Analysis of Volatile Chemicals in Lung Cancer Screen-Eligible Subjects Using Infrared Spectroscopy

Overview

The aim of this study is to sample and analyze volatile organic compounds (VOCs) from a high-risk population of subjects eligible for lung cancer screening as defined by the US Preventive Services Task Force (USPSTF) guidelines. The breath sample analysis will help investigators describe and identify real-world breath profiles from individuals at high risk of developing lung cancer and to use this to test machine learning (ML) algorithms for lung cancer screening. This study will also evaluate the feasibility and usability of Picomole's breath sampling technology in a mobile platform. with lung cancer when compared to normal breath profiles using infrared spectroscopy. This work will help validate early proof of concept results conducted with prototype technology and later stage NSCLC breath samples, and inform future breath testing analysis.

Lung cancer is the most common malignancy in the world in terms of both incidence and mortality (1.1 million new cases per year and 0.95 million deaths in males and 0.51 million new cases per year and 0.43 million deaths in females). The highest rates of lung cancer are found in Europe and North America. In addition, lung cancer is the leading cause of cancer death in Canada. Lung cancer is believed to develop slowly via a progressive accumulation of genetic mutations, hence the disease allows time for diagnosis and curative surgical treatment. When detected in the early stages, the 5-year survival rate for NSCLC is 57% to 61%, compared with a survival rate of approximately 6% once distant metastases are present. Unfortunately, most patients do not experience any signs or symptoms of lung cancer until the disease has progressed to an advanced stage. Therefore, technologies that facilitate detection of lung cancer in the earliest asymptomatic stages have significant potential to reduce lung cancer-specific mortality. Malignant transformation is facilitated by deregulation of fundamental cellular processes, including alterations in metabolism. Thus, metabolomic profiling may be a promising strategy for identifying lung cancer before it is detectable via conventional methods such as CT scans. Breathomics is a field of study dedicated to deconstructing the metabolomic profile or biological components of volatile organic compounds (VOC) in breath. To date, various analytical techniques including gas chromatography combined with mass spectrometry, ion mobility spectrometry, proton transfer reaction spectrometry, and selected ion flow tube mass spectrometry have been used to study breath VOCs. In a recent study, ion mobility spectrometry discriminated between lung cancer and chronic obstructive lung disease with 79% accuracy, 76.8% sensitivity, and 85.7% specificity. Furthermore, breath analysis can be used to not only identify lung cancer but also distinguish between lung cancers with particular somatic mutations. For example, electronic nose technology demonstrated 79% and 85% sensitivity and specificity, respectively, for identifying EGFR-mutant lung cancer. These studies suggest that breath analysis is a highly sensitive and specific approach to detecting lung cancer. This study will evaluate the performance characteristics of infrared spectroscopy for breath analysis. The spectrometer used to analyze breath gases is optimized to measure chemical concentrations down to the parts per trillion range. In a pilot study of 165 (67 newly diagnosed Non-Small Cell Lung Cancer (NSCLC) subjects which used infrared spectroscopy to analyze breath specimens from subjects with lung cancer and subjects without cancer, sensitivity and specificity for detecting lung cancer was 88.7% and 80%, respectively, with an accuracy of 86%. These preliminary results compare very favorably to mass spectrometry (the analytical platform that has been used in most breath analysis studies). This study will analyze breath VOCs from 300 subjects who meet the USPSTF eligibility guidelines for lung cancer screening. The study aims to better understand the VOC breath profiles in a larger group of subjects at high risk for developing lung cancer. By restricting the population to screen-eligible subjects, this study will approximate the potential future "real world" use of this screening strategy and better approximate its utility in the field, sampling high risk populations in rural settings. It is envisioned that this study will generate preliminary data that will inform the performance of machine learning algorithms developed to detect the presence of lung cancer in unselected populations.