Application of metagenomic next-generation sequencing in the diagnosis of pneumonia in patients with cancer

Application of metagenomic next-generation sequencing in the diagnosis of pneumonia in patients with cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Application of metagenomic next-generation sequencing in the diagnosis of pneumonia in patients with cancer Rong Qin, Chao Wang, Minghua Cong, Le Tian, Ning Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4909642/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background With the development of new sequencing technologies, metagenomic next-generation sequencing (mNGS) has become a diagnostic tool for respiratory tract infections. Patients with cancer may develop pneumonia caused by infections or antitumor therapy. Therefore, pneumonia in patients with cancer is more complex than that in healthy individuals. Currently, few reports are available on the use of mNGS for diagnosing pneumonia in patients with cancer. Methods In this retrospective study, 14 patients with cancer diagnosed with pneumonia in March 2023 were enrolled from the Emergency Department of the Chinese Academy of Medical Sciences Cancer Hospital. Sputum samples from the patients were examined using conventional tests and mNGS to identify pathogens. The mNGS and conventional test results were compared to assess the diagnostic yield and value of mNGS in improving the prognosis of pneumonia in patients with cancer. Results mNGS was more sensitive than conventional tests (sputum culture [SC] and polymerase chain reaction [PCR]) for detecting pathogens. The results were positive in 12/14 samples (86%) using mNGS compared with 8/14 samples (57%) using conventional testing. Compared with conventional tests, mNGS detected additional pathogens in 8 specimens. In 9/14 samples (64%), mNGS detected more pathogens than conventional testing. In nine patients (64%), the diagnosis was changed, and the antimicrobial regimen was adjusted based on the mNGS results. mNGS detected antibiotic resistance genes in five patients, which provided guidance for antibiotic selection. Conclusions mNGS is a promising technology for detecting pneumonia pathogens in patients with cancer and improves the diagnostic yield and prognosis. mNGS can be used to aid in early diagnosis and guide treatment of pneumonia in patients with cancer. Cancer Pneumonia Metagenomic next-generation sequencing Conventional tests Diagnosis Antimicrobial resistance Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Patients with cancer have an increased risk of pneumonia and poor prognosis due to systemic immunosuppression from the malignancy and cancer treatments, such as chemotherapy and surgery [ 1 ] . In patients with cancer, the incidence of pneumonia is further following immunotherapy or radiotherapy [ 2 , 3 ] . Approximately 10% of hospital admissions of patients with cancer are due to or complicated by pneumonia, particularly in patients with hematologic malignancies, in which the risk of pneumonia during treatment is estimated to be over 30% [ 4 – 6 ] . Pneumonia in patients with cancer is relatively more complicated and is more likely to involve mixed infections with multiple pathogens and the emergence of uncommon drug-resistant organisms [ 7 , 8 ] . Consequently, the incidence of severe pneumonia and the pneumonia case fatality rates are higher in patients with cancer than in other patients with pneumonia. In addition, the occurrence of immunotherapy- and radiotherapy-associated pneumonia increases the difficulty in diagnosing infectious pneumonia in patients with cancer, affecting the choice of antimicrobial agents and prognosis. Pneumonia can be caused by a variety of pathogens, including bacteria, viruses, mycoplasma, and fungi, which are difficult to differentiate clinically. Traditional pathogen detection methods, such as bacterial and fungal smears and cultures, PCR, and antigen detection, are time-consuming and inefficient. The cause of community-acquired pneumonia remains undetermined in up to 62% of cases using a combination of traditional diagnostic tests [ 9 ] . When traditional testing methods show negative results, patients are often administered empirical antibiotics, which can lead to exacerbation of the infection and misuse of broad-spectrum antibiotics. Early and targeted antimicrobial treatment can reduce mortality from pneumonia [ 10 ] . mNGS is a new tool that may overcome the shortcomings of traditional diagnostic methods [ 11 , 12 ] . mNGS directly sequences all nucleic acid fragments in samples to simultaneously identify all potentially infectious microorganisms. In addition to pathogen identification, mNGS provides genomic information necessary for airway microbiome analysis, human host response analysis, and drug resistance prediction [ 13 ] . mNGS also plays a critical role in the diagnosis of pneumonia caused by difficult-to-identify pathogens and pneumonia caused by multiple pathogens [ 14 ] . In this study, we aimed to assess the value of mNGS in the diagnosis of pneumonia in patients with cancer and differentiation between infectious pneumonia and pneumonia associated with anticancer therapy. Methods Study design and patient cohort In this retrospective study, data of 14 patients with cancer admitted to the Emergency Department of the Cancer Hospital of the Chinese Academy of Medical Sciences in March 2023 with pneumonia were analyzed. The cancer type and stage, and history of antitumor therapy was recorded for each patient. Pneumonia was diagnosed based on the clinical presentation, blood tests, microbiological tests, and chest computed tomography (CT). The study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Medical Ethical Committee of the Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College(reference number:24/351–4631). All participants were informed of the purpose of the study and provided signed informed consent. All methods were performed in accordance with relevant guidelines and regulations. Clinical presentation The presentation of pneumonia was characterized by acute onset of lower respiratory symptoms, such as fever, cough, pleurisy, dyspnea, and increased sputum production, with consistent radiographic imaging findings. All patients had cough and sputum production. Imaging Radiographic imaging features of lung parenchymal involvement are the gold standard for the diagnosis of pneumonia. All patients underwent chest CT imaging. Blood tests All patients underwent routine blood tests and measurement of their blood C-reactive protein and procalcitonin levels. Microbiological testing Specimens for conventional tests and mNGS were collected prior to initiating antimicrobial theraphy. The conventional tests included sputum bacterial and fungal smear and culture, and PCR tests for respiratory pathogens, including influenza A, influenza B, respiratory syncytial virus, parainfluenza virus, rhinovirus, metapneumovirus, adenovirus, bocavirus, Mycoplasma pneumoniae , and Chlamydia pneumoniae . All patient samples underwent sputum bacterial and fungal smear and culture. In addition, samples from two patients underwent PCR testing. For mNGS analysis, 1–4 mL of sputum sample was liquefied using 0.1% dithiothreitol (DTT) at room temperature for 30 min. After receiving the samples, the laboratory performed nucleic acid extraction, library construction, high-throughput sequencing, bioinformatics analysis, and pathogen data interpretation based on previous studies [ 15 ] . DNA was extracted from the samples using the QIAamp DNA Microbiome Kit (Cat#51704, Qiagen, Hilden, Germany), and RNA was extracted using the QIAamp Viral RNA Mini Kit (Cat#52904, Qiagen). The extracted RNA was reverse transcribed using random primers, and cDNA was pooled with DNA from the same clinical sample for sequencing library preparation. The pooled nucleic acid was enzymatically fragmented to a size of 200–300 bp, and sequencing libraries were constructed through end repair, adapter ligation, and PCR amplification. Sequencing templates were prepared using the OneTouch2 System (Life Technologies, Carlsbad, CA, USA) and sequenced using a BioelectronSeq 4000 sequencer (CapitalBio Corporation, Beijing, China) after quality control. A negative control water sample was used in each run to monitor for potential contamination. The original sequencing data were subjected to quality control, and reads with lengths of less than 50 bp, low-quality, or low complexity were removed. The remaining high-quality sequencing data were mapped to the human reference genome grch38 to deplete the human host sequences using Bowtie2 software. Subsequently, the non-human sequences were classified by simultaneous alignment to the genomic sequence databases downloaded from the US National Center for Biotechnology Information (NCBI) and Pathosystems Resource Integration Center (PATRIC) databases, which contained data of 13992 bacterial species, 1659 fungal species, 13000 virus species, and 287 parasite pathogens. To identify the suspected pathogens in clinical samples, the data of different types of samples from healthy people was reviewed and the relevant reference values were calculated, including the number of reads and coverage of all bacteria, fungi, viruses, and parasites detected. Pathogens detected in the negative control samples were excluded from the results of clinical samples. The final pathogen detection results included a list of suspected pathogens, the number of reads, and genome-level coverage statistics. Clinical treatment All patients were initially treated with empirical antimicrobial therapy according to the Chinese Adult Community-Acquired Pneumonia Diagnosis and Treatment Guide [ 16 ] . In patients with suspected radiation pneumonitis, the initial treatment was based on the Chinese expert consensus on the diagnosis and treatment of radiation pneumonitis [ 17 ] . The antimicrobial treatment regimen was adjusted according to the pathogens detected by conventional tests and mNGS. The assessment of effectiveness was based on the improvement in clinical manifestations and was assessed as effective or ineffective. Results Patient characteristics The 14 patients included 9 males and 5 females, with a mean age of 65 years (range, 49–84 years). Among the 14 patients, 10 had lung cancer, 2 had esophageal cancer, 1 had gastric cancer, and 1 had lymphoma (Table 1 ). 11 patients had received antitumor therapy such as surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy before the onset of pneumonia. The clinical manifestations, blood test results, and chest CT findings of the patients are shown in Table 1 . Table 1 Clinical characteristics of the patients enrolled in this study. Patient Age Gender Cancer type Stage of cancer Anticancer treatment before enrollment Body temperature Phlegm color CRP concentration PCT concentration WBC N% CT #1 58 M esophageal cancer III radiotherapy fever yellow high normal high high patchy #2 67 M gastric cancer IV none fever white high high high high patchy #3 55 M lung cancer III none fever white normal normal high high patchy #4 69 F esophageal cancer III radiotherapy fever white high normal high high patchy #5 64 M lung cancer I lung surgery fever white high high low normal patchy #6 67 M lung cancer IV chemotherapy and immunotherapy fever white high normal low high patchy #7 74 M lung cancer IV none fever yellow high high high high patchy #8 67 F lymphoma IV chemotherapy and targeted therapy fever yellow high normal low low patchy #9 66 F lung cancer II chemotherapy and immunotherapy normal white high normal high high patchy #10 59 M lung cancer III chemotherapy and immunotherapy normal white high normal low normal patchy #11 84 M lung cancer II radiotherapy normal white high normal high high patchy #12 49 F lung cancer IV chemotherapy and targeted therapy normal white high high high high patchy #13 57 M lung cancer III radiotherapy normal white normal normal normal normal patchy #14 75 F lung cancer IV chemotherapy and immunotherapy normal white normal normal normal normal patchy CRP: C-reactive protein; PCT: Procalcitonin; WBC: White blood cell; N%: Neutrophil percentage; CT: Computed Tomography. Normal range: CRP 0.0-0.6mg/dl; PCT: <0.5ng/ml; WBC: 3.5–9.5×10^/L; N%: 40.0–75.0%. Comparison of mNGS and conventional microbiological test results Comparison of pathogens detected by mNGS and conventional tests The pathogen positivity rate for the mNGS method was 86% (12/14) compared with 57% (8/14) for the combined results of conventional tests (sputum culture and PCR) (Table 2 ). The mNGS assay detected more pathogens than conventional assays (Fig. 1 ). As shown in Table 2 , 3 patient samples (21%) had completely different pathogens detected using mNGS and sputum culture. Eight patient samples (57%) had more pathogens detected with mNGS than with sputum culture, whereas 2 patient samples (14%) had identical results with both methods. Table 2 Pathogens detected in patient samples by different methods. Patient Sputum culture PCR mNGS Bacterium Fungus Virus Bacterium Fungus Virus #1 Klebsiella pneumoniae Candida albicans not tested Bilophila wadsworthia negative Human Herpesvirus 4 (EB virus) Human Herpesvirus 1 #2 negative Candida albicans not tested Staphylococcus epidermidis Bacteroides heparinolyticus Candida albicans Candida glabrata negative #3 negative Flavus Influenza A Streptococcus pneumoniae Saccharomyces cerevisiae Influenza A Human Herpesvirus 4 (EB virus) #4 negative negative not tested negative negative negative #5 Chryseobacterium indologenes negative not tested negative negative PBV virus #6 negative negative not tested Streptococcus pneumoniae negative Human Herpesvirus 4 #7 Stenotrophomonas maltophilia Staphylococcus haemolyticus negative not tested Klebsiella pneumoniae negative Papillomavirus type 8 #8 negative Candida albicans Influenza A Gordona bronchialis Candida albicans Candida parapsilosis Influenza A virus #9 negative Candida albicans not tested negative Candida albicans Human Herpesvirus 4 (EB virus) #10 negative negative not tested negative negative Molluscum contangiosum virus PBV virus #11 negative negative not tested negative negative negative #12 not tested not tested not tested Sphingomonas paucimobilis Acinetobacter baumannii Loprene Gordense Lactobacillus rhamnosus Candida albicans Influenza A virus Human Herpesvirus 4 (EB virus) #13 negative negative not tested Streptococcus pneumoniae Staphylococcus epidermidis negative negative #14 Pseudomonas aeruginosa negative not tested Streptococcus pneumoniae Pseudomonas aeruginosa negative negative Table 3 The adjustment of antimicrobial drug regimen based on the mNGS results. Patient Treatment before mNGS Treatment after mNGS #1 Meropenem Meropenem and Metronidazole #2 none Ceftriaxone #3 none Oseltamivir #7 Meropenem Sulbactam and Cefoperazone #8 Meropenem and Fluconazole Meropenem,Vancomycin,Voriconazole and Oseltamivir #9 Glucocorticoid Antifungal drug(specific unknown) in other hospitals #11 Ceftriaxone Glucocorticoid #12 Moxifloxacin Sulbactam and Cefoperazone,Fluconazole,Oseltamivir and Allicin #14 Levofloxacin Piperacillin and Tazobactam Table 4 Pathogens, ARGs and DRAs obtained from mNGS results. Patient Pathogens detected by mNGS ARGs detected by mNGS DRA #2 Staphylococcus epidermidis qacA Quaternary ammonium compounds msr(A) Macrolides blaR1 B-lactam dfrC Trimethoprim #3 Streptococcus pneumoniae msr(D) Macrolides #6 Streptococcus pneumoniae msr(D) Macrolides #13 Streptococcus pneumoniae msr(D) Macrolides #14 Streptococcus pneumoniae Pseudomonas aeruginosa msr(D) Macrolides tet(M) Tetracylines catB7 Chloramphenicol ARGs, antibiotic resistance genes. DRAs, drug-resistant antibiotic We analyzed the consistency of the mNGS results with those of pathogens identified using conventional microbiological methods (sputum culture and PCR). When mNGS identified the same pathogens as conventional tests, the results were considered to match. When mNGS identified more pathogens than conventional tests, the results were considered inconclusive. When the pathogens identified using the two methods were completely different, the results were considered mismatched. The pathogens identified were matched, inconclusive, and mismatched in two (14%), nine (64%), and three (21%) patients, respectively (Fig. 2 ). The sensitivity of mNGS and conventional tests for detecting co-infecting pathogens was compared. Conventional tests detected four cases of single pathogens, four cases of two pathogens, and no cases of three or more pathogens (Fig. 3 A). In contrast, mNGS detected one case of a single pathogen, six cases of two pathogens, and five cases of three or more pathogens (Fig. 3 B). mNGS and prediction of drug resistance Traditionally, antibiotic resistance prediction has relied on culture phenotyping and molecular testing [ 18 ] . mNGS detects antibiotic resistance genes (ARGs). In this study, mNGS detected seven ARGs in five patients, including more than one ARG in Patients #2 and #14 (Table 3 ). The ARGs results provided guidance for antibiotic selection, and the patients’ pneumonia improved. The mNGS results were notable for their ability to predict antibiotic resistance in patients with pneumonia. Outcomes of pneumonia in patients with cancer In 9 of the 14 patients (64%), the diagnosis of the cause of the pneumonia was changed and the antimicrobial drug regimen was adjusted based on the mNGS results (Table 3 ). Among these 9 patients, other antimicrobial drugs were added or switched in 4 patients; the dose of antimicrobial drugs was reduced and steroid was added in 1 patient owing to a negative mNGS result and a diagnosis of radiation pneumonitis; antifungal drugs were added in 1 patient; antiviral drugs were added in 1 patient; and antibacterial, antifungal, and antiviral drugs were added in 2 patients (Table 3 and Fig. 4 A, B). Eight of the nine patients showed improvement in their pneumonia, except for Patient #9, who showed no improvement after the addition of antifungal drugs, but subsequently improved after receiving hormonal therapy for immune pneumonia. All 14 patients’ pneumonia eventually improved, but two patients with advanced tumors subsequently died due to tumor progression. Discussion This retrospective study reported the use of mNGS for the diagnosis of pneumonia in patients with cancer and compared it with conventional laboratory tests. First, the sensitivity of mNGS for pathogen identification was significantly higher than that of conventional tests, especially for pathogens that are difficult to culture and require prolonged incubation. Second, mNGS requires less time to obtain results (≤ 30 hours) compared with sputum culture (usually 3–5 days). In addition, mNGS was more effective than conventional tests at detecting coinfecting pathogens. Finally, mNGS had the advantage of predicting antibiotic resistance. Compared with traditional assays, mNGS is an unbiased method for detecting all potentially infectious pathogens in a sample [ 13 ] . Previous studies have shown that mNGS has adequate accuracy and a significantly higher sensitivity for detecting pathogens [ 19 , 20 ] . Moreover, mNGS is less affected by prior antibiotic exposure [ 15 ] . In addition to pathogen identification, mNGS provides clinical microbiome analysis, human host response analysis and drug resistance prediction [ 13 ] . Therefore, it is valuable in the identification of pathogens causing pneumonia, particularly in cases of unexplained or mixed infection [ 14 ] . Immunocompromised patients with cancer are more susceptible to severe pneumonia, mixed infection, and pneumonia caused by pathogens that are difficult to detect using conventional tests [ 1 , 4 , 5 ] . Therefore, mNGS may be a crucial method for identifying pneumonia pathogens in patients with cancer. This retrospective study confirmed this finding. In terms of bacteria, mNGS detected additional Streptococcus pneumoniae , Klebsiella pneumoniae , Staphylococcus epidermidis , Acinetobacter baumannii , Bilophila wadsworthia , Bacteroides heparinolyticus , Gordona bronchialis , Sphingomonas spp., Gordonia polyisoprenivorans , and Ralstonia mannitolilytica , which are difficult to identify using conventional tests. In terms of viruses, mNGS detected additional Epstein-Barr virus (EBV) in Patients #1, #3, #6, #9, and #12; human herpesvirus 1 in Patient #1; small double-stranded RNA virus in Patients #5 and #10; influenza A virus in Patient #12; human papillomavirus 8 in Patient #7; and molluscum contagiosum virus in Patient #10. In terms of fungi, fungal culture of Patient #3 yielded only Aspergillus flavus , whereas mNGS identified Saccharomyces cerevisiae . mNGS also detected Candida glabrata in Patient #2, Candida parapsilosis in Patient #8, and Candida albicans in Patient #12. The results showed that mNGS can rapidly detect more pathogens, regardless of the type of infection. This could help guide timely antibiotic adjustment and improve the prognosis of pneumonia in patients with cancer. In our study, the most common pathogens detected by mNGS were Streptococcus pneumoniae and influenza A virus, both of which are common in non-cancer patients with pneumonia. Klebsiella pneumoniae , Acinetobacter baumannii and Pseudomonas aeruginosa are common pathogens that cause nosocomial infections in the general population [ 21 – 24 ] . In addition, mNGS detected a variety of uncommon pathogens, such as Bilophila wadsworthia spp., Bacteroides heparinolyticus , Gordona bronchialis , Sphingomonas spp., Gordonia polyisoprenivorans , Ralstonia mannitolilytica , and small double-stranded RNA virus. Pathogens in patients with cancer may differ from those in healthy individuals. Our study showed that mNGS can detect several opportunistic pathogens that are difficult to detect using conventional tests, such as opportunistic bacteria (e.g., Bilophila wadsworthia spp., Klebsiella pneumoniae, Leptospira hepatica ) and latent viruses (e.g., EBV). These pathogens are relatively harmless to healthy individuals but may cause infection in immunocompromised patients [ 25 – 27 ] . Our study confirmed that patients with cancer are susceptible to infection by these pathogens, possibly because of their immunocompromised status and frequent hospital treatments. Owing to the characteristics of opportunistic pathogens, it is necessary to determine whether they are pathogenic. In our study, the condition of Patient #1, who had Bilophila wadsworthia spp. detected on mNGS, improved after metronidazole administration. Similarly, the condition of Patient #7, who had Klebsiella pneumoniae detected on mNGS, improved after switching to Cefoperazone Sodium and Sulbactam Sodium. Although mNGS detected EBV in 5 patients, it was considered non-pathogenic because more than 90% of adults are latently infected with EBV during their lives, and these patients had no symptoms of associated viral infections [ 28 ] . In addition, owing to the large number of colonizing microorganisms in the respiratory tract, distinguishing between infection and colonization may be challenging. In our study, mNGS detected Candida albicans in Patient #9, whose symptoms did not improve with the addition of antifungal medication but improved with steroid therapy following a diagnosis of immune checkpoint inhibitor-associated pneumonitis [ 24 ] . Therefore, we considered Candida albicans to be a respiratory colonizer. However, clinicians should be aware of the possibility of fungal infections after long-term steroid therapy. In general, mNGS distinguishes between infection and colonization using quantitative or semi-quantitative statistical analyses. One study developed rule-based and logistic regression models to differentiate between lower respiratory tract infections and colonization, both of which had an accuracy of 95.5% in the validation cohort [ 29 ] . However, further studies on mNGS are required to better differentiate between infection and colonization. We suggest combining the patient's clinical presentation, blood test results, imaging findings, and empirical treatment outcomes to determine whether an opportunistic pathogen is pathogenic. Pneumonia is more complicated to diagnose and treat in patients with cancer, especially in those who have received immunotherapy or radiotherapy [ 3 , 30 ] . It is sometimes difficult to distinguish between immunological or radiological pneumonia and lung infection. Whereas traditional tests have the drawbacks of false-negative results and long culture times, mNGS can shorten the time to diagnosis and help with rapid decision-making regarding treatment options. In our study, Patient #11, who was diagnosed with radiation pneumonitis but had a negative mNGS result, showed rapid clinical improvement after the discontinuation of antibiotics and treatment with steroid therapy alone, which also prevented antibiotic abuse and flora destruction, as radiation pneumonitis tends to be complicated by fungal infections in the presence of prolonged steroid use [ 31 , 32 ] . In this study, mNGS detected five ARGs and predicted pathogen resistance to guide clinical treatment. Compared with sputum culture, mNGS can detect drug resistance more rapidly and comprehensively. Consistent with previous studies [ 19 , 33 ] , mNGS was able to predict drug resistance in slow-growing or unculturable pathogens, and detect inactive pathogens after antibiotic treatment. Patients with hospital-onset lower respiratory tract infections (LRTI) have a higher burden of ARGs in their respiratory microbiome than patients with community-onset LRTIs [ 34 ] . By identifying ARGs, mNGS may help in the early identification of future secondary lung infections [ 35 ] . The mNGS test is particularly indicated for tumor patients with severe or complicated pneumonia, as well as for tumor patients whose etiology is unknown on conventional examination, and for whom empirical treatment has not been effective [ 36 , 37 ] . This study has some limitations. First, it was a retrospective study with a small sample size, which may limit the generalizability of the results. Second, the mNGS specimens were sent to a commercial laboratory rather than a hospital microbiology laboratory, which may have reduced the sensitivity because the increased turnaround time reduced pathogen viability. In addition, mNGS is expensive and not currently covered by health insurance in China. This may have contributed to the bias in patient selection in this study. Prospective clinical studies are essential to investigate when to perform mNGS and to compare the diagnostic accuracy of mNGS with other methods. Conclusions mNGS can identify pathogens more rapidly and comprehensively than conventional methods. It is a valuable tool for the diagnosis, and treatment of pneumonia in patients with cancer. However, prospective studies are required to demonstrate the clinical utility of mNGS in cancer patients with pneumonia. Declarations Ethical approval and consent The study was planed and conducted in accordance with the principles of the Declaration of Helsinki, and was approved by the Medical Ethical Committee of the Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College(reference number:24/351-4631). All participants were informed of the purpose of this research and signed informed consent. All methods were carried out in accordance with relevant guidelines and regulations. Funding The research was supported by the Beijing Vlove Foundation. Competing interests The authors have no relevant financial or non-financial interests to disclose. Author Contribution Ning Li conceived and designed the study. Ning Li and Chao Wang did the data collection. Ning Li and Rong Qin did the data analysis and drafted the initial manuscript. Ming-hua Cong gave many valuable comments on the draft and polished it. All authors assisted with the interpretation of the findings, commented on drafts of the manuscript, and approved the final version. References Wong JL, Evans SE. Bacterial Pneumonia in Patients with Cancer: Novel Risk Factors and Management. Clin Chest Med. 2017 Jun;38(2):263-277. doi: 10.1016/j.ccm.2016.12.005. Wu J, Hong D, Zhang X, Lu X, Mia o J.PD-1 inhibitors increase the incidence and risk of pneumonitis in cancer patients in a dose-independent manner: a meta-analysis. Sci Rep. 2017 Mar 8;7:44173. doi: 10.1038/srep44173. 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Youssef J, Novosad SA, Winthrop KL. Infection Risk and Safety of Corticosteroid Use. Rheum Dis Clin North Am. 2016 Feb;42(1):157-76, ix-x. doi: 10.1016/j.rdc.2015.08.004. Wang K, Li P, Lin Y, Chen H, Yang L, Li J, Zhang T, Chen Q, Li Z, Du X, Zhou Y, Li P, Wang H, Song H.Metagenomic Diagnosis for a Culture-Negative Sample From a Patient With Severe Pneumonia by Nanopore and Next-Generation Sequencing. Front Cell Infect Microbiol. 2020;10:182. doi: 10.3389/fcimb.2020.00182. Serpa PH, Deng X, Abdelghany M, et al. Metagenomic prediction of antimicrobial resistance in critically ill patients with lower respiratory tract infections. Genome Med. 2022 Jul 12;14(1):74. doi: 10.1186/s13073-022-01072-4. Charalampous T, Alcolea-Medina A, Snell LB, et al. Evaluating the potential for respiratory metagenomics to improve treatment of secondary infection and detection of nosocomial transmission on expanded COVID-19 intensive care units. Genome Med. 2021;13:182. doi: 10.1186/s13073-021-00991-y. Chen H, Bai X, Gao Y, Liu W, Yao X, Wang J. Profile of Bacteria with ARGs Among Real-World Samples from ICU Admission Patients with Pulmonary Infection Revealed by Metagenomic NGS. Infect Drug Resist. 2021 Nov 27;14:4993-5004. doi: 10.2147/IDR.S335864. Lu H, Ma L, Zhang H, Feng L, Yu Y, Zhao Y, Li L, Zhou Y, Song L, Li W, Zhao J, Liu L. The Comparison of Metagenomic Next-Generation Sequencing with Conventional Microbiological Tests for Identification of Pathogens and Antibiotic Resistance Genes in Infectious Diseases. Infect Drug Resist. 2022 Oct 22;15:6115-6128. doi: 10.2147/IDR.S370964. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4909642","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":342891385,"identity":"68d55092-6fbf-40ea-a8ab-9f9ba8a21289","order_by":0,"name":"Rong Qin","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Qin","suffix":""},{"id":342891386,"identity":"ba9b25cb-6f24-476b-8c73-069262051487","order_by":1,"name":"Chao Wang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Wang","suffix":""},{"id":342891388,"identity":"f091b314-263e-4475-a3b6-dab1d79578a4","order_by":2,"name":"Minghua Cong","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Minghua","middleName":"","lastName":"Cong","suffix":""},{"id":342891390,"identity":"f02333fc-766d-4593-8843-c6ae4ef6c708","order_by":3,"name":"Le Tian","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Le","middleName":"","lastName":"Tian","suffix":""},{"id":342891392,"identity":"0791eb49-337c-4fef-8a1f-dbed2ead8627","order_by":4,"name":"Ning Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYFACHhCSkGNgJlGLjTHJWtISG4jWoNt/9vCLNwyH0+e38x78wFBjE01Qi9mBc2mWcxgO5244zJcswXAsLZegdWYHe8yMeUBamHkMJBgbDhOh5TAPWEu6fDOP8Q/itBzjMX4M9H4CA1Avkbac4UtjnMNgY7gBqMUigSi/nD97+MMbBgl5+f4zxjc+1NgQ1gIEbBKM/6DMBCKUgwDzByIVjoJRMApGwUgFANslOiSVtfYoAAAAAElFTkSuQmCC","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-08-13 23:11:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4909642/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4909642/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66631881,"identity":"a6c924ec-7dc9-442d-86f7-6569852a0371","added_by":"auto","created_at":"2024-10-15 04:55:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":579052,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of pathogens detected by mNGS and conventional tests in all 14 patients.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4909642/v1/d3ce946e41c10750e5157f78.png"},{"id":66631870,"identity":"a01ce3c2-fe4f-406f-927d-dd24ff05b797","added_by":"auto","created_at":"2024-10-15 04:55:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":185542,"visible":true,"origin":"","legend":"\u003cp\u003eConsistency of mNGS results with conventional tests results.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4909642/v1/ee0fda1440baacb4f7241c5a.png"},{"id":66632021,"identity":"cf471c34-08c9-4026-9a59-d9c68dc7c891","added_by":"auto","created_at":"2024-10-15 04:55:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":293854,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Number of patients with different number of pathogen types detected by conventional tests (SC or PCR). (b) Number of patients with different number of pathogen types detected by mNGS.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4909642/v1/8647f48eaa7302e7a8caaceb.png"},{"id":66631871,"identity":"c2d57c85-4369-4719-a52d-494c1e2805d5","added_by":"auto","created_at":"2024-10-15 04:55:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":335264,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Number of patients adjusted for antimicrobial agents based on mNGS results. (b) Percentage of antimicrobial modification types based on mNGS results.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4909642/v1/3ead39cc0cbe9ab5e7346ff6.png"},{"id":66633002,"identity":"73b01239-0aaa-4f58-8fd1-de925fc92cd7","added_by":"auto","created_at":"2024-10-15 05:03:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2031879,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4909642/v1/521184f2-87bd-4259-bad9-4bffc9fb762d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of metagenomic next-generation sequencing in the diagnosis of pneumonia in patients with cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePatients with cancer have an increased risk of pneumonia and poor prognosis due to systemic immunosuppression from the malignancy and cancer treatments, such as chemotherapy and surgery\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In patients with cancer, the incidence of pneumonia is further following immunotherapy or radiotherapy\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Approximately 10% of hospital admissions of patients with cancer are due to or complicated by pneumonia, particularly in patients with hematologic malignancies, in which the risk of pneumonia during treatment is estimated to be over 30%\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Pneumonia in patients with cancer is relatively more complicated and is more likely to involve mixed infections with multiple pathogens and the emergence of uncommon drug-resistant organisms\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Consequently, the incidence of severe pneumonia and the pneumonia case fatality rates are higher in patients with cancer than in other patients with pneumonia. In addition, the occurrence of immunotherapy- and radiotherapy-associated pneumonia increases the difficulty in diagnosing infectious pneumonia in patients with cancer, affecting the choice of antimicrobial agents and prognosis.\u003c/p\u003e \u003cp\u003ePneumonia can be caused by a variety of pathogens, including bacteria, viruses, mycoplasma, and fungi, which are difficult to differentiate clinically. Traditional pathogen detection methods, such as bacterial and fungal smears and cultures, PCR, and antigen detection, are time-consuming and inefficient. The cause of community-acquired pneumonia remains undetermined in up to 62% of cases using a combination of traditional diagnostic tests\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. When traditional testing methods show negative results, patients are often administered empirical antibiotics, which can lead to exacerbation of the infection and misuse of broad-spectrum antibiotics. Early and targeted antimicrobial treatment can reduce mortality from pneumonia\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003emNGS is a new tool that may overcome the shortcomings of traditional diagnostic methods\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. mNGS directly sequences all nucleic acid fragments in samples to simultaneously identify all potentially infectious microorganisms. In addition to pathogen identification, mNGS provides genomic information necessary for airway microbiome analysis, human host response analysis, and drug resistance prediction\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. mNGS also plays a critical role in the diagnosis of pneumonia caused by difficult-to-identify pathogens and pneumonia caused by multiple pathogens\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. In this study, we aimed to assess the value of mNGS in the diagnosis of pneumonia in patients with cancer and differentiation between infectious pneumonia and pneumonia associated with anticancer therapy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patient cohort\u003c/h2\u003e \u003cp\u003eIn this retrospective study, data of 14 patients with cancer admitted to the Emergency Department of the Cancer Hospital of the Chinese Academy of Medical Sciences in March 2023 with pneumonia were analyzed. The cancer type and stage, and history of antitumor therapy was recorded for each patient. Pneumonia was diagnosed based on the clinical presentation, blood tests, microbiological tests, and chest computed tomography (CT). The study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Medical Ethical Committee of the Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College(reference number:24/351\u0026ndash;4631). All participants were informed of the purpose of the study and provided signed informed consent. All methods were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinical presentation\u003c/h2\u003e \u003cp\u003eThe presentation of pneumonia was characterized by acute onset of lower respiratory symptoms, such as fever, cough, pleurisy, dyspnea, and increased sputum production, with consistent radiographic imaging findings. All patients had cough and sputum production.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eImaging\u003c/h2\u003e \u003cp\u003eRadiographic imaging features of lung parenchymal involvement are the gold standard for the diagnosis of pneumonia. All patients underwent chest CT imaging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBlood tests\u003c/h2\u003e \u003cp\u003eAll patients underwent routine blood tests and measurement of their blood C-reactive protein and procalcitonin levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiological testing\u003c/h2\u003e \u003cp\u003eSpecimens for conventional tests and mNGS were collected prior to initiating antimicrobial theraphy. The conventional tests included sputum bacterial and fungal smear and culture, and PCR tests for respiratory pathogens, including influenza A, influenza B, respiratory syncytial virus, parainfluenza virus, rhinovirus, metapneumovirus, adenovirus, bocavirus, \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e, and \u003cem\u003eChlamydia pneumoniae\u003c/em\u003e. All patient samples underwent sputum bacterial and fungal smear and culture. In addition, samples from two patients underwent PCR testing.\u003c/p\u003e \u003cp\u003eFor mNGS analysis, 1\u0026ndash;4 mL of sputum sample was liquefied using 0.1% dithiothreitol (DTT) at room temperature for 30 min. After receiving the samples, the laboratory performed nucleic acid extraction, library construction, high-throughput sequencing, bioinformatics analysis, and pathogen data interpretation based on previous studies\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. DNA was extracted from the samples using the QIAamp DNA Microbiome Kit (Cat#51704, Qiagen, Hilden, Germany), and RNA was extracted using the QIAamp Viral RNA Mini Kit (Cat#52904, Qiagen). The extracted RNA was reverse transcribed using random primers, and cDNA was pooled with DNA from the same clinical sample for sequencing library preparation. The pooled nucleic acid was enzymatically fragmented to a size of 200\u0026ndash;300 bp, and sequencing libraries were constructed through end repair, adapter ligation, and PCR amplification. Sequencing templates were prepared using the OneTouch2 System (Life Technologies, Carlsbad, CA, USA) and sequenced using a BioelectronSeq 4000 sequencer (CapitalBio Corporation, Beijing, China) after quality control. A negative control water sample was used in each run to monitor for potential contamination. The original sequencing data were subjected to quality control, and reads with lengths of less than 50 bp, low-quality, or low complexity were removed. The remaining high-quality sequencing data were mapped to the human reference genome grch38 to deplete the human host sequences using Bowtie2 software. Subsequently, the non-human sequences were classified by simultaneous alignment to the genomic sequence databases downloaded from the US National Center for Biotechnology Information (NCBI) and Pathosystems Resource Integration Center (PATRIC) databases, which contained data of 13992 bacterial species, 1659 fungal species, 13000 virus species, and 287 parasite pathogens. To identify the suspected pathogens in clinical samples, the data of different types of samples from healthy people was reviewed and the relevant reference values were calculated, including the number of reads and coverage of all bacteria, fungi, viruses, and parasites detected. Pathogens detected in the negative control samples were excluded from the results of clinical samples. The final pathogen detection results included a list of suspected pathogens, the number of reads, and genome-level coverage statistics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical treatment\u003c/h2\u003e \u003cp\u003eAll patients were initially treated with empirical antimicrobial therapy according to the Chinese Adult Community-Acquired Pneumonia Diagnosis and Treatment Guide\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. In patients with suspected radiation pneumonitis, the initial treatment was based on the Chinese expert consensus on the diagnosis and treatment of radiation pneumonitis\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The antimicrobial treatment regimen was adjusted according to the pathogens detected by conventional tests and mNGS. The assessment of effectiveness was based on the improvement in clinical manifestations and was assessed as effective or ineffective.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eThe 14 patients included 9 males and 5 females, with a mean age of 65 years (range, 49\u0026ndash;84 years). Among the 14 patients, 10 had lung cancer, 2 had esophageal cancer, 1 had gastric cancer, and 1 had lymphoma (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). 11 patients had received antitumor therapy such as surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy before the onset of pneumonia. The clinical manifestations, blood test results, and chest CT findings of the patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of the patients enrolled in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCancer type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStage of cancer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAnticancer treatment before enrollment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBody temperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePhlegm color\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCRP concentration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePCT concentration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eN%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eesophageal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eradiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003efever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eyellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003epatchy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003egastric cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003efever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003epatchy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003efever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003epatchy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eesophageal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eradiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003efever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003epatchy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003elung surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003efever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003epatchy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003echemotherapy and immunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003efever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003epatchy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003efever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eyellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003epatchy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003echemotherapy and targeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003efever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eyellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003epatchy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003echemotherapy and immunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003epatchy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003echemotherapy and immunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003epatchy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eradiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003epatchy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003echemotherapy and targeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003epatchy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eradiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003epatchy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003echemotherapy and immunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003epatchy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eCRP: C-reactive protein; PCT: Procalcitonin; WBC: White blood cell; N%: Neutrophil percentage; CT: Computed Tomography.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eNormal range: CRP 0.0-0.6mg/dl; PCT: \u0026lt;0.5ng/ml; WBC: 3.5\u0026ndash;9.5\u0026times;10^/L; N%: 40.0\u0026ndash;75.0%.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison of mNGS and conventional microbiological test results\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eComparison of pathogens detected by mNGS and conventional tests\u003c/h2\u003e \u003cp\u003eThe pathogen positivity rate for the mNGS method was 86% (12/14) compared with 57% (8/14) for the combined results of conventional tests (sputum culture and PCR) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The mNGS assay detected more pathogens than conventional assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e patient samples (21%) had completely different pathogens detected using mNGS and sputum culture. Eight patient samples (57%) had more pathogens detected with mNGS than with sputum culture, whereas 2 patient samples (14%) had identical results with both methods.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePathogens detected in patient samples by different methods.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSputum culture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003emNGS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBacterium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFungus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVirus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBacterium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFungus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVirus\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBilophila wadsworthia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHuman Herpesvirus 4 (EB virus)\u003c/p\u003e \u003cp\u003eHuman Herpesvirus 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eBacteroides heparinolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCandida glabrata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFlavus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInfluenza A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInfluenza A\u003c/p\u003e \u003cp\u003eHuman Herpesvirus 4 (EB virus)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eChryseobacterium indologenes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePBV virus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHuman Herpesvirus 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStaphylococcus haemolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePapillomavirus type 8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInfluenza A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eGordona bronchialis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCandida parapsilosis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInfluenza A virus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHuman Herpesvirus 4 (EB virus)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMolluscum\u0026nbsp;contangiosum\u0026nbsp;virus\u003c/p\u003e \u003cp\u003ePBV virus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSphingomonas paucimobilis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eLoprene Gordense\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eLactobacillus rhamnosus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInfluenza A virus\u003c/p\u003e \u003cp\u003eHuman Herpesvirus 4 (EB virus)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe adjustment of antimicrobial drug regimen based on the mNGS results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment before mNGS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment after mNGS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeropenem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeropenem and Metronidazole\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCeftriaxone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOseltamivir\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeropenem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSulbactam and Cefoperazone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeropenem and Fluconazole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeropenem,Vancomycin,Voriconazole and Oseltamivir\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlucocorticoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAntifungal drug(specific unknown) in other hospitals\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCeftriaxone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlucocorticoid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoxifloxacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSulbactam and Cefoperazone,Fluconazole,Oseltamivir and Allicin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevofloxacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePiperacillin and Tazobactam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePathogens, ARGs and DRAs obtained from mNGS results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePathogens detected by mNGS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eARGs detected by mNGS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDRA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e#2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eqacA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuaternary ammonium compounds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emsr(A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMacrolides\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eblaR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB-lactam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edfrC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrimethoprim\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emsr(D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMacrolides\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emsr(D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMacrolides\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emsr(D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMacrolides\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e#14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emsr(D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMacrolides\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etet(M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTetracylines\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecatB7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChloramphenicol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eARGs, antibiotic resistance genes. DRAs, drug-resistant antibiotic\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe analyzed the consistency of the mNGS results with those of pathogens identified using conventional microbiological methods (sputum culture and PCR). When mNGS identified the same pathogens as conventional tests, the results were considered to match. When mNGS identified more pathogens than conventional tests, the results were considered inconclusive. When the pathogens identified using the two methods were completely different, the results were considered mismatched. The pathogens identified were matched, inconclusive, and mismatched in two (14%), nine (64%), and three (21%) patients, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe sensitivity of mNGS and conventional tests for detecting co-infecting pathogens was compared. Conventional tests detected four cases of single pathogens, four cases of two pathogens, and no cases of three or more pathogens (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In contrast, mNGS detected one case of a single pathogen, six cases of two pathogens, and five cases of three or more pathogens (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003emNGS and prediction of drug resistance\u003c/h2\u003e \u003cp\u003eTraditionally, antibiotic resistance prediction has relied on culture phenotyping and molecular testing\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. mNGS detects antibiotic resistance genes (ARGs). In this study, mNGS detected seven ARGs in five patients, including more than one ARG in Patients #2 and #14 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The ARGs results provided guidance for antibiotic selection, and the patients\u0026rsquo; pneumonia improved. The mNGS results were notable for their ability to predict antibiotic resistance in patients with pneumonia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes of pneumonia in patients with cancer\u003c/h2\u003e \u003cp\u003eIn 9 of the 14 patients (64%), the diagnosis of the cause of the pneumonia was changed and the antimicrobial drug regimen was adjusted based on the mNGS results (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among these 9 patients, other antimicrobial drugs were added or switched in 4 patients; the dose of antimicrobial drugs was reduced and steroid was added in 1 patient owing to a negative mNGS result and a diagnosis of radiation pneumonitis; antifungal drugs were added in 1 patient; antiviral drugs were added in 1 patient; and antibacterial, antifungal, and antiviral drugs were added in 2 patients (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). Eight of the nine patients showed improvement in their pneumonia, except for Patient #9, who showed no improvement after the addition of antifungal drugs, but subsequently improved after receiving hormonal therapy for immune pneumonia. All 14 patients\u0026rsquo; pneumonia eventually improved, but two patients with advanced tumors subsequently died due to tumor progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective study reported the use of mNGS for the diagnosis of pneumonia in patients with cancer and compared it with conventional laboratory tests. First, the sensitivity of mNGS for pathogen identification was significantly higher than that of conventional tests, especially for pathogens that are difficult to culture and require prolonged incubation. Second, mNGS requires less time to obtain results (\u0026le;\u0026thinsp;30 hours) compared with sputum culture (usually 3\u0026ndash;5 days). In addition, mNGS was more effective than conventional tests at detecting coinfecting pathogens. Finally, mNGS had the advantage of predicting antibiotic resistance.\u003c/p\u003e \u003cp\u003eCompared with traditional assays, mNGS is an unbiased method for detecting all potentially infectious pathogens in a sample\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Previous studies have shown that mNGS has adequate accuracy and a significantly higher sensitivity for detecting pathogens\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Moreover, mNGS is less affected by prior antibiotic exposure\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. In addition to pathogen identification, mNGS provides clinical microbiome analysis, human host response analysis and drug resistance prediction\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is valuable in the identification of pathogens causing pneumonia, particularly in cases of unexplained or mixed infection\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Immunocompromised patients with cancer are more susceptible to severe pneumonia, mixed infection, and pneumonia caused by pathogens that are difficult to detect using conventional tests\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Therefore, mNGS may be a crucial method for identifying pneumonia pathogens in patients with cancer. This retrospective study confirmed this finding. In terms of bacteria, mNGS detected additional \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e, \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e, \u003cem\u003eBilophila wadsworthia\u003c/em\u003e, \u003cem\u003eBacteroides heparinolyticus\u003c/em\u003e, \u003cem\u003eGordona bronchialis\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e spp., \u003cem\u003eGordonia polyisoprenivorans\u003c/em\u003e, and \u003cem\u003eRalstonia mannitolilytica\u003c/em\u003e, which are difficult to identify using conventional tests. In terms of viruses, mNGS detected additional Epstein-Barr virus (EBV) in Patients #1, #3, #6, #9, and #12; human herpesvirus 1 in Patient #1; small double-stranded RNA virus in Patients #5 and #10; influenza A virus in Patient #12; human papillomavirus 8 in Patient #7; and molluscum contagiosum virus in Patient #10. In terms of fungi, fungal culture of Patient #3 yielded only \u003cem\u003eAspergillus flavus\u003c/em\u003e, whereas mNGS identified \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e. mNGS also detected \u003cem\u003eCandida glabrata\u003c/em\u003e in Patient #2, \u003cem\u003eCandida parapsilosis\u003c/em\u003e in Patient #8, and \u003cem\u003eCandida albicans\u003c/em\u003e in Patient #12. The results showed that mNGS can rapidly detect more pathogens, regardless of the type of infection. This could help guide timely antibiotic adjustment and improve the prognosis of pneumonia in patients with cancer.\u003c/p\u003e \u003cp\u003eIn our study, the most common pathogens detected by mNGS were \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e and influenza A virus, both of which are common in non-cancer patients with pneumonia. \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e are common pathogens that cause nosocomial infections in the general population\u003csup\u003e[\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. In addition, mNGS detected a variety of uncommon pathogens, such as \u003cem\u003eBilophila wadsworthia\u003c/em\u003e spp., \u003cem\u003eBacteroides heparinolyticus\u003c/em\u003e, \u003cem\u003eGordona bronchialis\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e spp., \u003cem\u003eGordonia polyisoprenivorans\u003c/em\u003e, \u003cem\u003eRalstonia mannitolilytica\u003c/em\u003e, and small double-stranded RNA virus.\u003c/p\u003e \u003cp\u003ePathogens in patients with cancer may differ from those in healthy individuals. Our study showed that mNGS can detect several opportunistic pathogens that are difficult to detect using conventional tests, such as opportunistic bacteria (e.g., \u003cem\u003eBilophila wadsworthia\u003c/em\u003e spp., \u003cem\u003eKlebsiella pneumoniae, Leptospira hepatica\u003c/em\u003e) and latent viruses (e.g., EBV). These pathogens are relatively harmless to healthy individuals but may cause infection in immunocompromised patients\u003csup\u003e[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Our study confirmed that patients with cancer are susceptible to infection by these pathogens, possibly because of their immunocompromised status and frequent hospital treatments. Owing to the characteristics of opportunistic pathogens, it is necessary to determine whether they are pathogenic. In our study, the condition of Patient #1, who had \u003cem\u003eBilophila wadsworthia\u003c/em\u003e spp. detected on mNGS, improved after metronidazole administration. Similarly, the condition of Patient #7, who had Klebsiella pneumoniae detected on mNGS, improved after switching to Cefoperazone Sodium and Sulbactam Sodium. Although mNGS detected EBV in 5 patients, it was considered non-pathogenic because more than 90% of adults are latently infected with EBV during their lives, and these patients had no symptoms of associated viral infections\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In addition, owing to the large number of colonizing microorganisms in the respiratory tract, distinguishing between infection and colonization may be challenging. In our study, mNGS detected \u003cem\u003eCandida albicans\u003c/em\u003e in Patient #9, whose symptoms did not improve with the addition of antifungal medication but improved with steroid therapy following a diagnosis of immune checkpoint inhibitor-associated pneumonitis\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Therefore, we considered \u003cem\u003eCandida albicans\u003c/em\u003e to be a respiratory colonizer. However, clinicians should be aware of the possibility of fungal infections after long-term steroid therapy.\u003c/p\u003e \u003cp\u003eIn general, mNGS distinguishes between infection and colonization using quantitative or semi-quantitative statistical analyses. One study developed rule-based and logistic regression models to differentiate between lower respiratory tract infections and colonization, both of which had an accuracy of 95.5% in the validation cohort\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. However, further studies on mNGS are required to better differentiate between infection and colonization. We suggest combining the patient's clinical presentation, blood test results, imaging findings, and empirical treatment outcomes to determine whether an opportunistic pathogen is pathogenic.\u003c/p\u003e \u003cp\u003ePneumonia is more complicated to diagnose and treat in patients with cancer, especially in those who have received immunotherapy or radiotherapy\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. It is sometimes difficult to distinguish between immunological or radiological pneumonia and lung infection. Whereas traditional tests have the drawbacks of false-negative results and long culture times, mNGS can shorten the time to diagnosis and help with rapid decision-making regarding treatment options. In our study, Patient #11, who was diagnosed with radiation pneumonitis but had a negative mNGS result, showed rapid clinical improvement after the discontinuation of antibiotics and treatment with steroid therapy alone, which also prevented antibiotic abuse and flora destruction, as radiation pneumonitis tends to be complicated by fungal infections in the presence of prolonged steroid use\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, mNGS detected five ARGs and predicted pathogen resistance to guide clinical treatment. Compared with sputum culture, mNGS can detect drug resistance more rapidly and comprehensively. Consistent with previous studies\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, mNGS was able to predict drug resistance in slow-growing or unculturable pathogens, and detect inactive pathogens after antibiotic treatment. Patients with hospital-onset lower respiratory tract infections (LRTI) have a higher burden of ARGs in their respiratory microbiome than patients with community-onset LRTIs\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. By identifying ARGs, mNGS may help in the early identification of future secondary lung infections\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. The mNGS test is particularly indicated for tumor patients with severe or complicated pneumonia, as well as for tumor patients whose etiology is unknown on conventional examination, and for whom empirical treatment has not been effective\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, it was a retrospective study with a small sample size, which may limit the generalizability of the results. Second, the mNGS specimens were sent to a commercial laboratory rather than a hospital microbiology laboratory, which may have reduced the sensitivity because the increased turnaround time reduced pathogen viability. In addition, mNGS is expensive and not currently covered by health insurance in China. This may have contributed to the bias in patient selection in this study. Prospective clinical studies are essential to investigate when to perform mNGS and to compare the diagnostic accuracy of mNGS with other methods.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003emNGS can identify pathogens more rapidly and comprehensively than conventional methods. It is a valuable tool for the diagnosis, and treatment of pneumonia in patients with cancer. However, prospective studies are required to demonstrate the clinical utility of mNGS in cancer patients with pneumonia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was planed and conducted in accordance with the principles of the Declaration of Helsinki, and was approved by the Medical Ethical Committee of the Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College(reference number:24/351-4631). All participants were informed of the purpose of this research and signed informed consent. All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was supported by the Beijing Vlove Foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNing Li conceived and designed the study. Ning Li and Chao Wang did the data collection. Ning Li and Rong Qin did the data analysis and drafted the initial manuscript. Ming-hua Cong gave many valuable comments on the draft and polished it. All authors assisted with the interpretation of the findings, commented on drafts of the manuscript, and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWong JL, Evans SE. Bacterial Pneumonia in Patients with Cancer: Novel Risk Factors and Management. Clin Chest Med. 2017 Jun;38(2):263-277. doi: 10.1016/j.ccm.2016.12.005.\u003c/li\u003e\n \u003cli\u003eWu J, Hong D, Zhang X, Lu X, Mia o J.PD-1 inhibitors increase the incidence and risk of pneumonitis in cancer patients in a dose-independent manner: a meta-analysis. Sci Rep. 2017 Mar 8;7:44173. doi: 10.1038/srep44173.\u003c/li\u003e\n \u003cli\u003eHanania AN, Mainwaring W, Ghebre YT, Hanania NA, Ludwig M. Radiation-Induced Lung Injury: Assessment and Management. Chest. 2019 Jul;156(1):150-162. doi: 10.1016/j.chest.2019.03.033. \u003c/li\u003e\n \u003cli\u003eEvans SE, Ost DE. Pneumonia in the neutropenic cancer patient. 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Infect Drug Resist. 2022 Oct 22;15:6115-6128. doi: 10.2147/IDR.S370964. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cancer, Pneumonia, Metagenomic next-generation sequencing, Conventional tests, Diagnosis, Antimicrobial resistance","lastPublishedDoi":"10.21203/rs.3.rs-4909642/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4909642/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWith the development of new sequencing technologies, metagenomic next-generation sequencing (mNGS) has become a diagnostic tool for respiratory tract infections. Patients with cancer may develop pneumonia caused by infections or antitumor therapy. Therefore, pneumonia in patients with cancer is more complex than that in healthy individuals. Currently, few reports are available on the use of mNGS for diagnosing pneumonia in patients with cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this retrospective study, 14 patients with cancer diagnosed with pneumonia in March 2023 were enrolled from the Emergency Department of the Chinese Academy of Medical Sciences Cancer Hospital. Sputum samples from the patients were examined using conventional tests and mNGS to identify pathogens. The mNGS and conventional test results were compared to assess the diagnostic yield and value of mNGS in improving the prognosis of pneumonia in patients with cancer.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003emNGS was more sensitive than conventional tests (sputum culture [SC] and polymerase chain reaction [PCR]) for detecting pathogens. The results were positive in 12/14 samples (86%) using mNGS compared with 8/14 samples (57%) using conventional testing. Compared with conventional tests, mNGS detected additional pathogens in 8 specimens. In 9/14 samples (64%), mNGS detected more pathogens than conventional testing. In nine patients (64%), the diagnosis was changed, and the antimicrobial regimen was adjusted based on the mNGS results. mNGS detected antibiotic resistance genes in five patients, which provided guidance for antibiotic selection.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003emNGS is a promising technology for detecting pneumonia pathogens in patients with cancer and improves the diagnostic yield and prognosis. mNGS can be used to aid in early diagnosis and guide treatment of pneumonia in patients with cancer.\u003c/p\u003e","manuscriptTitle":"Application of metagenomic next-generation sequencing in the diagnosis of pneumonia in patients with cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-15 04:54:43","doi":"10.21203/rs.3.rs-4909642/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5bd2668c-c1c0-4996-b524-de979cd28463","owner":[],"postedDate":"October 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-01T11:08:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-15 04:54:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4909642","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4909642","identity":"rs-4909642","version":["v1"]},"buildId":"ApUGefWb6u5IBVtyqm6d5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}