Prognosis of non-small cell lung cancer with postoperative regional lymph node recurrence

BACKGROUND

Regional lymph node recurrence after radical surgery for non-small cell lung cancer (NSCLC) is an oligo-recurrent disease; however, no treatment strategy has been established. In the present study we aimed to determine the clinical outcomes of postoperative regional lymph node recurrence and identify prognostic predictors in the era of molecular-targeted therapy.

METHODS

We retrospectively analyzed data on clinical characteristics and outcomes of patients with regional lymph node recurrence after surgery who underwent treatment for NSCLC between 2002 and 2022.

RESULTS

A total of 53 patients were included in this study. The median time between surgery and detection of recurrence was 1.21 years. Radiotherapy (RT) alone and chemoradiotherapy (CRT) were performed in 38 and six patients, respectively. Driver gene alterations were detected in eight patients (EGFR: 6, ROS1:1, and BRAF: 1) and programmed death-ligand 1 (PD-L1) expression was examined in 22 patients after 2016. Median progression-free survival (PFS) and overall survival (OS) after lymph node recurrences were 1.32 and 4.34 years, respectively. Multiple lymph node recurrence was an independent prognostic factor for PFS, whereas driver gene alteration was the only prognostic factor for OS. There was no significant difference in the OS between patients stratified according to the initial treatment modality for lymph node recurrence.

CONCLUSION

Our results suggest that the number of tumor recurrences may correlate with PFS, while detection of driver gene alterations could guide decision-making for the appropriate molecular-targeted therapy to achieve longer OS.

Copy number variations in esophageal squamous cell carcinoma: Emerging cancer drivers and biomarkers (Review)

The morbidity and mortality of esophageal squamous cell carcinoma (ESCC) remains high in China. ESCC is significantly influenced by a complex interplay of environmental and genetic factors. Copy number variations (CNVs) are a major form of genome‑scale changes in ESCC and are closely related to tumorigenesis and development. Genome‑wide detection and analysis allow the identification of important CNV‑affected genes with potential clinical applications. In both coding and non‑coding regions, CNVs have been identified frequently in certain segments of chromosomes. CNV‑impacted genes have crucial roles in multiple cellular processes, including proliferation, apoptosis, metastasis, and metabolic pathways. More importantly, they may serve as potential therapeutic targets for patients with ESCC. Therefore, studying the role of CNVs in ESCC is helpful to explore the pathogenesis of ESCC and to find effective treatment targets, which have profound implications for the diagnosis and therapy of ESCC.

Association of Germline Variation in Driver Genes with Breast Cancer Risk in Chilean Population

Cancer is a genomic disease, with driver mutations contributing to tumorigenesis. These potentially heritable variants influence risk and underlie familial breast cancer (BC). This study evaluated associations between BC risk and 13 SNPs in driver genes MAP3K1, SF3B1, SMAD4, ARID2, ATR, KMT2C, MAP3K13, NCOR1, and TBX3, in BRCA1/2-negative Chilean families. SNPs were genotyped using TaqMan Assay in 492 cases and 1285 controls. There were no associations between rs75704921:C>T (ARID2); rs2229032:A>C (ATR); rs3735156:C>G (KMT2C); rs2276738:G>C, rs2293906:C>T, rs4075943T:>A, rs13091808:C>T (MAP3K13); rs178831:G>A (NCOR1); or rs3759173:C>A (TBX3) and risk. The MAP3K1 rs832583 A allele (C/A+A/A) showed a protective effect in families with moderate BC history (OR = 0.7 [95% CI 0.5-0.9] p = 0.01). SF3B1 rs16865677-T (G/T+T/T) increased risk in sporadic early-onset BC (OR = 1.4 [95% CI 1.0-2.0] p = 0.01). SMAD4 rs3819122-C (A/C+C/C) increased risk in cases with moderate family history (OR = 2.0 [95% CI 1.3-2.9] p ≤ 0.0001) and sporadic cases diagnosed ≤50 years (OR = 1.6 [95% CI 1.1-2.2] p = 0.006). SMAD4 rs12456284:A>G increased BC risk in G-allele carriers (A/G + G/G) in cases with ≥2 BC/OC cases and early-onset cases (OR = 1.2 [95% CI 1.0-1.6] p = 0.04 and OR = 1.4 [95% CI 1.0-1.9] p = 0.03, respectively). Our study suggests that specific germline variants in driver genes MAP3K1, SF3B1, and SMAD4 contribute to BC risk in Chilean population.

Chromosome instability region analysis and identification of the driver genes of the epithelial ovarian cancer cell lines A2780 and SKOV3

Epithelial ovarian cancer (EOC) is one of the most prevalent gynaecological cancers worldwide. The molecular mechanisms of serous ovarian cancer (SOC) remain unclear and not well understood. SOC cases are primarily diagnosed at the late stage, resulting in a poor prognosis. Advances in molecular biology techniques allow us to obtain a better understanding of precise molecular mechanisms and to identify the chromosome instability region and key driver genes in the carcinogenesis and progression of SOC. Whole-exome sequencing was performed on the normal ovarian cell line IOSE80 and the EOC cell lines SKOV3 and A2780. The single-nucleotide variation burden, distribution, frequency and signature followed the known ovarian mutation profiles, without chromosomal bias. Recurrently mutated ovarian cancer driver genes, including LRP1B, KMT2A, ARID1A, KMT2C and ATRX were also found in two cell lines. The genome distribution of copy number alterations was found by copy number variation (CNV) analysis, including amplification of 17q12 and 4p16.1 and deletion of 10q23.33. The CNVs of MED1, GRB7 and MIEN1 located at 17q12 were found to be correlated with the overall survival of SOC patients (MED1: p = 0.028, GRB7: p = 0.0048, MIEN1: p = 0.0051), and the expression of the three driver genes in the ovarian cell line IOSE80 and EOC cell lines SKOV3 and A2780 was confirmed by western blot and cell immunohistochemistry.

Heterogeneity in the immune microenvironment of bone metastasis in driver-positive non-small cell lung cancer

Mutations in epidermal growth factor receptor and anaplastic lymphoma kinase are common driver events in non-small cell lung cancer (NSCLC), which are associated with a high frequency of bone metastases (BMs). While the bone marrow represents a specialized immune microenvironment, the immune repertoire of BMs remains unknown. Considering the higher incidence of BMs in driver gene-positive NSCLCs, and the unique biology of the bone, herein, we assessed the infiltrating immune cells and T cell receptor (TCR) profile of BMs in driver-positive NSCLCs. Immune profile of BMs in driver gene-positive NSCLC were assessed in 10 patients, where 6 had driver gene-positive mutation. TCR and bulk RNA sequencing were performed on malignant bone samples. The diversity and clonality of the TCR repertoire were analyzed. The cellular components were inferred from bulk gene expression profiles computationally by CIBERSORT. Although BMs were generally regarded as immune-cold tumors, immune cell composition analyses showed co-existence of cytotoxic and suppressor immune cells in driver-positive BM samples, as compared to primary lung. Analysis of the TCR repertoire indicated a trend of higher diversity and similar clonality in the driver-positive compared with the driver-negative subsets. In addition, we identified two cases that showed the opposite response to immune checkpoint blockade. A comparison of these two patients' BM samples showed more highly amplified clones, fewer M2 macrophages and more activated natural killer cells in the responder. In summary, BMs in NSCLC are heterogeneous in their immune microenvironment, which might be related to differential clinical outcomes to immune checkpoint blockade.

DRdriver: identifying drug resistance driver genes using individual-specific gene regulatory network

Drug resistance is one of principal limiting factors for cancer treatment. Several mechanisms, especially mutation, have been validated to implicate in drug resistance. In addition, drug resistance is heterogeneous, which makes an urgent need to explore the personalized driver genes of drug resistance. Here, we proposed an approach DRdriver to identify drug resistance driver genes in individual-specific network of resistant patients. First, we identified the differential mutations for each resistant patient. Next, the individual-specific network, which included the genes with differential mutations and their targets, was constructed. Then, the genetic algorithm was utilized to identify the drug resistance driver genes, which regulated the most differentially expressed genes and the least non-differentially expressed genes. In total, we identified 1202 drug resistance driver genes for 8 cancer types and 10 drugs. We also demonstrated that the identified driver genes were mutated more frequently than other genes and tended to be associated with the development of cancer and drug resistance. Based on the mutational signatures of all driver genes and enriched pathways of driver genes in brain lower grade glioma treated by temozolomide, the drug resistance subtypes were identified. Additionally, the subtypes showed great diversity in epithelial-mesenchyme transition, DNA damage repair and tumor mutation burden. In summary, this study developed a method DRdriver for identifying personalized drug resistance driver genes, which provides a framework for unlocking the molecular mechanism and heterogeneity of drug resistance.

Discovery of Drug-Responsive Phenomic Alteration-Related Driver Genes in the Treatment of Coronary Heart Disease

Background

The Xueyu Zheng (XYZ) phenome is central to coronary heart disease (CHD), but efforts to detect genetic associations in the XYZ phenome have been disappointing.

Methods

The phenomic alteration-related genes (PARGs) for the XYZ phenome were screened using |ρ| > 0.4 and p < 0.05 after treatment with Danhong injection at day 14 and day 30. Then, the driver genes for the Protein-Protein Interaction (PPI) networks of the PARGs established using STRING 11.0 were detected using a personalized network control algorithm (PNC). Finally, the molecular correlations of the driver genes with the XYZ phenome were analyzed with the Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways from a holistic viewpoint.

Results

A total of 525 and 309 PARGs in the XYZ phenome at day 14 and day 30 were identified. These genes were separately enriched in 48 and 35 pathways. Furthermore, five driver genes were detected. These genes were mainly correlated with endoplasmic reticulum stress-mediated apoptosis and autophagy regulation, which could suppress atherosclerosis progression.

Conclusion

Our study detected the drug-responsive PARGs of the XYZ phenome in CHD and provides an exemplary strategy to investigate the genetic associations among this common phenome and its component symptoms in patients with CHD.

Trial Registration

ClinicalTrials.gov, NCT01681316; registered on September 7, 2012.

Revealing the functions of clonal driver gene mutations in patients based on evolutionary dependencies

The clonal mutations in driver genes enable cells to gradually acquire growth advantage in tumor development. Therefore, revealing the functions of clonal driver gene mutations is important. Here, we proposed the method FCMP that considered evolutionary dependencies to analyze the functions of clonal driver gene mutations in a single patient. Applying our method to five cancer types from The Cancer Genome Atlas, we identified specific functions and common functions of clonal driver gene mutations. We found that the clonal driver gene mutations in the same patient played multiple functions. We also found that clonal mutations in the same driver gene performed different functions in different patients. These findings suggested that the clonal driver gene mutations showed strong tumor heterogeneity. In the pan-cancer analysis, the immune-related functions for clonal driver gene mutations were shared by multiple cancer types. In addition, clonal mutations in some driver genes predicted the survival of patients in cancers.

Concordance of Genomic Profiles in Matched Tissue and Plasma Samples From Chinese Patients With Lung Cancer

BACKGROUND

Next-generation sequencing (NGS) has been widely used to identify targetable variants for patients with solid tumors, especially lung cancer. Circulating tumor DNA (ctDNA) has emerged as an alternative approach for tumor biopsy. However, the feasibility of ctDNA in detecting molecular variants remains debatable.

METHODS

Herein, we performed NGS on matched tissue and plasma samples from 146 Chinese patients with lung cancer. The concordance of variants between tissue and plasma samples was explored at patient and variant levels.

RESULTS

More than 80% of patients harbored at least one concordant variant in tissue and plasma samples. A total of 506 variants were shared between tissue and plasma samples, and 432 variants were identified in tissue only and 92 variants were identified in plasma only. The sensitivity and positive predictive value (PPV) of all variants detected in plasma were 53.9% and 84.6%, respectively. High concordance was observed in several driver genes. In details, epidermal growth factor receptor exon 19 deletion (EGFR 19del), EGFR p.S768I, anaplastic lymphoma kinase (ALK) fusion, rearranged during transfection (RET) fusion, and kirsten rat sarcoma viral oncogene homolog (KRAS) p.G12C achieved a sensitivity of 90%, 100%, 85.7%, 100%, and 85.7%, respectively. Four EGFR-altered lung adenocarcinoma patients who underwent ctDNA-based NGS at initial diagnosis benefited from first-line gefitinib/icotinib with a median progression-free survival of 379.5 days.

CONCLUSIONS

Our work provided the clinical evidence of feasibility of ctDNA-based NGS in guiding decision-making in treatment. ctDNA-based NGA could be a reliable alternative approach for tissue biopsy in patients with lung cancer.

Autoantibody against Tumor-Associated Antigens as Diagnostic Biomarkers in Hispanic Patients with Hepatocellular Carcinoma

Background: Tumor-associated antigens (TAAs) have been investigated for many years as potential early diagnosis tools, especially for hepatocellular carcinoma (HCC). Nonetheless, very few studies have focused on the Hispanic HCC group that may be associated with distinct etiological risk factors. In the present study, we investigated novel anti-TAA autoantibodies as diagnostic biomarkers for Hispanic HCC patients. Methods: Novel TAA targets were identified by the serological proteome analysis (SERPA) and from differentially expressed HCC driver genes via bioinformatics. The autoantibody levels were validated by enzyme-linked immunosorbent assay (ELISA). Results: Among 19 potential TAA targets, 4 anti-TAA autoantibodies were investigated as potential diagnostic biomarkers with significantly high levels in Hispanic HCC sera, including DNA methyltransferase 3A (DNMT3A), p16, Hear shock protein 60 (Hsp60), and Heat shock protein A5 (HSPA5). The area under the ROC curve (AUC) value of the single autoantibodies varies from 0.7505 to 0.8885. After combining all 4 autoantibodies, the sensitivity of the autoantibody panel increased to 75% compared to the single one with the highest value of 45.8%. In a separate analysis of the Asian cohort, autoantibodies against HSPA5 and p16 showed significantly elevated levels in HCC compared to normal healthy controls, but not for DNMT3A or HSP60. Conclusion: Anti-DNMT3A, p16, HSPA5, and HSP60 autoantibodies have the potential to be diagnostic biomarkers for Hispanic HCC patients, of which DNMT3A and HSP60 might be exclusive for Hispanic HCC diagnosis.

Pan-KRAS inhibitors suppress proliferation through feedback regulation in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is currently one of the most lethal cancers worldwide. Several basic studies have confirmed that Kirsten rat sarcoma virus (KRAS) is a key driver gene for the occurrence of PDAC, and KRAS mutations have also been found in most patients in clinical studies. In this study, two pan-KRAS inhibitors, BI-2852 and BAY-293, were chosen as chemical probes to investigate their antitumor potency in PDAC. Their inhibitory effects on KRAS activation were validated in vitro and their antiproliferative potency in PDAC cell lines were profiled, with half-maximal inhibitory concentration (IC50) values of approximately 1 μM, demonstrating the therapeutic potential of pan-KRAS inhibitors in the treatment of PDAC. However, feedback regulation in the KRAS pathway weakened inhibitor activity, which was observed by a 50 times difference in BAY-293 from in vitro activity. Furthermore, pan-KRAS inhibitors effectively inhibited cell proliferation in 3D organoids cultured from PDAC patient samples; however, there were some variations between individuals. These results provide a sufficient theoretical foundation for KRAS as a clinical therapeutic target and for the application of pan-KRAS inhibitors in the treatment of PDAC, with important scientific significance in translational medicine.

Somatic variation in normal tissues: friend or foe of cancer early detection?

BACKGROUND

Seemingly normal tissues progressively become populated by mutant clones over time. Most of these clones bear mutations in well-known cancer genes but only rarely do they transform into cancer. This poses questions on what triggers cancer initiation and what implications somatic variation has for cancer early detection.

DESIGN

We analysed recent mutational screens of healthy and cancer-free diseased tissues to compare somatic drivers and the causes of somatic variation across tissues. We then reviewed the mechanisms of clonal expansion and their relationships with age and diseases other than cancer. We finally discussed the relevance of somatic variation for cancer initiation and how it can help or hinder cancer detection and prevention.

RESULTS

The extent of somatic variation is highly variable across tissues and depends on intrinsic features, such as tissue architecture and turnover, as well as the exposure to endogenous and exogenous insults. Most somatic mutations driving clonal expansion are tissue-specific and inactivate tumor suppressor genes involved in chromatin modification and cell growth signaling. Some of these genes are more frequently mutated in normal tissues than cancer, indicating a context-dependent cancer promoting or protective role. Mutant clones can persist over a long time or disappear rapidly, suggesting that their fitness depends on the dynamic equilibrium with the environment. The disruption of this equilibrium is likely responsible for their transformation into malignant clones and knowing what triggers this process is key for cancer prevention and early detection. Somatic variation should be considered in liquid biopsy, where it may contribute cancer-independent mutations, and in the identification of cancer drivers, since not all mutated genes favoring clonal expansion also drive tumorigenesis.

CONCLUSIONS

Somatic variation and the factors governing homeostasis of normal tissues should be taken into account when devising strategies for cancer prevention and early detection.

Novel Diagnostic Value of Driver Gene Transcription Signatures to Characterise Clear Cell Renal Cell Carcinoma, ccRCC

Routine molecular tumour diagnostics are augmented by DNA-based qualitative and quantitative molecular techniques detecting mutations of DNA. However, in the past decade, it has been unravelled that the phenotype of cancer, as it’s an extremely complex disease, cannot be fully described and explained by single or multiple genetic variants affecting only the coding regions of the genes. Moreover, studying the manifestation of these somatic mutations and the altered transcription programming—driven by genomic rearrangements, dysregulation of DNA methylation and epigenetic landscape—standing behind the tumorigenesis and detecting these changes could provide a more detailed characterisation of the tumour phenotype. Consequently, novel comparative cancer diagnostic pipelines, including DNA- and RNA-based approaches, are needed for a global assessment of cancer patients. Here we report, that by monitoring the expression patterns of key tumour driver genes by qPCR, the normal and the tumorous samples can be separated into distinct categories. Furthermore, we also prove that by examining the transcription signatures of frequently affected genes at 3p25, 3p21 and 9p21.3 genomic regions, the ccRCC (clear cell renal cell carcinoma) and non-tumorous kidney tissues can be distinguished based on the mRNA level of the selected genes. Our results open new diagnostics possibilities where the mRNA signatures of tumour drivers can supplement the DNA-based approaches providing a more precise diagnostics opportunity leading to determine more precise therapeutic protocols.

Evidence for reduced BRCA2 functional activity in Homo sapiens after divergence from the chimpanzee-human last common ancestor

We performed a comparative analysis of human and 12 non-human primates to identify sequence variations in known cancer genes. We identified 395 human-specific fixed non-silent substitutions that emerged during evolution of human. Using bioinformatics analyses for functional consequences, we identified a number of substitutions that are predicted to alter protein function; one of these mutations is located at the most evolutionarily conserved domain of human BRCA2.

Comprehensive Analysis of Subtype-Specific Molecular Characteristics of Colon Cancer: Specific Genes, Driver Genes, Signaling Pathways, and Immunotherapy Responses

Colon cancer is a complex, heterogeneous disease. The Colorectal Cancer Subtyping Consortium reported a novel classification system for colon cancer in 2015 to better understand its heterogeneity. This molecular classification system divided colon cancer into four distinct consensus molecular subtypes (CMS 1, 2, 3, and 4). However, the characteristics of different colon cancer molecular subtypes have not been fully elucidated. This study comprehensively analyzed the molecular characteristics of varying colon cancer subtypes using multiple databases and algorithms, including The Cancer Genome Atlas (TCGA) database, DriverDBv3 database, CIBERSORT, and MCP-counter algorithms. We analyzed the alterations in the subtype-specific genes of different colon cancer subtypes, such as the RNA levels and DNA alterations, and showed that specific subtype-specific genes significantly affected prognosis. We also explored the changes in colon cancer driver genes and representative genes of 10 signaling pathways in different subtypes. We identified genes that were altered in specific subtypes. We further detected the infiltration of 22 immune cell types in four colon cancer subtypes and the infiltration level of primary immune cells among these subtypes. Additionally, we explored changes in immune checkpoint genes (ICGs) and immunotherapy responses among different colon cancer subtypes. This study may provide clues for the molecular mechanism of tumorigenesis and progression in colon cancer. It also offers potential biomarkers and targets for the clinical diagnosis and treatment of different colon cancer subtypes.

Multidimensional Mutational Profiling of the Indian HNSCC Sub-Population Provides IRAK1, a Novel Driver Gene and Potential Druggable Target

Head and neck squamous cell carcinomas (HNSCC) include heterogeneous group of tumors, classified according to their anatomical site. It is the sixth most prevalent cancer globally. Among South Asian countries, India accounts for 40% of HNC malignancies with significant morbidity and mortality. In the present study, we have performed exome sequencing and analysis of 51 Head and Neck squamous cell carcinoma samples. Besides known mutations in the oncogenes and tumour suppressors, we have identified novel gene signatures differentiating buccal, alveolar, and tongue cancers. Around 50% of the patients showed mutation in tumour suppressor genes TP53 and TP63. Apart from the known mutations, we report novel mutations in the genes AKT1, SPECC1, and LRP1B, which are linked with tumour progression and patient survival. A highly curated process was developed to identify survival signatures. 36 survival-related genes were identified based on the correlation of functional impact of variants identified using exome-seq with gene expression from transcriptome data (GEPIA database) and survival. An independent LASSO regression analysis was also performed. Survival signatures common to both the methods led to identification of 4 dead and 3 alive gene signatures, the accuracy of which was confirmed by performing a ROC analysis (AUC=0.79 and 0.91, respectively). Also, machine learning-based driver gene prediction tool resulted in the identification of IRAK1 as the driver (p-value = 9.7 e-08) and also as an actionable mutation. Modelling of the IRAK1 mutation showed a decrease in its binding to known IRAK1 inhibitors.

Two-stage-vote ensemble framework based on integration of mutation data and gene interaction network for uncovering driver genes

Identifying driver genes, exactly from massive genes with mutations, promotes accurate diagnosis and treatment of cancer. In recent years, a lot of works about uncovering driver genes based on integration of mutation data and gene interaction networks is gaining more attention. However, it is in suspense if it is more effective for prioritizing driver genes when integrating various types of mutation information (frequency and functional impact) and gene networks. Hence, we build a two-stage-vote ensemble framework based on somatic mutations and mutual interactions. Specifically, we first represent and combine various kinds of mutation information, which are propagated through networks by an improved iterative framework. The first vote is conducted on iteration results by voting methods, and the second vote is performed to get ensemble results of the first poll for the final driver gene list. Compared with four excellent previous approaches, our method has better performance in identifying driver genes on $33$ types of cancer from The Cancer Genome Atlas. Meanwhile, we also conduct a comparative analysis about two kinds of mutation information, five gene interaction networks and four voting strategies. Our framework offers a new view for data integration and promotes more latent cancer genes to be admitted.

GET4 is a novel driver gene in colorectal cancer that regulates the localization of BAG6, a nucleocytoplasmic shuttling protein

Colorectal cancer (CRC) is one of the most common types of cancer and a significant cause of cancer mortality worldwide. Further improvements of CRC therapeutic approaches are needed. BCL2‐associated athanogene 6 (BAG6), a multifunctional scaffold protein, plays an important role in tumor progression. However, regulation of BAG6 in malignancies remains unclear. This study showed that guided entry of tail‐anchored proteins factor 4 (GET4), a component of the BAG6 complex, regulates the intercellular localization of BAG6 in CRC. Furthermore, GET4 was identified as a candidate driver gene on the short arm of chromosome 7, which is often amplified in CRC, by our bioinformatics approach using the CRC dataset from The Cancer Genome Atlas. Clinicopathologic and prognostic analyses using CRC datasets showed that GET4 was overexpressed in tumor cells due to an increased DNA copy number. High GET4 expression was an independent poor prognostic factor in CRC, whereas BAG6 was mainly overexpressed in the cytoplasm of tumor cells without gene alteration. The biological significance of GET4 was examined using GET4 KO CRC cells generated with CRISPR‐Cas9 technology or transfected CRC cells. In vitro and in vivo analyses showed that GET4 promoted tumor growth. It appears to facilitate cell cycle progression by cytoplasmic enrichment of BAG6‐mediated p53 acetylation followed by reduced p21 expression. In conclusion, we showed that GET4 is a novel driver gene and a prognostic biomarker that promotes CRC progression by inducing the cytoplasmic transport of BAG6. GET4 could be a promising therapeutic molecular target in CRC.

Uncovering the Subtype-Specific Molecular Characteristics of Breast Cancer by Multiomics Analysis of Prognosis-Associated Genes, Driver Genes, Signaling Pathways, and Immune Activity

Breast cancer is a heterogeneous malignant disease with different prognoses and has been divided into four molecular subtypes. It is believed that molecular events occurring in breast stem/progenitor cells contribute to the carcinogenesis and development of different breast cancer subtypes. However, these subtype-specific molecular characteristics are largely unknown. In this study, we employed 1217 breast cancer samples from The Cancer Genome Atlas (TCGA) database for a multiomics analysis of the molecular characteristics of different breast cancer subtypes based on PAM50 algorithms. We detected the expression changes of subtype-specific genes and revealed that the expression of particular subtype-specific genes significantly affected prognosis. We also investigated the mutations and copy number variations (CNVs) of breast cancer driver genes and the representative genes of ten signaling pathways in different subtypes and revealed several subtype-specifically altered genes. Moreover, we detected the infiltration of various immune cells in different subtypes of breast cancer and showed that the infiltration levels of major immune cell types are different among these subtypes. Additionally, we investigated the factors affecting the immune infiltration level and the immune cytolytic activity in different breast cancer subtypes, namely, the mutation burden, genome instability and cancer-associated fibroblast (CAF) infiltration. This study may shed light on the molecular events contributing to carcinogenesis and development and provide potential markers and targets for the clinical diagnosis and treatment of different breast cancer subtypes.

The novel driver gene ASAP2 is a potential druggable target in pancreatic cancer

Targeting mutated oncogenes is an effective approach for treating cancer. The 4 main driver genes of pancreatic ductal adenocarcinoma (PDAC) are KRAS, TP53, CDKN2A, and SMAD4, collectively called the "big 4" of PDAC, however they remain challenging therapeutic targets. In this study, ArfGAP with SH3 domain, ankyrin repeat and PH domain 2 (ASAP2), one of the ArfGAP family, was identified as a novel driver gene in PDAC. Clinical analysis with PDAC datasets showed that ASAP2 was overexpressed in PDAC cells based on increased DNA copy numbers, and high ASAP2 expression contributed to a poor prognosis in PDAC. The biological roles of ASAP2 were investigated using ASAP2-knockout PDAC cells generated with CRISPR-Cas9 technology or transfected PDAC cells. In vitro and in vivo analyses showed that ASAP2 promoted tumor growth by facilitating cell cycle progression through phosphorylation of epidermal growth factor receptor (EGFR). A repositioned drug targeting the ASAP2 pathway was identified using a bioinformatics approach. The gene perturbation correlation method showed that niclosamide, an antiparasitic drug, suppressed PDAC growth by inhibition of ASAP2 expression. These data show that ASAP2 is a novel druggable driver gene that activates the EGFR signaling pathway. Furthermore, niclosamide was identified as a repositioned therapeutic agent for PDAC possibly targeting ASAP2.

DriverSubNet: A Novel Algorithm for Identifying Cancer Driver Genes by Subnetwork Enrichment Analysis

Identification of driver genes from mass non-functional passenger genes in cancers is still a critical challenge. Here, an effective and no parameter algorithm, named DriverSubNet, is presented for detecting driver genes by effectively mining the mutation and gene expression information based on subnetwork enrichment analysis. Compared with the existing classic methods, DriverSubNet can rank driver genes and filter out passenger genes more efficiently in terms of precision, recall, and F1 score, as indicated by the analysis of four cancer datasets. The method recovered about 50% more known cancer driver genes in the top 100 detected genes than those found in other algorithms. Intriguingly, DriverSubNet was able to find these unknown cancer driver genes which could act as potential therapeutic targets and useful prognostic biomarkers for cancer patients. Therefore, DriverSubNet may act as a useful tool for the identification of driver genes by subnetwork enrichment analysis.

FAM83H-AS1 is a noncoding oncogenic driver and therapeutic target of lung adenocarcinoma

BACKGROUND

Little is known about noncoding oncogenes of lung adenocarcinoma (LUAD), and these potential drivers might provide novel therapeutic targets.

METHODS

Since abnormally overexpression of oncogenic drivers is induced by genomic variation, we here utilized genomic, transcriptomic, and clinical prognosis data of The Cancer Genome Atlas (TCGA) LUAD datasets to discover novel drivers from long noncoding RNAs. We further used zebrafish models to validate the biological function of candidates in vivo. The full length of FAM83H-AS1 was obtained by rapid amplification of the cDNA ends assay. RNA pull-down, RNA immunoprecipitation, quantitative mass spectrometry, and RNA sequencing assays were conducted to explore the potential mechanisms. Additionally, we used CRISPR interference (CRISPRi) method and patient-derived tumor xenograft (PDTX) model to evaluate the therapeutic potential of targeting FAM83H-AS1.

RESULTS

The results suggest that FAM83H-AS1 is a potential oncogenic driver due to chromosome 8q24 amplification. Upregulation of FAM83H-AS1 results in poor prognosis of LUAD patients in both Jiangsu Cancer Hospital (JSCH) and TCGA cohorts. Functional assays revealed that FAM83H-AS1 promotes malignant progression and inhibits apoptosis. Mechanistically, FAM83H-AS1 binds HNRNPK to enhance the translation of antiapoptotic oncogenes RAB8B and RAB14. Experiments using CRISPRi-mediated xenografts and PDTX models indicated that targeting FAM83H-AS1 inhibited LUAD progression in vivo.

CONCLUSIONS

Our work demonstrates that FAM83H-AS1 is a noncoding oncogenic driver that inhibits LUAD apoptosis via the FAM83H-AS1-HNRNPK-RAB8B/RAB14 axis, which highlights the importance and potential roles that FAM83H-AS1 may serve as a novel therapeutic target for LUAD.

Establishment of Criteria for Molecular Differential Diagnosis of MPLC and IPM

BACKGROUNDS

Differential diagnosis of multiple primary lung cancer (MPLC) and intrapulmonary metastasis (IPM) is one difficulty in lung cancer diagnosis, and crucial for establishment of treatment strategies and prognosis prediction. This study aims to establish the criteria for molecular differential diagnosis of synchronous MPLC and IPM by the next-generation sequencing (NGS) method.

METHODS

Training cohort included 30 synchronous MPLC (67 samples) patients and 5 synchronous IPM (13 samples) patients with adenocarcinoma. Criteria of MPLC/IPM differential diagnosis were established by results from a NGS-based 605-gene panel test. Subsequently, 16 patients (36 samples) were recruited as the validation cohort to verify the criteria.

RESULTS

IPM lesions showed a high degree of mutation overlap with an average concordance rate of 60.2% (range: 15.8%-91.7%). IPM lesions had at least three common alterations, including both high-frequency driver gene alterations and low-frequency gene alterations. In contrast, the average concordance rate of MPLC was 11.0% (range: 0.0%-100.0%), among which 66.7% (20/30) of patients had no common alterations (concordance rate: 0%). In the remaining 10 patients, 9 had only one overlapping alteration while 1 had two overlapping alterations, in which 6 patients had EGFR L858R overlapping mutation. Alterations were classified into trunk, shared, and branch subtypes. Branch alterations accounted for 94.4% of mutations in MPLC, while accounted for only 45.0% in IMP. In contrast, the ratio of trunk (38.3%) and shared (16.7%) alterations in IPM was significantly higher. The criteria for differentiating MPLC from IPM using 605-gene panel was established: 1) MPLC can be interpreted if no overlapping alterations is found; 2) MPLC is recommended if one overlapping high-frequency drive gene alteration and/or one overlapping low-frequency gene alteration are/is found; 3) IPM can be interpreted if more than three common alterations are found. Subsequently, 16 patients were recruited as the validation cohort in the single-blind manner to verify the criteria, and 14 MPLC and 2 IPM were identified, which was 100% consistent with the results from independent imaging and pathological diagnosis.

CONCLUSIONS

NGS detection can distinguish synchronous MPLC from IPM and is a useful tool to assist differential diagnosis.

Advances in Lung Cancer Driver Genes Associated With Brain Metastasis

Brain metastasis, one of the common complications of lung cancer, is an important cause of death in patients with advanced cancer, despite progress in treatment strategies. Lung cancers with positive driver genes have higher incidence and risk of brain metastases, suggesting that driver events associated with these genes might be biomarkers to detect and prevent disease progression. Common lung cancer driver genes mainly encode receptor tyrosine kinases (RTKs), which are important internal signal molecules that interact with external signals. RTKs and their downstream signal pathways are crucial for tumor cell survival, invasion, and colonization in the brain. In addition, new tumor driver genes, which also encode important molecules closely related to the RTK signaling pathway, have been found to be closely related to the brain metastases of lung cancer. In this article, we reviewed the relationship between lung cancer driver genes and brain metastasis, and summarized the mechanism of driver gene-associated pathways in brain metastasis. By understanding the molecular characteristics during brain metastasis, we can better stratify lung cancer patients and alert those at high risk of brain metastasis, which helps to promote individual therapy for lung cancer.

Identification of Driver Genes Regulating the T-Cell-Infiltrating Levels in Hepatocellular Carcinoma

The driver genes regulating T-cell infiltration are important for understanding immune-escape mechanisms and developing more effective immunotherapy. However, researches in this field have rarely been reported in hepatocellular carcinoma (HCC). In the present study, we identified cancer driver genes triggered by copy number alterations such as CDKN2B, MYC, TSC1, TP53, and GSK3B. The T-cell infiltration levels were significantly decreased in both HCC and recurrent HCC tissues compared with the adjacent normal liver tissues. Remarkably, we identified that copy number losses of MAX and TP53 were candidate driver genes that significantly suppress T-cell infiltration in HCC. Accordingly, their downstream oncogenic pathway, cell cycle, was significantly activated in the low T-cell infiltration HCC. Moreover, the chemokine-related target genes by TP53, which played key roles in T-cell recruitment, were also downregulated in HCC with TP53/MAX deletions, suggesting that copy number losses in MAX and TP53 might result in T-cell depletion in HCC via downregulating chemokines. Clinically, the T-cell infiltration levels and chemokines activity could accurately predict the response of sorafenib, and the prognostic outcomes in HCC. In conclusion, the systematic analysis not only facilitates identification of driver genes and signaling pathways involved in T-cell infiltration and immune escape, but also gains more insights into the functional roles of T cells in HCC.

Functional and Clinical Characterization of Tumor-Infiltrating T Cell Subpopulations in Hepatocellular Carcinoma

Tumor-infiltrating T-lymphocytes are defined as T-lymphocytes that infiltrated into tumor tissues; however, their composition, clinical significance, and underlying mechanism in hepatocellular carcinoma (HCC) and adjacent non-tumor tissues are still not completely understood. Herein, we collected marker genes of T cell subpopulations from a previous study and estimated their relative infiltrating levels in HCC and adjacent non-tumor tissues. Specifically, the infiltrating levels of all the T cells were significantly reduced in HCC as compared with non-tumor tissues. Unsupervised clustering of the HCC samples by the T cell infiltrating levels revealed that the HCC samples could be clearly classified into two groups. The driver genes, including PTK2B, ATM, PIK3C2B, and KIT, and several CNAs were observed to be associated with reduced T cell infiltrating levels. Particularly, deletion of TP53 more frequently occurred in low T cell infiltration HCC samples and resulted in its downregulation and cell cycle progression, indicating that cell cycle progression was closely associated with reduced T cell infiltration. In contrast, for the samples with high infiltration T cells, its immune evasion might be regulated by the immune checkpoint regulators, such as PD-1/PD-L1 and CTLA4. Moreover, Olaparib, one of the PARP inhibitors, and immune checkpoint inhibitors might be therapeutic candidates for the samples from the two T cell infiltrating clusters. Clinically, the tumor-infiltrating levels of cytotoxic CD4 cell, Mucosal associated invariant T (MAIT) cell, and exhausted CD8⁺ T cell might be used as predictors for vascular invasion, recurrence, and overall survival. Collectively, we systematically evaluated the clinical significance and potential molecular mechanisms of tumor-infiltrating T cell subpopulations in hepatocellular carcinoma, which might broaden our insights into the immunological features of HCC and provide potential immunotherapeutic targets.

Identification and Characterization of the Copy Number Dosage-Sensitive Genes in Colorectal Cancer

Dosage effect is one of the common mechanisms of somatic copy number alteration in the development of colorectal cancer, yet the roles of dosage-sensitive genes (DSGs) in colorectal cancer (CRC) remain to be characterized more deeply. In this study, we developed a five-step pipeline to identify DSGs and analyzed their characterization in CRC. Results showed that our pipeline performed better than existing methods, and the result was significantly overlapped between solid tumor and cell line. We also found that the top five DSGs (PSMF1, RAF1, PTPRA, MKRN2, and ELP3) were associated with the progression of CRC. By analyzing the characterization, DSGs were enriched in driver genes and they drove sub-pathways of CRC. In addition, immune-related DSGs are associated with CRC progression. Our results also showed that the CRC samples affected by high microsatellites have fewer DSGs, but a higher overlap with DSGs in microsatellite low instability and microsatellite stable samples. In addition, we applied DSGs to identify potential drug targets, with the results showing that 22 amplified DSGs were more sensitive to four drugs. In conclusion, DSGs play an important role in CRC, and our pipeline is effective to identify them.

Risk stratification of cutaneous melanoma reveals carcinogen metabolism enrichment and immune inhibition in high-risk patients

Cutaneous melanoma (CM) is the most lethal form of skin cancer. Risk assessment should facilitate stratified surveillance and guide treatment selection. Here, based on the mRNA-seq data from 419 CM patients in the Cancer Genome Atlas (TCGA), we developed a prognostic 21-gene signature to distinguish the outcomes of high- and low-risk patients, which was further validated in two external cohorts. The signature achieved a higher C-index as compared with other known biomarkers and clinical characteristics in both the TCGA and validation cohorts. Notably, in high-risk patients the expression levels of three driver genes, BRAF, NRAS, and NF1 in the MAPK pathway, were lower but exhibited a stronger positive correlation as compared with low-risk patients. Moreover, the genes involved in nicotinamide adenine dinucleotide metabolism were negatively correlated with the expression of BRAF in the high-risk group. Function analysis revealed that the upregulated genes in the high-risk group were enriched in the cytochrome P450-mediated metabolism of chemical carcinogens. Furthermore, the low-risk group had high levels of gamma delta T cells infiltration, while regulatory T cells were accumulated in the high-risk group. The present study offers a promising new prognostic signature for CM, and provides insight into the mechanisms of melanoma progression.

Prioritizing Cancer Genes Based on an Improved Random Walk Method

Identifying driver genes that contribute to cancer progression from numerous passenger genes, although a central goal, is a major challenge. The protein-protein interaction network provides convenient and reasonable assistance for driver gene discovery. Random walk-based methods have been widely used to prioritize nodes in social or biological networks. However, most studies select the next arriving node uniformly from the random walker's neighbors. Few consider transiting preference according to the degree of random walker's neighbors. In this study, based on the random walk method, we propose a novel approach named Driver_IRW (Driver genes discovery with Improved Random Walk method), to prioritize cancer genes in cancer-related network. The key idea of Driver_IRW is to assign different transition probabilities for different edges of a constructed cancer-related network in accordance with the degree of the nodes' neighbors. Furthermore, the global centrality (here is betweenness centrality) and Katz feedback centrality are incorporated into the framework to evaluate the probability to walk to the seed nodes. Experimental results on four cancer types indicate that Driver_IRW performs more efficiently than some previously published methods for uncovering known cancer-related genes. In conclusion, our method can aid in prioritizing cancer-related genes and complement traditional frequency and network-based methods.

Comprehensive characterization of driver genes in diffuse large B cell lymphoma

Diffuse large B cell lymphoma (DLBCL) is the most common hematological malignancy and is one of the most frequent non-Hodgkin lymphomas. Large-scale genomic studies have defined genetic drivers of DLBCL and their association with functional and clinical outcomes. However, the lymphomagenesis of DLBCL is yet to be fully understood. In the present study, four computational tools OncodriveFM, OncodriveCLUST, integrated Cancer Genome Score and Driver Genes and Pathways were used to detect driver genes and driver pathways involved in DLBCL. The aforementioned tools were also used to perform an integrative investigation of driver genes, including co-expression network, protein-protein interaction, copy number variation and survival analyses. The present study identified 208 driver genes and 31 driver pathways in DLBCL. IGLL5, MLL2, BTG2, B2M, PIM1, CARD11 were the top five frequently mutated genes in DLBCL. NOTCH3, LAMC1, COL4A1, PDGFRB and KDR were the 5 hub genes in the blue module that were associated with patient age. TP53, MYC, EGFR, PTEN, IL6, STAT3, MAPK8, TNF and CDH1 were at the core of the protein-protein interaction network. PRDM1, CDKN2A, CDKN2B, TNFAIP3, RSPO3 were the top five frequently deleted driver genes in DLBCL, while ACTB, BTG2, PLET1, CARD11, DIXDC1 were the top five frequently amplified driver genes in DLBCL. High EIF3B, MLH1, PPP1CA and RECQL4 expression was associated with decreased overall survival rate of patients with DLBCL. High XPO1 and LYN expression were associated with increased overall survival rate of patients with DLBCL. The present study improves the understanding of the biological processes and pathways involved in lymphomagenesis. The driver genes, EIF3B, MLH1, PPP1CA, RECQL4, XPO1 and LYN, pave the way for developing prognostic biomarkers and new therapeutic strategies for DLBCL.

Targeted molecular profiling of genetic alterations in colorectal cancer using next-generation sequencing

Colorectal cancer (CRC) is a major contributor to cancer-associated mortality in China and remains a vast challenge worldwide. Although the genetic basis of CRC has been investigated, the uncommonly mutated genes in CRC remain unknown, in particular in the Asian population. In the present study, targeted region sequencing on 22 CRC and 10 paired non-cancerous tissues was performed to determine the genetic pattern of CRC samples in the Chinese population. Driver genes were detected by three distinct softwares, including MutSigCV, oncodriveFM and iCAGES. A total of 1,335 reliable somatic mutations were identified in tumour samples compared with normal samples. Furthermore, mismatch repair (MMR) mutant patients presented significantly higher mutation density compared with MMR wild-type patients. The results from MutSigCV, oncodriveFM and iCAGES analyses simultaneously detected 29 unique driver genes. In addition, the genes APC regulator of WNT signaling pathway, SMAD family member 4, neurofibromin 1, AT-rich interaction domain 5B and nuclear receptor corepressor 1 were the top five most frequently mutated genes in CRC samples, with mutation rates of 68, 36, 36, 32 and 27%, respectively. The findings from the present study may therefore serve as a basis for future investigation on the diagnosis and oncogenesis of CRC.

The Integrated Analyses of Driver Genes Identify Key Biomarkers in Thyroid Cancer

AIM

Thyroid cancer is the most common endocrine cancer, the incidence rate has continuously increased worldwide. However, there are still lack of effective molecular biomarkers for the diagnosis and treatment of the disease. The study was conducted to identify driver genes that may serve as potential biomarkers for the disease.

METHODS

The computational tools oncodriveCLUST, oncodriveFM, icages and drgap were used to detect driver genes in thyroid cancer using somatic mutations from The Cancer Genome Atlas database. Integrated analyses were performed on the driver genes using multiomics data from the TCGA database.

RESULTS

A set of 291 driver genes were identified in thyroid cancer. BRAF, NRAS, HRAS, OTUD4, EIF1AX were the top 5 frequently mutated genes in thyroid cancer. The weighted gene co-expression network analysis identified 4 coexpression modules. The modules 1-3 were significantly associated with patients' tumor size, residual tumor, cancer stage, distant metastasis and multifocality. SEC24B, MET and ITGAL were the hub genes in the modules 1-3 respectively. Hierarchical clustering analysis of the 20 driver genes with the most frequent copy number changes revealed 3 clusters of PRAD patients. Cluster 1 tumors exhibited significantly older age, tumor size, cancer stages, and poorer prognosis than cluster 2 and 3 tumors. 16 genes were significantly associated with number of lymph nodes, tumor size and pathologic stage, such as IL7 R, IRS1, PTK2B, MAP3K3 and FGFR2.

CONCLUSIONS

The set of cancer genes and subgroups of patients shed insight on the tumorigenesis of thyroid cancer and open up avenues for developing prognostic biomarkers and driver gene-targeted therapies in thyroid cancer.

From somatic mutation to early detection: insights from molecular characterization of pancreatic cancer precursor lesions

Pancreatic cancer arises from noninvasive precursor lesions, including pancreatic intraepithelial neoplasia (PanIN), intraductal papillary mucinous neoplasm (IPMN), and mucinous cystic neoplasm (MCN), which are curable if detected early enough. Recently, these types of precursor lesions have been extensively characterized at the molecular level, defining the timing of critical genetic alterations in tumorigenesis pathways. The results of these studies deepen our understanding of tumorigenesis in the pancreas, providing novel insights into tumor initiation and progression. Perhaps more importantly, they also provide a rational foundation for early detection approaches that could allow clinical intervention prior to malignant transformation. In this review, we summarize the results of comprehensive molecular characterization of PanINs, IPMNs, and MCNs and discuss the implications for cancer biology as well as early detection. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Driver gene alterations and activated signaling pathways toward malignant progression of gastrointestinal stromal tumors

Mutually exclusive KIT and PDGFRA mutations are considered to be the earliest events in gastrointestinal stromal tumors (GIST), but insufficient for their malignant progression. Herein, we aimed to identify driver genes and signaling pathways relevant to GIST progression. We investigated genetic profiles of 707 driver genes, including mutations, gene fusions, copy number gain or loss, and gene expression for 65 clinical specimens of surgically dissected GIST, consisting of six metastatic tumors and 59 primary tumors from stomach, small intestine, rectum, and esophagus. Genetic alterations included oncogenic mutations and amplification‐dependent expression enhancement for oncogenes (OG), and loss of heterozygosity (LOH) and expression reduction for tumor suppressor genes (TSG). We assigned activated OG and inactivated TSG to 27 signaling pathways, the activation of which was compared between malignant GIST (metastasis and high‐risk GIST) and less malignant GIST (low‐ and very low‐risk GIST). Integrative molecular profiling indicated that a greater incidence of genetic alterations of driver genes was detected in malignant GIST (96%, 22 of 23) than in less malignant GIST (73%, 24 of 33). Malignant GIST samples groups showed mutations, LOH, and aberrant expression dominantly in driver genes associated with signaling pathways of PI3K (PIK3CA, AKT1, and PTEN) and the cell cycle (RB1, CDK4, and CDKN1B). Additionally, we identified potential PI3K‐related genes, the expression of which was upregulated (SNAI1 and TPX2) or downregulated (BANK1) in malignant GIST. Based on our observations, we propose that inhibition of PI3K pathway signals might potentially be an effective therapeutic strategy against malignant progression of GIST.

Intraductal Papillary Mucinous Neoplasms Arise From Multiple Independent Clones, Each With Distinct Mutations

BACKGROUND & AIMS

Intraductal papillary mucinous neoplasms (IPMNs) are lesions that can progress to invasive pancreatic cancer and constitute an important system for studies of pancreatic tumorigenesis. We performed comprehensive genomic analyses of entire IPMNs to determine the diversity of somatic mutations in genes that promote tumorigenesis.

METHODS

We microdissected neoplastic tissues from 6-24 regions each of 20 resected IPMNs, resulting in 227 neoplastic samples that were analyzed by capture-based targeted sequencing. Somatic mutations in genes associated with pancreatic tumorigenesis were assessed across entire IPMN lesions, and the resulting data were supported by evolutionary modeling, whole-exome sequencing, and in situ detection of mutations.

RESULTS

We found a high prevalence of heterogeneity among mutations in IPMNs. Heterogeneity in mutations in KRAS and GNAS was significantly more prevalent in IPMNs with low-grade dysplasia than in IPMNs with high-grade dysplasia (P < .02). Whole-exome sequencing confirmed that IPMNs contained multiple independent clones, each with distinct mutations, as originally indicated by targeted sequencing and evolutionary modeling. We also found evidence for convergent evolution of mutations in RNF43 and TP53, which are acquired during later stages of tumorigenesis.

CONCLUSIONS

In an analysis of the heterogeneity of mutations throughout IPMNs, we found that early-stage IPMNs contain multiple independent clones, each with distinct mutations, indicating their polyclonal origin. These findings challenge the model in which pancreatic neoplasms arise from a single clone. Increasing our understanding of the mechanisms of IPMN polyclonality could lead to strategies to identify patients at increased risk for pancreatic cancer.

Genome-wide profiling of prognosis-related alternative splicing signatures in sarcoma

BACKGROUND

Sarcomas (SARCs) are rare malignant tumors with poor prognosis. Increasing evidence has suggested that aberrant alternative splicing (AS) is strongly associated with tumor initiation and progression. We considered whether survival-related AS events might serve as prognosis predictors and underlying targeted molecules in SARC treatment.

METHODS

RNA-Seq data of the SARC cohort were downloaded from The Cancer Genome Atlas (TCGA) database. Survival-related AS events were selected by univariate and multivariate Cox regression analyses. Metascape was used for constructing a gene interaction network and performing functional enrichment analysis. Then, prognosis predictors were established based on statistically significant survival-related AS events and evaluated by receiver operator characteristic (ROC) curve analysis. Finally, the potential regulatory network was analyzed via Pearson's correlation between survival-related AS events and splicing factors (SFs).

RESULTS

A total of 3,610 AS events and 2,291 genes were found to be prognosis-related in 261 SARC samples. The focal adhesion pathway was identified as the most critical molecular mechanism corresponding to poor prognosis. Notably, several prognosis predictors based on survival-related AS events showed excellent performance in prognosis prediction. The area under the curve of the ROC of the risk score was 0.85 in the integrated predictor. The splicing network proved complicated regulation between prognosis-related SFs and AS events. Also, driver gene mutations were significantly associated with AS in SARC patients.

CONCLUSIONS

Survival-related AS events may become ideal indictors for the prognosis prediction of SARCs. Corresponding splicing regulatory mechanisms are worth further exploration.

CRISPR-Cas9-mediated gene knockout in intestinal tumor organoids provides functional validation for colorectal cancer driver genes

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths worldwide. Several genome sequencing studies have provided comprehensive CRC genomic datasets. Likewise, in our previous study, we performed genome-wide Sleeping Beauty transposon-based mutagenesis screening in mice and provided comprehensive datasets of candidate CRC driver genes. However, functional validation for most candidate CRC driver genes, which were commonly identified from both human and mice, has not been performed. Here, we describe a platform for functionally validating CRC driver genes that utilizes CRISPR-Cas9 in mouse intestinal tumor organoids and human CRC-derived organoids in xenograft mouse models. We used genetically defined benign tumor-derived organoids carrying 2 frequent gene mutations (Apc and Kras mutations), which act in the early stage of CRC development, so that we could clearly evaluate the tumorigenic ability of the mutation in a single gene. These studies showed that Acvr1b, Acvr2a, and Arid2 could function as tumor suppressor genes (TSGs) in CRC and uncovered a role for Trp53 in tumor metastasis. We also showed that co-occurrent mutations in receptors for activin and transforming growth factor-β (TGF-β) synergistically promote tumorigenesis, and shed light on the role of activin receptors in CRC. This experimental system can also be applied to mouse intestinal organoids carrying other sensitizing mutations as well as organoids derived from other organs, which could further contribute to identification of novel cancer driver genes and new drug targets.

Genome-Wide Profiling of Prognostic Alternative Splicing Pattern in Pancreatic Cancer

Alternative splicing (AS) has a critical role in tumor progression and prognosis. Our study aimed to investigate pancreatic cancer-specific AS events using RNA-seq data, gaining systematic insights into potential prognostic predictors. We downloaded 10,623 genes with 45,313 pancreatic cancer-specific AS events from the Cancer Genome Atlas (TCGA) and SpliceSeq database. Cox univariate analyses of overall survival suggested there was a remarkable association between 6,711 AS events and overall survival in pancreatic cancer patients (P < 0.05). The area under the curves (AUC) of the receiver operator characteristic curves (ROC) of risk score was 0.89 for final prognostic predictor. Results indicated that AS events of DAZAP1, RBM4, ESRP1, QKI, and SF1 were significantly associated with overall survival. The results of FunRich showed that transcription factors KLF7, GABPA, and SP1 were the most highly related to survival-associated AS genes. Furthermore, using DriverDBv2, we identified 13 driver genes associated with survival-associated AS events, including TP53 and CDC27. Thus, we concluded that the aberrant AS patterns in pancreatic cancer patients might serve as prognostic predictors.

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.

A gene interaction network‑based method to measure the common and heterogeneous mechanisms of gynecological cancer

Gynecological malignancies are a leading cause of mortality in the female population. The present study intended to identify the association between three severe types of gynecological cancer, specifically ovarian cancer, cervical cancer and endometrial cancer, and to identify the connective driver genes, microRNAs (miRNAs) and biological processes associated with these types of gynecological cancer. In the present study, individual driver genes for each type of cancer were identified using integrated analysis of multiple microarray data. Gene Ontology (GO) has been used widely in functional annotation and enrichment analysis. In the present study, GO enrichment analysis revealed a number of common biological processes involved in gynecological cancer, including 'cell cycle' and 'regulation of macromolecule metabolism'. Kyoto Encyclopedia of Genes and Genomes pathway analysis is a resource for understanding the high‑level functions and utilities of a biological system from molecular‑level information. In the present study, the most common pathway was 'cell cycle'. A protein‑protein interaction network was constructed to identify a hub of connective genes, including minichromosome maintenance complex component 2 (MCM2), matrix metalloproteinase 2 (MMP2), collagen type I α1 chain (COL1A1) and Jun proto‑oncogene AP‑1 transcription factor subunit (JUN). Survival analysis revealed that the expression of MCM2, MMP2, COL1A1 and JUN was associated with the prognosis of the aforementioned gynecological cancer types. By constructing an miRNA‑driver gene network, let‑7 targeted the majority of the driver genes. In conclusion, the present study demonstrated a connection model across three types of gynecological cancer, which was useful in identifying potential diagnostic markers and novel therapeutic targets, in addition to in aiding the prediction of the development of cancer as it progresses.

Identifying Cancer Specific Driver Modules Using a Network-Based Method

Detecting driver modules is a key challenge for understanding the mechanisms of carcinogenesis at the pathway level. Identifying cancer specific driver modules is helpful for interpreting the different principles of different cancer types. However, most methods are proposed to identify driver modules in one cancer, but few methods are introduced to detect cancer specific driver modules. We propose a network-based method to detect cancer specific driver modules (CSDM) in a certain cancer type to other cancer types. We construct the specific network of a cancer by combining specific coverage and mutual exclusivity in all cancer types, to catch the specificity of the cancer at the pathway level. To illustrate the performance of the method, we apply CSDM on 12 TCGA cancer types. When we compare CSDM with SpeMDP and HotNet2 with regard to specific coverage and the enrichment of GO terms and KEGG pathways, CSDM is more accurate. We find that the specific driver modules of two different cancers have little overlap, which indicates that the driver modules detected by CSDM are specific. Finally, we also analyze three specific driver modules of BRCA, BLCA, and LAML intersecting with well-known pathways. The source code of CSDM is freely accessible at https://github.com/fengli28/CSDM.git.

Molecular genomic landscapes of hepatobiliary cancer

Hepatocellular carcinoma (HCC) and biliary tract cancer are more frequent in East Asia including Japan than in Europe or North America. A compilation of 1340 multi‐ethnic HCC genomes, the largest cohort ever reported, identified a comprehensive landscape of HCC driver genes, comprised of three core drivers (TP53,TERT, and WNT signaling) and combinations of infrequent alterations in various cancer pathways. In contrast, five core driver genes (TP53,ARID1A,KRAS,SMAD4, and BAP1) with characteristic molecular alterations including fusion transcripts involving fibroblast growth factor receptor 2 and the protein kinase A pathway, and IDH1/2 mutation constituted the biliary tract cancer genomes. Consistent with their heterogeneous epidemiological backgrounds, mutational signatures and combinations of non‐core driver genes within these cancer genomes were found to be complex. Integrative analyses of multi‐omics data identified molecular classifications of these tumors that are associated with clinical outcome and enrichments of potential therapeutic targets, including immune checkpoint molecules. Translating comprehensive molecular‐genomic analysis together with further basic research and international collaborations are highly anticipated for developing precise and better treatments, diagnosis, and prevention of these tumor types.

Identification of driver modules in pan-cancer via coordinating coverage and exclusivity

It is widely accepted that cancer is driven by accumulated somatic mutations during the lifetime of an individual. Cancer mutations may target relatively small number of cell functional modules. The heterogeneity in different cancer patients makes it difficult to identify driver mutations or functional modules related to cancer. It is biologically desired to be capable of identifying cancer pathway modules through coordination between coverage and exclusivity. There have been a few approaches developed for this purpose, but they all have limitations in practice due to their computational complexity and prediction accuracy. We present a network based approach, CovEx, to predict the specific patient oriented modules by 1) discovering candidate modules for each considered gene, 2) extracting significant candidates by harmonizing coverage and exclusivity and, 3) further selecting the patient oriented modules based on a set cover model. Applying CovEx to pan-cancer datasets spanning 12 cancer types collecting from public database TCGA, it demonstrates significant superiority over the current leading competitors in performance. It is published under GNU GENERAL PUBLIC LICENSE and the source code is available at:https://sourceforge.net/projects/cancer-pathway/files/

Association between PD-L1 expression and driver gene status in non-small-cell lung cancer: a meta-analysis

OBJECTIVE

To assess the association between PD-L1 expression and driver gene mutations in patients with non-small-cell lung cancer (NSCLC).

METHOD

We performed a meta-analysis of 26 studies (7541 patients) which were published from 2015 to 2017. Pooled odds ratios (ORs) with 95% confidence intervals (CI) were calculated to describe the correlation. Subgroup analysis was performed based on population characteristics, types of PD-L1 antibodies and quality of individual studies.

RESULTS

A lower frequency of PD-L1 positivity was observed in NSCLCs harboring EGFR mutation (OR: 0.64, 95% CI, 0.45-0.91, p = 0.014). A negative correlation was also found at 1% (OR: 0.35, 95% CI, 0.22-0.55, p = 0.000) and 50% (OR: 0.33, 95% CI, 0.14-0.81, p = 0.015) cutoff for PD-L1 positive, elderly age group (OR: 0.56, 95% CI, 0.35-0.89, p = 0.013), female dominant group (OR: 0.55, 95% CI, 0.29-0.94, p = 0.030) and smoker dominant group (OR: 0.52, 95% CI, 0.29-0.96, p = 0.035). No significant differences in PD-L1 expression were observed among patients with different ALK, BRAF, HER2, PIK3CA status and MET expression level. Higher level of PD-L1 was found in tumors with KRAS mutation (OR: 1.45, 95% CI, 1.18-1.80, p = 0.001). PD-L1 expression level was not significantly different between triple (EGFR/ALK/KRAS) wild type NSCLCs and those with EGFR/ALK/KRAS mutation.

CONCLUSIONS

PD-L1 expression in EGFR mutated NSCLCs were lower than those in EGFR wild type NSCLCs, while tumors with KRAS mutation showed higher levels of PD-L1.

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications.

Extracting Fitness Relationships and Oncogenic Patterns among Driver Genes in Cancer

Driver mutation provides fitness advantage to cancer cells, the accumulation of which increases the fitness of cancer cells and accelerates cancer progression. This work seeks to extract patterns accumulated by driver genes (“fitness relationships”) in tumorigenesis. We introduce a network-based method for extracting the fitness relationships of driver genes by modeling the network properties of the “fitness” of cancer cells. Colon adenocarcinoma (COAD) and skin cutaneous malignant melanoma (SKCM) are employed as case studies. Consistent results derived from different background networks suggest the reliability of the identified fitness relationships. Additionally co-occurrence analysis and pathway analysis reveal the functional significance of the fitness relationships with signaling transduction. In addition, a subset of driver genes called the “fitness core” is recognized for each case. Further analyses indicate the functional importance of the fitness core in carcinogenesis, and provide potential therapeutic opportunities in medicinal intervention. Fitness relationships characterize the functional continuity among driver genes in carcinogenesis, and suggest new insights in understanding the oncogenic mechanisms of cancers, as well as providing guiding information for medicinal intervention.

Comprehensive characterization of cancer genes in hepatocellular carcinoma genomes

The present study was performed to detect moderate or low-frequency mutated cancer driver genes in hepatocellular carcinoma (HCC), using OncodriveFM and Dendrix. Following this, integrated analyses were conducted on these novel cancer driver genes. A total of 112,980 somatic mutations were retrieved from TCGA and classified into 11 categories based on their function. Driver genes and pathways were predicted by OncodriveFM and Dendrix, followed by differential expression, DNA-methylation, copy number variations and survival analyses. Overall, non-synonymous mutations accounted for >60% (72,149/112, 980) of total variants, 108 and 3 driver genes were determined by OncodriveFM and Dendrix, respectively. Tumor protein p53, SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4, smad family member 3, RB transcriptional corepressor 1, catenin β 1, smad family member 4, mitogen-activated protein kinase 1 and TSC complex subunit 2 are at the core of the driver gene interaction network. Two genes, transportin 1 (TNPO1) and chaperonin containing TCP1 subunit 3 (CCT3), were hypomethylated and overexpressed, and high expression of TNPO1 and CCT3 indicated a poor prognosis in patients with HCC. β-carotene oxygenase 2 was hypermethylated, under-expressed and associated with favorable prognosis in HCC. The present study has identified a set of novel cancer genes and pathways, offering potential therapeutic targets and prognostic biomarkers for the treatment of HCC.

Analysis of mutant allele fractions in driver genes in colorectal cancer - biological and clinical insights

Sequencing of tumors is now routine and guides personalized cancer therapy. Mutant allele fractions (MAFs, or the ‘mutation dose’) of a driver gene may reveal the genomic structure of tumors and influence response to targeted therapies. We performed a comprehensive analysis of MAFs of driver alterations in unpaired primary and metastatic colorectal cancer (CRC) at our institution from 2010 to 2015 and studied their potential clinical relevance. Of 763 CRC samples, 622 had detailed annotation on overall survival in the metastatic setting (OSmet) and 89 received targeted agents matched to KRAS (MEK inhibitors), BRAF (BRAF inhibitors), or PIK3CA mutations (PI3K pathway inhibitors). MAFs of each variant were normalized for tumor purity in the sample (adjMAFs). We found lower adjMAFs for BRAF^V600E and PIK3CA than for KRAS,NRAS, and BRAF non‐V600 variants. TP53 and BRAF^V600E adjMAFs were higher in metastases as compared to primary tumors, and high KRAS adjMAFs were found in CRC metastases of patients with KRAS wild‐type primary tumors previously exposed to EGFR antibodies. Patients with RAS‐ or BRAF^V600E‐mutated tumors, irrespective of adjMAFs, had worse OSmet. There was no significant association between adjMAFs and time to progression on targeted therapies matched to KRAS,BRAF, or PIK3CA mutations, potentially related to the limited antitumor activity of the employed drugs (overall response rate of 4.5%). In conclusion, the lower BRAF^V600E and PIK3CA adjMAFs in subsets of primary CRC tumors indicate subclonality of these driver genes. Differences in adjMAFs between metastases and primary tumors suggest that approved therapies may result in selection of BRAF^V600E‐ and KRAS‐resistant clones and an increase in genomic heterogeneity with acquired TP53 alterations. Despite significant differences in prognosis according to mutations in driver oncogenes, adjMAFs levels did not impact on survival and did not help predict benefit with matched targeted agents in the metastatic setting.

Exome Sequencing of Oral Squamous Cell Carcinoma Reveals Molecular Subgroups and Novel Therapeutic Opportunities

Oral squamous cell carcinoma (OSCC), an epithelial malignancy affecting a variety of subsites in the oral cavity, is prevalent in Asia. The survival rate of OSCC patients has not improved over the past decades due to its heterogeneous etiology, genetic aberrations, and treatment outcomes. Improvement in therapeutic strategies and tailored treatment options is an unmet need. To unveil the mutational spectrum, whole-exome sequencing of 120 OSCC from male individuals in Taiwan was conducted. Analyzing the contributions of the five mutational signatures extracted from the dataset of somatic variations identified four groups of tumors that were significantly associated with demographic and clinical features. In addition, known (TP53, FAT1, EPHA2, CDKN2A, NOTCH1, CASP8, HRAS, RASA1, and PIK3CA) and novel (CHUK and ELAVL1) genes that were significantly and frequently mutated in OSCC were discovered. Further analyses of gene alteration status with clinical parameters revealed that the tumors of the tongue were enriched with copy-number alterations in several gene clusters containing CCND1 and MAP4K2. Through defining the catalog of targetable genomic alterations, 58% of the tumors were found to carry at least one aberrant event potentially targeted by US Food and Drug Administration (FDA)-approved agents. Strikingly, if targeting the p53-cell cycle pathway (TP53 and CCND1) by the drugs studied in phase I-III clinical trials, those possibly actionable tumors are predominantly located in the tongue, suggesting a better prediction of sensitivity to current targeted therapies. Our work revealed molecular OSCC subgroups that reflect etiological and prognostic correlation as well as defined the landscape of major altered events in the coding regions of OSCC genomes. These findings provide clues for the design of clinical trials for targeted therapies and stratification of OSCC patients with differential therapeutic efficacy.