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Neuroblastoma (NB) is the most common cancer in infancy and most frequent cause of death from extracranial solid tumors in children. Ornithine decarboxylase (ODC) expression is an independent indicator of poor prognosis in NB patients. This study investigated safety, response, pharmacokinetics, genetic and metabolic factors associated with ODC in a clinical trial of the ODC inhibitor difluoromethylornithine (DFMO) ± etoposide for patients with relapsed or refractory NB. Twenty-one patients participated in a phase I study of daily oral DFMO alone for three weeks, followed by additional three-week cycles of DFMO plus daily oral etoposide. No dose limiting toxicities (DLTs) were identified in patients taking doses of DFMO between 500-1500 mg/m2 orally twice a day. DFMO pharmacokinetics, single nucleotide polymorphisms (SNPs) in the ODC gene and urinary levels of substrates for the tissue polyamine exporter were measured. Urinary polyamine levels varied among patients at baseline. Patients with the minor T-allele at rs2302616 of the ODC gene had higher baseline levels (p=0.02) of, and larger decreases in, total urinary polyamines during the first cycle of DFMO therapy (p=0.003) and had median progression free survival (PFS) that was over three times longer, compared to patients with the major G allele at this locus although this last result was not statistically significant (p=0.07). Six of 18 evaluable patients were progression free during the trial period with three patients continuing progression free at 663, 1559 and 1573 days after initiating treatment. Median progression-free survival was less among patients having increased urinary polyamines, especially diacetylspermine, although this result was not statistically significant (p=0.056). DFMO doses of 500-1500 mg/m2/day are safe and well tolerated in children with relapsed NB. Children with the minor T allele at rs2302616 of the ODC gene with relapsed or refractory NB had higher levels of urinary polyamine markers and responded better to therapy containing DFMO, compared to those with the major G allele at this locus. These findings suggest that this patient subset may display dependence on polyamines and be uniquely susceptible to therapies targeting this pathway. Clinicaltrials.gov NCT#01059071.

BackgroundNIZ985 is a recombinant heterodimer of physiologically active interleukin (IL-)15 and IL-15 receptor alpha. In preclinical models, NIZ985 promotes cytotoxic lymphocyte proliferation, killing function, and organ/tumor infiltration, with resultant anticancer effects. In this first-in-human study, we assessed the safety, pharmacokinetics, and immune effects of NIZ985 in patients with metastatic or unresectable solid tumors.MethodsSingle agent NIZ985 dose escalation data are reported from a phase I dose escalation/expansion study of NIZ985 as monotherapy. Adult patients (N=14) received 0.25, 0.5, 1, 2 or 4 µg/kg subcutaneous NIZ985 three times weekly (TIW) for the first 2 weeks of each 28-day cycle, in an accelerated 3+3 dose escalation trial design. IL-15 and endogenous cytokines were monitored by ELISA and multiplexed electrochemiluminescent assays. Multiparameter flow cytometry assessed the frequency, phenotype and proliferation of peripheral blood mononuclear cells. Preliminary antitumor activity was assessed by overall response rate (Response Evaluation Criteria in Solid Tumors V.1.1).ResultsAs of March 2, 2020, median treatment duration was 7.5 weeks (range 1.1–77.1). Thirteen patients had discontinued and one (uveal melanoma) remains on treatment with stable disease. Best clinical response was stable disease (3 of 14 patients; 21%). The most frequent adverse events (AEs) were circular erythematous injection site reactions (100%), chills (71%), fatigue (57%), and fever (50%). Treatment-related grade 3/4 AEs occurred in six participants (43%); treatment-related serious AEs (SAEs) in three (21%). The per-protocol maximum tolerated dose was not reached. Pharmacokinetic accumulation of serum IL-15 in the first week was followed by significantly lower levels in week 2, likely due to more rapid cytokine consumption by an expanding lymphocyte pool. NIZ985 treatment was associated with increases in several cytokines, including interferon (IFN)-γ, IL-18, C-X-C motif chemokine ligand 10, and tumor necrosis factor-β, plus significant induction of cytotoxic lymphocyte proliferation (including natural killer and CD8+ T cells), increased CD16+ monocytes, and increased CD163+ macrophages at injection sites.ConclusionsSubcutaneous NIZ985 TIW was generally well tolerated in patients with advanced cancer and produced immune activation paralleling preclinical observations, with induction of IFN-γ and proliferation of cytotoxic lymphocytes. Due to delayed SAEs at the two highest dose levels, administration is being changed to once-weekly in a revised protocol, as monotherapy and combined with checkpoint inhibitor spartalizumab. These alterations are expected to maximize the potential of NIZ985 as a novel immunotherapy.Trial registration numberNCT02452268.

Purpose Isocitrate dehydrogenase enzyme 1 (IDH1) mutations at 132nd amino acid residue (R132*) result in the cellular accumulation of the oncometabolite, 2-hydroxyglutarate (2-HG). IDH305 is an orally bioavailable, brain-penetrant, mutant-selective allosteric IDH1 inhibitor demonstrating target engagement in preclinical models. This first-in human study was designed to identify the recommended dose for expansion/maximum tolerated dose of IDH305 in patients with IDH1 R132 -mutant acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Methods IDH305 was given at doses 75–750 mg twice daily in 41 patients with IDH1 R132 -mutant AML/MDS. Dose escalation was designed using Bayesian hierarchical model with overdose control principle and relationship with dose-limiting toxicity. Results IDH305 exhibited rapid absorption with mean T 1/2 approximately 4–10 h across doses. Interpatient variability was moderate and exposure increased with dose in a less than dose proportional manner. Most patients (35/41) demonstrated target engagement with reduction in 2-HG concentration at all doses. Complete remission (CR) or CR with incomplete count recovery occurred in 10/37 (27%) patients with AML and 1/ 4 patients with MDS. Adverse events (AEs) suspected to be related to study drug were reported in 53.7% of patients: increased blood bilirubin (14.6%), nausea (14.6%), increased alanine aminotransferase and aspartate aminotransferase (12.2%, each); most frequent grade 3 or 4 AEs were differentiation syndrome and tumor lysis syndrome ( n  = 3; 7.3%, each). Hepatotoxicity was manageable with dose modification. Conclusion Due to potentially narrow therapeutic window, the study was prematurely halted and recommended phase 2 dose could not be declared. Trial registration Clinicaltrials.gov identifier: NCT02381886.

High risk neuroblastoma (HRNB) accounts for 15% of all pediatric cancer deaths. Despite aggressive therapy approximately half of patients will relapse, typically with only transient responses to second-line therapy. This study evaluated the ornithine decarboxylase inhibitor difluoromethylornithine (DFMO) as maintenance therapy to prevent relapse following completion of standard therapy (Stratum 1) or after salvage therapy for relapsed/refractory disease (Stratum 2). This Phase II single agent, single arm multicenter study enrolled from June 2012 to February 2016. Subjects received 2 years of oral DFMO (750 ± 250 mg/m 2 twice daily). Event free survival (EFS) and overall survival (OS) were determined on an intention-to-treat (ITT) basis. 101 subjects enrolled on Stratum 1 and 100 were eligible for ITT analysis; two-year EFS was 84% (±4%) and OS 97% (±2%). 39 subjects enrolled on Stratum 2, with a two-year EFS of 54% (±8%) and OS 84% (±6%). DFMO was well tolerated. The median survival time is not yet defined for either stratum. DFMO maintenance therapy for HRNB in remission is safe and associated with high EFS and OS. Targeting ODC represents a novel therapeutic mechanism that may provide a new strategy for preventing relapse in children with HRNB.

Isocitrate dehydrogenase enzyme 1 (IDH1) mutations at 132nd amino acid residue (R132*) result in the cellular accumulation of the oncometabolite, 2-hydroxyglutarate (2-HG). IDH305 is an orally bioavailable, brain-penetrant, mutant-selective allosteric IDH1 inhibitor demonstrating target engagement in preclinical models. This first-in human study was designed to identify the recommended dose for expansion/maximum tolerated dose of IDH305 in patients with IDH1 -mutant acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). IDH305 was given at doses 75-750 mg twice daily in 41 patients with IDH1 -mutant AML/MDS. Dose escalation was designed using Bayesian hierarchical model with overdose control principle and relationship with dose-limiting toxicity. IDH305 exhibited rapid absorption with mean T approximately 4-10 h across doses. Interpatient variability was moderate and exposure increased with dose in a less than dose proportional manner. Most patients (35/41) demonstrated target engagement with reduction in 2-HG concentration at all doses. Complete remission (CR) or CR with incomplete count recovery occurred in 10/37 (27%) patients with AML and 1/ 4 patients with MDS. Adverse events (AEs) suspected to be related to study drug were reported in 53.7% of patients: increased blood bilirubin (14.6%), nausea (14.6%), increased alanine aminotransferase and aspartate aminotransferase (12.2%, each); most frequent grade 3 or 4 AEs were differentiation syndrome and tumor lysis syndrome (n = 3; 7.3%, each). Hepatotoxicity was manageable with dose modification. Due to potentially narrow therapeutic window, the study was prematurely halted and recommended phase 2 dose could not be declared. Clinicaltrials.gov identifier: NCT02381886.

Background: High risk neuroblastoma is incurable using current treatment regimens in the majority of patients. Oncolytic virotherapy offers a novel approach to treating cancer. In vitro and in vivo studies in adult tumor models have shown efficacy with the use of cancer selective, attenuated, replication-competent Herpes Simplex Virus (HSV)-derived vectors. Such viruses are currently in clinical trials for several adult cancers.Objective: To determine the susceptibility of neuroblastoma tumor models to HSV-based virotherapy and delineate mechanisms of oncolysis.Design/Methods: Eight neuroblastoma cell lines were tested for susceptibility to HSV infection by measuring time and dose-dependent cell survival following exposure to a multi-mutated herpes simplex virus, NV1066. Two cell line-derived xenografts (CHLA-20 and LAN-5) in athymic nude mice were also treated by intratumoral injection of NV1066. Xenograft tumors were followed for response and analyzed histologically for tumor necrosis, viral production, and apoptosis.Results: All eight neuroblastoma cell lines tested in culture were uniformly sensitive to the NV1066 infection. Five of the eight cell lines were highly sensitive with a complete cytopathic effect (CPE) seen on day eight post infection at a multiplicity of infection of 0.05. At the same dose, the other three cell lines showed a CPE on day ten post infection. Animals in both xenograft tumor types treated with a single intratumoral injection of NV1066 showed a significantly prolonged survival at day 60 post infection in comparison to animals in the PBS-treated control group (p < .05). CHLA-20 xenografts treated with PBS showed a highly vascular tumor with absence of any significant areas of necrosis; in contrast, the NV1066 treated tumors showed large necrotic areas, apoptotic cells, cholesterol clefts, and calcification.Conclusions: Neuroblastoma tumor models are highly sensitive to herpes simplex mediated oncolysis. Therefore, further studies of HSV virotherapy as a novel treatment for neuroblastoma is warranted.

Early diagnosis remains a significant challenge for many neurological disorders, especially for rare disorders where studying large cohorts is not possible. A novel solution that investigators have undertaken is combining advanced machine learning algorithms with resting-state functional Magnetic Resonance Imaging to unveil hidden pathological brain connectome patterns to uncover diagnostic and prognostic biomarkers. Recently, state-of-the-art deep learning techniques are outperforming traditional machine learning methods and are hailed as a milestone for artificial intelligence. However, whole brain classification that combines brain connectome with deep learning has been hindered by insufficient training samples. Inspired by the transfer learning strategy employed in computer vision, we exploited previously collected resting-state functional MRI data for healthy subjects from existing databases and transferred this knowledge for new disease classification tasks. We developed a deep transfer learning neural network (DTL-NN) framework for enhancing the classification of whole brain functional connectivity patterns. Briefly, we trained a stacked sparse autoencoder (SSAE) prototype to learn healthy functional connectivity patterns in an offline learning environment. Then, the SSAE prototype was transferred to a DTL-NN model for a new classification task. To test the validity of our framework, we collected resting-state functional MRI data from the Autism Brain Imaging Data Exchange (ABIDE) repository. Using autism spectrum disorder (ASD) classification as a target task, we compared the performance of our DTL-NN approach with a traditional deep neural network and support vector machine models across four ABIDE data sites that enrolled at least 60 subjects. As compared to traditional models, our DTL-NN approach achieved an improved performance in accuracy, sensitivity, specificity and area under receiver operating characteristic curve. These findings suggest that DTL-NN approaches could enhance disease classification for neurological conditions, where accumulating large neuroimaging datasets has been challenging.

To identify perinatal clinical antecedents of white matter microstructural abnormalities in extremely preterm infants. A prospective cohort of extremely preterm infants (N = 86) and healthy term controls (N = 16) underwent diffusion tensor imaging (DTI) at term equivalent age. Region of interest-based measures of white matter microstructure - fractional anisotropy and mean diffusivity - were quantified in seven vulnerable cerebral regions and group differences assessed. In the preterm cohort, multivariable linear regression analyses were conducted to identify independent clinical factors associated with microstructural abnormalities. Preterm infants had a mean (standard deviation) gestational age of 26.1 (1.7) weeks and birth weight of 824 (182) grams. Compared to term controls, the preterm cohort exhibited widespread microstructural abnormalities in 9 of 14 regional measures. Chorioamnionitis, necrotizing enterocolitis, white matter injury on cranial ultrasound, and increasing duration of mechanical ventilation were adversely correlated with regional microstructure. Conversely, antenatal steroids, female sex, longer duration of caffeine therapy, and greater duration of human milk use were independent favorable factors. White matter injury on cranial ultrasound was associated with a five weeks or greater delayed maturation of the corpus callosum; every additional 10 days of human milk use were associated with a three weeks or greater advanced maturation of the corpus callosum. Diffusion tensor imaging is sensitive in detecting the widespread cerebral delayed maturation and/or damage increasingly observed in extremely preterm infants. In our cohort, it also aided identification of several previously known or suspected perinatal clinical antecedents of brain injury, aberrant development, and neurodevelopmental impairments.

The developmental significance of the frequently encountered white matter signal abnormality (WMSA) findings on MRI around term-equivalent age (TEA) in very preterm infants, remains in question. The use of conventional qualitative analysis methods is subjective, lacks sufficient reliability for producing accurate and reproducible WMSA diagnosis, and possibly contributes to suboptimal neurodevelopmental outcome prediction. The advantages of quantitative over qualitative diagnostic approaches have been widely acknowledged and demonstrated. The purpose of this study is to objectively and accurately quantify WMSA on TEA T2-weighted MRI in very preterm infants and to assess whether such quantifications predict 2-year language and cognitive developmental outcomes. To this end, we constructed a probabilistic brain atlas, exclusively for very preterm infants to embed tissue distributions (i.e. to encode shapes, locations and geometrical proportion of anatomical structures). Guided with this atlas, we then developed a fully automated method for WMSA detection and quantification using T2-weighted images. Computer simulations and experiments using in vivo very preterm data showed very high detection accuracy. WMSA volume, particularly in the centrum semiovale, on TEA MRI was a significant predictor of standardized language and cognitive scores at 2 years of age. Independent validation of our automated WMSA detection algorithm and school age follow-up are important next steps.

•Deep learning and radiomics to predict cognitive deficits in very preterm infants.•Self-supervised learning to address the lack of labeled radiomic data.•Collaborative self-supervised learning to learn latent radiomic representations.•Integrate pretext tasks of radiomics reconstruction and subject discrimination. The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on labeling radiological images, which is a time-consuming, labor-intensive, and expensive process. In this work, we present the first novel collaborative self-supervised learning method to solve the challenge of insufficient labeled radiomic data, whose characteristics are different from text and image data. To achieve this, we present two collaborative pretext tasks that explore the latent pathological or biological relationships between regions of interest and the similarity and dissimilarity of information between subjects. Our method collaboratively learns the robust latent feature representations from radiomic data in a self-supervised manner to reduce human annotation efforts, which benefits the disease diagnosis. We compared our proposed method with other state-of-the-art self-supervised learning methods on a simulation study and two independent datasets. Extensive experimental results demonstrated that our method outperforms other self-supervised learning methods on both classification and regression tasks. With further refinement, our method will have the potential advantage in automatic disease diagnosis with large-scale unlabeled data available.