Abstract
Invasive pulmonary aspergillosis (IPA) is an opportunistic fungal infection in patients undergoing chemotherapy for hematological malignancy, hematopoietic stem cell transplant, or other forms of immunosuppression. In this group, Aspergillus infections account for the majority of deaths due to mold pathogens. Although early detection is associated with improved outcomes, current diagnostic regimens lack sensitivity and specificity. Patients undergoing chemotherapy, stem cell transplantation and lung transplantation were enrolled in a multi-site prospective observational trial. Proven and probable IPA cases and matched controls were subjected to discovery proteomics analyses using a biofluid analysis platform, fractionating plasma into reproducible protein and peptide pools. From 556 spots identified by 2D gel electrophoresis, 66 differentially expressed post-translationally modified plasma proteins were identified in the leukemic subgroup only. This protein group was rich in complement components, acute-phase reactants and coagulation factors. Low molecular weight peptides corresponding to abundant plasma proteins were identified. A candidate marker panel of host response (9 plasma proteins, 4 peptides), fungal polysaccharides (galactomannan), and cell wall components (β-D glucan) were selected by statistical filtering for patients with leukemia as a primary underlying diagnosis. Quantitative measurements were developed to qualify the differential expression of the candidate host response proteins using selective reaction monitoring mass spectrometry assays, and then applied to a separate cohort of 57 patients with leukemia. In this verification cohort, a machine learning ensemble-based algorithm, generalized pathseeker (GPS) produced a greater case classification accuracy than galactomannan (GM) or host proteins alone. In conclusion, Integration of host response proteins with GM improves the diagnostic detection of probable IPA in patients undergoing treatment for hematologic malignancy. Upon further validation, early detection of probable IPA in leukemia treatment will provide opportunities for earlier interventions and interventional clinical trials.
Highlights
Aspergillus is an important opportunistic fungal pathogen affecting immunocompromised patients, and the disease is associated with high mortality [1, 2]
In 17 cases, blood samples were available from the surveillance study that were taken prior to the diagnosis of probable Invasive pulmonary aspergillosis (IPA); these samples termed “auto” controls were included in the discovery analysis to minimize th effects of individual variations in the plasma proteome on the biomarker identification
Recognizing that the data were not parametrically distributed, and the performance of machine learning classifiers are highly dependent on the underlying data structures [22, 25], we evaluated the performance of classification and regression trees (CART), random forests (RF), multivariate regression spine (MARS), and generalized pathseeker (GPS) machine learning techniques by their classification accuracy and area under the ROC curve (AUC)
Summary
Aspergillus is an important opportunistic fungal pathogen affecting immunocompromised patients, and the disease is associated with high mortality [1, 2]. Invasive aspergillosis is the most common type of fungal infection among stem cell transplant recipients and is the secondmost common type of fungal infection among solid organ transplant recipients, with a 12-month cumulative incidence of 19% [3]. Despite intense surveillance and institution of early aggressive anti-fungal therapy, case fatality rates are as high as 50 to 90% depending on underlying disease and site of infection [4, 5]. In patients with suppressed immunity such as those undergoing hematopoietic stem cell transplant, organ transplant, or those undergoing induction therapy for hematological malignancy, Aspergillus can cause aggressive and invasive infection leading to devastating outcome [6]. In patients with acute leukemia, prolonged periods of neutropenia and dysfunctional macrophages are major risk factors for invasive pulmonary aspergillosis [IPA; ref [8]]
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