Faculty Bibliography

BACKGROUND:Upadacitinib (UPA) is a novel selective JAK inhibitor approved for adults with ulcerative colitis (UC) and with positive phase 3 data for Crohn's disease (CD). Pediatric off-label use is common due to delays in pediatric approvals; real-world data on UPA are needed to understand the safety and effectiveness in pediatric IBD. METHODS:This is a single-center retrospective case series study of adolescents (12-17 years) with inflammatory bowel disease IBD on UPA. The primary outcome was postinduction steroid-free clinical remission (SF-CR) defined as Pediatric UC Activity Index (PUCAI) or Pediatric CD Activity Index (PCDAI) ≤10. Secondary outcomes include postinduction clinical response (decrease ≥12.5 in PUCAI/PCDAI), postinduction C-reactive protein (CRP) normalization, 6-month SF-CR, and intestinal ultrasound response and remission. Adverse events were recorded through last follow-up. RESULTS:Twenty patients (9 CD, 10 UC, 1 IBD-U; 55% female; median age 15 years, 90% ≥2 biologics) were treated with UPA for ≥12 weeks (median 51 [43-63] weeks). Upadacitinib was used as monotherapy in 55% and as combination with ustekinumab and vedolizumab in 35% and 10%, respectively. Week 12 SF-CR was achieved in 75% (15/20) and 80% (16/20) with CRP normalization. About 3/4 (14/19) achieved SF-CR at 6 months. Adverse event occurred in 2 patients (10%): Cytomegalovirus colitis requiring hospitalization and hyperlipidemia requiring no treatment. In the 75% with ultrasound monitoring, response and remission were achieved in 77% and 60%, respectively. CONCLUSION/CONCLUSIONS:While awaiting pediatric registration trials, our data suggest that UPA is effective in inducing and maintaining SF-CR in adolescents with highly-refractory IBD with an acceptable safety profile.

INTRODUCTION:High rates of screen failure for the minimum Simple Endoscopic Score for Crohn's Disease (SES-CD) plague Crohn's disease (CD) clinical trials. We aimed to determine the accuracy of segmental intestinal ultrasound (IUS) parameters and scores to detect segmental SES-CD activity. METHODS:A single-center, blinded, cross-sectional cohort study of children and young adult patients with CD undergoing IUS and ileocolonoscopy, comparing segmental IUS bowel wall thickness (BWT), hyperemia (modified Limberg score [MLS]), and scores to detect segmental SES-CD activity: (i) SES-CD ≤2, (ii) SES-CD ≥6, and (iii) SES-CD ≥4 in the terminal ileum (TI) only. Primary outcome was accuracy of BWT, MLS, and IUS scores to detect SES-CD ≤2 and SES-CD ≥6. Secondary outcomes were accuracy of TI BWT, MLS, and IUS scores to detect SES-CD ≥4 and correlation with the SES-CD. RESULTS:Eighty-two patients (median [interquartile range] age 16.5 [12.9-20.0] years) underwent IUS and ileocolonoscopy of 323 bowel segments. Segmental BWT ≤3.1 mm had a similar high accuracy to detect SES-CD ≤2 as IUS scores (area under the receiver operating curve [AUROC] 0.833 [95% confidence interval 0.76-0.91], 94% sensitivity, and 73% specificity). Segmental BWT ≥3.6 mm and ≥4.3 mm had similar high accuracy to detect SES-CD ≥6 (AUROC 0.950 [95% confidence interval 0.92-0.98], 89% sensitivity, 93% specificity) in the colon and an SES-CD ≥4 in the TI (AUROC 0.874 [0.79-0.96], 80% sensitivity, and 91% specificity) as IUS scores. Segmental IUS scores strongly correlated with the SES-CD. DISCUSSION:Segmental IUS BWT is highly accurate to detect moderate-to-severe endoscopic inflammation. IUS may be the ideal prescreening tool to reduce unnecessary trial screen failures.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.

Characterizing the effect of combination therapies is vital for treating diseases like cancer. We introduce correlated drug action (CDA), a baseline model for the study of drug combinations in both cell cultures and patient populations, which assumes that the efficacy of drugs in a combination may be correlated. We apply temporal CDA (tCDA) to clinical trial data, and demonstrate the utility of this approach in identifying possible synergistic combinations and others that can be explained in terms of monotherapies. Using MCF7 cell line data, we assess combinations with dose CDA (dCDA), a model that generalizes other proposed models (e.g., Bliss response-additivity, the dose equivalence principle), and introduce Excess over CDA (EOCDA), a new metric for identifying possible synergistic combinations in cell culture.

BACKGROUND:Clinical trials are vital for developing new therapies but can also delay drug development. Efficient trial data management, optimized trial protocol, and accurate patient identification are critical for reducing trial timelines. Natural language processing (NLP) has the potential to achieve these objectives. OBJECTIVE:This study aims to assess the feasibility of using data-driven approaches to optimize clinical trial protocol design and identify eligible patients. This involves creating a comprehensive eligibility criteria knowledge base integrated within electronic health records using deep learning-based NLP techniques. METHODS:We obtained data of 3281 industry-sponsored phase 2 or 3 interventional clinical trials recruiting patients with non-small cell lung cancer, prostate cancer, breast cancer, multiple myeloma, ulcerative colitis, and Crohn disease from ClinicalTrials.gov, spanning the period between 2013 and 2020. A customized bidirectional long short-term memory- and conditional random field-based NLP pipeline was used to extract all eligibility criteria attributes and convert hypernym concepts into computable hyponyms along with their corresponding values. To illustrate the simulation of clinical trial design for optimization purposes, we selected a subset of patients with non-small cell lung cancer (n=2775), curated from the Mount Sinai Health System, as a pilot study. RESULTS:-score (0.83, range 0.67-1), enabling the efficient extraction of granular criteria entities and relevant attributes from 3281 clinical trials. A standardized eligibility criteria knowledge base, compatible with electronic health records, was developed by transforming hypernym concepts into machine-interpretable hyponyms along with their corresponding values. In addition, an interface prototype demonstrated the practicality of leveraging real-world data for optimizing clinical trial protocols and identifying eligible patients. CONCLUSIONS:Our customized NLP pipeline successfully generated a standardized eligibility criteria knowledge base by transforming hypernym criteria into machine-readable hyponyms along with their corresponding values. A prototype interface integrating real-world patient information allows us to assess the impact of each eligibility criterion on the number of patients eligible for the trial. Leveraging NLP and real-world data in a data-driven approach holds promise for streamlining the overall clinical trial process, optimizing processes, and improving efficiency in patient identification.

From eukaryotes to prokaryotes, all cells secrete extracellular vesicles (EVs) as part of their regular homeostasis, intercellular communication, and cargo disposal. Accumulating evidence suggests that small EVs carry functional small RNAs, potentially serving as extracellular messengers and liquid-biopsy markers. Yet, the complete transcriptomic landscape of EV-associated small RNAs during disease progression is poorly delineated due to critical limitations including the protocols used for sequencing, suboptimal alignment of short reads (20-50 nt), and uncharacterized genome annotations-often denoted as the 'dark matter' of the genome. In this study, we investigate the EV-associated small unannotated RNAs that arise from endogenous genes and are part of the genomic 'dark matter', which may play a key emerging role in regulating gene expression and translational mechanisms. To address this, we created a distinct small RNAseq dataset from human prostate cancer & benign tissues, and EVs derived from blood (pre- & post-prostatectomy), urine, and human prostate carcinoma epithelial cell line. We then developed an unsupervised data-based bioinformatic pipeline that recognizes biologically relevant transcriptional signals irrespective of their genomic annotation. Using this approach, we discovered distinct EV-RNA expression patterns emerging from the un-annotated genomic regions (UGRs) of the transcriptomes associated with tissue-specific phenotypes. We have named these novel EV-associated small RNAs as 'EV-UGRs' or "EV-dark matter". Here, we demonstrate that EV-UGR gene expressions are downregulated by ∼100 fold (FDR < 0.05) in the circulating serum EVs from aggressive prostate cancer subjects. Remarkably, these EV-UGRs expression signatures were regained (upregulated) after radical prostatectomy in the same follow-up patients. Finally, we developed a stem-loop RT-qPCR assay that validated prostate cancer-specific EV-UGRs for selective fluid-based diagnostics. Overall, using an unsupervised data driven approach, we investigate the 'dark matter' of EV-transcriptome and demonstrate that EV-UGRs carry tissue-specific Information that significantly alters pre- and post-prostatectomy in the prostate cancer patients. Although further validation in randomized clinical trials is required, this new class of EV-RNAs hold promise in liquid-biopsy by avoiding highly invasive biopsy procedures in prostate cancer.

MOTIVATION/UNASSIGNED:The integration of vast, complex biological data with computational models offers profound insights and predictive accuracy. Yet, such models face challenges: poor generalization and limited labeled data. RESULTS/UNASSIGNED:To overcome these difficulties in binary classification tasks, we developed the Method for Optimal Classification by Aggregation (MOCA) algorithm, which addresses the problem of generalization by virtue of being an ensemble learning method and can be used in problems with limited or no labeled data. We developed both an unsupervised (uMOCA) and a supervised (sMOCA) variant of MOCA. For uMOCA, we show how to infer the MOCA weights in an unsupervised way, which are optimal under the assumption of class-conditioned independent classifier predictions. When it is possible to use labels, sMOCA uses empirically computed MOCA weights. We demonstrate the performance of uMOCA and sMOCA using simulated data as well as actual data previously used in Dialogue on Reverse Engineering and Methods (DREAM) challenges. We also propose an application of sMOCA for transfer learning where we use pre-trained computational models from a domain where labeled data are abundant and apply them to a different domain with less abundant labeled data. AVAILABILITY AND IMPLEMENTATION/UNASSIGNED:GitHub repository, https://github.com/robert-vogel/moca.

BACKGROUND:With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical record data is challenging given incompleteness of records, inaccurate medical diagnosis coding, as well as heterogeneity in clinical symptoms and procedures for specific disorders. We sought to develop a digital phenotyping algorithm (PheIndex) using electronic medical records to identify children aged 0-3 diagnosed with genetic disorders or who present with illness with an increased risk for genetic disorders. RESULTS:Through expert opinion, we established 13 criteria for the algorithm and derived a score and a classification. The performance of each criterion and the classification were validated by chart review. PheIndex identified 1,088 children out of 93,154 live births who may be at an increased risk for genetic disorders. Chart review demonstrated that the algorithm achieved 90% sensitivity, 97% specificity, and 94% accuracy. CONCLUSIONS:The PheIndex algorithm can help identify when a rare genetic disorder may be present, alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist.

Therapeutic vaccination has long been a promising avenue for cancer immunotherapy but is often limited by tumor heterogeneity. The genetic and molecular diversity between patients often results in variation in the antigens present on cancer cell surfaces. As a result, recent research has focused on personalized cancer vaccines. Although promising, this strategy suffers from time-consuming production, high cost, inaccessibility, and targeting of a limited number of tumor antigens. Instead, we explore an antigen-agnostic polymeric in situ cancer vaccination platform for treating blood malignancies, in our model here with acute myeloid leukemia (AML). Rather than immunizing against specific antigens or targeting adjuvant to specific cell-surface markers, this platform leverages a characteristic metabolic and enzymatic dysregulation in cancer cells that produces an excess of free cysteine thiols on their surfaces. These thiols increase in abundance after treatment with cytotoxic agents such as cytarabine, the current standard of care in AML. The resulting free thiols can undergo efficient disulfide exchange with pyridyl disulfide (PDS) moieties on our construct and allow for in situ covalent attachment to cancer cell surfaces and debris. PDS-functionalized monomers are incorporated into a statistical copolymer with pendant mannose groups and TLR7 agonists to target covalently linked antigen and adjuvant to antigen-presenting cells in the liver and spleen after IV administration. There, the compound initiates an anticancer immune response, including T-cell activation and antibody generation, ultimately prolonging survival in cancer-bearing mice.

Atherosclerosis is a chronic inflammatory disease associated with the accumulation of low-density lipoprotein (LDL) in arterial walls. Higher levels of the anti-inflammatory cytokine IL-10 in serum are correlated with reduced plaque burden. However, cytokine therapies have not translated well to the clinic, partially due to their rapid clearance and pleiotropic nature. Here, we engineered IL-10 to overcome these challenges by hitchhiking on LDL to atherosclerotic plaques. Specifically, we constructed fusion proteins in which one domain is IL-10 and the other is an antibody fragment (Fab) that binds to protein epitopes of LDL. In murine models of atherosclerosis, we show that systemically administered Fab-IL-10 constructs bind circulating LDL and traffic to atherosclerotic plaques. One such construct, 2D03-IL-10, significantly reduces aortic immune cell infiltration to levels comparable to healthy mice, whereas non-targeted IL-10 has no therapeutic effect. Mechanistically, we demonstrate that 2D03-IL-10 preferentially associates with foamy macrophages and reduces pro-inflammatory activation markers. This platform technology can be applied to a variety of therapeutics and shows promise as a potential targeted anti-inflammatory therapy in atherosclerosis.