Is the current diagnostic algorithm reliable for selecting cases for EGFR- and KRAS-mutation analysis in lung cancer?

Mass Spectrometry Imaging for Reliable and Fast Classification of Non-Small Cell Lung Cancer Subtypes

Simple Summary

Diagnostic subtyping of non-small cell lung cancer is paramount for therapy stratification. Our study shows that the subtyping into pulmonary adenocarcinoma and pulmonary squamous cell carcinoma by mass spectrometry imaging is rapid and accurate using limited tissue material.

Abstract

Subtyping of non-small cell lung cancer (NSCLC) is paramount for therapy stratification. In this study, we analyzed the largest NSCLC cohort by mass spectrometry imaging (MSI) to date. We sought to test different classification algorithms and to validate results obtained in smaller patient cohorts. Tissue microarrays (TMAs) from including adenocarcinoma (ADC, n = 499) and squamous cell carcinoma (SqCC, n = 440), were analyzed. Linear discriminant analysis, support vector machine, and random forest (RF) were applied using samples randomly assigned for training (66%) and validation (33%). The m/z species most relevant for the classification were identified by on-tissue tandem mass spectrometry and validated by immunohistochemistry (IHC). Measurements from multiple TMAs were comparable using standardized protocols. RF yielded the best classification results. The classification accuracy decreased after including less than six of the most relevant m/z species. The sensitivity and specificity of MSI in the validation cohort were 92.9% and 89.3%, comparable to IHC. The most important protein for the discrimination of both tumors was cytokeratin 5. We investigated the largest NSCLC cohort by MSI to date and found that the classification of NSCLC into ADC and SqCC is possible with high accuracy using a limited set of m/z species.

Clonality analysis of pulmonary tumors by genome-wide copy number profiling

Multiple tumors in patients are frequently diagnosed, either synchronous or metachronous. The distinction between a second primary and a metastasis is important for treatment. Chromosomal DNA copy number aberrations (CNA) patterns are highly unique to specific tumors. The aim of this study was to assess genome-wide CNA-patterns as method to identify clonally related tumors in a prospective cohort of patients with synchronous or metachronous tumors, with at least one intrapulmonary tumor. In total, 139 tumor pairs from 90 patients were examined: 35 synchronous and 104 metachronous pairs. Results of CNA were compared to histological type, clinicopathological methods (Martini-Melamed-classification (MM) and ACCP-2013-criteria), and, if available, EGFR- and KRAS-mutation analysis. CNA-results were clonal in 74 pairs (53%), non-clonal in 33 pairs (24%), and inconclusive in 32 pairs (23%). Histological similarity was found in 130 pairs (94%). Concordance between histology and conclusive CNA-results was 69% (74 of 107 pairs: 72 clonal and two non-clonal). In 31 of 103 pairs with similar histology, genetics revealed non-clonality. In two out of four pairs with non-matching histology, genetics revealed clonality. The subgroups of synchronous and metachronous pairs showed similar outcome for the comparison of histological versus CNA-results. MM-classification and ACCP-2013-criteria, applicable on 34 pairs, and CNA-results were concordant in 50% and 62% respectively. Concordance between mutation matching and conclusive CNA-results was 89% (8 of 9 pairs: six clonal and two non-clonal). Interestingly, in one patient both tumors had the same KRAS mutation, but the CNA result was non-clonal. In conclusion, although some concordance between histological comparison and CNA profiling is present, arguments exist to prefer extensive molecular testing to determine whether a second tumor is a metastasis or a second primary.

A robust six-gene prognostic signature for prediction of both disease-free and overall survival in non-small cell lung cancer

Background

The high mortality of patients with non-small cell lung cancer (NSCLC) emphasizes the necessity of identifying a robust and reliable prognostic signature for NSCLC patients. This study aimed to identify and validate a prognostic signature for the prediction of both disease-free survival (DFS) and overall survival (OS) of NSCLC patients by integrating multiple datasets.

Methods

We firstly downloaded three independent datasets under the accessing number of GSE31210, GSE37745 and GSE50081, and then performed an univariate regression analysis to identify the candidate prognostic genes from each dataset, and identified the gene signature by overlapping the candidates. Then, we built a prognostic model to predict DFS and OS using a risk score method. Kaplan–Meier curve with log-rank test was used to determine the prognostic significance. Univariate and multivariate Cox proportional hazard regression models were implemented to evaluate the influences of various variables on DFS and OS. The robustness of the prognostic gene signature was evaluated by re-sampling tests based on the combined GEO dataset (GSE31210, GSE37745 and GSE50081). Furthermore, a The Cancer Genome Atlas (TCGA)-NSCLC cohort was utilized to validate the prediction power of the gene signature. Finally, the correlation of the risk score of the gene signature and the Gene set variation analysis (GSVA) score of cancer hallmark gene sets was investigated.

Results

We identified and validated a six-gene prognostic signature in this study. This prognostic signature stratified NSCLC patients into the low-risk and high-risk groups. Multivariate regression and stratification analyses demonstrated that the six-gene signature was an independent predictive factor for both DFS and OS when adjusting for other clinical factors. Re-sampling analysis implicated that this six-gene signature for predicting prognosis of NSCLC patients is robust. Moreover, the risk score of the gene signature is correlated with the GSVA score of 7 cancer hallmark gene sets.

Conclusion

This study provided a robust and reliable gene signature that had significant implications in the prediction of both DFS and OS of NSCLC patients, and may provide more effective treatment strategies and personalized therapies.

Electronic supplementary material

The online version of this article (10.1186/s12967-019-1899-y) contains supplementary material, which is available to authorized users.

The diagnosis of non-small cell lung cancer in the molecular era

Lung carcinoma is the leading cause of cancer mortality for both genders in the United States and throughout the world. Many of these tumors are being diagnosed with minimally invasive means resulting in small samples. There is a need to extract an increasing amount of therapeutic and prognostic information from progressively smaller samples. Collaboration among clinicians and pathologists is needed to produce a comprehensive final diagnosis in patients with lung cancer. This collaboration facilitates triage of small samples for ancillary studies including molecular testing. What follows represents a review of the current required testing for lung cancer specimens, an example of an algorithm currently employed at the Cleveland Clinic so that all required tests can be performed even on the smallest of specimens and suggestions on how pathologists may approach this new era of "doing more with less".

PTEN inhibits non-small cell lung cancer cell growth by promoting G0/G1 arrest and cell apoptosis

Non-small cell lung cancer (NSCLC) is a major type of human lung cancer and the primary cause of cancer-associated cases of mortality worldwide. Phosphatase and tensin homolog (PTEN) is a potent tumor suppressor gene in various human cancer types. The aim of the current study was to explore the role of PTEN and its associated regulatory mechanisms in NSCLC. Firstly, the expression of PTEN was detected using western blotting in a variety of NSCLC cell lines. The results revealed that compared with normal control cells, PTEN levels were significantly decreased in NSCLC cell lines (P<0.01). Short hairpin (sh)RNAs specific to PTEN were also used to knockdown endogenous PTEN in NSCLC cells. The results indicated that cell viability was significantly increased in PTEN-knockdown cells compared with those transfected with negative control shRNA (P<0.01). Conversely, overexpression of PTEN in A549 and SK-MES-1 cells significantly decreased the optical density of NSCLC cells (P<0.01). Flow cytometry was used to investigate the cell cycle; the results revealed that PTEN knockdown significantly increased the percentage of cells at G₀/G₁ phase (P<0.01) and decreased the number of cells at S phase (P<0.01). The molecular mechanism was further explored using western blotting and the results demonstrated that PTEN overexpression increased the levels of cleaved caspase-3 (P<0.01). These results suggest that PTEN may be a potential target gene for gene therapy in patients with NSCLCs.

Any Place for Immunohistochemistry within the Predictive Biomarkers of Treatment in Lung Cancer Patients?

The identification of certain genomic alterations (EGFR, ALK, ROS1, BRAF) or immunological markers (PD-L1) in tissues or cells has led to targeted treatment for patients presenting with late stage or metastatic lung cancer. These biomarkers can be detected by immunohistochemistry (IHC) and/or by molecular biology (MB) techniques. These approaches are often complementary but depending on, the quantity and quality of the biological material, the urgency to get the results, the access to technological platforms, the financial resources and the expertise of the team, the choice of the approach can be questioned. The possibility of detecting simultaneously several molecular targets, and of analyzing the degree of tumor mutation burden and of the micro-satellite instability, as well as the recent requirement to quantify the expression of PD-L1 in tumor cells, has led to case by case development of algorithms and international recommendations, which depend on the quality and quantity of biological samples. This review will highlight the different predictive biomarkers detected by IHC for treatment of lung cancer as well as the present advantages and limitations of this approach. A number of perspectives will be considered.

Reliable Entity Subtyping in Non-small Cell Lung Cancer by Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry on Formalin-fixed Paraffin-embedded Tissue Specimens

Histopathological subtyping of non-small cell lung cancer (NSCLC) into adenocarcinoma (ADC), and squamous cell carcinoma (SqCC) is of utmost relevance for treatment stratification. However, current immunohistochemistry (IHC) based typing approaches on biopsies are imperfect, therefore novel analytical methods for reliable subtyping are needed. We analyzed formalin-fixed paraffin-embedded tissue cores of NSCLC by Matrix-assisted laser desorption/ionization (MALDI) imaging on tissue microarrays to identify and validate discriminating MALDI imaging profiles for NSCLC subtyping. 110 ADC and 98 SqCC were used to train a Linear Discriminant Analysis (LDA) model. Results were validated on a separate set of 58 ADC and 60 SqCC. Selected differentially expressed proteins were identified by tandem mass spectrometry and validated by IHC. The LDA classification model incorporated 339 m/z values. In the validation cohort, in 117 cases (99.1%) MALDI classification on tissue cores was in accordance with the pathological diagnosis made on resection specimen. Overall, three cases in the combined cohorts were discordant, after reevaluation two were initially misclassified by pathology whereas one was classified incorrectly by MALDI. Identification of differentially expressed peptides detected well-known IHC discriminators (CK5, CK7), but also less well known differentially expressed proteins (CK15, HSP27). In conclusion, MALDI imaging on NSCLC tissue cores as small biopsy equivalents is capable to discriminate lung ADC and SqCC with a very high accuracy. In addition, replacing multislide IHC by an one-slide MALDI approach may also save tissue for subsequent predictive molecular testing. We therefore advocate to pursue routine diagnostic implementation strategies for MALDI imaging in solid tumor typing.