Diagnostic Performance of a Noninvasive Breath Test for Colorectal Cancer: COBRA1 Study

Abstract

Colorectal cancer (CRC) is the third most common cancer globally.1Ferlay J. et al.https://www.who.int/news-room/fact-sheets/detail/cancerhttps://gco.iarc.fr/todayGoogle Scholar When diagnosed early, the 5-year survival rate is 92%,2Cancer Research UK https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/bowel-cancer/survival#heading-ThreeGoogle Scholar yet 23% of CRCs are diagnosed at an advanced stage3Cancer Research UK https://crukcancerintelligence.shinyapps.io/EarlyDiagnosis/Google Scholar in the United Kingdom with a 5-year survival rate of 10%.2Cancer Research UK https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/bowel-cancer/survival#heading-ThreeGoogle Scholar Early CRC has symptoms that are shared with common benign conditions.4Adelstein B.-A. et al.BMC Gastroenterol. 2011; 11: 65Crossref PubMed Scopus (108) Google Scholar Colonoscopy capacity is limited, and referring all symptomatic patients for colonoscopy would overwhelm available resources. An intermediate triage test to identify patients at risk of CRC could streamline referral pathways. A breath test based on detecting volatile organic compounds (VOCs) has the ideal characteristics for a triage tool because it is noninvasive, simple to undertake, and acceptable to patients of all ethnicities.5Woodfield G. et al.BMJ Open. 2021; 11e044691Crossref PubMed Scopus (8) Google Scholar COBRA1 is a prospective, multicenter diagnostic study aimed to develop a breath test to detect CRC (Research Ethics Committee no. 17/EE01/12; Bowel Cancer Screening Program identification 189; clinicaltrials.gov identifier NCT03699163). The target sample size was 1463 patients (117 CRC, 1346 control subjects) using the power diagnostic test function from the MKmisc R package, considering type I error (alpha) of 0.05, power (1-beta) of 0.8, prevalence value of 0.08, and assumed difference of 0.1. The study recruited patients aged 18–90 years attending 7 London hospitals (Appendix 1) for a bowel cancer screening program colonoscopy because of a positive fecal occult blood test (n = 664), colonoscopy for other indications (n = 645), or surgical colorectal adenocarcinoma resection (n = 123) (June 2017 to February 2020). We recruited a mixed population to ensure an adequate number of cancer cases for model development. The reference test was colonoscopy ± histopathology. Exclusion criteria was concurrent chemotherapy. Breath was collected by trained research nurses using the ReCIVA breath sampling device (Owlstone Medical Ltd, Cambridge, UK) onto thermal desorption tubes using standardized settings immediately before colonoscopy and surgery.6Doran S.L.F. et al.J Breath Res. 2017; 12016007Crossref PubMed Scopus (50) Google Scholar Patients fasted for 4 hours before breath collection. Quality control measures were performed for breath collection and analysis (Appendix 2). Thermal desorption tubes were couriered to the VOC laboratory (Imperial College London) for same-day analysis using gas chromatography-mass spectrometry (Agilent Technologies, Cheshire, UK) equipped with a midpolar column (ZB-62,460 m × 0.25 mm inner diameter × 1.40 μm df; Phenomenex Inc, Torrance, CA). When same-day analysis was not possible, thermal desorption tubes were stored at –80oC for subsequent analysis. Laboratory staff were blinded to disease status. Gas chromatography-mass spectrometry data were acquired using MassHunter software (B.07 SP1; Agilent Technologies) and processed using the custom-designed spectral deconvolution tool MSHub.7Aksenov A.A. et al.Nat Biotechnol. 2021; 39: 169-173Crossref PubMed Scopus (63) Google Scholar Machine learning pipelines identified predictive features (VOCs and clinical metadata) to develop a multivariate discriminant analysis model and receiver-operating characteristic (ROC) curves (Appendix 3). Included in the analysis were 1432 patients (828 men), with a median age of 66.5 years (range, 18–90). No adverse events were reported. Of 1432 patients, 357 had a normal colonoscopy; 188 had benign pathology (hemorrhoids or diverticular disease); 106 had inflammatory bowel disease; 348, 67, and 204 patients had low-, intermediate- and high-risk polyps, respectively; and 162 had colorectal adenocarcinoma. Polyp risk stratification was based on 2002 British Society of Gastroenterology (BSG) polyp surveillance guidelines,8Atkin W.S. et al.Gut. 2002; 51: v6-v9Crossref PubMed Scopus (273) Google Scholar adapted to include dysplasia status, and the BSG 2017 guidance on serrated polyps.9East J.E. Atkin W.S. Bateman A.C. et al.Gut. 2017; 66: 1181-1196Crossref PubMed Scopus (190) Google Scholar Patient demographics, cancer characteristics, and exclusion details are presented in Supplementary Table 1. CRC patients were older than control subjects and more likely to have had previous CRC or heart disease or used laxatives, antibiotics, or anticoagulants. CRCs were recruited from surgical lists (n = 119), bowel cancer screening programs (n = 30), and other colonoscopy lists (n = 13). Of the CRCs, 64.2% were T3 or T4. Of 1432 patients, 855 reported at least 1 symptom at the time of breath sampling. Across all breath samples, 1024 VOC product ions were detected. The top 99 most predictive features (97 VOCs, body mass index, and age) according to Random Forest Scores were annotated and identified using mass spectral libraries (NIST, version 2.0).10Shen VK, et al. NIST Standard Reference Simulation Website. https://www.nist.gov/programs-projects/nist-standard-reference-simulation-website.Google Scholar We assessed the origin of VOCs using the Human Metabolome Database 2018.11Wishart D.S. et al.Nucleic Acids Res. 2018; 46: D608-D617Crossref PubMed Scopus (2441) Google Scholar Thirty-five VOCs were deemed likely to be exogenous and 37 were of unknown identity, leaving 25 endogenous VOCs for further statistical analysis (Supplementary Table 2). A diagnostic model comparing all CRC (n = 162) and non-CRC patients (n = 1270) based on 14 endogenous VOCs and body mass index predicted CRC with area under the ROC curve of 0.87, sensitivity of 79%, specificity of 86%, and negative predictive value of 97% (Figure 1A). A model using data from symptomatic patients only, taken from the same cohort (CRC, n = 146; non-CRC, n = 709), predicted CRC with an area under the ROC curve of 0.91, sensitivity of 83%, specificity of 88%, and negative predictive value of 96% (Figure 1B). Predictive VOCs were largely from the alkane-, alcohol-, ester-, and sulfur-containing chemical groups. Higher levels of dimethyl sulfide and 2-ethoxypropane discriminated right-sided (cecum to transverse colon) from left-sided tumors (P = .002 and P = .045, respectively). Polyps of any risk category (n = 619) could be predicted with an area under the ROC curve of 0.67, sensitivity of 66%, and specificity of 58% when compared with patients with no polyps and no CRC (n = 651) based on 16 endogenous VOCs and age. A model based on high-risk polyps did not improve prediction. An acknowledged limitation of this study is the use of a selected population including patients known to have CRC to recruit enough cancer cases for model development, resulting in differences in age, medication use, and bowel preparation. It precluded symptom-based analysis (although this was not a study aim). Bowel preparation and age were not predictive features or confounding factors in the VOC-based model. Use of medications could not be examined in the machine learning analysis. COBRA1 achieved its aim of constructing a diagnostic model of a VOC-based breath test to detect CRC, with promising results. The high negative predictive value of the breath test suggests the possibility of use as a triage tool. The strength of this study lies in its multicenter design, large sample size, comprehensive quality control measures, and selecting only endogenous VOCs for model development. The feasibility of multicenter breath collection with centralized sample analysis is also demonstrated. These results support further evaluation of this technology for detecting CRC in an unselected screening-eligible population for CRC screening, either alone or in combination with other tests, such as the fecal immunochemical test. We thank the National Institute for Health Research–affiliated breath testing teams at St Mark’s, Charing Cross, St George’s, Homerton, West Middlesex, Chelsea and Westminster, and St Mary’s Hospitals for their patient recruitment and sample collection across 7 sites for this study. The full study protocol has been provided to the journal as a separate file and can be provided on request. Individual deidentified participant data will not be shared but are available on request to authors. The COBRA1 Working Group includes Piers R. Boshier,1 GengPing Lin,1 Antonis Myridakis,1 Oscar Ayrton,1 Patrik Španěl,1,2 Alberto Vidal-Diez,1 Andrea Romano,1 John Martin,3 Laura Marelli,4 Chris Groves,5 Kevin Monahan,1,6 Christos Kontovounisios,1,7 Brian P. Saunders,1,8 from the 1Department of Surgery and Cancer, Imperial College London, London, United Kingdom; 2J. Heyrovský Institute of Physical Chemistry of the Czech Academy of Sciences, Prague, Czech Republic; 3Department of Gastroenterology, Charing Cross Hospital, London, United Kingdom; 4Department of Gastroenterology, Homerton University Hospital, London, United Kingdom; 5Department of Gastroenterology, St George’s Hospital, London, United Kingdom; 6Department of Gastroenterology, West Middlesex University Hospital, London, United Kingdom; 7Department of Surgery, Chelsea and Westminster Hospital, London, United Kingdom; and 8Department of Gastroenterology, St Mark’s Hospital and Academic Institute, London, United Kingdom. Georgia Woodfield, PhD (Conceptualization: Equal; Data curation: Equal; Formal analysis: Supporting; Funding acquisition: Equal; Investigation: Equal; Methodology: Equal; Project administration: Equal; Writing – original draft: Equal; Writing – review & editing: Equal). Ilaria Belluomo, PhD (Conceptualization: Equal; Data curation: Equal; Formal analysis: Equal; Funding acquisition: Supporting; Investigation: Supporting; Methodology: Supporting; Project administration: Supporting; Resources: Supporting; Supervision: Equal; Writing – original draft: Supporting; Writing – review & editing: Equal). Piers R Boshier, PhD (Conceptualization: Supporting; Formal analysis: Supporting; Methodology: Supporting; Project administration: Supporting; Resources: Supporting; Supervision: Supporting; Writing – original draft: Supporting; Writing – review & editing:Equal). GengPing Lin, MD (Formal analysis: Supporting; Investigation: Equal; Methodology: Supporting; Project administration: Equal; Writing – review & editing: Supporting). Antonis Myridakis, PhD (Data curation: Supporting; Formal analysis: Supporting; Investigation: Supporting; Software: Supporting; Supervision: Supporting; Visualization: Supporting; Writing – review & editing: Supporting). Oscar Ayrton, MSc (Investigation: Supporting; Methodology: Supporting; Project administration: Supporting; Resources: Supporting; Software: Supporting; Writing –review & editing: Supporting). Ivan Laponogov, PhD (Data curation: Supporting; Formal analysis: Lead; Methodology: Supporting; Software: Lead; Supervision: Supporting; Validation: Lead; Writing – review & editing: Supporting). Kirill Veselkov, PhD (Data curation: Supporting; Formal analysis: Lead; Methodology: Supporting; Software: Lead; Supervision: Supporting; Validation: Supporting; Writing –review & editing: Supporting). Patrik Španěl, Dr. rer. nat (Data curation: Equal; Formal analysis: Equal; Investigation: Supporting; Methodology: Supporting; Resources: Supporting; Supervision: Supporting; Writing – review & editing: Supporting). Alberto Vidal-Diez, PhD (Data curation: Supporting; Formal analysis: Supporting; Methodology: Supporting; Software: Supporting; Writing – review & editing: Supporting). Andrea Romano, PhD (Formal analysis: Supporting; Investigation: Supporting; Software: Supporting; Writing – review & editing: Supporting). John Martin, MD (Project administration: Supporting; Resources: Supporting; Supervision: Supporting; Writing – review & editing: Supporting). Laura Marelli, MD (Project administration: Supporting; Resources: Supporting; Supervision: Supporting; Writing – review & editing: Supporting). Chris Groves, MD (Project administration: Supporting; Resources: Supporting; Supervision: Supporting; Writing – review & editing: Supporting). Kevin Monahan, PhD (Project administration: Supporting; Resources: Supporting; Supervision: Supporting; Writing – review & editing: Supporting). Christos Kontovounisios, PhD (Project administration: Supporting; Resources: Supporting; Supervision: Supporting; Writing – review & editing: Supporting). Brian P Saunders, MD (Supervision: Supporting; Visualization: Supporting; Writing –original draft: Supporting; Writing – review & editing: Supporting). Amanda J Cross, PhD (Conceptualization: Supporting; Formal analysis: Supporting; Methodology: Supporting; Resources: Supporting; Supervision: Supporting; Writing – original draft: Supporting; Writing – review & editing: Equal). George B Hanna, PhD (Conceptualization: Lead; Data curation: Supporting; Formal analysis: Equal; Funding acquisition: Lead; Investigation: Equal; Methodology: Lead; Project administration: Supporting; Resources: Equal; Software: Equal; Supervision: Lead; Writing – original draft: Supporting; Writing – review & editing: Equal). Patients attending for colonoscopy were recruited from Charing Cross, St Mark’s, St George’s and Homerton Hospitals in London. Surgical patients were recruited from St Mary’s, West Middlesex and Chelsea, and Westminster Hospitals in London. Breath samples were obtained on the same day before colonoscopy or elective surgery. Polyps were stratified into low-risk (1–2 subcentimeter tubular adenomas with low-grade dysplasia or subcentimeter serrated polyps without dysplasia), intermediate-risk (3–4 subcentimeter or one >1-cm tubular adenoma with low-grade dysplasia or >1-cm serrated polyps without dysplasia), and high-risk (≥5 subcentimeter adenomas, ≥3 adenomas if 1 was >1 cm, or any adenoma with high-grade dysplasia or villous change or any serrated polyp with dysplasia) categories. After conditioning (TC20; Markes Ltd, Llantrisant, UK), 30 tubes were randomly selected and assessed using proton transfer reaction time of flight mass spectrometry. If any VOC abundance was >1 ppb, tubes were rechecked and excluded if concentrations remained >1.5 ppb. We assessed VOC contamination in the CASPER filtration system (Owlstone Medical) every 3 months and replaced the filter if contamination was detected or after 450 hours of use; the flow rate by each pump every 4 weeks using a flowmeter to test the flow through an empty thermal desorption (TD) tube attached to each of tube inlets to ensure the intended 200 mL/min flow rate for each tube position; and recorded breath collection parameters by ReCIVA software for all breath samples (temperature, flow rates of both pumps, and pressure of the facemask against the patient’s face as a surrogate for a good seal). We interrogated the h5 files generated by the ReCIVA software using an in-house generated script written with R programming language11Wishart D.S. et al.Nucleic Acids Res. 2018; 46: D608-D617Crossref PubMed Scopus (2441) Google Scholar to create a graphic representation of quality control parameters. We visually assessed all outputs to exclude inadequate samples. We used a threshold-based system to quantify acetone (m/z = 58, RT = 8.97) within each TD tube using gas chromatography-mass spectroscopy, indicating the presence of enough breath sample for analysis. Acetone was selected as the reference compound because it is always present in human breath.2de Lacy Costello B. et al.J Breath Res. 2014; 8014001Crossref Scopus (749) Google Scholar Samples with an acetone abundance of <4,000,000 area (raw gas chromatography-mass spectroscopy data) were excluded. We identified this threshold by comparing 121 breath samples (500 mL) collected using identical protocol and analytical parameters as COBRA1 against 152 nonbiologic control subjects: empty conditioned TD tubes; 500-mL room air samples collected onto TD tubes using ReCIVA, following a similar procedure to that for patient breath; and TD tubes, previously conditioned and then loaded with a standard mixture not containing any of the tested compounds. All but 5 of 121 breath samples analyzed had a breath acetone level above the identified threshold (Mann-Whitney U test, P < .0001). We used retention time, peak shape, and peak area to assess consistency and accuracy of instrument analysis of 5 TD tubes loaded with a certified standard mixture using a permeation unit (ES 4050P; Eco Scientific, Gloucestershire UK).3Romano A. et al.Analytic Chem. 2018; 90: 10204-10210Crossref PubMed Scopus (22) Google Scholar The standard mixture consisted of benzene (63 ppb), phenol (90 ppb), butyric acid (20 ppb), pentanoic acid (5 ppb), hexanoic acid (5 ppb), decanal (4 ppb), and butanal (5 ppb), maintained at 30°C and with a nitrogen flow of 0.9 L/min. A machine learning pipeline using Python1Rossum GV, et al. Python 3 reference manual. Scotts Valley, CA: CreateSpace; Scholar and et al.J Res. 2011; Scholar processed all data to 1024 chemical ions breath sample and for each and identified of CRC and non-CRC using both analysis of and The data were and as of the machine learning Random support machine and machine learning were used to every combination and of pathology The same were for patients aged and years and all to age was confounding VOC The factors number of hours of body mass index, status, type of bowel preparation taken before and of and factors time of the TD tube from conditioning to breath time mass spectrometry and number of the TD tube was stored in the were not into the because were and if every was would have been common and would have only of a would have to the and not have been ROC curves were used to the accuracy of the diagnostic test in with and without colorectal The ROC curves were generated based on 25 5 of stratified with that samples were and then into 5 groups. was then used in as a test the other 4 were the and model were performed on a each time of the data) and then to the test of the data) to the was 5 and then the results from were to ROC curves and error of the model and any of an if the data to have been in a were not in any but to the most features in each If a was selected to be a of the data was the would be A that the in was more likely to be a CRC and Table Patient of CRC and patients only (n = control subjects = CRC and control of patients for from from of patients to from from of patients but not breath was time from from of patients had breath from from of patients excluded to inadequate reference test or of patients excluded before laboratory analysis to inadequate breath sample settings human tubes not of patients excluded to of ReCIVA quality control breath of patients excluded as samples gas chromatography-mass spectroscopy analysis instrument of patients excluded to of quality control for VOC presence in TD of patients with breath samples and reference test and in the to mass index, to preparation patients bowel No bowel for CRC by of CRC patients from for polyp surveillance inflammatory bowel resection patient for likely cancer of patients had >1 previous bowel blood heart pump of and and any other Patients were not at the time of breath symptoms (n = CRC patients and control Bowel of bowel symptoms was change in bowel fecal or of study patients of other symptoms was and The number of patients was because levels were not to transverse to of are n not of patients had >1 previous of and and any other Patients were not at the time of breath of bowel symptoms was change in bowel fecal or of other symptoms was and The number of patients was because levels were not in a Supplementary Table VOCs and of CRC Patients Patients in 1 = CRC = and Patients = CRC = are in of to the predictive time of peak CRC (n = 162) non-CRC (n = 1270) of acid has previously been to of the human and are in in breath have previously been to be predictive for CRC and sulfide is a as it is by in the with of the in CRC as the for its dimethyl sulfide within a of 4 other VOCs has also been to high-risk polyp patients from control subjects are also known to to be characteristic of CRC in the study. abundance in CRC breath be by the to the characteristic of cancer to a of A of are endogenous and are in the breath of this a The human and also would abundance in any blood from the through the were in the breath of and could be a to be in is a known risk for of CRC Research UK of all can also to to the with to breath is known to breath acetone levels acetone any other was to be for CRC in the machine learning model. of acid for 1 and have been as for CRC be present in breath to and that in cancer and is also a with and are known to be to in CRC could the similar to of acid for 1 for 3 or for 3 for 3 has been in the breath and of has also been in concentrations in the breath of cancer patients compared with control subjects for 7 for 3 for CRC (n = symptomatic non-CRC (n = of acid above above above above above above similar to of acid above of acid above above above above for 7 under 1 above or above above are according to of Random to 1 when all features that to the model are the the for the top most promising and only and is the the not to 1. The of the can only be in to the other features have high to the are in of to the predictive model. in a are n not are according to of Random to 1 when all features that to the model are the the for the top most promising and only and is the the not to 1. The of the can only be in to the other features have high to the

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