COPD-dependent effects of genetic variation in key inflammation pathway genes on lung cancer risk.

Genome-wide association studies (GWAS) have identified several loci contributing to lung cancer and COPD risk independently; however, inflammation-related pathways likely harbor additional lung cancer risk-associated variants in biologically relevant immune genes that differ dependent on COPD. We selected single nucleotide polymorphisms (SNPs) proximal to 2,069 genes within 48 immune pathways. We modeled the contribution of these variants to lung cancer risk in a discovery sample of 1,932 lung cancer cases and controls stratified by COPD status and validation sample of 953 cases and controls also stratified by COPD. There were 43 validated SNPs in those with COPD and 60 SNPs in those without COPD associated with lung cancer risk. Furthermore, 29 of 43 and 28 of 60 SNPs demonstrated a statistically significant interaction with COPD in the pooled sample. These variants demonstrated tissue-dependent effects on proximal gene expression, enhanced network connectivity and resided together in specific immune pathways. These results reveal that key inflammatory related genes and pathways, not found in prior GWAS, impact lung cancer risk in a COPD-dependent manner. Genetic variation identified in our study supplements prior lung cancer GWAS and serves as a foundation to further interrogate risk relationships in smoking and COPD populations.

Environmental perturbations lead to extensive directional shifts in RNA processing.

Environmental perturbations have large effects on both organismal and cellular traits, including gene expression, but the extent to which the environment affects RNA processing remains largely uncharacterized. Recent studies have identified a large number of genetic variants associated with variation in RNA processing that also have an important role in complex traits; yet we do not know in which contexts the different underlying isoforms are used. Here, we comprehensively characterized changes in RNA processing events across 89 environments in five human cell types and identified 15,300 event shifts (FDR = 15%) comprised of eight event types in over 4,000 genes. Many of these changes occur consistently in the same direction across conditions, indicative of global regulation by trans factors. Accordingly, we demonstrate that environmental modulation of splicing factor binding predicts shifts in intron retention, and that binding of transcription factors predicts shifts in alternative first exon (AFE) usage in response to specific treatments. We validated the mechanism hypothesized for AFE in two independent datasets. Using ATAC-seq, we found altered binding of 64 factors in response to selenium at sites of AFE shift, including ELF2 and other factors in the ETS family. We also performed AFE QTL mapping in 373 individuals and found an enrichment for SNPs predicted to disrupt binding of the ELF2 factor. Together, these results demonstrate that RNA processing is dramatically changed in response to environmental perturbations through specific mechanisms regulated by trans factors.

High-throughput allele-specific expression across 250 environmental conditions.

Gene-by-environment (GxE) interactions determine common disease risk factors and biomedically relevant complex traits. However, quantifying how the environment modulates genetic effects on human quantitative phenotypes presents unique challenges. Environmental covariates are complex and difficult to measure and control at the organismal level, as found in GWAS and epidemiological studies. An alternative approach focuses on the cellular environment using in vitro treatments as a proxy for the organismal environment. These cellular environments simplify the organism-level environmental exposures to provide a tractable influence on subcellular phenotypes, such as gene expression. Expression quantitative trait loci (eQTL) mapping studies identified GxE interactions in response to drug treatment and pathogen exposure. However, eQTL mapping approaches are infeasible for large-scale analysis of multiple cellular environments. Recently, allele-specific expression (ASE) analysis emerged as a powerful tool to identify GxE interactions in gene expression patterns by exploiting naturally occurring environmental exposures. Here we characterized genetic effects on the transcriptional response to 50 treatments in five cell types. We discovered 1455 genes with ASE (FDR < 10%) and 215 genes with GxE interactions. We demonstrated a major role for GxE interactions in complex traits. Genes with a transcriptional response to environmental perturbations showed sevenfold higher odds of being found in GWAS. Additionally, 105 genes that indicated GxE interactions (49%) were identified by GWAS as associated with complex traits. Examples include GIPR-caffeine interaction and obesity and include LAMP3-selenium interaction and Parkinson disease. Our results demonstrate that comprehensive catalogs of GxE interactions are indispensable to thoroughly annotate genes and bridge epidemiological and genome-wide association studies.

Prognostic modeling of the immune-centric transcriptome reveals interleukin signaling candidates contributing to differential patient outcomes.

Immunotherapy is a promising advancement in the treatment of non-small-cell lung carcinoma (NSCLC), although much of how lung tumors interact with the immune system in the natural course of disease remains unknown. We investigated the impact of the expression of immune-centric genes and pathways in tumors on patient survival to reveal novel candidates for immunotherapeutic research. Tumor transcriptomes and detailed clinical characteristics were obtained from patients with NSCLC who were participants of either the Inflammation, Health and Lung Epidemiology (INHALE) (discovery, N = 280) or The Cancer Genome Atlas (TCGA) Lung (replication, N = 1026) studies. Expressions of 2253 genes derived from 48 major immune pathways were assessed for association with patient prognosis using a multivariable Cox model and pathway effects were assessed with an in-house implementation of the Gene Set Enrichment Analysis (GSEA) algorithm. Prognosis-guided gene and pathway analysis of immune-centric expression in tumors revealed significant survival enrichments across both cohorts. The 'Interleukin Signaling' pathway, containing 430 genes, was found to be statistically and significantly enriched with prognostic signal in both the INHALE (P = 0.008) and TCGA (P = 0.039) datasets. Subsequent leading-edge analysis identified a subset of genes (N = 23) shared between both cohorts, driving the pathway enrichment. Cumulative expression of this leading-edge gene signature was a strong predictor of patient survival [discovery: hazard ratio (HR) = 1.59, P = 3.0 × 10-8; replication: HR = 1.29, P = 7.4 × 10-7]. These data demonstrate the impact of immune-centric expression on patient outcomes in NSCLC. Furthermore, prognostic gene effects were localized to discrete immune pathways, of which Interleukin Signaling had the greatest impact on overall survival and the subset of genes driving these effects have promise for future therapeutic intervention.

Whole-exome sequencing reveals genetic variability among lung cancer cases subphenotyped for emphysema.

Lung cancer continues to be a major public health challenge in the United States despite efforts to decrease the prevalence of smoking; outcomes are especially poor for African-American patients compared to other races/ethnicities. Chronic obstructive pulmonary disease (COPD) co-occurs with lung cancer frequently, but not always, suggesting both shared and distinct risk factors for these two diseases. To identify germline genetic variation that distinguishes between lung cancer in the presence and absence of emphysema, we performed whole-exome sequencing on 46 African-American lung cancer cases (23 with and 23 without emphysema frequency matched on age, sex, histology and pack years). Using conditional logistic regression, we found 6305 variants (of 168 150 varying sites) significantly associated with lung cancer subphenotype (P ≤ 0.05). Next, we validated 10 of these variants in an independent set of 612 lung cancer cases (267 with emphysema and 345 without emphysema) from the same population of inference as the sequenced cases. We found one variant that was significantly associated with lung cancer subphenotype in the validation sample. These findings contribute to teasing apart shared genetic factors from independent genetic factors for lung cancer and COPD.

Phylogenetic Relationships and Morphological Character Evolution of Photosynthetic Euglenids (Excavata) Inferred from Taxon-rich Analyses of Five Genes.

Photosynthetic euglenids acquired chloroplasts by secondary endosymbiosis, which resulted in changes to their mode of nutrition and affected the evolution of their morphological characters. Mapping morphological characters onto a reliable molecular tree could elucidate major trends of those changes. We analyzed nucleotide sequence data from regions of three nuclear-encoded genes (nSSU, nLSU, hsp90), one chloroplast-encoded gene (cpSSU) and one nuclear-encoded chloroplast gene (psbO) to estimate phylogenetic relationships among 59 photosynthetic euglenid species. Our results were consistent with previous works; most genera were monophyletic, except for the polyphyletic genus Euglena, and the paraphyletic genus Phacus. We also analyzed character evolution in photosynthetic euglenids using our phylogenetic tree and eight morphological traits commonly used for generic and species diagnoses, including: characters corresponding to well-defined clades, apomorphies like presence of lorica and mucilaginous stalks, and homoplastic characters like rigid cells and presence of large paramylon grains. This research indicated that pyrenoids were lost twice during the evolution of phototrophic euglenids, and that mucocysts, which only occur in the genus Euglena, evolved independently at least twice. In contrast, the evolution of cell shape and chloroplast morphology was difficult to elucidate, and could not be unambiguously reconstructed in our analyses.

Evaluation of the Immune Response within the Tumor Microenvironment in African American and Non-Hispanic White Patients with Non-Small Cell Lung Cancer.

BACKGROUND: African Americans have higher incidence and mortality from lung cancer than non-Hispanic Whites, but investigations into differences in immune response have been minimal. Therefore, we compared components of the tumor microenvironment among African Americans and non-Hispanic Whites diagnosed with non-small cell lung cancer based on PDL1 or tertiary lymphoid structure (TLS) status to identify differences of translational relevance. METHODS: Using a cohort of 280 patients with non-small cell lung cancer from the Inflammation, Health, Ancestry, and Lung Epidemiology study (non-Hispanic White: n = 155; African American: n = 125), we evaluated PDL1 tumor proportion score (<1% vs. ≥1%) and TLS status (presence/absence), comparing differences within the tumor microenvironment based on immune cell distribution and differential expression of genes. RESULTS: Tumors from African Americans had a higher proportion of plasma cell signatures within the tumor microenvironment than non-Hispanic Whites. In addition, gene expression patterns in African American PDL1-positive samples suggest that these tumors contained greater numbers of γδ T cells and resting dendritic cells, along with fewer CD8+ T cells after adjusting for age, sex, pack-years, stage, and histology. Investigation of differential expression of B cell/plasma cell-related genes between the two patient populations revealed that two immunoglobulin genes (IGKV2-29 and IGLL5) were associated with decreased mortality risk in African Americans. CONCLUSIONS: In the first known race-stratified analysis of tumor microenvironment components in lung cancer based on PDL1 expression or TLS status, differences within the immune cell composition and transcriptomic signature were identified that may have therapeutic implications. IMPACT: Future investigation of racial variation within the tumor microenvironment may help direct the use of immunotherapy.

ATR inhibition overcomes platinum tolerance associated with ERCC1- and p53-deficiency by inducing replication catastrophe.

ERCC1/XPF is a heterodimeric DNA endonuclease critical for repair of certain chemotherapeutic agents. We recently identified that ERCC1- and p53-deficient lung cancer cells are tolerant to platinum-based chemotherapy. ATR inhibition synergistically re-stored platinum sensitivity to platinum tolerant ERCC1-deficient cells. Mechanistically we show this effect is reliant upon several functions of ATR including replication fork protection and altered cell cycle checkpoints. Utilizing an inhibitor of replication protein A (RPA), we further demonstrate that replication fork protection and RPA availability are critical for platinum-based drug tolerance. Dual treatment led to increased formation of DNA double strand breaks and was associated with chromosome pulverization. Combination treatment was also associated with increased micronuclei formation which were capable of being bound by the innate immunomodulatory factor, cGAS, suggesting that combination platinum and ATR inhibition may also enhance response to immunotherapy in ERCC1-deficient tumors. In vivo studies demonstrate a significant effect on tumor growth delay with combination therapy compared with single agent treatment. Results of this study have led to the identification of a feasible therapeutic strategy combining ATR inhibition with platinum and potentially immune checkpoint blockade inhibitors to overcome platinum tolerance in ERCC1-deficient, p53-mutant lung cancers.

High-flow nasal cannula therapy in a predominantly African American population with COVID-19 associated acute respiratory failure.

IMPORTANCE: Use of non-invasive respiratory modalities in COVID-19 has the potential to reduce rates of intubation and mortality in severe disease however data regarding the use of high-flow nasal cannula (HFNC) in this population is limited. OBJECTIVE: To interrogate clinical and laboratory features of SARS-CoV-2 infection associated with high-flow failure. DESIGN: We conducted a retrospective cohort study to evaluate characteristics of high-flow therapy use early in the pandemic and interrogate factors associated with respiratory therapy failure. SETTING: Multisite single centre hospital system within the metropolitan Detroit region. PARTICIPANTS: Patients from within the Detroit Medical Center (n=104, 89% African American) who received HFNC therapy during a COVID-19 admission between March and May of 2020. PRIMARY OUTCOME: HFNC failure is defined as death or intubation while on therapy. RESULTS: Therapy failure occurred in 57% of the patient population, factors significantly associated with failure centred around markers of multiorgan failure including hepatic dysfunction/transaminitis (OR=6.1, 95% CI 1.9 to 19.4, p<0.01), kidney injury (OR=7.0, 95% CI 2.7 to 17.8, p<0.01) and coagulation dysfunction (OR=4.5, 95% CI 1.2 to 17.1, p=0.03). Conversely, comorbidities, admission characteristics, early oxygen requirements and evaluation just prior to HFNC therapy initiation were not significantly associated with success or failure of therapy. CONCLUSIONS: In a population disproportionately affected by COVID-19, we present key indicators of likely HFNC failure and highlight a patient population in which aggressive monitoring and intervention are warranted.

A stroma-corrected ZEB1 transcriptional signature is inversely associated with antitumor immune activity in breast cancer.

The epithelial-to-mesenchymal transition (EMT) is an essential developmental process which can be hijacked by cancer cells, leading to enhanced metastasis and chemoresistance in experimental models. Recent studies have linked gene expression of EMT-associated gene signatures to increased inflammatory immune response in multiple cancer types. However, these studies did not account for the potential confounding effects of gene expression by tumor-infiltrating mesenchymal stromal cells. In this study, we comprehensively dissect the associations between multiple EMT transcription factors and EMT markers with stromal and immune tumor infiltration. We find that EMT-related genes are highly correlated with intratumoral stromal cell abundance and identify a specific relationship between stroma-corrected ZEB1 expression and decreased immune activity in multiple cancer types. We derive a stroma-corrected ZEB1-activated transcriptional signature and demonstrate that this signature includes several known inhibitors of inflammation, including BMPR2. Finally, multivariate survival analysis reveals that ZEB1 and its expression signature are significantly associated with reduced overall survival in breast cancer patients. In conclusion, this study identifies a novel association between stroma-adjusted ZEB1 expression and tumor immune activity and addresses the critical issue of confounding between EMT-associated genes and tumor stromal content.

Profiling the Mutational Landscape in Known Driver Genes and Novel Genes in African American Non-Small Cell Lung Cancer Patients.

PURPOSE: Identifying novel driver genes and mutations in African American non-small cell lung cancer (NSCLC) cases can inform targeted therapy and improve outcomes for this traditionally underrepresented population. EXPERIMENTAL DESIGN: Tumor DNA, RNA, and germline DNA were collected from African American NSCLC patients who participated in research conducted at the Karmanos Cancer Institute (KCI) in Detroit, Michigan. Known mutations were ascertained through the Sequenom LungCarta panel of 214 mutations in 26 genes, RET/ROS1 fusions, amplification of FGFR1, and expression of ALK. Paired tumor and normal DNA was whole-exome sequenced for a subset of cases without known driver mutations. RESULTS: Of the 193 tumors tested, 77 known driver mutations were identified in 66 patients (34.2%). Sixty-seven of the 127 patients without a known driver mutation were sequenced. In 54 of these patients, 50 nonsynonymous mutations were predicted to have damaging effects among the 26 panel genes, 47 of which are not found in The Cancer Genome Atlas NSCLC white or African American samples. Analyzing the whole-exome sequence data using MutSig2CV identified a total of 88 genes significantly mutated at FDR q < 0.1. Only 5 of these genes were previously reported as oncogenic. CONCLUSIONS: These findings suggest that broader mutation profiling including both known and novel driver genes in African Americans with NSCLC will identify additional mutations that may be useful in treatment decision-making.

Identification and Characterization of Synthetic Viability with ERCC1 Deficiency in Response to Interstrand Crosslinks in Lung Cancer.

PURPOSE: ERCC1/XPF is a DNA endonuclease with variable expression in primary tumor specimens, and has been investigated as a predictive biomarker for efficacy of platinum-based chemotherapy. The failure of clinical trials utilizing ERCC1 expression to predict response to platinum-based chemotherapy suggests additional mechanisms underlying the basic biology of ERCC1 in the response to interstrand crosslinks (ICLs) remain unknown. We aimed to characterize a panel of ERCC1 knockout (Δ) cell lines, where we identified a synthetic viable phenotype in response to ICLs with ERCC1 deficiency. EXPERIMENTAL DESIGN: We utilized the CRISPR-Cas9 system to create a panel of ERCC1Δ lung cancer cell lines which we characterized. RESULTS: We observe that loss of ERCC1 hypersensitizes cells to cisplatin when wild-type (WT) p53 is retained, whereas there is only modest sensitivity in cell lines that are p53mutant/null. In addition, when p53 is disrupted by CRISPR-Cas9 (p53*) in ERCC1Δ/p53WT cells, there is reduced apoptosis and increased viability after platinum treatment. These results were recapitulated in 2 patient data sets utilizing p53 mutation analysis and ERCC1 expression to assess overall survival. We also show that kinetics of ICL-repair (ICL-R) differ between ERCC1Δ/p53WT and ERCC1Δ/p53* cells. Finally, we provide evidence that cisplatin tolerance in the context of ERCC1 deficiency relies on DNA-PKcs and BRCA1 function. CONCLUSIONS: Our findings implicate p53 as a potential confounding variable in clinical assessments of ERCC1 as a platinum biomarker via promoting an environment in which error-prone mechanisms of ICL-R may be able to partially compensate for loss of ERCC1.See related commentary by Friboulet et al., p. 2369.

Quantitative Imaging Markers of Lung Function in a Smoking Population Distinguish COPD Subgroups with Differential Lung Cancer Risk.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition with respect to onset, progression, and response to therapy. Incorporating clinical- and imaging-based features to refine COPD phenotypes provides valuable information beyond that obtained from traditional clinical evaluations. We characterized the spectrum of COPD-related phenotypes in a sample of former and current smokers and evaluated how these subgroups differ with respect to sociodemographic characteristics, COPD-related comorbidities, and subsequent risk of lung cancer. METHODS: White (N = 659) and African American (N = 520) male and female participants without lung cancer (controls) in the INHALE study who completed a chest CT scan, interview, and spirometry test were used to define distinct COPD-related subgroups based on hierarchical clustering. Seven variables were used to define clusters: pack years, quit years, FEV1/FVC, % predicted FEV1, and from quantitative CT (qCT) imaging, % emphysema, % air trapping, and mean lung density ratio. Cluster definitions were then applied to INHALE lung cancer cases (N = 576) to evaluate lung cancer risk. RESULTS: Five clusters were identified that differed significantly with respect to sociodemographic (e.g., race, age) and clinical (e.g., BMI, limitations due to breathing difficulties) characteristics. Increased risk of lung cancer was associated with increasingly detrimental lung function clusters (when ordered from most detrimental to least detrimental). CONCLUSIONS: Measures of lung function vary considerably among smokers and are not fully explained by smoking intensity. IMPACT: Combining clinical (spirometry) and radiologic (qCT) measures of COPD defines a spectrum of lung disease that predicts lung cancer risk differentially among patient clusters.

Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT.

PURPOSE: Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow-up procedures on the large number of false-positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment. METHODS: Lung nodule cases with pathology confirmed diagnosis (74 malignant, 289 benign) were used to extract quantitative imaging characteristics from computed tomography scans of the nodule and perinodular parenchyma tissue. A ML tool development pipeline was employed using k-medoids clustering and information theory to determine efficient predictor sets for different amounts of parenchyma inclusion and build an artificial neural network classifier. The resulting ML tool was validated using an independent cohort (50 malignant, 50 benign). RESULTS: The inclusion of parenchymal imaging features improved the performance of the ML tool over exclusively nodular features (P < 0.01). The best performing ML tool included features derived from nodule diameter-based surrounding parenchyma tissue quartile bands. We demonstrate similar high-performance values on the independent validation cohort (AUC-ROC = 0.965). A comparison using the independent validation cohort with the Fleischner pulmonary nodule follow-up guidelines demonstrated a theoretical reduction in recommended follow-up imaging and procedures. CONCLUSIONS: Radiomic features extracted from the parenchyma surrounding lung nodules contain valid signals with spatial relevance for the task of lung cancer risk classification. Through standardization of feature extraction regions from the parenchyma, ML tool validation performance of 100% sensitivity and 96% specificity was achieved.

Risk of Lung Cancer Associated with COPD Phenotype Based on Quantitative Image Analysis.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a risk factor for lung cancer. This study evaluates alternative measures of COPD based on spirometry and quantitative image analysis to better define a phenotype that predicts lung cancer risk. METHODS: A total of 341 lung cancer cases and 752 volunteer controls, ages 21 to 89 years, participated in a structured interview, standardized CT scan, and spirometry. Logistic regression, adjusted for age, race, gender, pack-years, and inspiratory and expiratory total lung volume, was used to estimate the odds of lung cancer associated with FEV1/FVC, percent voxels less than -950 Hounsfield units on the inspiratory scan (HUI) and percent voxels less than -856 HU on expiratory scan (HUE). RESULTS: The odds of lung cancer were increased 1.4- to 3.1-fold among those with COPD compared with those without, regardless of assessment method; however, in multivariable modeling, only percent voxels <-856 HUE as a continuous measure of air trapping [OR = 1.04; 95% confidence interval (CI), 1.03-1.06] and FEV1/FVC < 0.70 (OR = 1.71; 95% CI, 1.21-2.41) were independent predictors of lung cancer risk. Nearly 10% of lung cancer cases were negative on all objective measures of COPD. CONCLUSION: Measures of air trapping using quantitative imaging, in addition to FEV1/FVC, can identify individuals at high risk of lung cancer and should be considered as supplementary measures at the time of screening for lung cancer. IMPACT: Quantitative measures of air trapping based on imaging provide additional information for the identification of high-risk groups who might benefit the most from lung cancer screening. Cancer Epidemiol Biomarkers Prev; 25(9); 1341-7. ©2016 AACR.