Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer

Premalignant Progression in the Lung: Knowledge Gaps and Novel Opportunities for Interception of Non-Small Cell Lung Cancer. An Official American Thoracic Society Research Statement

Rationale: Despite significant advances in precision treatments and immunotherapy, lung cancer is the most common cause of cancer death worldwide. To reduce incidence and improve survival rates, a deeper understanding of lung premalignancy and the multistep process of tumorigenesis is essential, allowing timely and effective intervention before cancer development. Objectives: To summarize existing information, identify knowledge gaps, formulate research questions, prioritize potential research topics, and propose strategies for future investigations into the premalignant progression in the lung. Methods: An international multidisciplinary team of basic, translational, and clinical scientists reviewed available data to develop and refine research questions pertaining to the transformation of premalignant lung lesions to advanced lung cancer. Results: This research statement identifies significant gaps in knowledge and proposes potential research questions aimed at expanding our understanding of the mechanisms underlying the progression of premalignant lung lesions to lung cancer in an effort to explore potential innovative modalities to intercept lung cancer at its nascent stages. Conclusions: The identified gaps in knowledge about the biological mechanisms of premalignant progression in the lung, together with ongoing challenges in screening, detection, and early intervention, highlight the critical need to prioritize research in this domain. Such focused investigations are essential to devise effective preventive strategies that may ultimately decrease lung cancer incidence and improve patient outcomes.

Advances in proteomics methods for the analysis of exhaled breath condensate

The analysis of exhaled breath condensate (EBC) demonstrates a promising avenue of minimally invasive biopsies for diagnostics. EBC is obtained by cooling exhaled air and collecting the condensation to be utilized for downstream analysis using various analytical methods. The aqueous phase of breath contains a large variety of miscible small compounds including polar electrolytes, amino acids, cytokines, chemokines, peptides, small proteins, metabolites, nucleic acids, and lipids/eicosanoids-however, these analytes are typically present at minuscule levels in EBC, posing a considerable technical challenge. Along with recent improvements in devices for breath collection, the sensitivity and resolution of liquid chromatography coupled to online mass spectrometry-based proteomics has attained subfemtomole sensitivity, vastly enhancing the quality of EBC sample analysis. As a result, proteomics analysis of EBC has been expanding the field of breath biomarker research. We present an au courant overview of the achievements in proteomics of EBC, the advancement of EBC collection devices, and the current and future applications for EBC biomarker analysis.

Smoking-associated gene expression alterations in nasal epithelium reveal immune impairment linked to lung cancer risk

BACKGROUND

Lung cancer is the leading cause of cancer-related death in the world. In contrast to many other cancers, a direct connection to modifiable lifestyle risk in the form of tobacco smoke has long been established. More than 50% of all smoking-related lung cancers occur in former smokers, 40% of which occur more than 15 years after smoking cessation. Despite extensive research, the molecular processes for persistent lung cancer risk remain unclear. We thus set out to examine whether risk stratification in the clinic and in the general population can be improved upon by the addition of genetic data and to explore the mechanisms of the persisting risk in former smokers.

METHODS

We analysed transcriptomic data from accessible airway tissues of 487 subjects, including healthy volunteers and clinic patients of different smoking statuses. We developed a computational model to assess smoking-associated gene expression changes and their reversibility after smoking is stopped, comparing healthy subjects to clinic patients with and without lung cancer.

RESULTS

We find persistent smoking-associated immune alterations to be a hallmark of the clinic patients. Integrating previous GWAS data using a transcriptional network approach, we demonstrate that the same immune- and interferon-related pathways are strongly enriched for genes linked to known genetic risk factors, demonstrating a causal relationship between immune alteration and lung cancer risk. Finally, we used accessible airway transcriptomic data to derive a non-invasive lung cancer risk classifier.

CONCLUSIONS

Our results provide initial evidence for germline-mediated personalized smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality.

A Nasal Swab Classifier to Evaluate the Probability of Lung Cancer in Patients With Pulmonary Nodules

BACKGROUND

Accurate assessment of the probability of lung cancer (pCA) is critical in patients with pulmonary nodules (PNs) to help guide decision-making. We sought to validate a clinical-genomic classifier developed using whole-transcriptome sequencing of nasal epithelial cells from patients with a PN ≤ 30 mm who smoke or have previously smoked.

RESEARCH QUESTION

Can the pCA in individuals with a PN and a history of smoking be predicted by a classifier that uses clinical factors and genomic data from nasal epithelial cells obtained by cytologic brushing?

STUDY DESIGN AND METHODS

Machine learning was used to train a classifier using genomic and clinical features on 1,120 patients with PNs labeled as benign or malignant established by a final diagnosis or a minimum of 12 months of radiographic surveillance. The classifier was designed to yield low-, intermediate-, and high-risk categories. The classifier was validated in an independent set of 312 patients, including 63 patients with a prior history of cancer (other than lung cancer), comparing the classifier prediction with the known clinical outcome.

RESULTS

In the primary validation set, sensitivity and specificity for low-risk classification were 96% and 42%, whereas sensitivity and specificity for high-risk classification was 58% and 90%, respectively. Sensitivity was similar across stages of non-small cell lung cancer, independent of subtype. Performance compared favorably with clinical-only risk models. Analysis of 63 patients with prior cancer showed similar performance as did subanalyses of patients with light vs heavy smoking burden and those eligible for lung cancer screening vs those who were not.

INTERPRETATION

The nasal classifier provides an accurate assessment of pCA in individuals with a PN ≤ 30 mm who smoke or have previously smoked. Classifier-guided decision-making could lead to fewer diagnostic procedures in patients without cancer and more timely treatment in patients with lung cancer.

Effectiveness of Immunotherapy in Non-Small Cell Lung Cancer Patients with a Diagnosis of COPD: Is This a Hidden Prognosticator for Survival and a Risk Factor for Immune-Related Adverse Events?

The interplay between the immune system and chronic obstructive pulmonary disease (COPD) and non-small cell lung cancer (NSCLC) is complex and multifaceted. In COPD, chronic inflammation and oxidative stress can lead to immune dysfunction that can exacerbate lung damage, further worsening the respiratory symptoms. In NSCLC, immune cells can recognise and attack the cancer cells, which, however, can evade or suppress the immune response by various mechanisms, such as expressing immune checkpoint proteins or secreting immunosuppressive cytokines, thus creating an immunosuppressive tumour microenvironment that promotes cancer progression and metastasis. The interaction between COPD and NSCLC further complicates the immune response. In patients with both diseases, COPD can impair the immune response against cancer cells by reducing or suppressing the activity of immune cells, or altering their cytokine profile. Moreover, anti-cancer treatments can also affect the immune system and worsen COPD symptoms by causing lung inflammation and fibrosis. Immunotherapy itself can also cause immune-related adverse events that could worsen the respiratory symptoms in patients with COPD-compromised lungs. In the present review, we tried to understand the interplay between the two pathologies and how the efficacy of immunotherapy in NSCLC patients with COPD is affected in these patients.

An atlas of epithelial cell states and plasticity in lung adenocarcinoma

Understanding the cellular processes that underlie early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies1. Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8+ alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival. In mice exposed to tobacco carcinogen, KACs emerged before lung tumours and persisted for months after cessation of carcinogen exposure. Moreover, they acquired Kras mutations and conveyed sensitivity to targeted KRAS inhibition in KAC-enriched organoids derived from alveolar type 2 (AT2) cells. Last, lineage-labelling of AT2 cells or KRT8+ cells following carcinogen exposure showed that KACs are possible intermediates in AT2-to-tumour cell transformation. This study provides new insights into epithelial cell states at the root of LUAD development, and such states could harbour potential targets for prevention or intervention.

Emerging Strategies in Lung Cancer Screening: Blood and Beyond

BACKGROUND

Although low dose computed tomography (LDCT)-based lung cancer screening (LCS) can decrease lung cancer-related mortality among high-risk individuals, it remains an imperfect and substantially underutilized process. LDCT-based LCS may result in false-positive findings, which can lead to invasive procedures and potential morbidity. Conversely, current guidelines may fail to capture at-risk individuals, particularly those from under-represented minority populations. To address these limitations, numerous biomarkers have emerged to complement LDCT and improve early lung cancer detection.

CONTENT

This review focuses primarily on blood-based biomarkers, including protein, microRNAs, circulating DNA, and methylated DNA panels, in current clinical development for LCS. We also examine other emerging biomarkers-utilizing airway epithelia, exhaled breath, sputum, and urine-under investigation. We highlight challenges and limitations of biomarker testing, as well as recent strategies to integrate molecular strategies with imaging technologies.

SUMMARY

Multiple biomarkers are under active investigation for LCS, either to improve risk-stratification after nodule detection or to optimize risk-based patient selection for LDCT-based screening. Results from ongoing and future clinical trials will elucidate the clinical utility of biomarkers in the LCS paradigm.

A Bibliometric Analysis of Comorbidity of COPD and Lung Cancer: Research Status and Future Directions

Objective

Although studies on the association between COPD and lung cancer are of great significance, no bibliometric analysis has been conducted in the field of their comorbidity. This bibliometric analysis explores the current situation and frontier trends in the field of COPD and lung cancer comorbidity, and to lay a new direction for subsequent research.

Methods

Articles in the field of COPD and cancer comorbidity were retrieved from Web of Science Core Collections (WoSCC) from 2004 to 2023, and analyzed by VOSviewer, CiteSpace, Biblimatrix and WPS Office.

Results

In total, 3330 publications were included. The USA was the leading country with the most publications and great influence. The University of Groningen was the most productive institution. Edwin Kepner Silverman was the most influential scholar in this field. PLOS One was found to be the most prolific journal. Mechanisms and risk factors were of vital importance in this research field. Environmental pollution and pulmonary fibrosis may be future research prospects.

Conclusion

This bibliometric analysis provided new guidance for the development of the field of COPD and lung cancer comorbidity by visualizing current research hotspots, and predicting possible hot research directions in the future.

The Complex Role of the Microbiome in Non-Small Cell Lung Cancer Development and Progression

In recent years, there has been a growing interest in the relationship between microorganisms in the surrounding environment and cancer cells. While the tumor microenvironment predominantly comprises cancer cells, stromal cells, and immune cells, emerging research highlights the significant contributions of microbial cells to tumor development and progression. Although the impact of the gut microbiome on treatment response in lung cancer is well established, recent investigations indicate complex roles of lung microbiota in lung cancer. This article focuses on recent findings on the human lung microbiome and its impacts in cancer development and progression. We delve into the characteristics of the lung microbiome and its influence on lung cancer development. Additionally, we explore the characteristics of the intratumoral microbiome, the metabolic interactions between lung tumor cells, and how microorganism-produced metabolites can contribute to cancer progression. Furthermore, we provide a comprehensive review of the current literature on the lung microbiome and its implications for the metastatic potential of tumor cells. Additionally, this review discusses the potential for therapeutic modulation of the microbiome to establish lung cancer prevention strategies and optimize lung cancer treatment.

Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma

Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up. Through histologic, mutational, and transcriptomic profiling of tumor and adjacent-normal tissue, we identified an inflammatory gene signature in tumor-adjacent tissue as the strongest clinical predictor of disease progression. Single-cell transcriptomic analysis demonstrated the progression-associated inflammatory signature was expressed in both immune and non-immune cells, and cell type-specific profiling in monocytes further improved outcome predictions. Additional analyses of tumor-adjacent transcriptomic data from The Cancer Genome Atlas validated the association of the inflammatory signature with worse outcomes across cancers. Collectively, our study suggests that molecular profiling of tumor-adjacent tissue can identify patients at high risk for disease progression.

Clinical Adjuncts to Lung Cancer Screening: A Narrative Review

The updated US Preventive Services Task Force guidelines on lung cancer screening have significantly expanded the population of screening eligible adults, among whom the balance of benefits and harms associated with lung cancer screening vary considerably. Clinical adjuncts are additional information and tools that can guide decision-making to optimally screen individuals who are most likely to benefit. Proposed adjuncts include integration of clinical history, risk prediction models, shared-decision-making tools, and biomarker tests at key steps in the screening process. Although evidence regarding their clinical utility and implementation is still evolving, they carry significant promise in optimizing screening effectiveness and efficiency for lung cancer.

Update on Biomarkers for the Stratification of Indeterminate Pulmonary Nodules

Lung cancer is the leading cause of cancer-related deaths. Early detection and diagnosis are critical, as survival decreases with advanced stages. Approximately 1.6 million nodules are incidentally detected every year on chest CT scan images in the United States. This number of nodules identified is likely much larger after accounting for screening-detected nodules. Most of these nodules, whether incidentally or screening detected, are benign. Despite this, many patients undergo unnecessary invasive procedures to rule out cancer because our current stratification approaches are suboptimal, particularly for intermediate probability nodules. Thus, noninvasive strategies are urgently needed. Biomarkers have been developed to assist through the continuum of lung cancer care and include blood protein-based biomarkers, liquid biopsies, quantitative imaging analysis (radiomics), exhaled volatile organic compounds, and bronchial or nasal epithelium genomic classifiers, among others. Although many biomarkers have been developed, few have been integrated into clinical practice as they lack clinical utility studies showing improved patient-centered outcomes. Rapid technologic advances and large network collaborative efforts will continue to drive the discovery and validation of many novel biomarkers. Ultimately, however, randomized clinical utility studies showing improved patient outcomes will be required to bring biomarkers into clinical practice.

Validation of a Proteomic Signature of Lung Cancer Risk from Bronchial Specimens of Risk-Stratified Individuals

A major challenge in lung cancer prevention and cure hinges on identifying the at-risk population that ultimately develops lung cancer. Previously, we reported proteomic alterations in the cytologically normal bronchial epithelial cells collected from the bronchial brushings of individuals at risk for lung cancer. The purpose of this study is to validate, in an independent cohort, a selected list of 55 candidate proteins associated with risk for lung cancer with sensitive targeted proteomics using selected reaction monitoring (SRM). Bronchial brushings collected from individuals at low and high risk for developing lung cancer as well as patients with lung cancer, from both a subset of the original cohort (batch 1: n = 10 per group) and an independent cohort of 149 individuals (batch 2: low risk (n = 32), high risk (n = 34), and lung cancer (n = 83)), were analyzed using multiplexed SRM assays. ALDH3A1 and AKR1B10 were found to be consistently overexpressed in the high-risk group in both batch 1 and batch 2 brushing specimens as well as in the biopsies of batch 1. Validation of highly discriminatory proteins and metabolic enzymes by SRM in a larger independent cohort supported their use to identify patients at high risk for developing lung cancer.

Expression of CXCL8 (IL-8) in the Pathogenesis of T-Cell Acute Lymphoblastic Leukemia Patients

Background Inflammation plays a very important role in the pathogenesis of a wide range of diseases, such as atherosclerosis myocardial infarction, sepsis, rheumatoid arthritis, and cancer. This study aimed to investigate the association of IL-8 in T-cell acute lymphoblastic leukemia (T-ALL) patients. Methodology IL-8 levels were estimated in 52 individuals. Of the study population, 26 were T-ALL patients (all phases of leukemia were included in the study) and 26 were disease-free healthy volunteers. In this study, we employed flow cytometry, enzyme-linked immunosorbent assay, reverse transcription-polymerase chain reaction test, and western blot analysis. Results IL-8 was significantly higher in all T-ALL patients than in healthy volunteers. IL-8 levels showed a significant positive correlation in T-ALL patients at the genomic and proteomic levels. Conclusions Higher serum IL-8 levels were associated with the advanced disease stage of the clinicopathological parameters. Our results indicate that monitoring IL-8 has a role in modulating disease sensing in T-ALL and may represent a target for innovative diagnostic and therapeutic strategies.

Novel Screening Tools for Lung Cancer

Screening with low-dose computed tomography has been shown to decrease lung cancer mortality. However, the issues of low detection rates and false positive results remain, highlighting the need for adjunctive tools in lung cancer screening. To this end, researchers have investigated easily applicable, minimally invasive tests with high validity. We herein review some of the more promising novel markers utilizing plasma, sputum, and airway samples.

Integrative analysis of DNA methylomes reveals novel cell-free biomarkers in lung adenocarcinoma

Lung cancer is a leading cause of cancer-related deaths worldwide, with a low 5-year survival rate due in part to a lack of clinically useful biomarkers. Recent studies have identified DNA methylation changes as potential cancer biomarkers. The present study identified cancer-specific CpG methylation changes by comparing genome-wide methylation data of cfDNA from lung adenocarcinomas (LUAD) patients and healthy donors in the discovery cohort. A total of 725 cell-free CpGs associated with LUAD risk were identified. Then XGBoost algorithm was performed to identify seven CpGs associated with LUAD risk. In the training phase, the 7-CpGs methylation panel was established to classify two different prognostic subgroups and showed a significant association with overall survival (OS) in LUAD patients. We found that the methylation of cg02261780 was negatively correlated with the expression of its representing gene GNA11. The methylation and expression of GNA11 were significantly associated with LAUD prognosis. Based on bisulfite PCR, the methylation levels of five CpGs (cg02261780, cg09595050, cg20193802, cg15309457, and cg05726109) were further validated in tumor tissues and matched non-malignant tissues from 20 LUAD patients. Finally, validation of the seven CpGs with RRBS data of cfDNA methylation was conducted and further proved the reliability of the 7-CpGs methylation panel. In conclusion, our study identified seven novel methylation markers from cfDNA methylation data which may contribute to better prognosis for LUAD patients.

Initial development and testing of an exhaled microRNA detection strategy for lung cancer case-control discrimination

For detecting field carcinogenesis non-invasively, early technical development and case-control testing of exhaled breath condensate microRNAs was performed. In design, human lung tissue microRNA-seq discovery was reconciled with TCGA and published tumor-discriminant microRNAs, yielding a panel of 24 upregulated microRNAs. The airway origin of exhaled microRNAs was topographically "fingerprinted", using paired EBC, upper and lower airway donor sample sets. A clinic-based case-control study (166 NSCLC cases, 185 controls) was interrogated with the microRNA panel by qualitative RT-PCR. Data were analyzed by logistic regression (LR), and by random-forest (RF) models. Feasibility testing of exhaled microRNA detection, including optimized whole EBC extraction, and RT and qualitative PCR method evaluation, was performed. For sensitivity in this low template setting, intercalating dye-based URT-PCR was superior to fluorescent probe-based PCR (TaqMan). In application, adjusted logistic regression models identified exhaled miR-21, 33b, 212 as overall case-control discriminant. RF analysis of combined clinical + microRNA models showed modest added discrimination capacity (1.1-2.5%) beyond clinical models alone: all subjects 1.1% (p = 8.7e-04)); former smokers 2.5% (p = 3.6e-05); early stage 1.2% (p = 9.0e-03), yielding combined ROC AUC ranging from 0.74 to 0.83. We conclude that exhaled microRNAs are qualitatively measureable, reflect in part lower airway signatures; and when further refined/quantitated, can potentially help to improve lung cancer risk assessment.

Dual inhibition of EGFR‑VEGF: An effective approach to the treatment of advanced non‑small cell lung cancer with EGFR mutation (Review)

On a global scale, the incidence and mortality rates of lung cancer are gradually increasing year by year. A number of bad habits and environmental factors are associated with lung cancer, including smoking, second‑hand smoke exposure, occupational exposure, respiratory diseases and genetics. At present, low‑dose spiral computed tomography is routinely the first choice in the diagnosis of lung cancer. However, pathological examination is still the gold standard for the diagnosis of lung cancer. Based on the classification and stage of the cancer, treatment options such as surgery, radiotherapy, chemotherapy, targeted therapy and immunotherapy are available. The activation of the EGFR pathway can promote the survival and proliferation of tumor cells, and the VEGF pathway can promote the formation of blood vessels, thereby promoting tumor growth. In non‑small cell lung cancer (NSCLC) with EGFR mutation, EGFR activation can promote tumor growth by promoting VEGF upregulation through a hypoxia‑independent mechanism. The upregulation of VEGF can make tumor cells resistant to EGFR inhibitors. In addition, the expression of the VEGF signal is also affected by other factors. Therefore, the use of a single EGFR inhibitor cannot completely inhibit the expression of the VEGF signal. In order to overcome this problem, the combination of VEGF inhibitors and EGFR inhibitors has become the method of choice. Dual inhibition can not only overcome the resistance of tumor cells to EGFR inhibitors, but also significantly increase the progression‑free survival time of patients with NSCLC. The present review discusses the associations between the EGFR and VEGF pathways, and the characteristics of dual inhibition of the EGFR‑VEGF pathway.

Bronchial gene expression alterations associated with radiological bronchiectasis

OBJECTIVES

Discovering airway gene expression alterations associated with radiological bronchiectasis may improve the understanding of the pathobiology of early-stage bronchiectasis.

METHODS

Presence 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.

RESULTS

20 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 <0.1, log2 fold-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 (p<0.0001), more symptoms (p=0.002), higher SERPINA1 mutation rates (p=0.03) and higher computed tomography derived bronchiectasis scores (p<0.0001).

CONCLUSIONS

Genes 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.

Extracellular matrix profiles determine risk and prognosis of the squamous cell carcinoma subtype of non-small cell lung carcinoma

BACKGROUND

Squamous cell carcinoma (SqCC) is a subtype of non-small cell lung cancer for which patient prognosis remains poor. The extracellular matrix (ECM) is critical in regulating cell behavior; however, its importance in tumor aggressiveness remains to be comprehensively characterized.

METHODS

Multi-omics data of SqCC human tumor specimens was combined to characterize ECM features associated with initiation and recurrence. Penalized logistic regression was used to define a matrix risk signature for SqCC tumors and its performance across a panel of tumor types and in SqCC premalignant lesions was evaluated. Consensus clustering was used to define prognostic matreotypes for SqCC tumors. Matreotype-specific tumor biology was defined by integration of bulk RNAseq with scRNAseq data, cell type deconvolution, analysis of ligand-receptor interactions and enriched biological pathways, and through cross comparison of matreotype expression profiles with aging and idiopathic pulmonary fibrosis lung profiles.

RESULTS

This analysis revealed subtype-specific ECM signatures associated with tumor initiation that were predictive of premalignant progression. We identified an ECM-enriched tumor subtype associated with the poorest prognosis. In silico analysis indicates that matrix remodeling programs differentially activate intracellular signaling in tumor and stromal cells to reinforce matrix remodeling associated with resistance and progression. The matrix subtype with the poorest prognosis resembles ECM remodeling in idiopathic pulmonary fibrosis and may represent a field of cancerization associated with elevated cancer risk.

CONCLUSIONS

Collectively, this analysis defines matrix-driven features of poor prognosis to inform precision medicine prevention and treatment strategies towards improving SqCC patient outcome.

Smoking modulates different secretory subpopulations expressing SARS-CoV-2 entry genes in the nasal and bronchial airways

SARS-CoV-2 infection and disease severity are influenced by viral entry (VE) gene expression patterns in the airway epithelium. The similarities and differences of VE gene expression (ACE2, TMPRSS2, and CTSL) across nasal and bronchial compartments have not been fully characterized using matched samples from large cohorts. Gene expression data from 793 nasal and 1673 bronchial brushes obtained from individuals participating in lung cancer screening or diagnostic workup revealed that smoking status (current versus former) was the only clinical factor significantly and reproducibly associated with VE gene expression. The expression of ACE2 and TMPRSS2 was higher in smokers in the bronchus but not in the nose. scRNA-seq of nasal brushings indicated that ACE2 co-expressed genes were highly expressed in club and C15orf48+ secretory cells while TMPRSS2 co-expressed genes were highly expressed in keratinizing epithelial cells. In contrast, these ACE2 and TMPRSS2 modules were highly expressed in goblet cells in scRNA-seq from bronchial brushings. Cell-type deconvolution of the gene expression data confirmed that smoking increased the abundance of several secretory cell populations in the bronchus, but only goblet cells in the nose. The association of ACE2 and TMPRSS2 with smoking in the bronchus is due to their high expression in goblet cells which increase in abundance in current smoker airways. In contrast, in the nose, these genes are not predominantly expressed in cell populations modulated by smoking. In individuals with elevated lung cancer risk, smoking-induced VE gene expression changes in the nose likely have minimal impact on SARS-CoV-2 infection, but in the bronchus, smoking may lead to higher viral loads and more severe disease.

Early transcriptional responses of bronchial epithelial cells to whole cigarette smoke mirror those of in-vivo exposed human bronchial mucosa

BACKGROUND

Despite the well-known detrimental effects of cigarette smoke (CS), little is known about the complex gene expression dynamics in the early stages after exposure. This study aims to investigate early transcriptomic responses following CS exposure of airway epithelial cells in culture and compare these to those found in human CS exposure studies.

METHODS

Primary bronchial epithelial cells (PBEC) were differentiated at the air-liquid interface (ALI) and exposed to whole CS. Bulk RNA-sequencing was performed at 1 h, 4 h, and 24 h hereafter, followed by differential gene expression analysis. Results were additionally compared to data retrieved from human CS studies.

RESULTS

ALI-PBEC gene expression in response to CS was most significantly changed at 4 h after exposure. Early transcriptomic changes (1 h, 4 h post CS exposure) were related to oxidative stress, xenobiotic metabolism, higher expression of immediate early genes and pro-inflammatory pathways (i.e., Nrf2, AP-1, AhR). At 24 h, ferroptosis-associated genes were significantly increased, whereas PRKN, involved in removing dysfunctional mitochondria, was downregulated. Importantly, the transcriptome dynamics of the current study mirrored in-vivo human studies of acute CS exposure, chronic smokers, and inversely mirrored smoking cessation.

CONCLUSION

These findings show that early after CS exposure xenobiotic metabolism and pro-inflammatory pathways were activated, followed by activation of the ferroptosis-related cell death pathway. Moreover, significant overlap between these transcriptomic responses in the in-vitro model and human in-vivo studies was found, with an early response of ciliated cells. These results provide validation for the use of ALI-PBEC cultures to study the human lung epithelial response to inhaled toxicants.

Genomic Profiling of Bronchoalveolar Lavage Fluid in Lung Cancer

Genomic profiling of bronchoalveolar lavage (BAL) samples may be useful for tumor profiling and diagnosis in the clinic. Here, we compared tumor-derived mutations detected in BAL samples from subjects with non-small cell lung cancer (NSCLC) to those detected in matched plasma samples. Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) was used to genotype DNA purified from BAL, plasma, and tumor samples from patients with NSCLC. The characteristics of cell-free DNA (cfDNA) isolated from BAL fluid were first characterized to optimize the technical approach. Somatic mutations identified in tumor were then compared with those identified in BAL and plasma, and the potential of BAL cfDNA analysis to distinguish lung cancer patients from risk-matched controls was explored. In total, 200 biofluid and tumor samples from 38 cases and 21 controls undergoing BAL for lung cancer evaluation were profiled. More tumor variants were identified in BAL cfDNA than plasma cfDNA in all stages (P < 0.001) and in stage I to II disease only. Four of 21 controls harbored low levels of cancer-associated driver mutations in BAL cfDNA [mean variant allele frequency (VAF) = 0.5%], suggesting the presence of somatic mutations in nonmalignant airway cells. Finally, using a Random Forest model with leave-one-out cross-validation, an exploratory BAL genomic classifier identified lung cancer with 69% sensitivity and 100% specificity in this cohort and detected more cancers than BAL cytology. Detecting tumor-derived mutations by targeted sequencing of BAL cfDNA is technically feasible and appears to be more sensitive than plasma profiling. Further studies are required to define optimal diagnostic applications and clinical utility.

SIGNIFICANCE

Hybrid-capture, targeted deep sequencing of lung cancer mutational burden in cell-free BAL fluid identifies more tumor-derived mutations with increased allele frequencies compared with plasma cell-free DNA. See related commentary by Rolfo et al., p. 2826.

Feature Selection and Molecular Classification of Cancer Phenotypes: A Comparative Study

The classification of high dimensional gene expression data is key to the development of effective diagnostic and prognostic tools. Feature selection involves finding the best subset with the highest power in predicting class labels. Here, we conducted a comparative study focused on different combinations of feature selectors (Chi-Squared, mRMR, Relief-F, and Genetic Algorithms) and classification learning algorithms (Random Forests, PLS-DA, SVM, Regularized Logistic/Multinomial Regression, and kNN) to identify those with the best predictive capacity. The performance of each combination is evaluated through an empirical study on three benchmark cancer-related microarray datasets. Our results first suggest that the quality of the data relevant to the target classes is key for the successful classification of cancer phenotypes. We also proved that, for a given classification learning algorithm and dataset, all filters have a similar performance. Interestingly, filters achieve comparable or even better results with respect to the GA-based wrappers, while also being easier and faster to implement. Taken together, our findings suggest that simple, well-established feature selectors in combination with optimized classifiers guarantee good performances, with no need for complicated and computationally demanding methodologies.

Analyzing biomarker discovery: Estimating the reproducibility of biomarker sets

Many researchers try to understand a biological condition by identifying biomarkers. This is typically done using univariate hypothesis testing over a labeled dataset, declaring a feature to be a biomarker if there is a significant statistical difference between its values for the subjects with different outcomes. However, such sets of proposed biomarkers are often not reproducible – subsequent studies often fail to identify the same sets. Indeed, there is often only a very small overlap between the biomarkers proposed in pairs of related studies that explore the same phenotypes over the same distribution of subjects. This paper first defines the Reproducibility Score for a labeled dataset as a measure (taking values between 0 and 1) of the reproducibility of the results produced by a specified fixed biomarker discovery process for a given distribution of subjects. We then provide ways to reliably estimate this score by defining algorithms that produce an over-bound and an under-bound for this score for a given dataset and biomarker discovery process, for the case of univariate hypothesis testing on dichotomous groups. We confirm that these approximations are meaningful by providing empirical results on a large number of datasets and show that these predictions match known reproducibility results. To encourage others to apply this technique to analyze their biomarker sets, we have also created a publicly available website, https://biomarker.shinyapps.io/BiomarkerReprod/, that produces these Reproducibility Score approximations for any given dataset (with continuous or discrete features and binary class labels).

Clinical validation and utility of Percepta GSC for the evaluation of lung cancer

The Percepta Genomic Sequencing Classifier (GSC) was developed to up-classify as well as down-classify the risk of malignancy for lung lesions when bronchoscopy is non-diagnostic. We evaluated the performance of Percepta GSC in risk re-classification of indeterminate lung lesions. This multicenter study included individuals who currently or formerly smoked undergoing bronchoscopy for suspected lung cancer from the AEGIS I/ II cohorts and the Percepta Registry. The classifier was measured in normal-appearing bronchial epithelium from bronchial brushings. The sensitivity, specificity, and predictive values were calculated using predefined thresholds. The ability of the classifier to decrease unnecessary invasive procedures was estimated. A set of 412 patients were included in the validation (prevalence of malignancy was 39.6%). Overall, 29% of intermediate-risk lung lesions were down-classified to low-risk with a 91.0% negative predictive value (NPV) and 12.2% of intermediate-risk lesions were up-classified to high-risk with a 65.4% positive predictive value (PPV). In addition, 54.5% of low-risk lesions were down-classified to very low risk with >99% NPV and 27.3% of high-risk lesions were up-classified to very high risk with a 91.5% PPV. If the classifier results were used in nodule management, 50% of patients with benign lesions and 29% of patients with malignant lesions undergoing additional invasive procedures could have avoided these procedures. The Percepta GSC is highly accurate as both a rule-out and rule-in test. This high accuracy of risk re-classification may lead to improved management of lung lesions.

Hybrid feature selection based on SLI and genetic algorithm for microarray datasets

One of the major problems in microarray datasets is the large number of features, which causes the issue of "the curse of dimensionality" when machine learning is applied to these datasets. Feature selection refers to the process of finding optimal feature set by removing irrelevant and redundant features. It has a significant role in pattern recognition, classification, and machine learning. In this study, a new and efficient hybrid feature selection method, called Garank&rand, is presented. The method combines a wrapper feature selection algorithm based on the genetic algorithm (GA) with a proposed filter feature selection method, SLI-γ. In Garank&rand, some initial solutions are built regarding the most relevant features based on SLI-γ, and the remaining ones are only the random features. Eleven high-dimensional and standard datasets were used for the accuracy evaluation of the proposed SLI-γ. Additionally, four high-dimensional well-known datasets of microarray experiments were used to carry out an extensive experimental study for the performance evaluation of Garank&rand. This experimental analysis showed the robustness of the method as well as its ability to obtain highly accurate solutions at the earlier stages of the GA evolutionary process. Finally, the performance of Garank&rand was also compared to the results of GA to highlight its competitiveness and its ability to successfully reduce the original feature set size and execution time.

Lung cancer: Premalignant biology and medical prevention

Lung cancer (both adenocarcinoma and squamous cell) progress through a series of pre-malignant histologic changes before the development of invasive disease. Each of these carcinogenic cascades is defined by genetic and epigenetic alterations in pulmonary epithelial cells. Additionally, alterations in the immune response, progenitor cell function, mutational burden, and microenvironmental mediated survival of mutated clones contribute to the risk of pre-malignant lesions progressing to cancer. Medical preventions studies have been completed and current and future trials are informed by the improved understanding of pre-malignancy. This will lead to precision chemoprevention trials based on lesional biology and histologic characteristics.

Smartphone-Based Platforms for Clinical Detections in Lung-Cancer-Related Exhaled Breath Biomarkers: A Review

Lung cancer has been studied for decades because of its high morbidity and high mortality. Traditional methods involving bronchoscopy and needle biopsy are invasive and expensive, which makes patients suffer more risks and costs. Various noninvasive lung cancer markers, such as medical imaging indices, volatile organic compounds (VOCs), and exhaled breath condensates (EBCs), have been discovered for application in screening, diagnosis, and prognosis. However, the detection of markers still relies on bulky and professional instruments, which are limited to training personnel or laboratories. This seriously hinders population screening for early diagnosis of lung cancer. Advanced smartphones integrated with powerful applications can provide easy operation and real-time monitoring for healthcare, which demonstrates tremendous application scenarios in the biomedical analysis region from medical institutions or laboratories to personalized medicine. In this review, we propose an overview of lung-cancer-related noninvasive markers from exhaled breath, focusing on the novel development of smartphone-based platforms for the detection of these biomarkers. Lastly, we discuss the current limitations and potential solutions.

Percepta Genomic Sequencing Classifier and decision-making in patients with high-risk lung nodules: a decision impact study

BACKGROUND

Incidental and screening-identified lung nodules are common, and a bronchoscopic evaluation is frequently nondiagnostic. The Percepta Genomic Sequencing Classifier (GSC) is a genomic classifier developed in current and former smokers which can be used for further risk stratification in these patients. Percepta GSC has the capability of up-classifying patients with a pre-bronchoscopy risk that is high (> 60%) to "very high risk" with a positive predictive value of 91.5%. This prospective, randomized decision impact survey was designed to test the hypothesis that an up-classification of risk of malignancy from high to very high will increase the rate of referral for surgical or ablative therapy without additional intervening procedures while increasing physician confidence.

METHODS

Data were collected from 37 cases from the Percepta GSC validation cohort in which the pre-bronchoscopy risk of malignancy was high (> 60%), the bronchoscopy was nondiagnostic, and the patient was up-classified to very high risk by Percepta GSC. The cases were randomly presented to U.S pulmonologists in three formats: a pre-post cohort where each case is presented initially without and then with a GSG result, and two independent cohorts where each case is presented either with or without with a GSC result. Physicians were surveyed with respect to subsequent management steps and confidence in that decision.

RESULTS

One hundred and one survey takers provided a total of 1341 evaluations of the 37 patient cases across the three different cohorts. The rate of recommendation for surgical resection was significantly higher in the independent cohort with a GSC result compared to the independent cohort without a GSC result (45% vs. 17%, p < 0.001) In the pre-post cross-over cohort, the rate increased from 17 to 56% (p < 0.001) following the review of the GSC result. A GSC up-classification from high to very high risk of malignancy increased Pulmonologists' confidence in decision-making following a nondiagnostic bronchoscopy.

CONCLUSIONS

Use of the Percepta GSC classifier will allow more patients with early lung cancer to proceed more rapidly to potentially curative therapy while decreasing unnecessary intervening diagnostic procedures following a nondiagnostic bronchoscopy.

Olfactometric diagnosis of lung cancer by canine scent - A double-blinded study

BACKGROUND

Lung cancer is one of the most often diagnosed tumours in the world with the highest mortality. A major problem and reason for the high mortality from lung cancer is its diagnosis in the late stages. The main goal of preventing lung cancer deaths is early detection in the early stages and accurate diagnosis, which must be followed by targeted treatment. Nevertheless, even top diagnostic techniques do not have the same accuracy and sensitivity as a dog's sense of smell.

METHODS

The study aims to present the results of olfactometric detection of lung cancer using the smell of dogs in unblinded, single-blinded and double-blinded studies. 115 serum samples or breath from patients with lung cancer and 101 samples from healthy people were used for the training. The group consisted of women and men of Indo-European origin, mostly from the Moravian-Silesian region in Czech Republic. Two dogs were selected for the study.

RESULTS

In the case of tumor detection in the form of unblinded tests, Bugs had a sensitivity of 91% and a specificity of 92%. Boolomo had a sensitivity of 89% and a specificity of 81%. For single-blinded tests, Bugs had a sensitivity of 71%. The sensitivity of Boolomo was set at 90%. After meeting the sensitivity limit of 70%, dogs were included in the double-blinded studies. The highest accuracy was set at 68% for Bugs, 83% for Boolomo.

CONCLUSION

When a tumour is diagnosed in the late stages, it is a great burden on both the health and economic systems of the state. Unfortunately, there is still no suitable screening test to detect the tumour at an early stage, so any other method of detection seems desirable. Trained dogs are used in many fields, why not also in medicine and the diagnosis of tumours?

Sex and gender in respiratory physiology

Sex is a biological concept determined at conception. Gender is a social concept. Medicine recognises sex as a biological variable and recommends including sex as a factor in clinical practice norms and as a topic of bench and clinical research. Sex plays a role in respiratory physiology according to two pathways: hormones and anatomy, with females characterised by smaller dimensions at every level of the respiratory system. Sex hormones also play specific roles in lung inflammatory processes, breathing control and in response to diseases. The literature is extremely controversial because many factors need to be considered to avoid erroneous comparisons. The main difficulty lies in creating homogeneous groups of subjects according to age, body weight, lung/airway size, fluctuations in circulating hormone levels, and exercise protocol. Because almost all of the knowledge available in physiology is based on research in males, medicine for women is therefore less evidence-based than that being applied to men. Finally, the number of transsexual people is increasing and they represent new challenges for clinicians, due to the anatomical and physiological changes that they undergo.

Low-Coverage Whole Genome Sequencing Using Laser Capture Microscopy with Combined Digital Droplet PCR: An Effective Tool to Study Copy Number and Kras Mutations in Early Lung Adenocarcinoma Development

Defining detailed genomic characterization of early tumor progression is critical to identifying key regulators and pathways in carcinogenesis as potentially druggable targets. In human lung cancer, work to characterize early cancer development has mainly focused on squamous cancer, as the earliest lesions are more proximal in the airways and often accessible by repeated bronchoscopy. Adenocarcinomas are typically located distally in the lung, limiting accessibility for biopsy of pre-malignant and early stages. Mouse lung cancer models recapitulate many human genomic features and provide a model for tumorigenesis with pre-malignant atypical adenomatous hyperplasia and in situ adenocarcinomas often developing contemporaneously within the same animal. Here, we combined tissue characterization and collection by laser capture microscopy (LCM) with digital droplet PCR (ddPCR) and low-coverage whole genome sequencing (LC-WGS). ddPCR can be used to identify specific missense mutations in Kras (Kirsten rat sarcoma viral oncogene homolog, here focused on Kras Q61) and estimate the percentage of mutation predominance. LC-WGS is a cost-effective method to infer localized copy number alterations (CNAs) across the genome using low-input DNA. Combining these methods, the histological stage of lung cancer can be correlated with appearance of Kras mutations and CNAs. The utility of this approach is adaptable to other mouse models of human cancer.

Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders

Major complications arise from the recent increase in the amount of high-dimensional data, including high computational costs and memory requirements. Feature selection, which identifies the most relevant and informative attributes of a dataset, has been introduced as a solution to this problem. Most of the existing feature selection methods are computationally inefficient; inefficient algorithms lead to high energy consumption, which is not desirable for devices with limited computational and energy resources. In this paper, a novel and flexible method for unsupervised feature selection is proposed. This method, named QuickSelection (The code is available at: https://github.com/zahraatashgahi/QuickSelection), introduces the strength of the neuron in sparse neural networks as a criterion to measure the feature importance. This criterion, blended with sparsely connected denoising autoencoders trained with the sparse evolutionary training procedure, derives the importance of all input features simultaneously. We implement QuickSelection in a purely sparse manner as opposed to the typical approach of using a binary mask over connections to simulate sparsity. It results in a considerable speed increase and memory reduction. When tested on several benchmark datasets, including five low-dimensional and three high-dimensional datasets, the proposed method is able to achieve the best trade-off of classification and clustering accuracy, running time, and maximum memory usage, among widely used approaches for feature selection. Besides, our proposed method requires the least amount of energy among the state-of-the-art autoencoder-based feature selection methods.

Elevated T cell repertoire diversity is associated with progression of lung squamous cell premalignant lesions

Objective

The immune response to invasive carcinoma has been the focus of published work, but little is known about the adaptive immune response to bronchial premalignant lesions (PMLs), precursors of lung squamous cell carcinoma. This study was designed to characterize the T cell receptor (TCR) repertoire in PMLs and its association with clinical, pathological, and molecular features.

Methods

Endobronchial biopsies (n=295) and brushings (n=137) from high-risk subjects (n=50), undergoing lung cancer screening at approximately 1-year intervals via autofluorescence bronchoscopy and CT, were profiled by RNA-seq. We applied the TCR Repertoire Utilities for Solid Tissue/Tumor tool to the RNA-seq data to identify TCR CDR3 sequences across all samples. In the biopsies, we measured the correlation of TCR diversity with previously derived immune-associated PML transcriptional signatures and PML outcome. We also quantified the spatial and temporal distribution of shared and clonally expanded TCRs. Using the biopsies and brushes, the ratio of private (ie, found in one patient only) and public (ie, found in two or more patients) TCRs was quantified, and the CDR3 sequences were compared with those found in curated databases with known antigen specificities.

Results

We detected 39,303 unique TCR sequences across all samples. In PML biopsies, TCR diversity was negatively associated with a transcriptional signature of T cell mediated immune activation (p=4e-4) associated with PML outcome. Additionally, in lesions of the proliferative molecular subtype, TCR diversity was decreased in regressive versus progressive/persistent PMLs (p=0.045). Within each patient, TCRs were more likely to be shared between biopsies sampled at the same timepoint than biopsies sampled at the same anatomic location at different times. Clonally expanded TCRs, within a biopsied lesion, were more likely to be expanded at future time points than non-expanded clones. The majority of TCR sequences were found in a single sample, with only 3396 (8.6%) found in more than one sample and 1057 (2.7%) found in two or more patients (ie, public); however, when compared with a public database of CDR3 sequences, 4543 (11.6%) of TCRs were identified as public. TCRs with known antigen specificities were enriched among public TCRs (p<0.001).

Conclusions

Decreased TCR diversity may reflect nascent immune responses that contribute to PML elimination. Further studies are needed to explore the potential for immunoprevention of PMLs.

Mass spectrometry based proteome profiling of the exhaled breath condensate for lung cancer biomarkers search

INTRODUCTION

Lung cancer remains the most prevalent cause of cancer mortality worldwide mainly due to insufficient availability of early screening methods for wide-scale application. Exhaled breath condensate (EBC) is currently considered as one of the promising targets for early screening and is particularly attractive due to its absolutely noninvasive collection and possibility for long-term frozen storage. EBC proteome analysis can provide valuable information about the (patho)physiological changes in the respiratory system and may help to identify in time a high risk of lung cancer. Mass spectrometry (MS) profiling of EBC proteome seems to have no alternative in obtaining the most extensive data and characteristic marker panels for screening.

AREAS COVERED

This special report summarizes the data of several proteomic studies of EBC in normal and lung cancer (from 2012 to 2021, PubMed), focuses on the possible reasons for the significant discrepancy in the results, and discusses some aspects for special attention in further studies.

EXPERT OPINION

The significant discrepancy in the results of various studies primarily highlights the need to create standardized protocols for the collection and preparation of EBC for proteomic analysis. The application of quantitative and targeted LC-MS/MS based approaches seems to be the most promising in further EBC proteomic studies.

Current and Future Development in Lung Cancer Diagnosis

Lung cancer is the leading cause of cancer-related deaths in North America and other developed countries. One of the reasons lung cancer is at the top of the list is that it is often not diagnosed until the cancer is at an advanced stage. Thus, the earliest diagnosis of lung cancer is crucial, especially in screening high-risk populations, such as smokers, exposure to fumes, oil fields, toxic occupational places, etc. Based on the current knowledge, it looks that there is an urgent need to identify novel biomarkers. The current diagnosis of lung cancer includes different types of imaging complemented with pathological assessment of biopsies, but these techniques can still not detect early lung cancer developments. In this review, we described the advantages and disadvantages of current methods used in diagnosing lung cancer, and we provide an analysis of the potential use of body fluids as carriers of biomarkers as predictors of cancer development and progression.

Identifying complex gene-gene interactions: a mixed kernel omnibus testing approach

Genes do not function independently; rather, they interact with each other to fulfill their joint tasks. Identification of gene-gene interactions has been critically important in elucidating the molecular mechanisms responsible for the variation of a phenotype. Regression models are commonly used to model the interaction between two genes with a linear product term. The interaction effect of two genes can be linear or nonlinear, depending on the true nature of the data. When nonlinear interactions exist, the linear interaction model may not be able to detect such interactions; hence, it suffers from substantial power loss. While the true interaction mechanism (linear or nonlinear) is generally unknown in practice, it is critical to develop statistical methods that can be flexible to capture the underlying interaction mechanism without assuming a specific model assumption. In this study, we develop a mixed kernel function which combines both linear and Gaussian kernels with different weights to capture the linear or nonlinear interaction of two genes. Instead of optimizing the weight function, we propose a grid search strategy and use a Cauchy transformation of the P-values obtained under different weights to aggregate the P-values. We further extend the two-gene interaction model to a high-dimensional setup using a de-biased LASSO algorithm. Extensive simulation studies are conducted to verify the performance of the proposed method. Application to two case studies further demonstrates the utility of the model. Our method provides a flexible and computationally efficient tool for disentangling complex gene-gene interactions associated with complex traits.

fGAAM: A fast and resizable genetic algorithm with aggressive mutation for feature selection

The paper introduces a modified version of a genetic algorithm with aggressive mutation (GAAM) called fGAAM (fast GAAM) that significantly decreases the time needed to find feature subsets of a satisfactory classification accuracy. To demonstrate the time gains provided by fGAAM both algorithms were tested on eight datasets containing different number of features, classes, and examples. The fGAAM was also compared with four reference methods: the Holland GA with and without penalty term, Culling GA, and NSGA II. Results: (i) The fGAAM processing time was about 35% shorter than that of the original GAAM. (ii) The fGAAM was also 20 times quicker than two Holland GAs and 50 times quicker than NSGA II. (iii) For datasets of different number of features, classes, and examples, another number of individuals, stored for further processing, provided the highest acceleration. On average, the best results were obtained when individuals from the last 10 populations were stored (time acceleration: 36.39%) or when the number of individuals to be stored was calculated by the algorithm itself (time acceleration: 35.74%). (iv) The fGAAM was able to process all datasets used in the study, even those that, because of their high number of features, could not be processed by the two Holland GAs and NSGA II.

Application of a classifier combining bronchial transcriptomics and chest computed tomography features facilitates the diagnostic evaluation of lung cancer in smokers and nonsmokers

Lung cancer screening by computed tomography (CT) reduces mortality but exhibited high false-positive rates. We established a diagnostic classifier combining chest CT features with bronchial transcriptomics. Patients with CT-detected suspected lung cancer were enrolled. The sample collected by bronchial brushing was used for RNA sequencing. The e1071 and pROC packages in R software was applied to build the model. Eventually, a total of 283 patients, including 183 with lung cancer and 100 with benign lesions, were included into final analysis. When incorporating transcriptomic data with radiological characteristics, the advanced model yielded 0.903 AUC with 81.1% NPV. Moreover, the classifier performed well regardless of lesion size, location, stage, histologic type or smoking status. Pathway analysis showed enhanced epithelial differentiation, tumor metastasis, and impaired immunity were predominant in smokers with cancer, whereas tumorigenesis played a central role in nonsmokers with cancer. Apoptosis and oxidative stress contributed critically in metastatic lung cancer; by contrast, immune dysfunction was pivotal in locally advanced lung cancer. Collectively, we devised a minimal-to-noninvasive, efficient diagnostic classifier for smokers and nonsmokers with lung cancer, which provides evidence for different mechanisms of cancer development and metastasis associated with smoking. A negative classifier result will help the physician make conservative diagnostic decisions.

Analytical validation of the Percepta genomic sequencing classifier; an RNA next generation sequencing assay for the assessment of Lung Cancer risk of suspicious pulmonary nodules

BACKGROUND

Bronchoscopy is a common procedure used for evaluation of suspicious lung nodules, but the low diagnostic sensitivity of bronchoscopy often results in inconclusive results and delays in treatment. Percepta Genomic Sequencing Classifier (GSC) was developed to assist with patient management in cases where bronchoscopy is inconclusive. Studies have shown that exposure to tobacco smoke alters gene expression in airway epithelial cells in a way that indicates an increased risk of developing lung cancer. Percepta GSC leverages this idea of a molecular "field of injury" from smoking and was developed using RNA sequencing data generated from lung bronchial brushings of the upper airway. A Percepta GSC score is calculated from an ensemble of machine learning algorithms utilizing clinical and genomic features and is used to refine a patient's risk stratification.

METHODS

The objective of the analysis described and reported here is to validate the analytical performance of Percepta GSC. Analytical performance studies characterized the sensitivity of Percepta GSC test results to input RNA quantity, the potentially interfering agents of blood and genomic DNA, and the reproducibility of test results within and between processing runs and between laboratories.

RESULTS

Varying the amount of input RNA into the assay across a nominal range had no significant impact on Percepta GSC classifier results. Bronchial brushing RNA contaminated with up to 10% genomic DNA by nucleic acid mass also showed no significant difference on classifier results. The addition of blood RNA, a potential contaminant in the bronchial brushing sample, caused no change to classifier results at up to 11% contamination by RNA proportion. Percepta GSC scores were reproducible between runs, within runs, and between laboratories, varying within less than 4% of the total score range (standard deviation of 0.169 for scores on 4.57 scale).

CONCLUSIONS

The analytical sensitivity, analytical specificity, and reproducibility of Percepta GSC laboratory results were successfully demonstrated under conditions of expected day to day variation in testing. Percepta GSC test results are analytically robust and suitable for routine clinical use.

Distinct clinical phenotypes for Crohn's disease derived from patient surveys

Background

Defining clinical phenotypes provides opportunities for new diagnostics and may provide insights into early intervention and disease prevention. There is increasing evidence that patient-derived health data may contain information that complements traditional methods of clinical phenotyping. The utility of these data for defining meaningful phenotypic groups is of great interest because social media and online resources make it possible to query large cohorts of patients with health conditions.

Methods

We evaluated the degree to which patient-reported categorical data is useful for discovering subclinical phenotypes and evaluated its utility for discovering new measures of disease severity, treatment response and genetic architecture. Specifically, we examined the responses of 1961 patients with inflammatory bowel disease to questionnaires in search of sub-phenotypes. We applied machine learning methods to identify novel subtypes of Crohn’s disease and studied their associations with drug responses.

Results

Using the patients’ self-reported information, we identified two subpopulations of Crohn’s disease; these subpopulations differ in disease severity, associations with smoking, and genetic transmission patterns. We also identified distinct features of drug response for the two Crohn’s disease subtypes. These subtypes show a trend towards differential genotype signatures.

Conclusion

Our findings suggest that patient-defined data can have unplanned utility for defining disease subtypes and may be useful for guiding treatment approaches.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-021-01740-6.

Normal tissue adjacent to tumor expression profile analysis developed and validated a prognostic model based on Hippo-related genes in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the most common malignant disease worldwide. Although the diagnosis and treatment of HCC have greatly improved in the recent years, there is still a lack of accurate methods to predict the prognosis of patients. Evidence has shown that Hippo signaling in tissues adjacent to HCC plays a significant role in HCC development. In the present study, we aimed to construct a model based on the expression of Hippo‐related genes (HRGs) in tissues adjacent to HCC to predict the prognosis of HCC patients.

Methods

Gene expression data of paired normal tissues adjacent to HCC (PNTAH) and clinical information were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The HRG signature was constructed using four canonical Hippo‐related pathways. Univariate Cox regression analysis was used to screen survival‐related HRGs. LASSO and multivariate Cox regression analyses were used to construct the prognostic model. The true and false positive rates of the model were confirmed using receiver operating characteristic (ROC) analysis.

Results

The prognostic model was constructed based on the expression levels of five HRGs (NF2, MYC, BIRC3, CSNK1E, and MINK1) in PNTAH. The mortality rate of HCC patients increased as the risk score determined by the model increased. Furthermore, the risk score was found to be an independent risk factor for the survival of patients. ROC analysis showed that the prognostic model had a better predictive value than the other conventional clinical parameters. Moreover, the reliability of the prognostic model was confirmed in TCGA‐LIHC cohort. A nomogram was generated to predict patient survival. An exploration of the predictive value of the model in HCC tissues indicated that the model is PNTAH‐specific.

Conclusions

We developed and validated a prognostic model based on the expression levels of five HRGs in PNTAH, and this model should be helpful in predicting the prognosis of patients with HCC.

Binary Political Optimizer for Feature Selection Using Gene Expression Data

DNA Microarray technology is an emergent field, which offers the possibility of obtaining simultaneous estimates of the expression levels of several thousand genes in an organism in a single experiment. One of the most significant challenges in this research field is to select high relevant genes from gene expression data. To address this problem, feature selection is a well-known technique to eliminate unnecessary genes in order to ensure accurate classification results. This paper proposes a binary version of Political Optimizer (PO) to solve feature selection problem using gene expression data. Two transfer functions are used to design a binary PO. The first one is based on Sigmoid function and will be noted as BPO-S, while the second one is based on V-shaped function and will be noted as BPO-V. The proposed methods are evaluated using 9 biological datasets and compared with 8 binary well-known metaheuristics. The comparative results show the prevalent performance of the BPO methods especially BPO-V in comparison with other techniques.

Association of Cigarette Smoking, COPD, and Lung Cancer With Expression of SARS-CoV-2 Entry Genes in Human Airway Epithelial Cells

SARS-CoV-2 enters into human airway epithelial cells via membrane fusion or endocytosis, and this process is dependent on ACE2, TMPRSS2, and cathepsin L. In this study, we examined the expression profiles of the three SARS-CoV-2 entry genes in primary human airway epithelial cells isolated from smokers, non-smokers, patients with chronic obstructive pulmonary disease or lung cancer. An exhaustive search of the GEO database was performed to identify eligible data on 1st June 2020. In total, 46 GEO datasets comprising transcriptomic data of 3,053 samples were identified as eligible data for further analysis. All meta-analysis were performed using RStudio. Standardized mean difference was utilized to assess the effect size of a factor on the expression of targeted genes and 95% confidence intervals (CIs) were calculated. This study revealed that (i) cigarette smoking is associated with an increased expression of ACE2 and TMPRSS2 and a decreased expression of cathepsin L; (ii) significant alternations in expression of ACE2, TMPRSS2, and cathepsin L were observed between current smokers and former smokers, but not between former smokers and never smokers; (iii) when compared with healthy controls with identical smoking status, patients with COPD or lung cancer showed negligible changes in expression of ACE2, TMPRSS2, and cathepsin L. Therefore, this study implicates cigarette smoking might contribute to the development of COVID-19 by affecting the expression of SARS-CoV-2 entry genes, while smoking cessation could be effective to reduce the potential risk.

Development of novel monoclonal antibodies and immunoassays for sensitive and specific detection of SULF1 endosulfatase

BACKGROUND

Cell-surface heparan sulfate proteoglycans (HSPGs) function as receptors or co-receptors for ligand binding and mediate the transmission of critical extracellular signals into cells. The complex and dynamic modifications of heparan sulfates on the core proteins are highly regulated to achieve precise signaling transduction. Extracellular endosulfatase Sulf1 catalyzes the removal of 6-O sulfation from HSPGs and thus regulates signaling mediated by 6-O sulfation on HSPGs. The expression of Sulf1 is altered in many cancers. Further studies are needed to clarify Sulf1 role in tumorigenesis, and new tools that can expand our knowledge in this field are required.

METHODS

We have developed and validated novel SULF1 monoclonal antibodies (mAbs). The isotype and subclass for each of these antibodies were determined. These antibodies provide invaluable reagents to assess SULF1- tissue and blood levels by immunohistochemistry and ELISA assays, respectively.

RESULTS

This study reports novel mAbs and immunoassays developed for sensitive and specific human Sulf1 protein detection. Using these SULF1 mAbs, we developed an ELISA assay to investigate whether blood-derived SULF1 may be a useful biomarker for detecting cancer early. Furthermore, we have demonstrated the utility of these antibodies for Sulf1 protein detection, localization, and quantification in biospecimens using various immunoassays.

CONCLUSIONS

This study describes novel Sulf1 mAbs suitable for various immunoassays, including Western blot analysis, ELISA, and immunohistochemistry, which can help understand Sulf1 pathophysiological role.

GENERAL SIGNIFICANCE

New tools to assess and clarify SULF1 role in tumorigenesis are needed. Our novel Sulf1 mAbs and immunoassays assay may have utility for such application.

Biomarkers for Lung Cancer Screening and Detection

Lung cancer is the leading worldwide cause of cancer mortality, as it is often detected at an advanced stage. Since 2011, low-dose CT scan-based screening has promised a 20% reduction in lung cancer mortality. However, effectiveness of screening has been limited by eligibility only for a high-risk population of heavy smokers and a large number of false positives generated by CT. Biomarkers have tremendous potential to improve early detection of lung cancer by refining lung cancer risk, stratifying positive CT scans, and categorizing intermediate-risk pulmonary nodules. Three biomarker tests (Early CDT-Lung, Nodify XL2, Percepta) have undergone extensive validation and are available to the clinician. The authors discuss these tests, with their clinical applicability and limitations, current ongoing evaluation, and future directions for biomarkers in lung cancer screening and detection.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts

BACKGROUND

Bronchoscopy for suspected lung cancer has low diagnostic sensitivity, rendering many inconclusive results. The Bronchial Genomic Classifier (BGC) was developed to help with patient management by identifying those with low risk of lung cancer when bronchoscopy is inconclusive. The BGC was trained and validated on patients in the Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer (AEGIS) trials. A modern patient cohort, the BGC Registry, showed differences in key clinical factors from the AEGIS cohorts, with less smoking history, smaller nodules and older age. Additionally, we discovered interfering factors (inhaled medication and sample collection timing) that impacted gene expressions and potentially disguised genomic cancer signals.

METHODS

In this study, we leveraged multiple cohorts and next generation sequencing technology to develop a robust Genomic Sequencing Classifier (GSC). To address demographic composition shift and interfering factors, we synergized three algorithmic strategies: 1) ensemble of clinical dominant and genomic dominant models; 2) development of hierarchical regression models where the main effects from clinical variables were regressed out prior to the genomic impact being fitted in the model; and 3) targeted placement of genomic and clinical interaction terms to stabilize the effect of interfering factors. The final GSC model uses 1232 genes and four clinical covariates - age, pack-years, inhaled medication use, and specimen collection timing.

RESULTS

In the validation set (N = 412), the GSC down-classified low and intermediate pre-test risk subjects to very low and low post-test risk with a specificity of 45% (95% CI 37-53%) and a sensitivity of 91% (95%CI 81-97%), resulting in a negative predictive value of 95% (95% CI 89-98%). Twelve percent of intermediate pre-test risk subjects were up-classified to high post-test risk with a positive predictive value of 65% (95%CI 44-82%), and 27% of high pre-test risk subjects were up-classified to very high post-test risk with a positive predictive value of 91% (95% CI 78-97%).

CONCLUSIONS

The GSC overcame the impact of interfering factors and achieved consistent performance across multiple cohorts. It demonstrated diagnostic accuracy in both down- and up-classification of cancer risk, providing physicians actionable information for many patients with inconclusive bronchoscopy.

Molecular Analysis of Oncogenic Mutations in Resected Margins by Next-Generation Sequencing Predicts Relapse in Non-Small Cell Lung Cancer Patients

Objective

To investigate the genetic mutations in both tumor and marginal tissues in patients with non-small cell lung cancer (NSCLC), and to evaluate the potential prognostic value in patients with margins gene positive.

Methods

Next-generation sequencing (NGS) technique was used to detect genetic mutation in tumor and marginal tissues of the bronchus in 88 patients with NSCLC. Correlation of genetic mutations with pathology, lymph node metastasis, disease-free survival and overall survival was analyzed.

Results

Of the 88 patients, 83 cases (94.3%) had gene mutations in the tumor samples and 12 cases (13.6%) had genetic alterations in their margins. Most of the gene mutations detected were cancer drivers. Six common driver genes between tumor and marginal tissues were identified, including EGFR, TP53, CDKN2A, CTNNB1, BRAF, and NF1. Kaplan–Meier analysis revealed that the median disease-free survival (DFS) was significantly shorter in patients with detectable gene mutations in marginal tissues compared with patients without mutations in margins (30.7 versus 24.4 months, log-rank χ² = 4.78, P =0.029). Consistently, a shorter median OS was observed in patients harboring gene mutations in margins compared with patients with no mutations in margins (49.1 versus 32.2 months, log-rank χ² = 3.669, P =0.055).

Conclusion

These findings identify the presence of oncogenic alterations in microscopically negative margins in NSCLC patients associated with elevated risk of relapse and shorter survival time. Thus, examination of microscopically negative margins by NGS represents a valuable approach to predict the clinical outcome of NSCLC patients.