Dataset Information

Rapid diagnosis and comprehensive bacteria profiling of sepsis based on cell-free DNA.

ABSTRACT: BACKGROUND:Sepsis remains a major challenge in intensive care units, causing unacceptably high mortality rates due to the lack of rapid diagnostic tools with sufficient sensitivity. Therefore, there is an urgent need to replace time-consuming blood cultures with a new method. Ideally, such a method also provides comprehensive profiling of pathogenic bacteria to facilitate the treatment decision. METHODS:We developed a Random Forest with balanced subsampling to screen for pathogenic bacteria and diagnose sepsis based on cell-free DNA (cfDNA) sequencing data in a small blood sample. In addition, we constructed a bacterial co-occurrence network, based on a set of normal and sepsis samples, to infer unobserved bacteria. RESULTS:Based solely on cfDNA sequencing information from three independent datasets of sepsis, we distinguish sepsis from healthy samples with a satisfactory performance. This strategy also provides comprehensive bacteria profiling, permitting doctors to choose the best treatment strategy for a sepsis case. CONCLUSIONS:The combination of sepsis identification and bacteria-inferring strategies is a success for noninvasive cfDNA-based diagnosis, which has the potential to greatly enhance efficiency in disease detection and provide a comprehensive understanding of pathogens. For comparison, where a culture-based analysis of pathogens takes up to 5 days and is effective for only a third to a half of patients, cfDNA sequencing can be completed in just 1 day and our method can identify the majority of pathogens in all patients.

SUBMITTER: Chen P

PROVIDER: S-EPMC6943891 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Rapid diagnosis and comprehensive bacteria profiling of sepsis based on cell-free DNA.

Chen Pei P Li Shuo S Li Wenyuan W Ren Jie J Sun Fengzhu F Liu Rui R Zhou Xianghong Jasmine XJ

Journal of translational medicine 20200106 1

<h4>Background</h4>Sepsis remains a major challenge in intensive care units, causing unacceptably high mortality rates due to the lack of rapid diagnostic tools with sufficient sensitivity. Therefore, there is an urgent need to replace time-consuming blood cultures with a new method. Ideally, such a method also provides comprehensive profiling of pathogenic bacteria to facilitate the treatment decision.<h4>Methods</h4>We developed a Random Forest with balanced subsampling to screen for pathogeni ...[more]

PMID: 31906978

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Project description:Sepsis is a common life-threatening disease in the intensive care unit (ICU) that is usually treated empirically without pathogen identification. As a non-invasive and high-throughput technology, plasma microbial cell-free DNA (mcfDNA) sequencing can detect unknown pathogens independent of previous clinical or laboratory information. In this study, a total of 199 cases suspected of bloodstream infection (BSI) from January 2020 to June 2020 were collected, and potential pathogens were detected by simultaneous blood culture and plasma mcfDNA sequencing. Other clinical microbiological assays were performed within 7 days of plasma mcfDNA sequencing, including smear, culture of samples taken from relevant infected sites, and β-D-glucan/galactomannan (BDG/GM) tests, among others. The diagnoses were classified as sepsis [94 (47.2%)], non-sepsis [87 (43.7%)], and non-infectious disease [18 (9.0%)]. The sensitivity and specificity of plasma mcfDNA sequencing for diagnosing sepsis were 68.1 and 63.2%, respectively, which were significantly better than those of blood culture, especially for the common bacteria that cause hospital-acquired infection, namely, Acinetobacter baumannii (p < 0.01) and Klebsiella pneumoniae (p < 0.01), and DNA viruses (plasma mcfDNA sequencing only, p < 0.01). However, there was no significant difference in the rate of positivity between plasma mcfDNA sequencing and blood culture for antibiotic-non-exposed cases (43.6 vs. 30.9%, p = 0.17). In the non-sepsis group, 44.8% of cases (13/29) detected only by plasma mcfDNA sequencing showed infections in other parts of the body, such as lower respiratory infection (LRI), intra-abdominal infection (IAI) and central nervous system infection (CNSI). For some common pathogens (not including anaerobes), turnaround time (TAT) 3 (TAT from the initiation of blood sample processing by nucleic acid extraction to the completion of sequencing analysis) was longer than TAT1 (TAT from blood culture bottles in Virtuo to off Virtuo). With disease progression, significant dynamic changes in microbial species were clearly detected by plasma mcfDNA sequencing.

Project description:Mammography is used to detect breast cancer (BC), but its sensitivity is limited, especially for dense breasts. Circulating cell-free DNA (cfDNA) methylation tests is expected to compensate for the deficiency of mammography. We derived a specific panel of markers based on computational analysis of the DNA methylation profiles from The Cancer Genome Atlas (TCGA). Through training (n = 160) and validation set (n = 69), we developed a diagnostic prediction model with 26 markers, which yielded a sensitivity of 89.37% and a specificity of 100% for differentiating malignant disease from normal lesions [AUROC = 0.9816 (95% CI: 96.09-100%), and AUPRC = 0.9704 (95% CI: 94.54-99.46%)]. A simplified 4-marker model including cg23035715, cg16304215, cg20072171, and cg21501525 had a similar diagnostic power [AUROC = 0.9796 (95% CI: 95.56-100%), and AUPRC = 0.9220 (95% CI: 91.02-94.37%)]. We found that a single cfDNA methylation marker, cg23035715, has a high diagnostic power [AUROC = 0.9395 (95% CI: 89.72-99.27%), and AUPRC = 0.9111 (95% CI: 88.45-93.76%)], with a sensitivity of 84.90% and a specificity of 93.88%. In an independent testing dataset (n = 104), the obtained diagnostic prediction model discriminated BC patients from normal controls with high accuracy [AUROC = 0.9449 (95% CI: 90.07-98.91%), and AUPRC = 0.8640 (95% CI: 82.82-89.98%)]. We compared the diagnostic power of cfDNA methylation and mammography. Our model yielded a sensitivity of 94.79% (95% CI: 78.72-97.87%) and a specificity of 98.70% (95% CI: 86.36-100%) for differentiating malignant disease from normal lesions [AUROC = 0.9815 (95% CI: 96.75-99.55%), and AUPRC = 0.9800 (95% CI: 96.6-99.4%)], with better diagnostic power and had better diagnostic power than that of using mammography [AUROC = 0.9315 (95% CI: 89.95-96.34%), and AUPRC = 0.9490 (95% CI: 91.7-98.1%)]. In addition, hypermethylation profiling provided insights into lymph node metastasis stratifications (p < 0.05). In conclusion, we developed and tested a cfDNA methylation model for BC diagnosis with better performance than mammography.

Dataset Information

Rapid diagnosis and comprehensive bacteria profiling of sepsis based on cell-free DNA.

Publications

Rapid diagnosis and comprehensive bacteria profiling of sepsis based on cell-free DNA.

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