Analyses of lung cancer-derived volatiles in exhaled breath and in vitro models

Online monitoring and stable isotope tracing of cancer associated volatiles in murine model captures tumor associated markers in vivo

BACKGROUND

The imperative need for early cancer detection, which is crucial for improved survival rates in many severe cancers such as lung cancer, remains challenging due to the lack of reliable early-diagnosis technologies and robust biomarkers. To address this gap, innovative screening platforms are essential to unveil the chemical signatures of lung cancer and its treatments. It is established that the oxidative tumor environment induces alterations in host metabolic processes and influences endogenous volatile synthesis. Despite efforts, consensus on unique volatile markers for cancer detection has been elusive, partly due to genetic variation leading to metabolic heterogeneity in humans and the lack of standardized procedures for analytical analyses.

RESULTS

In this study, we utilized advanced secondary electrospray ionization (SESI) technique coupled with a high-resolution mass spectrometer (HRMS) to non-invasively monitor lung cancer volatiles in a pre-clinical mouse model in real time. Our findings revealed 651 dysregulated volatile features upon cancer onset and identified 36 features correlated with tumor size. Endogenous tracing of glucose metabolism highlighted the γ-glutamyl cycle as a downstream pathway implicated in lung cancer, driven by an imbalance in glutathione metabolism due to reactive oxygen species (ROS) accumulation. Notably, our study unveiled unique volatile changes associated with gemcitabine and cisplatin treatment, which significantly abrogated tumor growth in vivo. Furthermore, we identified 5-oxoproline as a volatile metabolite indicative of lung cancer response to treatment.

SIGNIFICANCE

In conclusion, our SESI-HRMS based analysis of pre-clinical model systematically explores the volatile signatures of lung cancer, and provides a novel non-invasive platform that possess great potential for the real-time, confident, and sensitive detection and monitoring of lung cancer.

Nanoparticle trends and hotspots in lung cancer diagnosis from 2006-2023: a bibliometric analysis

Background

Lung cancer possesses the highest incidence and mortality rates among malignancies globally. Despite substantial advancements in oncology, it is frequently diagnosed at an advanced stage, resulting in a poor prognosis. Over recent decades, the swift progress of nanotechnology has precipitated the extensive utilization of nanomaterials as carriers in cancer diagnosis and therapy. The deployment of nanoparticles as an innovative diagnostic strategy aspires to enable the earlier detection of lung cancer, thereby permitting earlier intervention and enhancing prognosis. This study endeavors to deepen our understanding of this domain through a comprehensive analysis employing bibliometric tools.

Method

Related articles were retrieved from the Web of Science Core Collection from January 1st, 2006, to December 14st, 2023. Thereaf CiteSpace, VOSviewer and the online platform of bibliometrics (http://bibliometric.com/) were utilized to visually analyze Author/Country/Institutions/Cited Journals/Keyword, et al.

Results

A total of 966 articles were retrieved for this study. The analysis unveils a progressive increase in annual publications within this field, with China at the forefront in publication volume, followed by the United States and India. Moreover, Chinese research institutions, notably the Chinese Academy of Sciences and Shanghai Jiao Tong University, prevail in publication output. Upon exclusion of irrelevant search terms, keywords clustering analysis highlights that "biomarkers", "sensors", "gold nanoparticles", and "silver nanoparticles" are predominant research focuses.

Conclusion

This bibliometric study furnishes a quantitative perspective on the extant literature, serving scholars in related fields. Furthermore, it anticipates future research trend concerning nanoparticles and lung cancer diagnosis, thereby aiding in the formulation of project planning and the design of experiments.

Precision detection of select human lung cancer biomarkers and cell lines using honeybee olfactory neural circuitry as a novel gas sensor

Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers at different concentrations and their mixtures at concentration ranges relevant to biomarkers in human breath from parts-per-billion to parts-per-trillion. We also validated this brain-based sensing technology by detecting human non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cell lines using the 'smell' of the cell cultures. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we classified individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create 'synthetic lung cancer' vs. 'synthetic healthy' human breath, honeybee population neuronal responses were able to classify those complex breath mixtures reliably with exceedingly high accuracy (93-100% success rate with a leave-one-trial-out classification method). Finally, we employed this sensor to detect human NSCLC and SCLC cell lines and we demonstrated that honeybee brain olfactory neurons could distinguish between lung cancer vs. healthy cell lines and could differentiate between different NSCLC and SCLC cell lines successfully (82% classification success rate). These results indicate that the honeybee olfactory system can be used as a sensitive biological gas sensor to detect human lung cancer.

Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform

BACKGROUND

The multitude of metabolites generated by physiological processes in the body can serve as valuable biomarkers for many clinical purposes. They can provide a window into relevant metabolic pathways for health and disease, as well as be candidate therapeutic targets. A subset of these metabolites generated in the human body are volatile, known as volatile organic compounds (VOCs), which can be detected in exhaled breath. These can diffuse from their point of origin throughout the body into the bloodstream and exchange into the air in the lungs. For this reason, breath VOC analysis has become a focus of biomedical research hoping to translate new useful biomarkers by taking advantage of the non-invasive nature of breath sampling, as well as the rapid rate of collection over short periods of time that can occur. Despite the promise of breath analysis as an additional platform for metabolomic analysis, no VOC breath biomarkers have successfully been implemented into a clinical setting as of the time of this review.

AIM OF REVIEW

This review aims to summarize the progress made to address the major methodological challenges, including standardization, that have historically limited the translation of breath VOC biomarkers into the clinic. We highlight what steps can be taken to improve these issues within new and ongoing breath research to promote the successful development of the VOCs in breath as a robust source of candidate biomarkers. We also highlight key recent papers across select fields, critically reviewing the progress made in the past few years to advance breath research.

KEY SCIENTIFIC CONCEPTS OF REVIEW

VOCs are a set of metabolites that can be sampled in exhaled breath to act as advantageous biomarkers in a variety of clinical contexts.

Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities

Non-invasive, simple, and fast tests for lung cancer diagnostics are one of the urgent needs for clinical practice. The work describes the results of exhaled breath analysis of 112 lung cancer patients and 120 healthy individuals using gas chromatography-mass spectrometry (GC-MS). Volatile organic compound (VOC) peak areas and their ratios were considered for data analysis. VOC profiles of patients with various histological types, tumor localization, TNM stage, and treatment status were considered. The effect of non-pulmonary comorbidities (chronic heart failure, hypertension, anemia, acute cerebrovascular accident, obesity, diabetes) on exhaled breath composition of lung cancer patients was studied for the first time. Significant correlations between some VOC peak areas and their ratios and these factors were found. Diagnostic models were created using gradient boosted decision trees (GBDT) and artificial neural network (ANN). The performance of developed models was compared. ANN model was the most accurate: 82-88% sensitivity and 80-86% specificity on the test data.

Mapping the landscape of lung cancer breath analysis: A scoping review (ELCABA)

Lung cancer is the leading cause of cancer death worldwide due to its late-stage detection. Lung cancer screening, including low-dose computed tomography (low-dose CT), provides an initial clinical solution. Nevertheless, further innovations and refinements would help to alleviate remaining limitations. The non-invasive, gentle, and fast nature of breath analysis (BA) makes this technology highly attractive to supplement low-dose CT for an improved screening algorithm. However, BA has not taken hold in everyday clinical practice. One reason might be the heterogeneity and variety of BA methods. This scoping review is a comprehensive summary of study designs, breath analytical methods, and suggested biomarkers in lung cancer. Furthermore, this synthesis provides a framework with core outcomes for future studies in lung cancer BA. This work supports future research for evidence synthesis, meta-analysis, and translation into clinical routine workflows.