The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research.

OBJECTIVE: Perform a scoping review of supervised machine learning in pediatric critical care to identify published applications, methodologies, and implementation frequency to inform best practices for the development, validation, and reporting of predictive models in pediatric critical care. DESIGN: Scoping review and expert opinion. SETTING: We queried CINAHL Plus with Full Text (EBSCO), Cochrane Library (Wiley), Embase (Elsevier), Ovid Medline, and PubMed for articles published between 2000 and 2022 related to machine learning concepts and pediatric critical illness. Articles were excluded if the majority of patients were adults or neonates, if unsupervised machine learning was the primary methodology, or if information related to the development, validation, and/or implementation of the model was not reported. Article selection and data extraction were performed using dual review in the Covidence tool, with discrepancies resolved by consensus. SUBJECTS: Articles reporting on the development, validation, or implementation of supervised machine learning models in the field of pediatric critical care medicine. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 5075 identified studies, 141 articles were included. Studies were primarily (57%) performed at a single site. The majority took place in the United States (70%). Most were retrospective observational cohort studies. More than three-quarters of the articles were published between 2018 and 2022. The most common algorithms included logistic regression and random forest. Predicted events were most commonly death, transfer to ICU, and sepsis. Only 14% of articles reported external validation, and only a single model was implemented at publication. Reporting of validation methods, performance assessments, and implementation varied widely. Follow-up with authors suggests that implementation remains uncommon after model publication. CONCLUSIONS: Publication of supervised machine learning models to address clinical challenges in pediatric critical care medicine has increased dramatically in the last 5 years. While these approaches have the potential to benefit children with critical illness, the literature demonstrates incomplete reporting, absence of external validation, and infrequent clinical implementation.

Development and Validation of a Risk Score Predicting Death Without Transplant in Adult Heart Transplant Candidates.

Importance: The US heart allocation system prioritizes medically urgent candidates with a high risk of dying without transplant. The current therapy-based 6-status system is susceptible to manipulation and has limited rank ordering ability. Objective: To develop and validate a candidate risk score that incorporates current clinical, laboratory, and hemodynamic data. Design, Setting, and Participants: A registry-based observational study of adult heart transplant candidates (aged ≥18 years) from the US heart allocation system listed between January 1, 2019, and December 31, 2022, split by center into training (70%) and test (30%) datasets. Adult candidates were listed between January 1, 2019, and December 31, 2022. Main Outcomes and Measures: A US candidate risk score (US-CRS) model was developed by adding a predefined set of predictors to the current French Candidate Risk Score (French-CRS) model. Sensitivity analyses were performed, which included intra-aortic balloon pumps (IABP) and percutaneous ventricular assist devices (VAD) in the definition of short-term mechanical circulatory support (MCS) for the US-CRS. Performance of the US-CRS model, French-CRS model, and 6-status model in the test dataset was evaluated by time-dependent area under the receiver operating characteristic curve (AUC) for death without transplant within 6 weeks and overall survival concordance (c-index) with integrated AUC. Results: A total of 16 905 adult heart transplant candidates were listed (mean [SD] age, 53 [13] years; 73% male; 58% White); 796 patients (4.7%) died without a transplant. The final US-CRS contained time-varying short-term MCS (ventricular assist-extracorporeal membrane oxygenation or temporary surgical VAD), the log of bilirubin, estimated glomerular filtration rate, the log of B-type natriuretic peptide, albumin, sodium, and durable left ventricular assist device. In the test dataset, the AUC for death within 6 weeks of listing for the US-CRS model was 0.79 (95% CI, 0.75-0.83), for the French-CRS model was 0.72 (95% CI, 0.67-0.76), and 6-status model was 0.68 (95% CI, 0.62-0.73). Overall c-index for the US-CRS model was 0.76 (95% CI, 0.73-0.80), for the French-CRS model was 0.69 (95% CI, 0.65-0.73), and 6-status model was 0.67 (95% CI, 0.63-0.71). Classifying IABP and percutaneous VAD as short-term MCS reduced the effect size by 54%. Conclusions and Relevance: In this registry-based study of US heart transplant candidates, a continuous multivariable allocation score outperformed the 6-status system in rank ordering heart transplant candidates by medical urgency and may be useful for the medical urgency component of heart allocation.

Prior emergency medical services utilization is a risk factor for in-hospital death among patients with substance misuse: a retrospective cohort study.

BACKGROUND: Substance misuse poses a significant public health challenge, characterized by premature morbidity and mortality, and heightened healthcare utilization. While studies have demonstrated that previous hospitalizations and emergency department visits are associated with increased mortality in patients with substance misuse, it is unknown whether prior utilization of emergency medical service (EMS) is similarly associated with poor outcomes among this population. The objective of this study is to determine the association between EMS utilization in the 30 days before a hospitalization or emergency department visit and in-hospital outcomes among patients with substance misuse. METHODS: We conducted a retrospective analysis of adult emergency department visits and hospitalizations (referred to as a hospital encounter) between 2017 and 2021 within the Substance Misuse Data Commons, which maintains electronic health records from substance misuse patients seen at two University of Wisconsin hospitals, linked with state agency, claims, and socioeconomic datasets. Using regression models, we examined the association between EMS use and the outcomes of in-hospital death, hospital length of stay, intensive care unit (ICU) admission, and critical illness events, defined by invasive mechanical ventilation or vasoactive drug administration. Models were adjusted for age, comorbidities, initial severity of illness, substance misuse type, and socioeconomic status. RESULTS: Among 19,402 encounters, individuals with substance misuse who had at least one EMS incident within 30 days of a hospital encounter experienced a higher likelihood of in-hospital mortality (OR 1.52, 95% CI [1.05 - 2.14]) compared to those without prior EMS use, after adjusting for confounders. Using EMS in the 30 days prior to an encounter was associated with a small increase in hospital length of stay but was not associated with ICU admission or critical illness events. CONCLUSIONS: Individuals with substance misuse who have used EMS in the month preceding a hospital encounter are at an increased risk of in-hospital mortality. Enhanced monitoring of EMS users in this population could improve overall patient outcomes.

Hypotensive Events in Pediatric Patients Receiving Dexmedetomidine for MRI.

PURPOSE: Dexmedetomidine, the preferred pediatric sedating agent for magnetic resonance imaging (MRI), has the side effect of hypotension. Newer recommendations for reporting adverse events in pediatric procedural sedation include using a two-pronged definition. Our aim was to describe the incidence of hypotension in patients undergoing sedated MRI and to identify demographic and clinical factors associated with hypotension, applying a two-pronged definition, where a numerical threshold/clinical criterion must be met as well as at least one clinical intervention performed. DESIGN: An observational cohort study. METHODS: Medical record data were extracted for outpatients less than 18 years of age sedated primarily with dexmedetomidine for MRI in a single center for over a seven-year period. Patients who received propofol as an adjunct were also included. Hypotension was defined using a two-pronged approach, as a 20% reduction in systolic blood pressure from baseline lasting ≥10 minutes, coupled with a fluid bolus. Analysis included descriptive statistics, t tests and logistic regression using discrete-time survival analysis. FINDINGS: Of the 1,590 patient encounters, 90 (5.7%) experienced hypotension. Males were significantly more likely to have hypotension. Patients with hypotension had overall longer appointment times, including longer sedation times and recovery time. Greater blood pressure (BP) variability in the preceding 20 minutes also increased the risk of hypotension. CONCLUSIONS: Our lower incidence of hypotension is likely related to the two-pronged intervention-based definition used, as it likely more accurately reflects clinically meaningful hypotension. To our knowledge, this is the first study using this approach with this population. Research further examining the relationship between prolonged sedation, blood pressure variability, gender, hypotension, and recovery time is needed. Understanding these relationships will help interdisciplinary teams, including nurses in pediatric procedural areas, to reduce the incidence of hypotension, potentially maximize patient safety, and optimize throughput.

Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest.

OBJECTIVES: Prognostication of neurologic status among survivors of in-hospital cardiac arrests remains a challenging task for physicians. Although models such as the Cardiac Arrest Survival Post-Resuscitation In-hospital score are useful for predicting neurologic outcomes, they were developed using traditional statistical techniques. In this study, we derive and compare the performance of several machine learning models with each other and with the Cardiac Arrest Survival Post-Resuscitation In-hospital score for predicting the likelihood of favorable neurologic outcomes among survivors of resuscitation. DESIGN: Analysis of the Get With The Guidelines-Resuscitation registry. SETTING: Seven-hundred fifty-five hospitals participating in Get With The Guidelines-Resuscitation from January 1, 2001, to January 28, 2017. PATIENTS: Adult in-hospital cardiac arrest survivors. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 117,674 patients in our cohort, 28,409 (24%) had a favorable neurologic outcome, as defined as survival with a Cerebral Performance Category score of less than or equal to 2 at discharge. Using patient characteristics, pre-existing conditions, prearrest interventions, and periarrest variables, we constructed logistic regression, support vector machines, random forests, gradient boosted machines, and neural network machine learning models to predict favorable neurologic outcome. Events prior to October 20, 2009, were used for model derivation, and all subsequent events were used for validation. The gradient boosted machine predicted favorable neurologic status at discharge significantly better than the Cardiac Arrest Survival Post-Resuscitation In-hospital score (C-statistic: 0.81 vs 0.73; p < 0.001) and outperformed all other machine learning models in terms of discrimination, calibration, and accuracy measures. Variables that were consistently most important for prediction across all models were duration of arrest, initial cardiac arrest rhythm, admission Cerebral Performance Category score, and age. CONCLUSIONS: The gradient boosted machine algorithm was the most accurate for predicting favorable neurologic outcomes in in-hospital cardiac arrest survivors. Our results highlight the utility of machine learning for predicting neurologic outcomes in resuscitated patients.

Multicenter Development and Validation of a Model for Predicting Retention in Care Among People with HIV.

Predictive analytics can be used to identify people with HIV currently retained in care who are at risk for future disengagement from care, allowing for prioritization of retention interventions. We utilized machine learning methods to develop predictive models of retention in care, defined as no more than a 12 month gap between HIV care appointments in the Center for AIDS Research Network of Integrated Clinical Systems (CNICS) cohort. Data were split longitudinally into derivation and validation cohorts. We created logistic regression (LR), random forest (RF), and gradient boosted machine (XGB) models within a discrete-time survival analysis framework and compared their performance to a baseline model that included only demographics, viral suppression, and retention history. 21,267 Patients with 507,687 visits from 2007 to 2018 were included. The LR model outperformed the baseline model (AUC 0.68 [0.67-0.70] vs. 0.60 [0.59-0.62], P < 0.001). RF and XGB models had similar performance to the LR model. Top features in the LR model included retention history, age, and viral suppression.

Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU.

OBJECTIVES: Unrecognized clinical deterioration during illness requiring hospitalization is associated with high risk of mortality and long-term morbidity among children. Our objective was to develop and externally validate machine learning algorithms using electronic health records for identifying ICU transfer within 12 hours indicative of a child's condition. DESIGN: Observational cohort study. SETTING: Two urban, tertiary-care, academic hospitals (sites 1 and 2). PATIENTS: Pediatric inpatients (age <18 yr). INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: Our primary outcome was direct ward to ICU transfer. Using age, vital signs, and laboratory results, we derived logistic regression with regularization, restricted cubic spline regression, random forest, and gradient boosted machine learning models. Among 50,830 admissions at site 1 and 88,970 admissions at site 2, 1,993 (3.92%) and 2,317 (2.60%) experienced the primary outcome, respectively. Site 1 data were split longitudinally into derivation (2009-2017) and validation (2018-2019), whereas site 2 constituted the external test cohort. Across both sites, the gradient boosted machine was the most accurate model and outperformed a modified version of the Bedside Pediatric Early Warning Score that only used physiologic variables in terms of discrimination ( C -statistic site 1: 0.84 vs 0.71, p < 0.001; site 2: 0.80 vs 0.74, p < 0.001), sensitivity, specificity, and number needed to alert. CONCLUSIONS: We developed and externally validated a novel machine learning model that identifies ICU transfers in hospitalized children more accurately than current tools. Our model enables early detection of children at risk for deterioration, thereby creating opportunities for intervention and improvement in outcomes.

Improving Prehospital Stroke Diagnosis Using Natural Language Processing of Paramedic Reports.

Background and Purpose: Accurate prehospital diagnosis of stroke by emergency medical services (EMS) can increase treatments rates, mitigate disability, and reduce stroke deaths. We aimed to develop a model that utilizes natural language processing of EMS reports and machine learning to improve prehospital stroke identification. Methods: We conducted a retrospective study of patients transported by the Chicago EMS to 17 regional primary and comprehensive stroke centers. Patients who were suspected of stroke by the EMS or had hospital-diagnosed stroke were included in our cohort. Text within EMS reports were converted to unigram features, which were given as input to a support-vector machine classifier that was trained on 70% of the cohort and tested on the remaining 30%. Outcomes included final diagnosis of stroke versus nonstroke, large vessel occlusion, severe stroke (National Institutes of Health Stroke Scale score >5), and comprehensive stroke center-eligible stroke (large vessel occlusion or hemorrhagic stroke). Results: Of 965 patients, 580 (60%) had confirmed acute stroke. In a test set of 289 patients, the text-based model predicted stroke nominally better than models based on the Cincinnati Prehospital Stroke Scale (c-statistic: 0.73 versus 0.67, P=0.165) and was superior to the 3-Item Stroke Scale (c-statistic: 0.73 versus 0.53, P<0.001) scores. Improvements in discrimination were also observed for the other outcomes. Conclusions: We derived a model that utilizes clinical text from paramedic reports to identify stroke. Our results require validation but have the potential of improving prehospital routing protocols.

Evaluation of Organ Dysfunction Scores for Allocation of Scarce Resources in Critically Ill Children and Adults During a Healthcare Crisis.

OBJECTIVES: When healthcare systems are overwhelmed, accurate assessments of patients' predicted mortality risks are needed to ensure effective allocation of scarce resources. Organ dysfunction scores can serve this essential role, but their evaluation in this context has been limited so far. In this study, we sought to assess the performance of three organ dysfunction scores in both critically ill adults and children at clinically relevant mortality thresholds and timeframes for resource allocation and compare it with two published prioritization schemas. DESIGN: Retrospective observational cohort study. SETTING: Three large academic medical centers in the United States. PATIENTS: Critically ill adults and children. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We calculated the daily Sequential Organ Failure Assessment score in adults and the Pediatric Logistic Organ Dysfunction 2 score and the Pediatric Sequential Organ Failure Assessment score in children. There were 49,290 (11.6% mortality) and 19,983 children (2.5% mortality) included in the analysis. Both the Sequential Organ Failure Assessment and Pediatric Sequential Organ Failure Assessment scores had adequate discrimination across relevant timeframes and adequate distribution across relevant mortality thresholds. Additionally, we found that the only published state prioritization schema that includes pediatric and adult patients had poor alignment of mortality risks, giving adults a systematic advantage over children. CONCLUSIONS: In the largest analysis of organ dysfunction scores in a general population of critically ill adults and children to date, we found that both the Sequential Organ Failure Assessment and Pediatric Sequential Organ Failure Assessment scores had adequate performance across relevant mortality thresholds and timeframes for resource allocation. Published prioritization schemas that include both pediatric and adult patients may put children at a disadvantage. Furthermore, the distribution of patient and mortality risk in the published schemas may not adequately stratify patients for some high-stakes allocation decisions. This information may be useful to bioethicists, healthcare leaders, and policy makers who are developing resource allocation policies for critically ill patients.

Multicenter Validation of the Neonatal Sequential Organ Failure Assessment Score for Prognosis in the Neonatal Intensive Care Unit.

Infants in the neonatal intensive care unit are at risk of life-threatening organ dysfunction, but few objective tools with utility exist. In a multicenter cohort of 20 152 infants, we show the neonatal sequential organ failure assessment score had good-to-excellent discrimination of mortality across centers, birth weights, and time points after admission.

Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes.

PURPOSE OF REVIEW: This manuscript reviews the use of electronic medical record (EMR) data for HIV care and research along the HIV care continuum with a specific focus on machine learning methods and clinical informatics interventions. RECENT FINDINGS: EMR-based clinical decision support tools and electronic alerts have been effectively utilized to improve HIV care continuum outcomes. Accurate EMR-based machine learning models have been developed to predict HIV diagnosis, retention in care, and viral suppression. Natural language processing (NLP) of clinical notes and data sharing between healthcare systems and public health agencies can enhance models for identifying people living with HIV who are undiagnosed or in need of relinkage to care. Challenges related to using these technologies include inconsistent EMR documentation, alert fatigue, and the potential for bias. Clinical informatics and machine learning models are promising tools for improving HIV care continuum outcomes. Future research should focus on methods for combining EMR data with additional data sources (e.g., social media, geospatial data) and studying how to effectively implement predictive models for HIV care into clinical practice.

Characteristics of Critically Ill Adults With Sacrococcygeal Unavoidable Hospital-Acquired Pressure Injuries: A Retrospective, Matched, Case-Control Study.

PURPOSE: To identify characteristics of critically ill adults with sacrococcygeal, unavoidable hospital-acquired pressure injuries (uHAPIs). DESIGN: Retrospective, matched, case-control design. SUBJECTS/SETTING: Patients admitted to adult intensive care units (ICUs) at an urban academic medical center from January 2014 through July 2016. METHODS: Thirty-four patients without uHAPI were matched to 34 patients with sacrococcygeal uHAPI. Time points of interest included admission to the ICU, the week preceding the definitive assessment date, and hospital discharge status. Variables of interest included length of stay, any diagnosis of sepsis, severity of illness, degree of organ dysfunction/failure, supportive therapies in use (eg, mechanical ventilation), and pressure injury risk (Braden Scale score). RESULTS: All 34 sacrococcygeal pressure injuries were classified as uHAPI using the pressure injury prevention inventory instrument. No statistically significant differences were noted between patients for severity of illness, degree of organ dysfunction/failure, or pressure injury risk at ICU admission. At 1 day prior to the definitive assessment date and at discharge, patients with uHAPI had significantly higher mean Sequential Organ Failure Assessment (SOFA) scores (greater organ dysfunction/failure) and lower mean Braden Scale scores (greater pressure injury risk) than patients without uHAPI. Patients with uHAPI had significantly longer lengths of stay, more supportive therapies in use, were more often diagnosed with sepsis, and were more likely to die during hospitalization. CONCLUSION: Sacrococcygeal uHAPI development was associated with progressive multiorgan dysfunction/failure, greater use of supportive therapies, sepsis diagnosis, and mortality. Additional research investigating the role of multiorgan dysfunction/failure and sepsis on uHAPI development is warranted.

External Validation of an Acute Respiratory Distress Syndrome Prediction Model Using Radiology Reports.

OBJECTIVES: Acute respiratory distress syndrome is frequently under recognized and associated with increased mortality. Previously, we developed a model that used machine learning and natural language processing of text from radiology reports to identify acute respiratory distress syndrome. The model showed improved performance in diagnosing acute respiratory distress syndrome when compared to a rule-based method. In this study, our objective was to externally validate the natural language processing model in patients from an independent hospital setting. DESIGN: Secondary analysis of data across five prospective clinical studies. SETTING: An urban, tertiary care, academic hospital. PATIENTS: Adult patients admitted to the medical ICU and at-risk for acute respiratory distress syndrome. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The natural language processing model was previously derived and internally validated in burn, trauma, and medical patients at Loyola University Medical Center. Two machine learning models were examined with the following text features from qualifying radiology reports: 1) word representations (n-grams) and 2) standardized clinical named entity mentions mapped from the National Library of Medicine Unified Medical Language System. The models were externally validated in a cohort of 235 patients at the University of Chicago Medicine, among which 110 (47%) were diagnosed with acute respiratory distress syndrome by expert annotation. During external validation, the n-gram model demonstrated good discrimination between acute respiratory distress syndrome and nonacute respiratory distress syndrome patients (C-statistic, 0.78; 95% CI, 0.72-0.84). The n-gram model had a higher discrimination for acute respiratory distress syndrome when compared with the standardized named entity model, although not statistically significant (C-statistic 0.78 vs 0.72; p = 0.09). The most important features in the model had good face validity for acute respiratory distress syndrome characteristics but differences in frequencies did occur between hospital settings. CONCLUSIONS: Our computable phenotype for acute respiratory distress syndrome had good discrimination in external validation and may be used by other health systems for case-identification. Discrepancies in feature representation are likely due to differences in characteristics of the patient cohorts.

A Vital Sign-Based Model to Predict Clinical Deterioration in Hospitalized Children.

OBJECTIVES: Clinical deterioration in hospitalized children is associated with increased risk of mortality and morbidity. A prediction model capable of accurate and early identification of pediatric patients at risk of deterioration can facilitate timely assessment and intervention, potentially improving survival and long-term outcomes. The objective of this study was to develop a model utilizing vital signs from electronic health record data for predicting clinical deterioration in pediatric ward patients. DESIGN: Observational cohort study. SETTING: An urban, tertiary-care medical center. PATIENTS: Patients less than 18 years admitted to the general ward during years 2009-2018. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary outcome of clinical deterioration was defined as a direct ward-to-ICU transfer. A discrete-time logistic regression model utilizing six vital signs along with patient characteristics was developed to predict ICU transfers several hours in advance. Among 31,899 pediatric admissions, 1,375 (3.7%) experienced the outcome. Data were split into independent derivation (yr 2009-2014) and prospective validation (yr 2015-2018) cohorts. In the prospective validation cohort, the vital sign model significantly outperformed a modified version of the Bedside Pediatric Early Warning System score in predicting ICU transfers 12 hours prior to the event (C-statistic 0.78 vs 0.72; p < 0.01). CONCLUSIONS: We developed a model utilizing six commonly used vital signs to predict risk of deterioration in hospitalized children. Our model demonstrated greater accuracy in predicting ICU transfers than the modified Bedside Pediatric Early Warning System. Our model may promote opportunities for timelier intervention and risk mitigation, thereby decreasing preventable death and improving long-term health.

Spatial and functional relationships among Pol V-associated loci, Pol IV-dependent siRNAs, and cytosine methylation in the Arabidopsis epigenome.

Multisubunit RNA polymerases IV and V (Pols IV and V) mediate RNA-directed DNA methylation and transcriptional silencing of retrotransposons and heterochromatic repeats in plants. We identified genomic sites of Pol V occupancy in parallel with siRNA deep sequencing and methylcytosine mapping, comparing wild-type plants with mutants defective for Pol IV, Pol V, or both Pols IV and V. Approximately 60% of Pol V-associated regions encompass regions of 24-nucleotide (nt) siRNA complementarity and cytosine methylation, consistent with cytosine methylation being guided by base-pairing of Pol IV-dependent siRNAs with Pol V transcripts. However, 27% of Pol V peaks do not overlap sites of 24-nt siRNA biogenesis or cytosine methylation, indicating that Pol V alone does not specify sites of cytosine methylation. Surprisingly, the number of methylated CHH motifs, a hallmark of RNA-directed de novo methylation, is similar in wild-type plants and Pol IV or Pol V mutants. In the mutants, methylation is lost at 50%-60% of the CHH sites that are methylated in the wild type but is gained at new CHH positions, primarily in pericentromeric regions. These results indicate that Pol IV and Pol V are not required for cytosine methyltransferase activity but shape the epigenome by guiding CHH methylation to specific genomic sites.

Automated Glycan Sequencing from Tandem Mass Spectra of N-Linked Glycopeptides.

Mass spectrometry has become a routine experimental tool for proteomic biomarker analysis of human blood samples, partly due to the large availability of informatics tools. As one of the most common protein post-translational modifications (PTMs) in mammals, protein glycosylation has been observed to alter in multiple human diseases and thus may potentially be candidate markers of disease progression. While mass spectrometry instrumentation has seen advancements in capabilities, discovering glycosylation-related markers using existing software is currently not straightforward. Complete characterization of protein glycosylation requires the identification of intact glycopeptides in samples, including identification of the modification site as well as the structure of the attached glycans. In this paper, we present GlycoSeq, an open-source software tool that implements a heuristic iterated glycan sequencing algorithm coupled with prior knowledge for automated elucidation of the glycan structure within a glycopeptide from its collision-induced dissociation tandem mass spectrum. GlycoSeq employs rules of glycosidic linkage as defined by glycan synthetic pathways to eliminate improbable glycan structures and build reasonable glycan trees. We tested the tool on two sets of tandem mass spectra of N-linked glycopeptides cell lines acquired from breast cancer patients. After employing enzymatic specificity within the N-linked glycan synthetic pathway, the sequencing results of GlycoSeq were highly consistent with the manually curated glycan structures. Hence, GlycoSeq is ready to be used for the characterization of glycan structures in glycopeptides from MS/MS analysis. GlycoSeq is released as open source software at https://github.com/chpaul/GlycoSeq/ .

A method for whole protein isolation from human cranial bone.

The presence of the dense hydroxyapatite matrix within human bone limits the applicability of conventional protocols for protein extraction. This has hindered the complete and accurate characterization of the human bone proteome thus far, leaving many bone-related disorders poorly understood. We sought to refine an existing method of protein extraction from mouse bone to extract whole proteins of varying molecular weights from human cranial bone. Whole protein was extracted from human cranial suture by mechanically processing samples using a method that limits protein degradation by minimizing heat introduction to proteins. The presence of whole protein was confirmed by western blotting. Mass spectrometry was used to sequence peptides and identify isolated proteins. The data have been deposited to the ProteomeXchange with identifier PXD003215. Extracted proteins were characterized as both intra- and extracellular and had molecular weights ranging from 9.4 to 629 kDa. High correlation scores among suture protein spectral counts support the reproducibility of the method. Ontology analytics revealed proteins of myriad functions including mediators of metabolic processes and cell organelles. These results demonstrate a reproducible method for isolation of whole protein from human cranial bone, representing a large range of molecular weights, origins and functions.

N-linked glycan profiling in neuroblastoma cell lines.

Although MYCN amplification has been associated with aggressive neuroblastoma, the molecular mechanisms that differentiate low-risk, MYCN-nonamplified neuroblastoma from high-risk, MYCN-amplified disease are largely unknown. Genomic and proteomic studies have been limited in discerning differences in signaling pathways that account for this heterogeneity. N-Linked glycosylation is a common protein modification resulting from the attachment of sugars to protein residues and is important in cell signaling and immune response. Aberrant N-linked glycosylation has been routinely linked to various cancers. In particular, glycomic markers have often proven to be useful in distinguishing cancers from precancerous conditions. Here, we perform a systematic comparison of N-linked glycomic variation between MYCN-nonamplified SY5Y and MYCN-amplified NLF cell lines with the aim of identifying changes in sugar abundance linked to high-risk neuroblastoma. Through a combination of liquid chromatography-mass spectrometry and bioinformatics analysis, we identified 16 glycans that show a statistically significant change in abundance between NLF and SY5Y samples. Closer examination revealed the preference for larger (in terms of total monosaccharide count) and more sialylated glycan structures in the MYCN-amplified samples in comparison to smaller, nonsialylated glycans that are more dominant in the MYCN-nonamplified samples. These results offer clues for deriving marker candidates for accurate neuroblastoma risk diagnosis.

Proteomic profiling of naïve multiple myeloma patient plasma cells identifies pathways associated with favourable response to bortezomib-based treatment regimens.

Toward our goal of personalized medicine, we comprehensively profiled pre-treatment malignant plasma cells from multiple myeloma patients and prospectively identified pathways predictive of favourable response to bortezomib-based treatment regimens. We utilized two complementary quantitative proteomics platforms to identify differentially-regulated proteins indicative of at least a very good partial response (VGPR) or complete response/near complete response (CR/nCR) to two treatment regimens containing either bortezomib, liposomal doxorubicin and dexamethasone (VDD), or lenalidomide, bortezomib and dexamethasone (RVD). Our results suggest enrichment of 'universal response' pathways that are common to both treatment regimens and are probable predictors of favourable response to bortezomib, including a subset of endoplasmic reticulum stress pathways. The data also implicate pathways unique to each regimen that may predict sensitivity to DNA-damaging agents, such as mitochondrial dysfunction, and immunomodulatory drugs, which was associated with acute phase response signalling. Overall, we identified patterns of tumour characteristics that may predict response to bortezomib-based regimens and their components. These results provide a rationale for further evaluation of the protein profiles identified herein for targeted selection of anti-myeloma therapy to increase the likelihood of improved treatment outcome of patients with newly-diagnosed myeloma.

Glycoproteomics: identifying the glycosylation of prostate specific antigen at normal and high isoelectric points by LC-MS/MS.

Prostate specific antigen (PSA) is currently used as a biomarker to diagnose prostate cancer. PSA testing has been widely used to detect and screen prostate cancer. However, in the diagnostic gray zone, the PSA test does not clearly distinguish between benign prostate hypertrophy and prostate cancer due to their overlap. To develop more specific and sensitive candidate biomarkers for prostate cancer, an in-depth understanding of the biochemical characteristics of PSA (such as glycosylation) is needed. PSA has a single glycosylation site at Asn69, with glycans constituting approximately 8% of the protein by weight. Here, we report the comprehensive identification and quantitation of N-glycans from two PSA isoforms using LC-MS/MS. There were 56 N-glycans associated with PSA, whereas 57 N-glycans were observed in the case of the PSA-high isoelectric point (pI) isoform (PSAH). Three sulfated/phosphorylated glycopeptides were detected, the identification of which was supported by tandem MS data. One of these sulfated/phosphorylated N-glycans, HexNAc5Hex4dHex1s/p1 was identified in both PSA and PSAH at relative intensities of 0.52 and 0.28%, respectively. Quantitatively, the variations were monitored between these two isoforms. Because we were one of the laboratories participating in the 2012 ABRF Glycoprotein Research Group (gPRG) study, those results were compared to that presented in this study. Our qualitative and quantitative results summarized here were comparable to those that were summarized in the interlaboratory study.