The lack of standardization of breath sampling is a major contributing factor to the poor repeatability of results and hence represents a barrier to the adoption of breath tests in clinical practice. On-line and bag breath sampling have advantages but do not suit multicentre clinical studies whereas storage and robust transport are essential for the conduct of wide-scale studies. Several devices have been developed to control sampling parameters and to concentrate volatile organic compounds (VOCs) onto thermal desorption (TD) tubes and subsequently transport those tubes for laboratory analysis. We conducted three experiments to investigate (i) the fraction of breath sampled (whole vs. lower expiratory exhaled breath); (ii) breath sample volume (125, 250, 500 and 1000ml) and (iii) breath sample flow rate (400, 200, 100 and 50 ml/min). The target VOCs were acetone and potential volatile biomarkers for oesophago-gastric cancer belonging to the aldehyde, fatty acids and phenol chemical classes. We also examined the collection execution time and the impact of environmental contamination. The experiments showed that the use of exhaled breath-sampling devices requires the selection of optimum sampling parameters. The increase in sample volume has improved the levels of VOCs detected. However, the influence of the fraction of exhaled breath and the flow rate depends on the target VOCs measured. The concentration of potential volatile biomarkers for oesophago-gastric cancer was not significantly different between the whole and lower airway exhaled breath. While the recovery of phenols and acetone from TD tubes was lower when breath sampling was performed at a higher flow rate, other VOCs were not affected. A dedicated 'clean air supply' overcomes the contamination from ambient air, but the breath collection device itself can be a source of contaminants. In clinical studies using VOCs to diagnose gastro-oesophageal cancer, the optimum parameters are 500mls sample volume of whole breath with a flow rate of 200ml/min. .

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.Given its ease of use and low operational cost, GC-MS has applications with broad societal effect, such as detection of metabolic disease in newborns, toxicology, doping, forensics, food science and clinical testing. The predominant ionization technique in GC-MS is electron ionization (EI), in which all compounds are ionized by high-energy (70-eV) electrons. Because fragmentation occurs with ionization, EI GC-MS data are subjected to spectral deconvolution, a process that separates fragmentation ion patterns for each eluting molecule into a composite mass spectrum.The 70 eV for ionizing electrons in GC-MS has been the standard, making it possible to use decades-old EI reference spectra for annotation 1 . There are ~1.2 million reference spectra that have been accumulated and curated over a period of more than 50 years 2 . Many tools and repositories for GC-MS data have been introduced [3][4][5][6][7][8][9][10][11][12][13][14][15] ; however, much of GC-MS data processing is restricted to vendor-specific formats and software 8 . Currently, deconvolution requires setting multiple parameters manually [3][4][5] or posessing computational skills to run the software 7 . Also, the lack of data sharing in a uniform format precludes data comparison between laboratories and prevents taking advantage of repository-scale information and community knowledge, resulting in infrequent reuse of GC-MS data 8,[11][12][13][14][15] .Although batch modes exist, deconvolution quality is currently not enhanced by using information from all other files. To leverage across-file information, improve scalability of spectral deconvolution and eliminate the need for manually setting the deconvolution parameters (m/z error correction of the ions and peak shapeslopes of raising and trailing edges, peak RT shifts and noise/intensity thresholds), we developed an algorithmic learning strategy for auto-deconvolution (Fig. 1a-f). We deployed this functionality within GNPS/MassIVE (https://gnps.ucsd.edu) 16 (Fig. 1f-i). To promote analysis reproducibility, all GNPS jobs performed are retained in the 'My User' space and can be shared as hyperlinks.This user-independent 'automatic' parameter optimization is accomplished via fast Fourier transform (FFT), multiplication and inverse Fourier transform for each ion across an entire data set, followed by an unsupervised non-negative matrix factorization (NMF) (one-layer neural network). Then, the compositional consistency of spectral patterns for each spec...

Breath analysis is highly acceptable to patients and health care professionals, but its implementation in clinical practice remains challenging. Clinical trials and routine practice require a robust system for collection, storage, and processing of large numbers of samples. This work describes a platform based upon the hyphenation of thermal desorption (TD) with proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS), coupled by means of an original modification of the TD interface. The performance of TD-PTR-ToF-MS was tested against seven oxygenated volatile organic compounds (VOCs), belonging to three chemical classes (i.e., fatty acids, aldehydes, and phenols), previously identified as possible biomarkers of colorectal and esophago-gastric adenocarcinoma. Limits of detection and quantification were on the order of 0.2-0.9 and 0.3-1.5 parts per billion by volume (ppbV), respectively. Analytical recoveries from TD tubes were 80% or higher, linear response was in the low- to mid-ppbV range ( R = 0.98-0.99), and coefficients of variation were within 20% of mean values. The usability of the platform was evaluated in the analysis of a set of breath samples of clinical origin, allowing for a throughput of nearly 100 TD tubes for 24 h of continuous operation. All of these characteristics enhance the implementation of TD-PTR-ToF-MS for large-scale clinical studies.

Disease breathomics is gaining importance nowadays due to its usefulness as non-invasive early cancer detection. Mass spectrometry (MS) technique is often used for analysis of volatile organic compounds (VOCs) associated with cancer in the exhaled breath but a long-standing challenge is the uncertainty in mass peak annotation for potential volatile biomarkers. This work describes a cross-platform MS strategy employing selected-ion flow tube mass spectrometry (SIFT-MS), high resolution gas chromatography-mass spectrometry (GC-MS) retrofitted with electron ionisation (EI) and GC-MS retrofitted with positive chemical ionisation (PCI) as orthogonal analytical approaches in order to provide facile identification of the oxygenated VOCs from breath of cancer patients. In addition, water infusion was applied as novel efficient PCI reagent in breathomics analysis, depicting unique diagnostic ions M+ or [M-17]+ for VOC identification. Identity confirmation of breath VOCs was deduced using the proposed multi-platform workflow, which reveals variation in breath oxygenated VOC composition of oesophageal-gastric (OG) cancer patients with dominantly ketones, followed by aldehydes, alcohols, acids and phenols in decreasing order of relative abundance. Accurate VOC identification provided by cross-platform approach would be valuable for the refinement of diagnostic VOC models and the understanding of molecular drivers of VOC production.

Gas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repository-scale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations. We introduce a “balance score” that quantifies the reproducibility of fragmentation patterns across all samples. We demonstrate the utility of the platform with breathomics analysis applied to the early detection of oesophago-gastric cancer, and by creating the first molecular spatial map of the human volatilome.

No abstract

201 Background: ICONIC is a single-arm phase II trial investigating the safety and efficacy of perioperative FLOT-A in resectable OGA. Following a 3+3 design safety run-in phase, standard dose FLOT with 10mg/kg IV avelumab (dose level 0) q2weeks was taken forward into the main study. The aims of this pre-planned interim analysis were to assess perioperative safety and R0 resection rates. Methods: The interim analysis occurred once the 15th patient treated at dose level 0 reached 30 days post-surgery. Results: At data cut-off, 15 patients had received at least one cycle of FLOT-A and had undergone resection. The median age of patients was 63y (range: 25 – 73). 71% had an ECOG PS of 0. 60% of tumours were staged as T3 at baseline and 40% T2; 67% were N0, 7% N1 and 27% N2. Due to 5-FU related cardiac toxicity, two patients switched to alternative chemotherapy without 5-FU and avelumab. 13/15 patients (87%) completed 4 cycles of pre-operative FLOT-A; of these, five patients had avelumab omitted for one cycle for toxicity evaluation and management. 9/15 patients (60%) experienced a G3/4 adverse event (AE). These were FLOT-A-related in 8/15 patients (53.3%). The commonest G3/4 AEs were febrile neutropenia, neutropenia and diarrhoea. Median time from last chemotherapy to surgery was 6.4 weeks. No delays or failure to proceed to surgery occurred due to avelumab-related complications. 7% of patients had an American Society of Anaesthesiologists (ASA) preoperative risk score of I, 47% a score of II and 47% a score of III. 73% of patients had operations involving a thoracic approach (10 minimally invasive Ivor-Lewis oesophagogastrectomy with two field radical lymphadenectomy, 1 left thoracoabdominal oesophagogastrectomy and 4 gastrectomy with D2 lymphadenectomy). Median time to extubation was 6h (IQR: 4-24). The median Acute Physiology and Chronic Health Evaluation (APACHE) score at day 1 post-op was 12 (IQR: 10-15) with a median of 3 days (IQR: 2-4) of CCU care. No unexpected complications were reported intra-operatively or during post-operative recovery in FLOT-A treated patients. 5/14 evaluable patients at data cut-off (35.7%) had Clavien-Dindo grade II post-operative complications and 3/14 (21.4%) grade IIIa complications; of these 1 patient had an anastomotic leak that was treated endoscopically. There were no emergency re-operations. All 15 patients achieved R0 resections and were discharged home after a median of 13d (IQR: 11-16) in hospital. Conclusions: To date, FLOT-A has not led to unexpected or unusually severe perioperative complications in the context of major complex upper GI surgery for resectable OGA. Clinical trial information: NCT03399071.

The feasibility and safety of enhanced recovery protocols (ERP) have been demonstrated in a large number of surgical specialties. Several studies have shown improved post-operative outcomes and economic benefit from the use of ERPs in oesophageal cancer surgery. However, these improvements are not always translated more widely into clinical practice due to variation in protocols, poor compliance and failure to implement a robust implementation strategy. ERP implementation strategies should reflect the fact that these are complex interventions that are influenced by a wide range of social, organizational and cultural factors.