Development of Proteomic Patterns for Detecting Lung Cancer

Abstract

Lung cancer is at present the number one cause of cancer death and no biomarker is available to detect early lung cancer in serum samples so far. The objective of this study is to find specific biomarkers for detection of lung cancer using Surface Enhanced Laser Desorption/Ionization (SELDI) technology. In this study, serum samples from 30 lung cancer patients and 51 age-and sex-matched healthy were analyzed by SELDI based ProteinChip reader, PBSII-C. The spectra were generated on WCX2 chips and protein peaks clustering and classification analyses were performed utilizing Biomarker Wizard and Biomarker Patterns software packages, respectively. Three protein peaks were automatically chosen for the system training and the development of a decision classification tree. The constructed model was then used to test an independent set of masked serum samples from 15 lung cancer patients and 31 healthy individuals. The analysis yielded a sensitivity of 93.3%, and a specificity of 96.7%. These results suggest that the serum is a capable resource for detection of specific lung cancer biomarkers. SELDI technique combined with an artificial intelligence classification algorithm can both facilitate the discovery of better biomarkers for lung cancer and provide a useful tool for molecular diagnosis in future.

Autoantibody Profiles Reveal Ubiquilin 1 as a Humoral Immune Response Target in Lung Adenocarcinoma

“…Unlike gene expression studies of tumor tissues, autoantibody profiling is done in serum, which can be much less invasively obtained and is easily monitored over time. Likewise, whereas there has been intensive activity in the use of proteomic approaches to identify biomarkers in sera (15), monitoring the immune response takes advantage of the inherent biological amplification provided by autoantibodies which can be more easily detected than low abundant proteins in a complex biological milieu, such as serum. Whereas our study suggests that the humoral immune response may be useful in the diagnosis and classification of tumors, it will also be important to investigate the role of these autoantibodies in promoting tumor development, especially in light of the growing evidence linking inflammation and cancer (24,25).…”

“…Several groups are using proteomic approaches using serum to identify biomarkers for the early detection of lung cancer (11)(12)(13)(14). This is a daunting challenge, as this requires identification of relatively low abundant proteins in a complex mixture of highly abundant serum proteins, such as albumin (15).…”

Evaluation of Serum Protein Profiling by Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry for the Detection of Prostate Cancer: I. Assessment of Platform Reproducibility

“…Several laboratories have demonstrated the feasibility of using mass spectrometric proteomic pattern analysis for the diagnosis of several categories of tumors, including ovarian, breast, lung, pancreas, and prostate cancer (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24). In one of the earliest contributions, investigators from Eastern Virginia Medical School (EVMS) 10 and the Fred Hutchison Cancer Research Center (FHCRC) demonstrated that surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) protein profiles may have clinical utility as a diagnostic tool for the early detection of prostate cancer, including prostatic adenocarcinoma (PCa) (6,8 ).…”

Classification tree analysis of second neoplasms in survivors of childhood cancer

“…In oncology the method has been used for tumor classification, evaluation of biomarkers [19][20][21][22][23]. The sample size represents a limitation in our study, but the method used is a potentially powerful tool for investigating multilevel interactions [24].…”