Global metabolomics revealed deviations from the metabolic aging clock in colorectal cancer patients

Background: Markers of aging hold promise in the context of colorectal cancer (CRC) care. Utilizing high-resolution metabolomic profiling, we can unveil distinctive age-related patterns that have the potential to predict early CRC development. Our study aims to unearth a panel of aging markers and delve into the metabolomic alterations associated with aging and CRC. Methods: We assembled a serum cohort comprising 5,649 individuals, consisting of 3,002 healthy volunteers, 715 patients diagnosed with colorectal advanced precancerous lesions (APL), and 1,932 CRC patients, to perform a comprehensive metabolomic analysis. Results: We successfully identified unique age-associated patterns across 42 metabolic pathways. Moreover, we established a metabolic aging clock, comprising 9 key metabolites, using an elastic net regularized regression model that accurately estimates chronological age. Notably, we observed significant chronological disparities among the healthy population, APL patients, and CRC patients. By combining the analysis of circulative carcinoembryonic antigen levels with the categorization of individuals into the "hypo" metabolic aging subgroup, our blood test demonstrates the ability to detect APL and CRC with positive predictive values of 68.4% (64.3%, 72.2%) and 21.4% (17.8%, 25.9%), respectively. Conclusions: This innovative approach utilizing our metabolic aging clock holds significant promise for accurately assessing biological age and enhancing our capacity to detect APL and CRC.

Wnt/β-catenin signaling activation promotes lipogenesis in the steatotic liver via physical mTOR interaction

Background and aims

Wnt/β-catenin signaling plays an important role in regulating hepatic metabolism. This study is to explore the molecular mechanisms underlying the potential crosstalk between Wnt/β-catenin and mTOR signaling in hepatic steatosis.

Methods

Transgenic mice (overexpress Wnt1 in hepatocytes, Wnt+) mice and wild-type littermates were given high fat diet (HFD) for 12 weeks to induce hepatic steatosis. Mouse hepatocytes cells (AML12) and those transfected to cause constitutive β-catenin stabilization (S33Y) were treated with oleic acid for lipid accumulation.

Results

Wnt+ mice developed more hepatic steatosis in response to HFD. Immunoblot shows a significant increase in the expression of fatty acid synthesis-related genes (SREBP-1 and its downstream targets ACC, AceCS1, and FASN) and a decrease in fatty acid oxidation gene (MCAD) in Wnt+ mice livers under HFD. Wnt+ mice also revealed increased Akt signaling and its downstream target gene mTOR in response to HFD. In vitro, increased lipid accumulation was detected in S33Y cells in response to oleic acid compared to AML12 cells reinforcing the in vivo findings. mTOR inhibition by rapamycin led to a down-regulation of fatty acid synthesis in S33Y cells. In addition, β-catenin has a physical interaction with mTOR as verified by co-immunoprecipitation in hepatocytes.

Conclusions

Taken together, our results demonstrate that β-catenin stabilization through Wnt signaling serves a central role in lipid metabolism in the steatotic liver through up-regulation of fatty acid synthesis via Akt/mTOR signaling. These findings suggest hepatic Wnt signaling may represent a therapeutic strategy in hepatic steatosis.

Exploring the feasibility of using long-term stored newborn dried blood spots to identify metabolic features for congenital heart disease screening

Congenital heart disease (CHD) represents a significant contributor to both morbidity and mortality in neonates and children. There's currently no analogous dried blood spot (DBS) screening for CHD immediately after birth. This study was set to assess the feasibility of using DBS to identify reliable metabolite biomarkers with clinical relevance, with the aim to screen and classify CHD utilizing the DBS. We assembled a cohort of DBS datasets from the California Department of Public Health (CDPH) Biobank, encompassing both normal controls and three pre-defined CHD categories. A DBS-based quantitative metabolomics method was developed using liquid chromatography with tandem mass spectrometry (LC-MS/MS). We conducted a correlation analysis comparing the absolute quantitated metabolite concentration in DBS against the CDPH NBS records to verify the reliability of metabolic profiling. For hydrophilic and hydrophobic metabolites, we executed significant pathway and metabolite analyses respectively. Logistic and LightGBM models were established to aid in CHD discrimination and classification. Consistent and reliable quantification of metabolites were demonstrated in DBS samples stored for up to 15 years. We discerned dysregulated metabolic pathways in CHD patients, including deviations in lipid and energy metabolism, as well as oxidative stress pathways. Furthermore, we identified three metabolites and twelve metabolites as potential biomarkers for CHD assessment and subtypes classifying. This study is the first to confirm the feasibility of validating metabolite profiling results using long-term stored DBS samples. Our findings highlight the potential clinical applications of our DBS-based methods for CHD screening and subtype classification.

High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengers

Background: Due to the poor prognosis and rising occurrence, there is a crucial need to improve the diagnosis of Primary Central Nervous System Lymphoma (PCNSL), which is a rare type of non-Hodgkin's lymphoma. This study utilized targeted metabolomics of cerebrospinal fluid (CSF) to identify biomarker panels for the improved diagnosis or differential diagnosis of primary central nervous system lymphoma (PCNSL). Methods: In this study, a cohort of 68 individuals, including patients with primary central nervous system lymphoma (PCNSL), non-malignant disease controls, and patients with other brain tumors, was recruited. Their cerebrospinal fluid samples were analyzed using the Ultra-high performance liquid chromatography - tandem mass spectrometer (UHPLC-MS/MS) technique for targeted metabolomics analysis. Multivariate statistical analysis and logistic regression modeling were employed to identify biomarkers for both diagnosis (Dx) and differential diagnosis (Diff) purposes. The Dx and Diff models were further validated using a separate cohort of 34 subjects through logistic regression modeling. Results: A targeted analysis of 45 metabolites was conducted using UHPLC-MS/MS on cerebrospinal fluid (CSF) samples from a cohort of 68 individuals, including PCNSL patients, non-malignant disease controls, and patients with other brain tumors. Five metabolic features were identified as biomarkers for PCNSL diagnosis, while nine metabolic features were found to be biomarkers for differential diagnosis. Logistic regression modeling was employed to validate the Dx and Diff models using an independent cohort of 34 subjects. The logistic model demonstrated excellent performance, with an AUC of 0.83 for PCNSL vs. non-malignant disease controls and 0.86 for PCNSL vs. other brain tumor patients. Conclusion: Our study has successfully developed two logistic regression models utilizing metabolic markers in cerebrospinal fluid (CSF) for the diagnosis and differential diagnosis of PCNSL. These models provide valuable insights and hold promise for the future development of a non-invasive and reliable diagnostic tool for PCNSL.

Altered expression of the L-arginine/nitric oxide pathway in ovarian cancer: metabolic biomarkers and biological implications

MOTIVATION

Ovarian cancer (OC) is a highly lethal gynecological malignancy. Extensive research has shown that OC cells undergo significant metabolic alterations during tumorigenesis. In this study, we aim to leverage these metabolic changes as potential biomarkers for assessing ovarian cancer.

METHODS

A functional module-based approach was utilized to identify key gene expression pathways that distinguish different stages of ovarian cancer (OC) within a tissue biopsy cohort. This cohort consisted of control samples (n = 79), stage I/II samples (n = 280), and stage III/IV samples (n = 1016). To further explore these altered molecular pathways, minimal spanning tree (MST) analysis was applied, leading to the formulation of metabolic biomarker hypotheses for OC liquid biopsy. To validate, a multiple reaction monitoring (MRM) based quantitative LCMS/MS method was developed. This method allowed for the precise quantification of targeted metabolite biomarkers using an OC blood cohort comprising control samples (n = 464), benign samples (n = 3), and OC samples (n = 13).

RESULTS

Eleven functional modules were identified as significant differentiators (false discovery rate, FDR < 0.05) between normal and early-stage, or early-stage and late-stage ovarian cancer (OC) tumor tissues. MST analysis revealed that the metabolic L-arginine/nitric oxide (L-ARG/NO) pathway was reprogrammed, and the modules related to "DNA replication" and "DNA repair and recombination" served as anchor modules connecting the other nine modules. Based on this analysis, symmetric dimethylarginine (SDMA) and arginine were proposed as potential liquid biopsy biomarkers for OC assessment. Our quantitative LCMS/MS analysis on our OC blood cohort provided direct evidence supporting the use of the SDMA-to-arginine ratio as a liquid biopsy panel to distinguish between normal and OC samples, with an area under the ROC curve (AUC) of 98.3%.

CONCLUSION

Our comprehensive analysis of tissue genomics and blood quantitative LC/MSMS metabolic data shed light on the metabolic reprogramming underlying OC pathophysiology. These findings offer new insights into the potential diagnostic utility of the SDMA-to-arginine ratio for OC assessment. Further validation studies using adequately powered OC cohorts are warranted to fully establish the clinical effectiveness of this diagnostic test.

Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.

Butyrate induces development-dependent necrotizing enterocolitis-like intestinal epithelial injury via necroptosis

BACKGROUND

The accumulation of short-chain fatty acids (SCFAs) from bacterial fermentation may adversely affect the under-developed gut as observed in premature newborns at risk for necrotizing enterocolitis (NEC). This study explores the mechanism by which specific SCFA fermentation products may injure the premature newborn intestine mucosa leading to NEC-like intestinal cell injury.

METHODS

Intraluminal injections of sodium butyrate were administered to 14- and 28-day-old mice, whose small intestine and stool were harvested for analysis. Human intestinal epithelial stem cells (hIESCs) and differentiated enterocytes from preterm and term infants were treated with sodium butyrate at varying concentrations. Necrosulfonamide (NSA) and necrostatin-1 (Nec-1) were used to determine the protective effects of necroptosis inhibitors on butyrate-induced cell injury.

RESULTS

The more severe intestinal epithelial injury was observed in younger mice upon exposure to butyrate (p = 0.02). Enterocytes from preterm newborns demonstrated a significant increase in sensitivity to butyrate-induced cell injury compared to term newborn enterocytes (p = 0.068, hIESCs; p = 0.038, differentiated cells). NSA and Nec-1 significantly inhibited the cell death induced by butyrate.

CONCLUSIONS

Butyrate induces developmental stage-dependent intestinal injury that resembles NEC. A primary mechanism of cell injury in NEC is necroptosis. Necroptosis inhibition may represent a potential preventive or therapeutic strategy for NEC.

IMPACT

Butyrate induces developmental stage-dependent intestinal injury that resembles NEC. A primary mechanism of cell injury caused by butyrate in NEC is necroptosis. Necroptosis inhibitors proved effective at significantly ameliorating the enteral toxicity of butyrate and thereby suggest a novel mechanism and approach to the prevention and treatment of NEC in premature newborns.

Data-driven longitudinal characterization of neonatal health and morbidity

Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children's Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. Maternal and newborn EHRs were extracted and used to train a multi-input multitask deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional cohort of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to validate the model. Areas under the receiver operating characteristic curve at delivery exceeded 0.9 for 10 of the 24 neonatal outcomes considered and were between 0.8 and 0.9 for 7 additional outcomes. Moreover, comprehensive association analysis identified multiple known associations between various maternal and neonatal features and specific neonatal outcomes. This study used linked EHRs from more than 30,000 mother-newborn dyads and would serve as a resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset: https://maternal-child-health-associations.shinyapps.io/shiny_app/.

Fecal Keratin 8 Is a Noninvasive and Specific Marker for Intestinal Injury in Necrotizing Enterocolitis

Specific biomarkers of intestinal injury associated with necrotizing enterocolitis (NEC) are needed to diagnose and monitor intestinal mucosal injury and recovery. This study aims to develop and test a modified enzyme-linked immunosorbent assay (ELISA) protocol to detect the total keratin 8 (K8) in the stool of newborns with NEC and investigate the clinical value of fecal K8 as a marker of intestinal injury specifically associated with NEC. We collected fecal samples from five newborns with NEC and five gestational age-matched premature neonates without NEC at the Lucile Packard Children's Hospital Stanford and Washington University School of Medicine, respectively. Fecal K8 levels were measured using a modified ELISA protocol and Western blot, and fecal calprotectin was measured using a commercial ELISA kit. Clinical data, including gestational age, birth weight, Bell stage for NEC, feeding strategies, total white blood cell (WBC) count, and other pertinent clinical variables, were collected and analyzed. Fecal K8 levels were significantly higher in the pre-NEC group (1-2 days before diagnosis of NEC) and NEC group than those in the non-NEC group (p = 0.013, p = 0.041). Moreover, fecal K8 was relatively higher at the onset of NEC and declined after the resolution of the disease (p = 0.019). Results with similar trends to fecal K8 were also seen in fecal calprotectin (p = 0.046), but not seen in total WBC count (p = 0.182). In conclusion, a modified ELISA protocol for the total K8 protein was successfully developed for the detection of fecal K8 in the clinical setting of premature newborns with NEC. Fecal K8 is noted to be significantly increased in premature newborns with NEC and may, therefore, serve as a noninvasive and specific marker for intestinal epithelial injury associated with NEC.

Early prediction and longitudinal modeling of preeclampsia from multiomics

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.

Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Taiwan

Background

Kawasaki disease (KD) is the leading cause of acquired heart disease in children. The major challenge in KD diagnosis is that it shares clinical signs with other childhood febrile control (FC) subjects. We sought to determine if our algorithmic approach applied to a Taiwan cohort.

Methods

A single center (Chang Gung Memorial Hospital in Taiwan) cohort of patients suspected with acute KD were prospectively enrolled by local KD specialists for KD analysis. Our previously single-center developed computer-based two-step algorithm was further tested by a five-center validation in US. This first blinded multi-center trial validated our approach, with sufficient sensitivity and positive predictive value, to identify most patients with KD diagnosed at centers across the US. This study involved 418 KDs and 259 FCs from the Chang Gung Memorial Hospital in Taiwan.

Findings

Our diagnostic algorithm retained sensitivity (379 of 418; 90.7%), specificity (223 of 259; 86.1%), PPV (379 of 409; 92.7%), and NPV (223 of 247; 90.3%) comparable to previous US 2016 single center and US 2020 fiver center results. Only 4.7% (15 of 418) of KD and 2.3% (6 of 259) of FC patients were identified as indeterminate. The algorithm identified 18 of 50 (36%) KD patients who presented 2 or 3 principal criteria. Of 418 KD patients, 157 were infants younger than one year and 89.2% (140 of 157) were classified correctly. Of the 44 patients with KD who had coronary artery abnormalities, our diagnostic algorithm correctly identified 43 (97.7%) including all patients with dilated coronary artery but one who found to resolve in 8 weeks.

Interpretation

This work demonstrates the applicability of our algorithmic approach and diagnostic portability in Taiwan.

Progressive Metabolic Abnormalities Associated with the Development of Neonatal Bronchopulmonary Dysplasia

Objective: To assess the longitudinal metabolic patterns during the evolution of bronchopulmonary dysplasia (BPD) development. Methods: A case-control dataset of preterm infants (<32-week gestation) was obtained from a multicenter database, including 355 BPD cases and 395 controls. A total of 72 amino acid (AA) and acylcarnitine (AC) variables, along with infants’ calorie intake and growth outcomes, were measured on day of life 1, 7, 28, and 42. Logistic regression, clustering methods, and random forest statistical modeling were utilized to identify metabolic variables significantly associated with BPD development and to investigate their longitudinal patterns that are associated with BPD development. Results: A panel of 27 metabolic variables were observed to be longitudinally associated with BPD development. The involved metabolites increased from 1 predominant different AC by day 7 to 19 associated AA and AC compounds by day 28 and 16 metabolic features by day 42. Citrulline, alanine, glutamate, tyrosine, propionylcarnitine, free carnitine, acetylcarnitine, hydroxybutyrylcarnitine, and most median-chain ACs (C5:C10) were the most associated metabolites down-regulated in BPD babies over the early days of life, whereas phenylalanine, methionine, and hydroxypalmitoylcarnitine were observed to be up-regulated in BPD babies. Most calorie intake and growth outcomes revealed similar longitudinal patterns between BPD cases and controls over the first 6 weeks of life, after gestational adjustment. When combining with birth weight, the derived metabolic-based discriminative model observed some differences between those with and without BPD development, with c-statistics of 0.869 and 0.841 at day 7 and 28 of life on the test data. Conclusions: The metabolic panel we describe identified some metabolic differences in the blood associated with BPD pathogenesis. Further work is needed to determine whether these compounds could facilitate the monitoring and/or investigation of early-life metabolic status in the lung and other tissues for the prevention and management of BPD.

A data-driven health index for neonatal morbidities

Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts twelve neonatal morbidities, as the basis for a new data-driven approach to define prematurity. Maternal demographics, medical history, obstetrical complications, and prenatal fetal findings were obtained from linked birth certificates and maternal/infant hospitalization records for 11,594,786 livebirths in California from 1991 to 2012. Overall, our model outperformed traditional models to assess prematurity which are based on GA and/or birthweight (area under the precision-recall curve was 0.326 for our model, 0.229 for GA, and 0.156 for small for GA). These findings highlight the potential of using machine learning techniques to predict multiple prematurity phenotypes and inform clinical decisions to prevent, diagnose and treat neonatal morbidities.

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Traditional definitions of prematurity based on gestational age need to be updated

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Deep learning of maternal clinical data improves predictions of neonatal morbidity

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Data-driven model leverages birthweight, type of delivery and maternal race

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Accurate risk prediction can inform clinical decisions

Serological Phenotyping Analysis Uncovers a Unique Metabolomic Pattern Associated With Early Onset of Type 2 Diabetes Mellitus

Background: Type 2 diabetes mellitus (T2DM) is a multifaceted disorder affecting epidemic proportion at global scope. Defective insulin secretion by pancreatic β-cells and the inability of insulin-sensitive tissues to respond effectively to insulin are the underlying biology of T2DM. However, circulating biomarkers indicative of early diabetic onset at the asymptomatic stage have not been well described. We hypothesized that global and targeted mass spectrometry (MS) based metabolomic discovery can identify novel serological metabolic biomarkers specifically associated with T2DM. We further hypothesized that these markers can have a unique pattern associated with latent or early asymptomatic stage, promising an effective liquid biopsy approach for population T2DM risk stratification and screening.

Methods: Four independent cohorts were assembled for the study. The T2DM cohort included sera from 25 patients with T2DM and 25 healthy individuals for the biomarker discovery and sera from 15 patients with T2DM and 15 healthy controls for the testing. The Pre-T2DM cohort included sera from 76 with prediabetes and 62 healthy controls for the model training and sera from 35 patients with prediabetes and 27 healthy controls for the model testing. Both global and targeted (amino acid, acylcarnitine, and fatty acid) approaches were used to deep phenotype the serological metabolome by high performance liquid chromatography-high resolution mass spectrometry. Different machine learning approaches (Random Forest, XGBoost, and ElasticNet) were applied to model the unique T2DM/Pre-T2DM metabolic patterns and contrasted with their effectiness to differentiate T2DM/Pre-T2DM from controls.

Results: The univariate analysis identified unique panel of metabolites (n = 22) significantly associated with T2DM. Global metabolomics and subsequent structure determination led to the identification of 8 T2DM biomarkers while targeted LCMS profiling discovered 14 T2DM biomarkers. Our panel can effectively differentiate T2DM (ROC AUC = 1.00) or Pre-T2DM (ROC AUC = 0.84) from the controls in the respective testing cohort.

Conclusion: Our serological metabolite panel can be utilized to identifiy asymptomatic population at risk of T2DM, which may provide utility in identifying population at risk at an early stage of diabetic development to allow for clinical intervention. This early detection would guide ehanced levels of care and accelerate development of clinical strategies to prevent T2DM.

Deviation from the precisely timed age-associated patterns revealed by blood metabolomics to find CRC patients at risk of relapse at the CRC diagnosis

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Background: Human serum metabolome profiles have been analyzed to explore the molecular changes that occur with aging. We hypothesized that deep metabolic profiling of sera with different ages would allow the identification of distinct metabolic chronologic patterns as a normal biological baseline to study personal aging. We further hypothesized that metabolic assessment of this chronologic deviation, resulting from advanced precancerous lesion (APL) and stage I/II/III CRC, from the normal reference baseline, would be instrumental for prognosis of relapse revealing underlying pathophysiology. Methods: A cohort of normal (n=3,616, training; n=1,170, testing), 631 advanced adenoma, 1,019 stage I, 404 stage II and 417 stage III serum samples were assembled. Innovative global LCMS metabolomic production were applied to deep profile these subjects. Identification of the age-associated molecular patterns in normal subjects, modeled with an elastic net algorithm, established the reference baseline to mirror a metabolic clock. CRC associated deviation from the precise chronologically paced metabolic patterns was quantified to associate the clinical endpoints of relapse, OSF and PFS, and to identify the tightly associated metabolic pathways. Results: We observed that for those CRCs, the predicted metabolic age can differ from the chronological age with consistent variations, resulting “older” or “younger” metabolic age subgroup in reference to the chronological age. Significant disruptions from the normal baseline were observed in CRCs patients, and consistent stage specific patterns were observed. Outlier, “Older” or “younger” metabolic age subgroup, CRC patients were found with significant future relapse enrichment. Predictive models were derived to case find the patients at risk of future relapse at the CRC diagnosis timepoint. Conclusions: Deviations from the meticulously timed metabolic aging patterns may provide utility to allow prognosis of future clinical endpoints of relapse and overall survival. Close examination of the underlying metabolic pathways, associated with CRC stage specific metabolic patterns, disrupting the baseline ageotypes, not only may improve the sensitivity and specificity of prognostic tests of CRC relapse, but also shed new insights into CRC therapeutics.

Metabolic model of necrotizing enterocolitis in the premature newborn gut resulting from enteric dysbiosis

Necrotizing enterocolitis (NEC) is a leading cause of premature newborn morbidity and mortality. The clinical features of NEC consistently include prematurity, gut dysbiosis and enteral inflammation, yet the pathogenesis remains obscure. Herein we combine metagenomics and targeted metabolomics, with functional in vivo and in vitro assessment, to define a novel molecular mechanism of NEC. One thousand six hundred and forty seven publicly available metagenomics datasets were analyzed (NEC = 245; healthy = 1,402) using artificial intelligence methodologies. Targeted metabolomic profiling was used to quantify the concentration of specified fecal metabolites at NEC onset (n = 8), during recovery (n = 6), and in age matched controls (n = 10). Toxicity assays of discovered metabolites were performed in vivo in mice and in vitro using human intestinal epithelial cells. Metagenomic and targeted metabolomic analyses revealed significant differences in pyruvate fermentation pathways and associated intermediates. Notably, the short chain fatty acid formate was elevated in the stool of NEC patients at disease onset (P = 0.005) dissipated during recovery (P = 0.02) and positively correlated with degree of intestinal injury (r ² = 0.86). In vitro, formate caused enterocyte cytotoxicity in human cells through necroptosis (P < 0.01). In vivo, luminal formate caused significant dose and development dependent NEC-like injury in newborn mice. Enterobacter cloacae and Klebsiella pneumoniae were the most discriminatory taxa related to NEC dysbiosis and increased formate production. Together, these data suggest a novel biochemical mechanism of NEC through the microbial production of formate. Clinical efforts to prevent NEC should focus on reducing the functional consequences of newborn gut dysbiosis associated metabolic pathways.

Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model

BACKGROUND

New-onset heart failure (HF) is associated with poor prognosis and high healthcare utilization. Early identification of patients at increased risk incident-HF may allow for focused allocation of preventative care resources. Health information exchange (HIE) data span the entire spectrum of clinical care, but there are no HIE-based clinical decision support tools for diagnosis of incident-HF. We applied machine-learning methods to model the one-year risk of incident-HF from the Maine statewide-HIE.

METHODS AND RESULTS

We included subjects aged ≥ 40 years without prior HF ICD9/10 codes during a three-year period from 2015 to 2018, and incident-HF defined as assignment of two outpatient or one inpatient code in a year. A tree-boosting algorithm was used to model the probability of incident-HF in year two from data collected in year one, and then validated in year three. 5,668 of 521,347 patients (1.09%) developed incident-HF in the validation cohort. In the validation cohort, the model c-statistic was 0.824 and at a clinically predetermined risk threshold, 10% of patients identified by the model developed incident-HF and 29% of all incident-HF cases in the state of Maine were identified.

CONCLUSIONS

Utilizing machine learning modeling techniques on passively collected clinical HIE data, we developed and validated an incident-HF prediction tool that performs on par with other models that require proactively collected clinical data. Our algorithm could be integrated into other HIEs to leverage the EMR resources to provide individuals, systems, and payors with a risk stratification tool to allow for targeted resource allocation to reduce incident-HF disease burden on individuals and health care systems.

Early-pregnancy prediction of risk for pre-eclampsia using maternal blood leptin/ceramide ratio: discovery and confirmation

OBJECTIVE

This study aimed to develop a blood test for the prediction of pre-eclampsia (PE) early in gestation. We hypothesised that the longitudinal measurements of circulating adipokines and sphingolipids in maternal serum over the course of pregnancy could identify novel prognostic biomarkers that are predictive of impending event of PE early in gestation.

STUDY DESIGN

Retrospective discovery and longitudinal confirmation.

SETTING

Maternity units from two US hospitals.

PARTICIPANTS

Six previously published studies of placental tissue (78 PE and 95 non-PE) were compiled for genomic discovery, maternal sera from 15 women (7 non-PE and 8 PE) enrolled at ProMedDx were used for sphingolipidomic discovery, and maternal sera from 40 women (20 non-PE and 20 PE) enrolled at Stanford University were used for longitudinal observation.

OUTCOME MEASURES

Biomarker candidates from discovery were longitudinally confirmed and compared in parallel to the ratio of placental growth factor (PlGF) and soluble fms-like tyrosine kinase (sFlt-1) using the same cohort. The datasets were generated by enzyme-linked immunosorbent and liquid chromatography-tandem mass spectrometric assays.

RESULTS

Our discovery integrating genomic and sphingolipidomic analysis identified leptin (Lep) and ceramide (Cer) (d18:1/25:0) as novel biomarkers for early gestational assessment of PE. Our longitudinal observation revealed a marked elevation of Lep/Cer (d18:1/25:0) ratio in maternal serum at a median of 23 weeks' gestation among women with impending PE as compared with women with uncomplicated pregnancy. The Lep/Cer (d18:1/25:0) ratio significantly outperformed the established sFlt-1/PlGF ratio in predicting impending event of PE with superior sensitivity (85% vs 20%) and area under curve (0.92 vs 0.52) from 5 to 25 weeks of gestation.

CONCLUSIONS

Our study demonstrated the longitudinal measurement of maternal Lep/Cer (d18:1/25:0) ratio allows the non-invasive assessment of PE to identify pregnancy at high risk in early gestation, outperforming the established sFlt-1/PlGF ratio test.

Novel Approaches to Develop Critical Reference Materials for Noninvasive Prenatal Testing: A Pilot Study

BACKGROUND

Highly characterized reference materials are required to expand noninvasive prenatal testing (NIPT) for low incidence aneuploidies and microdeletions. The goal of this study was to develop reference materials for the development of next generation circulating cell-free DNA (ccfDNA) assays.

METHODS

This was a prospective study of pregnancies complicated by positive prenatal genetic screening. ccfDNA was isolated from maternal plasma and amplified. Lymphoblastoid cell lines were prepared from maternal peripheral blood mononuclear cells and fetal cord blood cells. Cells were Epstein-Barr virus immortalized and expanded. Amplified DNA and to a limited extent formulated lymphoblastoid-derived ccfDNA was tested in SNP-based and chromosome counting (CC) based massively parallel sequencing assays.

RESULTS

Enrolled cases included fetuses with: T21 (2), T18 (1), T18-XXX (1), XYY (1), microdeletions (1), and euploid (2). Three lymphoblastoid cells lines were prepared. Genomic DNA was extracted from cell lines and fragmented to simulate ccfDNA. ccfDNA isolation yielded about 2000 usable genome equivalents of DNA for each case for amplification. Although the sonicated genomic DNA derived from lymphoblastoid cell lines did not yield results compatible with NIPT assays, when blinded, NIPT platforms correctly identified the amplified ccfDNA isolated from blood in the majority of cases.

CONCLUSIONS

This study showed that maternal blood samples from pregnancies complicated by common chromosomal abnormalities can be used to generate materials for the development and evaluation of NIPT assays.

Understanding how biologic and social determinants affect disparities in preterm birth and outcomes of preterm infants in the NICU

To understand the disparities in spontaneous preterm birth (sPTB) and/or its outcomes, biologic and social determinants as well as healthcare practice (such as those in neonatal intensive care units) should be considered. Disparities in sPTB have been largely intractable and remain obscure in most cases, despite a myriad of identified risk factors for and causes of sPTB. We still do not know how they lead to the different outcomes at different gestational ages and if they are independent of NICU practices. Here we describe an integrated approach to study the interplay between the genome and exposome, which may drive biochemistry and physiology and lead to health disparities.

Multi-omics longitudinal analyses in stages I to III CRC patients: Surveillance liquid biopsy test to predict early recurrence and enable risk-stratified postoperative CRC management

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Background: One-third of colorectal cancer (CRC) recurs following curative surgery and chemotherapy. Accordingly, novel methods are needed to predict recurrence to enable clinical course mitigating strategies. Serial monitoring of plasma by mass spectrometry (MS) and multi-omics modeling (MMO) of CRC relapse chronology provide the framework for liquid biopsy test development to supersede existing imaging modalities such as CT scans according to relapse related pathologies. We hypothesized that plasma MS and MMO analysis of relapse related pathologies can deconvolute high risk stratification for CRC recurrence within the cancer continuum of care pre/post-surgery and/or pre/post adjuvant chemotherapy (ACT). Methods: 189 CRC patients (Stage I-III) underwent one of three treatment modalities: Modality 1 (Surgery followed by ACT), Modality 2 (Surgery only), Modality 3 (Neoadjuvant chemotherapy followed by surgery and ACT). Plasma samples (n = 441) were collected from patients before surgery, 30 days post-op, and every 3 months until death or month 24 whichever came first. The MMO approach was used to analyze biological features encompassing native peptides, proteins, metabolites, lipids, and ceramides. MMO panels were developed comprising the significantly perturbed features as per the treatment modalities. These panels were used to predict relapse from plasma collected pre-op, 30-day post-op or after adjuvant chemotherapy. CEA levels were monitored in parallel. Results: Follow-up data was available for 135 patients (Stage I-III) and 25/135 had evidence of radiological recurrence. Irrespective of the treatment modality, longitudinal follow-up using the MMO panel was able to predict disease recurrence greater than 7 months before clinical progression was confirmed by CT scan. There was no significant correlation between longitudinal CEA levels and recurrence status, hence CEA levels alone did not provide any lead time advantage over the MMO panel or radiological surveillance. Kaplan-Meier (KM) survival analysis revealed that patients that were MMO panel positive had a poor survival irrespective of treatment modalities used: Modality 1 (HR = 6.2, p value = 0.003, test immediately post-surgery and immediately before ACT; HR = 31.6, p value = 0.01, test immediately after ACT); Modality 2 (HR = 11.2; p value = 0.01, test immediately after-surgery); Modality 3 (HR > 40, p value = 0.08, test immediately after neo-ACT and before-surgery; HR > 40, p value = 0.004, test immediately after-surgery). Conclusions: The MMO panel predicts CRC recurrence several months prior to detection by conventional CT scans, thus providing opportunity for alternative therapeutic strategies much earlier in the disease course.

Electronic Health Record-Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study

Background

Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death.

Objective

The aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia.

Methods

Retrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC).

Results

The cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year.

Conclusions

Our case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care.

Metagenomic insights of the infant microbiome community structure and function across multiple sites in the United States

The gut microbiome plays an important role in early life, protecting newborns from enteric pathogens, promoting immune system development and providing key functions to the infant host. Currently, there are limited data to broadly assess the status of the US healthy infant gut microbiome. To address this gap, we performed a multi-state metagenomic survey and found high levels of bacteria associated with enteric inflammation (e.g. Escherichia, Klebsiella), antibiotic resistance genes, and signatures of dysbiosis, independent of location, age, and diet. Bifidobacterium were less abundant than generally expected and the species identified, including B. breve, B. longum and B. bifidum, had limited genetic capacity to metabolize human milk oligosaccharides (HMOs), while B. infantis strains with a complete capacity for HMOs utilization were found to be exceptionally rare. Considering microbiome composition and functional capacity, this survey revealed a previously unappreciated dysbiosis that is widespread in the contemporary US infant gut microbiome.

Maternal metabolic profiling to assess fetal gestational age and predict preterm delivery: a two-centre retrospective cohort study in the US

OBJECTIVES

The aim of this study was to develop a single blood test that could determine gestational age and estimate the risk of preterm birth by measuring serum metabolites. We hypothesised that serial metabolic modelling of serum analytes throughout pregnancy could be used to describe fetal gestational age and project preterm birth with a high degree of precision.

STUDY DESIGN

A retrospective cohort study.

SETTING

Two medical centres from the USA.

PARTICIPANTS

Thirty-six patients (20 full-term, 16 preterm) enrolled at Stanford University were used to develop gestational age and preterm birth risk algorithms, 22 patients (9 full-term, 13 preterm) enrolled at the University of Alabama were used to validate the algorithms.

OUTCOME MEASURES

Maternal blood was collected serially throughout pregnancy. Metabolic datasets were generated using mass spectrometry.

RESULTS

A model to determine gestational age was developed (R²=0.98) and validated (R²=0.81). 66.7% of the estimates fell within ±1 week of ultrasound results during model validation. Significant disruptions from full-term pregnancy metabolic patterns were observed in preterm pregnancies (R²=-0.68). A separate algorithm to predict preterm birth was developed using a set of 10 metabolic pathways that resulted in an area under the curve of 0.96 and 0.92, a sensitivity of 0.88 and 0.86, and a specificity of 0.96 and 0.92 during development and validation testing, respectively.

CONCLUSIONS

In this study, metabolic profiling was used to develop and test a model for determining gestational age during full-term pregnancy progression, and to determine risk of preterm birth. With additional patient validation studies, these algorithms may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights into the pathophysiology of preterm birth. Metabolic pathway-based pregnancy modelling is a novel modality for investigation and clinical application development.

Kinetics of SARS-CoV-2 positivity of infected and recovered patients from a single center

Recurrence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive detection in infected but recovered individuals has been reported. Patients who have recovered from coronavirus disease 2019 (COVID-19) could profoundly impact the health care system. We sought to define the kinetics and relevance of PCR-positive recurrence during recovery from acute COVID-19 to better understand risks for prolonged infectivity and reinfection. A series of 414 patients with confirmed SARS-Cov-2 infection, at The Second Affiliated Hospital of Southern University of Science and Technology in Shenzhen, China from January 11 to April 23, 2020. Statistical analyses were performed of the clinical, laboratory, radiologic image, medical treatment, and clinical course of admission/quarantine/readmission data, and a recurrence predictive algorithm was developed. 16.7% recovered patients with PCR positive recurring one to three times, despite being in strict quarantine. Younger patients with mild pulmonary respiratory syndrome had higher risk of PCR positivity recurrence. The recurrence prediction model had an area under the ROC curve of 0.786. This case series provides characteristics of patients with recurrent SARS-CoV-2 positivity. Use of a prediction algorithm may identify patients at high risk of recurrent SARS-CoV-2 positivity and help to establish protocols for health policy.

High-throughput quantitation of serological ceramides/dihydroceramides by LC/MS/MS: Pregnancy baseline biomarkers and potential metabolic messengers

Ceramides and dihydroceramides are sphingolipids that present in abundance at the cellular membrane of eukaryotes. Although their metabolic dysregulation has been implicated in many diseases, our knowledge about circulating ceramide changes during the pregnancy remains limited. In this study, we present the development and validation of a high-throughput liquid chromatography-tandem mass spectrometric method for simultaneous quantification of 16 ceramides and 10 dihydroceramides in human serum within 5 min. by using stable isotope-labeled ceramides as internal standards. This method employs a protein precipitation method for high throughput sample preparation, reverse phase isocratic elusion for chromatographic separation, and Multiple Reaction Monitoring for mass spectrometric detection. To qualify for clinical applications, our assay has been validated against the FDA guidelines for Lower Limit of Quantitation (1 nM), linearity (R²>0.99), precision (imprecision<15 %), accuracy (inaccuracy<15 %), extraction recovery (>90 %), stability (>85 %), and carryover (<0.01 %). With enhanced sensitivity and specificity from this method, we have, for the first time, determined the serological levels of ceramides and dihydroceramides to reveal unique temporal gestational patterns. Our approach could have value in providing insights into disorders of pregnancy.

Racial/ethnic disparities and human milk use in necrotizing enterocolitis

BACKGROUND

The impact of human milk use on racial/ethnic disparities in necrotizing enterocolitis (NEC) incidence is unknown.

METHODS

Trends in NEC incidence and human milk use at discharge were evaluated by race/ethnicity among 47,112 very low birth weight infants born in California from 2008 to 2017. We interrogated the association between race/ethnicity and NEC using multilevel regression analysis, and evaluated the effect of human milk use at discharge on the relationship between race/ethnicity and NEC using mediation analysis.

RESULTS

Annual NEC incidence declined across all racial/ethnic groups from an aggregate average of 4.8% in 2008 to 2.6% in 2017. Human milk use at discharge increased over the time period across all racial groups, and non-Hispanic (NH) black infants received the least human milk each year. In multivariable analyses, Hispanic ethnicity (odds ratio (OR) 1.27, 95% confidence interval (CI) 1.02-1.57) and Asian or Pacific Islander race (OR 1.35, 95% CI 1.01-1.80) were each associated with higher odds of NEC, while the association of NH black race with NEC was attenuated after adding human milk use at discharge to the model. Mediation analysis revealed that human milk use at discharge accounted for 22% of the total risk of NEC in non-white vs. white infants, and 44% in black vs. white infants.

CONCLUSIONS

Although NEC incidence has declined substantially over the past decade, a sizable racial/ethnic disparity persists. Quality improvement initiatives augmenting human milk use may further reduce the incidence of NEC in vulnerable populations.

Treating Center Volume and Congenital Diaphragmatic Hernia Outcomes in California

OBJECTIVE

To examined outcomes for infants born with congenital diaphragmatic hernias (CDH), according to specific treatment center volume indicators.

STUDY DESIGN

A population-based retrospective cohort study was conducted involving neonatal intensive care units in California. Multivariable analysis was used to examine the outcomes of infants with CDH including mortality, total days on ventilation, and respiratory support at discharge. Significant covariables of interest included treatment center surgical and overall neonatal intensive care unit volumes.

RESULTS

There were 728 infants in the overall CDH cohort, and 541 infants (74%) in the lower risk subcohort according to a severity-weighted congenital malformation score and never requiring extracorporeal membrane oxygenation. The overall cohort mortality was 28.3% (n = 206), and 19.8% (n = 107) for the subcohort. For the lower risk subcohort, the adjusted odds of mortality were significantly lower at treatment centers with higher CDH repair volume (OR, 0.41; 95% CI, 0.23-0.75; P = .003), ventilator days were significantly lower at centers with higher thoracic surgery volume (OR, 0.56; 9 5% CI, 0.33-0.95; P = .03), and respiratory support at discharge trended lower at centers with higher neonatal intensive care unit admission volumes (OR, 0.51; 9 5% CI, 0.26-1.02; P = .06).

CONCLUSIONS

Overall and surgery-specific institutional experience significantly contribute to optimized outcomes for infants with CDH. These data and follow-on studies may help inform the ongoing debate over the optimal care setting and relevant quality indicators for newborn infants with major surgical anomalies.

Survival of infants with congenital diaphragmatic hernia in California: impact of hospital, clinical, and sociodemographic factors

OBJECTIVE

To understand factors associated with care and survival among babies with congenital diaphragmatic hernia (CDH).

STUDY DESIGN

We used data on California births (2006-2011) to examine birth hospital level of care, hospital transfer before repair, and survival.

RESULT

Among 577 infants, 25% were born at lower-level hospitals, 62% were transferred, and 31% died during infancy. Late or no prenatal care had the strongest association with birth at lower-level hospitals (adjusted relative risk (ARR) = 1.9, 95% confidence interval (CI) = 1.0-3.6). Birth at lower-level hospitals was associated with transfer (ARR = 1.2, CI = 1.1-1.4), and transferred infants tended to be less clinically complex. Infants with low birthweight, other birth defects, low Apgar scores, and late or no prenatal care had two- to fourfold higher risk of mortality than their comparison groups.

CONCLUSIONS

These data support the importance of prenatal care and delivery planning into higher-level hospitals for optimal care and outcomes for newborns with CDH.

Proteomic profiling to identify therapeutics targets in glioblastoma (GBM)

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Background: Glioblastoma (GBM) is an aggressive primary brain tumor with poor prognosis. Treatment at diagnosis is largely confined to surgery, radiation and temozolomide (TMZ) with median progression-free survival (PFS) of 7 months and median overall survival (mOS) of 15 months. GBM tumors recur in most cases and in patients with recurrent GBM, the mOS is 6.2 months. The lack of effective therapies underscores the importance of exploring other agents. We propose that quantitating therapy-associated protein biomarkers can improve treatment personalization for GBM. Methods: 97 FFPE GBM tissues were microdissected and solubilized for mass spectrometry-based proteomic analysis of therapy-associated protein biomarkers in our CLIA certified lab. We quantified protein levels of MGMT, hENT1, RRM1, TOPO1 and EGFR/TUBB3 (antibody target and payload resistance markers, respectively, for anti-EGFR ADCs) simultaneously. The multiplexed assay also quantified additional 24 clinically relevant proteins. Results: 43/57 patients were predicted to respond to TMZ based on undetectable levels of MGMT, confirming wide utility of this agent. 42/97(43%) patients were predicted to have gemcitabine sensitivity based on high expression of the response marker (hENT1 > 100 amol/ug) and low expression of the resistance marker (RRM1 < 700 amol/ug). 11/97(11%) patients expressed TOPO1 > 1350 amol/ug (75th percentile of all indications tested by author’s laboratory), suggesting likely response to irinotecan and topotecan. EGFR expression ranged from < 100 amol/ug to > 25000 amol/ug, including overexpression (> 1500 amol/ug) in 22%(21/97) of cases. While expression of EGFR(81/97, 84%) suggested likely response to anti-EGFR ADC, concurrent expression of TUBB3(78/81) may indicate resistance to several known payloads, such as taxanes and MMAE. Conjugation with another payload that targets sensitivity marker TOPO1 (68% expression) is a likely option. Proteomic analysis also revealed detectable levels of multiple RTKs (FGFR(4), AXL(20), IGF1R(10), MET overexpression(1), and HER2 overexpression(2)), indicating potential response to RTK inhibitors. Exploratory investigation in tumor vs TME using proteomics and metabolomics is ongoing. Conclusions: In this population of GBM patients, proteomic analysis identified protein targets of multiple approved and investigational therapies. Gemcitabine, which crosses the blood-brain barrier, may be considered as a salvage option after TMZ failure. Proteomic quantitation of EGFR and TUBB3 may improve patient selection for EGFR-targeting ADCs.

Deviation from the precisely timed phenomic ageotypes can assist in early CRC screening and reveal underlying pathophysiology

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Background: Human serum proteome and metabolome profiles have been analyzed to explore the molecular changes that occur with aging. We hypothesized that deep phenomic profiling of longitudinal sera would allow the identification of distinct phenomic chronologic patterns as a normal biological clock baseline to study personal aging. We further hypothesized that molecular assessment of this chronologic deviation, due to advance adenoma and early CRC, from the normal reference ageotypes would be instrumental as potential early diagnostics and reveal underlying pathophysiology. Methods: A cohort of 7673 normal, 746 advanced adenoma, 1177 stage I, 103 stage II and 119 stage III blood samples were assembled. Innovative multi-omics approaches, with global and targeted LCMS data production (metabolomics, lipidomics, and 2D proteomics), were applied to deep profile these subjects. Identification of the age-associated molecular patterns in normal subjects, modeled with an elastic net algorithm, established the reference baseline to mirror a biological clock. CRC associated deviation from this chronologically paced multi-omics clock was quantified to screen for early CRCs and explore the underlying pathophysiology. Results: Multiple mProbe aging indices of proteins and metabolites were identified, strongly predicting chronologic age ( P < 0.001, R > 0.90). Significant disruptions from normal molecular patterns were observed in advanced adenoma and early CRCs patients (R < 0.7). Pathway analysis of the proteins/metabolites with deviating patterns revealed both known and new pathways underlying CRC. Unsupervised cluster analysis identified unique aging subgroups among advanced adenoma and different stage CRC patients, indicating unique underlying biology relating to aging with different severities of cancer burdens. Conclusions: Deviations from the meticulously timed phenomic aging patterns may provide utility to allow future early CRC screening. Close examination of the underlying pathophysiology associated with early CRC, relating to ageotypes, not only may improve the sensitivity and specificity of prognostic and diagnostic tests of early CRCs, but also shed new insights into CRC therapeutics.

Changes in pregnancy-related serum biomarkers early in gestation are associated with later development of preeclampsia

Background

Placental protein expression plays a crucial role during pregnancy. We hypothesized that: (1) circulating levels of pregnancy-associated, placenta-related proteins throughout gestation reflect the temporal progression of the uncomplicated, full-term pregnancy, and can effectively estimate gestational ages (GAs); and (2) preeclampsia (PE) is associated with disruptions in these protein levels early in gestation; and can identify impending PE. We also compared gestational profiles of proteins in the human and mouse, using pregnant heme oxygenase-1 (HO-1) heterozygote (Het) mice, a mouse model reflecting PE-like symptoms.

Methods

Serum levels of placenta-related proteins–leptin (LEP), chorionic somatomammotropin hormone like 1 (CSHL1), elabela (ELA), activin A, soluble fms-like tyrosine kinase 1 (sFlt-1), and placental growth factor (PlGF)–were quantified by ELISA in blood serially collected throughout human pregnancies (20 normal subjects with 66 samples, and 20 subjects who developed PE with 61 samples). Multivariate analysis was performed to estimate the GA in normal pregnancy. Mean-squared errors of GA estimations were used to identify impending PE. The human protein profiles were then compared with those in the pregnant HO-1 Het mice.

Results

An elastic net-based gestational dating model was developed (R² = 0.76) and validated (R² = 0.61) using serum levels of the 6 proteins measured at various GAs from women with normal uncomplicated pregnancies. In women who developed PE, the model was not (R² = -0.17) associated with GA. Deviations from the model estimations were observed in women who developed PE (P = 0.01). The model developed with 5 proteins (ELA excluded) performed similarly from sera from normal human (R² = 0.68) and WT mouse (R² = 0.85) pregnancies. Disruptions of this model were observed in both human PE-associated (R² = 0.27) and mouse HO-1 Het (R² = 0.30) pregnancies. LEP outperformed sFlt-1 and PlGF in differentiating impending PE at early human and late mouse GAs.

Conclusions

Serum placenta-related protein profiles are temporally regulated throughout normal pregnancies and significantly disrupted in women who develop PE. LEP changes earlier than the well-established biomarkers (sFlt-1 and PlGF). There may be evidence of a causative action of HO-1 deficiency in LEP upregulation in a PE-like murine model.

Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm

OBJECTIVE

Predicting the risk of falls in advance can benefit the quality of care and potentially reduce mortality and morbidity in the older population. The aim of this study was to construct and validate an electronic health record-based fall risk predictive tool to identify elders at a higher risk of falls.

METHODS

The one-year fall prediction model was developed using the machine-learning-based algorithm, XGBoost, and tested on an independent validation cohort. The data were collected from electronic health records (EHR) of Maine from 2016 to 2018, comprising 265,225 older patients (≥65 years of age).

RESULTS

This model attained a validated C-statistic of 0.807, where 50 % of the identified high-risk true positives were confirmed to fall during the first 94 days of next year. The model also captured in advance 58.01 % and 54.93 % of falls that happened within the first 30 and 30-60 days of next year. The identified high-risk patients of fall showed conditions of severe disease comorbidities, an enrichment of fall-increasing cardiovascular and mental medication prescriptions and increased historical clinical utilization, revealing the complexity of the underlying fall etiology. The XGBoost algorithm captured 157 impactful predictors into the final predictive model, where cognitive disorders, abnormalities of gait and balance, Parkinson's disease, fall history and osteoporosis were identified as the top-5 strongest predictors of the future fall event.

CONCLUSIONS

By using the EHR data, this risk assessment tool attained an improved discriminative ability and can be immediately deployed in the health system to provide automatic early warnings to older adults with increased fall risk and identify their personalized risk factors to facilitate customized fall interventions.

Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records

Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to the risk of eventual death by suicide. In this study we sought to develop an EWS for high-risk suicide attempt patients through the development of a population-based risk stratification surveillance system. Advanced machine-learning algorithms and deep neural networks were utilized to build models with the data from electronic health records (EHRs). A final risk score was calculated for each individual and calibrated to indicate the probability of a suicide attempt in the following 1-year time period. Risk scores were subjected to individual-level analysis in order to aid in the interpretation of the results for health-care providers managing the at-risk cohorts. The 1-year suicide attempt risk model attained an area under the curve (AUC ROC) of 0.792 and 0.769 in the retrospective and prospective cohorts, respectively. The suicide attempt rate in the "very high risk" category was 60 times greater than the population baseline when tested in the prospective cohorts. Mental health disorders including depression, bipolar disorders and anxiety, along with substance abuse, impulse control disorders, clinical utilization indicators, and socioeconomic determinants were recognized as significant features associated with incident suicide attempt.

Expression of antibody-drug conjugates (ADC) biomarkers in colorectal cancer

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Background: Multiple ADCs are in clinical trials for CRC and the optimal strategy for selecting patients who may benefit from the treatment is evolving. Due to the unique mechanism of ADCs, patient selection should involve screening not only for the presence of the antibody target, but also for markers of resistance or response to the payload. We have built a multiplexed ADC biomarker panel in FFPE tumor tissue that simultaneously quantifies the protein levels of the antibody targets and also the payload markers. Methods: FFPE tumor tissues from 363 CRC patients were microdissected and solubilized for mass spectrometry-based targeted proteomic analysis in our CLIA certified laboratory. We quantified protein levels of EGFR, HER2, HER3, Axl, Mesothelin, FRalpha, Trop2 (antibody targets), tubulin-beta3 and TOPO1 (payload resistance and response markers, respectively) simultaneously. The multiplexed assay also quantified additional 22 clinically relevant proteins. Results: Expression of EGFR(83%), HER2(52%), HER3(21.5%), Axl(3.7%), Mesothelin(26.5%), FRalpha(3.7%), and Trop2(59.8%) may indicate likely response to ADCs. Expression of TUBB3(+) and TOPO1 (>1350amol/µg) in antibody target-positive subset may suggest resistance or response to payloads, such as taxanes and irinotecan, respectively (Table). Previously we identified that HER2 expression >750amol/µg correlated with HER2 positivity. Accordingly, 1.4% (5/355) of CRC patients were HER2 positive, of which 40% (2/5) had TOPO1 expression >1350amol/µg (75th percentile) suggesting that these 2 patients may receive benefit from a HER2/TOPO1 ADC. (+) indicates expression ≥LOQ; (-) indicates expression <LOQ. Conclusions: In patients with CRC, quantitative proteomics identified both antibody targets and markers of resistance or response to the payloads for multiple approved and investigational ADC therapies.

[Table: see text]

Integrating multiple “omics” analyses, on a triage concept, for effective case selection followed by diagnostic colonoscopy

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Background: Implementation of population screening for colorectal cancer (CRC) before colonoscopy can reduce the challenge of the overall capacity of bowel examination and improve survival. Blood based CRC assessment biomarkers, on a triage concept, can lead to improved selection to colonoscopy and cost-effective CRC care. Methods: Innovative multi-omics approaches, with global and targeted LCMS data production (metabolomics, lipidomics, and 2D proteomics) and integrative data analytics, were applied to discover serological biomarkers to assess nonadvanced adenoma and identify stage I/II colorectal bowel lesions. A cohort of 2396 normal, 660 adenoma, 953 stage I, and 101 stage II blood samples, was constructed to discover screening biomarkers to support case finding of patients at high risk for nonadvanced adenoma and stage I/II cancer for subsequent diagnostic colonoscopy. Results: A three-analyte mProbe panel was constructed which outperformed the commercial assays of plasma methylated septin 9 and fecal Cologuard tests. Sensitivity: (1) nonadvanced adenoma–Cologuard 17.2%, mProbe 76.0%; (2) stage I-III-Cologuard 93.3%, stage I-II Septin 9 (ARUP laboratories) 77%, stage I-II mProbe: 92.3%. Specificity–Cologuard 89.8%, Septin 9 (ARUP laboratories) 88%, mProbe 90.7%. Conclusions: mProbe triage concept of a blood-based protein biomarker panel promises the precision to allow future CRC screening, and reduce the low-risk utilization of unnecessary, unpleasant and risk-associated bowel examinations.

In fetuses with congenital lung masses, decreased ventricular and atrioventricular valve dimensions are associated with lesion size and clinical outcome

INTRODUCTION

The clinical importance of mass effect from congenital lung masses on the fetal heart is unknown. We aimed to report cardiac measurements in fetuses with congenital lung masses and to correlate lung mass severity/size with cardiac dimensions and clinical outcomes.

METHODS

Cases were identified from our institutional database between 2009 and 2016. We recorded atrioventricular valve (AVVz) annulus dimensions and ventricular widths (VWz) converted into z scores, ratio of aortic to total cardiac output (AoCO), lesion side, and congenital pulmonary airway malformation volume ratio (CVR). Respiratory intervention (RI) was defined as intubation, extracorporeal membrane oxygenation (ECMO), or use of surgical intervention prior to discharge.

RESULTS

Fifty-two fetuses comprised the study cohort. Mean AVVz and VWz were below expected for gestational age. CVR correlated with ipsilateral AVVz (R_S = -.59, P < .001) and ipsilateral VWz (-0.59, P < .001). Lower AVVz and AoCO and higher CVR were associated with RI. No patient had significant structural heart disease identified postnatally.

CONCLUSION

In fetuses with left-sided lung masses, ipsilateral cardiac structures tend to be smaller, but in our cohort, there were no patients with structural heart disease. However, smaller left-sided structures may contribute to the need for RI that affects a portion of these fetuses.

Obstetric and neonatal outcomes in pregnancies complicated by fetal lung masses: does final histology matter?. J Matern Fetal Neonatal Med 2019;34:3662-3668. [PMID: 31722592 DOI: 10.1080/14767058.2019.1689559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Purpose: Fetal lung masses complicate approximately 1 in 2000 live births. Our aim was to determine whether obstetric and neonatal outcomes differ by final fetal lung mass histology.Materials and methods: A review of all pregnancies complicated by a prenatally diagnosed fetal lung mass between 2009 and 2017 at a single academic center was conducted. All cases included in the final analysis underwent surgical resection and histology diagnosis was determined by a trained pathologist. Clinical data were obtained from review of stored electronic medical records which contained linked maternal and neonatal records. Imaging records included both prenatal ultrasound and magnetic resonance imaging. Fisher's exact test was used for categorical variables and the Kruskal-Wallis test was used for continuous variables. The level of significance was p<.05.Results: Of 61 pregnancies complicated by fetal lung mass during the study period, 45 cases underwent both prenatal care and postnatal resection. Final histology revealed 10 cases of congenital pulmonary airway malformation (CPAM) type 1, nine cases of CPAM type 2, and 16 cases of bronchopulmonary sequestration. There was no difference in initial, maximal, or final CPAM volume ratio between groups, with median final CPAM volume ratio of 0.6 for CPAM type 1, 0.7 for CPAM type 2, and 0.3 for bronchopulmonary sequestration (p = .12). There were no differences in any of the maternal or obstetric outcomes including gestational age at delivery and mode of delivery between the groups. The primary outcome of neonatal respiratory distress was not statistically different between groups (p = .66). Median neonatal length of stay following delivery ranged from 3 to 4 days, and time to postnatal resection was similar as well, with a median of 126 days for CPAM type 1, 122 days for CPAM type 2, and 132 days for bronchopulmonary sequestration (p = .76).Conclusions: In our cohort, there was no significant association between histologic lung mass subtypes and any obstetric or neonatal morbidity including respiratory distress.

A proteomic clock for malignant gliomas: The role of the environment in tumorigenesis at the presymptomatic stage

Malignant gliomas remain incurable with a poor prognosis despite of aggressive treatment. We have been studying the development of brain tumors in a glioma rat model, where rats develop brain tumors after prenatal exposure to ethylnitrosourea (ENU), and there is a sizable interval between when the first pathological changes are noted and tumors become detectable with MRI. Our aim to define a molecular timeline through proteomic profiling of the cerebrospinal fluid (CSF) such that brain tumor commitment can be revealed earlier than at the presymptomatic stage. A comparative proteomic approach was applied to profile CSF collected serially either before, at and after the time MRI becomes positive. Elastic net (EN) based models were developed to infer the timeline of normal or tumor development respectively, mirroring a chronology of precisely timed, “clocked”, adaptations. These CSF changes were later quantified by longitudinal entropy analyses of the EN predictive metric. False discovery rates (FDR) were computed to control the expected proportion of the EN models that are due to multiple hypothesis testing. Our ENU rat brain tumor dating EN model indicated that protein content in CSF is programmed even before tumor MRI detection. The findings of the precisely timed CSF tumor microenvironment changes at presymptomatic stages, deviation from the normal development timeline, may provide the groundwork for the understanding of adaptation of the brain environment in tumorigenesis to devise effective brain tumor management strategies.

Effects of gestational age at delivery and type of labor on neonatal outcomes among infants with gastroschisis†

OBJECTIVE

To investigate the effect of preterm gestational age (GA) on neonatal outcomes of gastroschisis and to compare the neonatal outcomes after spontaneous labor versus iatrogenic delivery both in the preterm and early term gestational periods.

STUDY DESIGN

A retrospective study of prenatally diagnosed gastroschisis cases born at Loma Linda University Medical Center and Lucile Packard Children's Hospital (Loma Linda, CA) between January 2009 and October 2016. A total of 194 prenatally diagnosed gastroschisis cases were identified and included in the analysis. We compared infants delivered <37 0/7 to those ≥37 0/7 weeks' gestation. Adverse neonatal outcome was defined as any of: sepsis, short bowel syndrome, prolonged ventilation, or death. Prolonged length of stay (LOS) was defined as ≥75th percentile value. Outcomes following spontaneous versus iatrogenic delivery were compared. Analyses were performed using chi-squared test or Fisher's exact test for categorical variables, and Student's t-test or Wilcoxon's rank-sum test for continuous variables.

RESULTS

One hundred and six neonates were born <37 weeks and 88 at ≥37 weeks. Adverse outcome was statistically similar among those born <37 weeks compared to ≥37 weeks (48 versus 34%, p = .07). Prolonged LOS was more frequent among neonates delivered <37 weeks (p = .03). Among neonates born <37 weeks, bowel atresia was more frequent in those with spontaneous versus iatrogenic delivery (p = .04). There was no significant difference in the adverse neonatal composite outcome between those with spontaneous preterm labor versus planned iatrogenic delivery at <37 weeks (n = 30 (58%) versus n = 21 (39%), p = .08).

CONCLUSIONS

Neonates with gastroschisis delivered <37 weeks had prolonged LOS whereas the rate of adverse neonatal outcomes was similar between those delivered preterm versus term. Neonates born after spontaneous preterm labor had a higher rate of bowel atresia compared to those born after planned iatrogenic preterm delivery.

A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data

BACKGROUND

The rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfect triage and level of care assignment after their admission. An early warning system (EWS) to identify patients at high risk of subsequent intrahospital death can be an effective tool for ensuring patient safety and quality of care and reducing avoidable harm and costs.

OBJECTIVE

The aim of this study was to prospectively validate a real-time EWS designed to predict patients at high risk of inpatient mortality during their hospital episodes.

METHODS

Data were collected from the system-wide electronic medical record (EMR) of two acute Berkshire Health System hospitals, comprising 54,246 inpatient admissions from January 1, 2015, to September 30, 2017, of which 2.30% (1248/54,246) resulted in intrahospital deaths. Multiple machine learning methods (linear and nonlinear) were explored and compared. The tree-based random forest method was selected to develop the predictive application for the intrahospital mortality assessment. After constructing the model, we prospectively validated the algorithms as a real-time inpatient EWS for mortality.

RESULTS

The EWS algorithm scored patients' daily and long-term risk of inpatient mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, the EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 69% (68/99) of whom died during the episodes. It accurately predicted the possibility of death for the top 13.3% (34/255) of the patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs, and laboratory test results were recognized as impactful predictors in the final EWS.

CONCLUSIONS

In this study, we prospectively demonstrated the capability of the newly-designed EWS to monitor and alert clinicians about patients at high risk of in-hospital death in real time, thereby providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for patients' better health outcomes in target medical facilities.

Prediction of the 1-Year Risk of Incident Lung Cancer: Prospective Study Using Electronic Health Records from the State of Maine

BACKGROUND

Lung cancer is the leading cause of cancer death worldwide. Early detection of individuals at risk of lung cancer is critical to reduce the mortality rate.

OBJECTIVE

The aim of this study was to develop and validate a prospective risk prediction model to identify patients at risk of new incident lung cancer within the next 1 year in the general population.

METHODS

Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. The study population consisted of patients with at least one EHR between April 1, 2016, and March 31, 2018, who had no history of lung cancer. A retrospective cohort (N=873,598) and a prospective cohort (N=836,659) were formed for model construction and validation. An Extreme Gradient Boosting (XGBoost) algorithm was adopted to build the model. It assigned a score to each individual to quantify the probability of a new incident lung cancer diagnosis from October 1, 2016, to September 31, 2017. The model was trained with the clinical profile in the retrospective cohort from the preceding 6 months and validated with the prospective cohort to predict the risk of incident lung cancer from April 1, 2017, to March 31, 2018.

RESULTS

The model had an area under the curve (AUC) of 0.881 (95% CI 0.873-0.889) in the prospective cohort. Two thresholds of 0.0045 and 0.01 were applied to the predictive scores to stratify the population into low-, medium-, and high-risk categories. The incidence of lung cancer in the high-risk category (579/53,922, 1.07%) was 7.7 times higher than that in the overall cohort (1167/836,659, 0.14%). Age, a history of pulmonary diseases and other chronic diseases, medications for mental disorders, and social disparities were found to be associated with new incident lung cancer.

CONCLUSIONS

We retrospectively developed and prospectively validated an accurate risk prediction model of new incident lung cancer occurring in the next 1 year. Through statistical learning from the statewide EHR data in the preceding 6 months, our model was able to identify statewide high-risk patients, which will benefit the population health through establishment of preventive interventions or more intensive surveillance.

Biomarker Discovery and Utility in Necrotizing Enterocolitis

Necrotizing enterocolitis (NEC) is a devastating disease of prematurity, with no current method for early diagnosis. Diagnosis is particularly challenging, frequently occurring after the disease has progressed to the point of significant and often irreversible intestinal damage. Biomarker research has tremendous potential to advance clinical management of NEC and our understanding of its pathogenesis. This review discusses the need for novel biomarkers in NEC management, evaluates studies investigating such biomarkers, and explains the difficulties associated with translating biomarker discovery into clinical use.

Predicting Pathology From Imaging in Children Undergoing Resection of Congenital Lung Lesions

BACKGROUND

Prenatal magnetic resonance imaging (MRI) is increasingly obtained to define congenital lung lesions (CLL) for surgical management. Postnatal, preoperative computed tomography (CT) provides further clarity at the cost of radiation. Depending on the lesion identified, the indication for resection remains controversial. We investigated the differences in detail found on prenatal MRI and postnatal CT compared with final pathology to determine their utility in preoperative decision-making.

MATERIALS AND METHODS

All children undergoing resection of CLLs at a single institution between July 2009 and February 2018 were retrospectively identified. Their imaging, operative, and pathology reports were compared. All imaging studies were examined by pediatric radiologists with experience in prenatal CLL diagnosis.

RESULTS

Fifty-five patients underwent CLL resection during the study period with 31 undergoing prenatal MRI, 45 postnatal CT, and 22 both. Resection was performed before 6 mo of age in 62% of patients. In the cohort undergoing both imaging studies, pathologic CLL diagnosis correlated with prenatal MRI and CT in 82% and 100% of patients, respectively (P = 0.13). Eight patients had systemic feeding vessels, of which 38% were identified on MRI, and 88% on CT (P = 0.13). Both studies had a specificity of 100% for detecting systemic feeding vessels.

CONCLUSIONS

For children where prenatal MRI detected a systemic feeding vessel, CT was redundant for preoperative planning but had greater sensitivity. Ultimately, the CLL type predicted from postnatal CT was not significantly different from that predicted by prenatal MRI; however, both imaging modalities had some level of discrepancy with pathology.

Abstract 5569: Donor-derived circulating cell-free DNA (ccfDNA) reference materials for concordance studies

The limited quantities of ccfDNA in plasma can make it difficult to assess the sensitivities and specificities of circulating tumor DNA (ctDNA) assays because there is often insufficient material to confirm the presence or absence of particular mutations by orthogonal assays. Additionally, there have been reports of discordance between different ctDNA assays with patient samples at lower variant allele frequencies (VAF), but the limited amount of material is a bottleneck to investigating the causes of discordance. To overcome this, we developed a method to amplify nanograms of ccfDNA from donors to microgram quantities. As an initial proof of concept, we amplified ccfDNA from pregnant female donors that were known to be carrying either a normal fetus or one with chromosomal abnormalities. These samples were analyzed externally at commercial Non-Invasive Prenatal Testing (NIPT) laboratories and were found to be compatible with both chromosome counting and SNP-based testing, and fetal fraction could also be assessed. In order to evaluate the suitability of amplified ccfDNA for ctDNA assays, we split ccfDNA samples so that part was analyzed as-is by a given ctDNA assay and part was analyzed after amplification. The same amplified material was also analyzed with four different ctDNA assays on different NGS platforms in order to assess concordance between assays in shared regions. Good concordance was seen between variants and their VAFs identified in native and amplified ccfDNA, although increasing amounts of discordant background errors were observed at lower VAF. With the Archer Reveal ctDNA 28 assay, obtaining more unique fragments was associated with obtaining more low VAF background errors that could be offset by obtaining more replicate reads of a given unique fragment. Discordant variants were often C&gt;T (or the corresponding G&gt;A) that can be generated by cytosine deamination, which can occur by heating the sample during PCR for library construction. In conclusion, we have developed a method for amplifying limited amounts of ccfDNA that generates sufficient donor ccfDNA-like material for analysis by multiple assays. This should enable the development, improvement and validation of assays that analyze ccfDNA - and especially ctDNA - because sufficient material can be generated in order to assess sensitivity and specificity using orthogonal assays. Additionally, this should also allow for concordance studies that attempt to analyze the same material at multiple sites and with multiple assays.

Citation Format: Yves Konigshofer, Matthew G. Butler, Jessica Dickens, Katherine Bianco, Karl G. Sylvester, Bharathi Anekella, Russell K. Garlick. Donor-derived circulating cell-free DNA (ccfDNA) reference materials for concordance studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5569.