Predictive biomarkers in nonsmall cell carcinoma and their clinico-pathological association

Personalized treatment using predictive biomarkers in solid organ malignancies: A review

In recent years, the influence of specific biomarkers in the diagnosis and prognosis of solid organ malignancies has been increasingly prominent. The relevance of the use of predictive biomarkers, which predict cancer response to specific forms of treatment provided, is playing a more significant role than ever before, as it affects diagnosis and initiation of treatment, monitoring for efficacy and side effects of treatment, and adjustment in treatment regimen in the long term. In the current review, we explored the use of predictive biomarkers in the treatment of solid organ malignancies, including common cancers such as colorectal cancer, breast cancer, lung cancer, prostate cancer, and cancers associated with high mortalities, such as pancreatic cancer, liver cancer, kidney cancer and cancers of the central nervous system. We additionally analyzed the goals and types of personalized treatment using predictive biomarkers, and the management of various types of solid organ malignancies using predictive biomarkers and their relative efficacies so far in the clinical settings.

Lung Cancer in Non-Smokers: Clinicopathological and Survival Differences from Smokers

Background Lung cancer in non-smokers is a clinically distinct entity based on unique epidemiology, clinicopathology, genetics, treatment response, and outcome. Data from Indian centres are scarce. The objective of this study was to compare the frequency, clinical characteristics, driver mutations, and survival of non-smoking and smoking lung cancer patients treated at a tertiary cancer centre in North India. Methodology Two years of data on 724 consecutive lung cancer patients were assessed. Clinical, demographics, smoking history, and EGFR and ALK mutation test results were collected. Descriptive and inferential statistics were applied. Survival analysis was performed using the Kaplan-Meier method. Results Non-smokers comprised 40.9% of the study sample. Non-smokers were more likely than smokers to experience disease onset at a younger age (P = 0.004) and metastasis (P < 0.001). The tumor histology showed significant differences (P < 0.001), with non-smokers more likely to be diagnosed with adenocarcinoma (77.4%), while squamous and small cell histologies were commonly found among smokers (37.6% and 13.8%, respectively). The EGFR mutation and ALK rearrangement rates in the cohort were 23.3% and 10.1%, respectively, and were more frequent in non-smoking patients. Overall, 10-year survival was 7%, with a significantly better survival rate of non-smokers than smokers (median survival time of 15.13 vs 10.17 months; P = 0.012). Conclusions About four out of 10 patients diagnosed with lung cancer at our centre were non-smokers. They were more often young, diagnosed at an advanced stage, with predominantly adenocarcinoma histology, and had a threefold higher frequency of EGFR mutations than smokers. In our cohort, non-smokers appear to be a targetable group with better survival than smokers.

Rare epidermal growth factor receptor gene alterations in non-small cell lung cancer patients, tyrosine kinase inhibitor response and outcome analysis

BACKGROUND

The predictive value of rare epidermal growth factor receptor gene (EGFR) mutations for non-small cell lung carcinoma (NSCLC) patients remain elusive. We evaluated the distribution, clinicopathological association, tyrosine kinase inhibitor (TKI) response, and outcome of NSCLC patients carrying uncommon EGFR aberrations in comparison to classical EGFR mutations.

METHODS

Treatment naïve, advanced NSCLC cases tested by Next-Generation sequencing (NGS) method between 2015 and 2020 were included. The objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and overall survival (OS) were analyzed.

RESULTS

A total of 237 tumor samples were sequenced. Among the sixty-nine (29%) EGFR mutated cases, 41 (59.4%) harbored classical mutation (37.7% Del19, 21.7% p.L858R). Non-classical aberrations included missense mutations in exon 18/20/21 (15.9%), EGFR amplification (8.7%), exon 20 insertions (7.2%), EGFR Variant III (4.3%), exon 18 indel (2.9%), exon 21 missense (2.9%) and exon 19 missense mutation (1.4%). These occurred as complex mutations in 16% of cases. Oral TKI was administered in 66.7% cases. The patients harboring non-classical variants had a lower ORR and DCR (23.1% and 61.5%) than those carrying a common mutation (57.6% and 84.8%). Collectively, compared to the patients with common EGFR mutations, the uncommon group showed early disease progression and had shorter overall survival.

CONCLUSION

NGS testing has the capacity to reveal a diverse spectrum of uncommon EGFR variants. Our study can contribute to the database of uncommon EGFR mutations with a positive influence on evidence-based care of advanced lung cancer patients with EGFR mutations.

Use of four genes in exosomes as biomarkers for the identification of lung adenocarcinoma and lung squamous cell carcinoma

The determination of biomarkers in the blood specific for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is crucial for the selection of effective treatment strategies and the prediction of prognosis. The purpose of the present study was to analyze the differentially expressed genes (DEGs) in LUSC and LUAD from The Cancer Genome Atlas (TCGA) database. In order to identify the potential biomarkers for non-small cell lung cancer (NSCLC) for clinical diagnosis, bioinformatics was used to analyze the DEGs of two subtypes of NSCLC, LUAD and LUSC. Exosomes were isolated from the serum of patients with LUAD or LUSC and identified using transmission electron microscopy, nanoparticle tracking analysis and western blot analysis. A total of four differential exosomal mRNAs were selected for validation with serum samples from 70 patients with NSCLC via reverse transcription-quantitative polymerase chain reaction. Receiver operating characteristic curves were established to evaluate the clinical diagnostic value of four DEGs for patients with LUAD and LUSC. The analysis based on TCGA data revealed the DEGs in LUSC and LUAD: A total of 1,619 genes were differentially expressed in patients with LUSC and LUAD. DEGs analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that inflammation-related signaling pathways, such as complement pathways, and multiple autoimmune diseases, such as systemic lupus erythematosus and asthma were mainly enriched in LUAD. The cell cycle, Hippo signaling pathway, Rap1 signaling pathway and Wnt signaling pathway were the main signaling pathways enriched in LUSC. The combination of tumor protein P63 (TP63), keratin 5 (KRT5), CEA cell adhesion molecule 6 (CEACAM6) and surfactant protein B (SFTPB) improved the specificity and sensitivity in the diagnosis of different lung cancer subtypes. Exosomal TP63, KRT5, CEACAM6 and SFTPB mRNAs can thus be used as biomarkers to differentiate between LUSC and LUAD, and may provide a novel strategy for their differential diagnosis and treatment.