Abstract
Better prognostic predictors for invasive candidiasis (IC) are needed to tailor and individualize therapeutic decision-making and minimize its high morbidity and mortality. We investigated whether molecular profiling of IgG-antibody response to the whole soluble Candida proteome could reveal a prognostic signature that may serve to devise a clinical-outcome prediction model for IC and contribute to known IC prognostic factors. By serological proteome analysis and data-mining procedures, serum 31-IgG antibody-reactivity patterns were examined in 45 IC patients randomly split into training and test sets. Within the training cohort, unsupervised two-way hierarchical clustering and principal-component analyses segregated IC patients into two antibody-reactivity subgroups with distinct prognoses that were unbiased by traditional IC prognostic factors and other patients-related variables. Supervised discriminant analysis with leave-one-out cross-validation identified a five-IgG antibody-reactivity signature as the most simplified and accurate IC clinical-outcome predictor, from which an IC prognosis score (ICPS) was derived. Its robustness was confirmed in the test set. Multivariate logistic-regression and receiver-operating-characteristic curve analyses demonstrated that the ICPS was able to accurately discriminate IC patients at high risk for death from those at low risk and outperformed conventional IC prognostic factors. Further validation of the five-IgG antibody-reactivity signature on a multiplexed immunoassay supported the serological proteome analysis results. The five IgG antibodies incorporated in the ICPS made biologic sense and were associated either with good-prognosis and protective patterns (those to Met6p, Hsp90p, and Pgk1p, putative Candida virulence factors and antiapoptotic mediators) or with poor-prognosis and risk patterns (those to Ssb1p and Gap1p/Tdh3p, potential Candida proapoptotic mediators). We conclude that the ICPS, with additional refinement in future larger prospective cohorts, could be applicable to reliably predict patient clinical-outcome for individualized therapy of IC. Our data further provide insights into molecular mechanisms that may influence clinical outcome in IC and uncover potential targets for vaccine design and immunotherapy against IC.
Highlights
From the ‡Department of Microbiology II, Faculty of Pharmacy, Complutense University of Madrid and Ramon y Cajal Institute of Health Research (IRYCIS), Spain
Its significant impact on patient clinical outcome, as reflected in its increased attributable mortality (10%– 49%), length of hospital stay (3–30 days per patient), and healthcare costs (US $ 6214 –92,266 per episode), could be ameliorated if early and appropriate antifungal therapeutic strategies were administered [1, 4]. This precondition highlights the need to search for prognostic features that may reliably predict the clinical outcome in invasive candidiasis (IC) patients at presentation to tailor and individualize therapeutic decision-making and, as a result, to minimize the burden of the invasive infections caused by Candida spp. (commonly Candida albicans [1])
Anti-Candida IgG Antibody-Reactivity Profiles of IC—To devise an antibody reactivity-based prognostic predictor for IC, serum samples from 45 IC patients with favorable (n ϭ 33) and fatal (n ϭ 12) clinical outcomes within 2 months following presentation were individually screened by Serological proteome analysis (SERPA) for IgG antibodies to the whole soluble C. albicans immunome, with the patients randomly, but split into a training set for prediction-model development and into a test set for model validation (Fig. 1)
Summary
Serum specimens from 48 adult IC patients belonging to different risk groups were obtained on the day of culture sampling at the Salamanca Clinic Hospital (Spain), a 750-bed tertiary care universityaffiliated hospital, between December 1997 and March 2003. Discriminant analysis was applied to model the relationship between the clinical outcome of IC patients and their anti-Candida IgG antibody patterns as well as between patient outcomes and traditional IC prognostic factors, and to find a linear combination of the minimum number of predictor variables that best separated the compared groups (classification algorithm or discriminant function). Model cutoff thresholds were defined on the basis of ROC curve analysis on training set data These discrimination threshold values correspond to the highest combined sensitivity and specificity (i.e. the minimum sum of false-negative plus false-positive test results) for poor or good clinical outcomes when mortality or survival predictors, respectively, for IC were determined. Statistical significance was set at p Ͻ 0.05 (two-sided)
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