Prediction of the Clinical Outcome in Invasive Candidiasis Patients Based on Molecular Fingerprints of Five Anti-Candida Antibodies in Serum

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.Despite recent advances in antifungal therapy, invasive candidiasis (IC)1 remains a leading infectious cause of morbidity and mortality in cancer, postsurgical, and intensive care patients (1–3). 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 however 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 IC patients at presentation to tailor and individualize therapeutic decision-making accordingly and, as a result, to minimize the burden of the invasive infections caused by Candida spp. (commonly Candida albicans (1)).Several factors have classically been reported to adversely influence the clinical outcome of IC patients (3, 5–7). Nonetheless, the prognostic potential of some of these traditional factors for IC is controversial (8, 9) and overall these have a limited prognostic power. For this reason, alternative laboratory tests based on measurement of Candida d-arabinitol/creatinine ratio, Candida antigen titer, or anti-Candida antibody levels (10–15) have been developed to explore their prognostic usefulness in IC. However, none of them has yet been validated for routine clinical practice. Furthermore, these few biomarkers may lack sensitivity for individual prediction of clinical outcomes in the first stages of infection and/or are not yet sufficiently accurate to attain widespread clinical use. In the light of these limitations, and considering the heterogeneity and intricacy of the host responses and molecular mechanisms underlying IC pathogenesis, it is likely that optimally combined multiple biomarkers may cover a broader range of IC patients and pathogenicity-related issues and more reliably predict IC prognosis in an early stage.Serological proteome analysis (SERPA) may be a promising tool in this context because this global profiling technique enables the simultaneous assessment of reactivities of antibodies to a large panel of immunogenic proteins (i.e. the immunome of a (micro)organism (16)) in one experimental approach (17–21). This strategy has widely been applied to antibody-reactivity profiling for diagnostic and therapeutic purposes in cancers, autoimmune disorders, allergies, and infectious diseases (including IC (13, 15, 22, 23)) (18, 24–30). Despite that attractive clinical value, little is known, however, about the potential of this immunoproteomic method to identify antibody-reactivity patterns or signatures (18, 31) that may have utility in predicting the prognosis of individual patients with these pathologies. These prognostic signatures might further offer insights into IC pathogenesis and uncover potential targets for molecular therapies against IC. This approach could also profit from bioinformatics to search for hidden trends within generated multidimensional data and derive useful new knowledge (models, algorithms or rules) (32, 33).Here, we examined the reactivity profiles of serum antibodies to the whole soluble Candida immunome at an early stage of IC by using SERPA and data-mining procedures in order to determine whether these could be indicative of distinct clinical outcomes in IC patients at presentation. We investigated whether these patterns could further reveal a prognostic signature that may serve to create a robust and consistent molecular predictor of clinical outcome for IC applicable to clinical practice and contribute to the traditional prognostic factors for IC. We then developed a multiplexed immunoassay to simultaneously and rapidly measure this simplified molecular fingerprint in each serum specimen and evaluate whether this could be a useful method for individual prediction of clinical outcomes in IC. We also explored whether this prognostic signature could yield biologic insights into molecular mechanisms that confer protection against IC and provide potential molecular targets for the design of novel vaccine- and/or immunotherapy-based strategies to prevent and control IC.