Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer.

The incidence of early-onset colorectal cancer (eoCRC) is rising, and its pathogenesis is not completely understood. We hypothesized that machine learning utilizing paired tissue microbiome and plasma metabolome features could uncover distinct host-microbiome associations between eoCRC and average-onset CRC (aoCRC). Individuals with stages I-IV CRC (n = 64) were categorized as eoCRC (age ≤ 50, n = 20) or aoCRC (age ≥ 60, n = 44). Untargeted plasma metabolomics and 16S rRNA amplicon sequencing (microbiome analysis) of tumor tissue were performed. We fit DIABLO (Data Integration Analysis for Biomarker Discovery using Latent variable approaches for Omics studies) to construct a supervised machine-learning classifier using paired multi-omics (microbiome and metabolomics) data and identify associations unique to eoCRC. A differential association network analysis was also performed. Distinct clustering patterns emerged in multi-omic dimension reduction analysis. The metabolomics classifier achieved an AUC of 0.98, compared to AUC 0.61 for microbiome-based classifier. Circular correlation technique highlighted several key associations. Metabolites glycerol and pseudouridine (higher abundance in individuals with aoCRC) had negative correlations with Parasutterella, and Ruminococcaceae (higher abundance in individuals with eoCRC). Cholesterol and xylitol correlated negatively with Erysipelatoclostridium and Eubacterium, and showed a positive correlation with Acidovorax with higher abundance in individuals with eoCRC. Network analysis revealed different clustering patterns and associations for several metabolites e.g.: urea cycle metabolites and microbes such as Akkermansia. We show that multi-omics analysis can be utilized to study host-microbiome correlations in eoCRC and demonstrates promising biomarker potential of a metabolomics classifier. The distinct host-microbiome correlations for urea cycle in eoCRC may offer opportunities for therapeutic interventions.

Adaptive selection at G6PD and disparities in diabetes complications.

Diabetes complications occur at higher rates in individuals of African ancestry. Glucose-6-phosphate dehydrogenase deficiency (G6PDdef), common in some African populations, confers malaria resistance, and reduces hemoglobin A1c (HbA1c) levels by shortening erythrocyte lifespan. In a combined-ancestry genome-wide association study of diabetic retinopathy, we identified nine loci including a G6PDdef causal variant, rs1050828 -T (Val98Met), which was also associated with increased risk of other diabetes complications. The effect of rs1050828 -T on retinopathy was fully mediated by glucose levels. In the years preceding diabetes diagnosis and insulin prescription, glucose levels were significantly higher and HbA1c significantly lower in those with versus without G6PDdef. In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, participants with G6PDdef had significantly higher hazards of incident retinopathy and neuropathy. At the same HbA1c levels, G6PDdef participants in both ACCORD and the Million Veteran Program had significantly increased risk of retinopathy. We estimate that 12% and 9% of diabetic retinopathy and neuropathy cases, respectively, in participants of African ancestry are due to this exposure. Across continentally defined ancestral populations, the differences in frequency of rs1050828 -T and other G6PDdef alleles contribute to disparities in diabetes complications. Diabetes management guided by glucose or potentially genotype-adjusted HbA1c levels could lead to more timely diagnoses and appropriate intensification of therapy, decreasing the risk of diabetes complications in patients with G6PDdef alleles.

Polygenic subtype identified in ACCORD trial displays a favorable type 2 diabetes phenotype in the UKBiobank population.

INTRODUCTION: We previously identified a genetic subtype (C4) of type 2 diabetes (T2D), benefitting from intensive glycemia treatment in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Here, we characterized the population of patients that met the C4 criteria in the UKBiobank cohort. RESEARCH DESIGN AND METHODS: Using our polygenic score (PS), we identified C4 individuals in the UKBiobank and tested C4 status with risk of developing T2D, cardiovascular disease (CVD) outcomes, and differences in T2D medications. RESULTS: C4 individuals were less likely to develop T2D, were slightly older at T2D diagnosis, had lower HbA1c values, and were less likely to be prescribed T2D medications (P < .05). Genetic variants in MAS1 and IGF2R, major components of the C4 PS, were associated with fewer overall T2D prescriptions. CONCLUSION: We have confirmed C4 individuals are a lower risk subpopulation of patients with T2D.

Proteomics identifies complement protein signatures in patients with alcohol-associated hepatitis.

Diagnostic challenges continue to impede development of effective therapies for successful management of alcohol-associated hepatitis (AH), creating an unmet need to identify noninvasive biomarkers for AH. In murine models, complement contributes to ethanol-induced liver injury. Therefore, we hypothesized that complement proteins could be rational diagnostic/prognostic biomarkers in AH. Here, we performed a comparative analysis of data derived from human hepatic and serum proteome to identify and characterize complement protein signatures in severe AH (sAH). The quantity of multiple complement proteins was perturbed in liver and serum proteome of patients with sAH. Multiple complement proteins differentiated patients with sAH from those with alcohol cirrhosis (AC) or alcohol use disorder (AUD) and healthy controls (HCs). Serum collectin 11 and C1q binding protein were strongly associated with sAH and exhibited good discriminatory performance among patients with sAH, AC, or AUD and HCs. Furthermore, complement component receptor 1-like protein was negatively associated with pro-inflammatory cytokines. Additionally, lower serum MBL associated serine protease 1 and coagulation factor II independently predicted 90-day mortality. In summary, meta-analysis of proteomic profiles from liver and circulation revealed complement protein signatures of sAH, highlighting a complex perturbation of complement and identifying potential diagnostic and prognostic biomarkers for patients with sAH.

An interpretable predictive deep learning platform for pediatric metabolic diseases.

OBJECTIVES: Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the development of these long-term complications. MATERIALS AND METHODS: No clinically available tools are currently in widespread use that can predict the onset of metabolic diseases in pediatric patients. Here, we use interpretable deep learning, leveraging longitudinal clinical measurements, demographical data, and diagnosis codes from electronic health record data from a large integrated health system to predict the onset of prediabetes, type 2 diabetes (T2D), and metabolic syndrome in pediatric cohorts. RESULTS: The cohort included 49 517 children with overweight or obesity aged 2-18 (54.9% male, 73% Caucasian), with a median follow-up time of 7.5 years and mean body mass index (BMI) percentile of 88.6%. Our model demonstrated area under receiver operating characteristic curve (AUC) accuracies up to 0.87, 0.79, and 0.79 for predicting T2D, metabolic syndrome, and prediabetes, respectively. Whereas most risk calculators use only recently available data, incorporating longitudinal data improved AUCs by 13.04%, 11.48%, and 11.67% for T2D, syndrome, and prediabetes, respectively, versus models using the most recent BMI (P < 2.2 × 10-16). DISCUSSION: Despite most risk calculators using only the most recent data, incorporating longitudinal data improved the model accuracies because utilizing trajectories provides a more comprehensive characterization of the patient's health history. Our interpretable model indicated that BMI trajectories were consistently identified as one of the most influential features for prediction, highlighting the advantages of incorporating longitudinal data when available.

Plasma metabolomic differences in early-onset compared to average-onset colorectal cancer.

Deleterious effects of environmental exposures may contribute to the rising incidence of early-onset colorectal cancer (eoCRC). We assessed the metabolomic differences between patients with eoCRC, average-onset CRC (aoCRC), and non-CRC controls, to understand pathogenic mechanisms. Patients with stage I-IV CRC and non-CRC controls were categorized based on age ≤ 50 years (eoCRC or young non-CRC controls) or  ≥ 60 years (aoCRC or older non-CRC controls). Differential metabolite abundance and metabolic pathway analyses were performed on plasma samples. Multivariate Cox proportional hazards modeling was used for survival analyses. All P values were adjusted for multiple testing (false discovery rate, FDR P < 0.15 considered significant). The study population comprised 170 patients with CRC (66 eoCRC and 104 aoCRC) and 49 non-CRC controls (34 young and 15 older). Citrate was differentially abundant in aoCRC vs. eoCRC in adjusted analysis (Odds Ratio = 21.8, FDR P = 0.04). Metabolic pathways altered in patients with aoCRC versus eoCRC included arginine biosynthesis, FDR P = 0.02; glyoxylate and dicarboxylate metabolism, FDR P = 0.005; citrate cycle, FDR P = 0.04; alanine, aspartate, and glutamate metabolism, FDR P = 0.01; glycine, serine, and threonine metabolism, FDR P = 0.14; and amino-acid t-RNA biosynthesis, FDR P = 0.01. 4-hydroxyhippuric acid was significantly associated with overall survival in all patients with CRC (Hazards ratio, HR = 0.4, 95% CI 0.3-0.7, FDR P = 0.05). We identified several unique metabolic alterations, particularly the significant differential abundance of citrate in aoCRC versus eoCRC. Arginine biosynthesis was the most enriched by the differentially altered metabolites. The findings hold promise in developing strategies for early detection and novel therapies.

Molecular subtypes of epilepsy associated with post-surgical seizure recurrence.

Approximately 50% of individuals who undergo resective epilepsy surgery experience seizure recurrence. The heterogenous post-operative outcomes are not fully explained by clinical, imaging and electrophysiological variables. We hypothesized that molecular features may be useful in understanding surgical response, and that individuals with epilepsy can be classified into molecular subtypes that are associated with seizure freedom or recurrence after surgical resection. Pre-operative blood samples, brain tissue and post-operative seizure outcomes were collected from a cohort of 40 individuals with temporal lobe epilepsy, 23 of whom experienced post-operative seizure recurrence. Messenger RNA and microRNA extracted from the blood and tissue samples were sequenced. The messenger RNA and microRNA expression levels from the blood and brain were each subjected to a novel clustering approach combined with multiple logistic regression to separate individuals into genetic clusters that identify novel subtypes associated with post-operative seizure outcomes. We then compared the microRNAs and messenger RNAs from patient blood and brain tissue that were significantly associated with each subtype to identify signatures that are similarly over- or under-represented for an outcome and more likely to represent endophenotypes with common molecular aetiology. These target microRNAs and messenger RNAs were further characterized by pathway analysis to assess their functional role in epilepsy. Using blood-derived microRNA and messenger RNA expression levels, we identified two subtypes of epilepsy that were significantly associated with seizure recurrence (clusters A1 and B4) (adjusted P < 0.20). A total of 551 microRNAs and 2486 messenger RNAs were associated with clusters A1 and B4, respectively (adjusted P < 0.05). Clustering of brain-tissue messenger RNA expression levels revealed an additional subtype (C2) associated with seizure recurrence that had high overlap of dysregulated messenger RNA transcripts with cluster B4. Clusters A1, B4 and C2 also shared significant overlap of subjects, which altogether suggests a coordinated mechanism by which microRNA and messenger RNA transcripts may be related to seizure recurrence. Epileptic subtypes A1, B4 and C2 reveal both known and novel microRNA and messenger RNA targets in seizure recurrence. Furthermore, targets identified in A1 and B4 are quantifiable in pre-operative blood samples and could potentially serve as biomarkers for surgical resection outcomes.

Characterization of Salivary and Plasma Metabolites as Biomarkers for HCC: A Pilot Study.

(1) Background: The incidence of hepatocellular carcinoma (HCC) is rising, and current screening methods lack sensitivity. This study aimed to identify distinct and overlapping metabolites in saliva and plasma that are significantly associated with HCC. (2) Methods: Saliva samples were collected from 42 individuals (HCC = 16, cirrhosis = 12, healthy = 14), with plasma samples from 22 (HCC = 14, cirrhosis = 2, healthy = 6). We performed untargeted mass spectrometry on blood and plasma, tested metabolites for associations with HCC or cirrhosis using a logistic regression, and identified enriched pathways with Metaboanalyst. Pearson's correlation was employed to test for correlations between salivary and plasma metabolites. (3) Results: Six salivary metabolites (1-hexadecanol, isooctanol, malonic acid, N-acetyl-valine, octadecanol, and succinic acid) and ten plasma metabolites (glycine, 3-(4-hydroxyphenyl)propionic acid, aconitic acid, isocitric acid, tagatose, cellobiose, fucose, glyceric acid, isocitric acid, isothreonic acid, and phenylacetic acid) were associated with HCC. Malonic acid was correlated between the paired saliva and plasma samples. Pathway analysis highlighted deregulation of the 'The Citric Acid Cycle' in both biospecimens. (4) Conclusions: Our study suggests that salivary and plasma metabolites may serve as independent sources for HCC detection. Despite the lack of correlation between individual metabolites, they converge on 'The Citric Acid Cycle' pathway, implicated in HCC pathogenesis.

Genetic variations that influence paclitaxel pharmacokinetics and intracellular effects that may contribute to chemotherapy-induced neuropathy: A narrative review.

Taxanes, particularly paclitaxel and docetaxel, are chemotherapeutic agents commonly used to treat breast cancers. A frequent side effect is chemotherapy-induced peripheral neuropathy (CIPN) that occurs in up to 70% of all treated patients and impacts the quality of life during and after treatment. CIPN presents as glove and stocking sensory deficits and diminished motor and autonomic function. Nerves with longer axons are at higher risk of developing CIPN. The causes of CIPN are multifactorial and poorly understood, limiting treatment options. Pathophysiologic mechanisms can include: (i) disruptions of mitochondrial and intracellular microtubule functions, (ii) disruption of axon morphology, and (iii) activation of microglial and other immune cell responses, among others. Recent work has explored the contribution of genetic variation and selected epigenetic changes in response to taxanes for any insights into their relation to pathophysiologic mechanisms of CIPN20, with the hope of identifying predictive and targetable biomarkers. Although promising, many genetic studies of CIPN are inconsistent making it difficult to develop reliable biomarkers of CIPN. The aims of this narrative review are to benchmark available evidence and identify gaps in the understanding of the role genetic variation has in influencing paclitaxel's pharmacokinetics and cellular membrane transport potentially related to the development of CIPN.

Biomarkers for Response to Anti-PD-1/Anti-PD-L1 Immune Checkpoint Inhibitors: A Large Meta-Analysis.

BACKGROUND: Immune checkpoint inhibitors (ICIs) that block PD-1/PD-L1 have consistently demonstrated durable clinical activity across multiple histologies but have low overall response rates for many cancers-indicating that too few patients benefit from ICIs. Many studies have explored potential predictive biomarkers (eg, PD-1/PD-L1 expression, tumor mutational burden [TMB]), no consensus biomarker has been identified. METHODS: This meta-analysis combined predictive accuracy metrics for various biomarkers, across multiple cancer types, to determine which biomarkers are most accurate for predicting ICI response. Data from 18,792 patients from 100 peer-reviewed studies that evaluated putative biomarkers for response to anti-PD-1/anti- PD-L1 treatment were meta-analyzed using bivariate linear mixed models. Biomarker performance was assessed based on the global area under the receiver operating characteristic curve (AUC) and 95% bootstrap confidence intervals. RESULTS: PD-L1 immunohistochemistry, TMB, and multimodal biomarkers discriminated responders and nonresponders better than random assignment (AUCs >.50). Excluding multimodal biomarkers, these biomarkers correctly classified at least 50% of the responders (sensitivity 95% CIs, >.50). Notably, variation in biomarker performance was observed across cancer types. CONCLUSIONS: Although some biomarkers consistently performed better, heterogeneity in performance was observed across cancer types, and additional research is needed to identify highly accurate and precise biomarkers for widespread clinical use.

Macrophage-derived MLKL in alcohol-associated liver disease: Regulation of phagocytosis.

BACKGROUND AND AIMS: Mixed lineage kinase domain-like pseudokinase (MLKL), a key terminal effector of necroptosis, also plays a role in intracellular vesicle trafficking that is critical for regulating liver inflammation and injury in alcohol-associated liver disease (ALD). Although receptor interacting protein kinase 3 (Rip3)-/- mice are completely protected from ethanol-induced liver injury, Mlkl-/- mice are only partially protected. Therefore, we hypothesized that cell-specific functions of MLKL may contribute to ethanol-induced injury. APPROACH AND RESULTS: Bone marrow transplants between Mlkl-/- mice and littermates were conducted to distinguish the role of myeloid versus nonmyeloid Mlkl in the Gao-binge model of ALD. Ethanol-induced hepatic injury, steatosis, and inflammation were exacerbated in Mlkl-/- →wild-type (WT) mice, whereas Mlkl deficiency in nonmyeloid cells (WT→ Mlkl-/- ) had no effect on Gao-binge ethanol-induced injury. Importantly, Mlkl deficiency in myeloid cells exacerbated ethanol-mediated bacterial burden and accumulation of immune cells in livers. Mechanistically, challenging macrophages with lipopolysaccharide (LPS) induced signal transducer and activator of transcription 1-mediated expression and phosphorylation of MLKL, as well as translocation and oligomerization of MLKL to intracellular compartments, including phagosomes and lysosomes but not plasma membrane. Importantly, pharmacological or genetic inhibition of MLKL suppressed the phagocytic capability of primary mouse Kupffer cells (KCs) at baseline and in response to LPS with/without ethanol as well as peripheral monocytes isolated from both healthy controls and patients with alcohol-associated hepatitis. Further, in vivo studies revealed that KCs of Mlkl-/- mice phagocytosed fewer bioparticles than KCs of WT mice. CONCLUSION: Together, these data indicate that myeloid MLKL restricts ethanol-induced liver inflammation and injury by regulating hepatic immune cell homeostasis and macrophage phagocytosis.

Host metabolome predicts the severity and onset of acute toxicities induced by CAR T-cell therapy.

Anti-CD19 chimeric antigen receptor (CAR) T-cell therapy is a highly effective treatment option for patients with relapsed/refractory large B-cell lymphoma. However, widespread use is deterred by the development of clinically significant acute inflammatory toxicities, including cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), that induce significant morbidity and require close monitoring. Identification of host biochemical signatures that predict the severity and time-to-onset of CRS and ICANS may assist patient stratification to enable timely mitigation strategies. Here, we report pretreatment host metabolites that are associated with CRS and ICANS induced by axicabtagene ciloleucel or tisagenlecleucel therapy. Both untargeted metabolomics analysis and validation using targeted assays revealed a significant association between the abundance of specific pretreatment biochemical entities and an increased risk and/or onset of clinically significant CRS (q < .1) and ICANS (q < .25). Higher pretreatment levels of plasma glucose and lower levels of cholesterol and glutamate were associated with a faster onset of CRS. In contrast, low baseline levels of the amino acids proline and glycine and the secondary bile acid isoursodeoxycholate were significantly correlated with clinically significant CRS. Lower concentration of the amino acid hydroxyproline was associated with higher grade and faster onset of ICANS, whereas low glutamine was negatively correlated with faster development of ICANS. Overall, our data indicate that the pretreatment host metabolome has biomarker potential in determining the risk of clinically significant CRS and ICANS, and may be useful in risk stratification of patients before anti-CD19 CAR T-cell therapy.

Impacts of COVID-19 on Glycemia and Risk of Diabetic Ketoacidosis.

Reports indicate that coronavirus disease 2019 (COVID-19) may impact pancreatic function and increase type 2 diabetes (T2D) risk, although real-world COVID-19 impacts on HbA1c and T2D are unknown. We tested whether COVID-19 increased HbA1c, risk of T2D, or diabetic ketoacidosis (DKA). We compared pre- and post-COVID-19 HbA1c and T2D risk in a large real-world clinical cohort of 8,755 COVID-19(+) patients and 11,998 COVID-19(-) matched control subjects. We investigated whether DKA risk was modified in COVID-19(+) patients with type 1 diabetes (T1D) (N = 701) or T2D (N = 21,830), or by race and sex. We observed a statistically significant, albeit clinically insignificant, HbA1c increase post-COVID-19 (all patients ΔHbA1c = 0.06%; with T2D ΔHbA1c = 0.1%) and no increase among COVID-19(-) patients. COVID-19(+) patients were 40% more likely to be diagnosed with T2D compared with COVID-19(-) patients and 28% more likely for the same HbA1c change as COVID-19(-) patients, indicating that COVID-19-attributed T2D risk may be due to increased recognition during COVID-19 management. DKA in COVID-19(+) patients with T1D was not increased. COVID-19(+) Black patients with T2D displayed disproportionately increased DKA risk (hazard ratio 2.46 [95% CI 1.48-6.09], P = 0.004) compared with White patients, suggesting a need for further clinical awareness and investigation.

Genetic and molecular features of seizure-freedom following surgical resections for focal epilepsy: A pilot study.

Objective: Seizure outcomes after brain surgery for drug-resistant epilepsy (DRE) are very heterogeneous and difficult to predict with models utilizing the current clinical, imaging, and electrophysiological variables. In this pilot study, we investigated whether genetic and molecular biomarkers (e.g., genomic, transcriptomic) can provide additional insight into differential response to surgery. Methods: Post-operative seizure-outcomes were collected at last follow-up (>6 months) for 201 adult patients with DRE who underwent surgery between 2004 and 2020. Resected tissue was sent for miRNA sequencing (n = 132) and mRNA sequencing (n = 135). Following the selection of 10 genes (SCN1A, NBEA, PTEN, GABRA1, LGL1, DEPDC5, IL1A, ABCB1, C3, CALHM1), we investigated SNPs in those 10 genes from previously acquired exome sequencing data (n = 106). Logistic regression was performed to test for associations between individual features (mRNAs, miRNAs, and SNPs) and post-operative seizure-outcome with an exploratory FDR P < 0.25 as the threshold for significance. Post-operative time-to-seizure analyses were performed for each SNP using a Cox proportional hazards model. Results: The majority of patients (83%) had temporal lobe epilepsy. Mean age at surgery was 38.3 years, and 56% were female. Three SNPs (rs10276036, rs11975994, rs1128503) in multi-drug resistance gene, ABCB1, were associated with post-operative seizure outcomes. Patients with alternate alleles in ABCB1 were more likely to be seizure-free at last follow-up (52-56% reduction in seizure recurrence; FDR P = 0.24). All three SNPs were in linkage disequilibrium and highly correlated with each other. Median post-operative time-to-seizure was 63 months for patients with 2 alternate alleles, 24-33 months with 1 alternate allele, and 10-11 months with 0 alternate alleles. These SNPs improved outcome prediction beyond MRI and sex alone. No independent miRNAs or mRNAs were significantly associated with seizure-outcome (P > 0.05). However, pathway analysis identified "cancer drug resistance by drug efflux" (mir-154 and mir-379) as enriched (P = 0.02), supporting the role of drug response genes in post-operative seizure recurrence. Significance: ABCB1 may have a role in epileptogenesis and surgery outcomes independent of its drug efflux activity necessitating further investigation. SNPs in ABCB1 may serve as independent predictors of post-operative outcome.

Cytomegalovirus US28 regulates cellular EphA2 to maintain viral latency.

Cytomegalovirus (CMV) reactivation from latency following immune dysregulation remains a serious risk for patients, often causing substantial morbidity and mortality. Here, we demonstrate the CMV-encoded G protein-coupled receptor, US28, in coordination with cellular Ephrin receptor A2, attenuates mitogen-activated protein kinase signaling, thereby limiting viral replication in latently infected primary monocytes. Furthermore, treatment of latently infected primary monocytes with dasatinib, a Food and Drug Association-approved kinase inhibitor used to treat a subset of leukemias, results in CMV reactivation. These ex vivo data correlate with our retrospective analyses of the Explorys electronic health record database, where we find dasatinib treatment is associated with a significant risk of CMV-associated disease (odds ratio 1.58, P = 0.0004). Collectively, our findings elucidate a signaling pathway that plays a central role in the balance between CMV latency and reactivation and identifies a common therapeutic cancer treatment that elevates the risk of CMV-associated disease.

A Multimodal Approach to Discover Biomarkers for Taxane-Induced Peripheral Neuropathy (TIPN): A Study Protocol.

Introduction: Taxanes are a class of chemotherapeutics commonly used to treat various solid tumors, including breast and ovarian cancers. Taxane-induced peripheral neuropathy (TIPN) occurs in up to 70% of patients, impacting quality of life both during and after treatment. TIPN typically manifests as tingling and numbness in the hands and feet and can cause irreversible loss of function of peripheral nerves. TIPN can be dose-limiting, potentially impacting clinical outcomes. The mechanisms underlying TIPN are poorly understood. As such, there are limited treatment options and no tools to provide early detection of those who will develop TIPN. Although some patients may have a genetic predisposition, genetic biomarkers have been inconsistent in predicting chemotherapy-induced peripheral neuropathy (CIPN). Moreover, other molecular markers (eg, metabolites, mRNA, miRNA, proteins) may be informative for predicting CIPN, but remain largely unexplored. We anticipate that combinations of multiple biomarkers will be required to consistently predict those who will develop TIPN. Methods: To address this clinical gap of identifying patients at risk of TIPN, we initiated the Genetics and Inflammatory Markers for CIPN (GENIE) study. This longitudinal multicenter observational study uses a novel, multimodal approach to evaluate genomic variation, metabolites, DNA methylation, gene expression, and circulating cytokines/chemokines prior to, during, and after taxane treatment in 400 patients with breast cancer. Molecular and patient reported data will be collected prior to, during, and after taxane therapy. Multi-modal data will be used to develop a set of comprehensive predictive biomarker signatures of TIPN. Conclusion: The goal of this study is to enable early detection of patients at risk of developing TIPN, provide a tool to modify taxane treatment to minimize morbidity from TIPN, and improved patient quality of life. Here we provide a brief review of the current state of research into CIPN and TIPN and introduce the GENIE study design.

Identification of robust deep neural network models of longitudinal clinical measurements.

Deep learning (DL) from electronic health records holds promise for disease prediction, but systematic methods for learning from simulated longitudinal clinical measurements have yet to be reported. We compared nine DL frameworks using simulated body mass index (BMI), glucose, and systolic blood pressure trajectories, independently isolated shape and magnitude changes, and evaluated model performance across various parameters (e.g., irregularity, missingness). Overall, discrimination based on variation in shape was more challenging than magnitude. Time-series forest-convolutional neural networks (TSF-CNN) and Gramian angular field(GAF)-CNN outperformed other approaches (P < 0.05) with overall area-under-the-curve (AUCs) of 0.93 for both models, and 0.92 and 0.89 for variation in magnitude and shape with up to 50% missing data. Furthermore, in a real-world assessment, the TSF-CNN model predicted T2D with AUCs reaching 0.72 using only BMI trajectories. In conclusion, we performed an extensive evaluation of DL approaches and identified robust modeling frameworks for disease prediction based on longitudinal clinical measurements.

Salivary miRNAs as non-invasive biomarkers of hepatocellular carcinoma: a pilot study.

BACKGROUND: Improved detection of hepatocellular carcinoma (HCC) is needed, as current detection methods, such as alpha fetoprotein (AFP) and ultrasound, suffer from poor sensitivity. MicroRNAs (miRNAs) are small, non-coding RNAs that regulate many cellular functions and impact cancer development and progression. Notably, miRNAs are detectable in saliva and have shown potential as non-invasive biomarkers for a number of cancers including breast, oral, and lung cancers. Here, we present, to our knowledge, the first report of salivary miRNAs in HCC and compare these findings to patients with cirrhosis, a high-risk cohort for HCC. METHODS: We performed small RNA sequencing in 20 patients with HCC and 19 with cirrhosis. Eleven patients with HCC had chronic liver disease, and analyses were performed with these samples combined and stratified by the presence of chronic liver disease. P values were adjusted for multiple comparisons using a false discovery rate (FDR) approach and miRNA with FDR P < 0.05 were considered statistically significant. Differential expression of salivary miRNAs was compared to a previously published report of miRNAs in liver tissue of patients with HCC vs cirrhosis. Support vector machines and leave-one-out cross-validation were performed to determine if salivary miRNAs have predictive potential for detecting HCC. RESULTS: A total of 4,565 precursor and mature miRNAs were detected in saliva and 365 were significantly different between those with HCC compared to cirrhosis (FDR P < 0.05). Interestingly, 283 of these miRNAs were significantly downregulated in patients with HCC. Machine-learning identified a combination of 10 miRNAs and covariates that accurately classified patients with HCC (AUC = 0.87). In addition, we identified three miRNAs that were differentially expressed in HCC saliva samples and in a previously published study of miRNAs in HCC tissue compared to cirrhotic liver tissue. CONCLUSIONS: This study demonstrates, for the first time, that miRNAs relevant to HCC are detectable in saliva, that salivary miRNA signatures show potential to be highly sensitive and specific non-invasive biomarkers of HCC, and that additional studies utilizing larger cohorts are needed.

Gut microbial trimethylamine is elevated in alcohol-associated hepatitis and contributes to ethanol-induced liver injury in mice.

There is mounting evidence that microbes residing in the human intestine contribute to diverse alcohol-associated liver diseases (ALD) including the most deadly form known as alcohol-associated hepatitis (AH). However, mechanisms by which gut microbes synergize with excessive alcohol intake to promote liver injury are poorly understood. Furthermore, whether drugs that selectively target gut microbial metabolism can improve ALD has never been tested. We used liquid chromatography tandem mass spectrometry to quantify the levels of microbe and host choline co-metabolites in healthy controls and AH patients, finding elevated levels of the microbial metabolite trimethylamine (TMA) in AH. In subsequent studies, we treated mice with non-lethal bacterial choline TMA lyase (CutC/D) inhibitors to blunt gut microbe-dependent production of TMA in the context of chronic ethanol administration. Indices of liver injury were quantified by complementary RNA sequencing, biochemical, and histological approaches. In addition, we examined the impact of ethanol consumption and TMA lyase inhibition on gut microbiome structure via 16S rRNA sequencing. We show the gut microbial choline metabolite TMA is elevated in AH patients and correlates with reduced hepatic expression of the TMA oxygenase flavin-containing monooxygenase 3 (FMO3). Provocatively, we find that small molecule inhibition of gut microbial CutC/D activity protects mice from ethanol-induced liver injury. CutC/D inhibitor-driven improvement in ethanol-induced liver injury is associated with distinct reorganization of the gut microbiome and host liver transcriptome. The microbial metabolite TMA is elevated in patients with AH, and inhibition of TMA production from gut microbes can protect mice from ethanol-induced liver injury.

Metagenomics and chemotherapy-induced nausea: A roadmap for future research.

Uncontrolled chemotherapy-induced nausea and vomiting can reduce patients' quality of life and may result in premature discontinuation of chemotherapy. Although nausea and vomiting are commonly grouped together, research has shown that antiemetics are clinically effective against chemotherapy-induced vomiting (CIV) but less so against chemotherapy-induced nausea (CIN). Nausea remains a problem for up to 68% of patients who are prescribed guideline-consistent antiemetics. Despite the high prevalence of CIN, relatively little is known regarding its etiology independent of CIV. This review summarizes a metagenomics approach to the study and treatment of CIN with the goal of encouraging future research. Metagenomics focuses on genetic risk factors and encompasses both human (ie, host) and gut microbial genetic variation. Little work to date has focused on metagenomics as a putative biological mechanism of CIN. Metagenomics has the potential to be a powerful tool in advancing scientific understanding of CIN by identifying new biological pathways and intervention targets. The investigation of metagenomics in the context of well-established demographic, clinical, and patient-reported risk factors may help to identify patients at risk and facilitate the prevention and management of CIN.