Proteogenomic data and resources for pan-cancer analysis.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer.

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

iProMix: A mixture model for studying the function of ACE2 based on bulk proteogenomic data.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused over six million deaths in the ongoing COVID-19 pandemic. SARS-CoV-2 uses ACE2 protein to enter human cells, raising a pressing need to characterize proteins/pathways interacted with ACE2. Large-scale proteomic profiling technology is not mature at single-cell resolution to examine the protein activities in disease-relevant cell types. We propose iProMix, a novel statistical framework to identify epithelial-cell specific associations between ACE2 and other proteins/pathways with bulk proteomic data. iProMix decomposes the data and models cell-type-specific conditional joint distribution of proteins through a mixture model. It improves cell-type composition estimation from prior input, and utilizes a non-parametric inference framework to account for uncertainty of cell-type proportion estimates in hypothesis test. Simulations demonstrate iProMix has well-controlled false discovery rates and favorable powers in non-asymptotic settings. We apply iProMix to the proteomic data of 110 (tumor adjacent) normal lung tissue samples from the Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma study, and identify interferon α/γ response pathways as the most significant pathways associated with ACE2 protein abundances in epithelial cells. Strikingly, the association direction is sex-specific. This result casts light on the sex difference of COVID-19 incidences and outcomes, and motivates sex-specific evaluation for interferon therapies.

Metabolic engineering and mechanical investigation of enhanced plant autoluminescence.

The fungal bioluminescence pathway (FBP) was identified from glowing fungi, which releases self-sustained visible green luminescence. However, weak bioluminescence limits the potential application of the bioluminescence system. Here, we screened and characterized a C3'H1 (4-coumaroyl shikimate/quinate 3'-hydroxylase) gene from Brassica napus, which efficiently converts p-coumaroyl shikimate to caffeic acid and hispidin. Simultaneous expression of BnC3'H1 and NPGA (null-pigment mutant in A. nidulans) produces more caffeic acid and hispidin as the natural precursor of luciferin and significantly intensifies the original fungal bioluminescence pathway (oFBP). Thus, we successfully created enhanced FBP (eFBP) plants emitting 3 × 1011 photons/min/cm2 , sufficient to illuminate its surroundings and visualize words clearly in the dark. The glowing plants provide sustainable and bio-renewable illumination for the naked eyes, and manifest distinct responses to diverse environmental conditions via caffeic acid biosynthesis pathway. Importantly, we revealed that the biosynthesis of caffeic acid and hispidin in eFBP plants derived from the sugar pathway, and the inhibitors of the energy production system significantly reduced the luminescence signal rapidly from eFBP plants, suggesting that the FBP system coupled with the luciferin metabolic flux functions in an energy-driven way. These findings lay the groundwork for genetically creating stronger eFBP plants and developing more powerful biological tools with the FBP system.

MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer.

Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study approaches predict genetically regulated gene expression at the tissue level, without considering cell-type heterogeneity, and test associations of predicted tissue-level expression with disease. Here we develop MiXcan, a cell-type-aware transcriptome-wide association study approach that predicts cell-type-level expression, identifies disease-associated genes via combination of cell-type-level association signals for multiple cell types, and provides insight into the disease-critical cell type. As a proof of concept, we conducted cell-type-aware analyses of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-aware transcriptome-wide analyses can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases.

Estimating the causal effects of multiple intermittent treatments with application to COVID-19.

To draw real-world evidence about the comparative effectiveness of multiple time-varying treatments on patient survival, we develop a joint marginal structural survival model and a novel weighting strategy to account for time-varying confounding and censoring. Our methods formulate complex longitudinal treatments with multiple start/stop switches as the recurrent events with discontinuous intervals of treatment eligibility. We derive the weights in continuous time to handle a complex longitudinal dataset without the need to discretize or artificially align the measurement times. We further use machine learning models designed for censored survival data with time-varying covariates and the kernel function estimator of the baseline intensity to efficiently estimate the continuous-time weights. Our simulations demonstrate that the proposed methods provide better bias reduction and nominal coverage probability when analyzing observational longitudinal survival data with irregularly spaced time intervals, compared to conventional methods that require aligned measurement time points. We apply the proposed methods to a large-scale COVID-19 dataset to estimate the causal effects of several COVID-19 treatments on the composite of in-hospital mortality and ICU admission.

Power of Bayesian and Heuristic Tests to Detect Cross-Species Introgression with Reference to Gene Flow in the Tamias quadrivittatus Group of North American Chipmunks.

In the past two decades, genomic data have been widely used to detect historical gene flow between species in a variety of plants and animals. The Tamias quadrivittatus group of North America chipmunks, which originated through a series of rapid speciation events, are known to undergo massive amounts of mitochondrial introgression. Yet in a recent analysis of targeted nuclear loci from the group, no evidence for cross-species introgression was detected, indicating widespread cytonuclear discordance. The study used the heuristic method HYDE to detect gene flow, which may suffer from low power. Here we use the Bayesian method implemented in the program BPP to re-analyze these data. We develop a Bayesian test of introgression, calculating the Bayes factor via the Savage-Dickey density ratio using the Markov chain Monte Carlo (MCMC) sample under the model of introgression. We take a stepwise approach to constructing an introgression model by adding introgression events onto a well-supported binary species tree. The analysis detected robust evidence for multiple ancient introgression events affecting the nuclear genome, with introgression probabilities reaching 63%. We estimate population parameters and highlight the fact that species divergence times may be seriously underestimated if ancient cross-species gene flow is ignored in the analysis. We examine the assumptions and performance of HYDE and demonstrate that it lacks power if gene flow occurs between sister lineages or if the mode of gene flow does not match the assumed hybrid-speciation model with symmetrical population sizes. Our analyses highlight the power of likelihood-based inference of cross-species gene flow using genomic sequence data. [Bayesian test; BPP; chipmunks; introgression; MSci; multispecies coalescent; Savage-Dickey density ratio.].

The miR408a-BBP-LAC3/CSD1 module regulates anthocyanin biosynthesis mediated by crosstalk between copper homeostasis and ROS homeostasis during light induction in Malus plants.

INTRODUCTION: The expression of miR408 is affected by copper (Cu) conditions and positively regulates anthocyanin biosynthesis in Arabidopsis. However, the underlying mechanisms by which miR408 regulates anthocyanin biosynthesis mediated by Cu homeostasis and reactive oxygen species (ROS) homeostasis remain unclear in Malus plants. OBJECTIVES: Our study aims to elucidate how miR408a and its target, basic blue protein (BBP) regulate Cu homeostasis and ROS homeostasis, and anthocyanin biosynthesis in Malus plants. METHODS: The roles of miR408a and its target BBP in regulating anthocyanin biosynthesis, Cu homeostasis, and ROS homeostasis were mainly identified in Malus plants. RESULTS: We found that the BBP protein interacted with the copper-binding proteins LAC3 (laccase) and CSD1 (Cu/Zn SOD superoxide dismutase), indicating a potential crosstalk between Cu homeostasis and ROS homeostasis might be mediated by miR408 to regulate the anthocyanin accumulation. Further studies showed that overexpressing miR408a or suppressing BBP transiently significantly increased the expression of genes related to Cu binding and Cu transport, leading to anthocyanin accumulation under light induction in apple fruit and Malus plantlets. Consistently, opposite results were obtained when repressing miR408a or overexpressing BBP. Moreover, light induction significantly increased the expression of miR408a, CSD1, and LAC3, but significantly reduced the BBP expression, resulting in increased Cu content and anthocyanin accumulation. Furthermore, excessive Cu significantly increased the anthocyanin accumulation, accompanied by reduced expression of miR408a and Cu transport genes, and upregulated expression of Cu binding proteins including BBP, LAC3, and CSD1 to maintain the Cu homeostasis and ROS homeostasis in Malus plantlets. CONCLUSION: Our findings provide new insights into the mechanism by which the miR408a-BBP-LAC3/CSD1 module perceives light and Cu signals regulating Cu and ROS homeostasis, ultimately affecting anthocyanin biosynthesis in Malus plants.

A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations.

Personalized medicine requires an understanding of treatment effect heterogeneity. Evolving toward causal evidence for scenarios not studied in randomized trials necessitates a methodology using real-world evidence. Herein, we demonstrate a methodology that generates causal effects, assesses the heterogeneity of the effects and adjusts for the clustered nature of the data. This study uses a state-of-the-art machine learning survival model, riAFT-BART, to draw causal inferences about individual survival treatment effects, while accounting for the variability in institutional effects; further, it proposes a data-driven approach to agnostically (as opposed to a priori hypotheses) ascertain which subgroups exhibit an enhanced treatment effect from which intervention, relative to global evidence-average treatment effects measured at the population level. Comprehensive simulations show the advantages of the proposed method in terms of bias, efficiency and precision in estimating heterogeneous causal effects. The empirically validated method was then used to analyze the National Cancer Database.

Homogeneous Lateral Lithium Intercalation into Transition Metal Dichalcogenides via Ion Backgating.

Lithium intercalation has become a versatile tool for realizing emergent quantum phenomena in two-dimensional (2D) materials. However, the insertion of lithium ions may be accompanied by the creation of wrinkles and cracks, which prevents the material from manifesting its intrinsic properties under substantial charge injection. By using the recently developed ion backgating technique, we successfully realize lateral intercalation in 1T-TiSe2 and 2H-NbSe2, which shows substantially improved sample homogeneity. The homogeneity at high lithium doping is not only demonstrated via low-temperature transport measurements but also directly visualized by topographical imaging through in situ atomic force microscopy (AFM). The application of lateral intercalation to a broad spectrum of 2D materials can greatly facilitate the search for exotic quantum phenomena.

A flexible approach for causal inference with multiple treatments and clustered survival outcomes.

When drawing causal inferences about the effects of multiple treatments on clustered survival outcomes using observational data, we need to address implications of the multilevel data structure, multiple treatments, censoring, and unmeasured confounding for causal analyses. Few off-the-shelf causal inference tools are available to simultaneously tackle these issues. We develop a flexible random-intercept accelerated failure time model, in which we use Bayesian additive regression trees to capture arbitrarily complex relationships between censored survival times and pre-treatment covariates and use the random intercepts to capture cluster-specific main effects. We develop an efficient Markov chain Monte Carlo algorithm to draw posterior inferences about the population survival effects of multiple treatments and examine the variability in cluster-level effects. We further propose an interpretable sensitivity analysis approach to evaluate the sensitivity of drawn causal inferences about treatment effect to the potential magnitude of departure from the causal assumption of no unmeasured confounding. Expansive simulations empirically validate and demonstrate good practical operating characteristics of our proposed methods. Applying the proposed methods to a dataset on older high-risk localized prostate cancer patients drawn from the National Cancer Database, we evaluate the comparative effects of three treatment approaches on patient survival, and assess the ramifications of potential unmeasured confounding. The methods developed in this work are readily available in the R $$ \mathsf{R}\kern.15em $$ package riAFTBART $$ \mathsf{riAFTBART} $$ .

Gestational Diabetes, Subsequent Type 2 Diabetes, and Food Security Status: National Health and Nutrition Examination Survey, 2007-2018.

INTRODUCTION: Despite many studies linking various risk factors to the association between gestational diabetes and subsequent type 2 diabetes, little is known about how food insecurity affects their association. We aimed to assess how the association between gestational diabetes and subsequent type 2 diabetes varies by food security status among women in the US. METHODS: This study is a secondary data analysis of 9,505 US women aged 20 years or older who had at least 1 live birth; we used cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) from 2007 through 2018. The main outcome was a diagnosis of type 2 diabetes in the subsequent years after the first live birth. We used multivariable survey-weighted negative binomial regressions to examine whether the association between gestational diabetes and subsequent type 2 diabetes differed by food security status, with and without adjusting for health behavior factors. RESULTS: Gestational diabetes was significantly associated with subsequent type 2 diabetes (incidence rate ratio [IRR], 2.57; 95% CI, 2.45-2.69). The association between gestational diabetes and subsequent type 2 diabetes was significantly different by food security status (IRR, 2.34; 95% CI, 2.23-2.45 among food-secure women; IRR, 2.99; 95% CI, 2.70-3.28 among food-insecure women). CONCLUSION: The association between gestational diabetes and subsequent type 2 diabetes differs significantly by food security status. Public health and health care practitioners should consider food security status when designing and implementing diabetes prevention interventions for women with a history of gestational diabetes.

Knowing What to Learn: A Metric-Oriented Focal Mechanism for Image Captioning.

Despite considerable progress, image captioning still suffers from the huge difference in quality between easy and hard examples, which is left unexploited in existing methods. To address this issue, we explore the hard example mining in image captioning, and propose a simple yet effective mechanism to instruct the model to pay more attention to hard examples, thereby improving the performance in both general and complex scenarios. We first propose a novel learning strategy, termed Metric-oriented Focal Mechanism (MFM), for hard example mining in image captioning. Differing from the existing strategies for classification tasks, MFM can adopt the generative metrics of image captioning to measure the difficulties of examples, and then up-weight the rewards of hard examples during training. To make MFM applicable to different datasets without tedious parameter tuning, we further introduce an adaptive reward metric called Effective CIDEr (ECIDEr), which considers the data distribution of easy and hard examples during reward estimation. Extensive experiments are conducted on the MS COCO benchmark, and the results show that while maintaining the performance on simple examples, MFM can significantly improve the quality of captions for hard examples. The ECIDEr-based MFM is equipped on the current SOTA method, e.g., DLCT (Luo et al., 2021), which outperforms all existing methods and achieves new state-of-the-art performance on both the off-line and on- line testing, i.e., 134.3 CIDEr for the off-line testing and 136.1 for the on- line testing of MSCOCO. To validate the generalization ability of ECIDEr-based MFM, we also apply it to another dataset, namely Flickr30k, and superior performance gains can also be obtained.

Immunotherapy Outcomes in Individuals With Non-Small Cell Lung Cancer and Poor Performance Status.

BACKGROUND: Patients with non-small cell lung cancer (NSCLC) treated in real-world practice typically have worse performance status (PS) compared with clinical trial patients, and the effectiveness of immunotherapy in this population in unknown. In this study, we assessed the effectiveness of standard of care immunotherapy for the first-line treatment of stage IV patients with NSCLC with Eastern Cooperative Oncology Group (ECOG) PS greater than or equal to 2. METHODS: We selected ECOG PS greater than or equal to 2 patients from real-world oncology data from a deidentified database and included them if they were diagnosed with stage IV NSCLC and had documented Programmed death-ligand 1 [PD-(L)1] expression greater than 0. Patients with tumor PD-(L)1 expression of at least 50% treated with pembrolizumab monotherapy were compared with those who did not have any documented treatment. Patients with tumor PD-(L)1 expression less than 50% treated with pembrolizumab and chemotherapy were compared with those treated with pembrolizumab monotherapy and those without documented treatment. RESULTS: In our propensity score-adjusted analysis, patients with ECOG PS of at least 2 and tumor PD-(L)1 expression of at least 50% treated with pembrolizumab monotherapy had statistically significantly better real-world overall survival compared with those without documented treatment (adjusted hazard ratio [HR] = 0.39, 95% confidence internal [CI] = 0.32 to 0.47). For patients with tumor PD-(L)1 expression less than 50%, there was also a statistically significant real-world overall survival benefit for those who received treatment either with combination pembrolizumab plus chemotherapy (adjusted HR = 0.39, 95% CI = 0.32 to 0.46) or pembrolizumab monotherapy (adjusted HR = 0.55, 95% CI = 0.41 to 0.70) compared with patients receiving no documented treatment. CONCLUSIONS: Among a highly representative sample of patients with advanced NSCLC and poor PS, our findings suggest that immunotherapy may provide an important survival benefit in individuals with high PD-(L)1-expressing tumors and in conjunction with chemotherapy in tumors with low PD-(L)1 expression.

NR2F1 Is a Barrier to Dissemination of Early-Stage Breast Cancer Cells.

Cancer cells can disseminate during very early and sometimes asymptomatic stages of tumor progression. Though biological barriers to tumorigenesis have been identified and characterized, the mechanisms that limit early dissemination remain largely unknown. We report here that the orphan nuclear receptor nuclear receptor subfamily 2, group F, member 1 (NR2F1)/COUP-TF1 serves as a barrier to early dissemination. NR2F1 expression was decreased in patient ductal carcinoma in situ (DCIS) samples. High-resolution intravital imaging of HER2+ early-stage cancer cells revealed that loss of function of NR2F1 increased in vivo dissemination and was accompanied by decreased E-cadherin expression, activation of wingless-type MMTV integration site family, member 1 (WNT)-dependent ß-catenin signaling, disorganized laminin 5 deposition, and increased expression of epithelial-mesenchymal transition (EMT) genes such as twist basic helix-loop-helix transcription factor 1 (TWIST1), zinc finger E-box binding homeobox 1 (ZEB1), and paired related homeobox 1 (PRRX1). Furthermore, downregulation of NR2F1 promoted a hybrid luminal/basal phenotype. NR2F1 expression was positively regulated by p38α signaling and repressed by HER2 and WNT4 pathways. Finally, early cancer cells with NR2F1LOW/PRRX1HIGH staining were observed in DCIS samples. Together, these findings reveal the existence of an inhibitory mechanism of dissemination regulated by NR2F1 in early-stage breast cancer cells. SIGNIFICANCE: During early stages of breast cancer progression, HER2-mediated suppression of NR2F1 promotes dissemination by inducing EMT and a hybrid luminal/basal-like program.

Prospective Cohort Study to Investigate the Safety of Preoperative Tumor Necrosis Factor Inhibitor Exposure in Patients With Inflammatory Bowel Disease Undergoing Intra-abdominal Surgery.

BACKGROUND & AIMS: Whether preoperative treatment of inflammatory bowel disease (IBD) with tumor necrosis factor inhibitors (TNFis) increases the risk of postoperative infectious complications remains controversial. The primary aim of this study was to determine whether preoperative exposure to TNFis is an independent risk factor for postoperative infectious complications within 30 days of surgery. METHODS: We conducted a multicenter prospective observational study of patients with IBD undergoing intra-abdominal surgery across 17 sites from the Crohn's & Colitis Foundation Clinical Research Alliance. Infectious complications were categorized as surgical site infections (SSIs) or non-SSIs. Current TNFi exposure was defined as use within 12 weeks of surgery, and serum was collected for drug-level analyses. Multivariable models for occurrence of the primary outcome, any infection, or SSI were adjusted by predefined covariates (age, sex, preoperative steroid use, and disease type), baseline variables significantly associated (P < .05) with any infection or SSI separately, and TNFi exposure status. Exploratory models used TNFi exposure based on serum drug concentration. RESULTS: A total of 947 patients were enrolled from September 2014 through June 2017. Current TNFi exposure was reported by 382 patients. Any infection (18.1% vs 20.2%, P = .469) and SSI (12.0% vs 12.6%, P = .889) rates were similar in patients currently exposed to TNFis and those unexposed. In multivariable analysis, current TNFi exposure was not associated with any infection (odds ratio, 1.050; 95% confidence interval, 0.716-1.535) or SSI (odds ratio, 1.249; 95% confidence interval, 0.793-1.960). Detectable TNFi drug concentration was not associated with any infection or SSI. CONCLUSIONS: Preoperative TNFi exposure was not associated with postoperative infectious complications in a large prospective multicenter cohort.

Prior Immunogenicity to Anti-TNF Biologics Is Not Associated with Increased Anti-drug Antibodies to Vedolizumab or Ustekinumab.

INTRODUCTION: Patients with inflammatory bowel disease (IBD) on biologic therapy may lose response to anti-tumor necrosis factor agents (anti-TNFs) due to the development of anti-drug antibodies (ADAs). A history of anti-TNF ADA increases the risk of developing ADA to subsequent anti-TNFs; however, it is not known whether ADA to anti-TNFs increases the risk of ADA development to vedolizumab (VDZ) or ustekinumab (UST). We aimed to investigate whether prior history of ADA to anti-TNF increases the risk of ADA to VDZ and UST. METHODS: We conducted a retrospective cohort study of patients at a tertiary care IBD center over the course of four years who had previous anti-TNF drug and ADA level data during maintenance treatment and subsequent VDZ or UST drug and antibody levels, all collected as standard of care. The primary outcome was the rate of ADA development to VDZ and UST in patients with and without prior anti-TNF immunogenicity. Descriptive statistics summarized the data and univariate tested associations. RESULTS: Of the 152 IBD patients analyzed, 41 (27%) had a history of previous anti-TNF ADA with 22 (53.7%) having simultaneously undetectable anti-TNF drug levels. There was no significant difference in the rates of ustekinumab and vedolizumab ADA development between patients with prior ADA and patients without prior ADA (1/41 [2.7%] vs 1/111 [0.9%]; p = 0.54). There was also no difference in concomitant immunomodulator use with ustekinumab or vedolizumab initiation in patients with or without prior ADA (13/41 [31.7%] vs 31/111 [27.9%], p = 0.84). Neither patient who developed ADA to VDZ or UST was on concomitant immunomodulator at drug initiation, and both patients had detectable drug levels at the time of antibody detection. CONCLUSIONS: We observed that prior immunogenicity to anti-TNF agents does not confer an increased risk of immunogenicity to ustekinumab or vedolizumab. Our data support the use of vedolizumab or ustekinumab as monotherapy for the treatment of IBD.

A Role for CXCR3 Ligands as Biomarkers of Post-Operative Crohn's Disease Recurrence.

BACKGROUND AND AIMS: Crohn's disease [CD] recurrence following ileocolic resection [ICR] is common. We sought to identify blood-based biomarkers associated with CD recurrence. METHODS: CD patients undergoing ICR were recruited across six centres. Serum samples were obtained at post-operative colonoscopy. A multiplex immunoassay was used to analyse 92 inflammation-related proteins [Olink Proteomics]. Bayesian analysis was used to identify proteins associated with increasing Rutgeerts score. Identified proteins were used in receiver operating characteristic [ROC] analysis to examine the ability to identify CD recurrence [Rutgeerts score ≥i2]. Existing single cell data were interrogated to further elucidate the role of the identified proteins. RESULTS: Data from 276 colonoscopies in 213 patients were available. Median time from surgery to first and second colonoscopy was 7 (interquartile range [IQR] 6-9) and 19 [IQR 16-23] months, respectively. Disease recurrence was evident at 60 [30%] first and 36 [49%] second colonoscopies. Of 14 proteins significantly associated with Rutgeerts score, the strongest signal was seen for CXCL9 and MMP1. Among patients on anti-tumour necrosis factor drugs, CXCL9 and CXCL11 were most strongly associated with Rutgeerts score. Both are CXCR3 ligands. Incorporation of identified proteins into ROC analysis improved the ability to identify disease recurrence as compared to C-reactive protein alone: area under the curve [AUC] 0.75 (95% confidence interval [CI]: 0.66-0.82] vs 0.64 [95% CI 0.56-0.72], p = 0.012. Single cell transcriptomic data provide evidence that innate immune cells are the primary source of the identified proteins. CONCLUSIONS: CXCR3 ligands are associated with CD recurrence following ICR. Incorporation of novel blood-based candidate biomarkers may aid in identification of CD recurrence.

Determinants of Total End-of-Life Health Care Costs of Medicare Beneficiaries: A Quantile Regression Forests Analysis.

BACKGROUND: To identify and rank the importance of key determinants of end-of-life (EOL) health care costs, and to understand how the key factors impact different percentiles of the distribution of health care costs. METHOD: We applied a principled, machine learning-based variable selection algorithm, using Quantile Regression Forests, to identify key determinants for predicting the 10th (low), 50th (median), and 90th (high) quantiles of EOL health care costs, including costs paid for by Medicare, Medicaid, Medicare Health Maintenance Organizations (HMOs), private HMOs, and patient's out-of-pocket expenditures. RESULTS: Our sample included 7 539 Medicare beneficiaries who died between 2002 and 2017. The 10th, 50th, and 90th quantiles of EOL health care cost are $5 244, $35 466, and $87 241, respectively. Regional characteristics, specifically, the EOL-Expenditure Index, a measure for regional variation in Medicare spending driven by physician practice, and the number of total specialists in the hospital referral region were the top 2 influential determinants for predicting the 50th and 90th quantiles of EOL costs but were not determinants of the 10th quantile. Black race and Hispanic ethnicity were associated with lower EOL health care costs among decedents with lower total EOL health care costs but were associated with higher costs among decedents with the highest total EOL health care costs. CONCLUSIONS: Factors associated with EOL health care costs varied across different percentiles of the cost distribution. Regional characteristics and decedent race/ethnicity exemplified factors that did not impact EOL costs uniformly across its distribution, suggesting the need to use a "higher-resolution" analysis for examining the association between risk factors and health care costs.