Integrating genomic features for non-invasive early lung cancer detection.

Radiologic screening of high-risk adults reduces lung-cancer-related mortality1,2; however, a small minority of eligible individuals undergo such screening in the United States3,4. The availability of blood-based tests could increase screening uptake. Here we introduce improvements to cancer personalized profiling by deep sequencing (CAPP-Seq)5, a method for the analysis of circulating tumour DNA (ctDNA), to better facilitate screening applications. We show that, although levels are very low in early-stage lung cancers, ctDNA is present prior to treatment in most patients and its presence is strongly prognostic. We also find that the majority of somatic mutations in the cell-free DNA (cfDNA) of patients with lung cancer and of risk-matched controls reflect clonal haematopoiesis and are non-recurrent. Compared with tumour-derived mutations, clonal haematopoiesis mutations occur on longer cfDNA fragments and lack mutational signatures that are associated with tobacco smoking. Integrating these findings with other molecular features, we develop and prospectively validate a machine-learning method termed 'lung cancer likelihood in plasma' (Lung-CLiP), which can robustly discriminate early-stage lung cancer patients from risk-matched controls. This approach achieves performance similar to that of tumour-informed ctDNA detection and enables tuning of assay specificity in order to facilitate distinct clinical applications. Our findings establish the potential of cfDNA for lung cancer screening and highlight the importance of risk-matching cases and controls in cfDNA-based screening studies.

Premalignant Progression in the Lung: Knowledge Gaps and Novel Opportunities for Interception of Non-Small Cell Lung Cancer. An Official American Thoracic Society Research Statement.

RATIONALE: Despite significant advances in precision treatments and immunotherapy, lung cancer is the most common cause of cancer death worldwide. To reduce incidence and improve survival rates, a deeper understanding of lung premalignancy and the multistep process of tumorigenesis is essential, allowing for timely and effective intervention before cancer development. OBJECTIVES: To summarize existing information, identify knowledge gaps, formulate research questions, prioritize potential research topics, and propose strategies for future investigations into the premalignant progression in the lung. METHODS: An international multidisciplinary team of basic, translational, and clinical scientists reviewed available data to develop and refine research questions pertaining to the transformation of premalignant lung lesions to advanced lung cancer. RESULTS: This research statement identifies significant gaps in knowledge and proposes potential research questions aimed at expanding our understanding of the mechanisms underlying the progression of premalignant lung lesions to lung cancer in an effort to explore potential innovative modalities to intercept lung cancer at its nascent stages. CONCLUSIONS: The identified gaps in knowledge about the biological mechanisms of premalignant progression in the lung, along with ongoing challenges in screening, detection, and early intervention, highlight the critical need to prioritize research in this domain. Such focused investigations are essential to devise effective preventive strategies that may ultimately decrease lung cancer incidence and improve patient outcomes.

Elucidating the role of EPPK1 in lung adenocarcinoma development.

BACKGROUND: We recently found that epiplakin 1 (EPPK1) alterations were present in 12% of lung adenocarcinoma (LUAD) cases and were associated with a poor prognosis in early-stage LUAD when combined with other molecular alterations. This study aimed to identify a probable crucial role for EPPK1 in cancer development. METHODS: EPPK1 mRNA and protein expression was analyzed with clinical variables. Normal bronchial epithelial cell lines were exposed to cigarette smoke for 16 weeks to determine whether EPPK1 protein expression was altered after exposure. Further, we used CRISPR-Cas9 to knock out (KO) EPPK1 in LUAD cell lines and observed how the cancer cells were altered functionally and genetically. RESULTS: EPPK1 protein expression was associated with smoking and poor prognosis in early-stage LUAD. Moreover, a consequential mesenchymal-to-epithelial transition was observed, subsequently resulting in diminished cell proliferation and invasion after EPPK1 KO. RNA sequencing revealed that EPPK1 KO induced downregulation of 11 oncogenes, 75 anti-apoptosis, and 22 angiogenesis genes while upregulating 8 tumor suppressors and 12 anti-cell growth genes. We also observed the downregulation of MYC and upregulation of p53 expression at both protein and RNA levels following EPPK1 KO. Gene ontology enrichment analysis of molecular functions highlighted the correlation of EPPK1 with the regulation of mesenchymal cell proliferation, mesenchymal differentiation, angiogenesis, and cell growth after EPPK1 KO. CONCLUSIONS: Our data suggest that EPPK1 is linked to smoking, epithelial to mesenchymal transition, and the regulation of cancer progression, indicating its potential as a therapeutic target for LUAD.

TP53 mutation prevalence in normal airway epithelium as a biomarker for lung cancer risk.

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 PLCOM2012 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.

Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT.

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.

Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules.

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.

Incorporating both genetic and tobacco smoking data to identify high-risk smokers for lung cancer screening.

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.

Small airway determinants of airflow limitation in chronic obstructive pulmonary disease.

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 (FEV1), 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.

Validation of the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a novel HRCT-based radiomic classifier for indeterminate pulmonary nodules.

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.

Risk stratification of indeterminate pulmonary nodules.

PURPOSE OF REVIEW: Lung cancer remains the leading cause of cancer-related death in the United States, with poor overall 5-year survival. Early detection and diagnosis are key to survival as demonstrated in lung cancer screening trials. However, with increasing implementation of screening guidelines and use of computed tomography, there has been a sharp rise in the incidence of indeterminate pulmonary nodules (IPNs). Risk stratification of IPNs, particularly those in the intermediate-risk category, remains challenging in clinical practice. Individual risk factors, imaging characteristics, biomarkers, and prediction models are currently used to assist in risk stratifying patients, but such strategies remain suboptimal. This review focuses on established risk stratification methods, current areas of research, and future directions. RECENT FINDINGS: The multitude of yearly incidental and screening-detected IPNs, its management-related healthcare costs, and risk of invasive procedures provides a strong rationale for risk stratification efforts. The development of new molecular and imaging biomarkers to discriminate benign from malignant lung nodules shows great promise. Yet, risk stratification methods need integration into the diagnostic workflow and await validation in prospective, biomarker-driven clinical trials. SUMMARY: Novel biomarkers and new imaging analysis, including radiomics and deep-learning methods, have been developed to optimize the risk stratification of IPNs. While promising, additional validation and clinical studies are needed before they can be part of routine clinical practice.

Comprehensive genomic profiles of small cell lung cancer.

We have sequenced the genomes of 110 small cell lung cancers (SCLC), one of the deadliest human cancers. In nearly all the tumours analysed we found bi-allelic inactivation of TP53 and RB1, sometimes by complex genomic rearrangements. Two tumours with wild-type RB1 had evidence of chromothripsis leading to overexpression of cyclin D1 (encoded by the CCND1 gene), revealing an alternative mechanism of Rb1 deregulation. Thus, loss of the tumour suppressors TP53 and RB1 is obligatory in SCLC. We discovered somatic genomic rearrangements of TP73 that create an oncogenic version of this gene, TP73Δex2/3. In rare cases, SCLC tumours exhibited kinase gene mutations, providing a possible therapeutic opportunity for individual patients. Finally, we observed inactivating mutations in NOTCH family genes in 25% of human SCLC. Accordingly, activation of Notch signalling in a pre-clinical SCLC mouse model strikingly reduced the number of tumours and extended the survival of the mutant mice. Furthermore, neuroendocrine gene expression was abrogated by Notch activity in SCLC cells. This first comprehensive study of somatic genome alterations in SCLC uncovers several key biological processes and identifies candidate therapeutic targets in this highly lethal form of cancer.

Genomic Underpinnings of Tumor Behavior in In Situ and Early Lung Adenocarcinoma.

Rationale: We have a limited understanding of the molecular underpinnings of early adenocarcinoma (ADC) progression. We hypothesized that the behavior of early ADC can be predicted based on genomic determinants.Objectives: To identify genomic alterations associated with resected indolent and aggressive early lung ADCs.Methods: DNA was extracted from 21 ADCs in situ (AISs), 27 minimally invasive ADCs (MIAs), and 54 fully invasive ADCs. This DNA was subjected to deep next-generation sequencing and tested against a custom panel of 347 cancer genes.Measurements and Main Results: Sequencing data was analyzed for associations among tumor mutation burden, frequency of mutations or copy number alterations, mutation signatures, intratumor heterogeneity, pathway alterations, histology, and overall survival. We found that deleterious mutation burden was significantly greater in invasive ADC, whereas more copy number loss was observed in AIS and MIA. Intratumor heterogeneity establishes early, as in AIS. Twenty-one significantly mutated genes were shared among the groups. Mutation signature profiling did not vary significantly, although the APOBEC signature was associated with ADC and poor survival. Subclonal KRAS mutations and a gene signature consisting of PIK3CG, ATM, EPPK1, EP300, or KMT2C mutations were also associated with poor survival. Mutations of KRAS, TP53, and NF1 were found to increase in frequency from AIS and MIA to ADC. A cancer progression model revealed selective early and late drivers.Conclusions: Our results reveal several genetic driver events, clonality, and mutational signatures associated with poor outcome in early lung ADC, with potential future implications for the detection and management of ADC.

Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules.

Rationale: The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regimens are needed.Objectives: To develop and validate a deep learning method to improve the management of IPNs.Methods: A Lung Cancer Prediction Convolutional Neural Network model was trained using computed tomography images of IPNs from the National Lung Screening Trial, internally validated, and externally tested on cohorts from two academic institutions.Measurements and Main Results: The areas under the receiver operating characteristic curve in the external validation cohorts were 83.5% (95% confidence interval [CI], 75.4-90.7%) and 91.9% (95% CI, 88.7-94.7%), compared with 78.1% (95% CI, 68.7-86.4%) and 81.9 (95% CI, 76.1-87.1%), respectively, for a commonly used clinical risk model for incidental nodules. Using 5% and 65% malignancy thresholds defining low- and high-risk categories, the overall net reclassifications in the validation cohorts for cancers and benign nodules compared with the Mayo model were 0.34 (Vanderbilt) and 0.30 (Oxford) as a rule-in test, and 0.33 (Vanderbilt) and 0.58 (Oxford) as a rule-out test. Compared with traditional risk prediction models, the Lung Cancer Prediction Convolutional Neural Network was associated with improved accuracy in predicting the likelihood of disease at each threshold of management and in our external validation cohorts.Conclusions: This study demonstrates that this deep learning algorithm can correctly reclassify IPNs into low- or high-risk categories in more than a third of cancers and benign nodules when compared with conventional risk models, potentially reducing the number of unnecessary invasive procedures and delays in diagnosis.

Evidence-based smoking cessation treatment: a comparison by healthcare system.

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.

Characterizing the Tumor Immune Microenvironment with Tyramide-Based Multiplex Immunofluorescence.

Multiplex immunofluorescence (mIF) allows simultaneous antibody-based detection of multiple markers with a nuclear counterstain on a single tissue section. Recent studies have demonstrated that mIF is becoming an important tool for immune profiling the tumor microenvironment, further advancing our understanding of the interplay between cancer and the immune system, and identifying predictive biomarkers of response to immunotherapy. Expediting mIF discoveries is leading to improved diagnostic panels, whereas it is important that mIF protocols be standardized to facilitate their transition into clinical use. Manual processing of sections for mIF is time consuming and a potential source of variability across numerous samples. To increase reproducibility and throughput we demonstrate the use of an automated slide stainer for mIF incorporating tyramide signal amplification (TSA). We describe two panels aimed at characterizing the tumor immune microenvironment. Panel 1 included CD3, CD20, CD117, FOXP3, Ki67, pancytokeratins (CK), and DAPI, and Panel 2 included CD3, CD8, CD68, PD-1, PD-L1, CK, and DAPI. Primary antibodies were first tested by standard immunohistochemistry and single-plex IF, then multiplex panels were developed and images were obtained using a Vectra 3.0 multispectral imaging system. Various methods for image analysis (identifying cell types, determining cell densities, characterizing cell-cell associations) are outlined. These mIF protocols will be invaluable tools for immune profiling the tumor microenvironment.

Identification of lipid biomarker from serum in patients with chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the United States with no effective treatment. The current diagnostic method, spirometry, does not accurately reflect the severity of COPD disease status. Therefore, there is a pressing unmet medical need to develop noninvasive methods and reliable biomarkers to detect early stages of COPD. Lipids are the fundamental components of cell membranes, and dysregulation of lipids was proven to be associated with COPD. Lipidomics is a comprehensive approach to all the pathways and networks of cellular lipids in biological systems. It is widely used for disease diagnosis, biomarker identification, and pathology disorders detection relating to lipid metabolism. METHODS: In the current study, a total of 25 serum samples were collected from 5 normal control subjects and 20 patients with different stages of COPD according to the global initiative for chronic obstructive lung disease (GOLD) (GOLD stages I ~ IV, 5 patients per group). After metabolite extraction, lipidomic analysis was performed using electrospray ionization mass spectrometry (ESI-MS) to detect the serum lipid species. Later, the comparisons of individual lipids were performed between controls and patients with COPD. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) and receiver operating characteristic (ROC) analysis were utilized to test the potential biomarkers. Finally, correlations between the validated lipidomic biomarkers and disease stages, age, FEV1% pack years and BMI were evaluated. RESULTS: Our results indicate that a panel of 50 lipid metabolites including phospholipids, sphingolipids, glycerolipids, and cholesterol esters can be used to differentiate the presence of COPD. Among them, 10 individual lipid species showed significance (p < 0.05) with a two-fold change. In addition, lipid ratios between every two lipid species were also evaluated as potential biomarkers. Further multivariate data analysis and receiver operating characteristic (ROC: 0.83 ~ 0.99) analysis suggest that four lipid species (AUC:0.86 ~ 0.95) and ten lipid ratios could be potential biomarkers for COPD (AUC:0.94 ~ 1) with higher sensitivity and specificity. Further correlation analyses indicate these potential biomarkers were not affected age, BMI, stages and FEV1%, but were associated with smoking pack years. CONCLUSION: Using lipidomics and statistical methods, we identified unique lipid signatures as potential biomarkers for diagnosis of COPD. Further validation studies of these potential biomarkers with large population may elucidate their roles in the development of COPD.

Multi-path x-D Recurrent Neural Networks for Collaborative Image Classification.

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.

Somatostatin receptor 2 signaling promotes growth and tumor survival in small-cell lung cancer.

Somatostatin receptor 2 (SSTR2) is overexpressed in a majority of neuroendocrine neoplasms, including small-cell lung carcinomas (SCLCs). SSTR2 was previously considered an inhibitory receptor on cell growth, but its agonists had poor clinical responses in multiple clinical trials. The role of this receptor as a potential therapeutic target in lung cancer merits further investigation. We evaluated the expression of SSTR2 in a cohort of 96 primary tumors from patients with SCLC and found 48% expressed SSTR2. Correlation analysis in both CCLE and an SCLC RNAseq cohort confirmed high-level expression and identified an association between NEUROD1 and SSTR2. There was a significant association with SSTR2 expression profile and poor clinical outcome. We tested whether SSTR2 expression might contribute to tumor progression through activation of downstream signaling pathways, using in vitro and in vivo systems and downregulated SSTR2 expression in lung cancer cells by shRNA. SSTR2 downregulation led to increased apoptosis and dramatically decreased tumor growth in vitro and in vivo in multiple cell lines with decreased AMPKα phosphorylation and increased oxidative metabolism. These results demonstrate a role for SSTR2 signaling in SCLC and suggest that SSTR2 is a poor prognostic biomarker in SCLC and potential future therapeutic signaling target.

Tobacco smoking induces cardiovascular mitochondrial oxidative stress, promotes endothelial dysfunction, and enhances hypertension.

Tobacco smoking is a major risk factor for cardiovascular disease and hypertension. It is associated with the oxidative stress and induces metabolic reprogramming, altering mitochondrial function. We hypothesized that cigarette smoke induces cardiovascular mitochondrial oxidative stress, which contributes to endothelial dysfunction and hypertension. To test this hypothesis, we studied whether the scavenging of mitochondrial H2O2 in transgenic mice expressing mitochondria-targeted catalase (mCAT) attenuates the development of cigarette smoke/angiotensin II-induced mitochondrial oxidative stress and hypertension compared with wild-type mice. Two weeks of exposure of wild-type mice with cigarette smoke increased systolic blood pressure by 17 mmHg, which was similar to the effect of a subpresssor dose of angiotensin II (0.2 mg·kg-1·day-1), leading to a moderate increase to the prehypertensive level. Cigarette smoke exposure and a low dose of angiotensin II cooperatively induced severe hypertension in wild-type mice, but the scavenging of mitochondrial H2O2 in mCAT mice completely prevented the development of hypertension. Cigarette smoke and angiotensin II cooperatively induced oxidation of cardiolipin (a specific biomarker of mitochondrial oxidative stress) in wild-type mice, which was abolished in mCAT mice. Cigarette smoke and angiotensin II impaired endothelium-dependent relaxation and induced superoxide overproduction, which was diminished in mCAT mice. To mimic the tobacco smoke exposure, we used cigarette smoke condensate, which induced mitochondrial superoxide overproduction and reduced endothelial nitric oxide (a hallmark of endothelial dysfunction in hypertension). Western blot experiments indicated that tobacco smoke and angiotensin II reduce the mitochondrial deacetylase sirtuin-3 level and cause hyperacetylation of a key mitochondrial antioxidant, SOD2, which promotes mitochondrial oxidative stress. NEW & NOTEWORTHY This work demonstrates tobacco smoking-induced mitochondrial oxidative stress, which contributes to endothelial dysfunction and development of hypertension. We suggest that the targeting of mitochondrial oxidative stress can be beneficial for treatment of pathological conditions associated with tobacco smoking, such as endothelial dysfunction, hypertension, and cardiovascular diseases.

Low Provider Knowledge Is Associated With Less Evidence-Based Lung Cancer Screening.

BACKGROUND: Despite widespread recommendation and supportive policies, screening with low-dose CT (LDCT) is incompletely implemented in the US healthcare system. Low provider knowledge of the lung cancer screening (LCS) guidelines represents a potential barrier to implementation. Therefore, we tested the hypothesis that low provider knowledge of guidelines is associated with less provider-reported screening with LDCT. PATIENTS AND METHODS: A cross-sectional survey was performed in a large academic medical center and affiliated Veterans Health Administration in the Mid-South United States that comprises hospital and community-based practices. Participants included general medicine providers and specialists who treat patients aged >50 years. The primary exposure was LCS guideline knowledge (US Preventive Services Task Force/Centers for Medicare & Medicaid Services). High knowledge was defined as identifying 3 major screening eligibility criteria (55 years as initial age of screening eligibility, smoking status as current or former smoker, and smoking history of ≥30 pack-years), and low knowledge was defined as not identifying these 3 criteria. The primary outcome was self-reported LDCT order/referral within the past year, and the secondary outcome was screening chest radiograph. Multivariable logistic regression evaluated the adjusted odds ratio (aOR) of screening by knowledge. RESULTS: Of 625 providers recruited, 407 (65%) responded, and 378 (60.5%) were analyzed. Overall, 233 providers (62%) demonstrated low LCS knowledge, and 224 (59%) reported ordering/referring for LDCT. The aOR of ordering/referring LDCT was less among providers with low knowledge (0.41; 95% CI, 0.24-0.71) than among those with high knowledge. More providers with low knowledge reported ordering screening chest radiographs (aOR, 2.7; 95% CI, 1.4-5.0) within the past year. CONCLUSIONS: Referring provider knowledge of LCS guidelines is low and directly proportional to the ordering rate for LDCT in an at-risk US population. Strategies to advance evidence-based LCS should incorporate provider education and system-level interventions to address gaps in provider knowledge.