Personalized medicine for non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) is a heterogeneous illness associated with a high mortality rate. Personalized therapy may improve treatment outcomes by identification of a specific genotypic anomaly and target-specific therapy. The most significant development in recent years was the discovery of activated EGF receptor (EGFR) mutations at exons 19 and 21. Patients with EGFR mutations respond dramatically to EGFR tyrosine kinase inhibitors such as gefitinib or erlotinib, resulting in longer progression-free survival. Multiple randomized studies, including the Iressa Pan-Asia Study and WJTOG3405, have confirmed the role of EGFR tyrosine kinase inhibitors as standard first-line therapy for patients with the EGFR mutation. In this article, we summarize the current nonpersonalized therapies and examine the available and investigational personalized therapies for patients with resectable early-stage, unresectable locally advanced, or metastatic disease.     

Correlation of PD-L1 Expression with Tumor Mutation Burden and Gene Signatures for Prognosis in Early-Stage Squamous Cell Lung Carcinoma

Objectives

Anti–programmed cell death 1 (PD-1)/programmed death ligand 1 (PD-L1) immunotherapy has demonstrated success in the treatment of advanced NSCLC. Recently, PD-1/PD-L1 blockade also has demonstrated interesting results in small trials of neoadjuvant treatment in stage IB to IIIA NSCLC. In addition, several clinical trials using anti–PD-1/PD-L1 immunotherapy as an adjuvant or neoadjuvant treatment in patients with resectable stage NSCLC are ongoing. However, few analyses of anti–PD-1/PD-L1 immunotherapy–related biomarkers in early-stage squamous cell lung carcinoma (SqCLC) have been reported. In this study, we evaluated PD-L1 protein expression, tumor mutation burden, and expression of an immune gene signature in early-stage SqCLC, providing data for identifying the potential role for patients with anti–PD-1/PD-L1 treatment in early-stage SqCLC.

Factor analysis for survival time prediction with informative censoring and diverse covariates

Fulfilling the promise of precision medicine requires accurately and precisely classifying disease states. For cancer, this includes prediction of survival time from a surfeit of covariates. Such data presents an opportunity for improved prediction, but also a challenge due to high dimensionality. Furthermore, disease populations can be heterogeneous. Integrative modeling is sensible, as the underlying hypothesis is that joint analysis of multiple covariates provides greater explanatory power than separate analyses. We propose an integrative latent variable model that combines factor analysis for various data types and an exponential proportional hazards (EPH) model for continuous survival time with informative censoring. The factor and EPH models are connected through low-dimensional latent variables that can be interpreted and visualized to identify subpopulations. We use this model to predict survival time. We demonstrate this model's utility in simulation and on four Cancer Genome Atlas datasets: diffuse lower-grade glioma, glioblastoma multiforme, lung adenocarcinoma, and lung squamous cell carcinoma. These datasets have small sample sizes, high-dimensional diverse covariates, and high censorship rates. We compare the predictions from our model to three alternative models. Our model outperforms in simulation and is competitive on real datasets. Furthermore, the low-dimensional visualization for diffuse lower-grade glioma displays known subpopulations.     

New therapeutics based on emerging concepts in pulmonary fibrosis

Introduction: Fibrosis is an irreversible pathological endpoint in many chronic diseases, including pulmonary fibrosis. Idiopathic pulmonary fibrosis (IPF) is a progressive and often fatal condition characterized by (myo)fibroblast proliferation and transformation in the lung, expansion of the extracellular matrix, and extensive remodeling of the lung parenchyma. Recent evidence indicates that IPF prevalence and mortality rates are growing in the United States and elsewhere. Despite decades of research on the pathogenic mechanisms of pulmonary fibrosis, few therapeutics have succeeded in the clinic, and they have failed to improve IPF patient survival.

Areas covered: Based on a literature search and our own results, we discuss the key cellular and molecular responses that contribute to (myo)fibroblast actions and pulmonary fibrosis pathogenesis; this includes signaling pathways in various cells that aberrantly and persistently activate (myo)fibroblasts in fibrotic lesions and promote scar tissue formation in the lung.

Expert opinion: Lessons learned from recent failures and successes with new therapeutics point toward approaches that can target multiple pro-fibrotic processes in IPF. Advances in preclinical modeling and single-cell genomics will also accelerate novel discoveries for effective treatment of IPF.     

COPD Assessment Test: A Simple Tool to Evaluate Disease Severity and Response to Treatment

The COPD assessment test (CAT) is a short questionnaire designed to assess the impairment in health status of COPD patients. We aimed to determine the change of the CAT in COPD patients after 1 year of treatment and test the association between the score and clinical and lung function variables. Methods A cohort of 111 newly diagnosed COPD patients in primary care was evaluated at baseline and one year after the implementation of the recommended treatment according to the Global Initiative for the management of COPD (GOLD). Results Most of the patients (82%) were diagnosed with mild to moderate airflow limitation (mean FEV₁ 72 ± 21.5% predicted) and the CAT score increased in proportion with the GOLD stage of severity. The CAT significantly correlated with the number of exacerbations, visits to general practitioners and days of hospitalization both at the beginning and at 1 year follow-up. A strong negative correlation between the CAT score and FEV₁ predicted was also observed. The CAT was responsive to the application of treatment with a significant improvement in the mean score (95% confidence interval) following 12 months of treatment by –2.4 (–2.9, –1.9) despite the small decline in lung function indices. The number of exacerbations in the preceding year and FEV₁ were independent predictors of the CAT score in the general linear model. Conclusion The CAT questionnaire may serve as a simple, measurable tool complementary to spirometry in the assessment of severity and of response to treatment in unselected COPD patients in primary care.     

Mycobacterium abscessus Complex – a Particular Challenge in the Setting of Lung Transplantation

Mycobacterium abscessus complex is an emerging pathogen in lung transplant candidates and recipients. M. abscessus complex is widespread in the environment and can cause pulmonary, skin and soft tissue, and disseminated infection, particularly in lung transplant recipients. It is innately resistant to many antibiotics making it difficult to treat. Herein we describe the epidemiology, clinical manifestations, diagnosis and treatment of M. abscessus with an emphasis on lung transplant candidates and recipients. We also outline the areas where data are lacking and the areas where further research is urgently needed.     

Elimination of socioeconomic and racial disparities related to lung cancer: Closing the gap at a high volume community cancer center

BackgroundHealthcare disparities have afflicted the healthcare industry for decades and there have been many campaigns in recent years to identify and eliminate disparities. The purpose of this study was to identify disparities in the lung cancer population of a single community cancer center and to report the results in accordance with industry goals.MethodsThis was a retrospective cohort study of data on non-small cell lung cancer patients recorded in the Christiana Care Tumor Registry (CCTR) in Delaware. Gender, age, race, socioeconomic status and insurance status were used as potential variables in identifying disparities.ResultsWe found no significant disparities between sexes, race or patients who were classified as having socioeconomic status 1–3. There was a lower survival rate associated with having the poorest socioeconomic status and in patients who used Medicare. Uninsured patients had the best survival outcomes and patients with Medicare had the poorest survival outcomes.ConclusionAlthough we have closed the gap on sex and racial disparities, there remains a difference in survival outcomes across socioeconomic classes and insurance types.