resulting in the first in-human clinical trials for monogenic disorders. Syndromic autism spectrum disorders can be caused by mutations in a single gene. Given the monogenic aspect and severity of syndromic ASD, it is an ideal candidate for gene therapies. Here, we selected 11 monogenic ASD syndromes, validated by animal models, and reviewed current gene therapies for each syndrome. Given the wide variety and novelty of some forms of gene therapy, the best possible option must be decided based on the gene and mutation.

Summary: Accurate prediction of TCR specificity forms a holy grail in immunology and large language models and computational structure predictions provide a path to achieve this. Importantly, current TCR-pMHC prediction models have been trained and evaluated using historical data of unknown quality. Here, we develop and utilize a high-throughput synthetic platform for TCR assembly and evaluation to assess a large fraction of VDJdb-deposited TCR-pMHC entries using a standardized readout of TCR function. Strikingly, this analysis demonstrates that claimed TCR reactivity is only confirmed for 50% of evaluated entries. Intriguingly, the use of TCRbridge to analyze AlphaFold3 confidence metrics reveals a substantial performance in distinguishing functionally validating and non-validating TCRs even though AlphaFold3 was not trained on this task, demonstrating the utility of the validated VDJdb (TCRvdb) database that we generated. We provide TCRvdb as a resource to the community to support training and evaluation of improved predictive TCR specificity models.

Tumors contain a mixture of T cells with bystander reactivities and reactivities towards different, frequently patient-specific, cancer (neo)antigens. The one-step identification of highly active TCR–antigen pairs in human tumors would be valuable, both as a monitoring tool and to boost T cell reactivities of interest. Here, we develop PAIR-Scan, an HLA-agnostic library-on-library screening technology that identifies functionally active TCR–neoantigen pairs among tens of thousands of candidate pairs in a single step. We demonstrate the value of PAIR-Scan on a range of tumor samples and for the direct identification of TCR-recognized minimal peptides. In addition, we demonstrate that PAIR-Scan correctly ranks TCRs reactive to the same antigen by their relative tumor-killing efficiency. Together, these data demonstrate the value of PAIR-Scan for both the dissection of T cell responses in clinical samples and to generate large-scale datasets for the development of predictive models of TCR reactivity.