Search

Ample studies suggest that the cyclooxygenase-2 (COX-2)/prostaglandin E2 (PGE2) pathway plays a pivotal role in carcinogenesis and that COX-2 inhibition may help prevent lung cancer. Therefore, we conducted a randomized, double-blind, placebo-controlled trial of the COX-2–selective inhibitor celecoxib (400 mg bid for 6 months) in former-smokers (age ≥ 45, ≥ 30 pack-years of smoking, ≥ 1 year of sustained abstinence from smoking). We assessed the impact of celecoxib on cellular and molecular events associated with lung cancer pathogenesis; the primary endpoint was bronchial Ki-67 labeling index (Ki-67 LI) after 6 months of treatment. Of 137 randomized subjects, 101 completed both baseline and 6-month bronchoscopies and were evaluable for the primary endpoint analysis. The beneficial effect on Ki-67 LI was greater in the celecoxib arm (versus placebo) in a mixed-effects analysis (P = 0.0006), and celecoxib significantly decreased Ki-67 LI by an average of 34%, whereas placebo increased Ki-67 LI by an average of 3.8% (P = 0.04; t test). In participants who crossed over to the other study arm at 6 months (all of whom had received 6 months of celecoxib at the end of a 12 months treatment period), the decreases in Ki-67 LI correlated with a reduction and/or resolution of lung nodules on computed tomography. Celecoxib significantly reduced plasma c-reactive protein and interleukin-6 mRNA and protein and increased 15(S)-hydroxy-eicosatetraenoic acid levels in bronchoalveolar lavage (BAL) samples. The baseline ratio of COX-2 to 15-hydroxyprostaglandin dehydrogenase mRNA in BAL cells was a significant predictive marker of Ki-67 response to celecoxib (P = 0.002). Our collective findings support the continued investigation of celecoxib for lung cancer chemoprevention in former smokers at a low risk of cardiovascular disease. Cancer Prev Res; 4(7); 984–93. ©2011 AACR.

Perspective on This Article from Lung Cancer Chemoprevention with Celecoxib in Former Smokers

Background CT screening for lung cancer results in a significant mortality reduction but is complicated by invasive procedures performed for evaluation of the many detected benign nodules. The purpose of this study was to evaluate measures of nodule location within the lung as predictors of malignancy. Methods We analyzed images and data from 3,483 participants in the National Lung Screening Trial (NLST). All nodules (4-20 mm) were characterized by 3D geospatial location using a Cartesian coordinate system and evaluated in logistic regression analysis. Model development and probability cutpoint selection was performed in the NLST testing set. The Geospatial test was then validated in the NLST testing set, and subsequently replicated in a new cohort of 147 participants from The Detection of Early Lung Cancer Among Military Personnel (DECAMP) Consortium. Results The Geospatial Test, consisting of the superior-inferior distance (Z distance), nodule diameter, and radial distance (carina to nodule) performed well in both the NLST validation set (AUC 0.85) and the DECAMP replication cohort (AUC 0.75). A negative Geospatial Test resulted in a less than 2% risk of cancer across all nodule diameters. The Geospatial Test correctly reclassified 19.7% of indeterminate nodules with a diameter over 6mm as benign, while only incorrectly classifying 1% of cancerous nodules as benign. In contrast, the parsimonious Brock Model applied to the same group of nodules correctly reclassified 64.5% of indeterminate nodules as benign but resulted in misclassification of a cancer as benign in 18.2% of the cases. Applying the Geospatial test would result in reducing invasive procedures performed for benign lesions by 11.3% with a low rate of misclassification (1.3%). In contrast, the Brock model applied to the same group of patients results in decreasing invasive procedures for benign lesion by 39.0% but misclassifying 21.1% of cancers as benign. Conclusions Utilizing information about geospatial location within the lung improves risk assessment for indeterminate lung nodules and may reduce unnecessary procedures. Trial registration NCT00047385, NCT01785342.

ImportanceScreening with low-dose computed tomography (CT) has been shown to reduce mortality from lung cancer in randomized clinical trials in which the rate of adherence to follow-up recommendations was over 90%; however, adherence to Lung Computed Tomography Screening Reporting & Data System (Lung-RADS) recommendations has been low in practice. Identifying patients who are at risk of being nonadherent to screening recommendations may enable personalized outreach to improve overall screening adherence.ObjectiveTo identify factors associated with patient nonadherence to Lung-RADS recommendations across multiple screening time points.Design, Setting, and ParticipantsThis cohort study was conducted at a single US academic medical center across 10 geographically distributed sites where lung cancer screening is offered. The study enrolled individuals who underwent low-dose CT screening for lung cancer between July 31, 2013, and November 30, 2021.ExposuresLow-dose CT screening for lung cancer.Main Outcomes and MeasuresThe main outcome was nonadherence to follow-up recommendations for lung cancer screening, defined as failing to complete a recommended or more invasive follow-up examination (ie, diagnostic dose CT, positron emission tomography–CT, or tissue sampling vs low-dose CT) within 15 months (Lung-RADS score, 1 or 2), 9 months (Lung-RADS score, 3), 5 months (Lung-RADS score, 4A), or 3 months (Lung-RADS score, 4B/X). Multivariable logistic regression was used to identify factors associated with patient nonadherence to baseline Lung-RADS recommendations. A generalized estimating equations model was used to assess whether the pattern of longitudinal Lung-RADS scores was associated with patient nonadherence over time.ResultsAmong 1979 included patients, 1111 (56.1%) were aged 65 years or older at baseline screening (mean [SD] age, 65.3 [6.6] years), and 1176 (59.4%) were male. The odds of being nonadherent were lower among patients with a baseline Lung-RADS score of 1 or 2 vs 3 (adjusted odds ratio [AOR], 0.35; 95% CI, 0.25-0.50), 4A (AOR, 0.21; 95% CI, 0.13-0.33), or 4B/X, (AOR, 0.10; 95% CI, 0.05-0.19); with a postgraduate vs college degree (AOR, 0.70; 95% CI, 0.53-0.92); with a family history of lung cancer vs no family history (AOR, 0.74; 95% CI, 0.59-0.93); with a high age-adjusted Charlson Comorbidity Index score (≥4) vs a low score (0 or 1) (AOR, 0.67; 95% CI, 0.46-0.98); in the high vs low income category (AOR, 0.79; 95% CI, 0.65-0.98); and referred by physicians from pulmonary or thoracic-related departments vs another department (AOR, 0.56; 95% CI, 0.44-0.73). Among 830 eligible patients who had completed at least 2 screening examinations, the adjusted odds of being nonadherent to Lung-RADS recommendations at the following screening were increased in patients with consecutive Lung-RADS scores of 1 to 2 (AOR, 1.38; 95% CI, 1.12-1.69).Conclusions and RelevanceIn this retrospective cohort study, patients with consecutive negative lung cancer screening results were more likely to be nonadherent with follow-up recommendations. These individuals are potential candidates for tailored outreach to improve adherence to recommended annual lung cancer screening.

Background Chronic tobacco smoke exposure results in a broad range of lung pathologies including emphysema, airway disease and parenchymal fibrosis as well as a multitude of extra-pulmonary comorbidities. Prior work using CT imaging has identified several clinically relevant subgroups of smoking related lung disease, but these investigations have generally lacked organ specific molecular correlates. Research Question Can CT imaging be used to identify clinical phenotypes of smoking related lung disease that have specific bronchial epithelial gene expression patterns to better understand disease pathogenesis? Study Design and Methods Using K-means clustering, we clustered participants from the COPDGene study (n = 5,273) based on CT imaging characteristics and then evaluated their clinical phenotypes. These clusters were replicated in the Detection of Early Lung Cancer Among Military Personnel (DECAMP) cohort (n = 360), and were further characterized using bronchial epithelial gene expression. Results Three clusters (preserved, interstitial predominant and emphysema predominant) were identified. Compared to the preserved cluster, the interstitial and emphysema clusters had worse lung function, exercise capacity and quality of life. In longitudinal follow-up, individuals from the emphysema group had greater declines in exercise capacity and lung function, more emphysema, more exacerbations, and higher mortality. Similarly, genes involved in inflammatory pathways (tumor necrosis factor-α, interferon-β) are more highly expressed in bronchial epithelial cells from individuals in the emphysema cluster, while genes associated with T-cell related biology are decreased in these samples. Samples from individuals in the interstitial cluster generally had intermediate levels of expression of these genes. Interpretation Using quantitative CT imaging, we identified three groups of individuals in older ever-smokers that replicate in two cohorts. Airway gene expression differences between the three groups suggests increased levels of inflammation in the most severe clinical phenotype, possibly mediated by the tumor necrosis factor-α and interferon-β pathways. Clinical Trial Registration COPDGene (NCT00608764), DECAMP-1 (NCT01785342), DECAMP-2 (NCT02504697)

ObjectivesDiscovering airway gene expression alterations associated with radiological bronchiectasis may improve the understanding of the pathobiology of early-stage bronchiectasis.MethodsPresence of radiological bronchiectasis in 173 individuals without a clinical diagnosis of bronchiectasis was evaluated. Bronchial brushings from these individuals were transcriptomically profiled and analysed. Single-cell deconvolution was performed to estimate changes in cellular landscape that may be associated with early disease progression.Results20 participants have widespread radiological bronchiectasis (three or more lobes). Transcriptomic analysis reflects biological processes associated with bronchiectasis including decreased expression of genes involved in cell adhesion and increased expression of genes involved in inflammatory pathways (655 genes, false discovery rate 2fold-change >0.25). Deconvolution analysis suggests that radiological bronchiectasis is associated with an increased proportion of ciliated and deuterosomal cells, and a decreased proportion of basal cells. Gene expression patterns separated participants into three clusters: normal, intermediate and bronchiectatic. The bronchiectatic cluster was enriched by participants with more lobes of radiological bronchiectasis (pConclusionsGenes involved in cell adhesion, Wnt signalling, ciliogenesis and interferon-γ pathways had altered expression in the bronchus of participants with widespread radiological bronchiectasis, possibly associated with decreased basal and increased ciliated cells. This gene expression pattern is not only highly enriched among individuals with radiological bronchiectasis, but also associated with airway-related symptoms in those without discernible radiological bronchiectasis, suggesting that it reflects a bronchiectasis-associated, but non-bronchiectasis-specific lung pathophysiological process.

IntroductionWe performed an external validation of the Brock model using the National Lung Screening Trial (NLST) data set, following strict guidelines set forth by the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis statement. We report how external validation results can be interpreted and highlight the role of recalibration and model updating.Materials and methodsWe assessed model discrimination and calibration using the NLST data set. Adhering to the inclusion/exclusion criteria reported by McWilliams et al, we identified 7879 non-calcified nodules discovered at the baseline low-dose CT screen with 2 years of follow-up. We characterised differences between Pan-Canadian Early Detection of Lung Cancer Study and NLST cohorts. We calculated the slope on the prognostic index and the intercept coefficient by fitting the original Brock model to NLST. We also assessed the impact of model recalibration and the addition of new covariates such as body mass index, smoking status, pack-years and asbestos.ResultsWhile the area under the curve (AUC) of the model was good, 0.905 (95% CI 0.882 to 0.928), a histogram plot showed that the model poorly differentiated between benign and malignant cases. The calibration plot showed that the model overestimated the probability of cancer. In recalibrating the model, the coefficients for emphysema, spiculation and nodule count were updated. The updated model had an improved calibration and achieved an optimism-corrected AUC of 0.912 (95% CI 0.891 to 0.932). Only pack-year history was found to be significant (pConclusionWhile the Brock model achieved a high AUC when validated on the NLST data set, the model benefited from updating and recalibration. Nevertheless, covariates used in the model appear to be insufficient to adequately discriminate malignant cases.

Background: Randomized controlled trials (RCT) play a central role in evidence-based healthcare. However, the clinical and policy implications of implementing RCTs in clinical practice are difficult to predict as the studied population is often different from the target population where results are being applied. This study illustrates the concepts of generalizability and transportability, demonstrating their utility in interpreting results from the National Lung Screening Trial (NLST). Methods: Using inverse-odds weighting, we demonstrate how generalizability and transportability techniques can be used to extrapolate treatment effect from (i) a subset of NLST to the entire NLST population and from (ii) the entire NLST to different target populations. Results: Our generalizability analysis revealed that lung cancer mortality reduction by LDCT screening across the entire NLST [16% (95% confidence interval [CI]: 4–24)] could have been estimated using a smaller subset of NLST participants. Using transportability analysis, we showed that populations with a higher prevalence of females and current smokers had a greater reduction in lung cancer mortality with LDCT screening [e.g., 27% (95% CI, 11–37) for the population with 80% females and 80% current smokers] than those with lower prevalence of females and current smokers. Conclusions: This article illustrates how generalizability and transportability methods extend estimation of RCTs' utility beyond trial participants, to external populations of interest, including those that more closely mirror real-world populations. Impact: Generalizability and transportability approaches can be used to quantify treatment effects for populations of interest, which may be used to design future trials or adjust lung cancer screening eligibility criteria.

Purpose: Integrative analysis combining diagnostic imaging and genomic information can uncover biological insights into lesions that are visible on radiologic images. We investigate techniques for interrogating a deep neural network trained to predict quantitative image (radiomic) features and histology from gene expression in non-small cell lung cancer (NSCLC). Approach: Using 262 training and 89 testing cases from two public datasets, deep feedforward neural networks were trained to predict the values of 101 computed tomography (CT) radiomic features and histology. A model interrogation method called gene masking was used to derive the learned associations between subsets of genes and a radiomic feature or histology class [adenocarcinoma (ADC), squamous cell, and other]. Results: Overall, neural networks outperformed other classifiers. In testing, neural networks classified histology with area under the receiver operating characteristic curves (AUCs) of 0.86 (ADC), 0.91 (squamous cell), and 0.71 (other). Classification performance of radiomics features ranged from 0.42 to 0.89 AUC. Gene masking analysis revealed new and previously reported associations. For example, hypoxia genes predicted histology ( >0.90 AUC ). Previously published gene signatures for classifying histology were also predictive in our model ( >0.80 AUC ). Gene sets related to the immune or cardiac systems and cell development processes were predictive ( >0.70 AUC ) of several different radiomic features. AKT signaling, tumor necrosis factor, and Rho gene sets were each predictive of tumor textures. Conclusions: This work demonstrates neural networks' ability to map gene expressions to radiomic features and histology types in NSCLC and to interpret the models to identify predictive genes associated with each feature or type.

Objective: Screening with low-dose CT (LDCT) effectively reduces mortality from lung cancer. Elective imaging procedures, including lung cancer screening (LCS) LDCT exams, were paused during the height of the COVID-19 pandemic at our institution to conserve healthcare resources and minimize risk as we learned how to mitigate the spread of COVID-19. We aimed to investigate the short-term impact of this COVID-related screening pause on patient participation and adherence to LCS. Methods: We analyzed data of 5133 LDCT screening exams performed at our institution from 2961 patients who were aged 50-80 at each screen between July 31, 2013 and Dec 30, 2020. Independent t-test, Pearson’s chi-square and Fisher’s exact tests were used to compare monthly average number of LDCTs, on-time adherence rates (i.e., completion of recommended or more invasive follow-up within 15, 9, 5, and 3 months for Lung-RADS 1/2, 3, 4A, and 4B/4X, respectively), percentages of positive screens (Lung-RADS 3 and 4), and lung cancer diagnoses across pre- (July 31, 2013 ~ Mar 18, 2020), during (Mar 19, 2020 ~ May 19, 2020), and post-COVID screening pause (May 20, 2020 and after) periods. Results: As expected, compared with the pre-COVID screening pause, there was a significant decrease in monthly average number of LDCTs during the COVID screening pause period (total monthly mean ± sd: pre 55±28 vs during 17±1, p0.05). Among the 88 patients diagnosed with lung cancers, 76 diagnoses were made before COVID, 12 diagnoses were made after the COVID pause, and no lung cancer diagnoses were made during the COVID screening pause period. There were no significant differences in terms of the rate of lung cancer (pre 2.9% vs post 1.9%, p>0.05) and the percent of advanced lung cancers (pre 20% vs post 0%, p>0.05) during the two periods. Conclusion: The rate of LCS exams performed at our institution declined during the early days of the COVID-19 pandemic, as elective exams were paused. Once screening resumed, we experienced a surge in the rate of LCS that surpassed pre-COVID rates. Although there were no significant changes in the percentages of positive screens and lung cancer diagnoses shortly after the COVID screening pause period, long-term follow-up is needed to monitor these trends. Additionally, interventions may be needed to improve rates of patients’ timely adherence to LCS follow-up recommendations, which decreased in the post-COVID period. Citation Format: Yannan Lin, William Hsu, Denise R. Aberle, Ashley E. Prosper. The short-term impact of COVID-19 on lung cancer screening participation and adherence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5275.

e21108 Background: RECIST 1.1 can underestimate treatment benefits of immunotherapy, with irRECIST and iRECIST accounting for atypical responses. Inter-reader discordances are known to occur in a dual reader paradigm. Our objective is to compare inter-reader reliability between RECIST 1.1, irRECIST, and iRECIST. Methods: This is a retrospective analysis of advanced NSCLC patients treated with pembrolizumab at our institution as part of the KEYNOTE-001 study. All trial imaging was interpreted by two radiologists. RECIST 1.1, irRECIST, and iRECIST categorical responses and agreement for progressive disease (PD) was compared by kappa statistic. Time to progression (TTP) or time to censor was compared between readers by paired t test. Relationship to disease progression and overall survival (OS) was assessed by log rank. Results: Of 98 patients, 77 had baseline and subsequent imaging available for 5.8 mean timepoints with 42.9 weeks of follow up. From this group, 45 patients had imaging beyond iUPD for confirmation and were analyzed. PD occurred by reader 1, reader 2 in 34, 33 patients by RECIST 1.1 (k = 0.591, CI = 0.320-0.863), 31, 29 patients by irRECIST (k = 0.501, CI = 0.234-0.768), and 27, 22 patients by iRECIST iCPD (confirmed-PD) (k = 0.690, CI = 0.485-0.896). There was no significant difference in reader agreement by RECIST 1.1, irRECIST, iRECIST (p = 0.38, 0.60, 0.26). There was a significant difference in time to progression between RECIST 1.1, irRECIST and iRECIST, with median PFS 3.4 months (2.6-4.6), 4.7 (3.5-6.8) and 8.7 (6.9-14.5) (p < 0.0001). PD by any criteria was not significantly correlated with OS. Conclusions: PD confirmation by iRECIST resulted in substantial reader agreement compared to moderate reader agreement by RECIST 1.1 and irRECIST. There were significant differences in TTP between the criteria, with iRECIST having the longest TTP. PD by each criteria did not correlate with a significant difference in OS.[Table: see text]

SummaryDetermining the clinical significance of CT scan-detected subsolid pulmonary nodules requires an understanding of the molecular and cellular features that may foreshadow disease progression. We studied the alterations at the transcriptome level in both immune and non-immune cells, utilizing single-cell RNA sequencing, to compare the microenvironment of subsolid, solid, and non-involved lung tissues from surgical resection specimens. This evaluation of early spectrum lung adenocarcinoma reveals a significant decrease in the cytolytic activities of natural killer and natural killer T cells, accompanied by a reduction of effector T cells as well as an increase of CD4+ regulatory T cells in subsolid lesions. Characterization of non-immune cells revealed that both cancer-associated alveolar type 2 cells and fibroblasts contribute to the deregulation of the extracellular matrix, potentially affecting immune infiltration in subsolid lesions through ligand-receptor interactions. These findings suggest a decrement of immune surveillance in subsolid lesions.

Globally, lung cancer is responsible for nearly one in five cancer deaths. The National Lung Screening Trial (NLST) demonstrated the efficacy of low-dose computed tomography (LDCT) to identify early-stage disease, setting the basis for widespread implementation of lung cancer screening programs. However, the specificity of LDCT lung cancer screening is suboptimal, with a significant false positive rate. Representing this imaging-based screening process as a sequential decision making problem, we combined multiple machine learning-based methods to learn a partially-observable Markov decision process that simultaneously optimizes lung cancer detection while enhancing test specificity. Using NLST data, we trained a dynamic Bayesian network as an observational model and used inverse reinforcement learning to discover a rewards function based on experts' decisions. Our resultant predictive model decreased the false positive rate while maintaining a high true positive rate at a level comparable to human experts. Our model also detected a number of lung cancers earlier.