Intestinal microbiota controls graft-versus-host disease independent of donor-host genetic disparity.

Acute graft-versus-host disease (aGVHD) remains a major limitation of allogeneic stem cell transplantation (SCT), and severe intestinal manifestation is the major cause of early mortality. Intestinal microbiota control MHC class II (MHC-II) expression by ileal intestinal epithelial cells (IECs) that promote GVHD. Here, we demonstrated that genetically identical mice of differing vendor origins had markedly different intestinal microbiota and ileal MHC-II expression, resulting in discordant GVHD severity. We utilized cohousing and antibiotic treatment to characterize the bacterial taxa positively and negatively associated with MHC-II expression. A large proportion of bacterial MHC-II inducers were vancomycin sensitive, and peri-transplant oral vancomycin administration attenuated CD4+ T cell-mediated GVHD. We identified a similar relationship between pre-transplant microbes, HLA class II expression, and both GVHD and mortality in a large clinical SCT cohort. These data highlight therapeutically tractable mechanisms by which pre-transplant microbial taxa contribute to GVHD independently of genetic disparity.

Prediction of Acute Graft versus Host Disease and Relapse by Endogenous Metabolomic Compounds in Patients Receiving Personalized Busulfan-Based Conditioning.

Busulfan-based conditioning is the most commonly used high-dose conditioning regimen for allogeneic hematopoietic cell transplant (HCT). The alkylating agent busulfan has a narrow therapeutic index, with busulfan doses personalized to a target plasma exposure (targeted busulfan). Using a global pharmacometabonomics approach, we sought to identify novel biomarkers of relapse or acute graft versus host disease (GVHD) in a cohort of 84 patients receiving targeted busulfan before allogeneic HCT. A total of 763 endogenous metabolomic compounds (EMCs) were quantitated in 230 longitudinal blood samples before, during, and shortly after intravenous busulfan administration. We performed both univariate linear regression and pathway enrichment analyses using global testing. The cysteine/methionine pathway and the glycine, serine, and threonine metabolism pathway were most associated with relapse. The latter be explained by the fact that glutathione S-transferases conjugate both busulfan and glutathione, which contains glycine as a component. The d-arginine and d-ornithine metabolism pathway and arginine and proline metabolism pathway were most associated with acute GVHD. None of these associations were significant after correcting for false discovery rate (FDR) with a strict cutoff of FDR-adjusted p < 0.1. Although larger studies are needed to substantiate these findings, the results show that EMCs may be used as predictive biomarkers in HCT patients.

Long-term association between diet quality and characteristics of the gut microbiome in the multiethnic cohort study.

As past usual diet quality may affect gut microbiome (GM) composition, we examined the association of the Healthy Eating Index (HEI)-2015 assessed 21 and 9 years before stool collection with measures of fecal microbial composition in a subset of the Multiethnic Cohort. A total of 5936 participants completed a validated quantitative FFQ (QFFQ) at cohort entry (Q1, 1993-1996), 5280 at follow-up (Q3, 2003-2008) and 1685 also at a second follow-up (Adiposity Phenotype Study (APS), 2013-2016). All participants provided a stool sample in 2013-2016. Fecal microbial composition was obtained from 16S rRNA gene sequencing (V1-V3 regions). HEI-2015 scores were computed based on each QFFQ. Using linear regression adjusted for relevant covariates, we calculated associations of HEI-2015 scores with gut microbial diversity and 152 individual genera. The mean HEI-2015 scores increased from Q1 (67 (sd 10)) to Q3 (71 (sd 11)) and APS (72 (sd 10)). Alpha diversity assessed by the Shannon Index was significantly higher with increasing tertiles of HEI-2015. Of the 152 bacterial genera tested, seven (Anaerostipes, Coprococcus_2, Eubacterium eligens, Lachnospira, Lachnospiraceae_ND3007, Ruminococcaceae_UCG-013 and Ruminococcus_1) were positively and five (Collinsella, Parabacteroides, Ruminiclostridium_5, Ruminococcus gnavus and Tyzzerella) were inversely associated with HEI-2015 assessed in Q1, Q3 and APS. The estimates of change per unit of the HEI-2015 score associated with the abundance of these twelve genera were consistent across the three questionnaires. The quality of past diet, assessed as far as ∼20 years before stool collection, is equally predictive of GM composition as concurrently assessed diet, indicative of the long-term consistency of this relation.

Connectivity-informed adaptive regularization for generalized outcomes.

One of the challenging problems in neuroimaging is the principled incorporation of information from different imaging modalities. Data from each modality are frequently analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization method, generalized ridgified Partially Empirical Eigenvectors for Regression (griPEER), to estimate associations between the brain structure features and a scalar outcome within the generalized linear regression framework. griPEER improves the regression coefficient estimation by providing a principled approach to use external information from the structural brain connectivity. Specifically, we incorporate a penalty term, derived from the structural connectivity Laplacian matrix, in the penalized generalized linear regression. In this work, we address both theoretical and computational issues and demonstrate the robustness of our method despite incomplete information about the structural brain connectivity. In addition, we also provide a significance testing procedure for performing inference on the estimated coefficients. Finally, griPEER is evaluated both in extensive simulation studies and using clinical data to classify HIV+ and HIV- individuals.

L'un des défis en imagerie cérébrale consiste à établir les principes pour incorporer de l'information provenant de différentes modalités d'imagerie. Les données de chaque modalité sont fréquemment analysées séparément, exploitant par exemple des techniques de réduction de la dimension, ce qui conduit à une perte d'information mutuelle. Les auteurs proposent une nouvelle méthode de régularisation, griPEER (ou par vecteurs propres ridgifiés partiellement empiriques généralisés pour la régression) afin d'estimer l'association entre des caratéristiques de structures du cerveau et une variable réponse scalaire dans le cadre d'une régression linéaire généralisée. Les griPEER améliorent l'estimation des coefficients de régression en établissant les principes d'une approche permettant d'utiliser des informations externes de connectivité des structures du cerveau. À cet effet, les auteurs ajoutent au modèle de régression pénalisée généralisé un terme de pénalité dérivé de la matrice laplacienne de connectivité structurelle. Les auteurs résolvent des problèmes théoriques et calculatoires, puis démontrent la robustesse de leur méthode lorsque l'information à propos de la connectivité du cerveau est incomplète. De plus, ils présentent une procédure de test d'hypothèse permettant de l'inférence au sujet des paramètres estimés. Finalement, les auteurs évaluent les griPEER dans de vastes études de simulation et en utilisant des données cliniques afin de classifier les individus en VIH+ et VIH−.

Mediation by differential DNA methylation of known associations between single nucleotide polymorphisms and bladder cancer risk.

BACKGROUND: Though bladder cancer has been the subject of many well-powered genome-wide association studies, the mechanisms involving bladder-cancer-associated single nucleotide polymorphisms (SNPs) remain largely unknown. This study focuses on rs798766, rs401681, rs2294008, and rs8102137, which have been associated with bladder cancer and are also cis-acting methylation quantitative loci (mQTL). METHODS: Among 412 bladder cancer cases and 424 controls from the Women's Health Initiative (WHI), we assessed whether the effects of these SNPs on bladder cancer are mediated through proximal DNA methylation changes in pre-diagnostic blood at mQTL-associated CpG sites, which we refer to as natural indirect effects (NIEs). We used a multiple-mediator mediation model for each of the four mQTL adjusted for matching variables and potential confounders, including race/ethnicity, smoking status, and pack-years of smoking. RESULTS: While not statistically significant, our results suggest that substantial proportions of the modest effects of rs401681 (ORNIE = 1.05, 95% confidence interval (CI) = 0.89 to 1.25; NIE percent = 98.5%) and rs2294008 (ORNIE = 1.10, 95% CI = 0.90 to 1.33; NIE percent = 77.6%) on bladder cancer risk are mediated through differential DNA methylation at nearby mQTL-associated CpG sites. The suggestive results indicate that rs2294008 may affect bladder cancer risk through a set of genes in the lymphocyte antigen 6 family, which involves genes that bind to and modulate nicotinic acetylcholine receptors. There was no suggestive evidence supporting mediation for rs8102137 and rs798766. CONCLUSIONS: Though larger studies are necessary, the methylation changes associated with rs401681 and rs2294008 at mQTL-associated CpG sites may be relevant for bladder carcinogenesis, and this study demonstrates how multi-omic data can be integrated to help understand the downstream effects of genetics variants.

Plasma lipidomic profiles after a low and high glycemic load dietary pattern in a randomized controlled crossover feeding study.

BACKGROUND: Dietary patterns low in glycemic load are associated with reduced risk of cardiometabolic diseases. Improvements in serum lipid concentrations may play a role in these observed associations. OBJECTIVE: We investigated how dietary patterns differing in glycemic load affect clinical lipid panel measures and plasma lipidomics profiles. METHODS: In a crossover, controlled feeding study, 80 healthy participants (n = 40 men, n = 40 women), 18-45 y were randomized to receive low-glycemic load (LGL) or high glycemic load (HGL) diets for 28 days each with at least a 28-day washout period between controlled diets. Fasting plasma samples were collected at baseline and end of each diet period. Lipids on a clinical panel including total-, VLDL-, LDL-, and HDL-cholesterol and triglycerides were measured using an auto-analyzer. Lipidomics analysis using mass-spectrometry provided the concentrations of 863 species. Linear mixed models and lipid ontology enrichment analysis were implemented. RESULTS: Lipids from the clinical panel were not significantly different between diets. Univariate analysis showed that 67 species on the lipidomics panel, predominantly in the triacylglycerol class, were higher after the LGL diet compared to the HGL (FDR < 0.05). Three species with FA 17:0 were lower after LGL diet with enrichment analysis (FDR < 0.05). CONCLUSION: In the context of controlled eucaloric diets with similar macronutrient distribution, these results suggest that there are relative shifts in lipid species, but the overall pool does not change. Further studies are needed to better understand in which compartment the different lipid species are transported in blood, and how these shifts are related to health outcomes. This trial was registered at clinicaltrials.gov as NCT00622661.

Tumor-derived Autoantibodies Identify Malignant Pulmonary Nodules.

Rationale: Screening for non-small cell lung cancer is associated with earlier diagnosis and reduced mortality but also increased harm caused by invasive follow-up of benign pulmonary nodules. Lung tumorigenesis activates the immune system, components of which could serve as tumor-specific biomarkers. Objectives: To profile tumor-derived autoantibodies as peripheral biomarkers of malignant pulmonary nodules. Methods: High-density protein arrays were used to define the specificity of autoantibodies isolated from B cells of 10 resected lung tumors. These tumor-derived autoantibodies were also examined as free or complexed to antigen in the plasma of the same 10 patients and matched benign nodule control subjects. Promising autoantibodies were further analyzed in an independent cohort of 250 nodule-positive patients. Measurements and Main Results: Thirteen tumor B-cell-derived autoantibodies isolated ex vivo showed greater than or equal to 50% sensitivity and greater than or equal to 70% specificity for lung cancer. In plasma, 11 of 13 autoantibodies were present both complexed to and free from antigen. In the larger validation cohort, 5 of 13 tumor-derived autoantibodies remained significantly elevated in cancers. A combination of four of these autoantibodies could detect malignant nodules with an area under the curve of 0.74 and had an area under the curve of 0.78 in a subcohort of indeterminate (8-20 mm in the longest diameter) pulmonary nodules. Conclusions: Our novel pipeline identifies tumor-derived autoantibodies that could effectively serve as blood biomarkers for malignant pulmonary nodule diagnosis. This approach has future implications for both a cost-effective and noninvasive approach to determine nodule malignancy for widespread low-dose computed tomography screening.

Fecal Microbial Diversity and Structure Are Associated with Diet Quality in the Multiethnic Cohort Adiposity Phenotype Study.

BACKGROUND: Variation in gut microbial community structure is partly attributed to variations in diet. A priori dietary indexes capture diet quality and have been associated with chronic disease risk. OBJECTIVES: The aim of this study was to examine the association of diet quality, as assessed by the Healthy Eating Index, Alternative Healthy Eating Index-2010, alternate Mediterranean Diet, and the Dietary Approaches to Stop Hypertension Trial, with measures of fecal microbial community structure assessed in the Adiposity Phenotype Study (APS), an ethnically diverse study population with varied food intakes. METHODS: Multiethnic Cohort Study members completed a validated quantitative food frequency questionnaire (QFFQ) at cohort entry (1993-1996) and, for the APS subset, at clinic visit (2013-2015), when they also provided a stool sample. DNA was extracted from stool, and the V1-V3 region of the 16S rRNA gene was amplified and sequenced. Dietary index scores were computed based on the QFFQ and an extensive nutritional database. Using linear regression adjusted for relevant covariates, we estimated associations of dietary quality with microbiome measures and computed adjusted mean values of microbial measures by tertiles of dietary index scores. RESULTS: The 858 men and 877 women of white, Japanese American, Latino, Native Hawaiian, and African American ancestry had a mean age of 69.2 years at stool collection. Alpha diversity according to the Shannon index increased by 1-2% across tertiles of all 4 diet indexes measured at clinic visit. The mean relative abundance of the phylum Actinobacteria was 13-19% lower with higher diet quality across all 4 indexes (difference between tertile 3 and tertile 1 divided by tertile 1). Of the 104 bacterial genera tested, 21 (primarily from the phylum Firmicutes) were positively associated with at least 1 index after Bonferroni adjustment. CONCLUSION: Diet quality was strongly associated with fecal microbial alpha diversity and beta diversity and several genera previously associated with human health.

Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study.

PURPOSE: To compare the ability of radiological semantic and quantitative texture features in lung cancer diagnosis of pulmonary nodules. MATERIALS AND METHODS: A total of N = 121 subjects with confirmed non-small-cell lung cancer were matched with 117 controls based on age and gender. Radiological semantic and quantitative texture features were extracted from CT images with or without contrast enhancement. Three different models were compared using LASSO logistic regression: "CS" using clinical and semantic variables, "T" using texture features, and "CST" using clinical, semantic, and texture variables. For each model, we performed 100 trials of fivefold cross-validation and the average receiver operating curve was accessed. The AUC of the cross-validation study (AUCCV) was calculated together with its 95% confidence interval. RESULTS: The AUCCV (and 95% confidence interval) for models T, CS, and CST was 0.85 (0.71-0.96), 0.88 (0.77-0.96), and 0.88 (0.77-0.97), respectively. After separating the data into two groups with or without contrast enhancement, the AUC (without cross-validation) of the model T was 0.86 both for images with and without contrast enhancement, suggesting that contrast enhancement did not impact the utility of texture analysis. CONCLUSIONS: The models with semantic and texture features provided cross-validated AUCs of 0.85-0.88 for classification of benign versus cancerous nodules, showing potential in aiding the management of patients. KEY POINTS: • Pretest probability of cancer can aid and direct the physician in the diagnosis and management of pulmonary nodules in a cost-effective way. • Semantic features (qualitative features reported by radiologists to characterize lung lesions) and radiomic (e.g., texture) features can be extracted from CT images. • Input of these variables into a model can generate a pretest likelihood of cancer to aid clinical decision and management of pulmonary nodules.

Oxidative DNA damage during night shift work.

OBJECTIVES: We previously reported that compared with night sleep, day sleep among shift workers was associated with reduced urinary excretion of 8-hydroxydeoxyguanosine (8-OH-dG), potentially reflecting a reduced ability to repair 8-OH-dG lesions in DNA. We identified the absence of melatonin during day sleep as the likely causative factor. We now investigate whether night work is also associated with reduced urinary excretion of 8-OH-dG. METHODS: For this cross-sectional study, 50 shift workers with the largest negative differences in night work versus night sleep circulating melatonin levels (measured as 6-sulfatoxymelatonin in urine) were selected from among the 223 shift workers included in our previous study. 8-OH-dG concentrations were measured in stored urine samples using high performance liquid chromatography with electrochemical detection. Mixed effects models were used to compare night work versus night sleep 8-OH-dG levels. RESULTS: Circulating melatonin levels during night work (mean=17.1 ng/mg creatinine/mg creatinine) were much lower than during night sleep (mean=51.7 ng/mg creatinine). In adjusted analyses, average urinary 8-OH-dG levels during the night work period were only 20% of those observed during the night sleep period (95% CI 10% to 30%; p<0.001). CONCLUSIONS: This study suggests that night work, relative to night sleep, is associated with reduced repair of 8-OH-dG lesions in DNA and that the effect is likely driven by melatonin suppression occurring during night work relative to night sleep. If confirmed, future studies should evaluate melatonin supplementation as a means to restore oxidative DNA damage repair capacity among shift workers.

Adaptive penalties for generalized Tikhonov regularization in statistical regression models with application to spectroscopy data.

Tikhonov regularization was proposed for multivariate calibration by Andries and Kalivas [1]. We use this framework for modeling the statistical association between spectroscopy data and a scalar outcome. In both the calibration and regression settings this regularization process has advantages over methods of spectral pre-processing and dimension-reduction approaches such as feature extraction or principal component regression. We propose an extension of this penalized regression framework by adaptively refining the penalty term to optimally focus the regularization process. We illustrate the approach using simulated spectra and compare it with other penalized regression models and with a two-step method that first pre-processes the spectra then fits a dimension-reduced model using the processed data. The methods are also applied to magnetic resonance spectroscopy data to identify brain metabolites that are associated with cognitive function.

Pharmacometabonomic Prediction of Busulfan Clearance in Hematopoetic Cell Transplant Recipients.

Intravenous (IV) busulfan doses are often personalized to a concentration at steady state (Css) using the patient's clearance, which is estimated with therapeutic drug monitoring. We sought to identify biomarkers of IV busulfan clearance using a targeted pharmacometabonomics approach. A total of 200 metabolites were quantitated in 106 plasma samples, each obtained before IV busulfan administration in hematopoietic cell transplant (HCT) recipients. Both univariate linear regression with false discovery rate (FDR) and pathway enrichment analyses using the Global test were performed. In the univariate analysis, glycine, N-acetylglycine, 2-hydroxyisovaleric acid, creatine, serine, and tyrosine were statistically significantly associated with IV busulfan clearance at P < 0.05, with the first three satisfying the FDR of q < 0.1. Using pathway enrichment analysis, the glycine, serine, and threonine metabolism pathway was statistically significantly associated with IV busulfan clearance at P < 0.05 and q < 0.1, and a pathway impact >0.1. Glycine is a component of glutathione, which is conjugated with busulfan via glutathione transferase enzymes. These results demonstrate the potential utility of pharmacometabonomics to inform IV busulfan dosing. Future studies are required to validate these findings.

Comprehensive site-specific whole genome profiling of stromal and epithelial colonic gene signatures in human sigmoid colon and rectal tissue.

The strength of associations between various exposures (e.g., diet, tobacco, chemopreventive agents) and colorectal cancer risk may partially depend on the complex interaction between epithelium and stroma across anatomic subsites. Currently, baseline data describing genome-wide coding and long noncoding gene expression profiles in the healthy colon specific to tissue type and location are lacking. Therefore, colonic mucosal biopsies from 10 healthy participants who were enrolled in a clinical study to evaluate effects of lignan supplementation on gut resiliency were used to characterize the site-specific global gene expression signatures associated with stromal vs. epithelial cells in the sigmoid colon and rectum. Using RNA-seq, we demonstrate that tissue type and location patterns of gene expression and upstream regulatory pathways are distinct. For example, consistent with a key role of stroma in the crypt niche, mRNAs associated with immunoregulatory and inflammatory processes (i.e., CXCL14, ANTXR1), smooth muscle contraction (CALD1), proliferation and apoptosis (GLP2R, IGFBP3), and modulation of extracellular matrix (MMP2, COL3A1, MFAP4) were all highly expressed in the stroma. In comparison, HOX genes (HOXA3, HOXD9, HOXD10, HOXD11, and HOXD-AS2, a HOXD cluster antisense RNA 2), and WNT5B expression were also significantly higher in sigmoid colon compared with the rectum. These findings provide strong impetus for considering colorectal tissue subtypes and location in future observational studies and clinical trials designed to evaluate the effects of exposures on colonic health.

Oxidative DNA damage during sleep periods among nightshift workers.

OBJECTIVES: Oxidative DNA damage may be increased among nightshift workers because of suppression of melatonin, a cellular antioxidant, and/or inflammation related to sleep disruption. However, oxidative DNA damage has received limited attention in previous studies of nightshift work. METHODS: From two previous cross-sectional studies, urine samples collected during a night sleep period for 217 dayshift workers and during day and night sleep (on their first day off) periods for 223 nightshift workers were assayed for 8-hydroxydeoxyguanosine (8-OH-dG), a marker of oxidative DNA damage, using high-performance liquid chromatography with electrochemical detection. Urinary measures of 6-sulfatoxymelatonin (aMT6s), a marker of circulating melatonin levels, and actigraphy-based sleep quality data were also available. RESULTS: Nightshift workers during their day sleep periods excreted 83% (p=0.2) and 77% (p=0.03) of the 8-OH-dG that dayshift workers and they themselves, respectively, excreted during their night sleep periods. Among nightshift workers, higher aMT6s levels were associated with higher urinary 8-OH-dG levels, and an inverse U-shaped trend was observed between 8-OH-dG levels and sleep efficiency and sleep duration. CONCLUSIONS: Reduced excretion of 8-OH-dG among nightshift workers during day sleep may reflect reduced functioning of DNA repair machinery, which could potentially lead to increased cellular levels of oxidative DNA damage. Melatonin disruption among nightshift workers may be responsible for the observed effect, as melatonin is known to enhance repair of oxidative DNA damage. Quality of sleep may similarly impact DNA repair. Cellular levels of DNA damage will need to be evaluated in future studies to help interpret these findings.

Longitudinal Functional Models with Structured Penalties.

This article addresses estimation in regression models for longitudinally-collected functional covariates (time-varying predictor curves) with a longitudinal scaler outcome. The framework consists of estimating a time-varying coefficient function that is modeled as a linear combination of time-invariant functions with time-varying coefficients. The model uses extrinsic information to inform the structure of the penalty, while the estimation procedure exploits the equivalence between penalized least squares estimation and a linear mixed model representation. The process is empirically evaluated with several simulations and it is applied to analyze the neurocognitive impairment of HIV patients and its association with longitudinally-collected magnetic resonance spectroscopy (MRS) curves.

Towards microbiome transplant as a therapy for periodontitis: an exploratory study of periodontitis microbial signature contrasted by oral health, caries and edentulism.

BACKGROUND: Conventional periodontal therapy aims at controlling supra- and subgingival biofilms. Although periodontal therapy was shown to improve periodontal health, it does not completely arrest the disease. Almost all subjects compliant with periodontal maintenance continue to experience progressive clinical attachment loss and a fraction of them loses teeth. An oral microbial transplant may be a new alternative for treating periodontitis (inspired by fecal transplant). First, it must be established that microbiomes of oral health and periodontitis are distinct. In that case, the health-associated microbiome could be introduced into the oral cavity of periodontitis patients. This relates to the goals of our study: (i) to assess if microbial communities of the entire oral cavity of subjects with periodontitis were different from or oral health contrasted by microbiotas of caries and edentulism patients; (ii) to test in vitro if safe concentration of sodium hypochlorite could be used for initial eradication of the original oral microbiota followed by a safe neutralization of the hypochlorite prior transplantation. METHODS: Sixteen systemically healthy white adults with clinical signs of one of the following oral conditions were enrolled: periodontitis, established caries, edentulism, and oral health. Oral biofilm samples were collected from sub- and supra-gingival sites, and oral mucosae. DNA was extracted and 16S rRNA genes were amplified. Amplicons from the same patient were pooled, sequenced and quantified. Volunteer's oral plaque was treated with saline, 16 mM NaOCl and NaOCl neutralized by ascorbate buffer followed by plating on blood agar. RESULTS: Ordination plots of rRNA gene abundances revealed distinct groupings for the oral microbiomes of subjects with periodontitis, edentulism, or oral health. The oral microbiome in subjects with periodontitis showed the greatest diversity harboring 29 bacterial species at significantly higher abundance compared to subjects with the other assessed conditions. Healthy subjects had significantly higher abundance in 10 microbial species compared to the other conditions. NaOCl showed strong antimicrobial properties; nontoxic ascorbate was capable of neutralizing the hypochlorite. CONCLUSIONS: Distinct oral microbial signatures were found in subjects with periodontitis, edentulism, or oral health. This finding opens up a potential for a new therapy, whereby a health-related entire oral microbial community would be transplanted to the diseased patient.

Matrix-Variate Regression for Sparse, Low-Rank Estimation of Brain Connectivities Associated With a Clinical Outcome.

OBJECTIVE: We address the problem of finding brain connectivities that are associated with a clinical outcome or phenotype. METHODS: The proposed framework regresses a (scalar) clinical outcome on matrix-variate predictors which arise in the form of brain connectivity matrices. For example, in a large cohort of subjects we estimate those regions of functional connectivities that are associated with neurocognitive scores. We approach this high-dimensional yet highly structured estimation problem by formulating a regularized estimation process that results in a low-rank coefficient matrix having a sparse set of nonzero entries which represent regions of biologically relevant connectivities. In contrast to the recent literature on estimating a sparse, low-rank matrix from a single noisy observation, our scalar-on-matrix regression framework produces a data-driven extraction of structures that are associated with a clinical response. The method, called Sparsity Inducing Nuclear-Norm Estimator (SpINNEr), simultaneously constrains the regression coefficient matrix in two ways: a nuclear norm penalty encourages low-rank structure while an l1 norm encourages entry-wise sparsity. RESULTS: Our simulations show that SpINNEr outperforms other methods in estimation accuracy when the response-related entries (representing the brain's functional connectivity) are arranged in well-connected communities. SpINNEr is applied to investigate associations between HIV-related outcomes and functional connectivity in the human brain. CONCLUSION AND SIGNIFICANCE: Overall, this work demonstrates the potential of SpINNEr to recover sparse and low-rank estimates under scalar-on-matrix regression framework.

CXCR1/2 antagonism inhibits neutrophil function and not recruitment in cancer.

The level of tumor and circulating CXCR1/2-expressing neutrophils and CXCR1/2 ligands correlate with poor patient outcomes, inversely correlate with tumoral lymphocyte content, and predict immune checkpoint inhibitor (ICI) treatment failure. Accordingly, CXCR2-selective and CXCR1/2 dual inhibitors exhibit activity both as single agents and in combination with ICI treatment in mouse tumor models. Based on such reports, clinical trials combining CXCR1/2 axis antagonists with ICI treatment for cancer patients are underway. It has been assumed that CXCR1/2 blockade impacts tumors by blocking neutrophil chemotaxis and reducing neutrophil content in tumors. Here, we show that while CXCR2 antagonism does slow tumor growth, it does not preclude neutrophil recruitment into tumor. Instead, CXCR1/2 inhibition alters neutrophil function by blocking the polarization of transcriptional programs toward immune suppressive phenotypes and rendering neutrophils incapable of suppressing lymphocyte proliferation. This is associated with decreased release of reactive oxygen species and Arginase-1 into the extracellular milieu. Remarkably, these therapeutics do not impact the ability of neutrophils to phagocytose and kill ingested bacteria. Taken together, these results mechanistically explain why CXCR1/2 inhibition has been active in cancer but without infectious complications.

Prediction of Busulfan Clearance by Predose Plasma Metabolomic Profiling.

Intravenous busulfan doses are often personalized to a target plasma exposure (targeted busulfan) using an individual's busulfan clearance (BuCL). We evaluated whether BuCL could be predicted by a predose plasma panel of 841 endogenous metabolomic compounds (EMCs). In this prospective cohort of 132 hematopoietic cell transplantation (HCT) patients, all had samples collected immediately before busulfan administration (preBU) and 96 had samples collected 2 weeks before busulfan (2-week-preBU). BuCL was significantly associated with 37 EMCs after univariate linear regression analysis and controlling for false discovery (< 0.05) in the 132 preBU samples. In parallel, with preBU samples, we included all 841 EMCs in a least absolute shrinkage and selection operator-penalized regression which selected 13 EMCs as predominantly associated with BuCL. Then, we constructed a prediction model by estimating coefficients for these 13 EMCs, along with sex, using ordinary least-squares. When the resulting linear prediction model was applied to the 2-week-preBU samples, it explained 40% of the variation in BuCL (adjusted R2  = 0.40). Pathway enrichment analysis revealed 18 pathways associated with BuCL. Lysine degradation followed by steroid biosynthesis, which aligned with the univariate analysis, were the top two pathways. BuCL can be predicted before busulfan administration with a linear regression model of 13 EMCs. This pharmacometabolomics method should be prioritized over use of a busulfan test dose or pharmacogenomics to guide busulfan dosing. These results highlight the potential of pharmacometabolomics as a precision medicine tool to improve or replace pharmacokinetics to personalize busulfan doses.

Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment.

The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody-antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules.