Faculty Bibliography

BACKGROUND AND AIMS/OBJECTIVE:STRIDE-II recommends early biomarker targets for treatment optimization to achieve treat-to-target [T2T] endoscopic remission [ER] in Crohn's disease [CD]. The predictive capabilities of intestinal ultrasound [IUS] for T2T ER remain unknown. We aimed to evaluate IUS response to predict ER in children with CD. METHODS:This was a prospective longitudinal cohort study of children with ileal [TI] CD initiating biologic therapy undergoing IUS, clinical disease activity, and C-reactive protein [CRP] assessments at baseline, week 8, 6 months, and T2T within 1 year. The primary outcome was the accuracy of optimal cut-points to predict TI ER [SES-CD ≤ 2] for change in bowel wall thickness [BWT] on IUS from baseline to week 8, and BWT at week 8. Area under the receiver operating curve [AUROC] analysis was performed and univariate analysis tested associations. RESULTS:In total, 44 children (median age 13 [IQR 12-17] years, 29 [66%] biologic naïve) were included, and 29 [66%] achieved ER. A ≥18% decrease in TI BWT at week 8 predicted ER with an AUROC of 0.99 [95% CI 0.98-1.00], 100% sensitivity, 93% specificity, 97% positive predictive value, and 100% negative predictive value, superior to a ≥46% decrease in PCDAI (AUROC 0.67 [95% CI 0.49-0.84]) and ≥84% decrease in CRP (AUROC 0.49 [95% CI 0.31-0.67]) at week 8. CONCLUSIONS:Early change in TI BWT on IUS is highly predictive of ER in children with CD and superior to symptoms and CRP. Our findings suggest that IUS could be used for treatment optimization and tight control to guide T2T strategies.

DESCRIPTION/METHODS:In the past 3 years, the use of intestinal ultrasound (IUS) for monitoring inflammatory bowel disease in clinical practice has grown substantially in the United States. This American Gastroenterological Association (AGA) Institute Clinical Practice Update (CPU) aims to review the available evidence and guidance regarding the role of intestinal ultrasound in inflammatory bowel disease care. METHODS:This CPU was commissioned and approved by the AGA Institute Clinical Practice Updates Committee (CPUC) and the AGA Governing Board to provide timely guidance on a topic of high clinical importance to the AGA membership and underwent internal peer review by the CPUC and external peer review through standard procedures of Clinical Gastroenterology and Hepatology. This expert commentary incorporates important and recently published studies in this field, and it reflects the experiences of the multidisciplinary group of authors composed of adult and pediatric gastroenterologists.

Novel technology is one of the five focus areas of the Challenges in Inflammatory Bowel Disease (IBD) Research 2024 document. Building off the Challenges in IBD Research 2019 document, the Foundation aims to provide a comprehensive overview of current gaps in IBD research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in interception, remission, and restoration for these diseases. The document is the result of a multidisciplinary collaboration from scientists, clinicians, patients, and funders and represents a valuable resource for patient-centric research prioritization. Specifically, the Novel Technologies section focuses on addressing key research gaps to enable interception and improve remission rates in IBD. This includes testing predictions of disease onset and progression, developing novel technologies tailored to specific phenotypes, and facilitating collaborative translation of science into diagnostics, devices, and therapeutics. Proposed priority actions outlined in the document include real-time measurement of biological changes preceding disease onset, more effective quantification of fibrosis, exploration of technologies for local treatment of fistulas, and the development of drug delivery platforms for precise, location-restricted therapies. Additionally, there is a strong emphasis on fostering collaboration between various stakeholders to accelerate progress in IBD research and treatment. Addressing these research gaps necessitates the exploration and implementation of bio-engineered novel technologies spanning a spectrum from materials to systems. By harnessing innovative ideas and technologies, there's a collective effort to enhance patient care and outcomes for individuals affected by IBD.

BACKGROUND:Data are limited on the safety and efficacy of combining advanced therapies for refractory patients with IBD. AIM/OBJECTIVE:To evaluate the real-world efficacy and safety of dual advanced therapy (DAT), combining 2 biologics or a biologic with a small molecule, in children and young adults with refractory IBD. METHODS:Primary outcome of this single IBD center cohort was DAT remission (clinical and biomarker remission) at first assessment (T1). Secondary outcomes included remission at T2, if DAT de-intensification (De-I) occurred and T3, if T2 DAT re-intensification (Re-I) occurred. Efficacy and safety outcomes were described. RESULTS:Of the 30 patients [43% female, 30% CD, median age of 18.3 [15.1-19.8] years], all 11 UST + TOFA achieved T1 remission; 6/10 De-I failed at T2; and 4/4 Re-I achieved T3 remission. Of 9 VDZ + TOFA, 6 achieved T1 remission; 5/6 De-I failed at T2; and 1/1 failed T3 Re-I. Of 4 UST + VDZ, 3 achieved T1 remission; 2/3 De-I failed at T2; and 0 had Re-I. Of 5 UST + UPA, 4 achieved T1 remission; 1/5 De-I failed at T2 but recaptured T3 remission post-Re-I. One VDZ + OZA achieved T1 remission and maintained T2 remission post-De-I to OZA monotherapy. At last follow-up, 43% were on original DAT, 17% on one of original DAT, and 40% neither. One UST + TOFA patient developed mild leukopenia and another developed septic arthritis and venous thromboembolism on VDZ + TOFA and prednisone. CONCLUSION/CONCLUSIONS:Most children and young adults treated with DAT achieved remission with minimal safety events; however, de-intensification had limited success.

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.

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.

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.

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.

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.