Performance of mitochondrial DNA mutations detecting early stage cancer

<p>Abstract</p> <p>Background</p> <p>Mutations in the mitochondrial genome (mtgenome) have been associated with cancer and many other disorders. These mutations can be point mutations or deletions, or admixtures (heteroplasmy). The detection of mtDNA mutations in body fluids using resequencing microarrays, which are more sensitive than other sequencing methods, could provide a strategy to measure mutation loads in remote anatomical sites.</p> <p>Methods</p> <p>We determined the mtDNA mutation load in the entire mitochondrial genome of 26 individuals with different early stage cancers (lung, bladder, kidney) and 12 heavy smokers without cancer. MtDNA was sequenced from three matched specimens (blood, tumor and body fluid) from each cancer patient and two matched specimens (blood and sputum) from smokers without cancer. The inherited wildtype sequence in the blood was compared to the sequences present in the tumor and body fluid, detected using the Affymetrix Genechip^®Human Mitochondrial Resequencing Array 1.0 and supplemented by capillary sequencing for noncoding region.</p> <p>Results</p> <p>Using this high-throughput method, 75% of the tumors were found to contain mtDNA mutations, higher than in our previous studies, and 36% of the body fluids from these cancer patients contained mtDNA mutations. Most of the mutations detected were heteroplasmic. A statistically significantly higher heteroplasmy rate occurred in tumor specimens when compared to both body fluid of cancer patients and sputum of controls, and in patient blood compared to blood of controls. Only 2 of the 12 sputum specimens from heavy smokers without cancer (17%) contained mtDNA mutations. Although patient mutations were spread throughout the mtDNA genome in the lung, bladder and kidney series, a statistically significant elevation of tRNA and ND complex mutations was detected in tumors.</p> <p>Conclusion</p> <p>Our findings indicate comprehensive mtDNA resequencing can be a high-throughput tool for detecting mutations in clinical samples with potential applications for cancer detection, but it is unclear the biological relevance of these detected mitochondrial mutations. Whether the detection of tumor-specific mtDNA mutations in body fluidsy this method will be useful for diagnosis and monitoring applications requires further investigation.</p

Identification of an autoantibody panel to separate lung cancer from smokers and nonsmokers

<p>Abstract</p> <p>Background</p> <p>Sera from lung cancer patients contain autoantibodies that react with tumor associated antigens (TAAs) that reflect genetic over-expression, mutation, or other anomalies of cell cycle, growth, signaling, and metabolism pathways.</p> <p>Methods</p> <p>We performed immunoassays to detect autoantibodies to ten tumor associated antigens (TAAs) selected on the basis of previous studies showing that they had preferential specificity for certain cancers. Sera examined were from lung cancer patients (22); smokers with ground-glass opacities (GGOs) (46), benign solid nodules (55), or normal CTs (35); and normal non-smokers (36). Logistic regression models based on the antibody biomarker levels among the high risk and lung cancer groups were developed to identify the combinations of biomarkers that predict lung cancer in these cohorts.</p> <p>Results</p> <p>Statistically significant differences in the distributions of each of the biomarkers were identified among all five groups. Using Receiver Operating Characteristic (ROC) curves based on age, c-myc, Cyclin A, Cyclin B1, Cyclin D1, CDK2, and survivin, we obtained a sensitivity = 81% and specificity = 97% for the classification of cancer vs smokers(no nodules, solid nodules, or GGO) and correctly predicted 31/36 healthy controls as noncancer.</p> <p>Conclusion</p> <p>A pattern of autoantibody reactivity to TAAs may distinguish patients with lung cancer versus smokers with normal CTs, stable solid nodules, ground glass opacities, or normal healthy never smokers.</p

CT Scan Screening for Lung Cancer: Risk Factors for Nodules and Malignancy in a High-Risk Urban Cohort

Low-dose computed tomography (CT) for lung cancer screening can reduce lung cancer mortality. The National Lung Screening Trial reported a 20% reduction in lung cancer mortality in high-risk smokers. However, CT scanning is extremely sensitive and detects non-calcified nodules (NCNs) in 24-50% of subjects, suggesting an unacceptably high false-positive rate. We hypothesized that by reviewing demographic, clinical and nodule characteristics, we could identify risk factors associated with the presence of nodules on screening CT, and with the probability that a NCN was malignant.We performed a longitudinal lung cancer biomarker discovery trial (NYU LCBC) that included low-dose CT-screening of high-risk individuals over 50 years of age, with more than 20 pack-year smoking histories, living in an urban setting, and with a potential for asbestos exposure. We used case-control studies to identify risk factors associated with the presence of nodules (n=625) versus no nodules (n=557), and lung cancer patients (n=30) versus benign nodules (n=128).The NYU LCBC followed 1182 study subjects prospectively over a 10-year period. We found 52% to have NCNs >4 mm on their baseline screen. Most of the nodules were stable, and 9.7% of solid and 26.2% of sub-solid nodules resolved. We diagnosed 30 lung cancers, 26 stage I. Three patients had synchronous primary lung cancers or multifocal disease. Thus, there were 33 lung cancers: 10 incident, and 23 prevalent. A sub-group of the prevalent group were stable for a prolonged period prior to diagnosis. These were all stage I at diagnosis and 12/13 were adenocarcinomas.NCNs are common among CT-screened high-risk subjects and can often be managed conservatively. Risk factors for malignancy included increasing age, size and number of nodules, reduced FEV1 and FVC, and increased pack-years smoking. A sub-group of screen-detected cancers are slow-growing and may contribute to over-diagnosis and lead-time biases

Molecular Characterization of the Peripheral Airway Field of Cancerization in Lung Adenocarcinoma

<div><p><i>Field of cancerization</i> in the airway epithelium has been increasingly examined to understand early pathogenesis of non-small cell lung cancer. However, the extent of field of cancerization throughout the lung airways is unclear. Here we sought to determine the differential gene and microRNA expressions associated with field of cancerization in the peripheral airway epithelial cells of patients with lung adenocarcinoma. We obtained peripheral airway brushings from smoker controls (n=13) and from the lung contralateral to the tumor in cancer patients (n=17). We performed gene and microRNA expression profiling on these peripheral airway epithelial cells using Affymetrix GeneChip and TaqMan Array. Integrated gene and microRNA analysis was performed to identify significant molecular pathways. We identified 26 mRNAs and 5 miRNAs that were significantly (FDR <0.1) up-regulated and 38 mRNAs and 12 miRNAs that were significantly down-regulated in the cancer patients when compared to smoker controls. Functional analysis identified differential transcriptomic expressions related to tumorigenesis. Integration of miRNA-mRNA data into interaction network analysis showed modulation of the extracellular signal-regulated kinase/mitogen-activated protein kinase (ERK/MAPK) pathway in the contralateral lung field of cancerization. In conclusion, patients with lung adenocarcinoma have tumor related molecules and pathways in histologically normal appearing peripheral airway epithelial cells, a substantial distance from the tumor itself. This finding can potentially provide new biomarkers for early detection of lung cancer and novel therapeutic targets.</p></div

ERK/MAPK Signaling Pathway.

<p>IPA was used to generate a network of molecules from mRNA and miRNA that were significantly different in peripheral airway field of cancerization in lung cancer patients compared to smoker controls. This composite network merge three top networks based on “network score,” which converges on the ERK/MAPk pathway: specifically merging on Extracellular signal-related kinases 1 and 2 (ERK1/2), c-Jun N-terminal kinases (JNKs), and p38 kinases subfamily. <b>Red</b> is up-regulated and <b>Green</b> is down-regulated.</p