Customised, Individualised Treatment of Metastatic Non-Small-Cell Lung Carcinoma (NSCLC)

Trends and Outcomes of Non-Small-Cell Lung Cancer in Omani Patients: Experience at a university hospital

OBJECTIVES

The incidence of lung cancer in Oman has shown a gradual but definitive increase since 2002. This study aimed to evaluate the demographic and epidemiological characteristics and survival outcomes of patients with non-small-cell lung cancer (NSCLC) at a university hospital in Oman.

METHODS

This study was conducted from January to June 2016. A retrospective analysis was performed of consecutive patients diagnosed with NSCLC presenting to the Sultan Qaboos University Hospital (SQUH) in Muscat, Oman, between March 2000 and December 2015. Clinical features at presentation and prognostic and predictive markers were reviewed. Kaplan-Meir estimates were used to determine relapse-free survival, progression-free survival (PFS) and overall survival (OS).

RESULTS

A total of 104 patients presented to SQUH during the study period. The median age at diagnosis was 64 years. Overall, 62 patients (59.6%) had adenocarcinomas. Only 12 patients (11.5%) presented in the early stages (I or II) of the disease and the majority of patients had an Eastern Cooperative Oncology Group performance status of 1 (27.9%) or 2 (26.0%). The prevalence of epidermal growth factor receptor mutations was 27.9%. The median PFS for patients with advanced disease (stages III or IV) was five months and the median OS for all patients was seven months. After five years, 50.0%, 60.0%, 10.0% and 8.0% of patients with disease stages I, II, III and IV, respectively, were alive.

CONCLUSION

Patients with NSCLC in Oman were found to present at an advanced stage. However, patient outcomes were similar to those reported in the USA.

Multivariate models from RNA-Seq SNVs yield candidate molecular targets for biomarker discovery: SNV-DA

BACKGROUND

It has recently been shown that significant and accurate single nucleotide variants (SNVs) can be reliably called from RNA-Seq data. These may provide another source of features for multivariate predictive modeling of disease phenotype for the prioritization of candidate biomarkers. The continuous nature of SNV allele fraction features allows the concurrent investigation of several genomic phenomena, including allele specific expression, clonal expansion and/or deletion, and copy number variation.

RESULTS

The proposed software pipeline and package, SNV Discriminant Analysis (SNV-DA), was applied on two RNA-Seq datasets with varying sample sizes sequenced at different depths: a dataset containing primary tumors from twenty patients with different disease outcomes in lung adenocarcinoma and a larger dataset of primary tumors representing two major breast cancer subtypes, estrogen receptor positive and triple negative. Predictive models were generated using the machine learning algorithm, sparse projections to latent structures discriminant analysis. Training sets composed of RNA-Seq SNV features limited to genomic regions of origin (e.g. exonic or intronic) and/or RNA-editing sites were shown to produce models with accurate predictive performances, were discriminant towards true label groupings, and were able to produce SNV rankings significantly different from than univariate tests. Furthermore, the utility of the proposed methodology is supported by its comparable performance to traditional models as well as the enrichment of selected SNVs located in genes previously associated with cancer and genes showing allele-specific expression. As proof of concept, we highlight the discovery of a previously unannotated intergenic locus that is associated with epigenetic regulatory marks in cancer and whose significant allele-specific expression is correlated with ER+ status; hereafter named ER+ associated hotspot (ERPAHS).

CONCLUSION

The use of models from RNA-Seq SNVs to identify and prioritize candidate molecular targets for biomarker discovery is supported by the ability of the proposed method to produce significantly accurate predictive models that are discriminant towards true label groupings. Importantly, the proposed methodology allows investigation of mutations outside of exonic regions and identification of interesting expressed loci not included in traditional gene annotations. An implementation of the proposed methodology is provided that allows the user to specify SNV filtering criteria and cross-validation design during model creation and evaluation.

Emerging role of long noncoding RNAs in lung cancer: Current status and future prospects

Lung cancer is the leading cause of cancer-related death worldwide with a 5-year survival rate of less than 15%, despite significant advances in both diagnostic and therapeutic approaches. Combined genomic and transcriptomic sequencing studies have identified numerous genetic driver mutations that are responsible for the development of lung cancer. Importantly, these approaches have also uncovered the widespread expression of "noncoding RNAs" including long noncoding RNAs (LncRNAs), which impact biologic responses through the regulation of mRNA transcription or translation. To date, most studies of the role of noncoding RNAs have focused on LncRNAs, which regulate mRNA translation via the RNA interference pathway. Although many of their attributes, such as patterns of expression, remain largely unknown, LncRNAs have key functions in transcriptional, post-transcriptional, and epigenetic gene regulation. Recent research showed that LncRNAs regulate flowering time in the lung cancer. In this review, we discuss these investigations into long noncoding RNAs were performed almost exclusively in lung cancer. Future work will need to extend these into lung cancer and to analyze how LncRNAs interact to regulate mRNA expression. From a clinical perspective, the targeting of LncRNAs as a novel therapeutic approach will require a deeper understanding of their function and mechanism of action.

Downregulation of both EGFR and ErbB3 improves the cellular response to pemetrexed in an established pemetrexed‑resistant lung adenocarcinoma A549 cell line

Epidermal growth factor receptor (EGFR) and ErbB3 (HER3) play important roles in the regulation of cell proliferation, differentiation, anti-apoptosis and chemoresistance; however, their dysregulation in pemetrexed (PEM) resistance remains unclear. The aim of the present study was to clarify the relationship between PEM resistance and gene expression of EGFR and ErbB3, by establishing the PEM-resistant lung adenocarcinoma A549 cell line, A549/PEM. Compared with A549 cells, the A549/PEM cells were significantly more resistant to PEM (P=0.0024). The downregulation of S phase and arrest at G1 stage were detected in the A549/PEM cell line when compared to the A549 cells (P<0.05). The apoptosis rate of A549/PEM cells was much lower than that of the A549 cells after a 24 h continuous exposure to PEM (P<0.001). Real-time PCR and western blotting demonstrated the overexpression of EGFR and ErbB3 in A549/PEM cells. However, downregulation of EGFR or ErbB3 by lentiviral delivered shRNAs in A549/PEM cells showed no significant correlation with PEM sensitivity while silencing both EGFR and ErbB3 increased the cellular response to PEM in the A549/PEM cells and significantly decreased phosphorylation of STAT3, AKT and ERK. Together, these data suggest that either high expression of EGFR or ErbB3 plays a critical role in the cellular response to PEM in human lung adenocarcinoma cells though EGFR/ErbB3-dependent pathways.