Deciphering the Molecular Profile of Lung Cancer: New Strategies for the Early Detection and Prognostic Stratification

MicroRNAs and Drug Resistance in Non-Small Cell Lung Cancer: Where Are We Now and Where Are We Going

Lung cancer is the leading cause of cancer-related mortality in the world. The development of drug resistance represents a major challenge for the clinical management of patients. In the last years, microRNAs have emerged as critical modulators of anticancer therapy response. Here, we make a critical appraisal of the literature available on the role of miRNAs in the regulation of drug resistance in non-small cell lung cancer (NSCLC). We performed a comprehensive annotation of miRNAs expression profiles in chemoresistant versus sensitive NSCLC, of the drug resistance mechanisms tuned up by miRNAs, and of the relative experimental evidence in support of these. Furthermore, we described the pros and cons of experimental approaches used to investigate miRNAs in the context of therapeutic resistance, to highlight potential limitations which should be overcome to translate experimental evidence into practice ultimately improving NSCLC therapy.

Validation of the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a novel HRCT-based radiomic classifier for indeterminate pulmonary nodules

INTRODUCTION

Implementation of low-dose chest computed tomography (CT) lung cancer screening and the ever-increasing use of cross-sectional imaging are resulting in the identification of many screen- and incidentally detected indeterminate pulmonary nodules. While the management of nodules with low or high pre-test probability of malignancy is relatively straightforward, those with intermediate pre-test probability commonly require advanced imaging or biopsy. Noninvasive risk stratification tools are highly desirable.

METHODS

We previously developed the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a conventional predictive radiomic model based on eight imaging features capturing nodule location, shape, size, texture and surface characteristics. Herein we report its external validation using a dataset of incidentally identified lung nodules (Vanderbilt University Lung Nodule Registry) in comparison to the Brock model. Area under the curve (AUC), as well as sensitivity, specificity, negative and positive predictive values were calculated.

RESULTS

For the entire Vanderbilt validation set (n=170, 54% malignant), the AUC was 0.87 (95% CI 0.81-0.92) for the Brock model and 0.90 (95% CI 0.85-0.94) for the BRODERS model. Using the optimal cut-off determined by Youden's index, the sensitivity was 92.3%, the specificity was 62.0%, the positive (PPV) and negative predictive values (NPV) were 73.7% and 87.5%, respectively. For nodules with intermediate pre-test probability of malignancy, Brock score of 5-65% (n=97), the sensitivity and specificity were 94% and 46%, respectively, the PPV was 78.4% and the NPV was 79.2%.

CONCLUSIONS

The BRODERS radiomic predictive model performs well on an independent dataset and may facilitate the management of indeterminate pulmonary nodules.

Serum Exosomes and Their miRNA Load-A Potential Biomarker of Lung Cancer

Early detection of lung cancer in screening programs is a rational way to reduce mortality associated with this malignancy. Low-dose computed tomography, a diagnostic tool used in lung cancer screening, generates a relatively large number of false-positive results, and its complementation with molecular biomarkers would greatly improve the effectiveness of such programs. Several biomarkers of lung cancer based on different components of blood, including miRNA signatures, were proposed. However, only a few of them have been positively validated in the context of early cancer detection yet, which imposes a constant need for new biomarker candidates. An emerging source of cancer biomarkers are exosomes and other types of extracellular vesicles circulating in body fluids. Hence, different molecular components of serum/plasma-derived exosomes were tested and showed different levels in lung cancer patients and healthy individuals. Several studies focused on the miRNA component of these vesicles. Proposed signatures of exosome miRNA had promising diagnostic value, though none of them have yet been clinically validated. These signatures involved a few dozen miRNA species overall, including a few species that recurred in different signatures. It is worth noting that all these miRNA species have cancer-related functions and have been associated with lung cancer progression. Moreover, a few of them, including known oncomirs miR-17, miR-19, miR-21, and miR-221, appeared in multiple miRNA signatures of lung cancer based on both the whole serum/plasma and serum/plasma-derived exosomes.

Non-Coding RNAs as Prognostic Biomarkers: A miRNA Signature Specific for Aggressive Early-Stage Lung Adenocarcinomas

Lung cancer burden can be reduced by adopting primary and secondary prevention strategies such as anti-smoking campaigns and low-dose CT screening for high risk subjects (aged >50 and smokers >30 packs/year). Recent CT screening trials demonstrated a stage-shift towards earlier stage lung cancer and reduction of mortality (~20%). However, a sizable fraction of patients (30–50%) with early stage disease still experience relapse and an adverse prognosis. Thus, the identification of effective prognostic biomarkers in stage I lung cancer is nowadays paramount. Here, we applied a multi-tiered approach relying on coupled RNA-seq and miRNA-seq data analysis of a large cohort of lung cancer patients (TCGA-LUAD, n = 510), which enabled us to identify prognostic miRNA signatures in stage I lung adenocarcinoma. Such signatures showed high accuracy (AUC ranging between 0.79 and 0.85) in scoring aggressive disease. Importantly, using a network-based approach we rewired miRNA-mRNA regulatory networks, identifying a minimal signature of 7 miRNAs, which was validated in a cohort of FFPE lung adenocarcinoma samples (CSS, n = 44) and controls a variety of genes overlapping with cancer relevant pathways. Our results further demonstrate the reliability of miRNA-based biomarkers for lung cancer prognostication and make a step forward to the application of miRNA biomarkers in the clinical routine.

Genetic Markers in Lung Cancer Diagnosis: A Review

Lung cancer is the most often diagnosed cancer in the world and the most frequent cause of cancer death. The prognosis for lung cancer is relatively poor and 75% of patients are diagnosed at its advanced stage. The currently used diagnostic tools are not sensitive enough and do not enable diagnosis at the early stage of the disease. Therefore, searching for new methods of early and accurate diagnosis of lung cancer is crucial for its effective treatment. Lung cancer is the result of multistage carcinogenesis with gradually increasing genetic and epigenetic changes. Screening for the characteristic genetic markers could enable the diagnosis of lung cancer at its early stage. The aim of this review was the summarization of both the preclinical and clinical approaches in the genetic diagnostics of lung cancer. The advancement of molecular strategies and analytic platforms makes it possible to analyze the genome changes leading to cancer development-i.e., the potential biomarkers of lung cancer. In the reviewed studies, the diagnostic values of microsatellite changes, DNA hypermethylation, and p53 and KRAS gene mutations, as well as microRNAs expression, have been analyzed as potential genetic markers. It seems that microRNAs and their expression profiles have the greatest diagnostic potential value in lung cancer diagnosis, but their quantification requires standardization.

MicroRNA-based risk scoring system to identify early-stage oral squamous cell carcinoma patients at high-risk for cancer-specific mortality

Background

For early‐stage oral squamous cell carcinoma (OSCC), there is no existing risk‐stratification modality beyond conventional TNM staging system to identify patients at high risk for cancer‐specific mortality.

Methods

A total of 568 early‐stage OSCC patients who had surgery only and also with available 5‐year clinical outcomes data were identified. Signature microRNAs (miRNAs) were discovered using deep sequencing analysis and validated by qRT‐PCR. The final 5‐plex prognostic marker panel was utilized to generate a cancer‐specific mortality risk score using the multivariate Cox regression analyses. The prognostic markers were validated in the internal and external validation cohorts.

Results

The risk score from the 5‐plex marker panel consisting of miRNAs‐127‐3p, 4736, 655‐3p, TNM stage and histologic grading stratified patients into four risk categories. Compared to the low‐risk group, the high‐risk group had 23‐fold increased mortality risk (hazard ratio 23, 95% confidence interval 13‐42), with a median time‐to‐recurrence of 6 months and time‐to‐death of 11 months (vs >60 months for each among low‐risk patient; p < .001).

Conclusion

The miRNA‐based 5‐plex marker panel driven mortality risk score formula provides clinically practical and reliable measures to assess the prognosis of patients assigned to an early‐stage OSCC.