Identification of a Biomarker Panel for Early Detection of Lung Cancer Patients

Abstract

Lung cancer is the most common cause of cancer related mortality worldwide, characterised by late clinical presentation (49-53% of patients are diagnosed at stage IV) and consequently poor outcomes. One challenge in identifying biomarkers of early disease is the collection of samples from patients prior to symptomatic presentation. We used blood collected during surgical resection of lung tumours in an iTRAQ isobaric tagging experiment to identify proteins effluxing from tumours into pulmonary veins. 40 proteins were identified as having an increased abundance in the vein draining from the tumour compared to "healthy" pulmonary veins. These protein markers were then assessed in a second cohort that utilised the mass spectrometry (MS) technique: Sequential window acquisition of all theoretical fragment ion spectra (SWATH) MS. SWATH-MS was used to measure proteins in serum samples taken from 25 patients <50 months prior to and at lung cancer diagnosis and 25 matched controls. The SWATH-MS analysis alone produced an 11 protein marker panel. A machine learning classification model was generated that could discriminate patient samples from patients within 12 months of lung cancer diagnosis and control samples. The model was evaluated as having a mean AUC of 0.89, with an accuracy of 0.89. This panel was combined with the SWATH-MS data from one of the markers from the first cohort to create a 12 protein panel. The proteome signature developed for lung cancer risk can now be developed on further cohorts.

Development of a machine learning-based multimode diagnosis system for lung cancer

“…Certainly, tumor markers in serum provide microscopic molecular information related to the occurrence and progression of cancer, which points out a new direction for the early detection of lung cancer [16,20]. In addition, blood sampling, minimally invasive and repeatable, can be easily performed, making serum an excellent matrix for lung cancer diagnosis [20,21]. Thus, the combination of tumor markers and the features of CT nodules, which offers microscopic molecular information and macroscopic imaging information, is supposed to be an ideal strategy for lung cancer diagnosis at early stages [22].…”

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

“…The plasma was immunodepleted and fractionated and peptides were quantified by iTRAQ. The results showed 50 proteins to be abundant in the vein draining part of the cancerous regions compared to the noncancerous sectors [158]. In another study done by Ahn et al [159] one hundred plasma samples from different stages of CRC and healthy controls were pooled to create a SWATH library.…”

Discovery and Evaluation of Protein Biomarkers as a Signature of Wellness in Late-Stage Cancer Patients in Early Phase Clinical Trials

“…SWATH-MS uses the reproducibility and relatively swift sample preparation and run time plus standardised informatics capabilities to generate digitised proteomic maps. SWATH-MS has been used to identify proteomics signatures in a wide range of diseases and conditions with recent cancer based studies finding signatures of interest in breast and colorectal cancer [ 29 , 30 ], endometrial cancer [ 31 ], ovarian [ 32 ] lung cancer [ 33 ] and other health conditions such as weight loss [ 34 ].…”