Zinpentraxin Alfa for Idiopathic Pulmonary Fibrosis: The Randomized Phase III STARSCAPE Trial.

Rationale: A phase II trial reported clinical benefit over 28 weeks in patients with idiopathic pulmonary fibrosis (IPF) who received zinpentraxin alfa. Objectives: To investigate the efficacy and safety of zinpentraxin alfa in patients with IPF in a phase III trial. Methods: This 52-week phase III, double-blind, placebo-controlled, pivotal trial was conducted at 275 sites in 29 countries. Patients with IPF were randomized 1:1 to intravenous placebo or zinpentraxin alfa 10 mg/kg every 4 weeks. The primary endpoint was absolute change from baseline to Week 52 in FVC. Secondary endpoints included absolute change from baseline to Week 52 in percent predicted FVC and 6-minute walk distance. Safety was monitored via adverse events. Post hoc analysis of the phase II and phase III data explored changes in FVC and their impact on the efficacy results. Measurements and Main Results: Of 664 randomized patients, 333 were assigned to placebo and 331 to zinpentraxin alfa. Four of the 664 randomized patients were never administered study drug. The trial was terminated early after a prespecified futility analysis that demonstrated no treatment benefit of zinpentraxin alfa over placebo. In the final analysis, absolute change from baseline to Week 52 in FVC was similar between placebo and zinpentraxin alfa (-214.89 ml and -235.72 ml; P = 0.5420); there were no apparent treatment effects on secondary endpoints. Overall, 72.3% and 74.6% of patients receiving placebo and zinpentraxin alfa, respectively, experienced one or more adverse events. Post hoc analysis revealed that extreme FVC decline in two placebo-treated patients resulted in the clinical benefit of zinpentraxin alfa reported by phase II. Conclusions: Zinpentraxin alfa treatment did not benefit patients with IPF over placebo. Learnings from this program may help improve decision making around trials in IPF. Clinical trial registered with www.clinicaltrials.gov (NCT04552899).

Biomarkers of fibrosis, inflammation, and extracellular matrix in the phase 3 trial of tocilizumab in systemic sclerosis.

Drug development for systemic sclerosis (SSc) benefits from understanding the relationship between disease and circulating biomarkers to enable activities such as patient stratification and evaluation of therapeutic response. We measured biomarkers in serum from SSc patients from a phase 3 trial of tocilizumab (focuSSced) and compared baseline levels with healthy controls (HCs). Several baseline biomarkers appeared elevated in SSc patients compared to HCs, suggesting activation of epithelial damage, inflammation, fibrosis, and extracellular matrix (ECM) remodeling. Baseline correlations among both periostin/COMP and ECM biomarker subsets implicated their participation in fibroblast activation. Tocilizumab treatment modulated serum biomarkers of macrophage activation, inflammation, and ECM turnover, including collagen formation and degradation neoepitopes. Baseline CRP, periostin, and SP-D showed prognostic trends for worsening lung function, and IL-6, COMP, periostin, and Pro-C3 showed prognostic trends for worsening skin thickness. These prognostic results warrant confirmation in additional patient cohorts to verify their utility.

A review of the challenges, learnings and future directions of home handheld spirometry in interstitial lung disease.

BACKGROUND: Patients with interstitial lung disease (ILD) require regular physician visits and referral to specialist ILD clinics. Difficulties or delays in accessing care can limit opportunities to monitor disease trajectory and response to treatment, and the COVID-19 pandemic has added to these challenges. Therefore, home monitoring technologies, such as home handheld spirometry, have gained increased attention as they may help to improve access to care for patients with ILD. However, while several studies have shown that home handheld spirometry in ILD is acceptable for most patients, data from clinical trials are not sufficiently robust to support its use as a primary endpoint. This review discusses the challenges that were encountered with handheld spirometry across three recent ILD studies, which included home spirometry as a primary endpoint, and highlights where further optimisation and research into home handheld spirometry in ILD is required. Rate of decline in forced vital capacity (FVC) as measured by daily home handheld spirometry versus site spirometry was of primary interest in three recently completed studies: STARLINER (NCT03261037), STARMAP and a Phase II study of pirfenidone in progressive fibrosing unclassifiable ILD (NCT03099187). Unanticipated practical and technical issues led to problems with estimating FVC decline. In all three studies, cross-sectional correlations for home handheld versus site spirometry were strong/moderate at baseline and later timepoints, but longitudinal correlations were weak. Other issues observed with the home handheld spirometry data included: high within-patient variability in home handheld FVC measurements; implausible longitudinal patterns in the home handheld spirometry data that were not reflected in site spirometry; and extreme estimated rates of FVC change. CONCLUSIONS: Home handheld spirometry in ILD requires further optimisation and research to ensure accurate and reliable FVC measurements before it can be used as an endpoint in clinical trials. Refresher training, automated alerts of problems and FVC changes, and patient support could help to overcome some practical issues. Despite the challenges, there is value in incorporating home handheld spirometry into clinical practice, and the COVID-19 pandemic has highlighted the potential for home monitoring technologies to help improve access to care for patients with ILD.

Untargeted metabolomics of newborn dried blood spots reveals sex-specific associations with pediatric acute myeloid leukemia.

The etiology of pediatric acute myeloid leukemia (AML) is largely unknown, but evidence for mutations in utero and long latency periods suggests that environmental factors play a role. Therefore, we used untargeted metabolomics of archived newborn dried blood spots (DBS) to investigate neonatal exposures as potential causal risk factors for AML. Untargeted metabolomics profiling was performed on DBS punches from 48 pediatric patients with AML and 46 healthy controls as part of the California Childhood Leukemia Study (CCLS). Because sex disparities are suggested by differences in AML incidence rates, metabolite features associated with AML were identified in analyses stratified by sex. There was no overlap between the 16 predictors of AML in females and 15 predictors in males, suggesting that neonatal metabolomic profiles of pediatric AML risk are sex-specific. In females, four predictors of AML were putatively annotated as ceramides, a class of metabolites that has been linked with cancer cell proliferation. In females, two metabolite predictors of AML were strongly correlated with breastfeeding duration, indicating a possible biological link between this putative protective risk factor and childhood leukemia. In males, a heterogeneous metabolite profile of AML predictors was observed. Replication with larger participant numbers is required to validate findings.

Untargeted adductomics of newborn dried blood spots identifies modifications to human serum albumin associated with childhood leukemia.

The developing fetus is exposed to chemicals, which are metabolized to electrophiles that form adducts with nucleophilic Cys34 of human serum albumin (HSA). By measuring these adducts in neonatal blood spots (NBS), we obtain information regarding fetal exposures during the last month of gestation. To discover potential risk factors for childhood leukemia resulting from in utero exposures, we used untargeted adductomics to measure HSA-Cys34 adducts in 782 archived NBS, collected from incident cases of childhood acute lymphoblastic leukemia (ALL) or acute myeloid leukemia (AML) and matched population-based controls. Among a total of 28 Cys34 modifications that were measured, we found no differences in adduct abundances between childhood leukemia cases and controls overall. However, cases of T-cell ALL had higher abundances of adducts of reactive carbonyl species and a Cys34 disulfide of homocysteine was present at lower levels in AML cases. These results suggest that oxidative stress and lipid peroxidation may be etiologic factors of T-cell ALL, and alterations in one-carbon metabolism and epigenetic changes may be predictors of AML. Future replication of the results with larger sample sizes is necessary.

Cys34 Adductomics Links Colorectal Cancer with the Gut Microbiota and Redox Biology.

Chronic inflammation is an established risk factor for colorectal cancer. To study reactive products of gut inflammation and redox signaling on colorectal cancer development, we used untargeted adductomics to detect adduct features in prediagnostic serum from the EPIC Italy cohort. We focused on modifications to Cys34 in human serum albumin, which is responsible for scavenging small reactive electrophiles that might initiate cancers. Employing a combination of statistical methods, we selected seven Cys34 adducts associated with colorectal cancer, as well as body mass index (BMI; a well-known risk factor). Five adducts were more abundant in colorectal cancer cases than controls and clustered with each other, suggesting a common pathway. Because two of these adducts were Cys34 modifications by methanethiol, a microbial-human cometabolite, and crotonaldehyde, a product of lipid peroxidation, these findings further implicate infiltration of gut microbes into the intestinal mucosa and the corresponding inflammatory response as causes of colorectal cancer. The other two associated adducts were Cys34 disulfides of homocysteine that were less abundant in colorectal cancer cases than controls and may implicate homocysteine metabolism as another causal pathway. The selected adducts and BMI ranked higher as potentially causal factors than variables previously associated with colorectal cancer (smoking, alcohol consumption, physical activity, and total meat consumption). Regressions of case-control differences in adduct levels on days to diagnosis showed no statistical evidence that disease progression, rather than causal factors at recruitment, contributed to the observed differences. These findings support the hypothesis that infiltration of gut microbes into the intestinal mucosa and the resulting inflammation are causal factors for colorectal cancer. SIGNIFICANCE: Infiltration of gut microbes into the intestinal mucosa and the resulting inflammation are causal factors for colorectal cancer.

Filtering procedures for untargeted LC-MS metabolomics data.

BACKGROUND: Untargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients. RESULTS: Using metabolomics datasets that were generated in our laboratory from samples of human blood, as well as two public LC-MS datasets, we compared our data-adaptive filtering method with traditional methods that rely on non-method specific thresholds. The data-adaptive approach outperformed traditional approaches in terms of removing noisy features and retaining high quality, biologically informative ones. The R code for running the data-adaptive filtering method is provided at https://github.com/courtneyschiffman/Metabolomics-Filtering . CONCLUSIONS: Our proposed data-adaptive filtering pipeline is intuitive and effectively removes uninformative features from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens.

Metabolomics of neonatal blood spots reveal distinct phenotypes of pediatric acute lymphoblastic leukemia and potential effects of early-life nutrition.

Early-life exposures are believed to influence the incidence of pediatric acute lymphoblastic leukemia (ALL). Archived neonatal blood spots (NBS), collected within the first days of life, offer a means to investigate small molecules that reflect early-life exposures. Using untargeted metabolomics, we compared abundances of small-molecule features in extracts of NBS punches from 332 children that later developed ALL and 324 healthy controls. Subjects were stratified by early (1-5 y) and late (6-14 y) diagnosis. Mutually-exclusive sets of metabolic features - representing putative lipids and fatty acids - were associated with ALL, including 9 and 19 metabolites in the early- and late-diagnosis groups, respectively. In the late-diagnosis group, a prominent cluster of features with apparent 18:2 fatty-acid chains suggested that newborn exposure to the essential nutrient, linoleic acid, increased ALL risk. Interestingly, abundances of these putative 18:2 lipids were greater in infants who were fed formula rather than breast milk (colostrum) and increased with the mother's pre-pregnancy body mass index. These results suggest possible etiologic roles of newborn nutrition in late-diagnosis ALL.

Untargeted adductomics of Cys34 modifications to human serum albumin in newborn dried blood spots.

Metabolism of chemicals from the diet, exposures to xenobiotics, the microbiome, and lifestyle factors (e.g., smoking, alcohol intake) produce electrophiles that react with nucleophilic sites in circulating proteins, notably Cys34 of human serum albumin (HSA). To discover potential risk factors resulting from in utero exposures, we are investigating HSA-Cys34 adducts in archived newborn dried blood spots (DBS) that reflect systemic exposures during the last month of gestation. The workflow includes extraction of proteins from DBS, measurement of hemoglobin (Hb) to normalize for blood volume, addition of methanol to enrich HSA by precipitation of Hb and other interfering proteins, digestion with trypsin, and detection of HSA-Cys34 adducts via nanoflow liquid chromatography-high-resolution mass spectrometry. As proof-of-principle, we applied the method to 49 archived DBS collected from newborns whose mothers either actively smoked during pregnancy or were nonsmokers. Twenty-six HSA-Cys34 adducts were detected, including Cys34 oxidation products, mixed disulfides with low molecular weight thiols (e.g., cysteine, homocysteine, glutathione, cysteinylglycine), and other modifications. Data were normalized with a novel method ("scone") to remove unwanted technical variation arising from HSA digestion, blood volume, DBS age, mass spectrometry analysis, and batch effects. Using an ensemble of linear and nonlinear models, the Cys34 adduct of cyanide was found to consistently discriminate between newborns of smoking and nonsmoking mothers with a mean fold change (smoking/nonsmoking) of 1.31. These results indicate that DBS adductomics is suitable for investigating in utero exposures to reactive chemicals and metabolites that may influence disease risks later in life.

Identification of gene expression predictors of occupational benzene exposure.

BACKGROUND: Previously, using microarrays and mRNA-Sequencing (mRNA-Seq) we found that occupational exposure to a range of benzene levels perturbed gene expression in peripheral blood mononuclear cells. OBJECTIVES: In the current study, we sought to identify gene expression biomarkers predictive of benzene exposure below 1 part per million (ppm), the occupational standard in the U.S. METHODS: First, we used the nCounter platform to validate altered expression of 30 genes in 33 unexposed controls and 57 subjects exposed to benzene (<1 to ≥5 ppm). Second, we used SuperLearner (SL) to identify a minimal number of genes for which altered expression could predict <1 ppm benzene exposure, in 44 subjects with a mean air benzene level of 0.55±0.248 ppm (minimum 0.203ppm). RESULTS: nCounter and microarray expression levels were highly correlated (coefficients >0.7, p<0.05) for 26 microarray-selected genes. nCounter and mRNA-Seq levels were poorly correlated for 4 mRNA-Seq-selected genes. Using negative binomial regression with adjustment for covariates and multiple testing, we confirmed differential expression of 23 microarray-selected genes in the entire benzene-exposed group, and 27 genes in the <1 ppm-exposed subgroup, compared with the control group. Using SL, we identified 3 pairs of genes that could predict <1 ppm benzene exposure with cross-validated AUC estimates >0.9 (p<0.0001) and were not predictive of other exposures (nickel, arsenic, smoking, stress). The predictive gene pairs are PRG2/CLEC5A, NFKBI/CLEC5A, and ACSL1/CLEC5A. They play roles in innate immunity and inflammatory responses. CONCLUSIONS: Using nCounter and SL, we validated the altered expression of multiple mRNAs by benzene and identified gene pairs predictive of exposure to benzene at levels below the US occupational standard of 1ppm.

Untargeted lipidomic features associated with colorectal cancer in a prospective cohort.

BACKGROUND: Epidemiologists are beginning to employ metabolomics and lipidomics with archived blood from incident cases and controls to discover causes of cancer. Although several such studies have focused on colorectal cancer (CRC), they all followed targeted or semi-targeted designs that limited their ability to find discriminating molecules and pathways related to the causes of CRC. METHODS: Using an untargeted design, we measured lipophilic metabolites in prediagnostic serum from 66 CRC patients and 66 matched controls from the European Prospective Investigation into Cancer and Nutrition (Turin, Italy). Samples were analyzed by liquid chromatography-high-resolution mass spectrometry (LC-MS), resulting in 8690 features for statistical analysis. RESULTS: Rather than the usual multiple-hypothesis-testing approach, we based variable selection on an ensemble of regression methods, which found nine features to be associated with case-control status. We then regressed each selected feature on time-to-diagnosis to determine whether the feature was likely to be either a potentially causal biomarker or a reactive product of disease progression (reverse causality). CONCLUSIONS: Of the nine selected LC-MS features, four appear to be involved in CRC etiology and merit further investigation in prospective studies of CRC. Four other features appear to be related to progression of the disease (reverse causality), and may represent biomarkers of value for early detection of CRC.

Electrophoretic cytopathology resolves ERBB2 forms with single-cell resolution.

In addition to canonical oncoproteins, truncated isoforms and proteolysis products are implicated in both drug resistance and disease progression. In HER2-positive breast tumors, expression of truncated HER2 isoforms resulting from alternative translation and/or carboxy-terminal fragments (CTFs) resulting from proteolysis (collectively, t-erbB2) have been associated with shortened progression-free survival of patients. Thus, to advance clinical pathology and inform treatment decisions, we developed a high-selectivity cytopathology assay capable of distinguishing t-erbB2 from full-length HER2 expression without the need for isoform-specific antibodies. Our microfluidic, single-cell western blot, employs electrophoretic separations to resolve full-length HER2 from the smaller t-erbB2 in each ~28 pL single-cell lysate. Subsequently, a pan-HER2 antibody detects all resolved HER2 protein forms via immunoprobing. In analysis of eight breast tumor biopsies, we identified two tumors comprised of 15% and 40% t-erbB2-expressing cells. By single-cell western blotting of the t-erbB2-expressing cells, we observed statistically different ratios of t-erbB2 proteins to full-length HER2 expression. Further, target multiplexing and clustering analyses scrutinized signaling, including ribosomal S6, within the t-erbB2-expressing cell subpopulation. Taken together, cytometric assays that report both protein isoform profiles and signaling state offer cancer classification taxonomies with unique relevance to precisely describing drug resistance mechanisms in which oncoprotein isoforms/fragments are implicated.

SIDEseq: A Cell Similarity Measure Defined by Shared Identified Differentially Expressed Genes for Single-Cell RNA sequencing Data.

One goal of single-cell RNA sequencing (scRNA seq) is to expose possible heterogeneity within cell populations due to meaningful, biological variation. Examining cell-to-cell heterogeneity, and further, identifying subpopulations of cells based on scRNA seq data has been of common interest in life science research. A key component to successfully identifying cell subpopulations (or clustering cells) is the (dis)similarity measure used to group the cells. In this paper, we introduce a novel measure, named SIDEseq, to assess cell-to-cell similarity using scRNA seq data. SIDEseq first identifies a list of putative differentially expressed (DE) genes for each pair of cells. SIDEseq then integrates the information from all the DE gene lists (corresponding to all pairs of cells) to build a similarity measure between two cells. SIDEseq can be implemented in any clustering algorithm that requires a (dis)similarity matrix. This new measure incorporates information from all cells when evaluating the similarity between any two cells, a characteristic not commonly found in existing (dis)similarity measures. This property is advantageous for two reasons: (a) borrowing information from cells of different subpopulations allows for the investigation of pairwise cell relationships from a global perspective and (b) information from other cells of the same subpopulation could help to ensure a robust relationship assessment. We applied SIDEseq to a newly generated human ovarian cancer scRNA seq dataset, a public human embryo scRNA seq dataset, and several simulated datasets. The clustering results suggest that the SIDEseq measure is capable of uncovering important relationships between cells, and outperforms or at least does as well as several popular (dis)similarity measures when used on these datasets.

An untargeted metabolomics method for archived newborn dried blood spots in epidemiologic studies.

INTRODUCTION: For pediatric diseases like childhood leukemia, a short latency period points to in-utero exposures as potentially important risk factors. Untargeted metabolomics of small molecules in archived newborn dried blood spots (DBS) offers an avenue for discovering early-life exposures that contribute to disease risks. OBJECTIVES: The purpose of this study was to develop a quantitative method for untargeted analysis of archived newborn DBS for use in an epidemiological study (California Childhood Leukemia Study, CCLS). METHODS: Using experimental DBS from the blood of an adult volunteer, we optimized extraction of small molecules and integrated measurement of potassium as a proxy for blood hematocrit. We then applied this extraction method to 4.7-mm punches from 106 control DBS samples from the CCLS. Sample extracts were analyzed with liquid chromatography high resolution mass spectrometry (LC-HRMS) and an untargeted workflow was used to screen for metabolites that discriminate population characteristics such as sex, ethnicity, and birth weight. RESULTS: Thousands of small molecules were measured in extracts of archived DBS. Normalizing for potassium levels removed variability related to varying hematocrit across DBS punches. Of the roughly 1,000 prevalent small molecules that were tested, multivariate linear regression detected significant associations with ethnicity (3 metabolites) and birth weight (15 metabolites) after adjusting for multiple testing. CONCLUSIONS: This untargeted workflow can be used for analysis of small molecules in archived DBS to discover novel biomarkers, to provide insights into the initiation and progression of diseases, and to provide guidance for disease prevention.