Non-small cell lung cancer (NSCLC)

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.

Folate/homocysteine (Hcy) metabolism facilitates the methylation of DNA, proteins and carcinogens; nucleic acid synthesis; and glutathione generation. Dysregulation of folate/Hcy metabolism, due to suboptimal vitamin status and/or functional polymorphisms of key enzymes, is an etiologic feature of some cancers, including lung cancer. Several drugs that target enzymes of folate/Hcy metabolism have become mainstays of cancer chemotherapy, most recently pemetrexed (PMX), which targets dihydrofolate reductase (DHFR) and thymidylate synthase (TS) and is used to treat patients with non-small cell lung cancer (NSCLC). Our primary and secondary hypotheses are that folate/Hcy phenotype, perhaps underpinned by genotype, is a major contributor to (i) clinical response to PMX, and (ii) risk of NSCLC itself. We will use archived and newly acquired samples from PMX-treated NSCLC patients to define pre-treatment and in-treatment folate/Hcy phenotypes using LC-MRM/MS technology that can precisely determine the concentrations and relative proportions of key folates (tetrahydrofolate; 5-methyltetrahydrofolate; 5,10-methenyltetrahydrofolate) and Hcy. Genotypes will be determined for approximately twenty folate-related genes. Biostatistical analyses will be used to establish

whether aspects of folate/Hcy phenotype and/or genotype are prospectively or concurrently associated with time to tumor progression, survival time, toxicity, etc. Case-control analysis of genetic and phenotypic data will also be undertaken to determine whether any genetic and/or biomarker variables predict case status. This project has the potential to identify biomarkers and/or genetic factors that can be used to predict or monitor responses of NSCLC patients to PMX, and may provide the basis for the future deployment of personalized medicine strategies in lung cancer treatment.

Inhibitors of the VEGF pathway, including the VEGF antibody bevacizumab (BV) and multitargeted VEGFR tyrosine kinase inhibitors such as vandetanib, have in some cases improved progression-free survival (PFS) and/or overall survival (OS) in patients with advanced NSCLC. While benefits in the overall NSCLC population have thus far been modest, it is clear that a subset of patients have dramatic benefit, while others may experience no benefit or even life-threatening toxicities. Furthermore, virtually all patients who respond inevitably develop therapeutic resistance. Thus, the development of markers predictive of response or resistance for VEGF inhibitors is critically needed to advance NSCLC treatment. There are currently no validated predictive markers for VEGF inhibitors, and the development of such markers is made more challenging by the impact of both tumor- and host-derived circulating factors on tumor angiogenesis. Useful predictive markers will likely need to reflect factors from both sources. We hypothesize that by comprehensively profiling angiogenesis-related CAFs and other peptides in the blood, as well as germline variations in angiogenesis-related genes, non-invasive predictive markers for identifying which patients benefit from VEGF inhibitors (alone or with chemotherapy) can be developed and validated.

The investigators of this proposal have a longstanding collaboration to develop predictive markers for VEGF inhibitors and, along with other investigators, have identified three promising and potentially complementary approaches using blood-based markers that will be tested in this grant. We have also played leadership roles in major randomized clinical trials of VEGF inhibitors, which have enabled us to leverage a unique set of resources to address these questions.

In Aim 1, we will use multiplexed CAF profiling to identify high-priority candidate predictive markers for differential benefit from VEGF inhibitors with chemotherapy compared to chemotherapy alone using two independent sets of samples from randomized phase II trials: one testing bevacizumab (BV) with chemotherapy (pemetrexed/carboplatin or Pem/C) vs. Pem/C alone (BATTLE-FL) trial, and another testing the VEGFR/EGFR tyrosine kinase inhibitor vandetanib (VAN) plus carboplatin/paclitaxel (VCP) vs. CP, in first-line NSCLC trial. We will also test CAF markers of benefit for VAN vs. standard therapy with erlotinib using specimens from a recently completed randomized phase III trial (ZEST).

In Aim 2, we will use MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectroscopy to develop a serum proteomic classifier for benefit for VEGF inhibitors, using samples from the same trials as in Aim 1. We previously used a similar approach to develop a serum proteomic marker of benefit (Veristrat) for EGFR inhibitors.

Finally, in Aim 3, we will validate a signature of SNPs from three angiogenesis-related genes that were found to be predictive of benefit for BV with CP (BCP) vs. CP in the ECOG4599 trial. This will be tested in the same three trials of VEGF inhibitors. It is worth noting that our interest is not in developing markers for a particular drug, but rather to identify markers and pathways relevant to predicting response or resistance to inhibitors of the VEGF pathway, a validated therapeutic target in NSCLC. Our data thus far suggest there will be overlap in markers for BV and VEGFR TKIs but it will be important to determine whether this is true, particularly as more potent and specific inhibitors are tested.

Impact. This project has the potential to address a critical unmet need in NSCLC by producing validated, non-invasive blood-based predictive markers for identifying patients more likely to benefit from VEGF inhibitors, and can provide critical insights into mechanisms of resistance to guide future combination regimens.

Targeted therapies such as erlotinib or gefitinib for EGFR-mutated lung cancer and crizotinib for ALK-translocated lung cancer can yield remarkable responses and significant clinical benefit. Other mutations in genes such as K-RAS, BRAF, and PI3K, among others, are also being actively investigated as targets of new drugs in clinical trials. However, a substantial number of patients (~40%) have no mutations when tested with a panel of known oncogenic mutations. The aim of this research proposal is to identify novel somatic genomic alterations in lung cancer, using an existing tumor bank of lung cancer cases with known clinical and tumor genetic information. We will focus on those lung cancers that are known to be wildtype on SNaPshot and ALK FISH (fluorescence in situ hybridization) (i.e., negative for over 50 tested oncogenic mutations), and perform both deep sequencing of exomes and a comprehensive search for translocations involving kinases. We believe that this effort will identify novel tumor genomic changes that can be targets for therapy.

There is recent evidence that metformin, a drug commonly used to treat diabetes, has anticancer effects and can significantly improve the rate of pathological response in breast cancers treated with chemotherapy. We propose to review clinical records of lung cancer patients treated over the past 25 years at Johns Hopkins to determine whether metformin treatment is associated with improved clinical response to chemotherapy for lung cancer. Because our preliminary laboratory work suggests that lung cancer cells with mutations of the LKB1 gene are particularly sensitive to metformin, we will secondarily determine whether mutations of the LKB1 gene (which is mutated in up to 40% of lung adenocarcinomas) are associated with a higher frequency of response to chemotherapy among these patients treated with metformin. In a second specific aim, we will conduct studies using xenografts of human lung cancers in the laboratory to further examine possible links between LKB1 mutations and response to metformin, with or without paclitaxel. Establishing such a link could lead to improved chemotherapy (using metformin in combination with conventional chemotherapy drugs) and to development of a biomarker that can identify patients with cancers who might have improved response to chemotherapy with metformin.

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.

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.