Integrative Genomic Analyses Identifies GGA2 as a Cooperative Driver of EGFR-Mediated Lung Tumorigenesis

Integrative genomics identifies SHPRH as a tumor suppressor gene in lung adenocarcinoma that regulates DNA damage response

BACKGROUND

Identification of driver mutations and development of targeted therapies has considerably improved outcomes for lung cancer patients. However, significant limitations remain with the lack of identified drivers in a large subset of patients. Here, we aimed to assess the genomic landscape of lung adenocarcinomas (LUADs) from individuals without a history of tobacco use to reveal new genetic drivers of lung cancer.

METHODS

Integrative genomic analyses combining whole-exome sequencing, copy number, and mutational information for 83 LUAD tumors was performed and validated using external datasets to identify genetic variants with a predicted functional consequence and assess association with clinical outcomes. LUAD cell lines with alteration of identified candidates were used to functionally characterize tumor suppressive potential using a conditional expression system both in vitro and in vivo.

RESULTS

We identified 21 genes with evidence of positive selection, including 12 novel candidates that have yet to be characterized in LUAD. In particular, SNF2 Histone Linker PHD RING Helicase (SHPRH) was identified due to its frequency of biallelic disruption and location within the familial susceptibility locus on chromosome arm 6q. We found that low SHPRH mRNA expression is associated with poor survival outcomes in LUAD patients. Furthermore, we showed that re-expression of SHPRH in LUAD cell lines with inactivating alterations for SHPRH reduces their in vitro colony formation and tumor burden in vivo. Finally, we explored the biological pathways associated SHPRH inactivation and found an association with the tolerance of LUAD cells to DNA damage.

CONCLUSIONS

These data suggest that SHPRH is a tumor suppressor gene in LUAD, whereby its expression is associated with more favorable patient outcomes, reduced tumor and mutational burden, and may serve as a predictor of response to DNA damage. Thus, further exploration into the role of SHPRH in LUAD development may make it a valuable biomarker for predicting LUAD risk and prognosis.

Establishment of a prognostic model for pancreatic cancer based on vesicle-mediated transport protein-related genes

This study attempted to build a prognostic riskscore model for pancreatic cancer (PC) patients based on vesicle-mediated transport protein-related genes (VMTGs). We initially conducted differential expression analysis and Cox regression analysis, followed by the construction of a riskscore model to classify PC patients into high-risk (HR) and low-risk (LR) groups. The GEO GSE62452 dataset further validated the model. Kaplan-Meier survival analysis was employed to analyze the survival rate of the HR group and LR group. Cox analysis confirmed the independent prognostic ability of the riskscore model. Additionally, we evaluated immune status in both HR and LR groups, utilizing data from the GDSC database to predict drug response among PC patients. We identified six PC-specific genes from 724 VMTGs. Survival analysis revealed that the survival rate of the HR group was lower than that of the LR group (P<0.05). Cox analysis confirmed that the prognostic riskscore model could independently predict the survival status of PC patients (P<0.001). Immunological analysis revealed that the ESTIMATE score, immune score, and stroma score of the HR group were considerably lower than those of the LR group, and the tumor purity score of the HR group was higher. The IC50 values of Gemcitabine, Irinotecan, Oxaliplatin, and Paclitaxel in the LR group were considerably lower than those in the HR group (P<0.001). In summary, the VMTG-based prognostic riskscore model could stratify PC risk and effectively predict the survival of PC patients.

Transferrin receptor-based circulating tumor cell enrichment provides a snapshot of the molecular landscape of solid tumors and correlates with clinical outcomes

Circulating tumor cells (CTCs) captured from the bloodstream of patients with solid tumors have the potential to accelerate precision oncology by providing insight into tumor biology, disease progression and response to treatment. However, their potential is hampered by the lack of standardized CTC enrichment platforms across tumor types. EpCAM-based CTC enrichment, the most commonly used platform, is limited by EpCAM downregulation during metastasis and the low EpCAM expression in certain tumor types, including the highly prevalent and lethal NSCLC. In this study we demonstrate that Transferrin Receptor (TfR) is a selective, efficient biomarker for CTC identification and capture in patients with prostate, pancreatic and NSCLC. TfR identifies significantly higher CTC counts than EpCAM, and TfR + -CTC enumeration correlates with disease progression in metastatic prostate and pancreatic cancers, and overall survival and osimetrinib-resistance in non-small cell lung cancer (NSCLC). Profiling of TfR + -CTCs provides a snapshot of the molecular landscape of each respective tumor type and identifies potential mechanisms underlying treatment response to EGFR TKi and immune checkpoint inhibitors in NSCLC.

One sentence summary

Transferrin Receptor identifies circulating tumor cells in solid tumors.

Integration of multi-omics data using adaptive graph learning and attention mechanism for patient classification and biomarker identification

With the rapid development and accumulation of high-throughput sequencing technology and omics data, many studies have conducted a more comprehensive understanding of human diseases from a multi-omics perspective. Meanwhile, graph-based methods have been widely used to process multi-omics data due to its powerful expressive ability. However, most existing graph-based methods utilize fixed graphs to learn sample embedding representations, which often leads to sub-optimal results. Furthermore, treating embedding representations of different omics equally usually cannot obtain more reasonable integrated information. In addition, the complex correlation between omics is not fully taken into account. To this end, we propose an end-to-end interpretable multi-omics integration method, named MOGLAM, for disease classification prediction. Dynamic graph convolutional network with feature selection is first utilized to obtain higher quality omic-specific embedding information by adaptively learning the graph structure and discover important biomarkers. Then, multi-omics attention mechanism is applied to adaptively weight the embedding representations of different omics, thereby obtaining more reasonable integrated information. Finally, we propose omic-integrated representation learning to capture complex common and complementary information between omics while performing multi-omics integration. Experimental results on three datasets show that MOGLAM achieves superior performance than other state-of-the-art multi-omics integration methods. Moreover, MOGLAM can identify important biomarkers from different omics data types in an end-to-end manner.

Diarylheptanoid 35d overcomes EGFR TKI resistance by inducing hsp70-mediated lysosomal degradation of EGFR in EGFR-mutant lung adenocarcinoma

EGFR-mutant lung adenocarcinomas (LUAD) patients often respond to EGFR tyrosine kinase inhibitors (TKIs) initially, but eventually develop resistance to TKIs. The switch of EGFR downstream signaling from TKI-sensitive to TKI-insensitive is a critical mechanism driving resistance to TKIs. Identification of potential therapies to target EGFR effectively is a potential strategy to treat TKI-resistant LUADs. In this study, we developed a small molecule diarylheptanoid 35d, a curcumin derivative, that effectively suppressed EGFR protein expression, killed multiple TKI-resistant LUAD cells in vitro, and suppressed tumor growth of EGFR-mutant LUAD xenografts with variant TKI-resistant mechanisms including EGFR C797S mutations in vivo. Mechanically, 35d triggers hsp70-mediated lysosomal pathway through transcriptional activation of several components in the pathway, such as HSPA1B, to induce EGFR protein degradation. Interestingly, higher HSPA1B expression in LUAD tumors associated with longer survival of EGFR-mutant TKI-treated patients, suggesting the role of HSPA1B on retarding TKI resistance and providing a rationale for combining 35d with EGFR TKIs. Our data showed that combination of 35d significantly inhibits tumor re-progression on osimertinib and prolongs mice survival. Overall, our results suggest 35d as a promising lead compound to suppress EGFR expression and provide important insights into the development of combination therapies for TKI-resistant LUADs, which could have translational potential for the treatment of this deadly disease.

Identification of eIF6 as a prognostic factor that drives tumor progression and predicts arsenic trioxide efficacy in lung adenocarcinoma

BACKGROUND

Lung cancer is the leading cause of cancer-related mortality worldwide. Dysregulation of mRNA translation can contribute to the development and progression of cancer whilst also having an impact on the prognosis of different types of malignancies. Eukaryotic translation initiation factors (eIFs) have been reported to serve a key role in the initiation of mRNA translation. However, little was known about the association between eIF6 and lung adenocarcinoma (LUAD) progression. We aimed to elucidate the roles of eIF6 in LUAD tumorigenesis.

METHODS

Bioinformatic analysis was conducted to assess the clinical significance of eIF6 in LUAD. CCK-8, colony formation assays were used to evaluate the biological roles of eIF6. The subcutaneous model was used to assess the in vivo roles of eIF6.

RESULTS

In the present study, it was found that eIF6 expression was significantly higher in LUAD samples compared with that in normal lung tissues. Higher expression levels of eIF6 were found to be associated with more advanced clinical stages of LUAD and poorer prognoses in patients with LUAD. Subsequently, overexpression of eIF6 was demonstrated to promote LUAD cell proliferation, migration and invasion, which are features of metastasis, in vitro. By contrast, inhibition of eIF6 induced cell cycle arrest and apoptosis in LUAD cells. Further bioinformatics analysis and experimental assays revealed that eIF6 expression positively correlated with the mRNA expression of stemness-associated genes in LUAD cells. Targeting eIF6 suppressed the sphere formation capacity of LUAD cells. In addition, data from the subcutaneous xenograft model in vivo also suggested that eIF6 deficiency could significantly delay tumor growth and improve the prognosis of mice. Targeting eIF6 rendered LUAD cells sensitive to arsenic trioxide treatment.

CONCLUSION

The present study suggest that eIF6 can serve as a prognostic biomarker and a potential therapeutic target for patients with LUAD.

Deciphering associations between three RNA splicing-related genetic variants and lung cancer risk

Limited efforts have been made in assessing the effect of genome-wide profiling of RNA splicing-related variation on lung cancer risk. In the present study, we first identified RNA splicing-related genetic variants linked to lung cancer in a genome-wide profiling analysis and then conducted a two-stage (discovery and replication) association study in populations of European ancestry. Discovery and validation were conducted sequentially with a total of 29,266 cases and 56,450 controls from both the Transdisciplinary Research in Cancer of the Lung and the International Lung Cancer Consortium as well as the OncoArray database. For those variants identified as significant in the two datasets, we further performed stratified analyses by smoking status and histological type and investigated their effects on gene expression and potential regulatory mechanisms. We identified three genetic variants significantly associated with lung cancer risk: rs329118 in JADE2 (P = 8.80E-09), rs2285521 in GGA2 (P = 4.43E-08), and rs198459 in MYRF (P = 1.60E-06). The combined effects of all three SNPs were more evident in lung squamous cell carcinomas (P = 1.81E-08, P = 6.21E-08, and P = 7.93E-04, respectively) than in lung adenocarcinomas and in ever smokers (P = 9.80E-05, P = 2.70E-04, and P = 2.90E-05, respectively) than in never smokers. Gene expression quantitative trait analysis suggested a role for the SNPs in regulating transcriptional expression of the corresponding target genes. In conclusion, we report that three RNA splicing-related genetic variants contribute to lung cancer susceptibility in European populations. However, additional validation is needed, and specific splicing mechanisms of the target genes underlying the observed associations also warrants further exploration.

Emerging Molecular Dependencies of Mutant EGFR-Driven Non-Small Cell Lung Cancer

Epidermal growth factor receptor (EGFR) mutations are the molecular driver of a subset of non-small cell lung cancers (NSCLC); tumors that harbor these mutations are often dependent on sustained oncogene signaling for survival, a concept known as “oncogene addiction”. Inhibiting EGFR with tyrosine kinase inhibitors has improved clinical outcomes for patients; however, successive generations of inhibitors have failed to prevent the eventual emergence of resistance to targeted agents. Although these tumors have a well-established dependency on EGFR signaling, there remain questions about the underlying genetic mechanisms necessary for EGFR-driven oncogenesis and the factors that allow tumor cells to escape EGFR dependence. In this review, we highlight the latest findings on mutant EGFR dependencies, co-operative drivers, and molecular mechanisms that underlie sensitivity to EGFR inhibitors. Additionally, we offer perspective on how these discoveries may inform novel combination therapies tailored to EGFR mutant NSCLC.

Identification of Six Prognostic Genes in EGFR-Mutant Lung Adenocarcinoma Using Structure Network Algorithms

This study aims to determine hub genes related to the incidence and prognosis of EGFR-mutant (MT) lung adenocarcinoma (LUAD) with weighted gene coexpression network analysis (WGCNA). From The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we used 253 EGFR-MT LUAD samples and 38 normal lung tissue samples. At the same time, GSE19188 was additionally included to verify the accuracy of the predicted gene. To discover differentially expressed genes (DEGs), the R package “limma” was used. The R packages “WGCNA” and “survival” were used to perform WGCNA and survival analyses, respectively. The functional analysis was carried out with the R package “clusterProfiler.” In total, 1450 EGFR-MT–specific DEGs were found, and 7 tumor-related modules were marked with WGCNA. We found 6 hub genes in DEGs that overlapped with the tumor-related modules, and the overexpression level of B3GNT3 was significantly associated with the worse OS (overall survival) of the EGFR-MT LUAD patients (p < 0.05). Functional analysis of the hub genes showed the metabolism and protein synthesis–related terms added value. In conclusion, we used WGCNA to identify hub genes in the development of EGFR-MT LUAD. The established prognostic factors could be used as clinical biomarkers. To confirm the mechanism of those genes in EGFR-MT LUAD, further molecular research is required.

Clathrin adapters AP-1 and GGA2 support expression of epidermal growth factor receptor for cell growth

The role of Golgi/endosome-localized clathrin adapters in the maintenance of steady-state cell surface epidermal growth factor receptor (EGFR) is not well known. Here, we show that EGFR associates preferentially with both AP-1 and GGA2 in vitro. AP-1 depletion caused a reduction in the EGFR protein by promoting its lysosomal degradation. Triple immunofluorescence microscopy and proximity ligation assays demonstrated that the interaction of EGFR with AP-1 or GGA2 occurred more frequently in Rab11-positive recycling endosomes than in Rab5-positive early endosomes. Biochemical recycling assay revealed that the depletion of AP-1 or GGA2 significantly suppressed EGFR recycling to the plasma membrane regardless of the EGF stimulation. Depletion of AP-1 or GGA2 also reduced cell contents of other tyrosine kinases, MET and ErbB4, and therefore, suppressed the growth of H1975 cancer cells in culture and xenograft model. Moreover, AP-1 was expressed in endosomes at higher levels in some cancer tissues. Collectively, these results suggest that AP-1 and GGA2 function in recycling endosomes to retrieve endocytosed EGFR, thereby sustaining its cell surface expression and, consequently, cancer cell growth.

Oxygen-carrying nanoparticle-based chemo-sonodynamic therapy for tumor suppression and autoimmunity activation

Sonodynamic therapy (SDT) is a promising non-invasive approach for cancer therapy. However, tumor hypoxia, a pathological characteristic of most solid tumor types, poses a major challenge in the application of SDT. In this study, a novel CD44 receptor-targeted and redox/ultrasound-responsive oxygen-carrying nanoplatform was constructed using chondroitin sulfate (CS), reactive oxygen species (ROS)-generating sonosensitizer Rhein (Rh), and perfluorocarbon (PFC). Perfluoroalkyl groups introduced into the structures preserved the oxygen carrying ability of PFC, increasing the oxygen content in B16F10 melanoma cells and enhancing the efficiency of SDT. Controlled nanoparticles without PFC generated lower ROS levels and exerted inferior tumor inhibition effects, both in vitro and in vivo, under ultrasound-treatment. In addition, SDT promoted immunogenic cell death (ICD) by inducing exposure of calreticulin (CRT) after treatment with CS-Rh-PFC nanoparticles (NPs). The immune system was significantly activated by docetaxel (DTX)-loaded NPs after SDT treatment due to the enhanced secretion of IFN-γ, TNF-α, IL-2 and IL-6 cytokines and tumor-infiltrating CD4+ and CD8+ T cell contents. Our findings support the utility of CS-Rh-PFC as an effective anti-tumor nanoplatform that promotes general immunity and accommodates multiple hydrophobic drugs to enhance the beneficial effects of chemo-SDT therapy.

Cancer-driving mutations and variants of components of the membrane trafficking core machinery

The core machinery for vesicular membrane trafficking broadly comprises of coat proteins, RABs, tethering complexes and SNAREs. As cellular membrane traffic modulates key processes of mitogenic signaling, cell migration, cell death and autophagy, its dysregulation could potentially results in increased cell proliferation and survival, or enhanced migration and invasion. Changes in the levels of some components of the core machinery of vesicular membrane trafficking, likely due to gene amplifications and/or alterations in epigenetic factors (such as DNA methylation and micro RNA) have been extensively associated with human cancers. Here, we provide an overview of association of membrane trafficking with cancer, with a focus on mutations and variants of coat proteins, RABs, tethering complex components and SNAREs that have been uncovered in human cancer cells/tissues. The major cellular and molecular cancer-driving or suppression mechanisms associated with these components of the core membrane trafficking machinery shall be discussed.

Identification of a 5-Gene Metabolic Signature for Predicting Prognosis Based on an Integrated Analysis of Tumor Microenvironment in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a common subtype of lung cancer with a depressing survival rate. The reprogramming of tumor metabolism was identified as a new hallmark of cancer in tumor microenvironment (TME), and we made a comprehensive exploration to reveal the prognostic role of the metabolic-related genes. Transcriptome profiling data of LUAD were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Based on the extracted metabolic-related genes, a novel 5-gene metabolic prognostic signature (including GNPNAT1, LPGAT1, TYMS, LDHA, and PTGES) was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. This signature confirmed its robustness and accuracy by external validation in multiple databases. It could be an independent risk factor for LUAD, and the nomograms possessed moderately accurate performance with the C-index of 0.755 (95% confidence interval: 0.706–0.804) and 0.691 (95% confidence interval: 0.636–0.746) in training set and testing set. This signature could reveal the metabolic features according to the results of gene set enrichment analysis (GSEA) and meanwhile monitor the status of TME through ESTIMATE scores and the infiltration levels of immune cells. In conclusion, this gene signature is a cost-effective tool which could indicate the status of TME to provide more clues in the exploration of new diagnostic and therapeutic strategy.

GGA2 and RAB13 promote activity-dependent β1-integrin recycling

β1-integrins mediate cell-matrix interactions and their trafficking is important in the dynamic regulation of cell adhesion, migration and malignant processes, including cancer cell invasion. Here, we employ an RNAi screen to characterize regulators of integrin traffic and identify the association of Golgi-localized gamma ear-containing Arf-binding protein 2 (GGA2) with β1-integrin, and its role in recycling of active but not inactive β1-integrin receptors. Silencing of GGA2 limits active β1-integrin levels in focal adhesions and decreases cancer cell migration and invasion, which is in agreement with its ability to regulate the dynamics of active integrins. By using the proximity-dependent biotin identification (BioID) method, we identified two RAB family small GTPases, i.e. RAB13 and RAB10, as novel interactors of GGA2. Functionally, RAB13 silencing triggers the intracellular accumulation of active β1-integrin, and reduces integrin activity in focal adhesions and cell migration similarly to GGA2 depletion, indicating that both facilitate active β1-integrin recycling to the plasma membrane. Thus, GGA2 and RAB13 are important specificity determinants for integrin activity-dependent traffic.