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Abstract Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up. Through histologic, mutational, and transcriptomic profiling of tumor and adjacent-normal tissue, we identified an inflammatory gene signature in tumor-adjacent tissue as the strongest clinical predictor of disease progression. Single-cell transcriptomic analysis demonstrated the progression-associated inflammatory signature was expressed in both immune and non-immune cells, and cell type-specific profiling in monocytes further improved outcome predictions. Additional analyses of tumor-adjacent transcriptomic data from The Cancer Genome Atlas validated the association of the inflammatory signature with worse outcomes across cancers. Collectively, our study suggests that molecular profiling of tumor-adjacent tissue can identify patients at high risk for disease progression.

Summary: Lung cancer treatment has benefited greatly through advancements in immunotherapies. However, immunotherapy often fails in patients with specific mutations like KEAP1, which are frequently found in lung adenocarcinoma. We established an antigenic lung cancer model and used it to explore how Keap1 mutations remodel the tumor immune microenvironment. Using single-cell technology and depletion studies, we demonstrate that Keap1-mutant tumors diminish dendritic cell and T cell responses driving immunotherapy resistance. This observation was corroborated in patient samples. CRISPR-Cas9-mediated gene targeting revealed that hyperactivation of the NRF2 antioxidant pathway is responsible for diminished immune responses in Keap1-mutant tumors. Importantly, we demonstrate that combining glutaminase inhibition with immune checkpoint blockade can reverse immunosuppression, making Keap1-mutant tumors susceptible to immunotherapy. Our study provides new insight into the role of KEAP1 mutations in immune evasion, paving the way for novel immune-based therapeutic strategies for KEAP1-mutant cancers.

Abstract Malignant pleural effusions (MPE) complicate malignancies and portend worse outcomes. MPE is comprised of various components, including immune cells, cancer cells, and cell-free DNA/RNA. There have been investigations into using these components to diagnose and prognosticate MPE. We hypothesize that the microbiome of MPE is unique and may be associated with diagnosis and prognosis. We compared the microbiota of MPE against microbiota of pleural effusions from non-malignant and paramalignant states. We collected a total of 165 pleural fluid samples from 165 subjects; Benign (n = 16), Paramalignant (n = 21), MPE-Lung (n = 57), MPE-Other (n = 22), and Mesothelioma (n = 49). We performed high throughput 16S rRNA gene sequencing on pleural fluid samples and controls. We showed that there are compositional differences among pleural effusions related to non-malignant, paramalignant, and malignant disease. Furthermore, we showed differential enrichment of bacterial taxa within MPE depending on the site of primary malignancy. Pleural fluid of MPE-Lung and Mesothelioma were associated with enrichment with oral and gut bacteria that are commonly thought to be commensals, including Rickettsiella, Ruminococcus, Enterococcus, and Lactobacillales. Mortality in MPE-Lung is associated with enrichment in Methylobacterium, Blattabacterium, and Deinococcus. These observations lay the groundwork for future studies that explore host-microbiome interactions and their influence on carcinogenesis.

Abstract Background Cancer recurrence after tumor resection in early-stage non-small cell lung cancer (NSCLC) is common, yet difficult to predict. The lung microbiota and systemic immunity may be important modulators of risk for lung cancer recurrence, yet biomarkers from the lung microbiome and peripheral immune environment are understudied. Such markers may hold promise for prediction as well as improved etiologic understanding of lung cancer recurrence. Methods In tumor and distant normal lung samples from 46 stage II NSCLC patients with curative resection (39 tumor samples, 41 normal lung samples), we conducted 16S rRNA gene sequencing. We also measured peripheral blood immune gene expression with nanoString®. We examined associations of lung microbiota and peripheral gene expression with recurrence-free survival (RFS) and disease-free survival (DFS) using 500 × 10-fold cross-validated elastic-net penalized Cox regression, and examined predictive accuracy using time-dependent receiver operating characteristic (ROC) curves. Results Over a median of 4.8 years of follow-up (range 0.2–12.2 years), 43% of patients experienced a recurrence, and 50% died. In normal lung tissue, a higher abundance of classes Bacteroidia and Clostridia, and orders Bacteroidales and Clostridiales, were associated with worse RFS, while a higher abundance of classes Alphaproteobacteria and Betaproteobacteria, and orders Burkholderiales and Neisseriales, were associated with better RFS. In tumor tissue, a higher abundance of orders Actinomycetales and Pseudomonadales were associated with worse DFS. Among these taxa, normal lung Clostridiales and Bacteroidales were also related to worse survival in a previous small pilot study and an additional independent validation cohort. In peripheral blood, higher expression of genes TAP1, TAPBP, CSF2RB, and IFITM2 were associated with better DFS. Analysis of ROC curves revealed that lung microbiome and peripheral gene expression biomarkers provided significant additional recurrence risk discrimination over standard demographic and clinical covariates, with microbiome biomarkers contributing more to short-term (1-year) prediction and gene biomarkers contributing to longer-term (2–5-year) prediction. Conclusions We identified compelling biomarkers in under-explored data types, the lung microbiome, and peripheral blood gene expression, which may improve risk prediction of recurrence in early-stage NSCLC patients. These findings will require validation in a larger cohort.

Malignant pleural mesothelioma (MPM) is an aggressive tumor with poor prognosis and limited therapeutic options. The extracellular matrix protein fibulin-3/EFEMP1 accumulates in the pleural effusions of MPM patients and has been proposed as a prognostic biomarker of these tumors. However, it is entirely unknown whether fibulin-3 plays a functional role on MPM growth and progression. Here, we demonstrate that fibulin-3 is upregulated in MPM tissue, promotes the malignant behavior of MPM cells, and can be targeted to reduce tumor progression. Overexpression of fibulin-3 increased the viability, clonogenic capacity and invasion of mesothelial cells, whereas fibulin-3 knockdown decreased these phenotypic traits as well as chemoresistance in MPM cells. At the molecular level, fibulin-3 activated PI3K/Akt signaling and increased the expression of a PI3K-dependent gene signature associated with cell adhesion, motility, and invasion. These pro-tumoral effects of fibulin-3 on MPM cells were disrupted by PI3K inhibition as well as by a novel, function-blocking, anti-fibulin-3 chimeric antibody. Anti-fibulin-3 antibody therapy tested in two orthotopic models of MPM inhibited fibulin-3 signaling, resulting in decreased tumor cell proliferation, reduced tumor growth, and extended animal survival. Taken together, these results demonstrate for the first time that fibulin-3 is not only a prognostic factor of MPM but also a relevant molecular target in these tumors. Further development of anti-fibulin-3 approaches are proposed to increase early detection and therapeutic impact against MPM.

The evolution of immune landscape in lung adenocarcinoma (ADC) is largely unknown. Here the authors use a cohort of resected invasive lung ADC and its precursors and show a gradual increase of immunosuppression and decrease of anti-tumor response associated with specific genomic and epigenetic features.

It is known that invasive lung adenocarcinomas evolve from pre-cancerous dysplastic lesions. In this study, the authors show that evolution of pre-cancerous lesions is accompanied by DNA methylation alterations, and that global hypomethylation correlates with immune infiltration, mutational burden and copy number alterations.

ObjectivesTo investigate the efficacy and safety of lung stereotactic body radiation therapy (SBRT) for non-small cell lung cancer (NSCLC) including oligorecurrent and oligoprogressive disease.MethodsSingle-institution retrospective analysis of 60 NSCLC patients with 62 discrete lesions treated with SBRT between 2008 and 2017. Patients were stratified into three groups, including early stage, locally recurrent, and oligoprogressive disease. Group 1 included early stage local disease with no prior local therapy. Group 2 included locally recurrent disease after local treatment of a primary lesion, and group 3 included regional or well-controlled distant metastatic disease receiving SBRT for a treatment naive lung lesion (oligoprogressive disease). Patient/tumor characteristics and adverse effects were recorded. Local failure free survival (LFFS), progression free survival (PFS), and overall survival (OS) were estimated using the Kaplan Meier method.ResultsAt median follow-up of 34 months, 67% of the study population remained alive. The estimated 3-year LFFS for group 1, group 2, and group 3 patients was 95% (95% CI: 86%-100%), 82%(62% - 100%), and 83% (58-100%), respectively. The estimated 3-year PFS was 59% (42-83%), 40% (21%-78%), and 33% (12%-95%), and the estimated 3-year OS was 58% (41-82%), 60% (37-96%), and 58% (31-100%)), respectively for each group. When adjusted for age and size of lesion, no significant difference in OS, LFFS, and PFS emerged between groups (p > 0.05). No patients experienced grade 3 to 5 toxicity. Eighteen patients (29%) experienced grade 1 to 2 toxicity. The most common toxicities reported were cough and fatigue.ConclusionsOur data demonstrates control rates in group 1 patients comparable to historical controls. Our study also reveals comparable clinical results for SBRT in the treatment of NSCLC by demonstrating similar rates of LFFS and OS in group 2 and group 3 patients with locally recurrent and treatment naïve lung lesion with well-controlled distant metastatic disease.

Abstract Background The potential utility of microRNA as biomarkers for early detection of cancer and other diseases is being investigated with genome-scale profiling of differentially expressed microRNA. Processes for measurement assurance are critical components of genome-scale measurements. Here, we evaluated the utility of a set of total RNA samples, designed with between-sample differences in the relative abundance of miRNAs, as process controls. Results Three pure total human RNA samples (brain, liver, and placenta) and two different mixtures of these components were evaluated as measurement assurance control samples on multiple measurement systems at multiple sites and over multiple rounds. In silico modeling of mixtures provided benchmark values for comparison with physical mixtures. Biomarker development laboratories using next-generation sequencing (NGS) or genome-scale hybridization assays participated in the study and returned data from the samples using their routine workflows. Multiplexed and single assay reverse-transcription PCR (RT-PCR) was used to confirm in silico predicted sample differences. Data visualizations and summary metrics for genome-scale miRNA profiling assessment were developed using this dataset, and a range of performance was observed. These metrics have been incorporated into an online data analysis pipeline and provide a convenient dashboard view of results from experiments following the described design. The website also serves as a repository for the accumulation of performance values providing new participants in the project an opportunity to learn what may be achievable with similar measurement processes. Conclusions The set of reference samples used in this study provides benchmark values suitable for assessing genome-scale miRNA profiling processes. Incorporation of these metrics into an online resource allows laboratories to periodically evaluate their performance and assess any changes introduced into their measurement process.

Abstract Background The number of pulmonary nodules detected in the US is expected to increase substantially following recent recommendations for nationwide CT-based lung cancer screening. Given the low specificity of CT screening, non-invasive adjuvant methods are needed to differentiate cancerous lesions from benign nodules to help avoid unnecessary invasive procedures in the asymptomatic population. We have constructed a serum-based multi-biomarker panel and assessed its clinical accuracy in a retrospective analysis of samples collected from participants with suspicious radiographic findings in the Prostate, Lung, Chest and Ovarian (PLCO) cancer screening trial. Methods Starting with a set of 9 candidate biomarkers, we identified 8 that exhibited limited pre-analytical variability with increasing clotting time, a key pre-analytical variable associated with the collection of serum. These 8 biomarkers were evaluated in a training study consisting of 95 stage I NSCLC patients and 186 smoker controls where a 5-biomarker pulmonary nodule classifier (PNC) was selected. The clinical accuracy of the PNC was determined in a blinded study of asymptomatic individuals comprising 119 confirmed malignant nodule cases and 119 benign nodule controls selected from the PLCO screening trial. Results A PNC comprising 5 biomarkers: CEA, CYFRA 21-1, OPN, SCC, and TFPI, was selected in the training study. In an independent validation study, the PNC resolved lung cancer cases from benign nodule controls with an AUC of 0.653 (p

Background: Malignant mesothelioma (MM) is a rare but deadly malignancy with about 3,000 new cases being diagnosed each year in the US. Very few studies have been performed to analyze factors associated with mesothelioma survival, especially for peritoneal presentation. The overarching aim of this study is to examine survival of the cohort of patients with malignant mesothelioma enrolled in the National Mesothelioma Virtual Bank (NMVB). Methods: 888 cases of pleural and peritoneal mesothelioma cases were selected from the NMVB database, which houses data and associated biospecimens for over 1400 cases that were diagnosed from 1990 to 2017. Kaplan Meier’s method was performed for survival analysis. The association between prognostic factors and survival was estimated using Cox Hazard Regression method and using R software for analysis. Results: The median overall survival (OS) rate of all MM patients, including pleural and peritoneal mesothelioma cases is 15 months (14 months for pleural and 31 months for peritoneal). Significant prognostic factors associated with improved survival of malignant mesothelioma cases in this NMVB cohort were younger than 45, female gender, epithelioid histological subtype, stage I, peritoneal occurrence, and having combination treatment of surgical therapy with chemotherapy. Combined surgical and chemotherapy treatment was associated with improved survival of 23 months in comparison to single line therapies. Conclusions: There has not been improvement in the overall survival for patients with malignant mesothelioma over many years with current available treatment options. Our findings show that combined surgical and chemotherapy treatment in peritoneal mesothelioma is associated with improved survival compared to local therapy alone.

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.

Recently a new high-throughput biomarker discovery platform based on printed glycan arrays (PGA) has emerged. PGAs are similar to DNA arrays but contain deposits of various carbohy-drate structures (glycans) instead of spotted DNAs. PGA-based biomarker discovery for the early detection, diagnosis and prognosis of human malignancies is based on the response of the immune system as measured by the level of binding of anti-glycan antibodies from human serum to the glycans on the ar-ray. Since the PGA offer a multitude of markers which can have moderate individual diagnostic power they can be combined in order to achieve maximal classification precision assessed by the popular performance measure area under the ROC curve (AUC). This paper presents an empirical analysis of several combination approaches including those that are specifically designed to maximize the AUC and those that are not, such as Fisher Linear Discriminant, Support Vector Machines and Gen-eralized Linear Model. The analysis is performed on real-life PGA data from three pilot studies involving malignant mesothe-lioma, lung cancer and ovarian cancer.