Identification of Driver Genes in Primary Liver Cancer by Integrating NGS and TCGA Mutation Data

Background: This study is aimed towards an exploration of mutant genes in primary liver cancer (PLC) patients by using bioinformatics and data mining techniques. Methods: Peripheral blood or paraffin-embedded tissues from 8 patients with PLC were analyzed using a 551 cancer-related gene panel on an Illumina NextSeq500 Sequencer (Illumina). Meanwhile, the data of 396 PLC cases were downloaded from The Cancer Genome Atlas (TCGA) database. The common mutated genes were obtained after integrating the mutation information of the above two cohorts, followed by functional enrichment and protein-protein interaction (PPI) analyses. Three well-known databases, including Vogelstein’s list, the Network of Cancer Gene (NCG), and the Catalog of Somatic Mutations in Cancer (COSMIC) database were used to screen driver genes. Furthermore, the Chi-square and logistic analysis were performed to analyze the correlation between the driver genes and clinicopathological characteristics, and Kaplan-Meier (KM) method and multivariate Cox analysis were conducted to evaluate the overall survival outcome. Results: In total, 84 mutation genes were obtained after 8 PLC patients undergoing gene mutation detection with next-generation sequencing (NGS). The top 100 most mutate gene data from PLC patients in TCGA database were downloaded. After integrating the above two cohorts, 17 common mutated genes were identified. Next, 11 driver genes were screened out by analyzing the intersection of the 17 mutation genes and the genes in the three well-known databases. Among them, RB1, TP53, and KRAS gene mutations were connected with clinicopathological characteristics, while all the 11 gene mutations had no relationship with overall survival. Conclusion: This study investigated the mutant genes with significant clinical implications in PLC patients, which may improve the knowledge of gene mutations in PLC molecular pathogenesis.

DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data

Identification of cancer driver genes plays an important role in precision oncology research,which is helpful to understand cancer initiation and progression.However,most existing computational methods mainly used the protein–protein interaction(PPI)networks,or treated the directed gene regulatory networks(GRNs)as the undirected gene–gene association networks to identify the cancer driver genes,which will lose the unique structure regulatory information in the directed GRNs,and then affect the outcome of the cancer driver gene identification.Here,based on the multi-omics pan-cancer data(i.e.,gene expression,mutation,copy number variation,and DNA methylation),we propose a novel method(called DGMP)to identify cancer driver genes by jointing directed graph convolutional network(DGCN)and multilayer perceptron(MLP).DGMP learns the multi-omics features of genes as well as the topological structure features in GRN with the DGCN model and uses MLP to weigh more on gene features for mitigating the bias toward the graph topological features in the DGCN learning process.The results on three GRNs show that DGMP outperforms other existing state-of-the-art methods.The ablation experimental results on the Dawn Net network indicate that introducing MLP into DGCN can offset the performance degradation of DGCN,and jointing MLP and DGCN can effectively improve the performance of identifying cancer driver genes.DGMP can identify not only the highly mutated cancer driver genes but also the driver genes harboring other kinds of alterations(e.g.,differential expression and aberrant DNA methylation)or genes involved in GRNs with other cancer genes.The source code of DGMP can be freely downloaded from https://github.com/NWPU-903PR/DGMP.

Annual progress of clinical research on targeted therapy for nonsmall cell lung cancer in 2022

With the rapid development of lung cancer molecular detection and precisiontherapy, targeted therapy has covered the entire process of diagnosis andtreatment of nonsmall cell lung cancer patients. Overall mortality from lungcancer has decreased significantly over the past 20 years, especially since theintroduction of targeted drugs in 2013. In 2022, targeted therapy for lungcancer has developed rapidly. The optimization of treatment modes and theexploration of new target drugs such as antibody‐drug conjugates will broadenthe selection range of nonsmall cell lung cancer patients with positive drivergenes. This article reviews the latest advances in targeted therapy for drivergene‐positive lung cancer in 2022.

T790M mutation in stage IV EGFR-mutated NSCLC patient with acquired resistance reverted to original 19Del mutation after administration of a series of precision treatments:a case report

Existing studies have yet to elucidate clearly the mechanisms of secondary resistance to third generation epidermal growth factor receptor(EGFR)tyrosine kinase inhibitors(TKIs),neither is there any established standard therapy for patients resistant to third generation EGFR-TKIs.This case report demonstrates a rare mutation pattern in a male patient with a pathologic diagnosis of non-small cell lung cancer(NSCLC)harboring an EGFR exon 19 deletion(19Del)mutation,who then acquired an EGFR-T790M mutation after developing resistance to the first generation EGFR-TKI(gefitinib).The mutation reverted to the original EGFR-19Del mutation after the patient developed secondary resistance against the third generation TKI(osimertinib).This patient eventually achieved partial response(PR)with second generation TKI(afatinib)as a fourth-line treatment.