Integrative omics to detect bacteremia in patients with febrile neutropenia

Background: Cancer chemotherapy-associated febrile neutropenia (FN) is a common condition that is deadly when bacteremia is present. Detection of bacteremia depends on culture, which takes days, and no accurate predictive tools applicable to the initial evaluation are available. We utilized metabolomics and transcriptomics to develop multivariable predictors of bacteremia among FN patients. Methods: We classified emergency department patients with FN and no apparent infection at presentation as bacteremic (cases) or not (controls), according to blood culture results. We assessed relative metabolite abundance in plasma, and relative expression of 2,560 immunology and cancer-related genes in whole blood. We used logistic regression to identify multivariable predictors of bacteremia, and report test characteristics of the derived predictors. Results: For metabolomics, 14 bacteremic cases and 25 non-bacteremic controls were available for analysis; for transcriptomics we had 7 and 22 respectively. A 5-predictor metabolomic model had an area under the receiver operating characteristic curve of 0.991 (95%CI: 0.972,1.000), 100% sensitivity, and 96% specificity for identifying bacteremia. Pregnenolone steroids were more abundant in cases and carnitine metabolites were more abundant in controls. A 3-predictor gene expression model had corresponding results of 0.961 (95%CI: 0.896,1.000), 100%, and 86%. Genes involved in innate immunity were differentially expressed. Conclusions: Classifiers derived from metabolomic and gene expression data hold promise as objective and accurate predictors of bacteremia among FN patients without apparent infection at presentation, and can provide insights into the underlying biology. Our findings should be considered illustrative, but may lay the groundwork for future biomarker development

Partial least square discriminant analysis demonstrating metabolomic differences in bacteremia cases (n = 14) and controls (n = 25).

<p>The first two components and the corresponding percentage of the total variance in the metabolome explained by these two components are presented.</p

Pathways differentially enriched at metabolomic and transcriptomic levels.

<p>Pathways differentially enriched at metabolomic and transcriptomic levels.</p

Sensitivity and specificity of the metabolomic and gene expression classifiers compared to existing clinical predictors in terms of area under the curve, sensitivity and specificity.

<p>Sensitivity and specificity of the metabolomic and gene expression classifiers compared to existing clinical predictors in terms of area under the curve, sensitivity and specificity.</p

Partial least square discriminant analysis demonstrating differential gene expression in bacteremia cases (n = 9) and controls (n = 21).

<p>Gene expression profiles based on 2560 genes quantified using EdgeSeq; the first two components and the corresponding percentage of variances in the gene expression profile explained by these two components are presented.</p

Association between gene expression and bacteremia using a logistic regression model.

<p>Top genes are names; nominal significance levels of 95% and 99% are indicated with dashed red line; the x axis represents the strength of the association and the y-axis the significance–genes to the right of the plot are more highly expressed in cases than controls, genes to the left are more highly expressed among the controls.</p

Association between metabolites and bacteremia using a multivariable logistic regression model after adjustment for age, sex, BMI and tumor type (liquid or solid).

<p>Metabolites are colored according to their super pathway assignment and top metabolites are named; nominal significance levels of 95% and 99% are indicated with dashed red line; the x axis represents the strength of the association and the y-axis the significance–metabolites to the right of the plot are at higher levels in cases than controls, metabolites to the left are at higher levels among the controls.</p

Baseline characteristics of the study population.

<p>Baseline characteristics of the study population.</p

Receiver operating characteristic curves showing the performance of gene expression based (logistic and LASSO) predictors compared to existing clinical (MASCC and high-risk) classifiers.

<p>AUC–Area under the receiver operating characteristic curve. MASCC binary classifier–Multinational Association for Supportive Care in Cancer score categorized into <21 (high risk) and ≥21(low risk). High risk binary classifier–defines a patient as high risk if the MASCC score is <21 or if any of the Infectious Diseases Society of America/American Society of Clinical Oncology high risk criteria are met. Logistic score–summary score based on the 153 genes associated with bacteremia under a logistic regression model. LASSO score–summary score based on the 2 genes associated with bacteremia under a penalized LASSO model.</p