Risk profile

Three independent principal investigators at the Fred Hutchinson Cancer Research Center with combined expertise in thoracic oncology, cancer epidemiology and prevention, and molecular diagnostics are joining efforts to respond to the Protect Your Lungs RFA. The proposed research program is focused on developing novel blood-based tests to 1) identify never-smoker subjects at increased risk of developing lung cancer (C. Li, PI), 2) detect lung cancer before onset of symptoms (G. Goodman, PI),  and 3) distinguish between benign and malignant lesions identified by CT scans (S. Hanash, PI). The research questions address important public health issues in lung cancer and build on discoveries of candidate markers by the applicant group for which we propose validation studies in this proposal. Validation studies to be conducted will take advantage of the availability to the investigators of pertinent bio-specimens consisting of samples derived from two large cohort studies (The Women’s Health Initiative and the Beta Carotene and Retinol Efficacy Trial (CARET)) and samples derived from CT screening studies. The samples available are sufficient to meet the needs of the proposed studies. The collaborative nature of the proposed research will ensure synergism and efficiency of scale to meet study objectives and fast-track the development of clinically relevant applications.

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.

Pulmonary nodules found on imaging studies are a significant diagnostic problem. Most lesions are indeterminate, but because of the concern for lung cancer all patients require further evaluation with resultant radiation risk, significant cost, and delays in diagnosis. We believe that the development of a serum test for lung cancer would have a significant impact on patient care and outcomes. We recently identified a novel panel of 25 serum autoantibodies associated with non-small cell lung cancer (NSCLC) and constructed a protein microarray containing the autoantigens. In a pilot study, we tested the microarray with human sera and developed a classification algorithm that incorporated microarray test results with nodule size. This algorithm had an overall sensitivity of 81% and specificity of 83% for the diagnosis of lung cancer. We now propose to further develop this panel and hypothesize that an autoantibody signature along with nodule size will be able to determine which patients have lung cancer.

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.

Better methods are required to accurately risk stratify patients with a solitary pulmonary nodule (SPN) who may undergo unnecessary procedures to exclude a cancer diagnosis. 18F-FDG Positron Emission Tomography (PET) is a widely available clinical test used to evaluate concerning SPNs and stage malignant lung nodules. It therefore facilitates clinical assessment but remains an imperfect tool for the diagnosis, localization, and prognostication of SPNs, be they infectious or malignant in nature. Furthermore, it performs well for staging lung cancer, but inaccurately categorizes patients in some cases who receive futile thoracotomies for disseminated disease, or conversely, are undertreated for localized disease. The applicant therefore plans on investigating whether circulating molecular biomarkers can increase the utility of 18F-FDG-PET imaging in clinical practice using statistical models that incorporate in vivo and in vitro diagnostics The concept here is “one-stop shopping,” where a patient can obtain routine molecular imaging and molecular diagnostics from routine blood work during an appointment that in combination increase the accuracy of a clinician’s diagnosis and facilitate the appropriate decision-making process. For this proposal we will develop a model that incorporates a circulating microRNA profile to discriminate infectious from malignant SPNs for patients along with clinical and FDG-PET data and, secondly, identify patients with malignant nodules and detectable circulating tumor cells to determine whether these cells upstage or augment clinical staging or prognosis post-operatively when compared to FDG-PET alone.

The development of non-invasive diagnostic tools to detect lung cancer at an early stage is the subject of active investigations because of its enormous potential impact on the number-one cause of cancer-related deaths. We have recently discovered by shotgun proteomics profiling 164 candidate biomarkers that are differentially expressed in stage 1 lung cancer tissue compared to non-malignant matched controls tissues and found in the circulation. Validation of these candidates by traditional methods using western blot or ELISA is a slow, low throughput, and expensive process. In this application, we propose to take advantage of the individual’s immune response to capture and detect autoantibodies directed against these 164 candidate protein targets identified by shotgun proteomics. To achieve this goal, we intend (1) to establish immune-based assays for the detection and quantitation  of lung cancer autoantibodies using an indirect ELISA; (2) to validate the diagnostic performance of our autoantibody signature in blood samples from individuals with early-stage lung cancer or matched nodule controls; and (3) to determine whether the diagnostic performance of our panel of autoantibody signatures augments the diagnostic accuracy of known predictive models for the evaluation of lung nodules. These studies may lead to the development and early validation of a new molecular diagnostic tool for lung cancer.