Characterization of tumor evolution by functional clonality and phylogenetics in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a molecularly heterogeneous solid malignancy, and its fitness may be shaped by how its tumor cells evolve. However, ability to monitor tumor cell evolution is hampered by the presence of numerous passenger mutations that do not provide any biological consequences. Here we develop a strategy to determine the tumor clonality of three independent HCC cohorts of 524 patients with diverse etiologies and race/ethnicity by utilizing somatic mutations in cancer driver genes. We identify two main types of tumor evolution, i.e., linear, and non-linear models where non-linear type could be further divided into classes, which we call shallow branching and deep branching. We find that linear evolving HCC is less aggressive than other types. GTF2IRD2B mutations are enriched in HCC with linear evolution, while TP53 mutations are the most frequent genetic alterations in HCC with non-linear models. Furthermore, we observe significant B cell enrichment in linear trees compared to non-linear trees suggesting the need for further research to uncover potential variations in immune cell types within genomically determined phylogeny types. These results hint at the possibility that tumor cells and their microenvironment may collectively influence the tumor evolution process.

Proteomic Indicators of Health Predict Alzheimer's Disease Biomarker Levels and Dementia Risk

OBJECTIVE

Few studies have comprehensively examined how health and disease risk influence Alzheimer's disease (AD) biomarkers. The present study examined the association of 14 protein-based health indicators with plasma and neuroimaging biomarkers of AD and neurodegeneration.

METHODS

In 706 cognitively normal adults, we examined whether 14 protein-based health indices (ie, SomaSignal® tests) were associated with concurrently measured plasma-based biomarkers of AD pathology (amyloid-β [Aβ]42/40 , tau phosphorylated at threonine-181 [pTau-181]), neuronal injury (neurofilament light chain [NfL]), and reactive astrogliosis (glial fibrillary acidic protein [GFAP]), brain volume, and cortical Aβ and tau. In a separate cohort (n = 11,285), we examined whether protein-based health indicators associated with neurodegeneration also predict 25-year dementia risk.

RESULTS

Greater protein-based risk for cardiovascular disease, heart failure mortality, and kidney disease was associated with lower Aβ42/40 and higher pTau-181, NfL, and GFAP levels, even in individuals without cardiovascular or kidney disease. Proteomic indicators of body fat percentage, lean body mass, and visceral fat were associated with pTau-181, NfL, and GFAP, whereas resting energy rate was negatively associated with NfL and GFAP. Together, these health indicators predicted 12, 31, 50, and 33% of plasma Aβ42/40 , pTau-181, NfL, and GFAP levels, respectively. Only protein-based measures of cardiovascular risk were associated with reduced regional brain volumes; these measures predicted 25-year dementia risk, even among those without clinically defined cardiovascular disease.

INTERPRETATION

Subclinical peripheral health may influence AD and neurodegenerative disease processes and relevant biomarker levels, particularly NfL. Cardiovascular health, even in the absence of clinically defined disease, plays a central role in brain aging and dementia. ANN NEUROL 2024;95:260-273.

Serum Proteomic Signatures of Common Health Outcomes among Older Adults

INTRODUCTION

In aging populations, the coexistence of multiple health comorbidities represents a significant challenge for clinicians and researchers. Leveraging advances in omics techniques to characterize these health conditions may provide insight into disease pathogenesis as well as reveal biomarkers for monitoring, prognostication, and diagnosis. Researchers have previously established the utility of big data approaches with respect to comprehensive health outcome measurements in younger populations, identifying protein markers that may provide significant health information with a single blood sample.

METHODS

Here, we employed a similar approach in two cohorts of older adults, the Baltimore Longitudinal Study of Aging (mean age = 76.12 years) and InCHIANTI Study (mean age = 66.05 years), examining the relationship between levels of serum proteins and 5 key health outcomes: kidney function, fasting glucose, physical activity, lean body mass, and percent body fat.

RESULTS

Correlations between proteins and health outcomes were primarily shared across both older adult cohorts. We further identified that most proteins associated with health outcomes in the older adult cohorts were not associated with the same outcomes in a prior study of a younger population. A subset of proteins, adiponectin, MIC-1, and NCAM-120, were associated with at least three health outcomes in both older adult cohorts but not in the previously published younger cohort, suggesting that they may represent plausible markers of general health in older adult populations.

CONCLUSION

Taken together, these findings suggest that comprehensive protein health markers have utility in aging populations and are distinct from those identified in younger adults, indicating unique mechanisms of disease with aging.

Cross-sectional analysis of healthy individuals across decades: Aging signatures across multiple physiological compartments

The study of age-related biomarkers from different biofluids and tissues within the same individual might provide a more comprehensive understanding of age-related changes within and between compartments as these changes are likely highly interconnected. Understanding age-related differences by compartments may shed light on the mechanism of their reciprocal interactions, which may contribute to the phenotypic manifestations of aging. To study such possible interactions, we carried out a targeted metabolomic analysis of plasma, skeletal muscle, and urine collected from healthy participants, age 22-92 years, and identified 92, 34, and 35 age-associated metabolites, respectively. The metabolic pathways that were identified across compartments included inflammation and cellular senescence, microbial metabolism, mitochondrial health, sphingolipid metabolism, lysosomal membrane permeabilization, vascular aging, and kidney function.

Chromosome 8p engineering reveals increased metastatic potential targetable by patient-specific synthetic lethality in liver cancer

Large-scale chromosomal aberrations are prevalent in human cancer, but their function remains poorly understood. We established chromosome-engineered hepatocellular carcinoma cell lines using CRISPR-Cas9 genome editing. A 33-mega-base pair region on chromosome 8p (chr8p) was heterozygously deleted, mimicking a frequently observed chromosomal deletion. Using this isogenic model system, we delineated the functional consequences of chr8p loss and its impact on metastatic behavior and patient survival. We found that metastasis-associated genes on chr8p act in concert to induce an aggressive and invasive phenotype characteristic for chr8p-deleted tumors. Genome-wide CRISPR-Cas9 viability screening in isogenic chr8p-deleted cells served as a powerful tool to find previously unidentified synthetic lethal targets and vulnerabilities accompanying patient-specific chromosomal alterations. Using this target identification strategy, we showed that chr8p deletion sensitizes tumor cells to targeting of the reactive oxygen sanitizing enzyme Nudix hydrolase 17. Thus, chromosomal engineering allowed for the identification of novel synthetic lethalities specific to chr8p loss of heterozygosity.

Diabetes-associated breast cancer is molecularly distinct and shows a DNA damage repair deficiency

Diabetes commonly affects patients with cancer. We investigated the influence of diabetes on breast cancer biology using a 3-pronged approach that included analysis of orthotopic human tumor xenografts, patient tumors, and breast cancer cells exposed to diabetes/hyperglycemia-like conditions. We aimed to identify shared phenotypes and molecular signatures by investigating the metabolome, transcriptome, and tumor mutational burden. Diabetes and hyperglycemia did not enhance cell proliferation but induced mesenchymal and stem cell-like phenotypes linked to increased mobility and odds of metastasis. They also promoted oxyradical formation and both a transcriptome and mutational signatures of DNA repair deficiency. Moreover, food- and microbiome-derived metabolites tended to accumulate in breast tumors in the presence of diabetes, potentially affecting tumor biology. Breast cancer cells cultured under hyperglycemia-like conditions acquired increased DNA damage and sensitivity to DNA repair inhibitors. Based on these observations, we conclude that diabetes-associated breast tumors may show an increased drug response to DNA damage repair inhibitors.

Calorie restriction modulates the transcription of genes related to stress response and longevity in human muscle: The CALERIE study

The lifespan extension induced by 40% caloric restriction (CR) in rodents is accompanied by postponement of disease, preservation of function, and increased stress resistance. Whether CR elicits the same physiological and molecular responses in humans remains mostly unexplored. In the CALERIE study, 12% CR for 2 years in healthy humans induced minor losses of muscle mass (leg lean mass) without changes of muscle strength, but mechanisms for muscle quality preservation remained unclear. We performed high-depth RNA-Seq (387-618 million paired reads) on human vastus lateralis muscle biopsies collected from the CALERIE participants at baseline, 12- and 24-month follow-up from the 90 CALERIE participants randomized to CR and "ad libitum" control. Using linear mixed effect model, we identified protein-coding genes and splicing variants whose expression was significantly changed in the CR group compared to controls, including genes related to proteostasis, circadian rhythm regulation, DNA repair, mitochondrial biogenesis, mRNA processing/splicing, FOXO3 metabolism, apoptosis, and inflammation. Changes in some of these biological pathways mediated part of the positive effect of CR on muscle quality. Differentially expressed splicing variants were associated with change in pathways shown to be affected by CR in model organisms. Two years of sustained CR in humans positively affected skeletal muscle quality, and impacted gene expression and splicing profiles of biological pathways affected by CR in model organisms, suggesting that attainable levels of CR in a lifestyle intervention can benefit muscle health in humans.

Identification of circulating proteins associated with general cognitive function among middle-aged and older adults

Identifying circulating proteins associated with cognitive function may point to biomarkers and molecular process of cognitive impairment. Few studies have investigated the association between circulating proteins and cognitive function. We identify 246 protein measures quantified by the SomaScan assay as associated with cognitive function (p < 4.9E-5, n up to 7289). Of these, 45 were replicated using SomaScan data, and three were replicated using Olink data at Bonferroni-corrected significance. Enrichment analysis linked the proteins associated with general cognitive function to cell signaling pathways and synapse architecture. Mendelian randomization analysis implicated higher levels of NECTIN2, a protein mediating viral entry into neuronal cells, with higher Alzheimer's disease (AD) risk (p = 2.5E-26). Levels of 14 other protein measures were implicated as consequences of AD susceptibility (p < 2.0E-4). Proteins implicated as causes or consequences of AD susceptibility may provide new insight into the potential relationship between immunity and AD susceptibility as well as potential therapeutic targets.

Identification of Clinically Relevant Brain Endothelial Cell Biomarkers in Plasma

BACKGROUND

Proteins expressed by brain endothelial cells (BECs), the primary cell type of the blood-brain barrier, may serve as sensitive plasma biomarkers for neurological and neurovascular conditions, including cerebral small vessel disease.

METHODS

Using data from the BLSA (Baltimore Longitudinal Study of Aging; n=886; 2009-2020), BEC-enriched proteins were identified among 7268 plasma proteins (measured with SomaScanv4.1) using an automated annotation algorithm that filtered endothelial cell transcripts followed by cross-referencing with BEC-specific transcripts reported in single-cell RNA-sequencing studies. To identify BEC-enriched proteins in plasma most relevant to the maintenance of neurological and neurovascular health, we selected proteins significantly associated with 3T magnetic resonance imaging-defined white matter lesion volumes. We then examined how these candidate BEC biomarkers related to white matter lesion volumes, cerebral microhemorrhages, and lacunar infarcts in the ARIC study (Atherosclerosis Risk in Communities; US multisite; 1990-2017). Finally, we determined whether these candidate BEC biomarkers, when measured during midlife, were related to dementia risk over a 25-year follow-up period.

RESULTS

Of the 28 proteins identified as BEC-enriched, 4 were significantly associated with white matter lesion volumes (CDH5 [cadherin 5], CD93 [cluster of differentiation 93], ICAM2 [intracellular adhesion molecule 2], GP1BB [glycoprotein 1b platelet subunit beta]), while another approached significance (RSPO3 [R-Spondin 3]). A composite score based on 3 of these BEC proteins accounted for 11% of variation in white matter lesion volumes in BLSA participants. We replicated the associations between the BEC composite score, CDH5, and RSPO3 with white matter lesion volumes in ARIC, and further demonstrated that the BEC composite score and RSPO3 were associated with the presence of ≥1 cerebral microhemorrhages. We also showed that the BEC composite score, CDH5, and RSPO3 were associated with 25-year dementia risk.

CONCLUSIONS

In addition to identifying BEC proteins in plasma that relate to cerebral small vessel disease and dementia risk, we developed a composite score of plasma BEC proteins that may be used to estimate blood-brain barrier integrity and risk for adverse neurovascular outcomes.

Epigenetic signature of human immune aging in the GESTALT study

Age-associated DNA methylation in blood cells convey information on health status. However, the mechanisms that drive these changes in circulating cells and their relationships to gene regulation are unknown. We identified age-associated DNA methylation sites in six purified blood-borne immune cell types (naive B, naive CD4+ and CD8+ T cells, granulocytes, monocytes, and NK cells) collected from healthy individuals interspersed over a wide age range. Of the thousands of age-associated sites, only 350 sites were differentially methylated in the same direction in all cell types and validated in an independent longitudinal cohort. Genes close to age-associated hypomethylated sites were enriched for collagen biosynthesis and complement cascade pathways, while genes close to hypermethylated sites mapped to neuronal pathways. In silico analyses showed that in most cell types, the age-associated hypo- and hypermethylated sites were enriched for ARNT (HIF1β) and REST transcription factor (TF) motifs, respectively, which are both master regulators of hypoxia response. To conclude, despite spatial heterogeneity, there is a commonality in the putative regulatory role with respect to TF motifs and histone modifications at and around these sites. These features suggest that DNA methylation changes in healthy aging may be adaptive responses to fluctuations of oxygen availability.

Circulating trans fatty acids are associated with prostate cancer in Ghanaian and American men

The association between fatty acids and prostate cancer remains poorly explored in African-descent populations. Here, we analyze 24 circulating fatty acids in 2934 men, including 1431 prostate cancer cases and 1503 population controls from Ghana and the United States, using CLIA-certified mass spectrometry-based assays. We investigate their associations with population groups (Ghanaian, African American, European American men), lifestyle factors, the fatty acid desaturase (FADS) genetic locus, and prostate cancer. Blood levels of circulating fatty acids vary significantly between the three population groups, particularly trans, omega-3 and omega-6 fatty acids. FADS1/2 germline genetic variants and lifestyle factors explain some of the variation in fatty acid levels, with the FADS1/2 locus showing population-specific associations, suggesting differences in their control by germline genetic factors. All trans fatty acids, namely elaidic, palmitelaidic, and linoelaidic acids, associated with an increase in the odds of developing prostate cancer, independent of ancestry, geographic location, or potential confounders.

Unbiased proteomics and multivariable regularized regression techniques identify SMOC1, NOG, APCS, and NTN1 in an Alzheimer's disease brain proteomic signature

Advancements in omics methodologies have generated a wealth of high-dimensional Alzheimer's disease (AD) datasets, creating significant opportunities and challenges for data interpretation. In this study, we utilized multivariable regularized regression techniques to identify a reduced set of proteins that could discriminate between AD and cognitively normal (CN) brain samples. Utilizing eNetXplorer, an R package that tests the accuracy and significance of a family of elastic net generalized linear models, we identified 4 proteins (SMOC1, NOG, APCS, NTN1) that accurately discriminated between AD (n = 31) and CN (n = 22) middle frontal gyrus (MFG) tissue samples from Religious Orders Study participants with 83 percent accuracy. We then validated this signature in MFG samples from Baltimore Longitudinal Study of Aging participants using leave-one-out logistic regression cross-validation, finding that the signature again accurately discriminated AD (n = 31) and CN (n = 19) participants with a receiver operating characteristic curve area under the curve of 0.863. These proteins were strongly correlated with the burden of neurofibrillary tangle and amyloid pathology in both study cohorts. We additionally tested whether these proteins differed between AD and CN inferior temporal gyrus (ITG) samples and blood serum samples at the time of AD diagnosis in ROS and BLSA, finding that the proteins differed between AD and CN ITG samples but not in blood serum samples. The identified proteins may provide mechanistic insights into the pathophysiology of AD, and the methods utilized in this study may serve as the basis for further work with additional high-dimensional datasets in AD.

Plasma amino acid signature for sarcopenic phenotypes in community-dwelling octogenarians: Results from the Kawasaki Aging Wellbeing Project

Sarcopenia is one of the primary risk factors for various adverse health events in later life. However, its pathophysiology in the very old population remains unclear. Hence, this study aimed to examine whether plasma free amino acids (PFAAs) correlate with major sarcopenic phenotypes (i.e., muscle mass, muscle strength, and physical performance) in community-dwelling adults aged 85-89 years living in Japan. Cross-sectional data from the Kawasaki Aging Well-being Project were used. We included 133 adults aged 85-89 years. In this study, fasting blood was collected to measure 20 plasma PFAAs. Measures for the three major sarcopenic phenotypes included appendicular lean mass assessed by multifrequency bioimpedance, isometric handgrip strength, and gait speed from a 5 m walk at a usual pace. Furthermore, we used phenotype-specific elastic net regression models adjusted for age centered at 85 years, sex, body mass index, education level, smoking status, and drinking habit to identify significant PFAAs for each sarcopenic phenotype. Higher histidine and lower alanine levels were associated with poor gait speed, but no PFAAs correlated with muscle strength or mass. In conclusion, PFAAs such as plasma histidine and alanine are novel blood biomarkers associated with physical performance in community-dwelling adults aged 85 years or older.

Transcriptomic and Proteomic of Gastrocnemius Muscle in Peripheral Artery Disease

BACKGROUND

Few effective therapies exist to improve lower extremity muscle pathology and mobility loss due to peripheral artery disease (PAD), in part because mechanisms associated with functional impairment remain unclear.

METHODS

To better understand mechanisms of muscle impairment in PAD, we performed in-depth transcriptomic and proteomic analyses on gastrocnemius muscle biopsies from 31 PAD participants (mean age, 69.9 years) and 29 age- and sex-matched non-PAD controls (mean age, 70.0 years) free of diabetes or limb-threatening ischemia.

RESULTS

Transcriptomic and proteomic analyses suggested activation of hypoxia-compensatory mechanisms in PAD muscle, including inflammation, fibrosis, apoptosis, angiogenesis, unfolded protein response, and nerve and muscle repair. Stoichiometric proportions of mitochondrial respiratory proteins were aberrant in PAD compared to non-PAD, suggesting that respiratory proteins not in complete functional units are not removed by mitophagy, likely contributing to abnormal mitochondrial activity. Supporting this hypothesis, greater mitochondrial respiratory protein abundance was significantly associated with greater complex II and complex IV respiratory activity in non-PAD but not in PAD. Rate-limiting glycolytic enzymes, such as hexokinase and pyruvate kinase, were less abundant in muscle of people with PAD compared with non-PAD participants, suggesting diminished glucose metabolism.

CONCLUSIONS

In PAD muscle, hypoxia induces accumulation of mitochondria respiratory proteins, reduced activity of rate-limiting glycolytic enzymes, and an enhanced integrated stress response that modulates protein translation. These mechanisms may serve as targets for disease modification.

Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition

Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based functional magnetic resonance imaging (fMRI). Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n = 3,989). We compared 9 predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and ordinary least squares (OLS) multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than nonlinear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects, and Friedman's H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.

Proteomic Mediators of Overall Cardiovascular Health on All-Cause Mortality

Measures of cardiovascular health (CVH) assessed by a combination of behavioral and biological factors has shown protective associations with all-cause mortality. The mechanisms underlying these associations have not been fully elucidated. In this study, we characterized the plasma proteomics profile of CVH and tested whether specific proteins mediated the associations between CVH and all-cause mortality in participants of the InCHIANTI study. Of the 1301 proteins tested, 92 proteins were associated with CVH (22 positively, 70 negatively). Proteins most strongly associated with CVH included leptin (LEP), fatty acid binding protein 3 (FABP3), Angiopoietin-2 (ANGPT2), and growth-differential factor 15 (GDF15). Of the 92 CVH-associated proteins, 33 proteins significantly mediated the associations between CVH and all-cause mortality, with percent mediation ranging from 5 to 30%. The most significant mediating proteins were GDF15 and insulin-like growth factor 2 (IGFBP2). Proteins associated with better CVH were enriched for proteins that reflect the suppression of the complement coagulation and GH/IGF pathways.

Effects of short-term unloading and active recovery on human motor unit properties, neuromuscular junction transmission and transcriptomic profile

Electrophysiological alterations of the neuromuscular junction (NMJ) and motor unit potential (MUP) with unloading are poorly studied. We aimed to investigate these aspects and the underlying molecular mechanisms with short-term unloading and active recovery (AR). Eleven healthy males underwent a 10-day unilateral lower limb suspension (ULLS) period, followed by 21-day AR based on resistance exercise. Quadriceps femoris (QF) cross-sectional area (CSA) and isometric maximum voluntary contraction (MVC) were evaluated. Intramuscular electromyographic recordings were obtained during 10% and 25% MVC isometric contractions from the vastus lateralis (VL). Biomarkers of NMJ molecular instability (serum c-terminal agrin fragment, CAF), axonal damage (neurofilament light chain) and denervation status were assessed from blood samples and VL biopsies. NMJ and ion channel transcriptomic profiles were investigated by RNA-sequencing. QF CSA and MVC decreased with ULLS. Increased CAF and altered NMJ transcriptome with unloading suggested the emergence of NMJ molecular instability, which was not associated with impaired NMJ transmission stability. Instead, increased MUP complexity and decreased motor unit firing rates were found after ULLS. Downregulation of ion channel gene expression was found together with increased neurofilament light chain concentration and partial denervation. The AR period restored most of these neuromuscular alterations. In conclusion, the human NMJ is destabilized at the molecular level but shows functional resilience to a 10-day unloading period at least at relatively low contraction intensities. However, MUP properties are altered by ULLS, possibly due to alterations in ion channel dynamics and initial axonal damage and denervation. These changes are fully reversed by 21 days of AR. KEY POINTS: We used integrative electrophysiological and molecular approaches to comprehensively investigate changes in neuromuscular integrity and function after a 10-day unilateral lower limb suspension (ULLS), followed by 21 days of active recovery in young healthy men, with a particular focus on neuromuscular junction (NMJ) and motor unit potential (MUP) properties alterations. After 10-day ULLS, we found significant NMJ molecular alterations in the absence of NMJ transmission stability impairment. These findings suggest that the human NMJ is functionally resilient against insults and stresses induced by short-term disuse at least at relatively low contraction intensities, at which low-threshold, slow-type motor units are recruited. Intramuscular electromyography analysis revealed that unloading caused increased MUP complexity and decreased motor unit firing rates, and these alterations could be related to the observed changes in skeletal muscle ion channel pool and initial and partial signs of fibre denervation and axonal damage. The active recovery period restored these neuromuscular changes.

Plasma Growth and Differentiation Factor 15 Predict Longitudinal Changes in Bone Parameters in Women, but Not in Men

Bone fragility can progress with aging, but biomarkers to detect emerging osteopenia have not been fully elucidated. Growth/differentiation factor 15 (GDF-15) has pleiotropic roles in a broad range of age-related conditions, but its association with osteopenia is unknown. We examined the relationship between plasma GDF-15 levels and rate of change in bone parameters over 9 years of follow-up in 596 adults in the InCHIANTI study (baseline age, 65-94 years; women, 52.4%; mean follow-up, 7.0 ± 3.0 years). Plasma GDF-15 concentrations were measured using the 1.3k HTS SOMAscan assay. Eight bone parameters were measured in the right tibia by peripheral quantitative computed tomography; total bone density, trabecular bone density, medullary plus trabecular bone density, cortical bone density, total bone area, cortical bone area, medullary bone area, and minimum moment of inertia (mMOI). We ran sex-specific linear mixed-effect models with random intercepts and slopes adjusted for age, age-squared, education, body mass index, the rate of change in weight, smoking, sedentary behavior, cross-sectional areas of calf muscles and fat, 25-hydroxyvitamin D, parathyroid hormone, calcium, diabetes mellitus, and follow-up time. We found a significant association of "baseline GDF-15 × time" in models predicting cortical bone density and the mMOI in women, suggesting that the rates of decline in these bone parameters increased with higher GDF-15 (false discovery rate <0.05). Higher plasma levels GDF-15 predicted an accelerated decline in bone parameters in women, but was less associated in men. Furthermore studies are needed to understand the mechanisms underlying these sex differences.

Serological responses to human virome define clinical outcomes of Italian patients infected with SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the pandemic respiratory infectious disease COVID-19. However, clinical manifestations and outcomes differ significantly among COVID-19 patients, ranging from asymptomatic to extremely severe, and it remains unclear what drives these disparities. Here, we studied 159 sequentially enrolled hospitalized patients with COVID-19-associated pneumonia from Brescia, Italy using the VirScan phage-display method to characterize circulating antibodies binding to 96,179 viral peptides encoded by 1,276 strains of human viruses. SARS-CoV-2 infection was associated with a marked increase in immune antibody repertoires against many known pathogenic and non-pathogenic human viruses. This antiviral antibody response was linked to longitudinal trajectories of disease severity and was further confirmed in additional 125 COVID-19 patients from the same geographical region in Northern Italy. By applying a machine-learning-based strategy, a viral exposure signature predictive of COVID-19-related disease severity linked to patient survival was developed and validated. These results provide a basis for understanding the role of memory B-cell repertoire to viral epitopes in COVID-19-related symptoms and suggest that a unique anti-viral antibody repertoire signature may be useful to define COVID-19 clinical severity.

Metabolomic Profile of Different Dietary Patterns and Their Association with Frailty Index in Community-Dwelling Older Men and Women

Diet quality has been associated with slower rates of aging; however, the mechanisms underlying the role of a healthy diet in aging are not fully understood. To address this question, we aimed to identify plasma metabolomic biomarkers of dietary patterns and explored whether these metabolites mediate the relationship between diet and healthy aging, as assessed by the frailty index (FI) in 806 participants of the Baltimore Longitudinal Study of Aging. Adherence to different dietary patterns was evaluated using the Mediterranean diet score (MDS), Mediterranean–DASH Diet Intervention for Neurodegenerative Delay (MIND) score, and Alternate Healthy Eating Index-2010 (AHEI). Associations between diet, FI, and metabolites were assessed using linear regression models. Higher adherence to these dietary patterns was associated with lower FI. We found 236, 218, and 278 metabolites associated with the MDS, MIND, and AHEI, respectively, with 127 common metabolites, which included lipids, tri/di-glycerides, lyso/phosphatidylcholine, amino acids, bile acids, ceramides, cholesterol esters, fatty acids and acylcarnitines, indoles, and sphingomyelins. Metabolomic signatures of diet explained 28%, 37%, and 38% of the variance of the MDS, MIND, and AHEI, respectively. Signatures of MIND and AHEI mediated 55% and 61% of the association between each dietary pattern with FI, while the mediating effect of MDS signature was not statistically significant. The high number of metabolites associated with the different dietary patterns supports the notion of common mechanisms that underly the relationship between diet and frailty. The identification of multiple metabolite classes suggests that the effect of diet is complex and not mediated by any specific biomarkers. Furthermore, these metabolites may serve as biomarkers for poor diet quality to identify individuals for targeted dietary interventions.

Proteins in the pathway from high red blood cell width distribution to all-cause mortality

Background

The pathophysiological mechanisms underlying the association between red blood cell distribution width (RDW) and all-cause mortality are unknown. We conducted a data-driven discovery investigation to identify plasma proteins that mediate the association between RDW and time to death in community-dwelling adults.

Methods

At baseline, 962 adults (women, 54·4%; age range, 21–98 years) participated in the InCHIANTI, “Aging in the Chianti Area” study, and proteomics data were generated from their plasma specimens. Of these, 623 participants had proteomics data available at the 9-year follow-up. For each visit, a total of 1301 plasma proteins were measured using SOMAscan technology. Complete data on vital status were available up to the 15-year follow-up period. Protein-specific exponential distribution accelerated failure time, and linear regression analyses adjusted for possible covariates were used for mortality and mediation analyses, respectively (survival data analysis).

Findings

Baseline values of EGFR, GHR, NTRK3, SOD2, KLRF1, THBS2, TIMP1, IGFBP2, C9, APOB, and LRP1B mediated the association between baseline RDW and all-cause mortality. Changes in IGFBP2 and C7 over 9 years mediated the association between changes in RDW and 6-year all-cause mortality.

Interpretation

Cellular senescence may contribute to the association between RDW and mortality.

Funding

This study was funded by grants from the National Institutes of Health (NIH) and the National Institute on Aging (NIA) contract and was supported by the Intramural Research Program of the NIA, NIH. The InCHIANTI study was supported as a ‘targeted project’ by the Italian Ministry of Health and in part by the U.S. NIA.

Abstract PR-11: Blood levels of TNFRSF9 and PTN predict lethal prostate cancer among African American men

OBJECTIVE: Differentiating men who have lethal forms of prostate cancer from those with a more slow-growing disease remains a major challenge in clinical oncology. Risk stratification strategies are particularly needed for men of African descent who disproportionately bear the prostate cancer burden. Methods: Using a high throughput proximal extension assay, we simultaneously measured 82 immune-oncological proteins in the blood of 819 prostate cancer patients at diagnosis of whom 394 were African American (AA) and 425 were European American (EA). These patients were followed up for a median of 8.6 years since their diagnosis during which 57 died of prostate cancer while 202 died of all causes. To identify an immune-oncology protein signature predictive of lethal prostate cancer, we applied a cross-validated, regularized Cox regression model. Included in this model were the 82 immune-oncology proteins and 6 covariates of clinical significance (age, education, BMI, smoking history, aspirin use, and diabetes). Results: We did not identify a robust predictive signature of lethal prostate cancer for EA patients. However, for AA patients a signature primarily driven by tumor necrosis factor receptor superfamily member 9 (TNFRSF9) and pleiotrophin (PTN) (both positively associated with the risk of lethal disease) and regular aspirin use (negatively associated with risk) were the top predictors (P &lt; 0.05) based on two selection criteria: the feature frequency and the weight of the features' contribution to the prediction. These features combined predicted prostate cancer-specific mortality with an accuracy of 83.7% (SE=3.8%). The two proteins alone, TNFRSF9 and PTN, predicted prostate cancer-specific mortality with an accuracy of 78.2% (SE=4.2%). AA prostate cancer patients with high levels (&gt; median) of both TNFRSF9 and PTN in their blood at diagnosis had the worst prostate cancer-specific survival. By 10 years, 33% of cases with high levels of both TNFRSF9 and PTN died of prostate cancer compared to only 5% of cases with low levels of both or either of these proteins. Conclusions: Our study describes novel blood markers of lethal prostate cancer that can be used for risk stratification of AA patients at the time of diagnosis. AA patients with high levels of both TNFRSF9 and PTN in their sera had the highest risk of dying from prostate cancer. These markers may also be applicable to African prostate cancer patients since the blood-based immunome of Ghanaian and AA men are similar, as shown by our data.

Citation Format: Tsion Zewdu Minas, Julián Candia, Tiffany H. Dorsey, Francine Baker, Wei Tang, Maeve Kiely, Cheryl J. Smith, Symone V. Jordan, Obadi M. Obadi, Anuoluwapo Ajao, Christopher A. Loffredo, Clayton Yates, Michael B. Cook, Stefan Ambs. Blood levels of TNFRSF9 and PTN predict lethal prostate cancer among African American men [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PR-11.

Single-cell atlas of tumor cell evolution in response to therapy in hepatocellular carcinoma and intrahepatic cholangiocarcinoma

BACKGROUND & AIMS

Intratumor molecular heterogeneity is a key feature of tumorigenesis and is linked to treatment failure and patient prognosis. Herein, we aimed to determine what drives tumor cell evolution by performing single-cell transcriptomic analysis.

METHODS

We analyzed 46 hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) biopsies from 37 patients enrolled in interventional studies at the NIH Clinical Center, with 16 biopsies collected before and after treatment from 7 patients. We developed a novel machine learning-based consensus clustering approach to track cellular states of 57,000 malignant and non-malignant cells including tumor cell transcriptome-based functional clonality analysis. We determined tumor cell relationships using RNA velocity and reverse graph embedding. We also studied longitudinal samples from 4 patients to determine tumor cellular state and its evolution. We validated our findings in bulk transcriptomic data from 488 patients with HCC and 277 patients with iCCA.

RESULTS

Using transcriptomic clusters as a surrogate for functional clonality, we observed an increase in tumor cell state heterogeneity which was tightly linked to patient prognosis. Furthermore, increased functional clonality was accompanied by a polarized immune cell landscape which included an increase in pre-exhausted T cells. We found that SPP1 expression was tightly associated with tumor cell evolution and microenvironmental reprogramming. Finally, we developed a user-friendly online interface as a knowledge base for a single-cell atlas of liver cancer.

CONCLUSIONS

Our study offers insight into the collective behavior of tumor cell communities in liver cancer as well as potential drivers of tumor evolution in response to therapy.

LAY SUMMARY

Intratumor molecular heterogeneity is a key feature of tumorigenesis that is linked to treatment failure and patient prognosis. In this study, we present a single-cell atlas of liver tumors from patients treated with immunotherapy and describe intratumoral cell states and their hierarchical relationship. We suggest osteopontin, encoded by the gene SPP1, as a candidate regulator of tumor evolution in response to treatment.

A brain proteomic signature of incipient Alzheimer's disease in young APOE ε4 carriers identifies novel drug targets

Aptamer-based proteomics revealed differentially abundant proteins in Alzheimer’s disease (AD) brains in the Baltimore Longitudinal Study of Aging and Religious Orders Study (mean age, 89 ± 9 years). A subset of these proteins was also differentially abundant in the brains of young APOE ε4 carriers relative to noncarriers (mean age, 39 ± 6 years). Several of these proteins represent targets of approved and experimental drugs for other indications and were validated using orthogonal methods in independent human brain tissue samples as well as in transgenic AD models. Using cell culture–based phenotypic assays, we showed that drugs targeting the cytokine transducer STAT3 and the Src family tyrosine kinases, YES1 and FYN, rescued molecular phenotypes relevant to AD pathogenesis. Our findings may accelerate the development of effective interventions targeting the earliest molecular triggers of AD.

The Plasma Proteome Fingerprint Associated with Circulating Carotenoids and Retinol in Older Adults

BACKGROUND

Although diets rich in carotenoids are associated with reduced risks of cardiovascular disease, age-related macular degeneration, disability, and other adverse aging outcomes, the underlying biological mechanisms are not fully elucidated.

OBJECTIVES

To characterize the plasma proteome fingerprint associated with circulating carotenoid and retinol concentrations in older adults.

METHODS

In 728 adults ≥65 y participating in the Invecchiare in Chianti (InCHIANTI) Study, plasma α-carotene, β-carotene, β-cryptoxanthin, lutein, zeaxanthin, and lycopene were measured using HPLC. The SOMAscan assay was used to measure 1301 plasma proteins. Multivariable linear regression models were used to examine the relationship of individual carotenoids and retinol with plasma proteins. A false discovery rate approach was used to deal with multiple comparisons using a q-value < 0.05.

RESULTS

Plasma β-carotene, β-cryptoxanthin, lutein, zeaxanthin, and lycopene were associated with 85, 39, 4, 2, and 5 plasma proteins, respectively, in multivariable linear regression models adjusting for potential confounders (q < 0.05). No proteins were associated with α-carotene or retinol. Two or more carotenoids were positively associated with ferritin, 6-phosphogluconate dehydrogenase (decarboxylating), hepcidin, thrombospondin-2, and choline/ethanolamine kinase. The proteins associated with circulating carotenoids were related to energy metabolism, sirtuin signaling, inflammation and oxidative stress, iron metabolism, proteostasis, innate immunity, and longevity.

CONCLUSIONS

The plasma proteomic fingerprint associated with elevated circulating carotenoids in older adults provides insight into the mechanisms underlying the protective role of carotenoids on health.

Identification and Validation of Nutrient State-Dependent Serum Protein Mediators of Human CD4+ T Cell Responsiveness

Intermittent fasting and fasting mimetic diets ameliorate inflammation. Similarly, serum extracted from fasted healthy and asthmatic subjects' blunt inflammation in vitro, implicating serum components in this immunomodulation. To identify the proteins orchestrating these effects, SOMAScan technology was employed to evaluate serum protein levels in healthy subjects following an overnight, 24-h fast and 3 h after refeeding. Partial least square discriminant analysis identified several serum proteins as potential candidates to confer feeding status immunomodulation. The characterization of recombinant IGFBP1 (elevated following 24 h of fasting) and PYY (elevated following refeeding) in primary human CD4⁺ T cells found that they blunted and induced immune activation, respectively. Furthermore, integrated univariate serum protein analysis compared to RNA-seq analysis from peripheral blood mononuclear cells identified the induction of IL1RL1 and MFGE8 levels in refeeding compared to the 24-h fasting in the same study. Subsequent quantitation of these candidate proteins in lean versus obese individuals identified an inverse regulation of serum levels in the fasted subjects compared to the obese subjects. In parallel, IL1RL1 and MFGE8 supplementation promoted increased CD4⁺ T responsiveness to T cell receptor activation. Together, these data show that caloric load-linked conditions evoke serological protein changes, which in turn confer biological effects on circulating CD4⁺ T cell immune responsiveness.

A replication-competent adenovirus-vectored influenza vaccine induces durable systemic and mucosal immunity

BACKGROUNDTo understand the features of a replicating vaccine that might drive potent and durable immune responses to transgene-encoded antigens, we tested a replication-competent adenovirus type 4 encoding influenza virus H5 HA (Ad4-H5-Vtn) administered as an oral capsule or via tonsillar swab or nasal spray.METHODSViral shedding from the nose, mouth, and rectum was measured by PCR and culturing. H5-specific IgG and IgA antibodies were measured by bead array binding assays. Serum antibodies were measured by a pseudovirus entry inhibition, microneutralization, and HA inhibition assays.RESULTSAd4-H5-Vtn DNA was shed from most upper respiratory tract-immunized (URT-immunized) volunteers for 2 to 4 weeks, but cultured from only 60% of participants, with a median duration of 1 day. Ad4-H5-Vtn vaccination induced increases in H5-specific CD4+ and CD8+ T cells in the peripheral blood as well as increases in IgG and IgA in nasal, cervical, and rectal secretions. URT immunizations induced high levels of serum neutralizing antibodies (NAbs) against H5 that remained stable out to week 26. The duration of viral shedding correlated with the magnitude of the NAb response at week 26. Adverse events (AEs) were mild, and peak NAb titers were associated with overall AE frequency and duration. Serum NAb titers could be boosted to very high levels 2 to 5 years after Ad4-H5-Vtn vaccination with recombinant H5 or inactivated split H5N1 vaccine.CONCLUSIONReplicating Ad4 delivered to the URT caused prolonged exposure to antigen, drove durable systemic and mucosal immunity, and proved to be a promising platform for the induction of immunity against viral surface glycoprotein targets.TRIAL REGISTRATIONClinicalTrials.gov NCT01443936 and NCT01806909.FUNDINGIntramural and Extramural Research Programs of the NIAID, NIH (U19 AI109946) and the Centers of Excellence for Influenza Research and Surveillance (CEIRS), NIAID, NIH (contract HHSN272201400008C).

Plasma proteomic biomarker signature of age predicts health and life span

Older age is a strong shared risk factor for many chronic diseases, and there is increasing interest in identifying aging biomarkers. Here, a proteomic analysis of 1301 plasma proteins was conducted in 997 individuals between 21 and 102 years of age. We identified 651 proteins associated with age (506 over-represented, 145 underrepresented with age). Mediation analysis suggested a role for partial cis-epigenetic control of protein expression with age. Of the age-associated proteins, 33.5% and 45.3%, were associated with mortality and multimorbidity, respectively. There was enrichment of proteins associated with inflammation and extracellular matrix as well as senescence-associated secretory proteins. A 76-protein proteomic age signature predicted accumulation of chronic diseases and all-cause mortality. These data support the use of proteomic biomarkers to monitor aging trajectories and to identify individuals at higher risk of disease to be targeted for in depth diagnostic procedures and early interventions.

Characterization of the plasma proteomic profile of frailty phenotype

Frailty is a risk factor for poor health outcomes in older adults. The aim of this study was to identify plasma proteomic biomarkers of frailty in 752 men and women older than 65 years of age from the InCHIANTI study. One thousand three hundred one plasma proteins were measured using an aptamer-based assay. Associations of each protein with frailty status were assessed using logistic regression and four proteins creatine kinase M-type (CKM), B-type (CKB), C-X-C motif chemokine ligand 13 (CXCL13), and thrombospondin 2 (THBS2) were associated with frailty status. Two proteins, cyclin-dependent kinase 5 (CDK5/CDK5R1) and interleukin 1 alpha (IL1A), were associated with worsening of frailty status over time in volunteers free of frailty at baseline. Using partial least squares discriminant analysis (PLS-DA), data of 1301 proteins was able to discriminate between frail and non-frail with a 2% error rate. The proteins with greater discriminatory ability represented the inflammation, blood coagulation, and cell growth pathways. The utility of these proteins as biomarkers of frailty should be further explored.

Serological Responses to Human Virome Define Clinical Outcomes of Italian Patients Infected with SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the pandemic respiratory infectious disease COVID-19. However, clinical manifestations and outcomes differ significantly among COVID-19 patients, ranging from asymptomatic to extremely severe, and it remains unclear what drives these disparities. Here, we studied 159 hospitalized Italian patients with pneumonia from the NIAID-NCI COVID-19 Consortium using a phage-display method to characterize circulating antibodies binding to 93,904 viral peptides encoded by 1,276 strains of human viruses. SARS-CoV-2 infection was associated with a marked increase in individual's immune memory antibody repertoires linked to trajectories of disease severity from the longitudinal analysis also including anti-spike protein antibodies. By applying a machine-learning-based strategy, we developed a viral exposure signature predictive of COVID-19-related disease severity linked to patient survival. These results provide a basis for understanding the roles of memory B-cell repertoires in COVID-19-related symptoms as well as a predictive tool for monitoring its clinical severity.

The genomic landscape of Mongolian hepatocellular carcinoma

Mongolia has the highest incidence of hepatocellular carcinoma (HCC) in the world, but its causative factors and underlying tumor biology remain unknown. Here, we describe molecular characteristics of HCC from 76 Mongolian patients by whole-exome and transcriptome sequencing. We present a comprehensive analysis of mutational signatures, driver genes, and molecular subtypes of Mongolian HCC compared to 373 HCC patients of different races and ethnicities and diverse etiologies. Mongolian HCC consists of prognostic molecular subtypes similar to those found in patients from other areas of Asia, Europe, and North America, as well as other unique subtypes, suggesting the presence of distinct etiologies linked to Mongolian patients. In addition to common driver mutations (TP53, CTNNB1) frequently found in pan-cancer analysis, Mongolian HCC exhibits unique drivers (most notably GTF2IRD2B, PNRC2, and SPTA1), the latter of which is associated with hepatitis D viral infection. These results suggest the existence of new molecular mechanisms at play in Mongolian hepatocarcinogenesis.

Abstract 5855: The genomic landscape of Mongolian hepatocellular carcinoma

Mongolia has the highest incidence of-and mortality from- hepatocellular carcinoma (HCC) in the world, which is between three and seven times higher than that observed in other high-incidence populations, such as South Korea, Thailand, and China. In Mongolia, where cancer is the second most common cause of death accounting for nearly a fifth of deaths, HCC is the most prevalent cancer type at about 40% of all cancers. Despite the daunting proportion of this longstanding health crisis, the molecular landscape of Mongolian HCC has not been studied yet. Filling this gap, we aim to identify robust molecular subclasses and driver features informative of the etiology and progression of the disease. Here, we describe molecular characteristics of 76 Mongolian HCC patients by whole-exome and whole-transcriptome sequencing. We present a comprehensive comparison of mutational signatures, driver genes and molecular subtypes of Mongolian HCC versus 373 HCC patients of different ethnicities and diverse etiologies. Mongolian HCC consists of several similar prognostic molecular subtypes in patients from other areas of Asia, Europe and North America as well as several unique types, suggesting potentially the presence of unique etiologies linked to Mongolian patients. Consistently, Mongolian HCC exhibits several common driver mutations (TP53, CTNNB1) frequently found in pan-cancer analyses but also a unique driver (SPTA1) that may be linked to hepatitis D viral infection. Furthermore, unique hotspot missense mutations were identified in driver genes GTF2IRD2B and PNRC2. These results indicate the existence of novel molecular mechanisms at play in Mongolian hepatocarcinogenesis.

Citation Format: Julián Candia, Enkhjargal Bayarsaikhan, Mayank Tandon, Anuradha Budhu, Marshonna Forgues, Justin Lack, Ann Chao, Jigjidsuren Chinburen, Xin W. Wang. The genomic landscape of Mongolian hepatocellular carcinoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5855.

A Viral Exposure Signature Defines Early Onset of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is an aggressive malignancy with its global incidence and mortality rate continuing to rise, although early detection and surveillance are suboptimal. We performed serological profiling of the viral infection history in 899 individuals from an NCI-UMD case-control study using a synthetic human virome, VirScan. We developed a viral exposure signature and validated the results in a longitudinal cohort with 173 at-risk patients who had long-term follow-up for HCC development. Our viral exposure signature significantly associated with HCC status among at-risk individuals in the validation cohort (area under the curve: 0.91 [95% CI 0.87-0.96] at baseline and 0.98 [95% CI 0.97-1] at diagnosis). The signature identified cancer patients prior to a clinical diagnosis and was superior to alpha-fetoprotein. In summary, we established a viral exposure signature that can predict HCC among at-risk patients prior to a clinical diagnosis, which may be useful in HCC surveillance.

Plasma proteomic signature of the risk of developing mobility disability: A 9-year follow-up

INTRODUCTION

Mobility disability is a powerful indicator of poor health in older adults. The biological and pathophysiological mechanism underlying the development of mobility disability remains unknown. This study conducted a data-driven discovery phase investigation to identify plasma proteins that predict the incidence of mobility disability in community-dwelling older adults without mobility disability at baseline.

METHODS

We investigated 660 women and men, aged 71.9 ± 6.0 (60-94) years, who participated in the Invecchiare in Chianti, "Aging in the Chianti Area" study and completed the 400-m walk at fast pace (400-m walk) at enrollment. Median follow-up time was 8.57 [interquartile, 3.20-9.08] years. SOMAscan technology was used to measure 1,301 plasma proteins at enrollment. The incident of mobility disability was defined as inability to complete the 400-m walk. Protein-specific Cox proportional hazard model was adjusted for sex, age, and other important covariates.

RESULTS

Plasma levels of 75 proteins predicted mobility disability (p < .05). Significant proteins were enriched for the KEGG "PI3K-Akt signaling," "phagosomes," and "cytokine-cytokine receptor interaction" pathways. After multiple comparison adjustment, plasma cathepsin S (CTSS; hazard ratio [HR] 1.33, 95% CI: 1.17, 1.51, q = 0.007), growth/differentiation factor 15 (GDF15; HR: 1.45, 95% CI: 1.23, 1.72, q = 0.007), and thrombospondin-2 (THBS2; HR: 1.44, 95% CI: 1.22, 1.69, q = 0.007) remained significantly associated with high risk of losing mobility.

CONCLUSION

CTSS, GDF15, and THBS2 are novel blood biomarkers associated with new mobility disability in community-dwelling individuals. Overall, our analysis suggests that cellular senescence and inflammation should be targeted for prevention of mobility disability.

Effects of rosuvastatin on the immune system in healthy volunteers with normal serum cholesterol

BACKGROUNDHMG-CoA reductase inhibitors (statins) are prescribed to millions of people. Statins are antiinflammatory independent of their cholesterol-reducing effects. To date, most reports on the immune effects of statins have assayed a narrow array of variables and have focused on cell lines, rodent models, or patient cohorts. We sought to define the effect of rosuvastatin on the "immunome" of healthy, normocholesterolemic subjects.METHODSWe conducted a prospective study of rosuvastatin (20 mg/d × 28 days) in 18 statin-naive adults with LDL cholesterol <130 mg/dL. A panel of >180 immune/biochemical/endocrinologic variables was measured at baseline and on days 14, 28, and 42 (14 days after drug withdrawal). Drug effect was evaluated using linear mixed-effects models. Potential interactions between drug and baseline high-sensitivity C-reactive protein (hsCRP) were evaluated.RESULTSA wide array of immune measures changed (nominal P < 0.05) during rosuvastatin treatment, although the changes were modest in magnitude, and few met an FDR of 0.05. Among changes noted were a concordant increase in proinflammatory cytokines (IFN-γ, IL-1β, IL-5, IL-6, and TNF-α) and peripheral blood neutrophil frequency, and a decline in activated Treg frequency. Several drug effects were significantly modified by baseline hsCRP, and some did not resolve after drug withdrawal. Among other unexpected rosuvastatin effects were changes in erythrocyte indices, glucose-regulatory hormones, CD8+ T cells, and haptoglobin.CONCLUSIONRosuvastatin induces modest changes in immunologic and metabolic measures in normocholesterolemic subjects, with several effects dependent on baseline CRP. Future, larger studies are warranted to validate these changes and their physiological significance.TRIAL REGISTRATIONClinicalTrials.gov NCT01200836.FUNDINGThis research was supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01 ES102005), and the trans-NIH Center for Human Immunology.

Abstract 3756: Highly multiplexed proteomic assessment of the human acute myeloid leukemia bone marrow microenvironment

Acute myeloid leukemia (AML) is a genetically heterogenous and often fatal cancer of the hematopoietic system. Even after achieving an initial complete remission, more than half of such AML patients experience clinical relapse due to the persistence of “minimal” residual disease (MRD). Ex-vivo studies have hypothesized that the interactions between residual leukemic cells and the local microenvironment in the bone marrow may play a key role in their survival and chemoresistance.

Therefore, we performed for the first time a global examination of the proteomic profile of the bone marrow microenvironment in AML patients using a highly multiplexed method based on the ability of slow off-rate modified aptamers (modified small single-stranded oligonucleotides) to bind target proteins with high specificity and affinity at slow dissociation rates. Ten relapsed/refractory adult AML patients and age-matched healthy subject controls were recruited, under an IRB-approved protocol, for research bone marrow aspirate (BMA) and blood serum collection. The supernatant resulting from centrifugation of BMA and serum samples were processed and analyzed on a SOMAscan™ hybridization microarray platform for the detection and quantification of 1,305 target proteins (Somalogic, CO). Data was corrected using Hybridization Control and Median Signal Normalization methods. Significant differences were found between AML and healthy donor bone marrow, such that 133 analytes were differentially expressed in the AML group (Wilcoxon rank sum test FDR p&lt;0.05, fold change &gt;1.5); 85 over-expressed and 48 under-expressed. In addition to proteins already known to be elevated in AML patients (eg: Erythropoietin, Thrombopoietin, Hepcidin and Ferritin) and dysregulation of pathways previously identified as disease relevant (eg: Arginase), we also discovered multiple statistically significant differences in levels of soluble proteins that are not currently known to be associated with AML pathogenesis or treatment. Comparative analysis between blood serum and bone marrow determined that 82 of the 133 candidates have differential expression that was specifically restricted to the bone marrow. The STRING database was queried for pathway analysis of enriched protein sets in the AML group and clustered analytes with molecular functions including cytokine activity (GO:0005125, n=11, p=1.93e-08), cytokine receptor binding (GO:0005126, n=12, p=3.17e-08) and signal transducer activity (GO:0004871, n=19, p=9.23e-05).

Using a high-throughput proteomic technology we have identified an AML bone marrow microenvironment-specific profile comprised of both proteins with known implications in leukemic pathogenesis and also several novel candidates from biologically plausible pathways that, once validated, may provide mechanistic insight and opportunity for therapeutic targeting.

Citation Format: Bogdan Popescu, Katherine Lindblad, Giovanna Fantoni, Gege Gui, Janet Valdez, Meghali Goswami, Christin DeStefano, Catherine Lai, Angélique Biancotto, Julián Candia, Foo Cheung, Julie Thompson, Laura W. Dillon, Christopher S. Hourigan. Highly multiplexed proteomic assessment of the human acute myeloid leukemia bone marrow microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3756.

eNetXplorer: an R package for the quantitative exploration of elastic net families for generalized linear models

Background

Regularized generalized linear models (GLMs) are popular regression methods in bioinformatics, particularly useful in scenarios with fewer observations than parameters/features or when many of the features are correlated. In both ridge and lasso regularization, feature shrinkage is controlled by a penalty parameter λ. The elastic net introduces a mixing parameter α to tune the shrinkage continuously from ridge to lasso. Selecting α objectively and determining which features contributed significantly to prediction after model fitting remain a practical challenge given the paucity of available software to evaluate performance and statistical significance.

Results

eNetXplorer builds on top of glmnet to address the above issues for linear (Gaussian), binomial (logistic), and multinomial GLMs. It provides new functionalities to empower practical applications by using a cross validation framework that assesses the predictive performance and statistical significance of a family of elastic net models (as α is varied) and of the corresponding features that contribute to prediction. The user can select which quality metrics to use to quantify the concordance between predicted and observed values, with defaults provided for each GLM. Statistical significance for each model (as defined by α) is determined based on comparison to a set of null models generated by random permutations of the response; the same permutation-based approach is used to evaluate the significance of individual features. In the analysis of large and complex biological datasets, such as transcriptomic and proteomic data, eNetXplorer provides summary statistics, output tables, and visualizations to help assess which subset(s) of features have predictive value for a set of response measurements, and to what extent those subset(s) of features can be expanded or reduced via regularization.

Conclusions

This package presents a framework and software for exploratory data analysis and visualization. By making regularized GLMs more accessible and interpretable, eNetXplorer guides the process to generate hypotheses based on features significantly associated with biological phenotypes of interest, e.g. to identify biomarkers for therapeutic responsiveness. eNetXplorer is also generally applicable to any research area that may benefit from predictive modeling and feature identification using regularized GLMs. The package is available under GPL-3 license at the CRAN repository, https://CRAN.R-project.org/package=eNetXplorer.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2778-5) contains supplementary material, which is available to authorized users.

Access to healthcare in Chilean prisons: an inmates' perspective

OBJECTIVES

To analyze the perception of access to Chilean prisons in a representative national sample of persons deprived of liberty as well as to examine the most important covariates of such access.

MATERIALS AND METHODS

This study uses secondary data from the First National Survey on the Quality of Prison Life (2014), which investigated inmates' perceptions regarding access to health services inside the prisons. To do this, it uses descriptive statistics and a logistic regression model.

RESULTS

Descriptive results at the national level show that access to health services in prisons tends to be "difficult" (44.7% of cases in this category). Multivariate logistic regression results indicate that men (OR=0.43) and those who reported better infrastructure (OR=0.70) were less likely to report "difficult access to health services". On the other hand, prison inmates (OR=1.61) and those who had reported higher levels of mistreatment (OR=1.26) were associated with a higher probability of reporting "difficult access to health services".

DISCUSSION

Our study suggests that access to health care is dynamically linked to other aspects of life within prisons such as the composition of the prison population (gender), some of the material aspects of prisons (infrastructure, type of facility), and even some relational aspects (level of mistreatment/abuse). Future studies could further extend the debate on healthcare in prisons, incorporating more complex both variables and analyses.

RefCell: multi-dimensional analysis of image-based high-throughput screens based on 'typical cells'

BACKGROUND

Image-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Currently available high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but suffer from the "curse of dimensionality" and non-standardized outputs.

RESULTS

Here we introduce RefCell, a multi-dimensional analysis pipeline for image-based HTS that reproducibly captures cells with typical combinations of features in reference states and uses these "typical cells" as a reference for classification and weighting of metrics. RefCell quantitatively assesses heterogeneous deviations from typical behavior for each analyzed perturbation or sample.

CONCLUSIONS

We apply RefCell to the analysis of data from a high-throughput imaging screen of a library of 320 ubiquitin-targeted siRNAs selected to gain insights into the mechanisms of premature aging (progeria). RefCell yields results comparable to a more complex clustering-based single-cell analysis method; both methods reveal more potential hits than a conventional analysis based on averages.

Aptamer-based proteomics of serum and plasma in acquired aplastic anemia

Single-stranded oligonucleotides containing deoxyuridine are aptamers (SOMAmers) that can bind proteins with high specificity and affinity and slow dissociation rates. SOMAscan, an aptamer-based proteomic technology, allows measurement of more than 1,300 proteins simultaneously for the identification of new disease biomarkers. The aim of the present study was to identify new serum and plasma protein markers for diagnosis of acquired aplastic anemia (AA) and response to immunosuppressive therapies (IST). SOMAscan was used to screen 1,141 serum proteins in 28 AA patients before and after therapy and 1,317 plasma proteins in seven SAA patients treated with standard IST and a thrombopoietin receptor agonist. From our analysis, 19 serum and 28 plasma proteins were identified as possible candidate diagnostic and prognostic markers. A custom immunobead-based multiplex assay with five selected serum proteins (BMP-10, CCL17, DKK1, HGF, and SELL) was used for validation in a verification set (n = 65) of samples obtained before and after IST and in a blinded validation cohort at baseline (n = 16). After technical validation, four biomarkers were employed to predict diagnosis (accuracy, 88%) and long-term response to IST (accuracy, 79%). In conclusion, SOMAscan is a powerful tool for the identification of new biomarkers. We propose further larger studies to validate new candidate serum and plasma diagnostic and prognostic markers of AA.

Relationship between Circulating Inflammation Proteins and Lung Cancer Diagnosis in the National Lung Screening Trial

BACKGROUND

Previously, we observed a strong relationship between circulating serum inflammation proteins in relation to lung cancer diagnosis and risk, both in case-control and prospective cohorts. Low-dose computed tomography (LDCT) screening has a high prevalence of false-positive nodules, thus companion noninvasive biomarkers that can distinguish between benign and malignant nodules could have clinical utility and positive impact on patient outcomes.

METHODS

We conducted a nested case-control study within the National Lung Screening Trial. Concentrations of 30 inflammation proteins were measured on plasma samples of 262 cases and 528 controls using a highly sensitive and analytically validated electrochemiluminescence V-PLEX immunoassay.

RESULTS

Comparing the fourth quartile with the first quartile, we found increased IFNγ and IL12/IL23p40 associated with increased odds of a lung cancer diagnosis [OR 1.89, 95% confidence intervals (CI), 1.16-3.09; OR 2.49, 95% CI, 1.46-4.23, respectively]. Confirming our previous observations, we also detected a relationship between increased IL6, IL8, and C-reactive protein (CRP) with lung cancer diagnosis. These relationships were significant after adjustment for age, gender, race, smoking, body mass index (BMI), family history of lung cancer, and previous diagnoses of inflammatory conditions. However, none of these proteins could distinguish between a benign and malignant lung nodule (IL6: OR 1.25, 95% CI, 0.59-2.64; IL8: OR 1.40, 95% CI, 0.70-2.81; CRP: OR 0.98, 95% CI, 0.45-2.12).

CONCLUSIONS

We have discovered new associations for IFNγ and IL12/IL23p40 with lung cancer but have no evidence that these proteins can distinguish between benign and malignant lung nodules.

IMPACT

Circulating inflammation proteins are unlikely to have utility as companion LDCT biomarkers.

Plasma proteomic signature of age in healthy humans

To characterize the proteomic signature of chronological age, 1,301 proteins were measured in plasma using the SOMAscan assay (SomaLogic, Boulder, CO, USA) in a population of 240 healthy men and women, 22-93 years old, who were disease- and treatment-free and had no physical and cognitive impairment. Using a p ≤ 3.83 × 10-5 significance threshold, 197 proteins were positively associated, and 20 proteins were negatively associated with age. Growth differentiation factor 15 (GDF15) had the strongest, positive association with age (GDF15; 0.018 ± 0.001, p = 7.49 × 10-56 ). In our sample, GDF15 was not associated with other cardiovascular risk factors such as cholesterol or inflammatory markers. The functional pathways enriched in the 217 age-associated proteins included blood coagulation, chemokine and inflammatory pathways, axon guidance, peptidase activity, and apoptosis. Using elastic net regression models, we created a proteomic signature of age based on relative concentrations of 76 proteins that highly correlated with chronological age (r = 0.94). The generalizability of our findings needs replication in an independent cohort.

Web Tool for Navigating and Plotting SomaLogic ADAT Files

SOMAscan™ is a complex proteomic platform created by SomaLogic. Experimental data resulting from the assay is provided by SomaLogic in a proprietary text-based format called ADAT. This manuscript describes a user-friendly point and click open source, platform-independent software tool designed to be used for navigating and plotting data from an ADAT file. This tool was used either alone or in conjunction with other tools as a first pass analysis of the data on several different on-going research projects. We have seen a need from our experience for a web interface to the ADAT file so that users can navigate, generate plots, perform QC and conduct statistical analysis on their own data in a point and click manner. After several rounds of interacting with biologists and their requirements with respect to data analysis, we present an online interactive Shiny Web Tool for Navigating and Plotting data contained within the ADAT file. Extensive video tutorials, example data, the tool and the source code are available online.

Cell Cycle and Cell Size Dependent Gene Expression Reveals Distinct Subpopulations at Single-Cell Level

Cell proliferation includes a series of events that is tightly regulated by several checkpoints and layers of control mechanisms. Most studies have been performed on large cell populations, but detailed understanding of cell dynamics and heterogeneity requires single-cell analysis. Here, we used quantitative real-time PCR, profiling the expression of 93 genes in single-cells from three different cell lines. Individual unsynchronized cells from three different cell lines were collected in different cell cycle phases (G0/G1 - S - G2/M) with variable cell sizes. We found that the total transcript level per cell and the expression of most individual genes correlated with progression through the cell cycle, but not with cell size. By applying the random forests algorithm, a supervised machine learning approach, we show how a multi-gene signature that classifies individual cells into their correct cell cycle phase and cell size can be generated. To identify the most predictive genes we used a variable selection strategy. Detailed analysis of cell cycle predictive genes allowed us to define subpopulations with distinct gene expression profiles and to calculate a cell cycle index that illustrates the transition of cells between cell cycle phases. In conclusion, we provide useful experimental approaches and bioinformatics to identify informative and predictive genes at the single-cell level, which opens up new means to describe and understand cell proliferation and subpopulation dynamics.

Uncovering low-dimensional, miR-based signatures of acute myeloid and lymphoblastic leukemias with a machine-learning-driven network approach

Complex phenotypic differences among different acute leukemias cannot be fully captured by analyzing the expression levels of one single molecule, such as a miR, at a time, but requires systematic analysis of large sets of miRs. While a popular approach for analysis of such datasets is principal component analysis (PCA), this method is not designed to optimally discriminate different phenotypes. Moreover, PCA and other low-dimensional representation methods yield linear or non-linear combinations of all measured miRs. Global human miR expression was measured in AML, B-ALL, and TALL cell lines and patient RNA samples. By systematically applying support vector machines to all measured miRs taken in dyad and triad groups, we built miR networks using cell line data and validated our findings with primary patient samples. All the coordinately transcribed members of the miR-23a cluster (which includes also miR-24 and miR-27a), known to function as tumor suppressors of acute leukemias, appeared in the AML, B-ALL and T-ALL centric networks. Subsequent qRT-PCR analysis showed that the most connected miR in the B-ALL-centric network, miR-708, is highly and specifically expressed in B-ALLs, suggesting that miR-708 might serve as a biomarker for B-ALL. This approach is systematic, quantitative, scalable, and unbiased. Rather than a single signature, our approach yields a network of signatures reflecting the redundant nature of biological signaling pathways. The network representation allows for visual analysis of all signatures by an expert and for future integration of additional information. Furthermore, each signature involves only small sets of miRs, such as dyads and triads, which are well suited for in depth validation through laboratory experiments. In particular, loss-and gain-of-function assays designed to drive changes in leukemia cell survival, proliferation and differentiation will benefit from the identification of multi-miR signatures that characterize leukemia subtypes and their normal counterpart cells of origin.

Far-from-equilibrium growth of magnetic thin films with Blume-Capel impurities

We investigate the irreversible growth of (2+1)-dimensional magnetic thin films. The spin variable can adopt three states (s(I)=±1,0), and the system is in contact with a thermal bath of temperature T. The deposition process depends on the change of the configuration energy, which, by analogy to the Blume-Capel Hamiltonian in equilibrium systems, depends on Ising-like couplings between neighboring spins (J) and has a crystal field (D) term that controls the density of nonmagnetic impurities (s(I)=0). Once deposited, particles are not allowed to flip, diffuse, or detach. By means of extensive Monte Carlo simulations, we obtain the phase diagram in the crystal field vs temperature parameter space. We show clear evidence of the existence of a tricritical point located at D(t)/J=1.145(10) and k(B)T(t)/J=0.425(10), which separates a first-order transition curve at lower temperatures from a critical second-order transition curve at higher temperatures, in analogy with the previously studied equilibrium Blume-Capel model. Furthermore, we show that, along the second-order transition curve, the critical behavior of the irreversible growth model can be described by means of the critical exponents of the two-dimensional Ising model under equilibrium conditions. Therefore, our findings provide a link between well-known theoretical equilibrium models and nonequilibrium growth processes that are of great interest for many experimental applications, as well as a paradigmatic topic of study in current statistical physics.

Understanding health and disease with multidimensional single-cell methods

Current efforts in the biomedical sciences and related interdisciplinary fields are focused on gaining a molecular understanding of health and disease, which is a problem of daunting complexity that spans many orders of magnitude in characteristic length scales, from small molecules that regulate cell function to cell ensembles that form tissues and organs working together as an organism. In order to uncover the molecular nature of the emergent properties of a cell, it is essential to measure multiple-cell components simultaneously in the same cell. In turn, cell heterogeneity requires multiple-cells to be measured in order to understand health and disease in the organism. This review summarizes current efforts towards a data-driven framework that leverages single-cell technologies to build robust signatures of healthy and diseased phenotypes. While some approaches focus on multicolor flow cytometry data and other methods are designed to analyze high-content image-based screens, we emphasize the so-called Supercell/SVM paradigm (recently developed by the authors of this review and collaborators) as a unified framework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond their specific contributions to basic and translational biomedical research, these efforts illustrate, from a larger perspective, the powerful synergy that might be achieved from bringing together methods and ideas from statistical physics, data mining, and mathematics to solve the most pressing problems currently facing the life sciences.

Stochastic resonance and dynamic first-order pseudo-phase-transitions in the irreversible growth of thin films under spatially periodic magnetic fields

We study the irreversible growth of magnetic thin films under the influence of spatially periodic fields by means of extensive Monte Carlo simulations. We find first-order pseudo-phase-transitions that separate a dynamically disordered phase from a dynamically ordered phase. By analogy with time-dependent oscillating fields applied to Ising-type models, we qualitatively associate this dynamic transition with the localization-delocalization transition of spatial hysteresis loops. Depending on the relative width of the magnetic film L compared to the wavelength of the external field λ, different transition regimes are observed. For small systems (L < λ), the transition is associated with the standard stochastic resonance regime, while for large systems (L > λ), the transition is driven by anomalous stochastic resonance. The origin of the latter is identified as due to the emergence of an additional relevant length scale, namely, the roughness of the spin domain switching interface. The distinction between different stochastic resonance regimes is discussed at length both qualitatively by means of snapshot configurations and quantitatively via residence-length and order-parameter probability distributions.