The Feasibility of Host Transcriptome Profiling as a Diagnostic Tool for Microbial Etiology in Childhood Cancer Patients with Febrile Neutropenia

Abstract

Infection is a common and serious complication of cancer treatment in children that often presents as febrile neutropenia (FN). Gene-expression profiling techniques can reveal transcriptional signatures that discriminate between viral, bacterial and asymptomatic infections in otherwise healthy children. Here, we examined whether gene-expression profiling was feasible in children with FN who were undergoing cancer treatment. The blood transcriptome of the children (n = 63) was investigated at time of FN diagnosed as viral, bacterial, co-infection or unknown etiology, respectively, and compared to control samples derived from 12 of the patients following the FN episode. RNA sequencing was successful in 43 (68%) of the FN episodes. Only two genes were significantly differentially expressed in the bacterial versus the control group. Significantly up-regulated genes in patients with the other three etiologies versus the control group were enriched with cellular processes related to proliferation and cellular stress response, with no clear enrichment with innate responses to pathogens. Among the significantly down-regulated genes, a few clustered into pathways connected to responses to infection. In the present study of children during cancer treatment, the blood transcriptome was not suitable for determining the etiology of FN because of too few circulating immune cells for reliable gene expression analysis.

Genetic Sequence Variants in TLR4, MBL or IL-1 Receptor Antagonist is not Associated to Increased Risk for Febrile Neutropenia in Children with ALL

“…Notably, in the current study, only ~40% of the FN had a proven etiology, which is a limitation of our study. However, it also highlights the importance of improved methods for microbial detection in children with ALL as suggested by us previously [ 35 ].…”

Weighted Gene Co-Expression Network Analysis and Machine learning Identify Critical Genes in the Development of Osteomyelitis after Staphylococcus aureus Infections

“…To our knowledge, this is the rst study to identify feature genes in OM development by WGCNA and machine learning. Some published studies suggested that the host blood transcriptional pro les play an essential role in diagnosing infectious diseases [18,[32][33][34]. It is worth noting that one previous study [11] divided samples into three comparison groups, including all of the S. aureus patients vs. Ctrl group (i.e., healthy people), OM patient vs. Ctrl group, and OM patients vs. non-OM patient following S. aureus infection.…”

Novel Biomarkers Differentiating Viral from Bacterial Infection in Febrile Children: Future Perspectives for Management in Clinical Praxis

“…By using a multi-data cohort approach including both adult and pediatric patients, and also animals in one of the studies, two research groups were able to identify a 7-and 4-script-Host RNA signature that discriminated viral from bacterial infection and viral from non-infective inflammation with high accuracy [75,76]. These gene expression-based diagnostic approaches are promising and have been proven superior to other biomarkers, such as white blood counts and PCT [63,64,78], but further evaluation in different patient cohorts is needed, especially since a recent publication could show that gene expression profiling was not feasible in children under treatment for cancer and presenting with febrile neutropenia [81].…”