Talimogene laherparepvec (T-VEC) is a genetically modified herpes simplex 1 virus (HSV-1), approved for cancer therapy. We investigated its effect on the clinical, histological, single-cell transcriptomic and immune repertoire level using repeated fine-needle aspirates (FNAs) of injected and noninjected lesions in primary cutaneous B-cell lymphoma (pCBCL). Thirteen patients received intralesional T-VEC, eleven of which demonstrated tumor response in the injected lesions. Upon single cell sequencing of the FNAs, we identified the malignant population and separated three pCBCL subtypes. HSV-1T-VEC transcripts were detected 24h after injection in malignant and non-malignant cells of the injected lesion but were absent in the noninjected lesion. Oncolytic virotherapy resulted in a rapid eradication of malignant cells by more than one death mechanism, IFN pathway activation, early influx of NK cells, monocytes, dendritic cells, followed by an enrichment in clonal cytotoxic T-cells and decrease of regulatory T-cells in both injected and noninjected lesions.

Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable conversion of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have vast potential as genome editing tools for basic research and gene therapy, their application has been hampered by a broad variation in editing efficiencies on different genomic loci. Here we perform an extensive analysis of adenine- and cytosine base editors on thousands of lentivirally integrated genetic sequences and establish BE-DICT, an attention-based deep learning algorithm capable of predicting base editing outcomes with high accuracy. BE-DICT is a versatile tool that in principle can be trained on any novel base editor variant, facilitating the application of base editing for research and therapy.

To help patients find high quality health information online, we developed a Deep Learning system that evaluates the quality of online health articles. The system implements the DISCERN criteria, which checks for references, balanced writing, and more.

Healthcare professionals have long envisioned using the enormous processing powers of computers to discover new facts and medical knowledge locked inside electronic health records. These vast medical archives contain time-resolved information about medical visits, tests and procedures, as well as outcomes, which together form individual patient journeys. By assessing the similarities among these journeys, it is possible to uncover clusters of common disease trajectories with shared health outcomes. The assignment of patient journeys to specific clusters may in turn serve as the basis for personalized outcome prediction and treatment selection. This procedure is a non-trivial computational problem, as it requires the comparison of patient data with multi-dimensional and multi-modal features that are captured at different times and resolutions. In this review, we provide a comprehensive overview of the tools and methods that are used in patient similarity analysis with longitudinal data and discuss its potential for improving clinical decision making.