Gene regulatory network analysis with drug sensitivity reveals synergistic effects of combinatory chemotherapy in gastric cancer

HER2-targeted ADC DX126-262 combined with chemotherapy demonstrates superior antitumor efficacy in HER2-positive gastric cancer

Gastric cancer is a common malignant tumor with high incidence and mortality. The overexpression of Human epidermal growth factor receptor 2 (HER2) is associated with increased metastatic potential and poor clinical outcome in gastric cancer. Despite the proven clinical response rates of approved HER2-targeted therapies, including Trastuzumab combined with chemotherapy, their limited long-term clinical benefits and inevitable disease progression still pose significant challenges to the clinical treatment of gastric cancer. Hence, exploring novel strategies to enhance therapeutic outcomes for HER2-positive patients is extremely crucial and urgent. Here, we reported that DX126-262, a novel HER2-targeted antibody-drug conjugate, generated by conjugating a potent Tubulysin B analogue (Tub-114) to humanized anti-HER2 monoclonal antibody, exhibited a significant synergistic inhibitory effect with both Cisplatin and 5-FU in HER2-positive gastric cancer NCI-N87 cells. Moreover, the triple-drug combination strategy of DX126-262 combined with Cisplatin and 5-FU showed much better in vitro and in vivo therapeutic efficacy than monotherapy or double-drug combination (Cisplatin plus 5-FU) or first-line standard-of-care (SOC, Herceptin plus Cisplatin and 5-FU), and comparable or even superior in vivo efficacy than third-line SOC (DS-8201a) in NCI-N87 cells and xenograft models. Meanwhile, the triple-drug combination therapy did not exhibit superimposed toxicity. Taken together, our findings provide compelling evidence that DX126-262 in combination with Cisplatin and 5-FU exerts synergistic antitumor activity and is a promising strategy to improve the clinical efficacy of HER2-positive advanced or metastatic gastric cancer.

Unveiling Gene Regulatory Networks That Characterize Difference of Molecular Interplays Between Gastric Cancer Drug Sensitive and Resistance Cell Lines

Gastric cancer is a leading cause of cancer-related deaths globally and chemotherapy is widely accepted as the standard treatment for gastric cancer. However, drug resistance in cancer cells poses a significant obstacle to the success of chemotherapy, limiting its effectiveness in treating gastric cancer. Although many studies have been conducted to unravel the mechanisms of acquired drug resistance, the existing studies were based on abnormalities of a single gene, that is, differential gene expression (DGE) analysis. Single gene-based analysis alone is insufficient to comprehensively understand the mechanisms of drug resistance in cancer cells, because the underlying processes of the mechanism involve perturbations of the molecular interactions. To uncover the mechanism of acquired gastric cancer drug resistance, we perform for identification of differentially regulated gene networks between drug-sensitive and drug-resistant cell lines. We develop a computational strategy for identifying phenotype-specific gene networks by extending the existing method, CIdrgn, that quantifies the dissimilarity of gene networks based on comprehensive information of network structure, that is, regulatory effect between genes, structure of edge, and expression levels of genes. To enhance the efficiency of identifying differentially regulated gene networks and improve the biological relevance of our findings, we integrate additional information and incorporate knowledge of network biology, such as hubness of genes and weighted adjacency matrices. The outstanding capabilities of the developed strategy are validated through Monte Carlo simulations. By using our strategy, we uncover gene regulatory networks that specifically capture the molecular interplays distinguishing drug-sensitive and drug-resistant profiles in gastric cancer. The reliability and significance of the identified drug-sensitive and resistance-specific gene networks, as well as their related markers, are verified through literature. Our analysis for differentially regulated gene network identification has the capacity to characterize the drug-sensitive and resistance-specific molecular interplays related to mechanisms of acquired drug resistance that cannot be revealed by analysis based solely on abnormalities of a single gene, for example, DGE analysis. Through our analysis and comprehensive examination of relevant literature, we suggest that targeting the suppressors of the identified drug-resistant markers, such as the Melanoma Antigen (MAGE) family, Trefoil Factor (TFF) family, and Ras-Associated Binding 25 (RAB25), while enhancing the expression of inducers of the drug sensitivity markers [e.g., Serum Amyloid A (SAA) family], could potentially reduce drug resistance and enhance the effectiveness of chemotherapy for gastric cancer. We expect that the developed strategy will serve as a useful tool for uncovering cancer-related phenotype-specific gene regulatory networks that provide essential clues for uncovering not only drug resistance mechanisms but also complex biological systems of cancer.

A review of computational methods for predicting cancer drug response at the single-cell level through integration with bulk RNAseq data

Cancer treatment failure is often attributed to tumor heterogeneity, where diverse malignant cell clones exist within a patient. Despite a growing understanding of heterogeneous tumor cells depicted by single-cell RNA sequencing (scRNA-seq), there is still a gap in the translation of such knowledge into treatment strategies tackling the pervasive issue of therapy resistance. In this review, we survey methods leveraging large-scale drug screens to generate cellular sensitivities to various therapeutics. These methods enable efficient drug screens in scRNA-seq data and serve as the bedrock of drug discovery for specific cancer cell groups. We envision that they will become an indispensable tool for tailoring patient care in the era of heterogeneity-aware precision medicine.

Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes

BACKGROUND

Previous pharmacovigilance studies and a retroactive review of cancer clinical trial studies identified that women were more likely to experience drug adverse events (i.e., any unintended effects of medication), and men were more likely to experience adverse events that resulted in hospitalization or death. These sex-biased adverse events (SBAEs) are due to many factors not entirely understood, including differences in body mass, hormones, pharmacokinetics, and liver drug metabolism enzymes and transporters.

METHODS

We first identified drugs associated with SBAEs from the FDA Adverse Event Reporting System (FAERS) database. Next, we evaluated sex-specific gene expression of the known drug targets and metabolism enzymes for those SBAE-associated drugs. We also constructed sex-specific tissue gene-regulatory networks to determine if these known drug targets and metabolism enzymes from the SBAE-associated drugs had sex-specific gene-regulatory network properties and predicted regulatory relationships.

RESULTS

We identified liver-specific gene-regulatory differences for drug metabolism genes between males and females, which could explain observed sex differences in pharmacokinetics and pharmacodynamics. In addition, we found that ~ 85% of SBAE-associated drug targets had sex-biased gene expression or were core genes of sex- and tissue-specific network communities, significantly higher than randomly selected drug targets. Lastly, we provide the sex-biased drug-adverse event pairs, drug targets, and drug metabolism enzymes as a resource for the research community.

CONCLUSIONS

Overall, we provide evidence that many SBAEs are associated with drug targets and drug metabolism genes that are differentially expressed and regulated between males and females. These SBAE-associated drug metabolism enzymes and drug targets may be useful for future studies seeking to explain or predict SBAEs.

Identification of Key Gene Targets for Sensitizing Colorectal Cancer to Chemoradiation: an Integrative Network Analysis on Multiple Transcriptomics Data

PURPOSE

Colorectal cancer (CRC) is a main cause of morbidity and mortality in the world. Chemoradioresistance is a major problem in CRC treatment. Identification of novel therapeutic targets in order to overcome treatment resistance in CRC is necessary.

METHODS

In this study, gene expression omnibus (GEO) database was searched to find microarray datasets. Data normalization/analyzing was performed using ExAtlas. The gene ontology (GO) and pathway enrichment analysis was performed using g:Profiler. Protein-protein interaction network (PPIN) was constructed by Search Tool for the Retrieval of Interacting Genes (STRING) and analyzed using Cytoscape. Survival analysis was done using Kaplan-Meier curve method.

RESULTS

Forty-one eligible datasets were included in study. A total of 12,244 differentially expressed genes (DEGs) and 7337 unique DEGs were identified. Among them, 1187 DEGs were overlapped in ≥ 3 datasets. Fifty-five overlapped genes were considered as hub genes. Common hub genes in chemo/radiation/chemoradiation datasets were chosen as the essential candidate genes (n = 13). Forty-one hub gene and 7 essential candidate genes were contributed in the significant modules. The modules were mainly enriched in the signaling pathways of senescence, autophagy, NF-κB, HIF-1, stem cell pluripotency, notch, neovascularization, cell cycle, p53, chemokine, and PI3K-Akt. NGFR, FGF2, and PROM1 genes were significantly predictors of CRC patient's survival.

CONCLUSION

Our study revealed three-gene signatures as potential therapeutic targets and also candidate molecular markers in CRC chemoradioresistance.

Chemosensitivity of gastric cancer: analysis of key pathogenic transcription factors

BACKGROUND

We aimed to screen the key pathogenic transcription factors of gastric cancer and analyzed the correlation between the expression of transcription factors and chemotherapy drugs in gastric cancer.

METHODS

Gastric cancer RNA sequencing data sets, single nucleotide polymorphism data sets, and corresponding clinical data sets were downloaded from The Cancer Genome Atlas, which is public data. The expression of transcription factors in gastric cancer and normal tissues was analyzed with R software. Pathway enrichment analysis of differentially expressed transcription factors was performed using the Kyoto Encyclopedia of Genes and Genomes database. Univariate Cox analysis was used to explore the correlation between the differential expression of transcription factors and prognosis. The interaction network among differentially expressed transcription factors was constructed using String database. Spearman test was used to explore the correlation between transcription factor mutation and gene expression. The Genomics of Drug Sensitivity in Cancer database was used to examine the relationship between the expression of transcription factors and chemotherapeutic drug sensitivity.

RESULTS

A total of 17 differentially expressed transcription factors were screened. The results indicated that CENPA, E2F1, EMX1, HOXA9, FOXM1, and MYBL2 were prognostic risk factors for gastric cancer patients (P<0.05), while RXRG and SOX4 were prognostic protective factors for gastric cancer patients (P<0.05). FDXM1 interacted with E2F7, MYBL2, E2F1, NCAPG, and SOX9. FOXM1 gene mutation was positively correlated with the expression level (P<0.05). Based on the median value of FOXM1, the patients were divided into high expression group and low expression group of FOXM1. There was no significant difference in IC50 of 5-fluorouracil between the FOXM1 high expression group and the FOXM1 low expression group (P>0.05). The IC50 of paclitaxel in the FOXM1 high expression group was higher than that in the FOXM1 low expression group (P<0.001). The expression of IC50 of cisplatin in the FOXM1 high expression group was higher than that in the FOXM1 low expression group (P<0.05).

CONCLUSIONS

FOXM1 was a highly expressed transcription factor in gastric cancer. High FOXM1 expression was associated with the resistance of gastric cancer patients to paclitaxel and cisplatin. Therefore, FOXM1 is a potential therapeutic target for gastric cancer.

Integrated Drug Mining Reveals Actionable Strategies Inhibiting Plexiform Neurofibromas

Neurofibromatosis Type 1 (NF1) is one of the most common genetic tumor predisposition syndromes, affecting up to 1 in 2500 individuals. Up to half of patients with NF1 develop benign nerve sheath tumors called plexiform neurofibromas (PNs), characterized by biallelic NF1 loss. PNs can grow to immense sizes, cause extensive morbidity, and harbor a 15% lifetime risk of malignant transformation. Increasingly, molecular sequencing and drug screening data from various preclinical murine and human PN cell lines, murine models, and human PN tissues are available to help identify salient treatments for PNs. Despite this, Selumetinib, a MEK inhibitor, is the only currently FDA-approved pharmacotherapy for symptomatic and inoperable PNs in pediatric NF1 patients. The discovery of alternative and additional treatments has been hampered by the rarity of the disease, which makes prioritizing drugs to be tested in future clinical trials immensely important. Here, we propose a gene regulatory network-based integrated analysis to mine high-throughput cell line-based drug data combined with transcriptomes from resected human PN tumors. Conserved network modules were characterized and served as drug fingerprints reflecting the biological connections among drug effects and the inherent properties of PN cell lines and tissue. Drug candidates were ranked, and the therapeutic potential of drug combinations was evaluated via computational predication. Auspicious therapeutic agents and drug combinations were proposed for further investigation in preclinical and clinical trials.

Alveolar Regeneration in COVID-19 Patients: A Network Perspective

A viral infection involves entry and replication of viral nucleic acid in a host organism, subsequently leading to biochemical and structural alterations in the host cell. In the case of SARS-CoV-2 viral infection, over-activation of the host immune system may lead to lung damage. Albeit the regeneration and fibrotic repair processes being the two protective host responses, prolonged injury may lead to excessive fibrosis, a pathological state that can result in lung collapse. In this review, we discuss regeneration and fibrosis processes in response to SARS-CoV-2 and provide our viewpoint on the triggering of alveolar regeneration in coronavirus disease 2019 (COVID-19) patients.

In Silico Inference of Synthetic Cytotoxic Interactions from Paclitaxel Responses

To exploit negatively interacting pairs of cancer somatic mutations in chemotherapy responses or synthetic cytotoxicity (SC), we systematically determined mutational pairs that had significantly lower paclitaxel half maximal inhibitory concentration (IC₅₀) values. We evaluated 407 cell lines with somatic mutation profiles and estimated their copy number and drug-inhibitory concentrations in Genomics of Drug Sensitivity in Cancer (GDSC) database. The SC effect of 142 mutated gene pairs on response to paclitaxel was successfully cross-validated using human cancer datasets for urogenital cancers available in The Cancer Genome Atlas (TCGA) database. We further analyzed the cumulative effect of increasing SC pair numbers on the TP53 tumor suppressor gene. Patients with TCGA bladder and urogenital cancer exhibited improved cancer survival rates as the number of disrupted SC partners (i.e., SYNE2, SON, and/or PRY) of TP53 increased. The prognostic effect of SC burden on response to paclitaxel treatment could be differentiated from response to other cytotoxic drugs. Thus, the concept of pairwise SC may aid the identification of novel therapeutic and prognostic targets.

Could microtubule inhibitors be the best choice of therapy in gastric cancer with high immune activity: mutant DYNC1H1 as a biomarker

Immune checkpoint blockade (ICB) has achieved unprecedented breakthroughs in various cancers, including gastric cancer (GC) with high immune activity (MSI-H or TMB-H), yet clinical benefits from ICB were moderate. Here we aimed to identify the most appropriate drugs which can improve outcomes in GC. We firstly compared MSI-H and TMB-H GC samples with normal samples in TCGA-STAD cohort, respectively. After that, Connectivity Map database repurposed nine candidate drugs (CMap score < -90). Then, microtubule inhibitors (MTIs) were screened as the significant candidate drugs with their representative gene sets strongly enriched (p < 0.05) via GSEA. GDSC database validated higher activities of some MTIs in GC cells with MSI-H and TMB-H (p < 0.05). Furthermore, some MTIs activities were positively associated with mutant Dynein Cytoplasmic 1 Heavy Chain 1 (DYNC1H1) (p < 0.05) based on NCI-60 cancer cell line panel. DYNC1H1 was high frequently alteration in GC and was positively associated with TMB-H and MSI-H. Mutant DYNC1H1 may be accompanied with down-regulation of MTIs-related genes in GC or change the binding pocket to sensitize MTIs. Overall, this study suggested that some MTIs may be the best candidate drugs to treat GC with high immune activity, especially patients with DYNC1H1 mutated.