Estrogen receptor-positive breast cancer (ERPBC) is the commonest subtype of breast cancer, with a high prevalence, incidence, and mortality. Herbal drugs are increasingly being used to treat ERPBC, although their mechanisms of action are not fully understood. Therefore, in this study, we aimed to analyze the therapeutic properties of FDY003, a herbal anti-ERPBC prescription, using a network pharmacology approach. FDY003 decreased the viability of human ERPBC cells and sensitized them to tamoxifen, an endocrine drug that is widely used in the treatment of ERPBC. The network pharmacology analysis revealed 18 pharmacologically active components in FDY003 that may interact with and regulate 66 therapeutic targets. The enriched gene ontology terms for the FDY003 targets were associated with the modulation of cell survival and death, cell proliferation and growth arrest, and estrogen-associated cellular processes. Analysis of the pathway enrichment of the targets showed that FDY003 may target a variety of ERPBC-associated pathways, including the PIK3-Akt, focal adhesion, MAPK, and estrogen pathways. Overall, these data provide a comprehensive mechanistic insight into the anti-ERPBC activity of FDY003.
Gastric cancer (GC) is one of the most common and deadly malignant tumors worldwide. While the application of herbal drugs for GC treatment is increasing, the multicompound–multitarget pharmacological mechanisms involved are yet to be elucidated. By adopting a network pharmacology strategy, we investigated the properties of the anticancer herbal drug FDY003 against GC. We found that FDY003 reduced the viability of human GC cells and enhanced their chemosensitivity. We also identified 8 active phytochemical compounds in FDY003 that target 70 GC-associated genes and proteins. Gene ontology (GO) enrichment analysis suggested that the targets of FDY003 are involved in various cellular processes, such as cellular proliferation, survival, and death. We further identified various major FDY003 target GC-associated pathways, including PIK3-Akt, MAPK, Ras, HIF-1, ErbB, and p53 pathways. Taken together, the overall analysis presents insight at the systems level into the pharmacological activity of FDY003 against GC.
Ovarian cancer (OC) is one of the deadliest gynecological tumors responsible for 0.21 million deaths per year worldwide. Despite the increasing interest in the use of herbal drugs for cancer treatment, their pharmacological effects in OC treatment are not understood from a systems perspective. Using network pharmacology, we determined the anti-OC potential of FDY003 from a comprehensive systems view. We observed that FDY003 suppressed the viability of human OC cells and further chemosensitized them to cytotoxic chemotherapy. Through network pharmacological and pharmacokinetic approaches, we identified 16 active ingredients in FDY003 and their 108 targets associated with OC mechanisms. Functional enrichment investigation revealed that the targets may coordinate diverse cellular behaviors of OC cells, including their growth, proliferation, survival, death, and cell cycle regulation. Furthermore, the FDY003 targets are important constituents of diverse signaling pathways implicated in OC mechanisms (eg, phosphoinositide 3-kinase [PI3K]-Akt, mitogen-activated protein kinase [MAPK], focal adhesion, hypoxia-inducible factor [HIF]-1, estrogen, tumor necrosis factor [TNF], erythroblastic leukemia viral oncogene homolog [ErbB], Janus kinase [JAK]-signal transducer and activator of transcription [STAT], and p53 signaling). In summary, our data present a comprehensive understanding of the anti-OC effects and mechanisms of action of FDY003.
Pancreatic cancer (PC) is the most lethal cancer with the lowest survival rate globally. Although the prescription of herbal drugs against PC is gaining increasing attention, their polypharmacological therapeutic mechanisms are yet to be fully understood. Based on network pharmacology, we explored the anti-PC properties and system-level mechanisms of the herbal drug FDY003. FDY003 decreased the viability of human PC cells and strengthened their chemosensitivity. Network pharmacological analysis of FDY003 indicated the presence of 16 active phytochemical components and 123 PC-related pharmacological targets. Functional enrichment analysis revealed that the PC-related targets of FDY003 participate in the regulation of cell growth and proliferation, cell cycle process, cell survival, and cell death. In addition, FDY003 was shown to target diverse key pathways associated with PC pathophysiology, namely, the PIK3-Akt, MAPK, FoxO, focal adhesion, TNF, p53, HIF-1, and Ras pathways. Our network pharmacological findings advance the mechanistic understanding of the anti-PC properties of FDY003 from a system perspective.
BackgroundThe development of personalized neoantigen-based therapeutic cancer vaccines relies on computational algorithm-based pipelines. One of the critical issues in the pipeline is obtaining higher positive predictive value (PPV) performance, i.e., how many are immunogenic when selecting the top 5 to 20 candidate neoepitopes for the vaccination. We attempted to test the PPV of a neoepitope prediction algorithm Neopepsee.MethodsSix breast cancer patients and patient-derived xenografts from three lung cancer patients and their paired peripheral blood samples were subjected to whole-exome and RNA sequencing. Neoantigen was predicted using two different algorithms (Neopepsee and pVACseq). Response of induced memory T cells to neopeptide candidates was evaluated by IFN-γ Enzyme-linked immune absorbent spot (ELISpot) assays of peripheral blood mononuclear cell (PBMC) from three HLA-matched donors. Positive ELISpot response to a candidate peptide in at least 2 of 3 donor PBMC was regarded as an immunogenic response.ResultsNeopepsee predicted 159 HLA-A matched neoepitope candidates out of 898 somatic mutations in nine patients (six breast and three lung cancer patients), whereas pVACseq predicted 84 HLA-A matched candidates. A total of 26 neopeptide candidates overlapped between the two predicted candidate pools. Among the candidates, 28 (20%, 28/ 137) and 15 (20%, 15/ 75) were positive by ELISpot assay, respectively. Among 26 overlapped candidates, 20 could be tested, and 7 of them (35%) were validated by ELISpot. Neopepsee identified at least one neoepitope in 7 of 9 patients (range 0-6), compared to 6 by pVACseq (range 0-5).ConclusionAs suggested by Tumor Neoantigen Selection Alliance (TESLA), our results demonstrate low PPV of individual prediction models as well as the complementary nature of the Neopepsee and pVACseq and may help design neoepitope targeted cancer vaccines. Our data contribute a significant addition to the database of tested neoepitope candidates that can be utilized to further train and improve the prediction algorithms.
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