Prognostic significance of grading in lung adenocarcinoma

Proposed novel grading system for stage I invasive lung adenocarcinoma and a comparison with the 2020 IASLC grading system

BACKGROUND

Several studies have proposed grading systems for risk stratification of early-stage lung adenocarcinoma based on histological patterns. However, the reproducibility of these systems is poor in clinical practice, indicating the need to develop a new grading system which is easy to apply and has high accuracy in prognostic stratification of patients.

METHODS

Patients with stage I invasive nonmucinous lung adenocarcinoma were retrospectively collected from pathology archives between 2009 and 2016. The patients were divided into a training and validation set at a 6:4 ratio. Histological features associated with patient outcomes (overall survival [OS] and progression-free survival [PFS]) identified in the training set were used to construct a new grading system. The newly proposed system was validated using the validation set. Survival differences between subgroups were assessed using the log-rank test. The prognostic performance of the novel grading system was compared with two previously proposed systems using the concordance index.

RESULTS

A total of 539 patients were included in this study. Using a multioutcome decision tree model, four pathological factors, including the presence of tumor spread through air space (STAS) and the percentage of lepidic, micropapillary and solid subtype components, were selected for the proposed grading system. Patients were accordingly classified into three groups: low, medium, and high risk. The high-risk group showed a 5-year OS of 52.4% compared to 89.9% and 97.5% in the medium and low-risk groups, respectively. The 5-year PFS of patients in the high-risk group was 38.1% compared to 61.7% and 90.9% in the medium and low-risk groups, respectively. Similar results were observed in the subgroup analysis. Additionally, our proposed grading system provided superior prognostic stratification compared to the other two systems with a higher concordance index.

CONCLUSION

The newly proposed grading system based on four pathological factors (presence of STAS, and percentage of lepidic, micropapillary, and solid subtypes) exhibits high accuracy and good reproducibility in the prognostic stratification of stage I lung adenocarcinoma patients.

The differential diagnosis of lung precursor glandular lesions, micro-invasive adenocarcinoma, and invasive adenocarcinoma using low dose spectral computed tomography perfusion imaging

Background

Few studies about the association between computed tomography (CT) perfusion imaging parameters and invasiveness in lung adenocarcinoma (LUAD) have been conducted using low dose spectral CT perfusion imaging. The purpose of this study was to investigate application of spectral revolution CT low-dose perfusion imaging in the differential diagnosis of different pathological subtypes of LUAD.

Methods

This was a cross-sectional study based on historical data from January 2018 to May 2019 in Peking University Cancer Hospital & Institute. A total of 62 cases were enrolled, including 2 cases of atypical adenomatous hyperplasia (AAH), 3 cases of adenocarcinoma in situ (AIS), 4 cases of minimally invasive adenocarcinoma (MIA), and 53 cases of invasive adenocarcinoma (IAC), all confirmed with pathology. The inclusion and exclusion criteria were regulated. Using Revolution low-dose CT perfusion imaging (GE, USA), the CT perfusion parameters of hemodynamics were obtained: blood flow (BF), blood volume (BV), impulse residue function time of arrival (IRF TO), maximum slope of increase (MSI), mean transit time (MTT), permeability surface area product (PS), positive enhancement integral (PEI), and maximum enhancement time (Tmax). Univariate analysis of variance (ANOVA) or Kruskal-Wallis test was used to compare the differences of CT perfusion quantitative parameters among AAH, AIS, MIA, and IAC. Mann-Whitney test was used to compare the difference of CT perfusion imaging parameters between preinvasive lesions (AAH and AIS) and invasive lung cancer (MIA and IAC).

Results

Statistically significant differences in IRF TO were observed in LUAD with different invasiveness, namely, among AIS, MIA, and IAC groups (0.56±0.74 vs. 0.54±1.08 vs. 4.39±2.19, P=0.004). Statistically significant differences in IRF TO were also observed between pre-invasive lesions group (AAH and AIS) and invasive lung cancer group (MIA and IAC) (1.12±1.27 vs. 3.75±2.79, P=0.031), and between AAH + AIS + MIA groups and IAC group (0.83±1.13 vs. 4.12±2.69, P<0.001). There were no statistically significant differences in other CT perfusion parameters of hemodynamics among different pathological subtypes of LUAD (P>0.05).

Conclusions

The low-dose perfusion parameter IRF TO of revolution CT has the potential to be employed in the differential diagnosis of different pathological subtypes of LUAD.

Updates on lung adenocarcinoma: invasive size, grading and STAS

Advancements in the classification of lung adenocarcinoma have resulted in significant changes in pathological reporting. The eighth edition of the tumour-node-metastasis (TNM) staging guidelines calls for the use of invasive size in staging in place of total tumour size. This shift improves prognostic stratification and requires a more nuanced approach to tumour measurements in challenging situations. Similarly, the adoption of new grading criteria based on the predominant and highest-grade pattern proposed by the International Association for the Study of Lung Cancer (IASLC) shows improved prognostication, and therefore clinical utility, relative to previous grading systems. Spread through airspaces (STAS) is a form of tumour invasion involving tumour cells spreading through the airspaces, which has been highly researched in recent years. This review discusses updates in pathological T staging, adenocarcinoma grading and STAS and illustrates the utility and limitations of current concepts in lung adenocarcinoma.

Development and validation of a nomogram for predicting overall survival in patients with stage III-N2 lung adenocarcinoma based on the SEER database

Background

There is variability in the prognosis of stage III-N2 lung adenocarcinoma (LUAD) patients. The current tumor-node-metastasis (TNM) staging is not sufficient to precisely estimate the prognosis of stage III-N2 LUAD patients. The Surveillance, Epidemiology, and End Results (SEER) database collected first-hand information from a large number of LUAD patients. Based on the SEER database, this study aimed to determine the prognostic factors that affect overall survival (OS) in stage III-N2 LUAD patients and then establish a nomogram for predicting OS in this type of cancer to identify the high-risk population that may require more frequent surveillance or intensive care.

Methods

Data for 1,844 stage III-N2 primary LUAD patients who were registered between 2010 and 2015 were obtained from the SEER database. These patients were randomly assigned to either training (n=1,290) or validation (n=554) cohorts at a 7:3 ratio. The univariate and multivariate Cox regression (UCR and MCR) analyses were performed to find the relevant independent prognostic factors. To predict the OS based on these prognostic factors, a nomogram was then developed. The performance of the nomogram was examined based on the calibration curves, and receiver operating characteristic (ROC) curves. The ability of nomogram to stratify patient risk was validated by Kaplan-Meier survival analysis.

Results

Age, gender, tumor location, T-stage and treatment modality (chemotherapy, radiation therapy, surgery and scope of lymph node dissection) of stage III-N2 LUAD patients were significantly associated with prognosis. The area under the curve (AUC) values of OS predicted by the nomogram constructed with these factors at 12-, 36- and 60-month were 0.784, 0.762 and 0.763 in the training cohort, whereas 0.707, 0.685 and 0.705 in the validation cohort, respectively. Additionally, calibration curves demonstrated concordance between predicted and observed outcomes. Nomogram risk stratification provides a meaningful distinction between patients with various survival risks.

Conclusions

A survival prediction model that may be useful for risk stratification and decision-making is developed and validated for stage III-N2 LUAD patients. A high-risk patient predicted by the prediction model may require more frequent surveillance or intensive care.

Inter- and intratumoral proteomics and glycosaminoglycan characterization of ALK rearranged lung adenocarcinoma tissues: a pilot study

Lung cancer is one of the most common types of cancer with limited therapeutic options, therefore a detailed understanding of the underlying molecular changes is of utmost importance. In this pilot study, we investigated the proteomic and glycosaminoglycan (GAG) profile of ALK rearranged lung tumor tissue regions based on the morphological classification, mucin and stromal content. Principal component analysis and hierarchical clustering revealed that both the proteomic and GAG-omic profiles are highly dependent on mucin content and to a lesser extent on morphology. We found that differentially expressed proteins between morphologically different tumor types are primarily involved in the regulation of protein synthesis, whereas those between adjacent normal and different tumor regions take part in several other biological processes (e.g. extracellular matrix organization, oxidation-reduction processes, protein folding) as well. The total amount and the sulfation profile of heparan sulfate and chondroitin sulfate showed small differences based on morphology and larger differences based on mucin content of the tumor, while an increase was observed in both the total amount and the average rate of sulfation in tumors compared to adjacent normal regions.

Tumor grade-associated genomic mutations in Chinese patients with non-small cell lung cancer

Background

Lung cancer is the most prevalent cancer worldwide and accounts for approximately 20% of cancer-related death in China every year. High-grade lung cancer poses a significant threat to patients, and developing a novel treatment for these patients requires an understanding of its underlying mechanism.

Methods

Chinese patients with lung cancer were enrolled. The tumor samples were collected by surgery or puncture and applied for next-generation sequencing. A panel of pan-cancer genes was targeted, and the sequencing depth was set to over 1,000 to improve the sensitivity of detecting mutations. Short-length mutations (substitution, insertion, and deletion), copy number variation, and gene fusion were called. Gene mutations were compared between low-grade, middle-grade, and high-grade tumors using Fisher’s exact test. The enriched pathways in each grade of tumors were also inferred.

Results

The study included 173 Chinese patients with non-small cell lung cancer, of whom 98 (56.6%) patients were female and 75 (43.4%) were male, with a mean age of 56.8 years. All patients were microsatellite stable; 66.4% were at the early stages (Stages 0, I, and II) with a tumor mutational burden of approximately 2.5 (confidence interval = [0, 48.3]). Compared to low-grade tumors, high-grade tumors had a significantly higher percentage of mutations in TP53 (75.9% vs 34.4%, p = 1.86e-3) and PIK3CA (24.1% vs. 0%, p = 3.58e-3). Pathway analysis found that high-grade tumors were enriched with mutations in bacterial invasion of epithelial cells (31% vs. 0%, p = 5.8e-4), Epstein–Barr virus infection (79.3% vs. 37.5%, p = 1.72e-3), and the Wnt signaling pathway (75.9% vs. 34.4%, p = 1.91e-3). High-grade tumors had a significantly higher tumor mutational burden than low-grade tumors (p-value = 0.0017). However, actionable mutations with high-level evidence were lower in high-grade tumors.

Conclusion

Patients with high-grade tumors from lung cancer may be more affected by bacteria and Epstein–Barr virus than low-grade tumors. High-grade tumors were specially mutated in TP53 and PIK3CA and may benefit more from immunotherapy. Further research on the underlying mechanism of high-grade lung cancer is necessary to develop new therapeutic options. Lung cancer, tumor grade, genomic mutations, Epstein–Barr virus, pathway analysis

The prognostic value of histopathology in invasive lung adenocarcinoma: a comparative review of the main proposed grading systems

INTRODUCTION

An accurate histological evaluation of invasive lung adenocarcinoma is essential for a correct clinical and pathological definition of the tumour. Different grading systems have been proposed to predict the prognosis of invasive lung adenocarcinoma.

AREAS COVERED

Invasive non mucinous lung adenocarcinoma is often morphologically heterogeneous, consisting of complex combinations of architectural patterns with different proportions. Several grading systems for non-mucinous lung adenocarcinoma have been proposed, being the main based on architectural differentiation and the predominant growth pattern. Herein we perform a thorough review of the literature using PubMed, Scopus and Web of Science and we highlight the peculiarities and the differences between the main grading systems and compare the data about their prognostic value. In addition, we carried out an evaluation of the proposed grading systems for less common histological variants of lung adenocarcinoma, such as fetal adenocarcinoma and invasive mucinous adenocarcinoma.

EXPERT OPINION

The current IASLC grading system, based on the combined score of predominant growth pattern plus high-grade histological pattern, shows the stronger prognostic significance than the previous grading systems in invasive non mucinous lung adenocarcinoma.

Morphologic Severity of Atypia Is Predictive of Lung Cancer Diagnosis

In cytologic analysis of lung nodules, specimens classified as atypia cannot be definitively diagnosed as benign or malignant. Atypia patients are typically subject to additional procedures to obtain repeat samples, thus delaying diagnosis. We evaluate morphologic categories predictive of lung cancer in atypia patients. This retrospective study stratified patients evaluated for primary lung nodules based on cytologic diagnoses. Atypia patients were further stratified based on the most severe verbiage used to describe the atypical cytology. Logistic regressions and receiver operator characteristic curves were performed. Of 129 patients with cytologic atypia, 62.8% later had cytologically or histologically confirmed lung cancer and 37.2% had benign respiratory processes. Atypia severity significantly predicted final diagnosis even while controlling for pack years and modified Herder score (p = 0.012). Pack years, atypia severity, and modified Herder score predicted final diagnosis independently and while adjusting for covariates (all p < 0.001). This model generated a significantly improved area under the curve compared to pack years, atypia severity, and modified Herder score (all p < 0.001) alone. Patients with severe atypia may benefit from repeat sampling for cytologic confirmation within one month due to high likelihood of malignancy, while those with milder atypia may be followed clinically.

Evaluation of Microscopic Tumour Extension in Localized Stage Non-Small-Cell Lung Cancer for Stereotactic Radiotherapy Planning

Background: Stereotactic radiotherapy for localised stage non-small-cell lung carcinoma (NSCLC) is an alternative indication for patients who are inoperable or refuse surgery. A study showed that the microscopic tumour extension (ME) of NSCLC varied according to the histological type, which allowed us to deduce adapted margins for the clinical target volume (CTV). However, to date, no study has been able to define the most relevant margins for patients with stage 1 tumours. Methods: We performed a retrospective analysis including patients with adenocarcinoma (ADC) or squamous cell carcinoma (SCC) of localised stage T1N0 or T2aN0 who underwent surgery. The ME was measured from this boundary. The profile of the type of tumour spread was also evaluated. Results: The margin required to cover the ME of a localised NSCLC with a 95% probability is 4.4 mm and 2.9 mm for SCC and ADC, respectively. A significant difference in the maximum distance of the ME between the tumour-infiltrating lymphocytes (TILs), 0−10% and 50−90% (p < 0.05), was noted for SCC. There was a significant difference in the maximum ME distance based on whether the patient had chronic obstructive pulmonary disease (COPD) (p = 0.011) for ADC. Multivariate analysis showed a statistically significant relationship between the maximum microextension distance and size with the shrinkage coefficient. Conclusion: This study definitively demonstrated that the ME depends on the pathology subtype of NSCLC. According to International Commission on Radiation Units and Measurements (ICRU) reports, 50, 62 and 83 CTV margins, proposed by these results, should be added to the GTV (Gross tumour volume). When stereotactic body radiation therapy is used, this approach should be considered in conjunction with the dataset and other margins to be applied.

Multiomics Analysis of Spatially Distinct Stromal Cells Reveals Tumor-Induced O-Glycosylation of the CDK4-pRB Axis in Fibroblasts at the Invasive Tumor Edge

The invasive leading edge represents a potential gateway for tumor metastasis. The role of fibroblasts from the tumor edge in promoting cancer invasion and metastasis has not been comprehensively elucidated. We hypothesize that cross-talk between tumor and stromal cells within the tumor microenvironment results in activation of key biological pathways depending on their position in the tumor (edge vs. core). Here we highlight phenotypic differences between tumor-adjacent-fibroblasts (TAF) from the invasive edge and tumor core fibroblasts from the tumor core, established from human lung adenocarcinomas. A multiomics approach that includes genomics, proteomics, and O-glycoproteomics was used to characterize cross-talk between TAFs and cancer cells. These analyses showed that O-glycosylation, an essential posttranslational modification resulting from sugar metabolism, alters key biological pathways including the cyclin-dependent kinase 4 (CDK4) and phosphorylated retinoblastoma protein axis in the stroma and indirectly modulates proinvasive features of cancer cells. In summary, the O-glycoproteome represents a new consideration for important biological processes involved in tumor-stroma cross-talk and a potential avenue to improve the anticancer efficacy of CDK4 inhibitors.

SIGNIFICANCE

A multiomics analysis of spatially distinct fibroblasts establishes the importance of the stromal O-glycoproteome in tumor-stroma interactions at the leading edge and provides potential strategies to improve cancer treatment. See related commentary by De Wever, p. 537.

DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

Simple Summary

Pathology images are vital for understanding solid cancers. In this study, we created DeepRePath using multi-scale pathology images with two-channel deep learning to predict the prognosis of patients with early-stage lung adenocarcinoma (LUAD). DeepRePath demonstrated that it could predict the recurrence of early-stage LUAD with average area under the curve scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Pathological features found to be associated with a high probability of recurrence included tumor necrosis, discohesive tumor cells, and atypical nuclei. In conclusion, DeepRePath can improve the treatment modality for patients with early-stage LUAD through recurrence prediction.

Abstract

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

Impact of Chronic Obstruction Pulmonary Disease on Survival in Patients with Advanced Stage Lung Squamous Cell Carcinoma Undergoing Concurrent Chemoradiotherapy

Simple Summary

No data are available regarding the effect of chronic obstruction pulmonary disease (COPD) and COPD with acute exacerbation (COPDAE) on survival in patients with lung squamous cell carcinoma (SCC) receiving definitive concurrent chemoradiotherapy (CCRT). This study is the first to examine the survival impact of COPD in patients with lung SCC receiving definitive CCRT. COPD and its severity are significant independent risk factors for all-cause mortality in patients with stage IIIA–IIIB lung SCC receiving definitive CCRT. Hospitalization for COPDAE within 1 year before CCRT is the significant independent risk factor for lung cancer death in the patients with stage IIIA–IIIB lung SCC receiving definitive CCRT.

Abstract

Background: To date, no data are available regarding the effect of chronic obstruction pulmonary disease (COPD) and COPD with acute exacerbation (COPDAE) on survival in patients with lung squamous cell carcinoma (SCC) receiving definitive concurrent chemoradiotherapy (CCRT). Patients and methods: We enrolled 3986 patients with clinical stage IIIA–IIIB, unresectable lung SCC, who had received standard definitive CCRT, and categorized them into two groups based on their COPD status to compare overall survival outcomes. We also examined the effects of COPD severity (0, 1, or ≥2 hospitalizations for COPDA within 1 year before CCRT). Results: In the inverse probability of treatment weighting (IPTW)-adjusted model, the adjusted hazard ratio (aHR) (95% confidence interval (CI)) of all-cause death for COPD was 1.04 (1.01, 1.16), compared no COPD in patients with stage IIIA–IIIB lung SCC receiving definitive CCRT. In the IPTW-adjusted model, the aHRs (95% CIs) of 1 and ≥ 2 hospitalizations for COPDAE within 1 year before CCRT were 1.32 (1.19, 1.46) and 1.81 (1.49, 2.19) respectively, compared with no hospitalization for COPDAE. Conclusion: COPD and its severity are significant independent risk factors for all-cause death in patients with stage IIIA–IIIB lung SCC receiving definitive CCRT. Hospitalization for COPDAE within 1 year before CCRT is the significant independent risk factor for lung cancer death in the patients with stage IIIA–IIIB lung SCC receiving definitive CCRT.

Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis

Background

Lung adenocarcinoma (LAC) is the predominant histologic subtype of lung cancer and has a complicated pathogenesis with high mortality. The purpose of this study was to identify differentially expressed genes (DEGs) with prognostic value and determine their underlying mechanisms.

Methods

Gene expression data of GSE27262 and GSE118370 were acquired from the Gene Expression Omnibus database, enrolling 31 LAC and 31 normal tissues. Common DEGs between LAC and normal tissues were identified using the GEO2R tool and Venn diagram software. Next, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to analyze the Gene Ontology and Kyoto Encyclopedia of Gene and Genome (KEGG) pathways. Then, protein-protein interaction (PPI) network of DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes and central genes were identified via Molecular Complex Detection. Furthermore, the expression and prognostic information of central genes were validated via Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier analysis, respectively. Finally, DAVID, real-time PCR and immunohistochemistry were applied to re-analyze the identified genes, which were also further validated in two additional datasets from ArrayExpress database.

Results

First, 189 common DEGs were identified among the two datasets, including 162 downregulated and 27 upregulated genes. Next, Gene Ontology and KEGG pathway analysis of the DEGs were conducted through DAVID. Then, PPI network of DEGs was constructed and 17 downregulated central genes were identified. Furthermore, the 17 downregulated central genes were validated via GEPIA and datasets from ArrayExpress, and 12 of them showed a significantly better prognosis. Finally, six genes were identified significantly enriched in neuroactive ligand-receptor interactions (EDNRB, RXFP1, P2RY1, CALCRL) and Rap1 signaling pathway (TEK, P2RY1, ANGPT1) via DAVID, which were further validated to be weakly expressed in LAC tissues via RNA quantification and immunohistochemistry analysis.

Conclusions

The low expression pattern and relation to prognosis indicated that the six genes were potential tumor suppressor genes in LAC. In conclusion, we identified six significantly downregulated DEGs as prognostic markers and potential tumor suppressor genes in LAC based on integrated bioinformatics methods, which could act as potential molecular markers and therapeutic targets for LAC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08308-3.

A 58-Year-Old Man with a Painful Gluteal Mass as the First Presentation of Metastatic Adenocarcinoma of the Lung

Patient: Male, 58-year-old

Final Diagnosis: Metastatic lung adenocarcinoma

Symptoms: Gluteal mass

Medication:—

Clinical Procedure: —

Specialty: Oncology • Pulmonology

Long-term outcomes of video-assisted lobectomy in non-small cell lung cancer

BACKGROUND

Video-assisted thoracoscopic surgery lobectomy combined with lymphadenectomy is widely utilized worldwide for treating non-small cell lung cancer. We evaluated the long-term survival outcomes of this approach and determined the prognostic factors of overall survival.

METHODS

This prospective observational study was performed in patients with non-small cell lung cancer who were subjected to video-assisted lobectomy and lymphadenectomy from 2012 to 2016. Independent prognostic factors were determined via uni- and multivariable Cox models.

RESULTS

There were 109 patients with the mean age of 59.2 years and males accounted for 54.1%. Postoperative staging determined 22.9% of stage IA, 31.2% of stage IB, 16.5% of stage IIA and 29.4% of stage IIIA. Median follow-up time was 27 months. The overall survival rate after 1, 2, 3, 4 and 5 years was 100%, 85.9%, 65.3%, 55.9% and 55.9%, respectively. In univariable analysis, smoking (hazard ratio (HR) [95% confidence interval (CI)]: 2.50 [1.18-5.31]), Tumor--nodes--metastases (TNM) stage (IIA: 7.60 [1.57-36.9]; IIIA: 14.3 [3.28-62.7] compared to IA), histological differentiation (moderately differentiated: 4.91 [1.04-23.2]; poorly differentiated: 8.25 [1.91-35.6] compared to well differentiated), lymph node size ≥1 cm (8.22 [3.11-21.7]), tumour size ≥3 cm (4.24 [1.01-17.9]), radical lymphadenectomy (6.67 [3.14-14.2]) were identified as prognostic factors of the long-term survival. In multivariable analysis, only radical lymphadenectomy was an independent prognostic factor (HR [95% CI]: 3.94 [1.41-11.0]).

CONCLUSION

Video-assisted thoracoscopic lobectomy combined with lymphadenectomy is feasible, safe and effective for the treatment of non-small cell lung cancer. The long-term outcomes of this method are favourable, especially at the early stage of cancer.

Risk Factors for The Growth of Residual Nodule in Surgical Patients with Adenocarcinoma Presenting as Multifocal Ground-glass Nodules

PURPOSE

We aim to investigate the risk factors influencing the growth of residual nodule (RN) in surgical patients with adenocarcinoma presenting as multifocal ground-glass nodules (GGNs).

METHOD

From January 2014 to June 2018, we enrolled 238 patients with multiple GGNs in a retrospective review. Patients were categorized into growth group 63 (26.5%), and non-growth group 175 (73.5%). The median follow-up time was 28.2 months (range, 6.3-73.0 months). To obtain the time of RN growth and find the risk factors for growth, data such as age, gender, history of smoking, history of malignancy, type of surgery, pathology and radiological characteristics were analyzed to use Kaplan-Meier method with the log-rank test and Cox regression analysis.

RESULTS

The median growth time of RN was 56.0 months (95% CI, 45.0-67.0 months) in all 238 patients. Roundness (HR 4.62, 95% CI 2.20-9.68), part-solid nodule (CTR ≥ 50%) (HR 4.39, 95% CI 2.29-8.45), vascular convergence sign (HR 2.32, 95% CI 1.36-3.96) of RN, and age (HR 1.04, 95% CI 1.01-1.07) were independent predictors of further nodule growth. However, radiological characteristics and pathology of domain tumour (DT) cannot be used as indicators to predict RN growth.

CONCLUSIONS

RN showed an indolent growth pattern in surgical patients with multifocal GGNs. RN with a higher roundness, presence of vascular convergence sign, more solid component, and in the elder was likely to grow. However, the growth of RN showed no association with the radiological features and pathology of DT.

A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study

Background

Intratumoural heterogeneity has been previously shown to be related to clonal evolution and genetic instability and associated with tumour progression. Phenotypically, it is reflected in the diversity of appearance and morphology within cell populations. Computer-extracted features relating to tumour cellular diversity on routine tissue images might correlate with outcome. This study investigated the prognostic ability of computer-extracted features of tumour cellular diversity (CellDiv) from haematoxylin and eosin (H&E)-stained histology images of non-small cell lung carcinomas (NSCLCs).

Methods

In this multicentre, retrospective study, we included 1057 patients with early-stage NSCLC with corresponding diagnostic histology slides and overall survival information from four different centres. CellDiv features quantifying local cellular morphological diversity from H&E-stained histology images were extracted from the tumour epithelium region. A Cox proportional hazards model based on CellDiv was used to construct risk scores for lung adenocarcinoma (LUAD; 270 patients) and lung squamous cell carcinoma (LUSC; 216 patients) separately using data from two of the cohorts, and was validated in the two remaining independent cohorts (comprising 236 patients with LUAD and 335 patients with LUSC). We used multivariable Cox regression analysis to examine the predictive ability of CellDiv features for 5-year overall survival, controlling for the effects of clinical and pathological parameters. We did a gene set enrichment and Gene Ontology analysis on 405 patients to identify associations with differentially expressed biological pathways implicated in lung cancer pathogenesis.

Findings

For prognosis of patients with early-stage LUSC, the CellDiv LUSC model included 11 discriminative CellDiv features, whereas for patients with early-stage LUAD, the model included 23 features. In the independent validation cohorts, patients predicted to be at a higher risk by the univariable CellDiv model had significantly worse 5-year overall survival (hazard ratio 1·48 [95% CI 1·06–2·08]; p=0·022 for The Cancer Genome Atlas [TCGA] LUSC group, 2·24 [1·04–4·80]; p=0·039 for the University of Bern LUSC group, and 1·62 [1·15–2·30]; p=0·0058 for the TCGA LUAD group). The identified CellDiv features were also found to be strongly associated with apoptotic signalling and cell differentiation pathways.

Interpretation

CellDiv features were strongly prognostic of 5-year overall survival in patients with early-stage NSCLC and also associated with apoptotic signalling and cell differentiation pathways. The CellDiv-based risk stratification model could potentially help to determine which patients with early-stage NSCLC might receive added benefit from adjuvant therapy.

Funding

National Institue of Health and US Department of Defense.

Predictive factors related to pleural dissemination in non-small cell lung cancer

Background

The prognosis of non-small-cell lung cancer (NSCLC) patients with pleural dissemination is poor, and pleural dissemination is generally considered a contraindication for radical surgery. However, if pleural dissemination is missed intraoperatively, patients with false-negative stage IV NSCLC cannot receive appropriate chemotherapy, and their prognosis might worsen.

Methods

In the present study, we enrolled 144 patients who received surgery for NSCLC between January 2008 and December 2019 with available data on the maximum standardized uptake value (SUV_max) on positron emission tomography (PET) with lesions adjacent to the visceral pleura and without lesions invading the chest wall.

Results

Seven patients who had pleural dissemination were compared with 137 patients who had not pleural dissemination. The relationships between pleural dissemination and the clinicopathological variables were analyzed, and significant differences in the histopathological type (P=0.03), and differentiation (P<0.01) were noted. It was suggested that squamous cell carcinoma tended not to show dissemination to the pleural cavity. The logistic regression analyses of the predictive factors related to pleural dissemination in non-squamous cell carcinoma patients were analyzed, and the age (P=0.01) and differentiation (P<0.01) were identified as significant predictive factors related to pleural dissemination.

Conclusions

Cases with non-squamous cell carcinoma, a young age, and poor differentiation of undifferentiated grade of histological differentiation are factors associated with early pleural cavity dissemination.

Differences in PET/CT standardized uptake values involvement and survival compared to histologic subtypes of lung adenocarcinoma

PURPOSE

Lung adenocarcinoma is histologically diverse but has distinct histologic growth patterns. There is no consensus on the clinical benefit of this histologic model. We aimed to evaluate the differences in the distribution of the preoperative primary tumor positron emission tomography (PET)/computed tomography (CT) standardized uptake values (SUVs) and survival in the lung adenocarcinoma subtypes.

METHODS

We retrospectively evaluated the data of 107 patients with resected lung adenocarcinoma who had preoperative PET/CT between 2005 and 2017 in a single center. Patients had lepidic, acinar, papillary, micropapillary, and solid histologic subtypes. We compared fluorodeoxyglucose SUVs and survival data of histologic subtypes.

RESULTS

The median age of the patients was 62 years (40-75), 76.4% were male, the median SUVmax was 9.4 (1-36.7), and the median follow-up time was 29 months (3-135 months). The median overall survival (OS) was 71 months and the median progression-free survival (PFS) was 33 months. SUVmax was significantly different in histologic subtypes: values for papillary, micropapillary, solid, acinar, and lepidic subtypes were 9.7, 8, 12, 9.1, and 3.9, respectively (p = 0.000). Solid predominant adenocarcinoma had significantly higher SUVmax than the other subtypes (p = 0.001). Lepidic predominant adenocarcinoma had significantly lower SUVmax than the other subtypes (p = 0.000). There was no significant difference in OS between histologic subtypes (p = 0.66), but PFS was significantly different between the groups (p = 0.017), and the solid subtype had a shorter PFS than the other histologic subtypes.

CONCLUSION

Lung adenocarcinoma consists of a diverse group of diseases. Different SUVmax values are seen in different histologic subtypes of nonmetastatic lung adenocarcinoma. Solid predominant types have high SUVmax values while lepidic predominant types have lower SUVmax values. The solid subtype had a shorter PFS than the other histologic subtypes.

Prognostic Impact of Histopathologic Features in Pulmonary Invasive Mucinous Adenocarcinomas

OBJECTIVES

The prognostic significance of pathologic features and invasive size has not been well studied for invasive mucinous adenocarcinoma (IMA). This study evaluates the significance of pathologic features and invasive size in relation to clinical outcome.

METHODS

We reviewed the pathologic features in 84 IMAs, including histologic pattern, nuclear atypia, mitosis, necrosis, and lymphovascular invasion. The invasive size was calculated from the total size using the percentage of invasive components. Cases were subdivided into two pathologic grades based on five pathologic features, and the pathologic grade and adjusted T (aT) stage were correlated with disease-free and overall survival (OS).

RESULTS

Necrosis and N stage were significantly associated with aT stage, and a significant association was noted between OS and aT stage. Nuclear atypia, mitosis, and lymphovascular and pleural invasion also showed a significant association with OS. High-grade tumors showing a significantly worse OS compared with low-grade tumors, as well as pathologic grade (hazard ratio [HR], 2.337; P = .043) and aT stage (HR, 1.875; P = .003), were independent prognostic factors in multivariate analysis.

CONCLUSIONS

The pathologic grading system stratified IMAs into high- and low-grade tumors with significant differences in OS. Invasive size may provide a better prognostic stratification for OS.

Long non-coding RNA RFPL3S is a novel prognostic biomarker in lung cancer

Long non-coding RNAs (lncRNAs) are functional components of the human genome. Recent studies have demonstrated that lncRNAs play essential roles in tumorigenesis, and are involved in cell proliferation, apoptosis, migration and invasion in several types of tumor, including lung cancer. However, the clinical relevance of lncRNA expression in lung cancer remains unknown. The aim of the present study was to investigate the expression pattern of RFPL3 antisense (RFPL3S) and its associations with clinicopathological characteristics in patients with lung cancer. Whether RFPL3S can act as a potential prognostic biomarker for lung cancer was also investigated. RFPL3S expression in tumor samples and cells was assessed using the Oncomine database and the Cancer Cell Line Encyclopedia, respectively. Based on Kaplan-Meier Plotter analyses, the prognostic values of RFPL3S were further evaluated. It was revealed that RFPL3S was highly expressed in lung cancer tissues when compared with normal tissues and was significantly associated with pN factor, pTNM stage and Ki-67 labeling index. In the survival analyses, increased RFPL3S expression was associated with poor survival and was inversely associated with first progression in all patients. These results indicate that RFPL3S may be of clinical significance and may act as a prognostic biomarker in lung cancer.

The Natural Growth of Subsolid Nodules Predicted by Quantitative Initial CT Features: A Systematic Review

Background: The detection rate for pulmonary nodules, particularly subsolid nodules (SSNs), has been significantly improved. The purpose of this review is to summarize the relationship between quantitative features of initial CT imaging and the subsequent natural growth of SSNs to explore potential reasons for these findings.

Methods: Relevant studies were collected from a literature search of PubMed, Embase, Web of Science, and Cochrane. Data extraction was performed on the patients' basic information, CT methods, and acquisition methods, including quantitative CT features, and statistical methods.

Results: A total of 10 relevant articles were included in our review, which included 850 patients with 1,026 SSNs. Overall, the results were variable, and the key findings were as follows. Seven studies looked at the relationship between the diameter and growth of SSNs, showing that SSNs with larger diameters were associated with increased growth. An additional three studies which focused on the relationship between CT attenuation and the growth of SSNs showed that SSNs with a high CT attenuation were associated with increased growth.

Conclusion: CT attenuation may be useful in predicting the natural growth of SSNs, and mean CT attenuation may be more useful in predicting the natural growth of pure ground glass nodules (GGNs) than part-solid GGNs. While evaluation by diameter did have some limitations, it demonstrates value in predicting the growth of SSNs.

A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images

Digital pathology imaging of tumor tissues, which captures histological details in high resolution, is fast becoming a routine clinical procedure. Recent developments in deep-learning methods have enabled the identification, characterization, and classification of individual cells from pathology images analysis at a large scale. This creates new opportunities to study the spatial patterns of and interactions among different types of cells. Reliable statistical approaches to modeling such spatial patterns and interactions can provide insight into tumor progression and shed light on the biological mechanisms of cancer. In this article, we consider the problem of modeling a pathology image with irregular locations of three different types of cells: lymphocyte, stromal, and tumor cells. We propose a novel Bayesian hierarchical model, which incorporates a hidden Potts model to project the irregularly distributed cells to a square lattice and a Markov random field prior model to identify regions in a heterogeneous pathology image. The model allows us to quantify the interactions between different types of cells, some of which are clinically meaningful. We use Markov chain Monte Carlo sampling techniques, combined with a double Metropolis-Hastings algorithm, in order to simulate samples approximately from a distribution with an intractable normalizing constant. The proposed model was applied to the pathology images of $205$ lung cancer patients from the National Lung Screening trial, and the results show that the interaction strength between tumor and stromal cells predicts patient prognosis (P = $0.005$). This statistical methodology provides a new perspective for understanding the role of cell-cell interactions in cancer progression.

Validating a targeted next-generation sequencing assay and profiling somatic variants in Chinese non-small cell lung cancer patients

Non-small cell lung cancer (NSCLC) is featured with complex genomic alterations. Molecular profiling of large cohort of NSCLC patients is thus a prerequisite for precision medicine. We first validated the detection performance of a next-generation sequencing (NGS) cancer hotspot panel, OncoAim, on formalin-fixed paraffin-embedded (FFPE) samples. We then utilized OncoAim to delineate the genomic aberrations in Chinese NSCLC patients. Overall detection performance was powerful for mutations with allele frequency (MAF) ≥ 5% at >500 × coverage depth, with >99% sensitivity, high specificity (positive predictive value > 99%), 94% accuracy and 96% repeatability. Profiling 422 NSCLC FFPE samples revealed that patient characteristics, including gender, age, lymphatic spread, histologic grade and histologic subtype were significantly associated with the mutation incidence of EGFR and TP53. Moreover, RTK signaling pathway activation was enriched in adenocarcinoma, while PI(3)K pathway activation, oxidative stress pathway activation, and TP53 pathway inhibition were more prevalent in squamous cell carcinoma. Additionally, novel co-existence (e.g., variants in BRAF and PTEN) and mutual-exclusiveness (e.g., alterations in EGFR and NFE2L2) were found. Finally, we revealed distinct mutation spectrum in TP53, as well as a previously undervalued PTEN aberration. Our findings could aid in improving diagnosis, prognosis and personalized therapeutic decisions of Chinese NSCLC patients.

Novel prognostic model for stratifying survival in stage I lung adenocarcinoma patients

PURPOSE

We combined conventional clinical and pathological characteristics and pathological architectural grading scores to develop a prognostic model to identify a specific group of patients with stage I lung adenocarcinomas with poor survival following surgery.

METHODS

This retrospective study included 198 patients with stage I lung adenocarcinomas recruited from 2004 to 2013. Multivariate analyses were used to confirm independent risk factors, which were checked for internal validity using the bootstrapping method. The prognostic scores, derived from β-coefficients using the Cox regression model, classified patients into high- and low-risk groups. The predictive performance and discriminative ability of the model were assessed by the area under the receiver operating characteristic curve (AUC), concordance index (C-index) and Kaplan-Meier survival analyses.

RESULTS

Three risk factors were identified: T2 (rounding of β-coefficients = 81), necrosis (rounding of β-coefficients = 67), and pathological architectural score of 5-6 (rounding of β-coefficients = 58). The final prognostic score was the sum of points. The derived prognostic scores stratified patients into low- (score ≤ 103) and high- (score > 103) risk groups, with significant differences in 5-year overall survival (high vs. low risk: 49.3% vs. 88.0%, respectively; hazard ratio: 4.55; p < 0.001). The AUC for the proposed model was 0.717. The C-index of the model was 0.693.

CONCLUSION

An integrated prognostic model was developed to discriminate resected stage I adenocarcinoma patients into low- and high-risk groups, which will help clinicians select individual treatment strategies.

Natural language processing for populating lung cancer clinical research data

Background

Lung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate cohort-related epidemiological studies using informatics approaches. Since manual extraction from large volumes of text materials is time consuming and labor intensive, some efforts have emerged to automatically extract information from text for lung cancer patients using natural language processing (NLP), an artificial intelligence technique.

Methods

In this study, using an existing cohort of 2311 lung cancer patients with information about stage, histology, tumor grade, and therapies (chemotherapy, radiotherapy and surgery) manually ascertained, we developed and evaluated an NLP system to extract information on these variables automatically for the same patients from clinical narratives including clinical notes, pathology reports and surgery reports.

Results

Evaluation showed promising results with the recalls for stage, histology, tumor grade, and therapies achieving 89, 98, 78, and 100% respectively and the precisions were 70, 88, 90, and 100% respectively.

Conclusion

This study demonstrated the feasibility and accuracy of automatically extracting pre-defined information from clinical narratives for lung cancer research.

A BAYESIAN MARK INTERACTION MODEL FOR ANALYSIS OF TUMOR PATHOLOGY IMAGES

With the advance of imaging technology, digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis. This process produces massive imaging data that capture histological details in high resolution. Recent developments in deep-learning methods have enabled us to identify and classify individual cells from digital pathology images at large scale. Reliable statistical approaches to model the spatial pattern of cells can provide new insight into tumor progression and shed light on the biological mechanisms of cancer. We consider the problem of modeling spatial correlations among three commonly seen cells observed in tumor pathology images. A novel geostatistical marking model with interpretable underlying parameters is proposed in a Bayesian framework. We use auxiliary variable MCMC algorithms to sample from the posterior distribution with an intractable normalizing constant. We demonstrate how this model-based analysis can lead to sharper inferences than ordinary exploratory analyses, by means of application to three benchmark datasets and a case study on the pathology images of 188 lung cancer patients. The case study shows that the spatial correlation between tumor and stromal cells predicts patient prognosis. This statistical methodology not only presents a new model for characterizing spatial correlations in a multitype spatial point pattern conditioning on the locations of the points, but also provides a new perspective for understanding the role of cell-cell interactions in cancer progression.

Clinical significance of the cribriform pattern in invasive adenocarcinoma of the lung

PURPOSE

According to the WHO, the cribriform pattern is a subtype of acinar (Aci) predominance in invasive adenocarcinoma (ADC) of the lung. Recently, several studies have demonstrated poor prognosis in patients with cribriform predominance. This study was performed to examine the correlations of cribriform pattern with the clinicopathology, molecular features and prognosis in patients with invasive ADC.

METHODS

Histological subtypes were evaluated in 279 patients who underwent complete resection for invasive ADC. Patients of the Aci-predominant subtype were divided into two subgroups according to the percentage of cribriform cancer (≥5% vs <5%). Clinicopathological characteristics, overall survival (OS), disease-free survival (DFS) and molecular changes were compared. In addition, both OS and DFS were compared between patients with cribriform-predominant (n=33) and pure Aci-predominant (n=88) ADCs.

RESULTS

A cribriform pattern was found in 111 (39.8%) cases and ranged from 5 % to 100 % of the total tumour volume (mean±SEM, 30%±2%). Of 117 patients with Aci predominance, 79 showed the cribriform pattern, while the remaining 38 did not. The cribriform pattern was associated with aggressive pathological behaviour, including advanced stages of cancer, nuclear atypia, mitoses, lymph node invasion, metastasis and larger tumour size. The subgroup with cribriform cancer (≥5%) had significantly poorer OS and DFS compared with the cribriform-negative (<5%) group. In addition, Cox multivariate analyses revealed that the cribriform pattern was an independent predictor of OS but not DFS. Moreover, OS was significantly lower in the cribriform-predominant group than in the Aci-predominant group.

CONCLUSION

The cribriform pattern is associated with aggressive pathological behaviour and is an independent poor prognostic indicator in patients with Aci-predominant ADC of the lung.

Differential expression of PD-L1 and IDO1 in association with the immune microenvironment in resected lung adenocarcinomas

Like programmed cell death ligand 1 (PD-L1), indoleamine 2,3-dioxygenase 1 (IDO1) is known to exert immunosuppressive effects and be variably expressed in human lung cancer. However, IDO1 expression has not been well studied in lung adenocarcinoma. PD-L1 and IDO1 expression was evaluated in 261 resected lung adenocarcinomas using tissue microarrays and H-scores (cutoff: 5). We compared IDO1 and PD-L1 expression with clinical features, tumor-infiltrating lymphocytes, HLA class I molecule expression, molecular alterations, and patient outcomes. There was expression of PD-L1 in 89 (34%) and IDO1 in 74 (29%) cases, with co-expression in 49 (19%). Both PD-L1 and IDO1 were significantly associated with smoking, aggressive pathologic features, and abundant CD8+ and T-bet+ (Th1 marker) tumor-infiltrating lymphocytes. PD-L1 expression was also associated with preserved HLA class I molecule expression (p = 0.002). Compared to PD-L1+/IDO1+ and PD-L1+ only cases, significantly fewer IDO1+ only cases had abundant CD8+ and T-bet+ tumor-infiltrating lymphocytes (p < 0.001, respectively). PD-L1 expression was significantly associated with EGFR wild-type (p < 0.001) and KRAS mutants (p = 0.021), whereas isolated IDO1 expression was significantly associated with EGFR mutations (p = 0.007). As for survival, PD-L1 was a significant predictor of decreased progression-free and overall survival by univariate but not multivariate analysis, while IDO1 was not associated with progression-free or overall survival. Interestingly, there was a significant difference in the 5-year progression-free and overall survival (p = 0.004 and 0.038, respectively), where cases without PD-L1 or IDO1 expression had the longest survival, and those with PD-L1 alone had the shortest survival. While PD-L1+/-IDO1 expression is observed in association with HLA class I expression, cytotoxic T lymphocyte/Th1 microenvironments, EGFR wild-type, and KRAS mutations, isolated IDO1 expression does not demonstrate these associations, suggesting that IDO1 may serve a distinct immunosuppressive role in lung adenocarcinomas. Thus, further investigation of IDO1 may demonstrate its role as a potential biomarker for patients who undergo anti-PD-1/PD-L1 therapy.

ABL kinase inhibition sensitizes primary lung adenocarcinomas to chemotherapy by promoting tumor cell differentiation

Lung cancer is the leading cause of cancer mortality in the United States, with an overall five-year survival rate of ~16%. Non-small cell lung cancer (NSCLC) accounts for ~80% of all lung cancer cases, and the majority (40%) of these are adenocarcinomas. Loss of function point mutations in TP53 (46%) and activating mutations in KRAS (33%) are the most common mutations in human lung adenocarcinomas. Because neither of these genetic alterations are clinically actionable, chemotherapy remains the mainstay of treatment in patients with oncogenic KRAS driver mutations. However, chemoresistance to genotoxic agents such as docetaxel remains a major clinical challenge facing lung cancer patients. Here we show that ABL kinase allosteric inhibitors can be effectively used for the treatment of KrasG12D/+; p53-/- lung adenocarcinomas in an autochthonous mouse model. Unexpectedly, we found that treatment of tumor-bearing mice with an ABL allosteric inhibitor promoted differentiation of lung adenocarcinomas from poorly differentiated tumors expressing basal cell markers to tumors expressing terminal differentiation markers in vivo, which rendered lung adenocarcinomas susceptible to chemotherapy. These findings uncover a novel therapeutic approach for the treatment of lung adenocarcinomas with poor response to chemotherapy.

Value of adjuvant chemotherapy in patients with resected stage IB solid predominant and solid non-predominant lung adenocarcinoma

BACKGROUND

The use of adjuvant chemotherapy (ACT) for stage IB lung adenocarcinoma remains controversial. We examined the benefits of ACT in stage IB patients with tumors composed of solid material.

METHODS

The records of 309 patients with stage IB lung adenocarcinoma who had undergone complete resection between 2006 and 2015 were reviewed. All pathological slides were evaluated for the composition of solid material.

RESULTS

Our data showed that although disease-free survival (DFS) and overall survival (OS) were not significantly different (P = 0.306 and P = 0.061, respectively) between patients displaying a solid pattern of tumor growth and treated with or without ACT, patients with a solid predominant pattern of tumor growth treated with ACT had longer DFS (hazard ratio 0.359; P = 0.033) and OS (hazard ratio 0.205; P = 0.003). In patients with solid non-predominant patterns, treatment with ACT had no effect on DFS (P = 0.326) or OS (P = 0.508).

CONCLUSIONS

Postoperative patients with the solid predominant pattern of stage IB lung adenocarcinoma may benefit from ACT, while those with the solid non-predominant pattern will not.

Clinicopathologic characteristics of non-small cell lung cancer in patients with smoking-related chronic obstructive pulmonary disease

BACKGROUND

The purpose of this study was to clarify the clinicopathologic characteristics of non-small cell lung cancer (NSCLC) patients with smoking-related chronic obstructive pulmonary disease (COPD) and to evaluate the biological behavior of this disease. We investigated the association between smoking-related COPD, the recurrence-free proportion (RFP) and the clinicopathological features of clinical stage I NSCLC patients.

METHODS

Between 2005 and 2014, 218 consecutive patients with clinical stage I NSCLC underwent complete resection with lobectomy or greater and systematic lymph node dissection. Differences in categorical outcomes were evaluated by the χ² test. RFPs were estimated using the Kaplan-Meier method, and differences were evaluated using the log-rank test.

RESULTS

The 5-year RFP of clinical stage I NSCLC patients with smoking-related COPD was 55%, which was significantly lower than in those without smoking-related COPD (85%; p < 0.001). Postoperative pathological factors, including moderate or poor histological differentiation, intratumoral vascular invasion and lymph node metastasis, were detected more often in patients with smoking-related COPD. In adenocarcinoma patients, the 5-year RFP of patients with smoking-related COPD was 47%, which was significantly lower than in those without smoking-related COPD (87%; p < 0.001). The presence of a solid component was more frequently found in patients with smoking-related COPD (p = 0.007).

CONCLUSION

Clinical stage I NSCLC patients with smoking-related COPD have histologically more invasive tumors than those without smoking-related COPD.

Post-operative AICS status in completely resected lung cancer patients with pre-operative AICS abnormalities: predictive significance of disease recurrence

The AminoIndex^TM Cancer Screening (AICS) system, a plasma-free amino acid (PFAA)-based multivariate discrimination index, is a blood screening test for lung cancer based on the comparison of PFAA concentrations between patients with lung cancer and healthy controls. Pre- and post-operative AICS values were compared among 72 patients who underwent curative resection for lung cancer. Post-operative changes in PFAA concentrations were also evaluated. AICS values were classified as rank A (0.0–4.9), B (5.0–7.9), or C (8.0–10.0). Rank B–C patients were evaluated for outcomes and post-operative changes in their AICS values. Twenty-three of the 44 pre-operative rank B–C patients experienced post-operative reductions in AICS rank. Only one patient experienced cancer recurrence. Post-operative changes in PFAA concentrations were associated with the risk of post-operative cancer recurrence (p = 0.001). Multivariate analysis revealed that the absence of a post-operative reduction in AICS rank independently predicted cancer recurrence (hazard ratio: 14.28; p = 0.012). The majority of patients had high pre-operative AICS values and exhibited a reduction in AICS rank after curative resection. However, the absence of a post-operative reduction in AICS rank was associated with cancer recurrence, suggesting that AICS rank may be a sensitive marker of post-operative recurrence.

CT texture analysis of histologically proven benign and malignant lung lesions

The purpose of our study was to determine accuracy of CT texture analysis (CTTA) for differentiating benign from malignant pulmonary nodules, and well-differentiated from poorly differentiated lung cancers, with histology as the standard of reference.In this IRB-approved study, 175 adult patients (average age 66 ± 12 years; age range 27-89 years, male 82: female 93) who underwent a noncontrast chest CT examination prior to CT-guided biopsy of pulmonary nodules were included. There were 57 benign (24 tumors or tumor-like lesions; 33 inflammatory conditions) and 120 malignant (29 well-differentiated adenocarcinomas, 48 poorly differentiated adenocarcinomas, and 43 squamous cell carcinomas) diagnoses on pathology. CTTA was performed on the prebiopsy noncontrast CT images using a commercially available software (TexRAD limited, UK). The CTCA features analyzed included mean HU values, percent positive pixels (PPP), mean value of positive pixels (MPP), standard deviation (SD), normalized SD, skewness, kurtosis, and entropy.The ROC analyses showed that normalized SD [AUC: 0.63, (CI: 0.55-72), P = .003] had moderate accuracy for differentiating between benign and malignant lesions. For differentiating among well-differentiated and poorly differentiated tumors, the ROC analysis showed that except skewness all other parameters were statistically significant The AUC values of other CTTA parameters were: mean (AUC: 0.73-0.76, P = .001- < .0001).CT texture analyses can reliably predict well- and poorly differentiated lung malignancies. However, inflammatory lung lesions with tissue heterogeneity negatively affect the performance of CTTA when it comes to differentiation between benign and malignant pulmonary nodules.

Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images

PURPOSE

To evaluate the usefulness of surrogate biomarkers as predictors of histopathologic tumor grade and aggressiveness using radiomics data from dual-energy computed tomography (DECT), with the ultimate goal of accomplishing stratification of early-stage lung adenocarcinoma for optimal treatment.

RESULTS

Pathologic grade was divided into grades 1, 2, and 3. Multinomial logistic regression analysis revealed i-uniformity and 97.5th percentile CT attenuation value as independent significant factors to stratify grade 2 or 3 from grade 1. The AUC value calculated from leave-one-out cross-validation procedure for discriminating grades 1, 2, and 3 was 0.9307 (95% CI: 0.8514-1), 0.8610 (95% CI: 0.7547-0.9672), and 0.8394 (95% CI: 0.7045-0.9743), respectively.

MATERIALS AND METHODS

A total of 80 patients with 91 clinically and radiologically suspected stage I or II lung adenocarcinoma were prospectively enrolled. All patients underwent DECT and F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT, followed by surgery. Quantitative CT and PET imaging characteristics were evaluated using a radiomics approach. Significant features for a tumor aggressiveness prediction model were extracted and used to calculate diagnostic performance for predicting all pathologic grades.

CONCLUSIONS

Quantitative radiomics values from DECT imaging metrics can help predict pathologic aggressiveness of lung adenocarcinoma.

Mitosis count and number of cancer cells in cases of primary pulmonary adenocarcinoma: Correlations among phosphorylated histone 3, number of cancer cells, nuclear grade, pathologic features and prognosis

Immunohistochemistry findings for the phosphorylated form of histone 3 (pHH3) have been shown to be a reliable mitosis-specific marker. We evaluated the correlation between pHH3-stained mitotic figures (PHMFs) and clinical outcome, and compared the results with findings for numbers of PHMFs and cancer cells. The primary tumor was obtained from 113 patients with pulmonary adenocarcinomas (≤2 cm maximum dimension). All specimens were stained with pHH3, then the number of cancer cells in each was determined. Cases with a cancer-cell index ≥1000 showed worse recurrence-free survival as compared to those with a value <1000 (P < 0.001). Also, cases with a pHH3 index ≥0.27 showed worse recurrence-free survival as compared to <0.27 (P = 0.001) and cases with a pHH3/cancer-cell index ≥0.001 showed worse recurrence-free survival as compared to <0.001 (P = 0.002). Multivariate analysis demonstrated that pHH3/cancer-cell index was significantly correlated with prognosis, but not Ki-67 index. The number of cancer cells was also strongly correlated with progression of Noguchi's classification and WHO pathologic type. pHH3/cancer-cell index was correlated with prognosis, and those were useful for prognostic evaluation of pulmonary adenocarcinoma patients. Furthermore, cancer cell number was correlated with Noguchi's classification and WHO pathologic type.

Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma

Adenocarcinoma accounts for more than 40% of lung malignancy, and microscopic pathology evaluation is indispensable for its diagnosis. However, how histopathology findings relate to molecular abnormalities remains largely unknown. Here, we obtained H&E-stained whole-slide histopathology images, pathology reports, RNA sequencing, and proteomics data of 538 lung adenocarcinoma patients from The Cancer Genome Atlas and used these to identify molecular pathways associated with histopathology patterns. We report cell-cycle regulation and nucleotide binding pathways underpinning tumor cell dedifferentiation, and we predicted histology grade using transcriptomics and proteomics signatures (area under curve >0.80). We built an integrative histopathology-transcriptomics model to generate better prognostic predictions for stage I patients (p = 0.0182 ± 0.0021) compared with gene expression or histopathology studies alone, and the results were replicated in an independent cohort (p = 0.0220 ± 0.0070). These results motivate the integration of histopathology and omics data to investigate molecular mechanisms of pathology findings and enhance clinical prognostic prediction.

A Grading System Combining Tumor Budding and Nuclear Diameter Predicts Prognosis in Resected Lung Squamous Cell Carcinoma

For lung squamous cell carcinomas, there are no histologic findings that have been universally accepted as prognostic factors. Tumor budding and nuclear grade have been recognized as prognostic factors in other carcinomas. In this study, we investigated whether pathologic findings could determine clinical outcome in Japanese patients with lung squamous cell carcinomas. Tumor slides from surgically resected lung squamous cell carcinomas (1999 to 2012) were reviewed (n=216). Tumors were evaluated for histologic subtypes, differentiation, tumor budding, nuclear diameter, and mitosis. Recurrence-free survival (RFS) and overall survival (OS) were analyzed using the log-rank test and the Cox proportional hazards model. Tumor budding and large nuclei were independent prognostic factors of a worse RFS (P<0.001 and P=0.002, respectively) and a worse OS (P<0.001 and P=0.038, respectively) on multivariate analysis after adjustment for pathologic stage and lymphatic invasion. However, histologic subtypes, differentiation, and mitotic count did not correlate with prognosis. A grading system combining tumor budding and nuclear diameter was an independent prognostic factors of a worse RFS (grade 2 vs. 1, hazard ratio [HR]=2.91; P<0.001, and grade 3 vs. 1, HR=7.60, P<0.001) and a worse OS (grade 2 vs. 1, HR=2.15; P=0.014, and grade 3 vs. 1, HR=4.54, P<0.001). We found that a grading system combining tumor budding and nuclear diameter was a significant prognostic factor among Japanese patients with resected lung squamous cell carcinoma.

LSD1 modulates the non-canonical integrin β3 signaling pathway in non-small cell lung carcinoma cells

The epigenetic writer lysine-specific demethylase 1 (LSD1) is aberrantly upregulated in many cancer types and its overexpression correlates with poor survival and tumor progression. In this study, we analysed LSD1 function in non-small cell lung cancer adenocarcinomas. Expression profiling of 182 cases of lung adenocarcinoma proved a significant correlation of LSD1 overexpression with lung adenocarcinoma progression and metastasis. KRAS-mutated lung cancer cell clones were stably silenced for LSD1 expression. RNA-seq and comprehensive pathway analysis revealed, that genes related to a recently described non-canonical integrin β3 pathway, were significantly downregulated by LSD1 silencing. Hence, invasion and self-renewal capabilities were strongly decreased. Notably, this novel defined LSD1/integrin β3 axis, was also detected in human lung adenocarcinoma specimens. Furthermore, the linkage of LSD1 to an altered expression pattern of lung-lineage specific transcription factors and genes, which are involved in alveolar epithelial differentiation, was demonstrated. Thus, our findings point to a LSD1-integrin β3 axis, conferring attributes of invasiveness and tumor progression to lung adenocarcinoma.

Ex vivo multiplex profiling of protein tyrosine kinase activities in early stages of human lung adenocarcinoma

Despite constant improvement in existing therapeutic efforts, the overall survival rate of lung cancer patients remains low. Enzyme activities may identify new therapeutically targetable biomarkers and overcome the marked lack of correlation between cellular abundance of translated proteins and corresponding mRNA expression levels. We analysed tyrosine kinase activities to classify lung adenocarcinoma (LuAdCa) resection specimens based on their underlying changes in cellular processes and pathways that are agents of or result from malignant transformation. We characterised 71 same-patient pairs of early-stage LuAdCa and non-neoplastic LuAdCa resection specimen lysates in the presence or absence of a tyrosine kinase inhibitor. We performed ex vivo multiplex tyrosine phosphorylation assays using 144 selected microarrayed kinase substrates. The obtained 76 selected phosphotyrosine signature peptides were subsequently analysed in terms of follow-up treatments and outcomes recorded in the patient files. For tumour, node, metastasis (TNM) stage 1 LuAdCa patients, we noticed a larger tyrosine kinase inhibitor-induced decrease in tyrosine phosphorylation for long-term as opposed to short-term disease survivors, for which 26 of 76 selected peptides were significantly (p < 0.01, FDR < 3%) more inhibited in the long-term survivors. Using statistical class prediction analysis, we obtained a 'prognostic-signature' for long- versus short-term disease survivors and correctly predicted the survival status of 73% of our patients. Our translational approach may assist clinical disease management after surgical resection and may help to direct patients for an optimal treatment strategy.

Evaluation of grading systems in stage I lung adenocarcinomas: a retrospective cohort study

AIMS

There is no internationally accepted grading system for lung adenocarcinoma despite the new WHO classification. The architectural grade, the Kadota grade and the Sica score were evaluated and compared with overall (OS) and disease-free survival (DFS).

METHODS

Comprehensive histological subtyping was used in a series of resected stage I lung adenocarcinoma to identify subtypes of adenocarcinomas, the architectural grade, the Kadota grade, the Sica grade, the mitotic count, nuclear atypia, the presence of lymphovascular, vascular and airway propagation, necrosis, and micropapillary or solid growth pattern in any percentage. Statistical models fitted included Kaplan-Meier estimates and Cox proportional hazard regression models.

RESULTS

261 stage I adenocarcinomas were included. The 5-year survivals of different subtypes were as follows: lepidic (n=40, OS: 92.5%; DFS 91.6%), acinar (n=54, OS: 81.8%; DFS: 68.6%), papillary (n=49, OS: 73.6%; DFS: 61.0%), solid (n=95, OS: 64.7%; DFS: 57.8%) and micropapillary (n=23, OS: 34.8%; DFS: 33.5%). Concerning the architectural grade, there were significant differences between OS and DFS of low and intermediate (pOS=0.005, pDFS<0.001), low and high (pOS<0.001, pDFS<0.001) and intermediate and high grades (pOS=0.002, pDFS<0.001). Low-grade and intermediate grade tumours did not differ in survival according to Kadota grade and Sica grade. In the multivariable model, architectural grade was found to be an independent prognostic marker. In another model, architectural pattern proved to be superior to architectural grade.

CONCLUSIONS

Of the three grading systems compared, the architectural grade makes the best distinction between the outcome of low-grade, intermediate-grade and high-grade stage I adenocarcinomas.

Histological features of malignancy correlate with growth patterns and patient outcome in lung adenocarcinoma

AIMS

Until the launch of the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society adenocarcinoma classification in 2011, there were no uniform histological grading criteria for pulmonary adenocarcinomas. The current classification highlights the prognostic importance of the various histological growth patterns observed in these morphologically heterogeneous neoplasias. In this study, we aimed to evaluate the classic histological parameters of malignancy in correlation with the growth patterns and patient outcomes in a series of 112 surgically operated stage I-IV lung adenocarcinomas.

METHODS AND RESULTS

Architectural growth pattern analysis was performed according to the current adenocarcinoma classification. Histological features including, for example, nuclear atypia, mitotic activity, tumour necrosis, and different patterns of invasion were assessed and correlated statistically with the architecture and the clinical data. A solid predominant histology was associated with increased levels of atypia (P = 0.027), mitotic activity (P < 0.001), necrosis (P < 0.001), and lymphovascular invasion (P = 0.001), and a non-predominant solid pattern was associated with intra-alveolar tumour spread (P = 0.004). The presence of a non-predominant lepidic tumour component showed inverse correlations with atypia (P = 0.002), mitotic rate (P = 0.009), and tumour necrosis (P < 0.001). Tumour size (P < 0.001), mitotic activity (P = 0.019), tumour necrosis (P = 0.002), lymphovascular invasion (P = 0.001) and visceral pleural involvement (P = 0.001) were all associated with reduced disease-specific survival.

CONCLUSIONS

The classic histological features of malignancy correlate with tumour architecture and patient outcome, confirming the prognostic value of the growth pattern analysis and questioning the need for a parallel grading system in pulmonary adenocarcinoma.

Lung Cancer Pathological Image Analysis Using a Hidden Potts Model

Nowadays, many biological data are acquired via images. In this article, we study the pathological images scanned from 205 patients with lung cancer with the goal to find out the relationship between the survival time and the spatial distribution of different types of cells, including lymphocyte, stroma, and tumor cells. Toward this goal, we model the spatial distribution of different types of cells using a modified Potts model for which the parameters represent interactions between different types of cells and estimate the parameters of the Potts model using the double Metropolis-Hastings algorithm. The double Metropolis-Hastings algorithm allows us to simulate samples approximately from a distribution with an intractable normalizing constant. Our numerical results indicate that the spatial interaction between the lymphocyte and tumor cells is significantly associated with the patient's survival time, and it can be used together with the cell count information to predict the survival of the patients.

Dynamic prognostication using conditional survival analysis for patients with operable lung adenocarcinoma

PURPOSE

To evaluate conditional survival among patients with surgically resected stage I-IIIa lung adenocarcinoma and identify changes in prognostic contributions for various prognostic factors over time.

PATIENTS AND METHODS

We performed conditional survival analysis at each t0 (=0, 1, 2, 3, 4, 5 years) for 723 consecutive patients who underwent surgical resection for lung adenocarcinoma, stratified by various clinico-demographic features, as well as pathologic and imaging (tumor-shadow disappearance ratio [TDR] on CT and maximum standardized uptake value [SUVmax] on PET) characteristics. Uni- and multivariableCox regression analyses were performed to evaluate relationships between those variables and conditional survival.

RESULTS

Three-year conditional overall survival (OS) and disease-free survival (DFS) were 92.12% and 75.51% at baseline, but improved steadily up to 98.33% and 95.95% at 5 years after surgery. In contrast to demographic factors, pathologic (stage, subtype, pathologic grade and differentiation) and radiologic factors (TDR and SUVmax) maintained a statistically significant association with subseqeunt 3-year OS until 3 years after surgery. According to the multivariableanalysis, high SUVmax and low TDR value were independent predictors of subsequent 3-year OS and DFS at baseline, 1 and 2 years after surgery, respectively.

CONCLUSION

Our findings based on CS provide theoretical background for clinicians to plan longer period of surveillance following lung adenocarcinoma resection in survivors with preoperatively high SUVmax and low TDR on PET-CT and chest CT, respectively.