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.

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.

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.

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.

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.

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.

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 p53^mutant/null. In addition, when p53 is disrupted by CRISPR-Cas9 (p53*) in ERCC1Δ/p53^WT 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/p53^WT 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.

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.

Abstract 1435: Immune/inflammatory polymorphisms predict lung cancer survival

Lung cancer is the leading cause of cancer related mortality in the United States, with a median five year survival of ~16%. Recent advancements in the treatment of lung cancer focus on modulating the immune system to improve patient outcomes with markedly reduced toxicity profiles. This study aims to investigate single nucleotide polymorphisms (SNPs) within functional units of the immune system genome to identify genetic markers indicative of overall survival in a cohort of metropolitan Detroit lung cancer patients. This cohort presents a unique population of lung cancer patients for analysis, consisting of 40% African Americans, whom are known to have worse outcomes than their Caucasian counterparts. To assess SNPs within functional units of the immune genome, a hierarchical immune system gene and pathway list was constructed, incorporating genes and pathway identifiers from the Reactome database as well as previous genetic and inflammatory specific cancer studies to generate a well curated gene and pathway dataset. Genomic locations for the genes, together with proximal regulatory regions, were obtained from the UCSC genome browser for cross-referencing with the Illumina MEGA SNP array to generate SNPs for inclusion. Preliminary analysis of 848 NSCLC patients in association with 29,126 immune specific SNPs identified six SNPs as significantly associated with survival (p-value &lt;10-4) in this lung cancer cohort, implicating the following six genes, PXN, GFRA3, HLA-DQA1, KSR2, BTBD1, and ATF2; although further gene/pathway analysis is necessary to elucidate how these associations interact in the context of genes and pathways of the immune system. PXN over expression has been previously implicated in both high risk lung epithelial dysplasia as well as in the development of lung adenocarcinoma in prior clinical studies. Additionally, GFRA3 has been identified as an activator of the RET kinase and increased GFRA3 activity in context of the Artemin pathway is a known promoter of NSCLC progression. As well, select polymorphisms in the HLA-DQA1 gene are known clinical modifiers of lung squamous cell carcinoma risk. Finally, ATF2 is a broadly active transcription factor and ATF2 upregulation has been associated with tumorigenesis and metastasis in both cell models and patient tumor samples in multiple cancer studies. The previous association of these genes in lung cancer is promising and further immune system gene/pathway analysis is warranted.

Citation Format: Donovan Watza, Chrissy Lusk, Angie Wenzlaff, Christine Neslund-Dudas, Gregory Dyson, Ayman Soubani, Shirish Gadgeel, Ann G. Schwartz. Immune/inflammatory polymorphisms predict lung cancer survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1435. doi:10.1158/1538-7445.AM2017-1435

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.

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.

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.

A high-throughput RNA-seq approach to profile transcriptional responses

In recent years RNA-seq protocols have been developed to investigate a variety of biological problems by measuring the abundance of different RNAs. Many study designs involve performing expensive preliminary studies to screen or optimize experimental conditions. Testing a large number of conditions in parallel may be more cost effective. For example, analyzing tissue/environment-specific gene expression generally implies screening a large number of cellular conditions and samples, without prior knowledge of which conditions are most informative (e.g., some cell types may not respond to certain treatments). To circumvent these challenges, we have established a new two-step high-throughput RNA-seq approach: the first step consists of gene expression screening of a large number of conditions, while the second step focuses on deep sequencing of the most relevant conditions (e.g., largest number of differentially expressed genes). This study design allows for a fast and economical screen in step one, with a more efficient allocation of resources for the deep sequencing of the most biologically relevant libraries in step two. We have applied this approach to study the response to 23 treatments in three lymphoblastoid cell lines demonstrating that it should also be useful for other high-throughput transcriptome profiling applications requiring iterative refinement or screening.