A Spatial Omnibus Test (SPOT) for Spatial Proteomic Data.

Spatial proteomics can reveal the spatial organization of immune cells in the tumor immune microenvironment. Relating measures of spatial clustering, such as Ripley's K or Besag's L, to patient outcomes may offer important clinical insights. However, these measures require pre-specifying a radius in which to quantify clustering, yet no consensus exists on the optimal radius which may be context-specific. We propose a SPatial Omnibus Test (SPOT) which conducts this analysis across a range of candidate radii. At each radius, SPOT evaluates the association between the spatial summary and outcome, adjusting for confounders. SPOT then aggregates results across radii using the Cauchy combination test, yielding an omnibus p-value characterizing the overall degree of association. Using simulations, we verify that the type I error rate is controlled and show SPOT can be more powerful than alternatives. We also apply SPOT to an ovarian cancer study. An R package and tutorial is provided at https://github.com/sarahsamorodnitsky/SPOT.

Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies.

Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19.

Pneumoproteins and biomarkers of inflammation and coagulation do not predict rapid lung function decline in people living with HIV.

Chronic obstructive pulmonary disease (COPD) is among the leading causes of death worldwide and HIV is an independent risk factor for the development of COPD. However, the etiology of this increased risk and means to identify persons with HIV (PWH) at highest risk for COPD have remained elusive. Biomarkers may reveal etiologic pathways and allow better COPD risk stratification. We performed a matched case:control study of PWH in the Strategic Timing of Antiretoviral Treatment (START) pulmonary substudy. Cases had rapid lung function decline (> 40 mL/year FEV1 decline) and controls had stable lung function (+ 20 to - 20 mL/year). The analysis was performed in two distinct groups: (1) those who were virally suppressed for at least 6 months and (2) those with untreated HIV (from the START deferred treatment arm). We used linear mixed effects models to test the relationship between case:control status and blood concentrations of pneumoproteins (surfactant protein-D and club cell secretory protein), and biomarkers of inflammation (IL-6 and hsCRP) and coagulation (d-dimer and fibrinogen); concentrations were measured within ± 6 months of first included spirometry. We included an interaction with treatment group (untreated HIV vs viral suppression) to test if associations varied by treatment group. This analysis included 77 matched case:control pairs in the virally suppressed batch, and 42 matched case:control pairs in the untreated HIV batch (n = 238 total) who were followed for a median of 3 years. Median (IQR) CD4 + count was lowest in the controls with untreated HIV at 674 (580, 838). We found no significant associations between case:control status and pneumoprotein or biomarker concentrations in either virally suppressed or untreated PWH. In this cohort of relatively young, recently diagnosed PWH, concentrations of pneumoproteins and biomarkers of inflammation and coagulation were not associated with subsequent rapid lung function decline.Trial registration: NCT00867048 and NCT01797367.

Lung proteome and metabolome endotype in HIV-associated obstructive lung disease.

Purpose: Obstructive lung disease is increasingly common among persons with HIV, both smokers and nonsmokers. We used aptamer proteomics to identify proteins and associated pathways in HIV-associated obstructive lung disease. Methods: Bronchoalveolar lavage fluid (BALF) samples from 26 persons living with HIV with obstructive lung disease were matched to persons living with HIV without obstructive lung disease based on age, smoking status and antiretroviral treatment. 6414 proteins were measured using SomaScan® aptamer-based assay. We used sparse distance-weighted discrimination (sDWD) to test for a difference in protein expression and permutation tests to identify univariate associations between proteins and forced expiratory volume in 1 s % predicted (FEV1 % pred). Significant proteins were entered into a pathway over-representation analysis. We also constructed protein-driven endotypes using K-means clustering and performed over-representation analysis on the proteins that were significantly different between clusters. We compared protein-associated clusters to those obtained from BALF and plasma metabolomics data on the same patient cohort. Results: After filtering, we retained 3872 proteins for further analysis. Based on sDWD, protein expression was able to separate cases and controls. We found 575 proteins that were significantly correlated with FEV1 % pred after multiple comparisons adjustment. We identified two protein-driven endotypes, one of which was associated with poor lung function, and found that insulin and apoptosis pathways were differentially represented. We found similar clusters driven by metabolomics in BALF but not plasma. Conclusion: Protein expression differs in persons living with HIV with and without obstructive lung disease. We were not able to identify specific pathways differentially expressed among patients based on FEV1 % pred; however, we identified a unique protein endotype associated with insulin and apoptotic pathways.

Lung and Plasma Metabolome in HIV-Associated Obstructive Lung Disease.

BACKGROUND: HIV is a risk factor for obstructive lung disease (OLD), independent of smoking. We used mass spectrometry (MS) approaches to identify metabolomic biomarkers that inform mechanistic pathogenesis of OLD in persons with HIV (PWH). METHODS: We obtained bronchoalveolar lavage fluid (BALF) samples from 52 PWH, in case:control (+OLD/-OLD) pairs matched on age, smoking status, and antiretroviral treatment. Four hundred nine metabolites from 8 families were measured on BALF and plasma samples using a MS-based Biocrates platform. After filtering metabolites with a high proportion of missing values and values below the level of detection, we performed univariate testing using paired t tests followed by false discovery rate corrections. We used distance-weighted discrimination (DWD) to test for an overall difference in the metabolite profile between cases and controls. RESULTS: After filtering, there were 252 BALF metabolites for analysis from 8 metabolite families. DWD testing found that collectively, BALF metabolites differentiated cases from controls, whereas plasma metabolites did not. In BALF samples, we identified 3 metabolites that correlated with OLD at the false discovery rate of 10%; all were in the phosphatidylcholine family. We identified additional BALF metabolites when analyzing lung function as a continuous variable, and these included acylcarnitines, triglycerides, and a cholesterol ester. CONCLUSIONS: Collectively, BALF metabolites differentiate PWH with and without OLD. These included several BALF lipid metabolites. These findings were limited to BALF and were not found in plasma from the same individuals. Phosphatidylcholine, the most common lipid component of surfactant, was the predominant lipid metabolite differentially expressed.

A hierarchical spike-and-slab model for pan-cancer survival using pan-omic data.

BACKGROUND: Pan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict clinical outcomes. We address the issue of prediction across multiple high-dimensional sources of data and sample sets by using molecular patterns identified by BIDIFAC+, a method for integrative dimension reduction of bidimensionally-linked matrices, in a Bayesian hierarchical model. Our model performs variable selection through spike-and-slab priors that borrow information across clustered data. We use this model to predict overall patient survival from the Cancer Genome Atlas with data from 29 cancer types and 4 omics sources and use simulations to characterize the performance of the hierarchical spike-and-slab prior. RESULTS: We found that molecular patterns shared across all or most cancers were largely not predictive of survival. However, our model selected patterns unique to subsets of cancers that differentiate clinical tumor subtypes with markedly different survival outcomes. Some of these subtypes were previously established, such as subtypes of uterine corpus endometrial carcinoma, while others may be novel, such as subtypes within a set of kidney carcinomas. Through simulations, we found that the hierarchical spike-and-slab prior performs best in terms of variable selection accuracy and predictive power when borrowing information is advantageous, but also offers competitive performance when it is not. CONCLUSIONS: We address the issue of prediction across multiple sources of data by using results from BIDIFAC+ in a Bayesian hierarchical model for overall patient survival. By incorporating spike-and-slab priors that borrow information across cancers, we identified molecular patterns that distinguish clinical tumor subtypes within a single cancer and within a group of cancers. We also corroborate the flexibility and performance of using spike-and-slab priors as a Bayesian variable selection approach.

A Pan-Cancer and Polygenic Bayesian Hierarchical Model for the Effect of Somatic Mutations on Survival.

We built a novel Bayesian hierarchical survival model based on the somatic mutation profile of patients across 50 genes and 27 cancer types. The pan-cancer quality allows for the model to "borrow" information across cancer types, motivated by the assumption that similar mutation profiles may have similar (but not necessarily identical) effects on survival across different tissues of origin or tumor types. The effect of a mutation at each gene was allowed to vary by cancer type, whereas the mean effect of each gene was shared across cancers. Within this framework, we considered 4 parametric survival models (normal, log-normal, exponential, and Weibull), and we compared their performance via a cross-validation approach in which we fit each model on training data and estimate the log-posterior predictive likelihood on test data. The log-normal model gave the best fit, and we investigated the partial effect of each gene on survival via a forward selection procedure. Through this we determined that mutations at TP53 and FAT4 were together the most useful for predicting patient survival. We validated the model via simulation to ensure that our algorithm for posterior computation gave nominal coverage rates. The code used for this analysis can be found at https://github.com/sarahsamorodnitsky/Pan-Cancer-Survival-Modeling.git, and the results are summarized at http://ericfrazerlock.com/surv_figs/SurvivalDisplay.html.