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

doi:10.21203/rs.3.rs-4909642/v1

Download PDF

Research Article

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

https://doi.org/10.21203/rs.3.rs-4909642/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

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

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.

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.

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
Patient	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. 3A). In contrast, mNGS detected one case of a single pathogen, six cases of two pathogens, and five cases of three or more pathogens (Fig. 3B).

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. 4A, 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.

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.

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.

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.

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.
Hanania 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.
Evans SE, Ost DE. Pneumonia in the neutropenic cancer patient. Curr Opin Pulm Med. 2015;21(3):260–71. doi: 10.1097/MCP.0000000000000156.
Safdar A, Armstrong D. Infectious morbidity in critically ill patients with cancer. Crit Care Clin. 2001;17(3):531–70, vii–viii. doi: 10.1016/s0749-0704(05)70198-6.
Gonzalez C, Johnson T, Rolston K, Merriman K, Warneke C, Evans S. Predicting pneumonia mortality using CURB-65, PSI, and patient characteristics in patients presenting to the emergency department of a comprehensive cancer center. Cancer Med. 2014;3(4):962–70. doi: 10.1002/cam4.240.
Ashour HM, el-Sharif A. Microbial spectrum and antibiotic susceptibility profile of gram-positive aerobic bacteria isolated from cancer patients. J Clin Oncol. 2007;25(36):5763–9. doi: 10.1200/JCO.2007.14.0947.
Steele RW. Managing infection in cancer patients and other immunocompromised children. Ochsner J. 2012;12(3):202–10.
Jain S, Self WH, Wunderink RG, et al. Community-acquired pneumonia requiring hospitalization among U.S. adults. N Engl J Med. 2015;373(5):415–427. doi: 10.1056/NEJMoa1500245.
Xie Y, Du J, Jin W, Teng X, Cheng R, Huang P, Xie H, Zhou Z, Tian R, Wang R, Feng T. Next generation sequencing for diagnosis of severe pneumonia: China, 2010-2018. J Infect. 2019 Feb;78(2):158-169. doi: 10.1016/j.jinf.2018.09.004.
Behjati S, Tarpey PS. What is next generation sequencing? Arch Dis Child Educ Pract Ed. 2013 Dec;98(6):236-8. doi: 10.1136/archdischild-2013-304340.
Hao J, Li W, Wang Y, Zhao J, Chen Y. Clinical utility of metagenomic next-generation sequencing in pathogen detection for lower respiratory tract infections and impact on clinical outcomes in southernmost China. Front Cell Infect Microbiol. 2023 Dec 8;13:1271952. doi: 10.3389/fcimb.2023.1271952.
Chiu CY, Miller SA. Clinical metagenomics. Nat Rev Genet. 2019 Jun;20(6):341-355. doi: 10.1038/s41576-019-0113-7.
Wang J, Han Y, Feng J. Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis. BMC Pulm Med. 2019 Dec 19;19(1):252. doi: 10.1186/s12890-019-1022-4.
Miao Q, Ma Y, Wang Q, et al. Microbiological Diagnostic Performance of Metagenomic Next-generation Sequencing When Applied to Clinical Practice. Clin Infect Dis. 2018 Nov 13;67(suppl_2):S231-S240. doi: 10.1093/cid/ciy693.
Cao B, Huang Y, She DY, et al. Diagnosis and treatment of community-acquired pneumonia in adults: 2016 clinical practice guidelines by the Chinese Thoracic Society, Chinese Medical Association. Clin Respir J. 2018 Apr;12(4):1320-1360. doi: 10.1111/crj.12674.
Zhou C, Yu J. Chinese expert consensus on diagnosis and treatment of radiation pneumonitis. Prec Radiat Oncol. 2022;1-10.
Ransom EM, Potter RF, Dantas G, Burnham CD. Genomic Prediction of Antimicrobial Resistance: Ready or Not, Here It Comes! Clin Chem. 2020 Oct 1;66(10):1278–1289. doi: 10.1093/clinchem/hvaa172.
Chao L, Li J, Zhang Y, Pu H, Yan X. Application of next generation sequencing-based rapid detection platform for microbiological diagnosis and drug resistance prediction in acute lower respiratory infection. Ann Transl Med. 2020 Dec;8(24):1644. doi: 10.21037/atm-20-7081.
Liu Y, Zhang R, Yao B, Yang J, Ge H, Zheng S, Guo Q, Xing J. Metagenomics next-generation sequencing provides insights into the causative pathogens from critically ill patients with pneumonia and improves treatment strategies. Front Cell Infect Microbiol. 2023 Jan 13;12:1094518. doi: 10.3389/fcimb.2022.1094518.
Chen IR, Lin SN, Wu XN, Chou SH, Wang FD, Lin YT. Clinical and Microbiological Characteristics of Bacteremic Pneumonia Caused by Klebsiella pneumoniae. Front Cell Infect Microbiol. 2022 Jun 23;12:903682. doi: 10.3389/fcimb.2022.903682.
Amaya-Villar R, Garnacho-Montero J. How should we treat acinetobacter pneumonia? Curr Opin Crit Care. 2019 Oct;25(5):465-472. doi: 10.1097/MCC.0000000000000649.
Ding C, Yang Z, Wang J, Liu X, Cao Y, Pan Y, Han L, Zhan S. Prevalence of Pseudomonas aeruginosa and antimicrobial-resistant Pseudomonas aeruginosa in patients with pneumonia in mainland China: a systematic review and meta-analysis. Int J Infect Dis. 2016 Aug;49:119-28. doi: 10.1016/j.ijid.2016.06.014.
Pfaller MA, Diekema DJ. Epidemiology of invasive candidiasis: a persistent public health problem. Clin Microbiol Rev. 2007 Jan;20(1):133-63. doi: 10.1128/CMR.00029-06.
Godoy MCB, Ferreira Dalla Pria HR, Truong MT, Shroff GS, Marom EM. Invasive Fungal Pneumonia in Immunocompromised Patients. Radiol Clin North Am. 2022 May;60(3):497-506. doi: 10.1016/j.rcl.2022.01.006.
Varthalitis I, Meunier F. Pneumocystis carinii pneumonia in cancer patients. Cancer Treat Rev. 1993 Oct;19(4):387-413. doi: 10.1016/0305-7372(93)90011-f.
Neville K, Renbarger J, Dreyer Z. Pneumonia in the immunocompromised pediatric cancer patient. Semin Respir Infect. 2002 Mar;17(1):21-32. doi: 10.1053/srin.2002.31691.
Kerr JR. Epstein-Barr virus (EBV) reactivation and therapeutic inhibitors. J Clin Pathol. 2019 Oct;72(10):651-658. doi: 10.1136/jclinpath-2019-205822.
Langelier C, Kalantar KL, Moazed F, et al. Integrating host response and unbiased microbe detection for lower respiratory tract infection diagnosis in critically ill adults. Proceedings of the National Academy of Sciences of the United States of America. 2018 Dec 26;115(52):E12353-e12362. doi: 10.1073/pnas.1809700115.
Gomatou G, Tzilas V, Kotteas E, Syrigos K, Bouros D. Immune Checkpoint Inhibitor-Related Pneumonitis. Respiration. 2020;99(11):932-942. doi: 10.1159/000509941.
McAleese J, Mooney L, Walls GM. Reducing the Risk of Death From Pneumocystis jirovecii Pneumonia After Radical Radiation Therapy to the Lung. Clin Oncol (R Coll Radiol). 2021 Dec;33(12):780-787. doi: 10.1016/j.clon.2021.06.010.
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.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

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

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

Study design and patient cohort

Clinical presentation

Imaging

Blood tests

Microbiological testing

Clinical treatment

Results

Patient characteristics

Comparison of mNGS and conventional microbiological test results

Comparison of pathogens detected by mNGS and conventional tests

mNGS and prediction of drug resistance

Outcomes of pneumonia in patients with cancer

Discussion

Conclusions

Declarations

Author Contribution

References

Additional Declarations

Status:

Version 1