Sequential Whole Exome Sequencing Reveals Somatic Mutations Associated with Platinum Response in NSCLC

Read Count Patterns and Detection of Cancerous Copy Number Alterations in Plasma Cell-Free DNA Whole Exome Sequencing Data for Advanced Non-Small Cell Lung Cancer

Plasma cell-free DNA (cfDNA) sequencing data have been widely studied for early diagnosis and treatment response or recurrence monitoring of cancers because of the non-invasive benefits. In cancer studies, whole exome sequencing (WES) is mostly used for discovering single nucleotide variants (SNVs), but it also has the potential to detect copy number alterations (CNAs) that are mostly discovered by whole genome sequencing or microarray. In clinical settings where the quantity of the acquired blood from the patients is limited and where various sequencing experiments are not possible, providing various types of mutation information such as CNAs and SNVs using only WES will be helpful in the treatment decision. Here, we questioned whether the plasma cfDNA WES data for patients with advanced non-small cell lung cancer (NSCLC) could be exploited for CNA detection. When the read count (RC) signals of the WES data were investigated, a similar fluctuation pattern was observed among the signals of different samples, and it can be a major challenge hindering CNA detection. When these RC patterns among cfDNA were suppressed by the method we proposed, the cancerous CNAs were more distinguishable in some samples with higher cfDNA quantity. Although the potential to detect CNAs using the plasma cfDNA WES data for NSCLC patients was studied here, further studies with other cancer types, with more samples, and with more sophisticated techniques for bias correction are required to confirm our observation. In conclusion, the detection performance for cancerous CNAs can be improved by controlling RC bias, but it depends on the quantity of cfDNA in plasma.

Identification of the Prognostic Significance of Somatic Mutation-Derived LncRNA Signatures of Genomic Instability in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is a highly heterogeneous tumor with substantial somatic mutations and genome instability, which are emerging hallmarks of cancer. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in genomic instability. However, the identification of genome instability-related lncRNAs (GInLncRNAs) and their clinical significance has not been investigated in LUAD.

Methods: We determined GInLncRNAs by combining somatic mutation and transcriptome data of 457 patients with LUAD and probed their potential function using co-expression network and Gene Ontology (GO) enrichment analyses. We then filtered GInLncRNAs by Cox regression and LASSO regression to construct a genome instability-related lncRNA signature (GInLncSig). We subsequently evaluated GInLncSig using correlation analyses with mutations, external validation, model comparisons, independent prognostic significance analyses, and clinical stratification analyses. Finally, we established a nomogram for prognosis prediction in patients with LUAD and validated it in the testing set and the entire TCGA dataset.

Results: We identified 161 GInLncRNAs, of which seven were screened to develop a prognostic GInLncSig model (LINC01133, LINC01116, LINC01671, FAM83A-AS1, PLAC4, MIR223HG, and AL590226.1). GInLncSig independently predicted the overall survival of patients with LUAD and displayed an improved performance compared to other similar signatures. Furthermore, GInLncSig was related to somatic mutation patterns, suggesting its ability to reflect genome instability in LUAD. Finally, a nomogram comprising the GInLncSig and tumor stage exhibited improved robustness and clinical practicability for predicting patient prognosis.

Conclusion: Our study identified a signature for prognostic prediction in LUAD comprising seven lncRNAs associated with genome instability, which may provide a useful indicator for clinical stratification management and treatment decisions for patients with LUAD.

Effect of Wenxia Changfu Formula Combined With Cisplatin Reversing Non-Small Cell Lung Cancer Cell Adhesion-Mediated Drug Resistance

Non-small cell lung cancer (NSCLC), the major form of primary lung cancer, is a common cause of cancer-related death worldwide. Cell adhesion-mediated drug resistance (CAM-DR), a form of chemotherapy resistance, has been reported to confer resistance to various chemotherapeutic agents. Integrin β1 signaling plays an important role in CAM-DR and has been proposed as a potential target for NSCLC. Wenxia Changfu Formula (WCF) is a Traditional Chinese Compound Prescription for the intervention treatment of NSCLC combined with cisplatin (DDP). This study aims to investigate the effect and mechanism of WCF combined with DDP in reversing CAM-DR. Firstly, the chemical profile of WCF was characterized by UPLC/Q-TOF-MS analysis. A total of 237 compounds with mzCloud Best Match of greater than 70 were identified by using the online database mzCloud. Secondly, we established A549 three-dimensional(3D) cells cultured in vitro and nude mice xenografts models of the A549 cell line with Integrin β1 overexpression. In vitro, the cell viability, migration and adhesion were measured though MTT Assay, Wound Healing Assay and Cell Adhesion Assay, the Integrin β1 expression of the A549 cells was assessed through immunocytochemistry; in vitro, the transplanted tumor morphology and the colocalization of Integrin β1 and its ligands were tested by HE staining and immunofluorescence. As a result, we found that the combination effectively reduced cell viability, suppressed migration and adhesion, and downregulated the protein level of Integrin β1 in three-dimensional cultured A549 cells. And the combination of WCF with DDP significantly inhibited tumor growth, increased organelle vacuolations and decreased colocalization of Integrin β1 and its ligands including fibulin-2 and laminin. Taken together, our results confirm that the combination of WCF with DDP could reverse the lung cancer CAM-DR through the Integrin β1 signaling pathway.