Faculty Bibliography

Approximately 30% of human lung cancers acquire mutations in either Keap1 or Nfe2l2, resulting in the stabilization of Nrf2, the Nfe2l2 gene product, which controls oxidative homeostasis. Here, we show that heme triggers the degradation of Bach1, a pro-metastatic transcription factor, by promoting its interaction with the ubiquitin ligase Fbxo22. Nrf2 accumulation in lung cancers causes the stabilization of Bach1 by inducing Ho1, the enzyme catabolizing heme. In mouse models of lung cancers, loss of Keap1 or Fbxo22 induces metastasis in a Bach1-dependent manner. Pharmacological inhibition of Ho1 suppresses metastasis in a Fbxo22-dependent manner. Human metastatic lung cancer display high levels of Ho1 and Bach1. Bach1 transcriptional signature is associated with poor survival and metastasis in lung cancer patients. We propose that Nrf2 activates a metastatic program by inhibiting the heme- and Fbxo22-mediated degradation of Bach1, and that Ho1 inhibitors represent an effective therapeutic strategy to prevent lung cancer metastasis.

There has been a dramatic increase in the detection of lung nodules, many of which are preneoplasia atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (ADC). The molecular landscape and the evolutionary trajectory of lung preneoplasia have not been well defined. Here, we perform multi-region exome sequencing of 116 resected lung nodules including AAH (n = 22), AIS (n = 27), MIA (n = 54) and synchronous ADC (n = 13). Comparing AAH to AIS, MIA and ADC, we observe progressive genomic evolution at the single nucleotide level and demarcated evolution at the chromosomal level supporting the early lung carcinogenesis model from AAH to AIS, MIA and ADC. Subclonal analyses reveal a higher proportion of clonal mutations in AIS/MIA/ADC than AAH suggesting neoplastic transformation of lung preneoplasia is predominantly associated with a selective sweep of unfit subclones. Analysis of multifocal pulmonary nodules from the same patients reveal evidence of convergent evolution.

INTRODUCTION/BACKGROUND:Detailed guidelines regarding the use of radiation therapy for malignant pleural mesothelioma (MPM) are currently lacking because of the rarity of the disease, the wide spectrum of clinical presentations, and the paucity of high-level data on individual treatment approaches. METHODS:In March 2017, a multidisciplinary meeting of mesothelioma experts was cosponsored by the U.S. National Cancer Institute, International Association for the Study of Lung Cancer Research, and Mesothelioma Applied Research Foundation. Among the outcomes of this conference was the foundation of detailed, multidisciplinary consensus guidelines. RESULTS:Here we present consensus recommendations on the use of radiation therapy for MPM in three discrete scenarios: (1) hemithoracic radiation therapy to be used before or after extrapleural pneumonectomy; (2) hemithoracic radiation to be used as an adjuvant to lung-sparing procedures (i.e., without pneumonectomy); and (3) palliative radiation therapy for focal symptoms caused by the disease. We discuss appropriate simulation techniques, treatment volumes, dose fractionation regimens, and normal tissue constraints. We also assess the role of particle beam therapy, specifically, proton beam therapy, for MPM. CONCLUSION/CONCLUSIONS:The recommendations provided in this consensus statement should serve as important guidelines for developing future clinical trials of treatment approaches for MPM.

BACKGROUND:Previous studies have reported volatile organic compounds (VOCs) in the breath as apparent biomarkers of lung cancer. We tested the hypothesis that a robust breath VOC biomarker of lung cancer should also predict pulmonary nodules in chest CT images. METHODS:Biomarker discovery study (unblinded): 301 subjects were screened for lung cancer with low dose chest CT (LDCT), and donated duplicate samples of alveolar breath for analysis with gas chromatography mass spectrometry (GC MS). Monte Carlo analysis of breath chromatograms revealed a mass ion as a biomarker that identified biopsy-proven lung cancer as well as suspicious pulmonary nodules on LDCT. The biomarker was termed MAGIIC (Mass Abnormalities in Gaseous Ions with Imaging Correlates). The chemical structure of MAGIIC was tentatively identified from the NIST library of mass spectra; the best-fit compounds included C4 and C5 alkane derivatives that were consistent with metabolic products of oxidative stress. Blinded validation of MAGIIC: The abundance of the MAGIIC biomarker was determined in a different group of 161 subjects undergoing screening with LDCT. They donated duplicate alveolar breath VOC samples that were analyzed at two independent laboratories. The study was blinded and monitored with Good Clinical Practice. The abundance of MAGIIC in breath predicted biopsy-proven lung cancer with 84% accuracy, sensitivity = 75.4% and specificity = 85.0%. MAGIIC also predicted pulmonary nodules in LDCT with 80.5% accuracy, sensitivity = 80.1% and specificity = 75.0%. Breath MAGIIC abundance was not significantly affected by tobacco smoking history. CONCLUSIONS:In a blinded study, breath VOC MAGIIC accurately predicted lung cancer confirmed on a tissue biopsy, as well as suspicious pulmonary nodules observed on LDCT. MAGIIC may have been a product of oxidative stress and it could potentially be employed as an ancillary to LDCT to predict the likelihood that a pulmonary nodule is malignant.

BACKGROUND:Human microbiota have many functions that could contribute to cancer initiation and/or progression at local sites, yet the relation of the lung microbiota to lung cancer prognosis has not been studied. METHODS:In a pilot study, 16S rRNA gene sequencing was performed on paired lung tumor and remote normal samples from the same lobe/segment in 19 non-small cell lung cancer patients. We explored associations of tumor or normal tissue microbiome diversity and composition with recurrence-free and disease-free survival, and compared microbiome diversity and composition between paired tumor and normal samples. RESULTS:Higher richness and diversity in normal tissue were associated with reduced recurrence-free survival (richness p=0.08, Shannon index p=0.03) and disease-free survival (richness p=0.03, Shannon index p=0.02), as was normal tissue overall microbiome composition (Bray-Curtis p=0.09 for recurrence-free and p=0.02 for disease-free survival). In normal tissue, greater abundance of family Koribacteraceae was associated with increased recurrence-free and disease-free survival, while greater abundance of families Bacteroidaceae, Lachnospiraceae, and Ruminococcaceae were associated with reduced recurrence-free or disease-free survival (p<0.05). Tumor tissue diversity and overall composition were not associated with recurrence-free or disease-free survival. Tumor tissue had lower richness and diversity (p≤0.0001) than paired normal tissue, though overall microbiome composition did not differ between paired samples. CONCLUSIONS:We demonstrate, for the first time, a potential relationship between the normal lung microbiota and lung cancer prognosis, which requires confirmation in a larger study. IMPACT/CONCLUSIONS:Definition of bacterial biomarkers of prognosis may lead to improved survival outcomes for lung cancer patients.

: Low-dose CT (LDCT) is widely accepted as the preferred method for detecting pulmonary nodules. However, the determination of whether a nodule is benign or malignant involves either repeated scans or invasive procedures that sample the lung tissue. Noninvasive methods to assess these nodules are needed to reduce unnecessary invasive tests. In this study, we have developed a pulmonary nodule classifier (PNC) using RNA from whole blood collected in RNA-stabilizing PAXgene tubes that addresses this need. Samples were prospectively collected from high-risk and incidental subjects with a positive lung CT scan. A total of 821 samples from 5 clinical sites were analyzed. Malignant samples were predominantly stage 1 by pathologic diagnosis and 97% of the benign samples were confirmed by 4 years of follow-up. A panel of diagnostic biomarkers was selected from a subset of the samples assayed on Illumina microarrays that achieved a ROC-AUC of 0.847 on independent validation. The microarray data were then used to design a biomarker panel of 559 gene probes to be validated on the clinically tested NanoString nCounter platform. RNA from 583 patients was used to assess and refine the NanoString PNC (nPNC), which was then validated on 158 independent samples (ROC-AUC = 0.825). The nPNC outperformed three clinical algorithms in discriminating malignant from benign pulmonary nodules ranging from 6-20 mm using just 41 diagnostic biomarkers. Overall, this platform provides an accurate, noninvasive method for the diagnosis of pulmonary nodules in patients with non-small cell lung cancer. SIGNIFICANCE: These findings describe a minimally invasive and clinically practical pulmonary nodule classifier that has good diagnostic ability at distinguishing benign from malignant pulmonary nodules.

Malignant pleural mesothelioma (MPM) is an aggressive inflammatory cancer with a poor survival rate. Treatment options are limited at best and drug resistance is common. Thus, there is an urgent need to identify novel therapeutic targets in this disease in order to improve patient outcomes and survival times. MST1R (RON) is a trans-membrane receptor tyrosine kinase (RTK), which is part of the c-MET proto-oncogene family. The only ligand recognized to bind MST1R (RON) is Macrophage Stimulating 1 (MST1), also known as Macrophage Stimulating Protein (MSP) or Hepatocyte Growth Factor-Like Protein (HGFL). In this study, we demonstrate that the MST1-MST1R (RON) signaling axis is active in MPM. Targeting this pathway with a small molecule inhibitor, LCRF-0004, resulted in decreased proliferation with a concomitant increase in apoptosis. Cell cycle progression was also affected. Recombinant MST1 treatment was unable to overcome the effect of LCRF-0004 in terms of either proliferation or apoptosis. Subsequently, the effect of an additional small molecular inhibitor, BMS-777607 (which targets MST1R (RON), MET, Tyro3, and Axl) also resulted in a decreased proliferative capacity of MPM cells. In a cohort of MPM patient samples, high positivity for total MST1R by IHC was an independent predictor of favorable prognosis. Additionally, elevated expression levels of MST1 also correlated with better survival. This study also determined the efficacy of LCRF-0004 and BMS-777607 in xenograft MPM models. Both LCRF-0004 and BMS-777607 demonstrated significant anti-tumor efficacy in vitro, however BMS-777607 was far superior to LCRF-0004. The in vivo and in vitro data generated by this study indicates that a multi-TKI, targeting the MST1R/MET/TAM signaling pathways, may provide a more effective therapeutic strategy for the treatment of MPM as opposed to targeting MST1R alone.

Lung cancer remains the leading cause of cancer death worldwide1. With new treatment modalities, there has been a shift in focus to how we can predict who may respond to targeted treatments. Current data suggest that the human microbiome can affect lung cancer treatment through its effects on the systemic immune tone. Our group has shown that the lower airway microbiota of lung cancer patients is characterized by enrichment with oral commensals2 which triggers transcriptomic signatures (PI3K, MAPK) previously described in NSCLC 2,3. The impact of local lung dysbiosis on lung cancer progression and survival is unknown. Patients with suspicious nodules on imaging who underwent bronchoscopy were recruited. High-throughput sequencing of bacterial 16S rRNA-encoding gene amplicons was performed. Clustering was based on Dirichlet-Multinomial mixtures (DMM) modeling. RNAseq was performed on bronchial epithelial cells obtained by airway brushing. We focused our analysis on 83 NSCLC samples. Overall alpha-diversity showed that advanced stage (IIIb-VI) lower airway samples were more similar to buccal samples than local stage (I-IIIa), p<0.0001. In addition, worse 6-month and 1-year survival was associated with more similar alpha-diversity between lower airway and buccal samples (Figure 1A-D). Utilizing DMM two clusters were identified, Supraglottic-Predominant-Taxa (SPT) and Background-Predominant-Taxa (BPT). There was a significant increase in percentage of SPT in advance stage compared to local stage (p<0.008) Kaplan-Meir survival analysis shows worse survival in those with NSCLC who were clustered into the SPT group compared to BPT (p=0.0003, Figure 1E). With RNAseq, differentially expressed genes between advanced stage vs. local stage and 6-month vs. 1-year survival were not as pronounced as SPT vs. BPT (Figure 1F) suggesting that globally, transcriptomic changes between different stage and NSCLC survival were difficult to detect as compared to when airway microbiome were differentiated. In lung cancer, dysbiosis within the lower airway microenvironment, possibly by micro-aspiration, is associated with a worse 6-month and 1-year survival. This change is also associated with transcriptome changes in the local environment