Survival associated with chronic obstructive pulmonary disease among SEER-Medicare beneficiaries with non-small-cell lung cancer

International expert consensus on diagnosis and treatment of lung cancer complicated by chronic obstructive pulmonary disease

Background

Lung cancer combined by chronic obstructive pulmonary disease (LC-COPD) is a common comorbidity and their interaction with each other poses significant clinical challenges. However, there is a lack of well-established consensus on the diagnosis and treatment of LC-COPD.

Methods

A panel of experts, comprising specialists in oncology, respiratory medicine, radiology, interventional medicine, and thoracic surgery, was convened. The panel was presented with a comprehensive review of the current evidence pertaining to LC-COPD. After thorough discussions, the panel reached a consensus on 17 recommendations with over 70% agreement in voting to enhance the management of LC-COPD and optimize the care of these patients.

Results

The 17 statements focused on pathogenic mechanisms (n=2), general strategies (n=4), and clinical application in COPD (n=2) and lung cancer (n=9) were developed and modified. These statements provide guidance on early screening and treatment selection of LC-COPD, the interplay of lung cancer and COPD on treatment, and considerations during treatment. This consensus also emphasizes patient-centered and personalized treatment in the management of LC-COPD.

Conclusions

The consensus highlights the need for concurrent treatment for both lung cancer and COPD in LC-COPD patients, while being mindful of the mutual influence of the two conditions on treatment and monitoring for adverse reactions.

Chronic obstructive pulmonary disease alters the genetic landscape and tumor immune microenvironment in lung cancer patients

Background

Chronic obstructive pulmonary disease (COPD) and lung cancer are leading causes of morbidity and mortality worldwide. Studies have reported molecular alterations in patients with lung cancer and in patients with COPD. However, few investigation has been conducted on the molecular characteristics of lung cancer patients with COPD.

Materials and methods

We performed a retrospective cohort study that included 435 patients with pathologically confirmed lung cancer at the Ruijin Hospital. For patients with documented spirometry, Global Initiative for Chronic Obstructive Lung Disease criteria were used to define COPD. For patients without documented spirometry, chest computed tomography and other clinical information were used to define COPD. Tumor tissue DNA was extracted from formalin-fixed paraffin-embedded samples. DNA mutation analysis, multiplex immunohistochemistry (mIHC), calculation of tumor mutational burden (TMB), mutant-allele tumor heterogeneity (MATH), and predication of neoantigens were performed.

Results

Although SNV mutations in lung cancer patients with COPD (G1 group) were generally higher than those in lung cancer patients without COPD (G2 group), the difference in the number of mutations was insignificant between the two groups. Of the 35 mutated genes, the number of them was higher in G1 than in G2, except that of EGFR. PI3K-Akt signaling pathway was enriched from significantly different genes. While TMB and MATH levels were not significantly different, the tumor neoantigen burdenwas markedly higher in G1 than that in G2. The level of CD68+ macrophages was significant higher in the stroma and total areas in the G1 group than in G2 group. The level of CD8+ lymphocytes was markedly higher in the stroma and showed a clear tendency forhigher expression in the G1 group than inthe G2 group. No significant differences were observed for the level of programmed death-ligand 1+ (PD-L1+), programmed death 1+ (PD-1+), and CD68PD-L1 in the stroma, tumor and total areas.

Conclusion

Our study revealed different genetic aberrations and pathways, higher neoantigen burden, and higher level of CD68+ macrophages and CD8+ T lymphocytes in lung cancer patients with COPD. Our investigation implies that the existence of COPD should be considered and immunotherapy is a potential choice when treating lung cancer patients with COPD.

Chronic Obstructive Pulmonary Disease Is Associated with Worse Oncologic Outcomes in Early-Stage Resected Pancreatic and Periampullary Cancers

Pancreatic ductal adenocarcinoma (PDAC) is the 3rd leading cause of cancer mortality in the United States. Hypoxic and hypercapnic tumor microenvironments have been suggested to promote tumor aggressiveness. The objective of this study was to evaluate the association between chronic obstructive pulmonary disease (COPD) and oncologic survival outcomes in patients with early-stage PDAC and periampullary cancers. In this case-control study, patients who underwent a pancreaticoduodenectomy during 2014-2021 were assessed. Demographic, perioperative, histologic, and oncologic data were collected. A total of 503 PDAC and periampullary adenocarcinoma patients were identified, 257 males and 246 females, with a mean age of 68.1 (±9.8) years and a mean pre-operative BMI of 26.6 (±4.7) kg/m². Fifty-two percent of patients (N = 262) reported a history of smoking. A total of 42 patients (8.3%) had COPD. The average resected tumor size was 2.9 ± 1.4 cm and 65% of the specimens (N = 329) were positive for lymph-node involvement. Kaplan-Meier analysis showed that COPD was associated with worse overall and disease-specific survival (p < 0.05). Cox regression analysis showed COPD to be an independent prognostic factor (HR = 1.5, 95% CI 1.0-2.3, p = 0.039) along with margin status, lymphovascular invasion, and perineural invasion (p < 0.05 each). A 1:3 nearest neighbor propensity score matching was also employed and revealed COPD to be an independent risk factor for overall and disease-specific survival (OR 1.8 and OR 1.6, respectively; p < 0.05 each). These findings may support the rationale posed by in vitro laboratory studies, suggesting an important impact of hypoxic and hypercapnic tumor respiratory microenvironments in promoting therapy resistance in cancer.

Clinical characteristics and gene mutation profiles of chronic obstructive pulmonary disease in non-small cell lung cancer

Purpose

The coexistence of chronic obstructive pulmonary disease (COPD) often leads to a worse prognosis in patients with non-small cell lung cancer (NSCLC). Meanwhile, approaches targeting specific genetic alterations have been shown to significantly improve the diagnosis and treatment outcomes of patients with NSCLC. Herein, we sought to evaluate the impact of COPD on the clinical manifestations and gene mutation profiles of NSCLC patients with both circulating tumor (ctDNA) and tumor DNA (tDNA).

Materials and methods

The influence of COPD on clinical features was observed in 285 NSCLC cohorts suffering from NSCLC alone, NSCLC coexisting with COPD, or NSCLC coexisting with prodromal changes in COPD (with emphysema, bullae, or chronic bronchitis). The gene mutation profiles of specific 168 NSCLC-related genes were further analyzed in the NSCLC sub-cohorts with formalin-fixed and paraffin-embedded tumor DNA (FFPE tDNA) samples and plasma circulating tumor DNA (PLA ctDNA) samples. Moreover, mutation concordance was assessed in tDNA and paired ctDNA of 110 NSCLC patients.

Results

Relative to patients with NSCLC alone, patients with NSCLC coexisting with COPD and prodromal changes presented with worse lung functions, more clinical symptoms, signs and comorbidities, and inconsistent gene mutation profiles. In addition, patients in the latter two groups exhibited a higher average frequency of gene mutation. Lastly, mutation concordance between tDNA and ctDNA samples was significantly reduced in NSCLC patients coexisting with COPD.

Conclusions

Collectively, our findings revealed that coexistence of COPD leads to worse clinical manifestations and altered gene mutation profiles in patients with NSCLC. Additionally, for NSCLC patients with COPD, the use of ctDNA instead of tDNA may not be the most efficient approach to identifying gene mutations.

An Interpretable Two-Phase Modeling Approach for Lung Cancer Survivability Prediction

Although lung cancer survival status and survival length predictions have primarily been studied individually, a scheme that leverages both fields in an interpretable way for physicians remains elusive. We propose a two-phase data analytic framework that is capable of classifying survival status for 0.5-, 1-, 1.5-, 2-, 2.5-, and 3-year time-points (phase I) and predicting the number of survival months within 3 years (phase II) using recent Surveillance, Epidemiology, and End Results data from 2010 to 2017. In this study, we employ three analytical models (general linear model, extreme gradient boosting, and artificial neural networks), five data balancing techniques (synthetic minority oversampling technique (SMOTE), relocating safe level SMOTE, borderline SMOTE, adaptive synthetic sampling, and majority weighted minority oversampling technique), two feature selection methods (least absolute shrinkage and selection operator (LASSO) and random forest), and the one-hot encoding approach. By implementing a comprehensive data preparation phase, we demonstrate that a computationally efficient and interpretable method such as GLM performs comparably to more complex models. Moreover, we quantify the effects of individual features in phase I and II by exploiting GLM coefficients. To the best of our knowledge, this study is the first to (a) implement a comprehensive data processing approach to develop performant, computationally efficient, and interpretable methods in comparison to black-box models, (b) visualize top factors impacting survival odds by utilizing the change in odds ratio, and (c) comprehensively explore short-term lung cancer survival using a two-phase approach.

Does the Combined Use of Aspirin and Immunotherapy Result in Better Outcomes in Non-Small Cell Lung Cancer Than Immunotherapy Alone?

Introduction: Immunotherapy works by stimulating the immune system against cancer cells. Resistance to immunotherapy represents a significant challenge in the field of medical oncology. The mechanisms by which cancer cells evade immunotherapy are not well understood. Prior research suggested overexpression of prostaglandin E-2 (PGE-2) by cancer cells, which bind to EP-2 and EP-4 receptors on the tumor-specific cytotoxic T-lymphocytes (CTLs) and suppress their anticancer role. This immunosuppressive effect is involved in evading the programmed cell death-1 (PD-1)/programmed death-ligand 1 (PD-L1) blockade of immunotherapy, which fuels cancer cell growth and recurrence. Studies found that combining PGE-2 blockade and a PD-1 signaling inhibitor helped promote the anticancer immunity cells. If confirmed in a clinical setting, the above in vitro findings could be of great clinical significance.

Methods: Given that aspirin (ASA) blocks PGE-2 production, this work aimed to evaluate whether ASA use with immunotherapy results in better outcomes than immunotherapy alone. We performed a retrospective chart review of 500 non-small cell lung cancer (NSCLC) patients aged 21 years or older treated with PD-1 and/or PD-L1 directed immunotherapy at St. Luke’s University Health Network between July 2015 and July 2021. Relevant patient, disease, and treatment-related variables were collected, including ASA use (≥ 81 mg daily) and the type of immunotherapy. Bivariate analyses were conducted to determine which variables to include in a multivariable model. 

The four primary outcomes included survival at 18-months, both after diagnosis and starting immunotherapy, achieving complete remission (CR), and having a progressive disease (PD), as defined by RECIST (Response Evaluation Criteria in Solid Tumors) criteria. Secondary outcomes included therapy-related toxicities and complications in the different treatment groups.  

Results: After bivariate analysis, no statistical significance was found for a difference in 18-month survival between ASA and non-ASA groups (50.3% vs 49.7%, p-value = 0.79). ASA with PD-L1 inhibitor showed a trend towards a higher likelihood of achieving CR [adjusted odds ratio (AOR) 1.85] with a p-value close to statistical significance (0.06). However, ASA with PD-L1 showed high statistical significance as an independent variable associated with a decreased likelihood of having PD (AOR 0.44, p < 0.001). These findings suggest that NSCLC patients receiving PD-L1 inhibitors could benefit more from daily ASA than patients treated with PD-1 inhibitors. Our study emphasizes using the Eastern Cooperative Oncology Group (ECOG) scoring of the performance status (PS) in NSCLC patients. Poorer PS was associated with lower survival, decreased likelihood of CR, and more PD. Other variables associated with worse outcomes were advanced cancer stage at diagnosis and male gender. Low-PD-L1 expression in NSCLC was associated with an increased likelihood of survival; this could be of clinical significance, especially with previous studies suggesting better outcomes of using ASA in PD-L1 low tumors. 

Conclusion: These findings suggest that daily ASA use with PD-L1 inhibitors is associated with more favorable outcomes in NSCLC. More studies are needed to investigate further the potential benefits vs. risks of using ASA with different immunotherapies and the other possible variables affecting treatment outcomes. 

Identification of Survival-Associated Gene Signature in Lung Cancer Coexisting With COPD

Background: Chronic obstructive pulmonary disease (COPD) and lung cancer often coexist, which is associated with a worse prognosis. Thousands of biomarkers related to the survival of lung cancer have been investigated. However, those which can predict the survival of lung cancer coexisting with COPD are currently lacking. The present study aimed to identify novel gene signatures to predict the survival of patients with lung cancer coexisting COPD.

Method: RNA-sequence data of lung cancer and control accompanying with matched clinical information were retrieved from the Cancer Genome Atlas (TCGA). Differently expressed genes (DEGs) associated with lung cancer coexisting COPD were screened. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed. Univariate and multivariate Cox regression analyses were applied to identify survival-associated DEGs and to construct survival-associated gene signature. Kaplan-Meier survival analysis and calibration plots of the nomogram were performed to test the predictive accuracy of the gene signature. qPCR was performed to validate the genes in the prognostic signature.

Results: Sequence data from 70 patients with lung cancer coexisting COPD, 127 with lung cancer alone and 108 control tissues were included for analysis. A total of 2424 DEGs were identified when comparing lung cancer coexisting COPD with controls. The biological process was primarily associated with DNA-binding transcription activator activity, peptidase inhibitor activity, endopeptidase inhibitor activity, et al. KEGG pathways were mainly enriched in neuroactive ligand-receptor interaction, cell cycle, and Staphylococcus aureus infection. A survival-associated gene signature consisting of CEACAM5, RASAL1, CSTL1, CNGB1, and SLC4A3 was identified and represented as risk score. The high-risk score group had significantly worse survival than the low-risk score group (P < 0.001). Areas under receiver operating characteristic curves were 0.943, 0.773, 0.888 for predicting overall survival at 1-, 3-, and 5-year, respectively. The risk score was an independent predictor of survival, independent of clinical factors. High conformity of the actual survival and the nomogram–predicted probability of survival by applying the risk score. Upregulation of the five genes in patients with lung cancer coexisting COPD were confirmed by qPCR in an independent cohort.

Conclusion: Our study constructed and validated a novel prognostic gene signature for predicting survival of patient with lung cancer coexisting COPD, which may contribute to the clinical treatment decisions.

High expression of SPP1 in patients with chronic obstructive pulmonary disease (COPD) is correlated with increased risk of lung cancer

Chronic obstructive pulmonary disease (COPD) is characterized by persistent airway inflammation and fixed airflow obstruction. Patients with COPD have increased risk of lung cancer (LC), and the coexistence of both diseases is associated with poorer survival. However, the mechanisms predisposing patients with COPD to LC development and poor prognosis remain unclear. Gene expression profiles were downloaded from the Gene Expression Omnibus. Twenty‐two data sets were included (n = 876). We identified 133 DEGs and 145 DEGs in patients with COPD and LC compared with healthy controls, respectively. There were 1544 DEGs in patients with LC and coexisting COPD compared with COPD, and these DEGs are mainly involved in the cell cycle, DNA replication, p53 signalling and insulin signalling. The biological processes primarily associated with these DEGs are oxidation reduction and apoptosis. SPP1 was the only overlapping DEG that was up‐regulated in patients with COPD and/or LC, and this was validated by qPCR in an independent cohort. The area under the curve value for SPP1 was 0.893 (0.822–0.963) for the prediction of LC in patients with COPD. High expression of SPP1 in patients with LC was associated with shorter survival time. Up‐regulation of SPP1 may be associated with increased risk of LC in patients with COPD and therefore may have potential as a therapeutic target for LC in patients with COPD.

Lung cancer survival period prediction and understanding: Deep learning approaches

INTRODUCTION

Survival period prediction through early diagnosis of cancer has many benefits. It allows both patients and caregivers to plan resources, time and intensity of care to provide the best possible treatment path for the patients. In this paper, by focusing on lung cancer patients, we build several survival prediction models using deep learning techniques to tackle both cancer survival classification and regression problems. We also conduct feature importance analysis to understand how lung cancer patients' relevant factors impact their survival periods. We contribute to identifying an approach to estimate survivability that are commonly and practically appropriate for medical use.

METHODOLOGIES

We have compared the performance across three of the most popular deep learning architectures - Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) while comparing the performing of deep learning models against traditional machine learning models. The data was obtained from the lung cancer section of Surveillance, Epidemiology, and End Results (SEER) cancer registry.

RESULTS

The deep learning models outperformed traditional machine learning models across both classification and regression approaches. We obtained a best of 71.18 % accuracy for the classification approach when patients' survival periods are segmented into classes of '<=6 months',' 0.5 - 2 years' and '>2 years' and Root Mean Squared Error (RMSE) of 13.5 % andR² value of 0.5 for the regression approach for the deep learning models while the traditional machine learning models saturated at 61.12 % classification accuracy and 14.87 % RMSE in regression.

CONCLUSIONS

This approach can be a baseline for early prediction with predictions that can be further improved with more temporal treatment information collected from treated patients. In addition, we evaluated the feature importance to investigate the model interpretability, gaining further insight into the survival analysis models and the factors that are important in cancer survival period prediction.

Effects of the recurrence pattern on patient survival following SABR for stage I lung cancer

Background: Information on the effect of the recurrence pattern on survival for stage I lung cancer following stereotactic ablative radiotherapy (SABR) is limited.Materials and Methods: The recurrence pattern was analyzed for 100 consecutive stage I non-small-cell lung cancer patients treated with SABR using predominantly 12 Gy × 4. Recurrences were classified as local, regional lymph nodes and distant. Distant recurrences included recurrences in the lung and outside the chest. Single lung recurrences were named solitary, if no other location was involved. Kaplan-Meier survival estimates were calculated for different locations of recurrence. Clinical and dosimetrical factors affecting survival were also analyzed.Results: Median follow-up was 32 months (3-123), median age 70 years (49-95). In total, 31 patients had recurrences after a median 21 months (4-60): 5 local; 10 regional; 8 distant outside the chest; 25 non-local lung recurrences, of which 15 were single - 10 of which solitary - and 10 multiple lung nodules. Patients with a solitary lung recurrence had longer survival compared to local or distant recurrences (p = .04 each), and compared to multiple lung nodules (p = .09). 3-year local recurrence-free survival was 92%, disease-free survival 69% and overall survival 59%. On multivariate analysis, disease-free survival was associated with statin use (p = .038) and tumor location (p = .035). Smoking history predicted overall survival (p < .0006).Conclusions: A total of 81% of recurrences involved the lungs. Patients with solitary lung recurrences/second primary lung cancers had the longest overall survival suggesting definitive treatment should be considered. The effects of statin use need to be explored further.