Differential gene expression profiles according to the Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society histopathological classification in lung adenocarcinoma subtypes

Spatial whole exome sequencing reveals the genetic features of highly-aggressive components in lung adenocarcinoma

In invasive lung adenocarcinoma (LUAD), patients with micropapillary (MIP) or solid (SOL) components had a significantly poorer prognosis than those with only lepidic (LEP), acinar (ACI) or papillary (PAP) components. It is interesting to explore the genetic features of different histologic subtypes, especially the highly aggressive components. Based on a cohort of 5,933 patients, this study observed that in different tumor size groups, LUAD with MIP/SOL components showed a different prevalence, and patients with ALK alteration or TP53 mutations had a higher probability of developing MIP/SOL components. To control individual differences, this research used spatial whole-exome sequencing (WES) via laser-capture microdissection of five patients harboring these five coexistent components and identified genetic features among different histologic components of the same tumor. In tracing the evolution of components, we found that titin (TTN) mutation might serve as a crucial intratumor potential driver for MIP/SOL components, which was validated by a cohort of 146 LUAD patients undergoing bulk WES. Functional analysis revealed that TTN mutations enriched the complement and coagulation cascades, which correlated with the pathway of cell adhesion, migration, and proliferation. Collectively, the histologic subtypes of invasive LUAD were genetically different, and certain trunk genotypes might synergize with branching TTN mutation to develop highly aggressive components.

Selective Targeting and Eradication of Various Human Non-Small Cell Lung Cancer Cell Lines Using Self-Assembled Aptamer-Decorated Nanoparticles

The leading cause of cancer mortality remains lung cancer (LC), of which non-small cell lung cancer (NSCLC) is the predominant type. Chemotherapy achieves only low response rates while inflicting serious untoward toxicity. Herein, we studied the binding and internalization of S15-aptamer (S15-APT)-decorated polyethylene glycol-polycaprolactone (PEG-PCL) nanoparticles (NPs) by various human NSCLC cell lines. All the NSCLC cell lines were targeted by S15-APT-decorated NPs. Confocal microscopy revealed variable levels of NP binding and uptake amongst these NSCLC cell lines, decreasing in the following order: Adenocarcinoma (AC) A549 cells > H2228 (AC) > H1299 (large cell carcinoma) > H522 (AC) > H1975 (AC). Flow cytometry analysis showed a consistent variation between these NSCLC cell lines in the internalization of S15-APT-decorated quantum dots. We obtained a temperature-dependent NP uptake, characteristic of active internalization. Furthermore, cytotoxicity assays with APT-NPs entrapping paclitaxel, revealed that A549 cells had the lowest IC50 value of 0.03 µM PTX (determined previously), whereas H2228, H1299, H522 and H1975 exhibited higher IC50 values of 0.38 µM, 0.92 µM, 2.31 µM and 2.59 µM, respectively (determined herein). Cytotoxicity was correlated with the binding and internalization of APT-NPs in the various NSCLC cells, suggesting variable expression of the putative S15 target receptor. These findings support the development of APT-targeted NPs in precision nanomedicine for individual NSCLC patient treatment.

Risk of development of brain metastases according to the IASLC/ATS/ERS lung adenocarcinoma classification in locally advanced and metastatic disease

INTRODUCTION

Brain metastases (BM) are frequent among lung cancer patients, affecting prognosis and quality of life. The International Association for the Study of Lung Cancer (IASLC), American Thoracic Society (ATS) and European Respiratory Society (ERS) lung adenocarcinoma (LADC) classification (IASLC/ATS/ERS) has prognostic impact in early-stage disease; however, its role in the advanced setting is not precise. This study aims to determine the correlation between the predominant histological subtype and the risk of developing brain metastases (BM) in locally advanced and metastatic (stages IIIB-IV) LADC.

METHODS

A total of 710 patients with LADC were treated at our institution from January 2010 to December 2017. After excluding patients with brain metastases at diagnoses (n = 151), they were categorized according to the IASLC/ATS/ERS LADC classification to estimate the risk of developing brain metastases. A competing risk analysis was employed, considering death a competing risk event.

RESULTS

From 559 patients, the mean age was 59 ± 13.2 years, women (52.4 %), and clinical-stage IV (79.2 %). LADC subtypes distribution was lepidic (11.6 %), acinar (37.9 %), papillary (10.2 %), micropapillary (6.8 %), and solid (33.5 %). A total of 27.0 % of patients developed BM, 32.9 % died without brain affection, and 40.0 % did not progress. The predominantly solid subtype showed the greatest probability of all subtypes for developing BM [HR 4.0; 95 % CI (1.80-8.91), p = 0.0006], followed by micropapillary [HR1.11; 95 % CI (0.36-3.39), p = 0.85). The solid subtype, moderately differentiated tumors, age, and ECOG PS (>2) were associated with increased hazards in the multivariate analysis.

CONCLUSION

According to the IASLC/ATS/ERS classification, the predominantly solid pattern was significantly associated with an increased risk of developing BM in patients with locally advanced and metastatic LADC. Its prognostic value might help explore novel clinical approaches, modify monitoring for earlier detection, prevent complications, and reduce morbidity.

Single-cell RNA sequencing reveals distinct tumor microenvironmental patterns in lung adenocarcinoma

Recent developments in immuno-oncology demonstrate that not only cancer cells, but also the tumor microenvironment can guide precision medicine. A comprehensive and in-depth characterization of the tumor microenvironment is challenging since its cell populations are diverse and can be important even if scarce. To identify clinically relevant microenvironmental and cancer features, we applied single-cell RNA sequencing to ten human lung adenocarcinomas and ten normal control tissues. Our analyses revealed heterogeneous carcinoma cell transcriptomes reflecting histological grade and oncogenic pathway activities, and two distinct microenvironmental patterns. The immune-activated CP²E microenvironment was composed of cancer-associated myofibroblasts, proinflammatory monocyte-derived macrophages, plasmacytoid dendritic cells and exhausted CD8+ T cells, and was prognostically unfavorable. In contrast, the inert N³MC microenvironment was characterized by normal-like myofibroblasts, non-inflammatory monocyte-derived macrophages, NK cells, myeloid dendritic cells and conventional T cells, and was associated with a favorable prognosis. Microenvironmental marker genes and signatures identified in single-cell profiles had progonostic value in bulk tumor profiles. In summary, single-cell RNA profiling of lung adenocarcinoma provides additional prognostic information based on the microenvironment, and may help to predict therapy response and to reveal possible target cell populations for future therapeutic approaches.

Gene expression profiling by targeted RNA sequencing in pathological stage I lung adenocarcinoma with a solid component

OBJECTIVES

Solid predominant adenocarcinoma is considered an independent predictor of an unfavorable prognosis in patients with stage I lung adenocarcinoma (LUAD). Furthermore, solid minor components are related to poor prognosis in patients with stage I LUAD. Therefore, it is imperative to elucidate the molecular determinants of the malignant potential of solid components (SC). Several studies reported the gene expression profiling specific for lepidic predominant adenocarcinoma or solid predominant adenocarcinoma, however; there is no report identifying the differentially expressed genes (DEGs) between SC and acinar component (AC) within the same tumor tissue in pathological (p)-stage I LUAD patients.

MATERIALS AND METHODS

LUAD tissue samples containing both SC and AC were obtained from 8 patients with p-stage I LUAD and each component was microdissected. Targeted RNA sequencing was performed by a high-throughput chip-based approach.

RESULTS

In total, 1272 DEGs were identified, including 677 upregulated genes and 595 downregulated genes in SC compared with AC. The most highly upregulated gene was TATA binding protein associated factor 7 (TAF7) and the most highly downregulated gene was homeobox B3 (HOXB3), which acts as a metastasis suppressor. A protein-protein interaction (PPI) network analysis of upregulated genes in SC identified ribosomal protein S27a (RPS27a) as a hub gene with the highest degree. First neighbors of RPS27a included PSMA6, which is a highly promising target for lung cancer. The subnetwork of PD-L1 had 10 first neighbors, including CMTM6, which enhances the ability of PD-L1-expressing tumor cells to inhibit T cells. The staining score for PD-L1 in SC was significantly higher than that in AC by immunohistochemistry (p = 0.001).

CONCLUSION

Our results revealed several new DEGs and key PPI network in SC compared to AC, contributing to understanding the biological features of SC and providing therapeutic targets for early-stage LUAD with SC in the future.

Automated sample preparation with SP3 for low-input clinical proteomics

High-throughput and streamlined workflows are essential in clinical proteomics for standardized processing of samples from a variety of sources, including fresh-frozen tissue, FFPE tissue, or blood. To reach this goal, we have implemented single-pot solid-phase-enhanced sample preparation (SP3) on a liquid handling robot for automated processing (autoSP3) of tissue lysates in a 96-well format. AutoSP3 performs unbiased protein purification and digestion, and delivers peptides that can be directly analyzed by LCMS, thereby significantly reducing hands-on time, reducing variability in protein quantification, and improving longitudinal reproducibility. We demonstrate the distinguishing ability of autoSP3 to process low-input samples, reproducibly quantifying 500-1,000 proteins from 100 to 1,000 cells. Furthermore, we applied this approach to a cohort of clinical FFPE pulmonary adenocarcinoma (ADC) samples and recapitulated their separation into known histological growth patterns. Finally, we integrated autoSP3 with AFA ultrasonication for the automated end-to-end sample preparation and LCMS analysis of 96 intact tissue samples. Collectively, this constitutes a generic, scalable, and cost-effective workflow with minimal manual intervention, enabling reproducible tissue proteomics in a broad range of clinical and non-clinical applications.

MLW-gcForest: a multi-weighted gcForest model towards the staging of lung adenocarcinoma based on multi-modal genetic data

Background

Lung cancer is one of the most common types of cancer, among which lung adenocarcinoma accounts for the largest proportion. Currently, accurate staging is a prerequisite for effective diagnosis and treatment of lung adenocarcinoma. Previous research has used mainly single-modal data, such as gene expression data, for classification and prediction. Integrating multi-modal genetic data (gene expression RNA-seq, methylation data and copy number variation) from the same patient provides the possibility of using multi-modal genetic data for cancer prediction. A new machine learning method called gcForest has recently been proposed. This method has been proven to be suitable for classification in some fields. However, the model may face challenges when applied to small samples and high-dimensional genetic data.

Results

In this paper, we propose a multi-weighted gcForest algorithm (MLW-gcForest) to construct a lung adenocarcinoma staging model using multi-modal genetic data. The new algorithm is based on the standard gcForest algorithm. First, different weights are assigned to different random forests according to the classification performance of these forests in the standard gcForest model. Second, because the feature vectors generated under different scanning granularities have a diverse influence on the final classification result, the feature vectors are given weights according to the proposed sorting optimization algorithm. Then, we train three MLW-gcForest models based on three single-modal datasets (gene expression RNA-seq, methylation data, and copy number variation) and then perform decision fusion to stage lung adenocarcinoma. Experimental results suggest that the MLW-gcForest model is superior to the standard gcForest model in constructing a staging model of lung adenocarcinoma and is better than the traditional classification methods. The accuracy, precision, recall, and AUC reached 0.908, 0.896, 0.882, and 0.96, respectively.

Conclusions

The MLW-gcForest model has great potential in lung adenocarcinoma staging, which is helpful for the diagnosis and personalized treatment of lung adenocarcinoma. The results suggest that the MLW-gcForest algorithm is effective on multi-modal genetic data, which consist of small samples and are high dimensional.

An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data

The application of gene expression data to the diagnosis and classification of cancer has become a hot issue in the field of cancer classification. Gene expression data usually contains a large number of tumor-free data and has the characteristics of high dimensions. In order to select determinant genes related to breast cancer from the initial gene expression data, we propose a new feature selection method, namely, support vector machine based on recursive feature elimination and parameter optimization (SVM-RFE-PO). The grid search (GS) algorithm, the particle swarm optimization (PSO) algorithm, and the genetic algorithm (GA) are applied to search the optimal parameters in the feature selection process. Herein, the new feature selection method contains three kinds of algorithms: support vector machine based on recursive feature elimination and grid search (SVM-RFE-GS), support vector machine based on recursive feature elimination and particle swarm optimization (SVM-RFE-PSO), and support vector machine based on recursive feature elimination and genetic algorithm (SVM-RFE-GA). Then the selected optimal feature subsets are used to train the SVM classifier for cancer classification. We also use random forest feature selection (RFFS), random forest feature selection and grid search (RFFS-GS), and minimal redundancy maximal relevance (mRMR) algorithm as feature selection methods to compare the effects of the SVM-RFE-PO algorithm. The results showed that the feature subset obtained by feature selection using SVM-RFE-PSO algorithm results has a better prediction performance of Area Under Curve (AUC) in the testing data set. This algorithm not only is time-saving, but also is capable of extracting more representative and useful genes.

Dissecting the heterogeneity of stage III non-small-cell lung cancer through incorporation of grade and histology

AIM

This study evaluated a grade-integrated American Joint Committee on Cancer (AJCC) staging system for non-small-cell lung cancer.

PATIENTS & METHODS

Surveillance, Epidemiology and End Results database was queried through SEER*Stat program. Through recursive partitioning analysis and subsequent decision-tree formation, suggested grade-modified stages were formulated.

RESULTS

All pairwise hazard ratio comparisons among AJCC eighth stages were significant (p < 0.05) except stage IIIB versus stage IIIC; while all pairwise hazard ratio comparisons among modified AJCC stages were significant (p < 0.05). When stratified by histology, there was a benefit for the modified system among adenocarcinoma rather than squamous cell carcinoma patients.

CONCLUSION

Grade integration improved the prognostication of the AJCC staging system particularly for stage III adenocarcinoma. This should be considered in future revisions of the AJCC staging system.