Introduction The pattern of exhaled breath volatile organic compounds represents a metabolic biosignature with the potential to identify and characterize lung cancer. Breath biosignature-based classification of homogeneous subgroups of lung cancer may be more accurate than a global breath signature. Combining breath biosignatures with clinical risk factors may improve the accuracy of the signature. Objectives Develop an exhaled breath biosignature of lung cancer using a colorimetric sensor array. Determine the accuracy of breath biosignatures of lung cancer characteristics with and without the inclusion of clinical risk factors. Methods The exhaled breath of 229 study subjects, 92 with lung cancer and 137 controls, was drawn across a colorimetric sensor array. Logistic prediction models were developed and statistically validated based on the color changes of the sensor. Age, sex, smoking history, and COPD were incorporated in the prediction models. Results The validated prediction model of the combined breath and clinical biosignature was moderately accurate at distinguishing lung cancer from control subjects (C-statistic 0.811). The accuracy improved when the model focused on only one histology (C-statistic 0.825 – 0.890). Individuals with different histologies could be accurately distinguished from one another (C-statistic 0.889 for adenocarcinoma vs. squamous cell carcinoma). Moderate accuracies were noted for validated breath biosignatures of stage and survival (C-statistic 0.793, 0.770 respectively). Conclusions A colorimetric sensor array is capable of identifying exhaled breath biosignatures of lung cancer. The accuracy of breath biosignatures can be optimized by evaluating specific histologies and incorporating clinical risk factors.

ClinicalTrials.gov; No.: NCT01752114; URL: www.clinicaltrials.gov).

BackgroundMetformin and the thiazolidinediones (TZDs) may have a protective effect against the development of lung cancer.MethodsPatients with diabetes mellitus (DM) were identified from the electronic medical records of the Cleveland Clinic. Diabetics with lung cancer were identified then verified by direct review of their records. Control subjects were matched with cancer subjects 1:1 by date of birth, sex, and smoking history. The frequency and duration of diabetic medication use was compared between the groups. The cancer characteristics were compared between those with lung cancer who had and had not been using metformin and/or a TZD.Results93,939 patients were identified as having DM. 522 lung cancers in 507 patients were confirmed. The matched control group was more likely to have used metformin and/or a TZD (61.0% vs. 41.2%, p < 0.001 for any use; 55.5% vs. 24.6%, p < 0.001 for >24 months vs. 0–12 months). In the group with lung cancer, those who had used metformin alone had a different histology distribution than those who received neither metformin nor a TZD, were more likely to present with metastatic disease (40.8% vs. 28.2%, p = 0.013), and had a shorter survival from the time of diagnosis (HR 1.47, p < 0.005).ConclusionsThe use of metformin and/or the TZDs is associated with a lower likelihood of developing lung cancer in diabetic patients. Diabetics who develop lung cancer while receiving metformin may have a more aggressive cancer phenotype.

Differences in the serum metabolite profile exist between patients with stage I through stage III non-small cell lung cancer and matched controls.

Objective Abnormal eye gaze is a hallmark characteristic of autism spectrum disorder (ASD), and numerous studies have identified abnormal attention patterns in ASD. The primary aim of the present study was to create an objective, eye tracking-based autism risk index. Method In initial and replication studies, children were recruited after referral for comprehensive multidisciplinary evaluation of ASD and subsequently grouped by clinical consensus diagnosis (ASD n=25/15, non-ASD n=20/19 for initial/replication samples). Remote eye tracking was blinded to diagnosis and included multiple stimuli. Dwell times were recorded to each a priori-defined region-of-interest (ROI) and averaged across ROIs to create an autism risk index. Receiver operating characteristic curve analyses examined classification accuracy. Correlations with clinical measures evaluated whether the autism risk index was associated with autism symptom severity independent of language ability. Results In both samples, the autism risk index had high diagnostic accuracy (area under the curve [AUC]=.91 and .85, 95%CIs=.81–.98 and .71–.96), was strongly associated with Autism Diagnostic Observation Schedule–Second Edition (ADOS-2) severity scores (r=.58 and .59, p<.001), and not significantly correlated with language ability (r≤|−.28|, p>.095). Conclusion The autism risk index may be a useful quantitative and objective measure of risk for autism in at-risk settings. Future research in larger samples is needed to cross-validate these findings. If a validated scale for clinical use, this measure could inform clinical judgment regarding ASD diagnosis and track symptom improvements.