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Rationale: Tobacco smoking is responsible for 85% of all lung cancers. To further our understanding of the molecular pathogenesis of lung cancer, we determined whether smoking history leads to the emergence of specific genomic alterations found in non-small cell lung cancer (NSCLC).Objectives: To identify gene copy number alterations in NSCLCs associated with smoking history or DNA repair capacity.Methods: Seventy-five NSCLCs were selected for this study from patients with current, none, or past smoking history, including pack year information. Tissue sections were microdissected, and DNA was extracted, purified, and labeled by random priming before hybridization onto bacterial artificial chromosome (BAC) arrays. Normalized ratios were correlated with smoking history and DNA repair capacity was measured by an in vitro lymphocyte assay in the same patients.Measurements and Main Results: We identified smoking-related genomic signatures in NSCLCs that could be predicted with an overall 74% accuracy. Lung tumors arising from current-smokers had the greatest number of copy number alterations. The genomic regions most significantly associated with smoking were located within 60 regions and were functionally associated with genes controlling the M phase of the cell cycle, the segregation of chromosomes, and the methylation of DNA. Verification of the data is provided from data in the public domain and by quantitative real-time polymerase chain reaction. The associations between genomic abnormalities and DNA repair capacity did not reach statistical significance.Conclusions: These findings indicate that smoking history leaves a specific genomic signature in the DNA of lung tumors and suggest that these alterations may reflect new molecular pathways to cancer development. [ABSTRACT FROM AUTHOR]

Lung adenocarcinoma (LUAD) is the predominant type of lung cancer in the U.S. and exhibits a broad variety of behaviors ranging from indolent to aggressive. Identification of the biological determinants of LUAD behavior at early stages can improve existing diagnostic and treatment strategies. Extracellular matrix (ECM) remodeling and cancer-associated fibroblasts play a crucial role in the regulation of cancer aggressiveness and there is a growing need to investigate their role in the determination of LUAD behavior at early stages. We analyzed tissue samples isolated from patients with LUAD at early stages and used imaging-based biomarkers to predict LUAD behavior. Single-cell RNA sequencing and histological assessment showed that aggressive LUADs are characterized by a decreased number of ADH1B + CAFs in comparison to indolent tumors. ADH1B + CAF enrichment is associated with distinct ECM and immune cell signatures in early-stage LUADs. Also, we found a positive correlation between the gene expression of ADH1B + CAF markers in early-stage LUADs and better survival. We performed TCGA dataset analysis to validate our findings. Identified associations can be used for the development of the predictive model of LUAD aggressiveness and novel therapeutic approaches., (© 2023. Springer Nature Limited.)

A major challenge in lung cancer prevention and cure hinges on identifying the at-risk population that ultimately develops lung cancer. Previously, we reported proteomic alterations in the cytologically normal bronchial epithelial cells collected from the bronchial brushings of individuals at risk for lung cancer. The purpose of this study is to validate, in an independent cohort, a selected list of 55 candidate proteins associated with risk for lung cancer with sensitive targeted proteomics using selected reaction monitoring (SRM). Bronchial brushings collected from individuals at low and high risk for developing lung cancer as well as patients with lung cancer, from both a subset of the original cohort (batch 1: n = 10 per group) and an independent cohort of 149 individuals (batch 2: low risk (n = 32), high risk (n = 34), and lung cancer (n = 83)), were analyzed using multiplexed SRM assays. ALDH3A1 and AKR1B10 were found to be consistently overexpressed in the high-risk group in both batch 1 and batch 2 brushing specimens as well as in the biopsies of batch 1. Validation of highly discriminatory proteins and metabolic enzymes by SRM in a larger independent cohort supported their use to identify patients at high risk for developing lung cancer.

Background: There is a need for biomarkers that improve accuracy compared with current demographic risk indices to detect individuals at the highest lung cancer risk. Improved risk determination will enable more effective lung cancer screening and better stratification of lung nodules into high or low-risk category. We previously reported discovery of a biomarker for lung cancer risk characterized by increased prevalence of TP53 somatic mutations in airway epithelial cells (AEC). Here we present results from a validation study in an independent retrospective case-control cohort., Methods: Targeted next generation sequencing was used to identify mutations within three TP53 exons spanning 193 base pairs in AEC genomic DNA., Results: TP53 mutation prevalence was associated with cancer status (P < 0.001). The lung cancer detection receiver operator characteristic (ROC) area under the curve (AUC) for the TP53 biomarker was 0.845 (95% confidence limits 0.749-0.942). In contrast, TP53 mutation prevalence was not significantly associated with age or smoking pack-years. The combination of TP53 mutation prevalence with PLCO M2012 risk score had an ROC AUC of 0.916 (0.846-0.986) and this was significantly higher than that for either factor alone (P < 0.03)., Conclusions: These results support the validity of the TP53 mutation prevalence biomarker and justify taking additional steps to assess this biomarker in AEC specimens from a prospective cohort and in matched nasal brushing specimens as a potential non-invasive surrogate specimen., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: Deficiencies in risk assessment for patients with pulmonary nodules (PNs) contribute to unnecessary invasive testing and delays in diagnosis., Research Question: What is the accuracy of a novel PN risk model that includes plasma proteins and clinical factors? How does the accuracy compare with that of an established risk model?, Study Design and Methods: Based on technology using magnetic nanosensors, assays were developed with seven plasma proteins. In a training cohort (n = 429), machine learning approaches were used to identify an optimal algorithm that subsequently was evaluated in a validation cohort (n = 489), and its performance was compared with the Mayo Clinic model., Results: In the training set, we identified a support vector machine algorithm that included the seven plasma proteins and six clinical factors that demonstrated an area under the receiver operating characteristic curve of 0.87 and met other selection criteria. The resulting risk reclassification model (RRM) was used to recategorize patients with a pretest risk of between 10% and 84%, and its performance was assessed across five risk strata (low, ≤ 10%; moderate, 10%-34%; intermediate, 35%-70%; high, 71%-84%; very high, > 85%). Stratification by the RRM decreased the proportion of intermediate-risk patients from 26.7% to 10.8% (P < .001) and increased the low-risk and high-risk strata from 16.8% to 21.9% (P < .001) and from 3.7% to 12.1% (P < .001), respectively. Among patients classified as low risk by the RRM and Mayo Clinic model, the corresponding true-negative to false-negative ratios were 16.8 and 19.5, respectively. Among patients classified as very high risk by the RRM and Mayo Clinic model, the corresponding true-positive to false-positive ratios were 28.5 and 17.0, respectively. Compared with the Mayo Clinic model, the RRM provided higher specificity at the low-risk threshold and higher sensitivity at the very high-risk threshold., Interpretation: The RRM accurately reclassified some patients into low-risk and very high-risk categories, suggesting the potential to improve PN risk assessment., (Copyright © 2022 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Introduction: Lung cancer is the deadliest cancer in the United States and worldwide, and lung adenocarcinoma (LUAD) is the most prevalent histologic subtype in the United States. LUAD exhibits a wide range of aggressiveness and risk of recurrence, but the biological underpinnings of this behavior are poorly understood. Past studies have focused on the biological characteristics of the tumor itself, but the ability of the immune response to contain tumor growth represents an alternative or complementary hypothesis. Emerging technologies enable us to investigate the spatial distribution of specific cell types within the tumor nest and characterize this immune response. This study aimed to investigate the association between immune cell density within the primary tumor and recurrence-free survival (RFS) in stage I and II LUAD., Methods: This study is a prospective collection with retrospective evaluation. A total of 100 patients with surgically resected LUAD and at least 5-year follow-ups, including 69 stage I and 31 stages II tumors, were enrolled. Multiplexed immunohistochemistry panels for immune markers were used for measurement., Results: Cox regression models adjusted for sex and EGFR mutation status revealed that the risk of recurrence was reduced by 50% for the unit of one interquartile range (IQR) change in the tumoral T-cell (adjusted hazard ratio per IQR increase = 0.50, 95% confidence interval: 0.27-0.93) and decreased by 64% in mast cell density (adjusted hazard ratio per IQR increase = 0.36, confidence interval: 0.15-0.84). The analyses were reported without the type I error correction for the multiple types of immune cell testing., Conclusions: Analysis of the density of immune cells within the tumor and surrounding stroma reveals an association between the density of T-cells and RFS and between mast cells and RFS in early-stage LUAD. This preliminary result is a limited study with a small sample size and a lack of an independent validation set., (© 2023 The Authors.)

Although when used as a lung cancer screening tool low-dose computed tomography (LDCT) has demonstrated a significant reduction in lung cancer related mortality, it is not without pitfalls. The associated high false positive rate, inability to distinguish between benign and malignant nodules, cumulative radiation exposure, and resulting patient anxiety have all demonstrated the need for adjunctive testing in lung cancer screening. Current research focuses on developing liquid biomarkers to complement imaging as non-invasive lung cancer diagnostics. Biomarkers can be useful for both the early detection and diagnosis of disease, thereby decreasing the number of unnecessary radiologic tests performed. Biomarkers can stratify cancer risk to further enrich the screening population and augment existing risk prediction. Finally, biomarkers can be used to distinguish benign from malignant nodules in lung cancer screening. While many biomarkers require further validation studies, several, including autoantibodies and blood protein profiling, are available for clinical use. This paper describes the need for biomarkers as a lung cancer screening tool, both in terms of diagnosis and risk assessment. Additionally, this paper will discuss the goals of biomarker use, describe properties of a good biomarker, and review several of the most promising biomarkers currently being studied including autoantibodies, complement fragments, microRNA, blood proteins, circulating tumor DNA, and DNA methylation. Finally, we will describe future directions in the field of biomarker development.

Small cell lung cancer (SCLC) elicits the generation of autoantibodies that result in unique paraneoplastic neurological syndromes. The mechanistic basis for the formation of such autoantibodies is largely unknown but is key to understanding their etiology. We developed a high-dimensional technique that enables detection of autoantibodies in complex with native antigens directly from patient plasma. Here, we used our platform to screen 1009 human plasma samples for 3600 autoantibody-antigen complexes, finding that plasma from patients with SCLC harbors, on average, fourfold higher disease-specific autoantibody signals compared with plasma from patients with other cancers. Across three independent SCLC cohorts, we identified a set of common but previously unknown autoantibodies that are produced in response to both intracellular and extracellular tumor antigens. We further characterized several disease-specific posttranslational modifications within extracellular proteins targeted by these autoantibodies, including citrullination, isoaspartylation, and cancer-specific glycosylation. Because most patients with SCLC have metastatic disease at diagnosis, we queried whether these autoantibodies could be used for SCLC early detection. We created a risk prediction model using five autoantibodies with an average area under the curve of 0.84 for the three cohorts that improved to 0.96 by incorporating cigarette smoke consumption in pack years. Together, our findings provide an innovative approach to identify circulating autoantibodies in SCLC with mechanistic insight into disease-specific immunogenicity and clinical utility.

Small cell lung cancer (SCLC) is characterized by morphologic, epigenetic and transcriptomic heterogeneity. Subtypes based upon predominant transcription factor expression have been defined that, in mouse models and cell lines, exhibit potential differential therapeutic vulnerabilities, with epigenetically distinct SCLC subtypes also described. The clinical relevance of these subtypes is unclear, due in part to challenges in obtaining tumor biopsies for reliable profiling. Here we describe a robust workflow for genome-wide DNA methylation profiling applied to both patient-derived models and to patients' circulating cell-free DNA (cfDNA). Tumor-specific methylation patterns were readily detected in cfDNA samples from patients with SCLC and were correlated with survival outcomes. cfDNA methylation also discriminated between the transcription factor SCLC subtypes, a precedent for a liquid biopsy cfDNA-methylation approach to molecularly subtype SCLC. Our data reveal the potential clinical utility of cfDNA methylation profiling as a universally applicable liquid biopsy approach for the sensitive detection, monitoring and molecular subtyping of patients with SCLC., (© 2022. The Author(s).)

Background: The Veterans Health Administration issued policy for lung cancer screening resources at eight Veterans Affairs Medical Centers (VAMCs) in a demonstration project (DP) from 2013 through 2015., Research Question: Do policies that provide resources increase lung cancer screening rates?, Study Design and Methods: Data from eight DP VAMCs (DP group) and 20 comparable VAMCs (comparison group) were divided into before DP (January 2011-June 2013), DP (July 2013-June 2015), and after DP (July 2015-December 2018) periods. Coprimary outcomes were unique veterans screened per 1,000 eligible per month and those with 1-year (9-15 months) follow-up screening. Eligible veterans were estimated using yearly counts and the percentage of those with eligible smoking histories. Controlled interrupted time series and difference-in-differences analyses were performed., Results: Of 27,746 veterans screened, the median age was 66.5 years and most were White (77.7%), male (95.6%), and urban dwelling (67.3%). During the DP, the average rate of unique veterans screened at DP VAMCs was 17.7 per 1,000 eligible per month, compared with 0.3 at comparison VAMCs. Adjusted analyses found a higher rate increase at DP VAMCs by 0.93 screening per 1,000 eligible per month (95% CI, 0.25-1.61) during this time, with an average facility-level difference of 17.4 screenings per 1,000 eligible per month (95% CI, 12.6-22.3). Veterans with 1-year follow-up screening also increased more rapidly at DP VAMCs during the DP, by 0.39 screening per 1,000 eligible per month (95% CI, 0.18-0.60), for an average facility-level difference of 7.2 more screenings per 1,000 eligible per month (95% CI, 5.2-9.2). Gains were not maintained after the DP., Interpretation: In this cohort, provision of resources for lung cancer screening implementation was associated with an increase in veterans screened and those with 1-year follow-up screening. Screening gains associated with the DP were not maintained., (Published by Elsevier Inc.)

Background Limited data are available regarding whether computer-aided diagnosis (CAD) improves assessment of malignancy risk in indeterminate pulmonary nodules (IPNs). Purpose To evaluate the effect of an artificial intelligence-based CAD tool on clinician IPN diagnostic performance and agreement for both malignancy risk categories and management recommendations. Materials and Methods This was a retrospective multireader multicase study performed in June and July 2020 on chest CT studies of IPNs. Readers used only CT imaging data and provided an estimate of malignancy risk and a management recommendation for each case without and with CAD. The effect of CAD on average reader diagnostic performance was assessed using the Obuchowski-Rockette and Dorfman-Berbaum-Metz method to calculate estimates of area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Multirater Fleiss κ statistics were used to measure interobserver agreement for malignancy risk and management recommendations. Results A total of 300 chest CT scans of IPNs with maximal diameters of 5-30 mm (50.0% malignant) were reviewed by 12 readers (six radiologists, six pulmonologists) (patient median age, 65 years; IQR, 59-71 years; 164 [55%] men). Readers' average AUC improved from 0.82 to 0.89 with CAD ( P < .001). At malignancy risk thresholds of 5% and 65%, use of CAD improved average sensitivity from 94.1% to 97.9% ( P = .01) and from 52.6% to 63.1% ( P < .001), respectively. Average reader specificity improved from 37.4% to 42.3% ( P = .03) and from 87.3% to 89.9% ( P = .05), respectively. Reader interobserver agreement improved with CAD for both the less than 5% (Fleiss κ, 0.50 vs 0.71; P < .001) and more than 65% (Fleiss κ, 0.54 vs 0.71; P < .001) malignancy risk categories. Overall reader interobserver agreement for management recommendation categories (no action, CT surveillance, diagnostic procedure) also improved with CAD (Fleiss κ, 0.44 vs 0.52; P = .001). Conclusion Use of computer-aided diagnosis improved estimation of indeterminate pulmonary nodule malignancy risk on chest CT scans and improved interobserver agreement for both risk stratification and management recommendations. © RSNA, 2022 Online supplemental material is available for this article . See also the editorial by Yanagawa in this issue.

Background: Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer., Methods: Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index., Results: Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy., Conclusions: A combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management., (Copyright © 2022. Published by Elsevier B.V.)

CYFRA 21.1, a cytokeratin fragment of epithelial origin, has long been a valuable blood-based biomarker. As with most biomarkers, the clinical diagnostic value of CYFRA 21.1 is dependent on the quantitative performance of the assay. Looking toward translation, it is shown here that a free-solution assay (FSA) coupled with a compensated interferometric reader (CIR) can be used to provide excellent analytical performance in quantifying CYFRA 21.1 in patient serum samples. This report focuses on the analytical performance of the high-sensitivity (hs)-CYFRA 21.1 assay in the context of quantifying the biomarker in two indeterminate pulmonary nodule (IPN) patient cohorts totaling 179 patients. Each of the ten assay calibrations consisted of 6 concentrations, each run as 7 replicates (e.g., 10 × 6 × 7 data points) and were performed on two different instruments by two different operators. Coefficients of variation (CVs) for the hs-CYFRA 21.1 analytical figures of merit, limit of quantification (LOQ) of ca. 60 pg/mL, B max , initial slope, probe-target binding affinity, and reproducibility of quantifying an unknown were found to range from 2.5 to 8.3%. Our results demonstrate the excellent performance of our FSA-CIR hs-CYFRA 21-1 assay and a proof of concept for potentially redefining the performance characteristics of this existing important candidate biomarker., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Genomic profiling of bronchoalveolar lavage (BAL) samples may be useful for tumor profiling and diagnosis in the clinic. Here, we compared tumor-derived mutations detected in BAL samples from subjects with non-small cell lung cancer (NSCLC) to those detected in matched plasma samples. Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) was used to genotype DNA purified from BAL, plasma, and tumor samples from patients with NSCLC. The characteristics of cell-free DNA (cfDNA) isolated from BAL fluid were first characterized to optimize the technical approach. Somatic mutations identified in tumor were then compared with those identified in BAL and plasma, and the potential of BAL cfDNA analysis to distinguish lung cancer patients from risk-matched controls was explored. In total, 200 biofluid and tumor samples from 38 cases and 21 controls undergoing BAL for lung cancer evaluation were profiled. More tumor variants were identified in BAL cfDNA than plasma cfDNA in all stages (P < 0.001) and in stage I to II disease only. Four of 21 controls harbored low levels of cancer-associated driver mutations in BAL cfDNA [mean variant allele frequency (VAF) = 0.5%], suggesting the presence of somatic mutations in nonmalignant airway cells. Finally, using a Random Forest model with leave-one-out cross-validation, an exploratory BAL genomic classifier identified lung cancer with 69% sensitivity and 100% specificity in this cohort and detected more cancers than BAL cytology. Detecting tumor-derived mutations by targeted sequencing of BAL cfDNA is technically feasible and appears to be more sensitive than plasma profiling. Further studies are required to define optimal diagnostic applications and clinical utility., Significance: Hybrid-capture, targeted deep sequencing of lung cancer mutational burden in cell-free BAL fluid identifies more tumor-derived mutations with increased allele frequencies compared with plasma cell-free DNA. See related commentary by Rolfo et al., p. 2826., (©2022 The Authors; Published by the American Association for Cancer Research.)

Integrins - the principal extracellular matrix (ECM) receptors of the cell - promote cell adhesion, migration, and proliferation, which are key events for cancer growth and metastasis. To date, most integrin-targeted cancer therapeutics have disrupted integrin-ECM interactions, which are viewed as critical for integrin functions. However, such agents have failed to improve cancer patient outcomes. We show that the highly expressed integrin β1 subunit is required for lung adenocarcinoma development in a carcinogen-induced mouse model. Likewise, human lung adenocarcinoma cell lines with integrin β1 deletion failed to form colonies in soft agar and tumors in mice. Mechanistically, we demonstrate that these effects do not require integrin β1-mediated adhesion to ECM but are dependent on integrin β1 cytoplasmic tail-mediated activation of focal adhesion kinase (FAK). These studies support a critical role for integrin β1 in lung tumorigenesis that is mediated through constitutive, ECM binding-independent signaling involving the cytoplasmic tail.

Here we focus on the molecular characterization of clinically significant histological subtypes of early-stage lung adenocarcinoma (esLUAD), which is the most common histological subtype of lung cancer. Within lung adenocarcinoma, histology is heterogeneous and associated with tumor invasion and diverse clinical outcomes. We present a gene signature distinguishing invasive and non-invasive tumors among esLUAD. Using the gene signatures, we estimate an Invasiveness Score that is strongly associated with survival of esLUAD patients in multiple independent cohorts and with the invasiveness phenotype in lung cancer cell lines. Regulatory network analysis identifies aurora kinase as one of master regulators of the gene signature and the perturbation of aurora kinases in vitro and in a murine model of invasive lung adenocarcinoma reduces tumor invasion. Our study reveals aurora kinases as a therapeutic target for treatment of early-stage invasive lung adenocarcinoma., (© 2022. The Author(s).)

Background: 18F-fluorodeoxyglucose (FDG) PET/CT is recommended for evaluation of intermediate-risk indeterminate pulmonary nodules (IPNs). While highly sensitive, the specificity of FDG remains suboptimal for differentiating malignant from benign nodules, particularly in areas where fungal lung diseases are prevalent. Thus, a cancer-specific imaging probe is greatly needed. In this study, we tested the hypothesis that a PET radiotracer (S)-4-(3-[18F]-fluoropropyl)-L-glutamic acid (FSPG) improves the diagnostic accuracy of IPNs compared to 18F-FDG PET/CT., Methods: This study was conducted at a major academic medical center and an affiliated VA medical center. Twenty-six patients with newly discovered IPNs 7-30mm diameter or newly diagnosed lung cancer completed serial PET/CT scans utilizing 18F-FDG and 18F-FSPG, without intervening treatment of the lesion. The scans were independently reviewed by two dual-trained diagnostic radiology and nuclear medicine physicians. Characteristics evaluated included quantitative SUVmax values of the pulmonary nodules and metastases., Results: A total of 17 out of 26 patients had cancer and 9 had benign lesions. 18F-FSPG was negative in 6 of 9 benign lesions compared to 7 of 9 with 18F-FDG. 18F-FSPG and 18F-FDG were positive in 14 of 17 and 12 of 17 malignant lesions, respectively. 18F-FSPG detected brain and intracardiac metastases missed by 18F-FDG PET in one case, while 18F-FDG detected a metastasis to the kidney missed by 18F-FSPG., Conclusion: In this pilot study, there was no significant difference in overall diagnostic accuracy between 18F-FSPG and 18F-FDG for the evaluation of IPNs and staging of lung cancer. Additional studies will be needed to determine the clinical utility of this tracer in the management of IPNs and lung cancer., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: [HCM is a scientific consultant for Parthenon Therapeutics] The other authors have declared that no competing interests exist.

Lung cancer is the most common cause of cancer-related deaths in both men and women, accounting for one-quarter of total cancer-related mortality globally. Lung adenocarcinoma is the major subtype of non-small cell lung cancer (NSCLC) and accounts for around 40% of lung cancer cases. Lung adenocarcinoma is a highly heterogeneous disease and patients often display variable histopathological morphology, genetic alterations, and genomic aberrations. Recent advances in transcriptomic and genetic profiling of lung adenocarcinoma by investigators, including our group, has provided better stratification of this heterogeneous disease, which can facilitate devising better treatment strategies suitable for targeted patient cohorts. In a recent study we have shown gene expression profiling identified novel clustering of early stage LUAD patients and correlated with tumor invasiveness and patient survival. In this study, we focused on copy number alterations in LUAD patients. SNP array data identified amplification at chromosome 12q15 on MDM2 locus and protein overexpression in a subclass of LUAD patients with an invasive subtype of the disease. High copy number amplification and protein expression in this subclass correlated with poor overall survival. We hypothesized that MDM2 copy number and overexpression predict response to MDM2-targeted therapy. In vitro functional data on a panel of LUAD cells showed that MDM2-targeted therapy effectively suppresses cell proliferation, migration, and invasion in cells with MDM2 amplification/overexpression but not in cells without MDM2 amplification, independent of p53 status. To determine the key signaling mechanisms, we used RNA sequencing (RNA seq) to examine the response to therapy in MDM2-amplified/overexpressing p53 mutant and wild-type LUAD cells. RNA seq data shows that in MDM2-amplified/overexpression with p53 wild-type condition, the E2F → PEG10 → MMPs pathway is operative, while in p53 mutant genetic background, MDM2-targeted therapy abrogates tumor progression in LUAD cells by suppressing epithelial to mesenchymal transition (EMT) signaling. Our study provides a potentially clinically relevant strategy of selecting LUAD patients for MDM2-targeted therapy that may provide for increased response rates and, thus, better survival.

The Early Detection Research Network's (EDRN) purpose is to discover, develop and validate biomarkers and imaging methods to detect early-stage cancers or at-risk individuals. The EDRN is composed of sites that fall into four categories: Biomarker Developmental Laboratories (BDL), Biomarker Reference Laboratories (BRL), Clinical Validation Centers (CVC) and Data Management and Coordinating Centers. Each component has a crucial role to play within the mission of the EDRN. The primary role of the CVCs is to support biomarker developers through validation trials on promising biomarkers discovered by both EDRN and non-EDRN investigators. The second round of funding for the EDRN Lung CVC at Vanderbilt University Medical Center (VUMC) was funded in October 2016 and we intended to accomplish the three missions of the CVCs: To conduct innovative research on the validation of candidate biomarkers for early cancer detection and risk assessment of lung cancer in an observational study; to compare biomarker performance; and to serve as a resource center for collaborative research within the Network and partner with established EDRN BDLs and BRLs, new laboratories and industry partners. This report outlines the impact of the VUMC EDRN Lung CVC and describes the role in promoting and validating biological and imaging biomarkers.

Purpose: Lung cancer causes the largest number of cancer-related deaths in the United States. Lung cancer incidence rates, mortality rates, and rates of advanced stage disease are higher among those who live in rural areas. Known disparities in lung cancer outcomes between rural and nonrural populations may be in part because of barriers faced by rural populations. The authors tested the hypothesis that among Veterans who receive initial lung cancer screening, rural Veterans would be less likely to complete annual repeat screening than nonrural Veterans., Methods: A retrospective cohort study was conducted of 10 Veterans Affairs medical centers from 2015 to 2019. Rural and nonrural Veterans undergoing lung cancer screening were identified. Rural status was defined using the rural-urban commuting area codes. The primary outcome was annual repeat lung cancer screening in the 9- to 15-month window (primary analysis) and 31-day to 18-month window (sensitivity analysis) after the first documented lung cancer screening. To examine rurality as a predictor of annual repeat lung cancer screening, multivariable logistic regression models were used., Results: In the final analytic sample of 11,402 Veterans, annual repeat lung cancer screening occurred in 27.7% of rural Veterans (641 of 2,316) and 31.8% of nonrural Veterans (2,891 of 9,086) (adjusted odds ratio: 0.86; 95% confidence interval: 0.73-1.03). Similar results were seen in the sensitivity analysis, with 41.6% of rural Veterans (963 of 2,316) versus 45.2% of nonrural Veterans (4,110 of 9,086) (adjusted odds ratio: 0.88; 95% confidence interval: 0.73-1.04) having annual repeat screening in the expanded 31-day to 18-month window., Conclusions: Among a national cohort of Veterans, rural residence was associated with numerically lower odds of annual repeat lung cancer screening than nonrural residence. Continued, intentional outreach efforts to increase annual repeat lung cancer screening among rural Veterans may offer an opportunity to decrease deaths from lung cancer., (Published by Elsevier Inc.)

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 ( n  = 170) and validated in cohorts 2-4 (total n  = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P  < 2 × 10 -16 ). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

Background: Although a variety of pathological changes have been described in small airways of patients with COPD, the critical anatomic features determining airflow limitation remain incompletely characterised., Methods: We examined lung tissue specimens from 18 non-smokers without chronic lung disease and 55 former smokers with COPD for pathological features of small airways that could contribute to airflow limitation. Morphometric evaluation was performed for epithelial and subepithelial tissue thickness, collagen and elastin content, luminal mucus and radial alveolar attachments. Immune/inflammatory cells were enumerated in airway walls. Quantitative emphysema scoring was performed on chest CT scans., Results: Small airways from patients with COPD showed thickening of epithelial and subepithelial tissue, mucus plugging and reduced collagen density in the airway wall (in severe COPD). In patients with COPD, we also observed a striking loss of alveolar attachments, which are connective tissue septa that insert radially into the small airway adventitia. While each of these parameters correlated with reduced airflow (FEV 1 ), multivariable regression analysis indicated that loss of alveolar attachments was the major determinant of airflow limitation related to small airways. Neutrophilic infiltration of airway walls and collagen degradation in airway adventitia correlated with loss of alveolar attachments. In addition, quantitative analysis of CT scans identified an association between the extent of emphysema and loss of alveolar attachments., Conclusion: In COPD, loss of radial alveolar attachments in small airways is the pathological feature most closely related to airflow limitation. Destruction of alveolar attachments may be mediated by neutrophilic inflammation., Competing Interests: Competing interests: SR was an employee of AstraZeneca from 2015 to 2019 and continues to own shares that were received as part of his compensation. As part of that employment, he represented AstraZeneca on the Board of Directors of Dizal Pharma without additional compensation. In the last three years, he has consulted for Bergenbio, GlaxoSmithKline, NovoVentures and Verona. Between 1996 and 2007, his university received funding from tobacco companies that supported studies relating to harm reduction and to the impact of tobacco smoke on stem cells. As part of this work, he consulted with RJ Reynolds without personal fee on the topic of harm reduction, received funding from RJ Reynolds to evaluate the effect of a harm reduction product in normal smokers (1996) and in subjects with chronic bronchitis (1999) and to assess the effect of smoking cessation on lower respiratory tract inflammation (2000); he participated in a Philip Morris multicentre study to assess biomarkers of smoke exposure (2002); he received funding for a clinical trial from the Institute for Science and Health (2005), which receives support from the tobacco industry, to evaluate biomarkers in exhaled breath associated with smoking cessation and reduction. This study was supplemented with funding from Lorillard and RJ Reynolds to expand the spectrum of biomarkers assessed. He received a grant from the Philip Morris External Research Program (2005) to assess the impact of cigarette smoking on circulating stem cells in the mouse. There are no active tobacco-industry funded projects. All ties with tobacco industry companies and entities supported by tobacco companies were terminated in 2007., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: To develop a model to estimate lung cancer risk using lung cancer screening CT and clinical data elements (CDEs) without manual reading efforts., Materials and Methods: Two screening cohorts were retrospectively studied: the National Lung Screening Trial (NLST; participants enrolled between August 2002 and April 2004) and the Vanderbilt Lung Screening Program (VLSP; participants enrolled between 2015 and 2018). Fivefold cross-validation using the NLST dataset was used for initial development and assessment of the co-learning model using whole CT scans and CDEs. The VLSP dataset was used for external testing of the developed model. Area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve were used to measure the performance of the model. The developed model was compared with published risk-prediction models that used only CDEs or imaging data alone. The Brock model was also included for comparison by imputing missing values for patients without a dominant pulmonary nodule., Results: A total of 23 505 patients from the NLST (mean age, 62 years ± 5 [standard deviation]; 13 838 men, 9667 women) and 147 patients from the VLSP (mean age, 65 years ± 5; 82 men, 65 women) were included. Using cross-validation on the NLST dataset, the AUC of the proposed co-learning model (AUC, 0.88) was higher than the published models predicted with CDEs only (AUC, 0.69; P < .05) and with images only (AUC, 0.86; P < .05). Additionally, using the external VLSP test dataset, the co-learning model had a higher performance than each of the published individual models (AUC, 0.91 [co-learning] vs 0.59 [CDE-only] and 0.88 [image-only]; P < .05 for both comparisons)., Conclusion: The proposed co-learning predictive model combining chest CT images and CDEs had a higher performance for lung cancer risk prediction than models that contained only CDE or only image data; the proposed model also had a higher performance than the Brock model. Keywords: Computer-aided Diagnosis (CAD), CT, Lung, Thorax Supplemental material is available for this article. © RSNA, 2021., Competing Interests: Disclosures of Conflicts of Interest: R.G. grant from National Institutes of Health. Y.T. No relevant relationships. M.S.K. No relevant relationships. K.X. No relevant relationships. A.B.P. employed by Vanderbilt University Medical Center. S.S. No relevant relationships. Y.H. grants from NIH and NSF; university employee at Vanderbilt University. S.D. grant from National Cancer Institute (U01CA152662). P.P.M. No relevant relationships. K.L.S. grant from Vanderbilt-Ingram Cancer Center (VICC) (received funding through the Martineau Innovation Award from VICC). B.A.L. grant from NIH., (2021 by the Radiological Society of North America, Inc.)

Background: There is a strong rationale for proposing transpulmonary pressure-guided protective ventilation in acute respiratory distress syndrome. The reference esophageal balloon catheter method requires complex in vivo calibration, expertise and specific material order. A simple, inexpensive, accurate and reproducible method of measuring esophageal pressure would greatly facilitate the measure of transpulmonary pressure to individualize protective ventilation in the intensive care unit., Results: We propose an air-filled esophageal catheter method without balloon, using a disposable catheter that allows reproducible esophageal pressure measurements. We use a 49-cm-long 10 Fr thin suction catheter, positioned in the lower-third of the esophagus and connected to an air-filled disposable blood pressure transducer bound to the monitor and pressurized by an air-filled infusion bag. Only simple calibration by zeroing the transducer to atmospheric pressure and unit conversion from mmHg to cmH 2 O are required. We compared our method with the reference balloon catheter both ex vivo, using pressure chambers, and in vivo, in 15 consecutive mechanically ventilated patients. Esophageal-to-airway pressure change ratios during the dynamic occlusion test were close to one (1.03 ± 0.19 and 1.00 ± 0.16 in the controlled and assisted modes, respectively), validating the proper esophageal positioning. The Bland-Altman analysis revealed no bias of our method compared with the reference and good precision for inspiratory, expiratory and delta esophageal pressure measurements in both the controlled (largest bias -0.5 cmH 2 O [95% confidence interval: -0.9; -0.1] cmH 2 O; largest limits of agreement -3.5 to 2.5 cmH 2 O) and assisted modes (largest bias -0.3 [-2.6; 2.0] cmH 2 O). We observed a good repeatability (intra-observer, intraclass correlation coefficient, ICC: 0.89 [0.79; 0.96]) and reproducibility (inter-observer ICC: 0.89 [0.76; 0.96]) of esophageal measurements. The direct comparison with pleural pressure in two patients and spectral analysis by Fourier transform confirmed the reliability of the air-filled catheter-derived esophageal pressure as an accurate surrogate of pleural pressure. A calculator for transpulmonary pressures is available online., Conclusions: We propose a simple, minimally invasive, inexpensive and reproducible method for esophageal pressure monitoring with an air-filled esophageal catheter without balloon. It holds the promise of widespread bedside use of transpulmonary pressure-guided protective ventilation in ICU patients., (© 2021. The Author(s).)

Lung adenocarcinoma (ADC) is a heterogeneous group of tumors associated with different survival rates, even when detected at an early stage. Here, we aim to investigate whether CyTOF identifies cellular and molecular predictors of tumor behavior. We developed and validated a CyTOF panel of 34 antibodies in four ADC cell lines and PBMC. We tested our panel in a set of 10 ADCs, classified into long- (LPS) (n = 4) and short-predicted survival (SPS) (n = 6) based on radiomics features. We identified cellular subpopulations of epithelial cancer cells (ECC) and their microenvironment and validated our results by multiplex immunofluorescence (mIF) applied to a tissue microarray (TMA) of LPS and SPS ADCs. The antibody panel captured the phenotypical differences in ADC cell lines and PBMC. LPS ADCs had a higher proportion of immune cells. ECC clusters (ECCc) were identified and uncovered two ADC groups. ECCc with high HLA-DR expression were correlated with CD4+ and CD8+ T cells, with LPS samples being enriched for those clusters. We confirmed a positive correlation between HLA-DR expression on ECC and T cell number by mIF staining on TMA slides. Spatial analysis demonstrated shorter distances from T cells to the nearest ECC in LPS. Our results demonstrate a distinctive cellular profile of ECC and their microenvironment in ADC. We showed that HLA-DR expression in ECC is correlated with T cell infiltration, and that a set of ADCs with high abundance of HLA-DR+ ECCc and T cells is enriched in LPS samples. This suggests new insights into the role of antigen presenting tumor cells in tumorigenesis., (© 2021. The Author(s).)

Lung cancer screening detects early-stage cancers, but also a large number of benign nodules. Molecular markers can help in the lung cancer screening process by refining inclusion criteria or guiding the management of indeterminate pulmonary nodules. In this study, we developed a diagnostic model based on the quantification in plasma of complement-derived fragment C4c, cytokeratin fragment 21-1 (CYFRA 21-1) and C-reactive protein (CRP). The model was first validated in two independent cohorts, and showed a good diagnostic performance across a range of lung tumor types, emphasizing its high specificity and positive predictive value. We next tested its utility in two clinically relevant contexts: assessment of lung cancer risk and nodule malignancy. The scores derived from the model were associated with a significantly higher risk of having lung cancer in asymptomatic individuals enrolled in a computed tomography (CT)-screening program (OR = 1.89; 95% CI = 1.20-2.97). Our model also served to discriminate between benign and malignant pulmonary nodules (AUC: 0.86; 95% CI = 0.80-0.92) with very good specificity (92%). Moreover, the model performed better in combination with clinical factors, and may be used to reclassify patients with intermediate-risk indeterminate pulmonary nodules into patients who require a more aggressive work-up. In conclusion, we propose a new diagnostic biomarker panel that may dictate which incidental or screening-detected pulmonary nodules require a more active work-up., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Rationale: A prospective longitudinal cohort of individuals at high risk of developing lung cancer was established to build a biorepository of carefully annotated biological specimens and low-dose computed tomography (LDCT) chest images for derivation and validation of candidate biomarkers for early detection of lung cancer. Objectives: The goal of this study is to characterize individuals with high risk for lung cancer, accumulating valuable biospecimens and LDCT chest scans longitudinally over 5 years. Methods: Participants 55-80 years of age with a 5-year estimated risk of developing lung cancer >1.5% were recruited and enrolled from clinics at the Vanderbilt University Medical Center, Veteran Affairs Medical Center, and Meharry Medical Center. Individual demographic characteristics were assessed via questionnaire at baseline. Participants underwent an LDCT scan, spirometry, sputum cytology, and research bronchoscopy at the time of enrollment. Participants will be followed yearly for 5 years. Positive LDCT scans are followed-up according to standard of care. The clinical, imaging, and biospecimen data are collected prospectively and stored in a biorepository. Participants are offered smoking cessation counseling at each study visit. Results: A total of 480 participants were enrolled at study baseline and consented to sharing their data and biospecimens for research. Participants are followed with yearly clinic visits to collect imaging data and biospecimens. To date, a total of 19 cancers (13 adenocarcinomas, four squamous cell carcinomas, one large cell neuroendocrine, and one small-cell lung cancer) have been identified. Conclusions: We established a unique prospective cohort of individuals at high risk for lung cancer, enrolled at three institutions, for whom full clinical data, well-annotated LDCT scans, and biospecimens are being collected longitudinally. This repository will allow for the derivation and independent validation of clinical, imaging, and molecular biomarkers of risk for diagnosis of lung cancer.Clinical trial registered with ClinicalTrials.gov (NCT01475500).

The US Preventive Services Task Force (USPSTF) recently proposed to widen the current lung cancer screening guideline to include less-heavy smokers. We sought to incorporate both genetic and tobacco smoking data to evaluate the proposed new guideline in white smokers. We constructed a polygenic risk score (PRS) using lung cancer risk variants. Using data from 308 490 participants of European descent in the UK Biobank, a population-based cohort study, we estimated hazard ratios of lung cancer associated with both tobacco smoking and PRS to identify individuals at a similar or higher risk than the group of heavy smokers who are recommended for screening under the USPSTF-2014 guideline (≥30 pack-years, either current or former smokers who quit within 15 years). During a median follow-up of 5.8 years, 1449 incident cases of lung cancer were identified. We found a similar lung cancer risk for current smokers with 20-29 pack-years [hazard ratio = 20.7, 95% confidence interval: 16.3-26.4] and the 'heavy smoker group' defined above (hazard ratio = 19.9, 95% confidence interval: 16.8-23.6) compared with never smokers. Current smokers with 20-29 pack-years did not reach a 6-year absolute risk of 0.0151, a suggested risk threshold for using low-dose computed tomography screening, until the age of 55 years. However, these smokers at high genetic risk (PRS ≥ 80%) reached this risk level at the age of 50. Our findings support the USPSTF proposal to lower the smoking pack-year eligibility to 20 pack-years for current smokers and suggest that PRS for lung cancer could be considered to identify high-risk smokers for screening., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Objectives: Lung cancer has the highest cancer-related mortality in the United States and among Veterans. Screening of high-risk individuals with low-dose CT (LDCT) can improve survival through detection of early-stage lung cancer. Organizational factors that aid or impede implementation of this evidence-based practice in diverse populations are not well described. We evaluated organizational readiness for change and change valence (belief that change is beneficial and valuable) for implementation of LDCT screening., Methods: We performed a cross-sectional survey of providers, staff, and administrators in radiology and primary care at a single Veterans Affairs Medical Center. Survey measures included Shea's validated Organizational Readiness for Implementing Change (ORIC) scale and Shea's 10 items to assess change valence. ORIC and change valence were scored on a scale from 1 to 7 (higher scores representing higher readiness for change or valence). Multivariable linear regressions were conducted to determine predictors of ORIC and change valence., Results: Of 523 employees contacted, 282 completed survey items (53.9% overall response rate). Higher ORIC scores were associated with radiology versus primary care (mean 5.48, SD 1.42 versus 5.07, SD 1.22, β = 0.37, P = .039). Self-identified leaders in lung cancer screening had both higher ORIC (5.56, SD 1.39 versus 5.11, SD 1.26, β = 0.43, P = .050) and change valence scores (5.89, SD 1.21 versus 5.36, SD 1.19, β = 0.51, P = .012)., Discussion: Radiology health professionals have higher levels of readiness for change for implementation of LDCT screening than those in primary care. Understanding health professionals' behavioral determinants for change can inform future lung cancer screening implementation strategies., (Published by Elsevier Inc.)

Introduction: Lung cancer is the most common cause of cancer deaths worldwide, accounting for 1.8 million deaths each year. Only 20% of lung cancer cases are reported to occur in low- and middle-income countries. An estimated 1.5% of all Ethiopian cancers involved the lung; however, no nationwide cancer registry exists in Ethiopia. Thus, accurate data on clinical history, histopathology, molecular characteristics, and risk factors for lung cancer are not available. The aim of this study was to describe the clinical, radiologic, and pathologic characteristics, including available molecular profiles, for lung cancer at Tikur Anbessa Specialized Hospital (TASH), the main tertiary referral center in Addis Ababa, Ethiopia., Methods: A cross-sectional study was conducted at TASH among 146 patients with pathologically confirmed primary lung cancer, diagnosed from 2015 to 2019 and recorded in the Addis Ababa Cancer Registry at TASH. Clinical data were extracted from patient medical records, entered into a Research Electronic Data Capture database, and analyzed using Statistical Package for the Social Sciences statistical software. Variables collected included sociodemographics, personal exposures, comorbidities, clinical manifestations at presentation, chest imaging results, diagnostic procedures performed, histopathological classification, cancer staging, and type of treatment (if any). A subset of lung biopsies fixed in formalin for 2 to 7 days, which could be retrieved from the files of the Pathology Department of TASH, were reviewed, and molecular analysis was performed using next-generation sequencing to identify the tumor-oncogenic drivers., Results: Among the 146 patients studied, the mean (SD) age was 54 plus or minus 13 years; 61.6% (n = 90) were male and 25.3% (n = 37) had a history of tobacco use. The most common clinical manifestations included cough (88.4%, n = 129), chest pain (60.3%, n = 88), and dyspnea (53.4%, n = 78). The median duration of any symptoms was 6 months (interquartile range: 3-12 mo). The most common radiologic features were lung mass (84.9%, n = 129) and pleural effusion (52.7%, n = 77). Adenocarcinoma accounted for 35.7% of lung cancers (n = 52) and squamous cell carcinoma 19.2% (n = 28) from those specimens was reported. Among patients on whom staging of lung cancer was documented, 92.2% (n = 95) of the subjects presented at advanced stages (stages III and IV). EGFR mutation, exons 19 and 20, was found in 7 of 14 tissue blocks analyzed. No specific risk factors were identified, possibly reflecting the relatively small sample size and limited exposures., Conclusions: There are marked differences in the presentation, risk factors, and molecular characteristics of lung cancer in Ethiopia as compared with other African and non-African countries. Adenocarcinoma was the most common histologic type of lung cancer detected in our study, similar to findings from other international studies. Nevertheless, compared with high-income countries, lung cancer in Ethiopia presents at a younger age, a later stage, and without considerable personal tobacco use. The relatively higher prevalence of EGFR mutation, from the limited molecular analyses, suggests that factors other than smoking history, such as exposure to biomass fuel, may be a more important risk factor. Country-specific screening guidelines and treatment protocols, in addition to a national tumor registry and greater molecular mutation analyses, are needed to improve prevention and management of lung cancer in Ethiopia., (© 2021 The Authors.)

Introduction: The Veterans Affairs Partnership to increase Access to Lung Screening (VA-PALS) is an enterprise-wide initiative to implement lung cancer screening programs at VA medical centers (VAMCs). VA-PALS will be using implementation strategies that include program navigators to coordinate screening activities, trainings for navigators and radiologists, an open-source software management system, tools to standardize low-dose computed tomography image quality, and access to a support network. VAMCs can utilize strategies according to their local needs. In this protocol, we describe the planned program evaluation for the initial 10 VAMCs participating in VA-PALS., Materials and Methods: The implementation of programs will be evaluated using the Consolidated Framework for Implementation Research to ensure broad contextual guidance. Program evaluation measures have been developed using the Reach, Effectiveness, Adoption, Implementation and Maintenance framework. Adaptations of screening processes will be assessed using the Framework for Reporting Adaptations and Modifications to Evidence Based Interventions. Measures collected will reflect the inner settings, estimate and describe the population reached, adoption by providers, implementation of the programs, report clinical outcomes and maintenance of programs. Analyses will include descriptive statistics and regression to evaluate predictors and assess implementation over time., Discussion: This theory-based protocol will evaluate the implementation of lung cancer screening programs across the Veterans Health Administration using scientific frameworks. The findings will inform plans to expand the VA-PALS initiative beyond the original sites and can guide implementation of lung cancer screening programs more broadly., (Published by Elsevier Inc.)

Introduction: Implementation of low-dose chest computed tomography (CT) lung cancer screening and the ever-increasing use of cross-sectional imaging are resulting in the identification of many screen- and incidentally detected indeterminate pulmonary nodules. While the management of nodules with low or high pre-test probability of malignancy is relatively straightforward, those with intermediate pre-test probability commonly require advanced imaging or biopsy. Noninvasive risk stratification tools are highly desirable., Methods: We previously developed the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a conventional predictive radiomic model based on eight imaging features capturing nodule location, shape, size, texture and surface characteristics. Herein we report its external validation using a dataset of incidentally identified lung nodules (Vanderbilt University Lung Nodule Registry) in comparison to the Brock model. Area under the curve (AUC), as well as sensitivity, specificity, negative and positive predictive values were calculated., Results: For the entire Vanderbilt validation set (n=170, 54% malignant), the AUC was 0.87 (95% CI 0.81-0.92) for the Brock model and 0.90 (95% CI 0.85-0.94) for the BRODERS model. Using the optimal cut-off determined by Youden's index, the sensitivity was 92.3%, the specificity was 62.0%, the positive (PPV) and negative predictive values (NPV) were 73.7% and 87.5%, respectively. For nodules with intermediate pre-test probability of malignancy, Brock score of 5-65% (n=97), the sensitivity and specificity were 94% and 46%, respectively, the PPV was 78.4% and the NPV was 79.2%., Conclusions: The BRODERS radiomic predictive model performs well on an independent dataset and may facilitate the management of indeterminate pulmonary nodules., Competing Interests: Conflict of interest: F. Maldonado reports grants from Department of Defense (Lung Cancer Research Program under award number W81XWH-15-1-0110), during the conduct of the study; and holds intellectual property rights as an inventor of the CANARY software and BRODERS classifier, but does not receive any financial relationships regarding this software. Conflict of interest: C. Varghese has nothing to disclose. Conflict of interest: S. Rajagopalan reports grants from Department of Defense (Lung Cancer Research Program under award number W81XWH-15-1-0110 to F. Maldonado), during the conduct of the study; CALIPER software is licensed to Imbio, LLC, from whom The Mayo Clinic and B.J. Bartholmai receive royalties related to CALIPER (also known as Imbio Lung Texture Analysis, LTA); and S. Rajagoplan holds intellectual property rights as an inventor of the CANARY software and BRODERS classifier, but does not receive any financial relationships regarding this software. Conflict of interest: F. Duan reports grants from Department of Defense (Lung Cancer Research Program under award number W81XWH-15-1-0110 to F. Maldonado), during the conduct of the study. Conflict of interest: A.B. Balar has nothing to disclose. Conflict of interest: D.A. Lakhani has nothing to disclose. Conflict of interest: S.L. Antic has nothing to disclose. Conflict of interest: P.P. Massion has nothing to disclose. Conflict of interest: T.F. Johnson reports grants from Department of Defense, during the conduct of the study. Conflict of interest: R.A. Karwoski reports grants from Department of Defense (Lung Cancer Research Program under award number W81XWH-15-1-0110 to F. Maldonado), during the conduct of the study; CALIPER software is licensed to Imbio, LLC, from whom The Mayo Clinic and B.J. Bartholmai receive royalties related to CALIPER (also known as Imbio Lung Texture Analysis, LTA); and R.A. Karwoski holds intellectual property rights as an inventor of the CANARY software and BRODERS classifier, but does not receive any financial relationships regarding this software. Conflict of interest: R.A. Robb reports grants from Department of Defense (Lung Cancer Research Program under award number W81XWH-15-1-0110 to F. Maldonado), during the conduct of the study; CALIPER software is licensed to Imbio, LLC, from whom The Mayo Clinic and B.J. Bartholmai receive royalties related to CALIPER (also known as Imbio Lung Texture Analysis, LTA); and R.A. Robb holds intellectual property rights as an inventor of the CANARY software and BRODERS classifier, but does not receive any financial relationships regarding this software. Conflict of interest: B.J. Bartholmai reports grants from Department of Defense (Lung Cancer Research Program under award number W81XWH-15-1-0110 to F. Maldonado), during the conduct of the study; personal fees for advisory board work from Promedior, LLC, outside the submitted work; CALIPER software is licensed to Imbio, LLC, from whom The Mayo Clinic and B.J. Bartholmai receive royalties related to CALIPER (also known as Imbio Lung Texture Analysis, LTA); and B.J. Bartholmai holds intellectual property rights as an inventor of the CANARY software and BRODERS classifier, but does not receive any financial relationships regarding this software. Conflict of interest: T. Peikert reports grants from Department of Defense (Lung Cancer Research Program under award number W81XWH-15-1-0110 to F. Maldonado), during the conduct of the study; fees paid to institution for advisory board work from AstraZeneca and Novocure, outside the submitted work; and holds intellectual property rights as an inventor of the CANARY software and BRODERS classifier, but does not receive any financial relationships regarding this software., (Copyright ©ERS 2021.)

Clinical data elements (CDEs) (e.g., age, smoking history), blood markers and chest computed tomography (CT) structural features have been regarded as effective means for assessing lung cancer risk. These independent variables can provide complementary information and we hypothesize that combining them will improve the prediction accuracy. In practice, not all patients have all these variables available. In this paper, we propose a new network design, termed as multi-path multi-modal missing network (M3Net), to integrate the multi-modal data (i.e., CDEs, biomarker and CT image) considering missing modality with multiple paths neural network. Each path learns discriminative features of one modality, and different modalities are fused in a second stage for an integrated prediction. The network can be trained end-to-end with both medical image features and CDEs/biomarkers, or make a prediction with single modality. We evaluate M3Net with datasets including three sites from the Consortium for Molecular and Cellular Characterization of Screen-Detected Lesions (MCL) project. Our method is cross validated within a cohort of 1291 subjects (383 subjects with complete CDEs/biomarkers and CT images), and externally validated with a cohort of 99 subjects (99 with complete CDEs/biomarkers and CT images). Both cross-validation and external-validation results show that combining multiple modality significantly improves the predicting performance of single modality. The results suggest that integrating subjects with missing either CDEs/biomarker or CT imaging features can contribute to the discriminatory power of our model (p < 0.05, bootstrap two-tailed test). In summary, the proposed M3Net framework provides an effective way to integrate image and non-image data in the context of missing information.

Background: Granulomas caused by infectious lung diseases can present as indeterminate pulmonary nodules (IPN). This study aims to validate an enzyme immunoassay (EIA) for Histoplasma immunoglobulin G (IgG) and immunoglobulin M (IgM) for diagnosing benign IPN in areas with endemic histoplasmosis., Methods: Prospectively collected serum samples from patients at Vanderbilt University Medical Center (VUMC [n = 204]), University of Pittsburgh Medical Center (n = 71), and University of Cincinnati (n = 51) with IPN measuring 6 to 30 mm were analyzed for Histoplasma IgG and IgM with EIA. Diagnostic test characteristics were compared with results from the VUMC pilot cohort (n = 127). A multivariable logistic regression model was developed to predict granuloma in IPN., Results: Cancer prevalence varied by cohort: VUMC pilot 60%, VUMC validation 65%, University of Pittsburgh Medical Center 35%, and University of Cincinnati 75%. Across all cohorts, 19% of patients had positive IgG titers, 5% had positive IgM, and 3% had positive both IgG and IgM. Of patients with benign disease, 33% were positive for at least one antibody. All patients positive for both IgG and IgM antibodies at acute infection levels had benign disease (n = 13), with a positive predictive value of 100%. The prediction model for granuloma in IPN demonstrated an area under the receiver-operating characteristics curve of 0.84 and Brier score of 0.10., Conclusions: This study confirmed that Histoplasma EIA testing can be useful for diagnosing benign IPN in areas with endemic histoplasmosis in a population at high risk for lung cancer. Integrating Histoplasma EIA testing into the current diagnostic algorithm where histoplasmosis is endemic could improve management of IPN and potentially decrease unnecessary invasive biopsies., (Copyright © 2021 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Background: A systems-level approach to smoking cessation treatment may optimize healthcare provider adherence to guidelines. Institutions such as the Veterans Health Administration (VHA) are unique in their systematic approach, but comparisons of provider behavior in different healthcare systems are limited., Methods: We surveyed general medicine providers and specialists in a large academic health center (AHC) and its affiliated VHA in the Mid-South in 2017 to determine the cross-sectional association of healthcare system in which the provider practiced (exposure: AHC versus VHA) with self-reported provision of evidence-based smoking cessation treatment (delivery of counseling plus smoking cessation medication or referral) at least once in the past 12 months (composite outcome). Multivariable logistic regression with adjustment for specialty was performed in 2017-2019., Results: Of 625 healthcare providers surveyed, 407 (65%) responded, and 366 (59%) were analyzed. Most respondents practiced at the AHC (273[75%] vs VHA 93[25%]) and were general internists (215[59%]); pulmonologists (39[11%]); hematologists/oncologists (69[19%]); and gynecologists (43[12%]). Most respondents (328[90%]) reported the primary outcome. The adjusted odds of evidence-based smoking cessation treatment were higher among VHA vs. AHC healthcare providers (aOR = 4.3; 95% CI 1.3-14.4; p = .02). Health systems differed by provision of individual treatment components, including smoking cessation medication use (98% VHA vs. 90% AHC, p = 0.02) and referral to smoking cessation services (91% VHA vs. 65% AHC p = 0.001)., Conclusions: VHA healthcare providers were significantly more likely to provide evidence-based smoking cessation treatment compared to AHC healthcare providers. Healthcare systems' prioritization of and investment in smoking cessation treatment is critical to improving providers' adherence to guidelines.

A major goal of lung cancer screening is to identify individuals with particular phenotypes that are associated with high risk of cancer. Identifying relevant phenotypes is complicated by the variation in body position and body composition. In the brain, standardized coordinate systems (e.g., atlases) have enabled separate consideration of local features from gross/global structure. To date, no analogous standard atlas has been presented to enable spatial mapping and harmonization in chest computational tomography (CT). In this paper, we propose a thoracic atlas built upon a large low dose CT (LDCT) database of lung cancer screening program. The study cohort includes 466 male and 387 female subjects with no screening detected malignancy (age 46-79 years, mean 64.9 years). To provide spatial mapping, we optimize a multi-stage inter-subject non-rigid registration pipeline for the entire thoracic space. Briefly, with 50 scans of 50 randomly selected female subjects as fine tuning dataset, we search for the optimal configuration of the non-rigid registration module in a range of adjustable parameters including: registration searching radius, degree of keypoint dispersion, regularization coefficient and similarity patch size, to minimize the registration failure rate approximated by the number of samples with low Dice similarity score (DSC) for lung and body segmentation. We evaluate the optimized pipeline on a separate cohort (100 scans of 50 female and 50 male subjects) relative to two baselines with alternative non-rigid registration module: the same software with default parameters and an alternative software. We achieve a significant improvement in terms of registration success rate based on manual QA. For the entire study cohort, the optimized pipeline achieves a registration success rate of 91.7%. The application validity of the developed atlas is evaluated in terms of discriminative capability for different anatomic phenotypes, including body mass index (BMI), chronic obstructive pulmonary disease (COPD), and coronary artery calcification (CAC).

Liver stiffness is an essential clinical biomarker for diagnosing liver fibrosis and cirrhosis. In current clinical practice, elastography techniques are standard non-invasive diagnosis tools to assess stiffness of liver, using either Ultrasound (US) or magnetic resonance imaging (MRI). However, the US elastography yields ≈ 10 % failure rate and degraded performance on obese patients, while the MR elastography is costlier and less available. Compared with US and MRI, the computerized tomography (CT) imaging has not been widely used in measuring liver stiffness. In this paper, we performed a pilot study to assess if volumetric variations of liver can be captured from paired inspiratory-expiratory chest (PIEC) CT scans. To enable the assessment, we propose a Hierarchical Intra-Patient Organ-specific (HIPO) registration pipeline to quantify the partial liver volumetric variations with lung pressure from a respiratory cycle. The PIEC protocol is employed since it naturally provides two paired CT scans with liver deformation from regulated respiratory motions. For the subjects whose registration results passed both an automatic quantitative quality assurance (QA) and another visual qualitative QA, 6.0% average volumetric variations of liver were measured, from inspiratory phase to expiratory phase. Future clinical validations will be required to validate the findings in this pilot study.

Case Presentation: A 56-year-old man presented to the lung nodule clinic with abnormal chest imaging prompted by a chronic cough and hemoptysis. Approximately 2.5 years earlier, while kneeling beside his car fixing a flat tire, he fell backwards while holding the tire cap in his mouth, causing him to inhale sharply and aspirate the cap. He immediately developed an intractable cough productive of flecks of blood. He presented to an emergency room but left before being seen because of a long wait time and his lack of health-care insurance. He self-medicated for severe cough and chest discomfort with codeine, eventually developing a dependency. Approximately 3 weeks after aspirating the tire cap, his cough became productive, and he developed fever and chills. His symptoms improved transiently with antibiotics and additional narcotics. Ultimately, his chronic cough with intermittent hemoptysis affected his ability to work, and 30 months later he sought medical attention and was diagnosed with pneumonia and reactive airway disease. He was prescribed doxycycline, steroids, inhaled albuterol, and dextromethorphan, with initial improvement, but his symptoms recurred multiple times despite quitting smoking, leading to repeated medication courses., (Published by Elsevier Inc.)

Lung squamous carcinoma (LUSC) is a highly metastatic disease with a poor prognosis. Using an integrated screening approach, we found that miR-671-5p reduces LUSC metastasis by inhibiting a circular RNA (circRNA), CDR1as. Although the putative function of circRNA is through miRNA sponging, we found that miR-671-5p more potently silenced an axis of CDR1as and its antisense transcript, cerebellar degeneration related protein 1 (CDR1). Silencing of CDR1as or CDR1 significantly inhibited LUSC metastases and CDR1 was sufficient to promote migration and metastases. CDR1, which directly interacted with adaptor protein 1 (AP1) complex subunits and coatomer protein I (COPI) proteins, no longer promoted migration upon blockade of Golgi trafficking. Therapeutic inhibition of the CDR1as/CDR1 axis with miR-671-5p mimics reduced metastasis in vivo . This report demonstrates a novel role for CDR1 in promoting metastasis and Golgi trafficking. These findings reveal an miRNA/circRNA axis that regulates LUSC metastases through a previously unstudied protein, CDR1. SIGNIFICANCE: This study shows that circRNA, CDR1as, promotes lung squamous migration, metastasis, and Golgi trafficking through its complimentary transcript, CDR1., (©2020 American Association for Cancer Research.)

Introduction: Blood-based next-generation sequencing assays of circulating tumor DNA (ctDNA) have the ability to detect tumor-associated mutations in patients with SCLC. We sought to characterize the relationship between ctDNA mean variant allele frequency (VAF) and radiographic total-body tumor volume (TV) in patients with SCLC., Methods: We identified matched blood draws and computed tomography (CT) or positron emission tomography (PET) scans within a prospective SCLC blood banking cohort. We sequenced plasma using our previously developed 14-gene SCLC-specific ctDNA assay. Three-dimensional TV was determined from PET and CT scans using MIM software and reviewed by radiation oncologists. Univariate association and multivariate regression analyses were performed to evaluate the association between mean VAF and total-body TV., Results: We analyzed 75 matched blood draws and CT or PET scans from 25 unique patients with SCLC. Univariate analysis revealed a positive association between mean VAF and total-body TV (Spearman's ρ = 0.292, p < 0.01), and when considering only treatment-naive and pretreatment patients (n = 11), there was an increase in the magnitude of association (ρ = 0.618, p  = 0.048). The relationship remained significant when adjusting for treatment status and bone metastases ( p  = 0.046). In the subgroup of patients with TP53 variants, univariate analysis revealed a significant association (ρ = 0.762, p  = 0.037) only when considering treatment-naive and pretreatment patients (n = 8)., Conclusions: We observed a positive association between mean VAF and total-body TV in patients with SCLC, suggesting mean VAF may represent a dynamic biomarker of tumor burden that could be followed to monitor disease status., (© 2020 The Authors.)

The Long Short-Term Memory (LSTM) network is widely used in modeling sequential observations in fields ranging from natural language processing to medical imaging. The LSTM has shown promise for interpreting computed tomography (CT) in lung screening protocols. Yet, traditional image-based LSTM models ignore interval differences, while recently proposed interval-modeled LSTM variants are limited in their ability to interpret temporal proximity. Meanwhile, clinical imaging acquisition may be irregularly sampled, and such sampling patterns may be commingled with clinical usages. In this paper, we propose the Distanced LSTM (DLSTM) by introducing time-distanced (i.e., time distance to the last scan) gates with a temporal emphasis model (TEM) targeting at lung cancer diagnosis (i.e., evaluating the malignancy of pulmonary nodules). Briefly, (1) the time distance of every scan to the last scan is modeled explicitly, (2) time-distanced input and forget gates in DLSTM are introduced across regular and irregular sampling sequences, and (3) the newer scan in serial data is emphasized by the TEM. The DLSTM algorithm is evaluated with both simulated data and real CT images (from 1794 National Lung Screening Trial (NLST) patients with longitudinal scans and 1420 clinical studied patients). Experimental results on simulated data indicate the DLSTM can capture families of temporal relationships that cannot be detected with traditional LSTM. Cross-validation on empirical CT datasets demonstrates that DLSTM achieves leading performance on both regularly and irregularly sampled data (e.g., improving LSTM from 0.6785 to 0.7085 on F1 score in NLST). In external-validation on irregularly acquired data, the benchmarks achieved 0.8350 (CNN feature) and 0.8380 (with LSTM) on AUC score, while the proposed DLSTM achieves 0.8905. In conclusion, the DLSTM approach is shown to be compatible with families of linear, quadratic, exponential, and log-exponential temporal models. The DLSTM can be readily extended with other temporal dependence interactions while hardly increasing overall model complexity., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier B.V.)

Tumor evolution from a single cell into a malignant, heterogeneous tissue remains poorly understood. Here, we profile single-cell transcriptomes of genetically engineered mouse lung tumors at seven stages, from pre-neoplastic hyperplasia to adenocarcinoma. The diversity of transcriptional states increases over time and is reproducible across tumors and mice. Cancer cells progressively adopt alternate lineage identities, computationally predicted to be mediated through a common transitional, high-plasticity cell state (HPCS). Accordingly, HPCS cells prospectively isolated from mouse tumors and human patient-derived xenografts display high capacity for differentiation and proliferation. The HPCS program is associated with poor survival across human cancers and demonstrates chemoresistance in mice. Our study reveals a central principle underpinning intra-tumoral heterogeneity and motivates therapeutic targeting of the HPCS., Competing Interests: Declaration of Interests T.J. is a member of the Board of Directors of Amgen and Thermo Fisher Scientific, and a co-Founder of Dragonfly Therapeutics and T2 Biosystems. T.J. serves on the Scientific Advisory Board of Dragonfly Therapeutics, SQZ Biotech, and Skyhawk Therapeutics. T.J. also received funding from Calico and currently receives funding from Johnson & Johnson, but this funding did not support the research described in this manuscript. A.R. is a co-founder and equity holder in Celsius Therapeutics and a SAB member for Thermo Fisher, Asimov, Neogene Therapeutics, and Syros Pharmaceuticals, and an equity holder of Immunitas Therapeutics. C.M.R. serves on the SAB of Bridge Medicines and Harpoon Therapeutics, and has consulted regarding oncology drug development with AbbVie, Amgen, Ascentage, Bicycle, Celgene, Daiichi Sankyo, Genentech, Ipsen, Loxo, Pharmamar, and Vavotek. None of the affiliations listed above represent a conflict of interest with the design or execution of this study or interpretation of data presented in this manuscript. Other authors have nothing to disclose., (Copyright © 2020 Elsevier Inc. All rights reserved.)

With the rapid development of image acquisition and storage, multiple images per class are commonly available for computer vision tasks (e.g., face recognition, object detection, medical imaging, etc.). Recently, the recurrent neural network (RNN) has been widely integrated with convolutional neural networks (CNN) to perform image classification on ordered (sequential) data. In this paper, by permutating multiple images as multiple dummy orders, we generalize the ordered "RNN+CNN" design (longitudinal) to a novel unordered fashion, called Multi-path x-D Recurrent Neural Network (MxDRNN) for image classification. To the best of our knowledge, few (if any) existing studies have deployed the RNN framework to unordered intra-class images to leverage classification performance. Specifically, multiple learning paths are introduced in the MxDRNN to extract discriminative features by permutating input dummy orders. Eight datasets from five different fields (MNIST, 3D-MNIST, CIFAR, VGGFace2, and lung screening computed tomography) are included to evaluate the performance of our method. The proposed MxDRNN improves the baseline performance by a large margin across the different application fields (e.g., accuracy from 46.40% to 76.54% in VGGFace2 test pose set, AUC from 0.7418 to 0.8162 in NLST lung dataset). Additionally, empirical experiments show the MxDRNN is more robust to category-irrelevant attributes (e.g., expression, pose in face images), which may introduce difficulties for image classification and algorithm generalizability. The code is publicly available., Competing Interests: Conflict of Interest None.