In utero delivery of targeted ionizable lipid nanoparticles facilitates in vivo gene editing of hematopoietic stem cells.

Monogenic blood diseases are among the most common genetic disorders worldwide. These diseases result in significant pediatric and adult morbidity, and some can result in death prior to birth. Novel ex vivo hematopoietic stem cell (HSC) gene editing therapies hold tremendous promise to alter the therapeutic landscape but are not without potential limitations. In vivo gene editing therapies offer a potentially safer and more accessible treatment for these diseases but are hindered by a lack of delivery vectors targeting HSCs, which reside in the difficult-to-access bone marrow niche. Here, we propose that this biological barrier can be overcome by taking advantage of HSC residence in the easily accessible liver during fetal development. To facilitate the delivery of gene editing cargo to fetal HSCs, we developed an ionizable lipid nanoparticle (LNP) platform targeting the CD45 receptor on the surface of HSCs. After validating that targeted LNPs improved messenger ribonucleic acid (mRNA) delivery to hematopoietic lineage cells via a CD45-specific mechanism in vitro, we demonstrated that this platform mediated safe, potent, and long-term gene modulation of HSCs in vivo in multiple mouse models. We further optimized this LNP platform in vitro to encapsulate and deliver CRISPR-based nucleic acid cargos. Finally, we showed that optimized and targeted LNPs enhanced gene editing at a proof-of-concept locus in fetal HSCs after a single in utero intravenous injection. By targeting HSCs in vivo during fetal development, our Systematically optimized Targeted Editing Machinery (STEM) LNPs may provide a translatable strategy to treat monogenic blood diseases before birth.

An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia.

Background: Datasets on rare diseases, like pediatric acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), have small sample sizes that hinder machine learning (ML). The objective was to develop an interpretable ML framework to elucidate actionable insights from small tabular rare disease datasets. Methods: The comprehensive framework employed optimized data imputation and sampling, supervised and unsupervised learning, and literature-based discovery (LBD). The framework was deployed to assess treatment-related infection in pediatric AML and ALL. Results: An interpretable decision tree classified the risk of infection as either "high risk" or "low risk" in pediatric ALL (n = 580) and AML (n = 132) with accuracy of ∼79%. Interpretable regression models predicted the discrete number of developed infections with a mean absolute error (MAE) of 2.26 for bacterial infections and an MAE of 1.29 for viral infections. Features that best explained the development of infection were the chemotherapy regimen, cancer cells in the central nervous system at initial diagnosis, chemotherapy course, leukemia type, Down syndrome, race, and National Cancer Institute risk classification. Finally, SemNet 2.0, an open-source LBD software that links relationships from 33+ million PubMed articles, identified additional features for the prediction of infection, like glucose, iron, neutropenia-reducing growth factors, and systemic lupus erythematosus (SLE). Conclusions: The developed ML framework enabled state-of-the-art, interpretable predictions using rare disease tabular datasets. ML model performance baselines were successfully produced to predict infection in pediatric AML and ALL.

Fetal allotransplant recipients are resistant to graft-versus-host disease.

In utero hematopoietic cell transplantation (IUHCT) is an experimental treatment for congenital hemoglobinopathies, including Sickle cell disease and thalassemias. One of the principal advantages of IUHCT is the predisposition of the developing fetus toward immunologic tolerance. This allows for engraftment across immune barriers without immunosuppression and, potentially, decreased susceptibility to graft-versus-host disease (GVHD). We demonstrate fetal resistance to GVHD following T cell-replete allogeneic hematopoietic cell transplantation compared with the neonate. We show that this resistance is associated with elevated fetal serum interleukin-10 conducive to the induction of regulatory T cells (Tregs). Finally, we demonstrate that the adoptive transfer of Tregs from IUHCT recipients to neonates uniformly prevents GVHD, recapitulating the predisposition to tolerance observed after fetal allotransplantation. These findings demonstrate fetal resistance to GVHD following hematopoietic cell transplantation and elucidate Tregs as important contributors.

In utero adenine base editing corrects multi-organ pathology in a lethal lysosomal storage disease.

In utero base editing has the potential to correct disease-causing mutations before the onset of pathology. Mucopolysaccharidosis type I (MPS-IH, Hurler syndrome) is a lysosomal storage disease (LSD) affecting multiple organs, often leading to early postnatal cardiopulmonary demise. We assessed in utero adeno-associated virus serotype 9 (AAV9) delivery of an adenine base editor (ABE) targeting the Idua G→A (W392X) mutation in the MPS-IH mouse, corresponding to the common IDUA G→A (W402X) mutation in MPS-IH patients. Here we show efficient long-term W392X correction in hepatocytes and cardiomyocytes and low-level editing in the brain. In utero editing was associated with improved survival and amelioration of metabolic, musculoskeletal, and cardiac disease. This proof-of-concept study demonstrates the possibility of efficiently performing therapeutic base editing in multiple organs before birth via a clinically relevant delivery mechanism, highlighting the potential of this approach for MPS-IH and other genetic diseases.

Meta-Analysis of Gastrointestinal Adverse Events from Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia.

Tyrosine kinase inhibitors (TKIs) are the frontline therapy for BCR-ABL (Ph+) chronic myeloid leukemia (CML). A systematic meta-analysis of 43 peer-reviewed studies with 10,769 CML patients compared the incidence of gastrointestinal adverse events (GI AEs) in a large heterogeneous CML population as a function of TKI type. Incidence and severity of nausea, vomiting, and diarrhea were assessed for imatinib, dasatinib, bosutinib, and nilotinib. Examination of combined TKI average GI AE incidence found diarrhea most prevalent (22.5%), followed by nausea (20.6%), and vomiting (12.9%). Other TKI GI AEs included constipation (9.2%), abdominal pain (7.6%), gastrointestinal hemorrhage (3.5%), and pancreatitis (2.2%). Mean GI AE incidence was significantly different between TKIs (p < 0.001): bosutinib (52.9%), imatinib (24.2%), dasatinib (20.4%), and nilotinib (9.1%). Diarrhea was the most prevalent GI AE with bosutinib (79.2%) and dasatinib (28.1%), whereas nausea was most prevalent with imatinib (33.0%) and nilotinib (13.2%). Incidence of grade 3 or 4 severe GI AEs was ≤3% except severe diarrhea with bosutinib (9.5%). Unsupervised clustering revealed treatment efficacy measured by the complete cytogenetic response, major molecular response, and overall survival is driven most by disease severity, not TKI type. For patients with chronic phase CML without resistance, optimal TKI selection should consider TKI AE profile, comorbidities, and lifestyle.

Publisher Correction: Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection.

Deregulation of transcription factors (TFs) is an important driver of tumorigenesis, but non-invasive assays for assessing transcription factor activity are lacking. Here we develop and validate a minimally invasive method for assessing TF activity based on cell-free DNA sequencing and nucleosome footprint analysis. We analyze whole genome sequencing data for >1,000 cell-free DNA samples from cancer patients and healthy controls using a bioinformatics pipeline developed by us that infers accessibility of TF binding sites from cell-free DNA fragmentation patterns. We observe patient-specific as well as tumor-specific patterns, including accurate prediction of tumor subtypes in prostate cancer, with important clinical implications for the management of patients. Furthermore, we show that cell-free DNA TF profiling is capable of detection of early-stage colorectal carcinomas. Our approach for mapping tumor-specific transcription factor binding in vivo based on blood samples makes a key part of the noncoding genome amenable to clinical analysis.

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA.

BACKGROUND: Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer. METHODS: Whole-genome sequencing was performed on cfDNA extracted from plasma samples (N = 546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validations to assess generalization performance. RESULTS: In a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance. CONCLUSIONS: A machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

Reliability of the supraorbital ethmoid cell vs Keros classification in predicting the course of the anterior ethmoid artery.

BACKGROUND: We previously showed that the supraorbital ethmoid cell (SOEC) is a reliable landmark for identifying the anterior ethmoid artery (AEA). Recent data have suggested that Keros classification is also a dependable predictor. We aim to characterize the location of the AEA and its relation to the skull base in patients with and without SOEC using the Keros classification. METHODS: Retrospective radiographic evaluation of computed tomography (CT) scans of 76 patients (40 with SOEC, 36 without) was conducted. Distance of AEA from skull base and prevalence of AEA outside of the skull base were measured on each side and compared between groups using the 2-sample t test and χ2 test, respectively. Subgroup analysis was carried out based on the Keros classification. RESULTS: Mean distance of AEA from the skull base was 1.32 ± 1.5 mm in patients with SOEC and 0.47 ± 1.08 mm in those without (p < 0.001). Prevalence of AEA outside of the skull base was 53.8% in those with SOEC and 18.1% in those without (p < 0.001). Comparing patients with SOEC to those without, AEA was found below the skull base in 30% vs 0% of cases with Keros type 1 (p = 0.45), 58% vs 14.5% with Keros type 2 (p < 0.001), and 60% vs 50% with Keros type 3 (p = 0.72). CONCLUSION: The presence of SOEC is associated with a higher prevalence of the AEA coursing below the level of the skull base in all Keros types, thus placing the artery at greater risk for injury. Careful surgical planning is needed to avoid potential orbital complications.

Notch transactivates Rheb to maintain the multipotency of TSC-null cells.

Differentiation abnormalities are a hallmark of tuberous sclerosis complex (TSC) manifestations; however, the genesis of these abnormalities remains unclear. Here we report on mechanisms controlling the multi-lineage, early neuronal progenitor and neural stem-like cell characteristics of lymphangioleiomyomatosis (LAM) and angiomyolipoma cells. These mechanisms include the activation of a previously unreported Rheb-Notch-Rheb regulatory loop, in which the cyclic binding of Notch1 to the Notch-responsive elements (NREs) on the Rheb promoter is a key event. This binding induces the transactivation of Rheb. The identified NRE2 and NRE3 on the Rheb promoter are important to Notch-dependent promoter activity. Notch cooperates with Rheb to block cell differentiation via similar mechanisms in mouse models of TSC. Cell-specific loss of Tsc1 within nestin-expressing cells in adult mice leads to the formation of kidney cysts, renal intraepithelial neoplasia, and invasive papillary renal carcinoma.

Opossum peptide that can neutralize rattlesnake venom is expressed in Escherichia coli.

An eleven amino acid ribosomal peptide was shown to completely neutralize Western Diamondback Rattlesnake (Crotalus atrox) venom in mice when a lethal dose of the venom was pre-incubated with the peptide prior to intravenous injection. We have expressed the peptide as a concatenated chain of peptides and cleaved them apart from an immobilized metal affinity column using a protease. After ultrafiltration steps, the mixture was shown to partially neutralize rattlesnake venom in mice. Preliminary experiments are described here that suggest a potential life-saving therapy could be developed. To date, no recombinant therapies targeting cytotoxic envenomation have been reported. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:81-86, 2017.