Performance and Clinical Impact of Radiomics and 3D-CNN Models for the Diagnosis of Neurodegenerative Parkinsonian Syndromes on 18F-FDOPA PET.

PURPOSE: The aim of this study was to compare the performance and added clinical value of a semiautomated radiomics model and an automated 3-dimensinal convolutional neural network (3D-CNN) model for diagnosing neurodegenerative parkinsonian syndromes on 18F-FDOPA PET images. PATIENTS AND METHODS: This 2-center retrospective study included 687 patients with motor symptoms consistent with parkinsonian syndrome. All patients underwent 18F-FDOPA brain PET scans, acquired on 3 PET systems from 2 different hospitals, and classified as pathological or nonpathological (by an expert nuclear physician). Artificial intelligence models were trained to replicate this medical expert's classification using 2 pipelines. The radiomics pipeline was semiautomated and involved manually segmenting the bilateral caudate and putamen nuclei; 43 radiomic features were extracted and combined using the support vector machine method. The deep learning pipeline was fully automatic and used a 3D-CNN model. Both models were trained on 417 patients and tested on an internal (n = 100) and an external (n = 170) test set. The final models' performance was evaluated using balanced accuracy and compared with that of a junior medical expert and nonexpert nuclear physician. RESULTS: On the internal test set, the 3D-CNN model outperformed the radiomic model with a balanced accuracy of 99% (vs 96%). It led to diagnostic performance similar to that of a junior medical expert (only 1 in 100 patients misclassified by both). On the external test set from a less experienced hospital, the 3D-CNN model allowed physicians to correctly reclassify the diagnosis of 10 out 170 patients (6%). CONCLUSIONS: The developed 3D-CNN model can automatically diagnose neurodegenerative parkinsonian syndromes, also reducing diagnostic errors by 6% in less-experienced hospitals.

Plasmatic Inactive IL-18 Predicts a Worse Overall Survival for Advanced Non-Small-Cell Lung Cancer with Early Metabolic Progression after Immunotherapy Initiation.

The aim of this study was to assess the potential value of circulating active and inactive IL-18 levels in distinguishing pseudo and true tumor progression among NSCLC patients receiving immune checkpoint inhibitor treatments (ICIs). METHODS: This ancillary study includes 195 patients with metastatic non-small-cell lung cancer (NSCLC) treated with ICI in monotherapy, either pembrolizumab or nivolumab. Plasmatic levels of IL-18-related compounds, comprising the inhibitor IL-18 binding protein (IL-18BP), the inactive IL-18 (corresponding to IL-18/IL-18BP complex), and the active free IL-18, were assayed by ELISA. Objective tumoral response was analyzed by 18FDG PET-CT at baseline, 7 weeks, and 3 months post treatment induction, using PERCIST criteria. RESULTS: Plasmatic IL-18BP and total IL-18 levels are increased at baseline in NSCLC patients compared with healthy controls, whereas IL-18/IL-18BP complexes are decreased, and free IL-18 levels remain unchanged. Neither of the IL-18-related compounds allowed to discriminate ICI responding to nonresponding patients. However, inactive IL-18 levels allowed to discriminate patients with a first tumor progression, assessed after 7 weeks of treatment, with worse overall survival. In addition, we showed that neutrophil concentration is also a predictive indicator of patients' outcomes with OS (HR = 2.6, p = 0.0001) and PFS (HR = 2.2, p = 0.001). CONCLUSIONS: Plasmatic levels of inactive IL-18, combined with circulating neutrophil concentrations, can effectively distinguish ICI nonresponding patients with better overall survival (OS), potentially guiding rapid decisions for therapeutic intensification.

[18F]FDG-PET/CT atypical response patterns to immunotherapy in non-small cell lung cancer patients: long term prognosis assessment and clinical management proposal.

AIM: To determine the long-term prognosis of immune-related response profiles (pseudoprogression and dissociated response), not covered by conventional PERCIST criteria, in patients with non-small-cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICPIs). METHODS: 109 patients were prospectively included and underwent [18F]FDG-PET/CT at baseline, after 7 weeks (PETinterim1), and 3 months (PETinterim2) of treatment. On PETinterim1, tumor response was assessed using standard PERCIST criteria. In the event of PERCIST progression at this time-point, the study design provided for continued immunotherapy for 6 more weeks. Additional response patterns were then considered on PETinterim2: pseudo-progression (PsPD, subsequent metabolic response); dissociated response (DR, coexistence of responding and non-responding lesions), and confirmed progressive metabolic disease (cPMD, subsequent homogeneous progression of lesions). Patients were followed up for at least 12 months. RESULTS: Median follow-up was 21 months. At PETinterim1, PERCIST progression was observed in 60% (66/109) of patients and ICPI was continued in 59/66. At the subsequent PETinterim2, 14% of patients showed PsPD, 11% DR, 35% cPMD, and 28% had a sustained metabolic response. Median overall survival (OS) and progression-free-survival (PFS) did not differ between PsPD and DR (27 vs 29 months, p = 1.0; 17 vs 12 months, p = 0.2, respectively). The OS and PFS of PsPD/DR patients were significantly better than those with cPMD (29 vs 9 months, p < 0.02; 16 vs 2 months, p < 0.001), but worse than those with sustained metabolic response (p < 0.001). This 3-group prognostic stratification enabled better identification of true progressors, outperforming the prognostic value of standard PERCIST criteria (p = 0.03). CONCLUSION: [18F]FDG-PET/CT enables early assessment of response to immunotherapy. The new wsPERCIST ("wait and see") PET criteria proposed, comprising immune-related atypical response patterns, can refine conventional prognostic stratification based on PERCIST criteria. TRIAL REGISTRATION: HDH F20230309081206. Registered 20 April 2023. Retrospectively registered.

Total metabolic tumor volume on 18F-FDG PET/CT is a game-changer for patients with metastatic lung cancer treated with immunotherapy.

PURPOSE: Because of atypical response imaging patterns in patients with metastatic non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICPIs), new biomarkers are needed for a better monitoring of treatment efficacy. The aim of this prospective study was to evaluate the prognostic value of volume-derived positron-emission tomography (PET) parameters on baseline and follow-up 18F-fluoro-deoxy-glucose PET (18F-FDG-PET) scans and compare it with the conventional PET Response Criteria in Solid Tumors (PERCIST). METHODS: Patients with metastatic NSCLC were included in two different single-center prospective trials. 18F-FDG-PET studies were performed before the start of immunotherapy (PETbaseline), after 6-8 weeks (PETinterim1) and after 12-16 weeks (PETinterim2) of treatment, using PERCIST criteria for tumor response assessment. Different metabolic parameters were evaluated: absolute values of maximum standardized uptake value (SUVmax) of the most intense lesion, total metabolic tumor volume (TMTV), total lesion glycolysis (TLG), but also their percentage changes between PET studies (ΔSUVmax, ΔTMTV and ΔTLG). The median follow-up of patients was 31 (7.3-31.8) months. Prognostic values and optimal thresholds of PET parameters were estimated by ROC (Receiver Operating Characteristic) curve analysis of 12-month overall survival (12M-OS) and 6-month progression-free survival (6M-PFS). Tumor progression needed to be confirmed by a multidisciplinary tumor board, considering atypical response patterns on imaging. RESULTS: 110 patients were prospectively included. On PETbaseline, TMTV was predictive of 12M-OS [AUC (Area Under Curve) =0.64; 95% CI: 0.61 to 0.66] whereas SUVmax and TLG were not. On PETinterim1 and PETinterim2, all metabolic parameters were predictive for 12M-OS and 6M-PFS, the residual TMTV on PETinterim1 (TMTV1) being the strongest prognostic biomarker (AUC=0.83 and 0.82; 95% CI: 0.74 to 0.91, for 12M-OS and 6M-PFS, respectively). Using the optimal threshold by ROC curve to classify patients into three TMTV1 subgroups (0 cm3; 0-57 cm3; >57 cm3), TMTV1 prognostic stratification was independent of PERCIST criteria on both PFS and OS, and significantly outperformed them. Subgroup analysis demonstrated that TMTV1 remained a strong prognostic biomarker of 12M-OS for non-responding patients (p=0.0003) according to PERCIST criteria. In the specific group of patients with PERCIST progression on PETinterim1, low residual tumor volume (<57 cm3) was still associated with a very favorable patients' outcome (6M-PFS=73%; 24M-OS=55%). CONCLUSION: The absolute value of residual metabolic tumor volume, assessed 6-8 weeks after the start of ICPI, is an optimal and independent prognostic measure, exceeding and complementing conventional PERCIST criteria. Oncologists should consider it in patients with first tumor progression according to PERCIST criteria, as it helps identify patients who benefit from continued treatment. TRIAL REGISTRATION NUMBER: 2018-A02116-49; NCT03584334.

Privacy preserving image registration.

Image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame. Current approaches to image registration are generally based on the assumption that the content of the images is usually accessible in clear form, from which the spatial transformation is subsequently estimated. This common assumption may not be met in practical applications, since the sensitive nature of medical images may ultimately require their analysis under privacy constraints, preventing to openly share the image content. In this work, we formulate the problem of image registration under a privacy preserving regime, where images are assumed to be confidential and cannot be disclosed in clear. We derive our privacy preserving image registration framework by extending classical registration paradigms to account for advanced cryptographic tools, such as secure multi-party computation and homomorphic encryption, that enable the execution of operations without leaking the underlying data. To overcome the problem of performance and scalability of cryptographic tools in high dimensions, we propose several techniques to optimize the image registration operations by using gradient approximations, and by revisiting the use of homomorphic encryption trough packing, to allow the efficient encryption and multiplication of large matrices. We focus on registration methods of increasing complexity, including rigid, affine, and non-linear registration based on cubic splines or diffeomorphisms parameterized by time-varying velocity fields. In all these settings, we demonstrate how the registration problem can be naturally adapted for accounting to privacy-preserving operations, and illustrate the effectiveness of PPIR on a variety of registration tasks.

Survival analysis of patient groups defined by unsupervised machine learning clustering methods based on patient metabolomic data.

Purpose: Meta-analyses failed to accurately identify patients with non-metastatic breast cancer who are likely to benefit from chemotherapy, and metabolomics could provide new answers. In our previous published work, patients were clustered using five different unsupervised machine learning (ML) methods resulting in the identification of three clusters with distinct clinical and simulated survival data. The objective of this study was to evaluate the survival outcomes, with extended follow-up, using the same 5 different methods of unsupervised machine learning. Experimental design: Forty-nine patients, diagnosed between 2013 and 2016, with non-metastatic BC were included retrospectively. Median follow-up was extended to 85.8 months. 449 metabolites were extracted from tumor resection samples by combined Liquid chromatography-mass spectrometry (LC-MS). Survival analyses were reported grouping together Cluster 1 and 2 versus cluster 3. Bootstrap optimization was applied. Results: PCA k-means, K-sparse and Spectral clustering were the most effective methods to predict 2-year progression-free survival with bootstrap optimization (PFSb); as bootstrap example, with PCA k-means method, PFSb were 94% for cluster 1&2 versus 82% for cluster 3 (p = 0.01). PCA k-means method performed best, with higher reproducibility (mean HR=2 (95%CI [1.4-2.7]); probability of p ≤ 0.05 85%). Cancer-specific survival (CSS) and overall survival (OS) analyses highlighted a discrepancy between the 5 ML unsupervised methods. Conclusion: Our study is a proof-of-principle that it is possible to use unsupervised ML methods on metabolomic data to predict PFS survival outcomes, with the best performance for PCA k-means. A larger population study is needed to draw conclusions from CSS and OS analyses.

Efficient long-term multilineage engraftment of CD33-edited hematopoietic stem/progenitor cells in nonhuman primates.

Current immunotherapeutic targets are often shared between neoplastic and normal hematopoietic stem and progenitor cells (HSPCs), leading to unwanted on-target, off-tumor toxicities. Deletion or modification of such targets to protect normal HSPCs is, therefore, of great interest. Although HSPC modifications commonly aim to mimic naturally occurring phenotypes, the long-term persistence and safety of gene-edited cells need to be evaluated. Here, we deleted the V-set domain of CD33, the immune-dominant domain targeted by most anti-CD33 antibodies used to treat CD33-positive malignancies, including acute myeloid leukemia, in the HSPCs of two rhesus macaques, performed autologous transplantation after myeloablative conditioning, and followed the animals for up to 3 years. CD33-edited HSPCs engrafted without any delay in recovery of neutrophils, the primary cell type expressing CD33. No impact on the blood composition, reconstitution of the bone marrow stem cell compartment, or myeloid differentiation potential was observed. Up to 20% long-term gene editing in HSPCs and blood cell lineages was seen with robust loss of CD33 detection on myeloid lineages. In conclusion, deletion of the V-set domain of CD33 on HSPCs, progenitors, and myeloid lineages did not show any adverse effects on their homing and engraftment potential or the differentiation and functionality of myeloid progenitors and lineages.

Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma.

Radiomics is a discipline that involves studying medical images through their digital data. Using "artificial intelligence" algorithms, radiomics utilizes quantitative and high-throughput analysis of an image's textural richness to obtain relevant information for clinicians, from diagnosis assistance to therapeutic guidance. Exploitation of these data could allow for a more detailed characterization of each phenotype, for each patient, making radiomics a new biomarker of interest, highly promising in the era of precision medicine. Moreover, radiomics is non-invasive, cost-effective, and easily reproducible in time. In the field of oncology, it performs an analysis of the entire tumor, which is impossible with a single biopsy but is essential for understanding the tumor's heterogeneity and is known to be closely related to prognosis. However, current results are sometimes less accurate than expected and often require the addition of non-radiomics data to create a performing model. To highlight the strengths and weaknesses of this new technology, we take the example of hepatocellular carcinoma and show how radiomics could facilitate its diagnosis in difficult cases, predict certain histological features, and estimate treatment response, whether medical or surgical.

Metabolomic Signatures of Scarff-Bloom-Richardson (SBR) Grade in Non-Metastatic Breast Cancer.

PURPOSE: Identification of metabolomic biomarkers of high SBR grade in non-metastatic breast cancer. METHODS: This retrospective bicentric metabolomic analysis included a training set (n = 51) and a validation set (n = 49) of breast cancer tumors, all classified as high-grade (grade III) or low-grade (grade I-II). Metabolomes of tissue samples were studied by liquid chromatography coupled with mass spectrometry. RESULTS: A molecular signature of the top 12 metabolites was identified from a database of 602 frequently predicted metabolites. Partial least squares discriminant analyses showed that accuracies were 0.81 and 0.82, the R2 scores were 0.57 and 0.55, and the Q2 scores were 0.44431 and 0.40147 for the training set and validation set, respectively; areas under the curve for the Receiver Operating Characteristic Curve were 0.882 and 0.886. The most relevant metabolite was diacetylspermine. Metabolite set enrichment analyses and metabolic pathway analyses highlighted the tryptophan metabolism pathway, but the concentration of individual metabolites varied between tumor samples. CONCLUSIONS: This study indicates that high-grade invasive tumors are related to diacetylspermine and tryptophan metabolism, both involved in the inhibition of the immune response. Targeting these pathways could restore anti-tumor immunity and have a synergistic effect with immunotherapy. Recent studies could not demonstrate the effectiveness of this strategy, but the use of theragnostic metabolomic signatures should allow better selection of patients.

Multiplex Base Editing to Protect from CD33-Directed Therapy: Implications for Immune and Gene Therapy.

On-target toxicity to normal cells is a major safety concern with targeted immune and gene therapies. Here, we developed a base editing (BE) approach exploiting a naturally occurring CD33 single nucleotide polymorphism leading to removal of full-length CD33 surface expression on edited cells. CD33 editing in human and nonhuman primate (NHP) hematopoietic stem and progenitor cells (HSPCs) protects from CD33-targeted therapeutics without affecting normal hematopoiesis in vivo , thus demonstrating potential for novel immunotherapies with reduced off-leukemia toxicity. For broader applications to gene therapies, we demonstrated highly efficient (>70%) multiplexed adenine base editing of the CD33 and gamma globin genes, resulting in long-term persistence of dual gene-edited cells with HbF reactivation in NHPs. In vitro , dual gene-edited cells could be enriched via treatment with the CD33 antibody-drug conjugate, gemtuzumab ozogamicin (GO). Together, our results highlight the potential of adenine base editors for improved immune and gene therapies.

Learning a confidence score and the latent space of a new supervised autoencoder for diagnosis and prognosis in clinical metabolomic studies.

BACKGROUND: Presently, there is a wide variety of classification methods and deep neural network approaches in bioinformatics. Deep neural networks have proven their effectiveness for classification tasks, and have outperformed classical methods, but they suffer from a lack of interpretability. Therefore, these innovative methods are not appropriate for decision support systems in healthcare. Indeed, to allow clinicians to make informed and well thought out decisions, the algorithm should provide the main pieces of information used to compute the predicted diagnosis and/or prognosis, as well as a confidence score for this prediction. METHODS: Herein, we used a new supervised autoencoder (SAE) approach for classification of clinical metabolomic data. This new method has the advantage of providing a confidence score for each prediction thanks to a softmax classifier and a meaningful latent space visualization and to include a new efficient feature selection method, with a structured constraint, which allows for biologically interpretable results. RESULTS: Experimental results on three metabolomics datasets of clinical samples illustrate the effectiveness of our SAE and its confidence score. The supervised autoencoder provides an accurate localization of the patients in the latent space, and an efficient confidence score. Experiments show that the SAE outperforms classical methods (PLS-DA, Random Forests, SVM, and neural networks (NN)). Furthermore, the metabolites selected by the SAE were found to be biologically relevant. CONCLUSION: In this paper, we describe a new efficient SAE method to support diagnostic or prognostic evaluation based on metabolomics analyses.

Development and validation of a radiomic model for the diagnosis of dopaminergic denervation on [18F]FDOPA PET/CT.

PURPOSE: FDOPA PET shows good performance for the diagnosis of striatal dopaminergic denervation, making it a valuable tool for the differential diagnosis of Parkinsonism. Textural features are image biomarkers that could potentially improve the early diagnosis and monitoring of neurodegenerative parkinsonian syndromes. We explored the performances of textural features for binary classification of FDOPA scans. METHODS: We used two FDOPA PET datasets: 443 scans for feature selection, and 100 scans from a different PET/CT system for model testing. Scans were labelled according to expert interpretation (dopaminergic denervation versus no dopaminergic denervation). We built LASSO logistic regression models using 43 biomarkers including 32 textural features. Clinical data were also collected using a shortened UPDRS scale. RESULTS: The model built from the clinical data alone had a mean area under the receiver operating characteristics (AUROC) of 63.91. Conventional imaging features reached a maximum score of 93.47 but the addition of textural features significantly improved the AUROC to 95.73 (p < 0.001), and 96.10 (p < 0.001) when limiting the model to the top three features: GLCM_Correlation, Skewness and Compacity. Testing the model on the external dataset yielded an AUROC of 96.00, with 95% sensitivity and 97% specificity. GLCM_Correlation was one of the most independent features on correlation analysis, and systematically had the heaviest weight in the classification model. CONCLUSION: A simple model with three radiomic features can identify pathologic FDOPA PET scans with excellent sensitivity and specificity. Textural features show promise for the diagnosis of parkinsonian syndromes.

Prognostic value of immunotherapy-induced organ inflammation assessed on 18FDG PET in patients with metastatic non-small cell lung cancer.

PURPOSE: We evaluated the prognostic value of immunotherapy-induced organ inflammation observed on 18FDG PET in patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICPIs). METHODS: Data from patients with IIIB/IV NSCLC included in two different prospective trials were analyzed. 18FDG PET/CT exams were performed at baseline (PETBaseline) and repeated after 7-8 weeks (PETInterim1) and 12-16 weeks (PETInterim2) of treatment, using iPERCIST for tumor response evaluation. The occurrence of abnormal organ 18FDG uptake, deemed to be due to ICPI-related organ inflammation, was collected. RESULTS: Exploratory cohort (Nice, France): PETInterim1 and PETInterim2 revealed the occurrence of at least one ICPI-induced organ inflammation in 72.8% of patients, including midgut/hindgut inflammation (33.7%), gastritis (21.7%), thyroiditis (18.5%), pneumonitis (17.4%), and other organ inflammations (9.8%). iPERCIST tumor response was associated with improved progression-free survival (p < 0.001). iPERCIST tumor response and immuno-induced gastritis assessed on PET were both associated with improved overall survival (OS) (p < 0.001 and p = 0.032). Combining these two independent variables, we built a model predicting patients' 2-year OS with a sensitivity of 80.3% and a specificity of 69.2% (AUC = 72.7). Validation cohort (Genova, Italy): Immuno-induced gastritis (19.6% of patients) was associated with improved OS (p = 0.04). The model built previously predicted 2-year OS with a sensitivity and specificity of 72.0% and 63.6% (AUC = 70.7) and 3-year OS with a sensitivity and specificity of 69.2% and 80.0% (AUC = 78.2). CONCLUSION: Immuno-induced gastritis revealed by early interim 18FDG PET in around 20% of patients with NSCLC treated with ICPI is a novel and reproducible imaging biomarker of improved OS.

Efficient polymer nanoparticle-mediated delivery of gene editing reagents into human hematopoietic stem and progenitor cells.

Clinical applications of hematopoietic stem cell (HSC) gene editing are limited due to their complex and expensive logistics. HSC editing is commonly performed ex vivo using electroporation and requires good manufacturing practice (GMP) facilities, similar to bone marrow transplant centers. In vivo gene editing could overcome this limitation; however, electroporation is unsuitable for systemic in vivo applications to HSCs. Here we evaluated polymer-based nanoparticles (NPs), which could also be used for in vivo administration, for the delivery of mRNA and nucleases to human granulocyte colony-stimulating factor (GCSF)-mobilized CD34+ cells. NP-mediated ex vivo delivery showed no toxicity, and the efficiency was directly correlated with the charge of the NPs. In a side-by-side comparison with electroporation, NP-mediated gene editing allowed for a 3-fold reduction in the amount of reagents, with similar efficiency. Furthermore, we observed enhanced engraftment potential of human HSCs in the NSG mouse xenograft model using NPs. Finally, mRNA- and nuclease-loaded NPs were successfully lyophilized for storage, maintaining their transfection potential after rehydration. In conclusion, we show that polymer-based NP delivery of mRNA and nucleases has the potential to overcome current limitations of HSC gene editing. The predictable transfection efficiency, low toxicity, and ability to lyophilize NPs will greatly enhance the portability and provide a highly promising platform for HSC gene therapy.

Synthetic introns enable splicing factor mutation-dependent targeting of cancer cells.

Many cancers carry recurrent, change-of-function mutations affecting RNA splicing factors. Here, we describe a method to harness this abnormal splicing activity to drive splicing factor mutation-dependent gene expression to selectively eliminate tumor cells. We engineered synthetic introns that were efficiently spliced in cancer cells bearing SF3B1 mutations, but unspliced in otherwise isogenic wild-type cells, to yield mutation-dependent protein production. A massively parallel screen of 8,878 introns delineated ideal intronic size and mapped elements underlying mutation-dependent splicing. Synthetic introns enabled mutation-dependent expression of herpes simplex virus-thymidine kinase (HSV-TK) and subsequent ganciclovir (GCV)-mediated killing of SF3B1-mutant leukemia, breast cancer, uveal melanoma and pancreatic cancer cells in vitro, while leaving wild-type cells unaffected. Delivery of synthetic intron-containing HSV-TK constructs to leukemia, breast cancer and uveal melanoma cells and GCV treatment in vivo significantly suppressed the growth of these otherwise lethal xenografts and improved mouse host survival. Synthetic introns provide a means to exploit tumor-specific changes in RNA splicing for cancer gene therapy.

Intracellular RNase activity dampens zinc finger nuclease-mediated gene editing in hematopoietic stem and progenitor cells.

Over the past decade, numerous gene-editing platforms which alter host DNA in a highly specific and targeted fashion have been described. Two notable examples are zinc finger nucleases (ZFNs), the first gene-editing platform to be tested in clinical trials, and more recently, CRISPR/Cas9. Although CRISPR/Cas9 approaches have become arguably the most popular platform in the field, the therapeutic advantages and disadvantages of each strategy are only beginning to emerge. We have established a nonhuman primate (NHP) model that serves as a strong predictor of successful gene therapy and gene-editing approaches in humans; our recent work shows that ZFN-edited hematopoietic stem and progenitor cells (HSPCs) engraft at lower levels than CRISPR/Cas9-edited cells. Here, we investigate the mechanisms underlying this difference. We show that optimized culture conditions, including defined serum-free media, augment engraftment of gene-edited NHP HSPCs in a mouse xenograft model. Furthermore, we identify intracellular RNases as major barriers for mRNA-encoded nucleases relative to preformed enzymatically active CRISPR/Cas9 ribonucleoprotein (RNP) complexes. We conclude that CRISPR/Cas9 RNP gene editing is more stable and efficient than ZFN mRNA-based delivery and identify co-delivered RNase inhibitors as a strategy to enhance the expression of gene-editing proteins from mRNA intermediates.

Multiplex CRISPR/Cas9 genome editing in hematopoietic stem cells for fetal hemoglobin reinduction generates chromosomal translocations.

Sickle cell disease and ß-thalassemia are common monogenic disorders that cause significant morbidity and mortality globally. The only curative treatment currently is allogeneic hematopoietic stem cell transplantation, which is unavailable to many patients due to a lack of matched donors and carries risks including graft-versus-host disease. Genome editing therapies targeting either the BCL11A erythroid enhancer or the HBG promoter are already demonstrating success in reinducing fetal hemoglobin. However, where a single locus is targeted, reliably achieving levels high enough to deliver an effective cure remains a challenge. We investigated the application of a CRISPR/Cas9 multiplex genome editing approach, in which both the BCL11A erythroid enhancer and HBG promoter are disrupted within human hematopoietic stem cells. We demonstrate superior fetal hemoglobin reinduction with this dual-editing approach without compromising engraftment or lineage differentiation potential of edited cells post-xenotransplantation. However, multiplex editing consistently resulted in the generation of chromosomal rearrangement events that persisted in vivo following transplantation into immunodeficient mice. The risk of oncogenic events resulting from such translocations therefore currently prohibits its clinical translation, but it is anticipated that, in the future, alternative editing platforms will help alleviate this risk.

Primal-dual for classification with rejection (PD-CR): a novel method for classification and feature selection-an application in metabolomics studies.

BACKGROUND: Supervised classification methods have been used for many years for feature selection in metabolomics and other omics studies. We developed a novel primal-dual based classification method (PD-CR) that can perform classification with rejection and feature selection on high dimensional datasets. PD-CR projects data onto a low dimension space and performs classification by minimizing an appropriate quadratic cost. It simultaneously optimizes the selected features and the prediction accuracy with a new tailored, constrained primal-dual method. The primal-dual framework is general enough to encompass various robust losses and to allow for convergence analysis. Here, we compare PD-CR to three commonly used methods: partial least squares discriminant analysis (PLS-DA), random forests and support vector machines (SVM). We analyzed two metabolomics datasets: one urinary metabolomics dataset concerning lung cancer patients and healthy controls; and a metabolomics dataset obtained from frozen glial tumor samples with mutated isocitrate dehydrogenase (IDH) or wild-type IDH. RESULTS: PD-CR was more accurate than PLS-DA, Random Forests and SVM for classification using the 2 metabolomics datasets. It also selected biologically relevant metabolites. PD-CR has the advantage of providing a confidence score for each prediction, which can be used to perform classification with rejection. This substantially reduces the False Discovery Rate. CONCLUSION: PD-CR is an accurate method for classification of metabolomics datasets which can outperform PLS-DA, Random Forests and SVM while selecting biologically relevant features. Furthermore the confidence score provided with PD-CR can be used to perform classification with rejection and reduce the false discovery rate.

Genome editing in large animal models.

Although genome editing technologies have the potential to revolutionize the way we treat human diseases, barriers to successful clinical implementation remain. Increasingly, preclinical large animal models are being used to overcome these barriers. In particular, the immunogenicity and long-term safety of novel gene editing therapeutics must be evaluated rigorously. However, short-lived small animal models, such as mice and rats, cannot address secondary pathologies that may arise years after a gene editing treatment. Likewise, immunodeficient mouse models by definition lack the ability to quantify the host immune response to a novel transgene or gene-edited locus. Large animal models, including dogs, pigs, and non-human primates (NHPs), bear greater resemblance to human anatomy, immunology, and lifespan and can be studied over longer timescales with clinical dosing regimens that are more relevant to humans. These models allow for larger scale and repeated blood and tissue sampling, enabling greater depth of study and focus on rare cellular subsets. Here, we review current progress in the development and evaluation of novel genome editing therapies in large animal models, focusing on applications in human immunodeficiency virus 1 (HIV-1) infection, cancer, and genetic diseases including hemoglobinopathies, Duchenne muscular dystrophy (DMD), hypercholesterolemia, and inherited retinal diseases.