Stage I

Despite focused research in conventional therapies, the 5-year survival rate of lung cancer patients remains only 15%. Due to the lack of reliable early diagnostic techniques, most patients are diagnosed with advanced-stage disease and therefore have a poor prognosis. There are more than 100 million current and former smokers in the US who have elevated risk for lung cancer and may benefit from a non-invasive test for early detection. Patients at risk may have subclinical disease for years prior to diagnosis. Therefore the clinical challenge is to develop lung cancer detection methods that are effective during this window of opportunity to increase the rate of early detection and thereby spur early treatment and improve lung cancer patient outcomes. During tumorigenesis, the developing tumor forms a complex cellular microenvironment that produces many soluble mediators, such as proteins and miRNAs, which are important for tumor initiation and growth and thus can serve as lung cancer biomarkers. Because the pulmonary vascular bed comprises approximately 50% of the body’s total vasculature, the peripheral blood is expected to reflect even small variations in lung cancer biomarkers.

Over the past decade, our laboratory has gathered substantial data supporting the analysis of soluble mediators of lung carcinogenesis including inflammatory cytokines, growth and angiogenic factors, and miRNA. Additionally, a considerable literature has developed describing the contribution of these factors to lung carcinogenesis. Based on these findings, in our preliminary studies we have developed a plasma protein-based biomarker panel that accurately distinguished between lung cancer patients and at-risk control subjects. Our predictive model provided 97% sensitivity, 77% specificity, and an area under the ROC of .93 for stage 1 vs. high-risk controls. Because recent studies demonstrated that circulating miRNA can serve as robust and specific diagnostic biomarkers, we developed a panel of 17 miRNAs relevant to lung carcinogenesis. In our preliminary studies utilizing this miRNA panel, we were able to detect miRNAs that are differentially expressed in the plasma of lung cancer patients as compared to high-risk controls. We hypothesize that the proteins and miRNA profiles will each contribute unique information and thus, an integrated panel of protein and miRNA markers may provide higher sensitivity for early lung cancer detection than either platform alone.

Specific aims for the current proposal include: 1) to identify protein profiles in plasma associated with early-stage lung cancer, 2) to identify plasma miRNA profiles associated with early stage lung cancer, and 3) to integrate miRNA and cytokine biomarkers established in Aims 1 and 2 to improve early-stage lung cancer detection. The first and second aims address the clinical problems of patients at risk for lung cancer who present with a suspicious pulmonary nodule of indeterminate significance that has be further evaluated.

The most effective curative modality we have for lung cancer is still surgery, but, even with optimum surgery, many patients relapse and die of their lung cancer, and this fraction is improved only slightly by adjuvant chemotherapy, which is nonetheless toxic for everybody. Thus, we need to identify prognostic biomarkers (to define which patients are likely to relapse after surgery) and predictive biomarkers (to define who will benefit from adjuvant therapy, and what specific type of adjuvant therapy should be used on each particular patient). In this proposal, collaborators from two different lung cancer SPOREs in three different universities will use two sets of high information-content analyses of protein and RNA expression already acquired on a cohort of early-stage lung cancer patients who did not receive adjuvant chemotherapy and in a panel of cell lines rigorously characterized for sensitivity to platinum doublets, and will add to that data funded by this proposal on a new cohort of patients treated with adjuvant chemotherapy to generate our classifier. This classifier will then be reduced to a small number of RNA or protein assays and tested in a larger set of resected patients with and without adjuvant chemotherapy.

Lung cancer is the leading cause of death for men and women in the United States. Screening methods for the early detection of lung cancer must be developed to circumvent the phenomenon that sees the majority of patients being diagnosed with advanced, and thus incurable, lung cancer. While computerized tomography was demonstrated to reduce mortality by 20% in a high-risk smoker patient cohort, further examination of this method, complementary screening methods, and tools to screen less at-risk populations are needed to make the detection of early-stage lung cancer most beneficial. Using the expertise of our multidisciplinary clinical lung cancer team (Drs. Kelly, Gandara, Cooke, Yoneda, and Murin) with leaders in metabolomics and lipid biology (Dr. Fiehn) and glycomics (Drs. Miyamoto and Lebrilla), we aim to identify mass spectroscopy-detectible biomarkers of early lung cancer. The advent of “omics” technologies will allow us to expand the current pool of DNA and protein biomarkers and facilitate our vision of a systems-biology screening and/or diagnostic tool based on “biomarker panels.” Our approach begins with a discovery phase that will identify biomarkers in tumor and blood with the aim of finding blood-based biomarkers that most closely reflect tumor biology. Next we will proceed to the testing phase to determine the performance of these biomarkers in four independent cohorts (two lung cancer cohorts, healthy controls, and benign nodules). Biomarkers that meet our pre-specified criteria will require further evaluation that is beyond the scope of this proposal.

The overarching concept guiding our work is that combinations of biomarkers and imaging characteristics will be most robust in predicting which patients with lung nodules should receive an aggressive diagnostic and/or therapeutic approach and which patients should be monitored for nodule stability over time. Our objectives are to improve two biomarker tests that already have shown significant promise-- sputum chromosomal aneusomy by fluorescence in situ hybridization (CA-FISH) and the analysis of multiple serum analytes by a highly multiplexed aptamer-based assay developed by SomaLogic, Inc.-- and then to compare test performance with predictive models based on nodule and patient characteristics. These objectives are based on two separate hypotheses: 1. The current CA-FISH assay panel can be further improved by the development of two sub-panels, one for detection of squamous cell carcinoma of the lung (SCC) and the second for the detection of adenocarcinoma of the lung (AC). 2. The current aptamer-based analysis can be further improved by developing separate assays specifically designed for the detection of AC, SCC, K-ras, and EGFR-mutated lung tumors. The assays will then be applied to sputum and blood specimens from the Pittsburgh SPORE Pittsburgh Lung Screening Study (PLuSS) and Colorado SPORE Lung Nodule cohorts, in concert with analysis of the CT characteristics of the corresponding lung nodules, including size, volume, density, location, or airway involvement, to compare test performances and to assess whether combining tests would be valuable.

Evaluating suspicious pulmonary nodules in the setting of a lung cancer diagnostic work-up is a common clinical management challenge. High resolution multi-detector computed tomography visualizes many noncalcified lesions that could represent either potentially lethal cancers or benign processes. Although the vast majority of these nodules are benign, non-neoplastic lesions, a subset will be malignant and in these cases, patients could benefit from immediate treatment. A non-invasive test that maintains a high sensitivity and specificity across the spectrum of clinical presentations of pulmonary nodules would be of tremendous clinical benefit by reducing the number unnecessary invasive procedures and minimizing the time between detection and definitive treatment. The overall goal for the proposed studies is to develop a convenient and low-cost serum test that will, either alone or in combination with clinical features, accurately classify indeterminate nodules and direct proper clinical management. Our approach to develop such a test will be to profile and identify which proteins detected in the serum at the time of the detection of these nodules were immunogenic and induced the production of circulating autoantibodies. Differences in the autoantibody profile between the malignant and non-malignant groups will then be further evaluated against additional clinical specimens from both our institution and two pre-specified collaborator institutions to identify the optimal test for this urgent clinical need. Once completed, we anticipate this test will aid clinicians in deciding the optimal treatment for patients found to have suspicious nodules and improve the current standard of care.

Lung cancer remains the leading cause of cancer-related death due to the limited ability to detect the disease at an early and potentially curable stage. Low-dose computerized tomography (LDCT) screening of high-risk patients improves mortality. However, the high proportion of false positive tests, the majority of which are pulmonary nodules, has limited widespread adoption of this life-saving screening modality. Since screen-detected pulmonary nodules contribute to the overall cost of screening and potential morbidity due to the need for further diagnostic testing, there is an urgent need for accurate tools to stratify patients with screen-detected nodules for routine follow-up or additional intervention. Our data suggests that, similar to the smoking-associated field effect, the lung cancer-associated airway field of injury can be measured less invasively using gene-expression profiling of the nasal epithelium. Because early-stage lung cancers detected by CT screening may have distinct biologic characteristics compared to those diagnosed in patients evaluated for clinical symptoms, we propose to develop an accurate biomarker integrating expression profiling of genes and microRNA, clinical variables, and nodule characteristics in order to distinguish early lung malignancies from benign pulmonary nodules. The immediate clinical application of this test will be in the evaluation of patients with LDCT-detected lung nodules, and will allow clinicians to more accurately stratify patients to or from further diagnostic intervention. Ultimately, this test may also have utility for further stratifying lung cancer risk in patients eligible for screening prior to undergoing LDCT.

The release of small amounts of cell-free DNA into the bloodstream from dying tumor cells has been well documented in patients with various malignancies. Driven by improvements in assay technologies, such circulating tumor DNA (ctDNA) is beginning to show excellent promise as a non-invasive cancer biomarker. Because unique somatic mutations can serve as telltale marks to distinguish tumor-derived DNA in plasma, the cancer specificity of ctDNA is expected to be very high, making it an attractive candidate as a biomarker for cancer screening. Because many somatic mutations in non-small cell lung cancer (NSCLC) are found clustered with high frequency within a small set of genes, detection of ctDNA in such patients is technically more tractable. We have created a highly multiplexed, ultrasensitive assay that uses next-generation sequencing techniques to quantify low-abundance mutant DNA fragments in plasma against a large excess of background wild-type DNA. Our assay can simultaneously evaluate mutations in 40 different hotspots in up to 96 patient samples, using novel error suppression methods to optimize detection sensitivity. We have shown that the assay is capable of measuring very small amounts of mutant ctDNA in a limited set of patients with early-stage disease or with small, localized recurrences. We now aim to test the clinical performance of the assay on a larger scale. We propose to perform a case-control study comparing patients with early-stage NSCLC to individuals with a heavy smoking history or individuals having benign pulmonary nodules.

Prognosis for non-small cell lung cancer (NSCLC) is substantially better in early-stage as opposed to late-stage disease. If high-risk individuals could be effectively identified, they could significantly benefit from intensive surveillance, early detection, and prevention strategies. While smoking is a primary risk factor, only about 15% of smokers develop the disease. Consistent with a genetic predisposition, some patients have a family history of the disease and are affected at a young age, although they have never smoked. Unfortunately, NSCLC predisposing gene mutations are poorly characterized. We aim to identify novel NSCLC germline risk gene mutations. Our team members have previously identified novel risk gene mutations for several familial cancers. We have assembled a large clinically and molecularly well-characterized NSCLC case-control cohort of never-smokers, performed whole-exome sequencing (WES) on familial and early onset NSCLC probands, identified promising candidate risk genes, and externally validated selected samples. We follow a resource-conscious approach by focusing on a founder population, Ashkenazi Jews (AJ), to decrease genetic heterogeneity and to increase the allele frequencies of candidate recurrent risk mutations. We will perform WES on additional familial and early onset NSCLC probands to identify recurrently mutated NSCLC genes and cross-validate the genes using whole-genome sequencing on matching tumors. The gene candidates identified will be validated using a targeted sequencing approach in a larger non-AJ cohort. These are anticipated to provide new biological insights, serve as future chemoprevention targets, and be used in the development of a genetic diagnostic test to identify high-risk individuals.