Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival.

Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease's complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learning methods. Leveraging multi-omics data, this study seeks to enhance the accuracy of survival prediction by proposing new feature extraction techniques combined with unbiased feature selection. Two lung adenocarcinoma multi-omics datasets, originating from the TCGA and CPTAC-3 projects, were employed for this purpose, emphasizing gene expression, methylation, and mutations as the most relevant data sources that provide features for the survival prediction models. Additionally, gene set aggregation was shown to be the most effective feature extraction method for mutation and copy number variation data. Using the TCGA dataset, we identified 32 molecular features that allowed the construction of a 2-year survival prediction model with an AUC of 0.839. The selected features were additionally tested on an independent CPTAC-3 dataset, achieving an AUC of 0.815 in nested cross-validation, which confirmed the robustness of the identified features.

Bronchoscopic procedures to diagnose peripheral lung lesions.

Bronchoscopy has a low risk of complications when diagnosing peripheral lung lesions suspected of malignancy, however the procedures do not always determine a diagnosis. Several modalities have been invented to improve the diagnostic yield, including radial endobronchial ultrasound and electromagnetic navigation, which are currently used by several departments in Denmark. Augmented fluoroscopy, CT-guided bronchoscopy and robotic bronchoscopy are not yet available in Denmark, but may improve the diagnostic work-up, as argued in this review.

Lung adenocarcinoma discovered during the follow-up of lung-dominant connective tissue disease: a case report and literature review.

Interstitial lung disease (ILD) can lead to lung cancer, which brings great challenges to differential diagnosis and comprehensive treatment. However, the clinical features of lung-dominant connective tissue disease (LD-CTD) related ILD combined with lung cancer has not been validated. We report the case of an 80-year-old woman with LD-CTD treated regularly with nintedanib who presented progressive dyspnoea and hypoxemia after recurrent viral infections. Her chest computed tomography (CT) showed aggravated interstitial fibrosis in both lower lungs with moderate right pleural effusion. Clinicians should be alert to lung cancer in patients who are experiencing poor responsiveness to treatment or acute progression of ILD. The available literatures about the differential diagnosis of clinical manifestations, imaging, treatment and prognosis of LD-CTD are reviewed and discussed in this study.

Perspectives of potentially eligible Indigenous Maori on a lung cancer screening programme: a qualitative study.

AIMS: Lung cancer causes more deaths than any other cancer, globally and in Aotearoa New Zealand, where it disproportionately affects Maori. We aimed to understand Maori perspectives on lung cancer screening in Aotearoa New Zealand to guide its equity-focussed implementation, including identifying enablers and barriers. METHODS: We took a Kaupapa Maori based co-design approach to inform future screening, recruiting Maori current/ex-smokers and members of their whanau (family) for three focus group phases held in Auckland, Aotearoa New Zealand in August 2019. Participants responded to a proposed lung cancer screening pathway and shared their attitudes and beliefs about lung cancer and screening. Results were thematically analysed. RESULTS: The 21 Maori participants supported future lung cancer screening in Aotearoa New Zealand. Perceived benefits included being more informed about lung cancer and screening and enabling healthier future generations. Barriers to screening were previous negative health service experiences; fear; stigma; and access, including time, cost and transport. Enablers included providers' cultural competence; clear communication; a one-stop shop; and support with transport. A range of factors could potentially influence a decision to participate in screening. CONCLUSIONS: Participants favoured future lung cancer screening and identified key barriers and facilitators of screening.

Liquid biopsy techniques and lung cancer: diagnosis, monitoring and evaluation.

Lung cancer stands as the most prevalent form of cancer globally, posing a significant threat to human well-being. Due to the lack of effective and accurate early diagnostic methods, many patients are diagnosed with advanced lung cancer. Although surgical resection is still a potential means of eradicating lung cancer, patients with advanced lung cancer usually miss the best chance for surgical treatment, and even after surgical resection patients may still experience tumor recurrence. Additionally, chemotherapy, the mainstay of treatment for patients with advanced lung cancer, has the potential to be chemo-resistant, resulting in poor clinical outcomes. The emergence of liquid biopsies has garnered considerable attention owing to their noninvasive nature and the ability for continuous sampling. Technological advancements have propelled circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), extracellular vesicles (EVs), tumor metabolites, tumor-educated platelets (TEPs), and tumor-associated antigens (TAA) to the forefront as key liquid biopsy biomarkers, demonstrating intriguing and encouraging results for early diagnosis and prognostic evaluation of lung cancer. This review provides an overview of molecular biomarkers and assays utilized in liquid biopsies for lung cancer, encompassing CTCs, ctDNA, non-coding RNA (ncRNA), EVs, tumor metabolites, TAAs and TEPs. Furthermore, we expound on the practical applications of liquid biopsies, including early diagnosis, treatment response monitoring, prognostic evaluation, and recurrence monitoring in the context of lung cancer.

[Modern approaches to studying the molecular mechanisms of lung functioning in normal and pathological conditions]. / Sovremennye podkhody k izucheniyu molekulyarnykh mekhanizmov funktsionirovaniya legkikh v norme i pri patologii.

Problems with breathing and lung function are caused by the development of various lung diseases associated with lifestyle, harmful environmental factors and genetic predisposition. Knowledge of the molecular mechanisms of the development of the pathological process will allow on time identification of the disease or the development of targeted therapy. The article provides an overview of modern methods that make it possible to most accurately reproduce the structural, functional and mechanical properties of the lung (organ-on-a-chip), to perform non-invasive molecular studies of biomarkers of bronchopulmonary pathology using saliva diagnostics, as well as using DNA and RNA aptamers, verify tumor markers in biological samples of human tissue. Analysis of alterations in the pattern of protein glycosylation using glycodiagnostic methods makes it possible to detect lung cancer in the early stages.

Segmentation of patients with small cell lung cancer into responders and non-responders using the optimal cross-validation technique.

BACKGROUND: The timing of treating cancer patients is an essential factor in the efficacy of treatment. So, patients who will not respond to current therapy should receive a different treatment as early as possible. Machine learning models can be built to classify responders and nonresponders. Such classification models predict the probability of a patient being a responder. Most methods use a probability threshold of 0.5 to convert the probabilities into binary group membership. However, the cutoff of 0.5 is not always the optimal choice. METHODS: In this study, we propose a novel data-driven approach to select a better cutoff value based on the optimal cross-validation technique. To illustrate our novel method, we applied it to three clinical trial datasets of small-cell lung cancer patients. We used two different datasets to build a scoring system to segment patients. Then the models were applied to segment patients into the test data. RESULTS: We found that, in test data, the predicted responders and non-responders had significantly different long-term survival outcomes. Our proposed novel method segments patients better than the standard approach using a cutoff of 0.5. Comparing clinical outcomes of responders versus non-responders, our novel method had a p-value of 0.009 with a hazard ratio of 0.668 for grouping patients using the Cox proportion hazard model and a p-value of 0.011 using the accelerated failure time model which approved a significant difference between responders and non-responders. In contrast, the standard approach had a p-value of 0.194 with a hazard ratio of 0.823 using the Cox proportion hazard model and a p-value of 0.240 using the accelerated failure time model indicating the responders and non-responders do not differ significantly in survival. CONCLUSION: In summary, our novel prediction method can successfully segment new patients into responders and non-responders. Clinicians can use our prediction to decide if a patient should receive a different treatment or stay with the current treatment.

Comprehensive analysis and validation reveal DEPDC1 as a potential diagnostic biomarker associated with tumor immunity in non-small-cell lung cancer.

Current evidence suggests that DEP domain containing 1 (DEPDC1) has an important effect on non-small-cell lung cancer (NSCLC). However, the diagnostic value and the regulatory function within NSCLC are largely unclear. This work utilized publicly available databases and in vitro experiments for exploring, DEPDC1 expression, clinical features, diagnostic significance and latent molecular mechanism within NSCLC. According to our results, DEPDC1 was remarkably upregulated in the tissues of NSCLC patients compared with non-carcinoma tissues, linked with gender, stage, T classification and N classification based on TCGA data and associated with smoking status and stage according to GEO datasets. Meanwhile, the summary receiver operating characteristic (sROC) curve analysis result showed that DEPDC1 had a high diagnostic value in NSCLC (AUC = 0.96, 95% CI: 0.94-0.98; diagnostic odds ratio = 99.08, 95%CI: 31.91-307.65; sensitivity = 0.89, 95%CI: 0.81-0.94; specificity = 0.92, 95%CI: 0.86-0.96; positive predictive value = 0.94, 95%CI: 0.89-0.98; negative predictive value = 0.78, 95%CI: 0.67-0.90; positive likelihood ratio = 11.77, 95%CI: 6.11-22.68; and negative likelihood ratio = 0.12, 95%CI: 0.06-0.22). Subsequently, quantitative real-time PCR (qRT-PCR) and western blotting indicated that DEPDC1 was high expressed in NSCLC cells. According to the in vitro MTS and apoptotic assays, downregulated DEPDC1 expression targeting P53 signaling pathway inhibited the proliferation of NSCLC cells while promoting apoptosis of NSCLC cells. Moreover, DEPDC1 was significantly correlated with immune cell infiltrating levels in NSCLC based on TCGA data, which were primarily associated with T cells CD4 memory activated, macrophages M1, B cells memory, mast cells resting, T cells regulatory, monocytes, and T cells CD4 memory resting. Compared with the group with high expression of DEPDC1, the group with low expression level had higher scores for immune checkpoint inhibitors (ICIs) treatment. GSEA confirmed that DEPDC1 was involved in gene expression and tumor-related signaling pathways. Finally, DEPDC1 and its associated immune-related genes were shown to be enriched in 'receptor ligand activity', 'external side of plasma membrane', 'regulation of innate immune response', and 'Epstein-Barr virus infection' pathways. The present study demonstrates that DEPDC1 may contribute to NSCLC tumorigenesis and can be applied as the biomarker for diagnosis and immunology.

Comparison between next-generation sequencing and multiplex polymerase chain reaction assays for nonsmall-cell lung cancer molecular diagnosis.

PURPOSE: Genetic mutation detection has become an important step in nonsmall-cell lung cancer (NSCLC) treatment because of the increasing number of drugs that target genomic rearrangements. A multiplex test that can detect multiple gene mutations prior to treatment is thus necessary. Currently, either next-generation sequencing (NGS)-based or polymerase chain reaction (PCR)-based tests are used. We evaluated the performance of the Oncomine Dx Target Test (ODxTT), an NGS-based multiplex biomarker panel test, and the AmoyDx Pan Lung Cancer PCR Panel (AmoyDx PLC panel), a real-time PCR-based multiplex biomarker panel test. MATERIALS AND METHODS: Patients with histologically diagnosed NSCLC and a sufficient sample volume to simultaneously perform the AmoyDx PLC panel and ODxTT-M were included in the study. The success and detection rates of both tests were evaluated. RESULTS: Biopsies revealed 116 cases of malignancies, 100 of which were NSCLC. Of these, 59 met the inclusion criteria and were eligible for analysis. The success rates were 100% and 98% for AmoyDx PLC panel and ODxTT-M, respectively. Nine driver mutations were detected in 35.9% and 37.3% of AmoyDx PLC and ODxTT-M panels, respectively. EGFR mutations were detected in 14% and 12% of samples using the AmoyDx PLC panel and ODxTT-M, respectively. Of the 58 cases in which both NGS and AmoyDx PLC panels were successful, discordant results were observed in seven cases. These differences were mainly due to different sensitivities of the detection methods used and the gene variants targeted in each test. DISCUSSION: The AmoyDx PLC panel, a PCR-based multiplex diagnostic test, exhibits a high success rate. The frequency of the nine genes targeted for treatment detected by the AmoyDx PLC panel was comparable to the frequency of mutations detected by ODxTT-M. Clinicians should understand and use the AmoyDx PLC panel and ODxTT-M with respect to their respective performances and limitations.

Performance characteristics of a polymerase chain reaction-based assay for the detection of EGFR mutations in plasma cell-free DNA from patients with non-small cell lung cancer using cell-free DNA collection tubes.

Survival rates in non-small cell lung cancer (NSCLC) are low. Detection of circulating tumor DNA in liquid biopsy (plasma) is increasingly used to identify targeted therapies for clinically actionable mutations, including EGFR mutations in NSCLC. The cobas® EGFR Mutation Test v2 (cobas EGFR test) is FDA-approved for EGFR mutation detection in tissue or liquid biopsy from NSCLC. Standard K2EDTA tubes require plasma separation from blood within 4 to 8 hours; however, Roche Cell-Free DNA (cfDNA) Collection Tubes (Roche cfDNA tube) enable whole blood stability for up to 7 days prior to plasma separation. This analysis assessed performance of Roche cfDNA tubes with the cobas EGFR test for the detection of EGFR mutations in plasma from healthy donors or patients with NSCLC. Overall, test performance was equally robust with either blood collection tube, eg, regarding limit of detection, linearity, and reproducibility, making Roche cfDNA tubes suitable for routine clinical laboratory use in this setting. Importantly, the Roche cfDNA tubes provided more flexibility for specimen handling versus K2EDTA tubes, eg, in terms of tube mixing, plasma separation, and sample stability, and do not require processing of blood within 8 hours thereby increasing the reach of plasma biopsies in NSCLC.

An alternating-intervention pilot trial on the impact of an informational handout on patient-reported outcomes and follow-up after lung cancer screening.

INTRODUCTION: Lung cancer screening (LCS) can reduce lung cancer mortality; however, poor understanding of results may impact patient experience and follow-up. We sought to determine whether an informational handout accompanying LCS results can improve patient-reported outcomes and adherence to follow-up. STUDY DESIGN: This was a prospective alternating intervention pilot trial of a handout to accompany LCS results delivery. SETTING/PARTICIPANTS: Patients undergoing LCS in a multisite program over a 6-month period received a mailing containing either: 1) a standardized form letter of LCS results (control) or 2) the LCS results letter and the handout (intervention). INTERVENTION: A two-sided informational handout on commonly asked questions after LCS created through iterative mixed-methods evaluation with both LCS patients and providers. OUTCOME MEASURES: The primary outcomes of 1)patient understanding of LCS results, 2)correct identification of next steps in screening, and 3)patient distress were measured through survey. Adherence to recommended follow-up after LCS was determined through chart review. Outcomes were compared between the intervention and control group using generalized estimating equations. RESULTS: 389 patients were eligible and enrolled with survey responses from 230 participants (59% response rate). We found no differences in understanding of results, identification of next steps in follow-up or distress but did find higher levels of knowledge and understanding on questions assessing individual components of LCS in the intervention group. Follow-up adherence was overall similar between the two arms, though was higher in the intervention group among those with positive findings (p = 0.007). CONCLUSIONS: There were no differences in self-reported outcomes between the groups or overall follow-up adherence. Those receiving the intervention did report greater understanding and knowledge of key LCS components, and those with positive results had a higher rate of follow-up. This may represent a feasible component of a multi-level intervention to address knowledge and follow-up for LCS. TRIAL REGISTRATION: ClinicalTrials.gov NCT05265897.

Saliva­microbiome­derived signatures: expected to become a potential biomarker for pulmonary nodules (MCEPN-1).

BACKGROUND: Oral microbiota imbalance is associated with the progression of various lung diseases, including lung cancer. Pulmonary nodules (PNs) are often considered a critical stage for the early detection of lung cancer; however, the relationship between oral microbiota and PNs remains unknown. METHODS: We conducted a 'Microbiome with pulmonary nodule series study 1' (MCEPN-1) where we compared PN patients and healthy controls (HCs), aiming to identify differences in oral microbiota characteristics and discover potential microbiota biomarkers for non-invasive, radiation-free PNs diagnosis and warning in the future. We performed 16 S rRNA amplicon sequencing on saliva samples from 173 PN patients and 40 HCs to compare the characteristics and functional changes in oral microbiota between the two groups. The random forest algorithm was used to identify PN salivary microbial markers. Biological functions and potential mechanisms of differential genes in saliva samples were preliminarily explored using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of Orthologous Groups (COG) analyses. RESULTS: The diversity of salivary microorganisms was higher in the PN group than in the HC group. Significant differences were noted in community composition and abundance of oral microorganisms between the two groups. Neisseria, Prevotella, Haemophilus and Actinomyces, Porphyromonas, Fusobacterium, 7M7x, Granulicatella and Selenomonas were the main differential genera between the PN and HC groups. Fusobacterium, Porphyromonas, Parvimonas, Peptostreptococcus and Haemophilus constituted the optimal marker sets (area under curve, AUC = 0.80), which can distinguish between patients with PNs and HCs. Further, the salivary microbiota composition was significantly correlated with age, sex, and smoking history (P < 0.001), but not with personal history of cancer (P > 0.05). Bioinformatics analysis of differential genes showed that patients with PN showed significant enrichment in protein/molecular functions related to immune deficiency and energy metabolisms, such as the cytoskeleton protein RodZ, nicotinamide adenine dinucleotide phosphate dehydrogenase (NADPH) dehydrogenase, major facilitator superfamily transporters and AraC family transcription regulators. CONCLUSIONS: Our study provides the first evidence that the salivary microbiota can serve as potential biomarkers for identifying PN. We observed a significant association between changes in the oral microbiota and PNs, indicating the potential of salivary microbiota as a new non-invasive biomarker for PNs. TRIAL REGISTRATION: Clinical trial registration number: ChiCTR2200062140; Date of registration: 07/25/2022.

FTIR spectroscopy for assessment of hair from lung cancer patients and its application in monitoring the chemotherapy treatment effect.

Lung cancer is the most common cancer and the leading cause of death in China. The current gold standard for clinical lung cancer diagnosis is based on histopathological examination of tumors, but it has the limitation for easy operation and convenient applications. Therefore, researchers are still striving to develop other tools and methods for non-invasive and rapid assessment of the health conditions of lung cancer patients. Hair, as a reflection of the metabolism of the body, is closely related to human health conditions. In principle, Fourier-transform infrared (FTIR) spectroscopy can probe the major chemical compositions in the hair. However, as indicated by previous studies, there is still the challenge to make good use of FTIR spectroscopy for achieving reliable analysis of hair from cancer patients. In this study, hair samples from 82 lung cancer patients were collected and subjected to FTIR measurements and analysis, which showed the protein content in the hair is closely related to the protein content in the blood serum of patients, and the contents of protein and lipid are statistically lower in the lung cancer patients. Furthermore, we demonstrated that FTIR spectroscopy could be employed to monitor the hair of lung cancer patients undergoing chemotherapy, and confirmed that the FTIR spectra of the hair may reflect the resultant effect of the chemotherapy. As such, this work validates the way of using FTIR spectroscopy in hair analysis for the assistance of medical diagnosis of lung cancer as well as monitoring the conditions of the patients under the medical treatment.

Detection of lung cancer through SERS analysis of serum.

Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.

Predicting the risk of lung cancer using machine learning: A large study based on UK Biobank.

In response to the high incidence and poor prognosis of lung cancer, this study tends to develop a generalizable lung-cancer prediction model by using machine learning to define high-risk groups and realize the early identification and prevention of lung cancer. We included 467,888 participants from UK Biobank, using lung cancer incidence as an outcome variable, including 49 previously known high-risk factors and less studied or unstudied predictors. We developed multivariate prediction models using multiple machine learning models, namely logistic regression, naïve Bayes, random forest, and extreme gradient boosting models. The performance of the models was evaluated by calculating the areas under their receiver operating characteristic curves, Brier loss, log loss, precision, recall, and F1 scores. The Shapley additive explanations interpreter was used to visualize the models. Three were ultimately 4299 cases of lung cancer that were diagnosed in our sample. The model containing all the predictors had good predictive power, and the extreme gradient boosting model had the best performance with an area under curve of 0.998. New important predictive factors for lung cancer were also identified, namely hip circumference, waist circumference, number of cigarettes previously smoked daily, neuroticism score, age, and forced expiratory volume in 1 second. The predictive model established by incorporating novel predictive factors can be of value in the early identification of lung cancer. It may be helpful in stratifying individuals and selecting those at higher risk for inclusion in screening programs.

Clinical values of different specimen preparation methods for the diagnosis of lung cancer by EBUS-TBNA.

BACKGROUND AND OBJECTIVE: EBUS-TBNA has emerged as an important minimally invasive procedure for the diagnosis and staging of lung cancer. Our objective was to evaluate the effect of different specimen preparation from aspirates on the diagnosis of lung cancer. METHODS: 181 consecutive patients with known or suspected lung cancer accompanied by hilar / mediastinal lymphadenopathy underwent EBUS-TBNA from January 2019 to December 2022. Specimens obtained by EBUS-TBNA were processed by three methods: Traditional smear cytology of aspirates (TSC), liquid-based cytology of aspirates (LBC) and histopathology of core biopsies. RESULTS: EBUS-TBNA was performed in 181 patients on 213 lymph nodes, the total positive rate of the combination of three specimen preparation methods was 80.7%. The diagnostic positive rate of histopathology was 72.3%, TSC was 68.1%, and LBC was 65.3%, no significant differences was observed (p = 0.29); however, statistically significant difference was noted between the combination of three preparation methods and any single specimen preparation methods (p = 0.002). The diagnostic sensitivity of histopathology combined with TSC and histopathology combined with LBC were 96.5 and 94.8%, the specificity was 95.0% and 97.5%, the PPV was 98.8% and 99.4%, the NPV was 86.4% and 81.2%, the diagnostic accuracy was 96.2% and 95.3%, respectively; The sensitivity and accuracy of above methods were higher than that of single specimen preparation, but lower than that of combination of three preparation methods. CONCLUSION: When EBUS-TBNA is used for the diagnosis and staging of lung cancer, histopathology combined with TSC can achieve enough diagnostic efficiency and better cost-effectiveness.