Outcomes of hematopoietic stem cell gene therapy for Wiskott-Aldrich syndrome

Wiskott-Aldrich syndrome (WAS) is a rare X-linked disorder characterized by combined immunodeficiency, eczema, microthrombocytopenia, autoimmunity, and lymphoid malignancies. Gene therapy (GT) to modify autologous CD34+ cells is an emerging alternative treatment with advantages over standard allogeneic hematopoietic stem cell transplantation for patients who lack well-matched donors, avoiding graft-versus-host-disease. We report the outcomes of a phase 1/2 clinical trial in which 5 patients with severe WAS underwent GT using a self-inactivating lentiviral vector expressing the human WAS complementary DNA under the control of a 1.6-kB fragment of the autologous promoter after busulfan and fludarabine conditioning. All patients were alive and well with sustained multilineage vector gene marking (median follow-up: 7.6 years). Clinical improvement of eczema, infections, and bleeding diathesis was universal. Immune function was consistently improved despite subphysiologic levels of transgenic WAS protein expression. Improvements in platelet count and cytoskeletal function in myeloid cells were most prominent in patients with high vector copy number in the transduced product. Two patients with a history of autoimmunity had flares of autoimmunity after GT, despite similar percentages of WAS protein-expressing cells and gene marking to those without autoimmunity. Patients with flares of autoimmunity demonstrated poor numerical recovery of T cells and regulatory T cells (Tregs), interleukin-10-producing regulatory B cells (Bregs), and transitional B cells. Thus, recovery of the Breg compartment, along with Tregs appears to be protective against development of autoimmunity after GT. These results indicate that clinical and laboratory manifestations of WAS are improved with GT with an acceptable safety profile. This trial is registered at clinicaltrials.gov as #NCT01410825.

PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework

Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore's ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 37 of 40 investigated syndromes against clinical features observed in individuals with other neurodevelopmental disorders and show it is an improvement on existing approaches. PhenoScore provides predictions for individuals with variants of unknown significance and enables sophisticated genotype-phenotype studies by testing hypotheses on possible phenotypic (sub)groups. PhenoScore confirmed previously known phenotypic subgroups caused by variants in the same gene for SATB1, SETBP1 and DEAF1 and provides objective clinical evidence for two distinct ADNP-related phenotypes, already established functionally.