A Noninvasive Multianalytical Approach for Lung Cancer Diagnosis of Patients with Pulmonary Nodules

Circulating tumour DNA analysis for early detection of lung cancer: a systematic review

Background

Circulating tumor DNA (ctDNA) analysis has been applied in cancer diagnostics including lung cancer. Specifically for the early detection purpose, various modalities of ctDNA analysis have demonstrated their potentials. Such analyses have showed diverse performance across different studies.

Methods

We performed a systematic review of original studies published before 1 January 2023. Studies that evaluated ctDNA alone and in combination with other biomarkers for early detection of lung cancer were included.

Results

The systematic review analysis included 56 original studies that were aimed for early detection of lung cancer. There were 39 studies for lung cancer only and 17 for pan-cancer early detection. Cancer and control cases included were heterogenous across studies. Different molecular features of ctDNA have been evaluated, including 7 studies on cell-free DNA concentration, 17 on mutation, 29 on methylation, 5 on hydroxymethylation and 8 on fragmentation patterns. Among these 56 studies, 17 have utilised different combinations of the above-mentioned ctDNA features and/or circulation protein markers. For all the modalities, lower sensitivities were reported for the detection of early-stage cancer.

Conclusions

The systematic review suggested the clinical utility of ctDNA analysis for early detection of lung cancer, alone or in combination with other biomarkers. Future validation with standardised testing protocols would help integration into clinical care.

Saliva‑microbiome‑derived signatures: expected to become a potential biomarker for pulmonary nodules (MCEPN-1)

BACKGROUND

Oral microbiota imbalance is associated with the progression of various lung diseases, including lung cancer. Pulmonary nodules (PNs) are often considered a critical stage for the early detection of lung cancer; however, the relationship between oral microbiota and PNs remains unknown.

METHODS

We conducted a 'Microbiome with pulmonary nodule series study 1' (MCEPN-1) where we compared PN patients and healthy controls (HCs), aiming to identify differences in oral microbiota characteristics and discover potential microbiota biomarkers for non-invasive, radiation-free PNs diagnosis and warning in the future. We performed 16 S rRNA amplicon sequencing on saliva samples from 173 PN patients and 40 HCs to compare the characteristics and functional changes in oral microbiota between the two groups. The random forest algorithm was used to identify PN salivary microbial markers. Biological functions and potential mechanisms of differential genes in saliva samples were preliminarily explored using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of Orthologous Groups (COG) analyses.

RESULTS

The diversity of salivary microorganisms was higher in the PN group than in the HC group. Significant differences were noted in community composition and abundance of oral microorganisms between the two groups. Neisseria, Prevotella, Haemophilus and Actinomyces, Porphyromonas, Fusobacterium, 7M7x, Granulicatella and Selenomonas were the main differential genera between the PN and HC groups. Fusobacterium, Porphyromonas, Parvimonas, Peptostreptococcus and Haemophilus constituted the optimal marker sets (area under curve, AUC = 0.80), which can distinguish between patients with PNs and HCs. Further, the salivary microbiota composition was significantly correlated with age, sex, and smoking history (P < 0.001), but not with personal history of cancer (P > 0.05). Bioinformatics analysis of differential genes showed that patients with PN showed significant enrichment in protein/molecular functions related to immune deficiency and energy metabolisms, such as the cytoskeleton protein RodZ, nicotinamide adenine dinucleotide phosphate dehydrogenase (NADPH) dehydrogenase, major facilitator superfamily transporters and AraC family transcription regulators.

CONCLUSIONS

Our study provides the first evidence that the salivary microbiota can serve as potential biomarkers for identifying PN. We observed a significant association between changes in the oral microbiota and PNs, indicating the potential of salivary microbiota as a new non-invasive biomarker for PNs.

TRIAL REGISTRATION

Clinical trial registration number: ChiCTR2200062140; Date of registration: 07/25/2022.

Liquid biopsy techniques and lung cancer: diagnosis, monitoring and evaluation

Lung cancer stands as the most prevalent form of cancer globally, posing a significant threat to human well-being. Due to the lack of effective and accurate early diagnostic methods, many patients are diagnosed with advanced lung cancer. Although surgical resection is still a potential means of eradicating lung cancer, patients with advanced lung cancer usually miss the best chance for surgical treatment, and even after surgical resection patients may still experience tumor recurrence. Additionally, chemotherapy, the mainstay of treatment for patients with advanced lung cancer, has the potential to be chemo-resistant, resulting in poor clinical outcomes. The emergence of liquid biopsies has garnered considerable attention owing to their noninvasive nature and the ability for continuous sampling. Technological advancements have propelled circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), extracellular vesicles (EVs), tumor metabolites, tumor-educated platelets (TEPs), and tumor-associated antigens (TAA) to the forefront as key liquid biopsy biomarkers, demonstrating intriguing and encouraging results for early diagnosis and prognostic evaluation of lung cancer. This review provides an overview of molecular biomarkers and assays utilized in liquid biopsies for lung cancer, encompassing CTCs, ctDNA, non-coding RNA (ncRNA), EVs, tumor metabolites, TAAs and TEPs. Furthermore, we expound on the practical applications of liquid biopsies, including early diagnosis, treatment response monitoring, prognostic evaluation, and recurrence monitoring in the context of lung cancer.

Plasma metabolomics reveals risk factors for lung adenocarcinoma

Background

Metabolic reprogramming plays a significant role in the advancement of lung adenocarcinoma (LUAD), yet the precise metabolic changes remain incompletely understood. This study aims to uncover metabolic indicators associated with the progression of LUAD.

Methods

A total of 1083 subjects were recruited, including 670 LUAD, 135 benign lung nodules (BLN) and 278 healthy controls (HC). Gas chromatography-mass spectrometry (GC/MS) was used to identify and quantify plasma metabolites. Odds ratios (ORs) were calculated to determine LUAD risk factors, and machine learning algorithms were utilized to differentiate LUAD from BLN.

Results

High levels of oxalate, glycolate, glycine, glyceric acid, aminomalonic acid, and creatinine were identified as risk factors for LUAD (adjusted ORs>1.2, P<0.03). Remarkably, oxalate emerged as a distinctive metabolic risk factor exhibiting a strong correlation with the progression of LUAD (adjusted OR=5.107, P<0.001; advanced-stage vs. early-stage). The Random Forest (RF) model demonstrated a high degree of efficacy in distinguishing between LUAD and BLN (accuracy = 1.00 and 0.73, F1-score= 1.00 and 0.79, and AUC = 1.00 and 0.76 in the training and validation sets, respectively). TCGA and GTEx gene expression data have shown that lactate dehydrogenase A (LDHA), a crucial enzyme involved in oxalate metabolism, is increasingly expressed in the progression of LUAD. High LDHA expression levels in LUAD patients are also linked to poor prognoses (HR=1.66, 95% CI=1.34-2.07, P<0.001).

Conclusions

This study reveals risk factors associated with LUAD.

Circulating tumor DNA methylation: a promising clinical tool for cancer diagnosis and management

Cancer continues to pose significant challenges to the medical community. Early detection, accurate molecular profiling, and adequate assessment of treatment response are critical factors in improving the quality of life and survival of cancer patients. Accumulating evidence shows that circulating tumor DNA (ctDNA) shed by tumors into the peripheral blood preserves the genetic and epigenetic information of primary tumors. Notably, DNA methylation, an essential and stable epigenetic modification, exhibits both cancer- and tissue-specific patterns. As a result, ctDNA methylation has emerged as a promising molecular marker for noninvasive testing in cancer clinics. In this review, we summarize the existing techniques for ctDNA methylation detection, describe the current research status of ctDNA methylation, and present the potential applications of ctDNA-based assays in the clinic. The insights presented in this article could serve as a roadmap for future research and clinical applications of ctDNA methylation.

Serum Protein Fishing for Machine Learning-Boosted Diagnostic Classification of Small Nodules of Lung

Diagnosis of benign and malignant small nodules of the lung remains an unmet clinical problem which is leading to serious false positive diagnosis and overtreatment. Here, we developed a serum protein fishing-based spectral library (ProteoFish) for data independent acquisition analysis and a machine learning-boosted protein panel for diagnosis of early Non-Small Cell Lung Cancer (NSCLC) and classification of benign and malignant small nodules. We established an extensive NSCLC protein bank consisting of 297 clinical subjects. After testing 5 feature extraction algorithms and six machine learning models, the Lasso algorithm for a 15-key protein panel selection and Random Forest was chosen for diagnostic classification. Our random forest classifier achieved 91.38% accuracy in benign and malignant small nodule diagnosis, which is superior to the existing clinical assays. By integrating with machine learning, the 15-key protein panel may provide insights to multiplexed protein biomarker fishing from serum for facile cancer screening and tackling the current clinical challenge in prospective diagnostic classification of small nodules of the lung.

Circulating proteome for pulmonary nodule malignancy

BACKGROUND

Although lung cancer screening with low-dose computed tomography is rolling out in many areas of the world, differentiating indeterminate pulmonary nodules remains a major challenge. We conducted one of the first systematic investigations of circulating protein markers to differentiate malignant from benign screen-detected pulmonary nodules.

METHODS

Based on 4 international low-dose computed tomography screening studies, we assayed 1078 protein markers using prediagnostic blood samples from 1253 participants based on a nested case-control design. Protein markers were measured using proximity extension assays, and data were analyzed using multivariable logistic regression, random forest, and penalized regressions. Protein burden scores (PBSs) for overall nodule malignancy and imminent tumors were estimated.

RESULTS

We identified 36 potentially informative circulating protein markers differentiating malignant from benign nodules, representing a tightly connected biological network. Ten markers were found to be particularly relevant for imminent lung cancer diagnoses within 1 year. Increases in PBSs for overall nodule malignancy and imminent tumors by 1 standard deviation were associated with odds ratios of 2.29 (95% confidence interval: 1.95 to 2.72) and 2.81 (95% confidence interval: 2.27 to 3.54) for nodule malignancy overall and within 1 year of diagnosis, respectively. Both PBSs for overall nodule malignancy and for imminent tumors were substantially higher for those with malignant nodules than for those with benign nodules, even when limited to Lung Computed Tomography Screening Reporting and Data System (LungRADS) category 4 (P < .001).

CONCLUSIONS

Circulating protein markers can help differentiate malignant from benign pulmonary nodules. Validation with an independent computed tomographic screening study will be required before clinical implementation.

DNA-Guided Extracellular-Vesicle Metallization with High Catalytic Activity for Accurate Diagnosis of Pulmonary Nodules

Sensitive and specific analysis of extracellular vesicles (EVs) offers a promising minimally invasive way to identify malignant pulmonary nodules from benign lesions. However, accurate analysis of EVs is subject to free target proteins in blood samples, which compromises the clinical diagnosis value of EVs. Here a DNA-guided extracellular-vesicle metallization (DEVM) strategy is described for ultrasensitive and specific analysis of EV protein biomarkers and classification of pulmonary nodules. The facile DEVM process mainly includes the incorporation of DNA labeled with cholesterol and thiol groups into EV membranes and subsequent deposition of Au3+ and Pt4+ to allow the DNA-functionalized EVs to be encapsulated with AuPt nanoshells. It is found that the synthesized AuPt-metallized EVs possess extrinsic peroxidase-like activity. Utilizing the feature of the catalytic metal nanoshells just growth on the EV membranes, the DEVM method enables multiparametric recognition of target proteins and EV membranes and can produce an amplified colorimetric signal, avoiding the interference of free proteins. By profiling four surface proteins of EVs from 48 patients with pulmonary nodules, the highest area under the receiver operating characteristic curve (0.9983) is obtained. Therefore, this work provides a feasible EVs analysis tool for accurate pulmonary nodules management.

Circulating cell-free DNA sequencing for early detection of lung cancer

INTRODUCTION

Lung cancer is a leading cause of death in patients with cancer. Early diagnosis is crucial to improve the prognosis of patients with lung cancer. Plasma circulating cell-free DNA (cfDNA) contains comprehensive genetic and epigenetic information from tissues throughout the body, suggesting that early detection of lung cancer can be done non-invasively, conveniently, and cost-effectively using high-sensitivity techniques such as sequencing.

AREAS COVERED

In this review, we summarize the latest technological innovations, coupled with next-generation sequencing (NGS), regarding genomic alterations, methylation, and fragmentomic features of cfDNA for the early detection of lung cancer, as well as their clinical advances. Additionally, we discuss the suitability of study designs for diagnostic accuracy evaluation for different target populations and clinical questions.

EXPERT OPINION

Currently, cfDNA-based early screening and diagnosis of lung cancer faces many challenges, such as unsatisfactory performance, lack of quality control standards, and poor repeatability. However, the progress of several large prospective studies employing epigenetic features has shown promising predictive performance, which has inspired cfDNA sequencing for future clinical applications. Furthermore, the development of multi-omics markers for lung cancer, including genome-wide methylation and fragmentomics, is expected to play an increasingly important role in the future.

Potential clinical utility of liquid biopsy in early-stage non-small cell lung cancer

BACKGROUND

Liquid biopsy has been widely researched for early diagnosis, prognostication and disease monitoring in lung cancer, but there is a need to investigate its clinical utility for early-stage non-small cell lung cancer (NSCLC).

METHODS

We performed a meta-analysis and systematic review to evaluate diagnostic and prognostic values of liquid biopsy for early-stage NSCLC, regarding the common biomarkers, circulating tumor cells, circulating tumor DNA (ctDNA), methylation signatures, and microRNAs. Cochrane Library, PubMed, EMBASE databases, ClinicalTrials.gov, and reference lists were searched for eligible studies since inception to 17 May 2022. Sensitivity, specificity and area under the curve (AUC) were assessed for diagnostic values. Hazard ratio (HR) with a 95% confidence interval (CI) was extracted from the recurrence-free survival (RFS) and overall survival (OS) plots for prognostic analysis. Also, potential predictive values and treatment response evaluation were further investigated.

RESULTS

In this meta-analysis, there were 34 studies eligible for diagnostic assessment and 21 for prognostic analysis. The estimated diagnostic values of biomarkers for early-stage NSCLC with AUCs ranged from 0.84 to 0.87. The factors TNM stage I, T1 stage, N0 stage, adenocarcinoma, young age, and nonsmoking contributed to a lower tumor burden, with a median cell-free DNA concentration of 8.64 ng/ml. For prognostic analysis, the presence of molecular residual disease (MRD) detection was a strong predictor of disease relapse (RFS, HR, 4.95; 95% CI, 3.06-8.02; p < 0.001) and inferior OS (HR, 3.93; 95% CI, 1.97-7.83; p < 0.001), with average lead time of 179 ± 74 days between molecular recurrence and radiographic progression. Predictive values analysis showed adjuvant therapy significantly benefited the RFS of MRD + patients (HR, 0.27; p < 0.001), while an opposite tendency was detected for MRD - patients (HR, 1.51; p = 0.19). For treatment response evaluation, a strong correlation between pathological response and ctDNA clearance was detected, and both were associated with longer survival after neoadjuvant therapy.

CONCLUSIONS

In conclusion, our study indicated liquid biopsy could reliably facilitate more precision and effective management of early-stage NSCLC. Improvement of liquid biopsy techniques and detection approaches and platforms is still needed, and higher-quality trials are required to provide more rigorous evidence prior to their routine clinical application.

CT and CEA-based machine learning model for predicting malignant pulmonary nodules

Computed tomography (CT), an efficient radiological technology, is used to detect lung cancer in the clinic. Carcinoembryonic antigen (CEA), a common tumor biomarker, is applied in the detection of various tumors. To highlight the advantages of two-dimensional techniques and assist clinicians in optimizing lung cancer diagnostic schemes, we established a favorable model combining CT and CEA. In the study, univariate analysis was performed to screen independent predictors in a training cohort of 271 patients with malignant pulmonary nodules (MPNs) and 92 with benign pulmonary nodules (BPNs). Six machine learning-based models involving five CT predictors (mediastinal lymph node enlargement, lobulation, vascular notch sign, spiculation, and nodule number) and lnCEA were constructed and validated in an independent cohort of 129 participants (92 MPNs and 37 BPNs) by SPSS Modeler. A nomogram and the Delong test were generated by R software. Finally, the model established by logistic regression had highest diagnostic efficiency (area under the curve [AUC] = 0.912). Moreover, the diagnostic ability of the logistic model in the validation cohort (AUC = 0.882, 80.4% sensitivity, 75.7% specificity) was higher than that of the Peking University model (AUC = 0.712, 68.5% sensitivity, 70.3% specificity) and the Mayo model (AUC = 0.745, 62.0% sensitivity, 75.7% specificity). Interestingly, for the participants with intermediate (10-30 mm) and CEA-negative nodule, the model reached an AUC of 0.835 (72.3% sensitivity, 83.3% specificity). The AUC for the early lung cancer was as high as 0.822 with 67.3% sensitivity and 78.9% specificity. As a conclusion, this promising model presents a new diagnostic strategy for the clinic to distinguish MPNs from BPNs.

Epigenome-wide DNA methylation signature of plasma zinc and their mediation roles in the association of zinc with lung cancer risk

Essential trace element zinc is associated with decreased lung cancer risk, but underlying mechanisms remain unclear. This study aimed to investigate role of DNA methylation in zinc-lung cancer association. We conducted a case-cohort study within prospective Dongfeng-Tongji cohort, including 359 incident lung cancer cases and a randomly selected sub-cohort of 1399 participants. Epigenome-wide association study (EWAS) was used to examine association of plasma zinc with DNA methylation in peripheral blood. For the zinc-related CpGs, their mediation effects on zinc-lung cancer association were assessed; their diagnostic performance for lung cancer was testified in the case-cohort study and further validated in another 126 pairs of lung cancer case-control study. We identified 28 CpGs associated with plasma zinc at P < 1.0 × 10^-5 and seven of them (cg07077080, cg01077808, cg17749033, cg15554270, cg26125625, cg10669424, and cg15409013 annotated to GSR, CALR3, SLC16A3, PHLPP2, SLC12A8, VGLL4, and ADAMTS16, respectively) were associated with incident risk of lung cancer. Moreover, the above seven CpGs were differently methylated between 126 pairs of lung cancer and adjacent normal lung tissues and had the same directions with EWAS of zinc. They could mediate a separate 7.05%∼22.65% and a joint 29.42% of zinc-lung cancer association. Compared to using traditional factors, addition of methylation risk score exerted improved discriminations for lung cancer both in case-cohort study [area under the curve (AUC) = 0.818 vs. 0.738] and in case-control study (AUC = 0.816 vs. 0.646). Our results provide new insights for the biological role of DNA methylation in the inverse association of zinc with incident lung cancer.