Denoising perturbation signatures reveal an actionable AKT-signaling gene module underlying a poor clinical outcome in endocrine-treated ER+ breast cancer

A Network Pharmacology Study on the Molecular Mechanisms of FDY003 for Breast Cancer Treatment

Herbal medicines have drawn considerable attention with regard to their potential applications in breast cancer (BC) treatment, a frequently diagnosed malignant disease, considering their anticancer efficacy with relatively less adverse effects. However, their mechanisms of systemic action have not been understood comprehensively. Based on network pharmacology approaches, we attempted to unveil the mechanisms of FDY003, an herbal drug comprised of Lonicera japonica Thunberg, Artemisia capillaris Thunberg, and Cordyceps militaris, against BC at a systemic level. We found that FDY003 exhibited pharmacological effects on human BC cells. Subsequently, detailed data regarding the biochemical components contained in FDY003 were obtained from comprehensive herbal medicine-related databases, including TCMSP and CancerHSP. By evaluating their pharmacokinetic properties, 18 chemical compounds in FDY003 were shown to be potentially active constituents interacting with 140 BC-associated therapeutic targets to produce the pharmacological activity. Gene ontology enrichment analysis using g:Profiler indicated that the FDY003 targets were involved in the modulation of cellular processes, involving the cell proliferation, cell cycle process, and cell apoptosis. Based on a KEGG pathway enrichment analysis, we further revealed that a variety of oncogenic pathways that play key roles in the pathology of BC were significantly enriched with the therapeutic targets of FDY003; these included PI3K-Akt, MAPK, focal adhesion, FoxO, TNF, and estrogen signaling pathways. Here, we present a network-perspective of the molecular mechanisms via which herbal drugs treat BC.

Formiminotransferase Cyclodeaminase Suppresses Hepatocellular Carcinoma by Modulating Cell Apoptosis, DNA Damage, and Phosphatidylinositol 3-Kinases (PI3K)/Akt Signaling Pathway

Background

Formiminotransferase cyclodeaminase (FTCD) is a candidate tumor suppressor gene in hepatocellular carcinoma (HCC). However, the mechanism for reduced expression of FTCD and its functional role in HCC remains unclear. In this study, we explored the biological functions of FTCD in HCC.

Material/Methods

The expression and clinical correlation of FTCD in HCC tissue were analyzed using TCGA (The Cancer Genome Atlas) and a cohort of 60 HCC patients. The MEXPRESS platform was accessed to identify the methylation level in promoter region FTCD. CCK-8 assay and flow cytometry analysis were used to explore the proliferation, cell apoptosis proportion, and DNA damage in HCC cells with FTCD overexpression. Western blot analysis was performed to identify the downstream target of FTCD.

Results

FTCD is significantly downregulated in HCC tissues and cell lines. Low FTCD expression is correlated with a poor prognosis (P<0.001) and an aggressive tumor phenotype, including AFP levels (P=0.009), tumor size (P=0.013), vascular invasion (P=0.001), BCLC stage (P=0.024), and pTNM stage (P<0.001). Bioinformatics analysis indicated promoter hypermethylation can result in decreased expression of FTCD. FTCD overexpression suppressed cell proliferation by promoting DNA damage and inducing cell apoptosis in HCC cells. FTCD overexpression resulted in increased level of PTEN protein, but a decrease in PI3K, total Akt, and phosphorylated Akt protein in HCC cells, suggesting involvement of the PI3K/Akt pathway.

Conclusions

FTCD acts as a tumor suppressor gene in HCC pathogenesis and progression and is a candidate prognostic marker and a possible therapeutic target for this disease.

Bronchial airway gene expression signatures in mouse lung squamous cell carcinoma and their modulation by cancer chemopreventive agents

Due to exposure to environmental toxicants, a “field cancerization” effect occurs in the lung resulting in the development of a field of initiated but morphologically normal appearing cells in the damaged epithelium of bronchial airways with dysregulated gene expression patterns. Using a mouse model of lung squamous cell carcinoma (SCC), we performed transcriptome sequencing (RNA-Seq) to profile bronchial airway gene expression and found activation of the PI3K and Myc signaling networks in cytologically normal bronchial airway epithelial cells of mice with preneopastic lung SCC lesions, which was reversed by treatment with the PI3K Inhibitor XL-147 and pioglitazone, respectively. Activated MYC signaling was also present in premalignant and tumor tissues from human lung SCC patients. In addition, we identified a key microRNA, mmu-miR-449c-5p, whose suppression significantly up-regulated Myc expression in the normal bronchial airway epithelial cells of mice with early stage SCC lesions. We developed a novel bronchial genomic classifier in mice and validated it in humans. In the classifier, Ppbp (pro-platelet basic protein) was overexpressed 115 fold in the bronchial airways of mice with preneoplastic lung SCC lesions. This is the first report that demonstrates Ppbp as a novel biomarker in the bronchial airway for lung cancer diagnosis.

Detecting gene signature activation in breast cancer in an absolute, single-patient manner

Background

The ability to reliably identify the state (activated, repressed, or latent) of any molecular process in the tumor of a patient from an individual whole-genome gene expression profile obtained from microarray or RNA sequencing (RNA-seq) promises important clinical utility. Unfortunately, all previous bioinformatics tools are only applicable in large and diverse panels of patients, or are limited to a single specific pathway/process (e.g. proliferation).

Methods

Using a panel of 4510 whole-genome gene expression profiles from 10 different studies we built and selected models predicting the activation status of a compendium of 1733 different biological processes. Using a second independent validation dataset of 742 patients we validated the final list of 1773 models to be included in a de novo tool entitled absolute inference of patient signatures (AIPS). We also evaluated the prognostic significance of the 1773 individual models to predict outcome in all and in specific breast cancer subtypes.

Results

We described the development of the de novo tool entitled AIPS that can identify the activation status of a panel of 1733 different biological processes from an individual breast cancer microarray or RNA-seq profile without recourse to a broad cohort of patients. We demonstrated that AIPS is stable compared to previous tools, as the inferred pathway state is not affected by the composition of a dataset. We also showed that pathway states inferred by AIPS are in agreement with previous tools but use far fewer genes. We determined that several AIPS-defined pathways are prognostic across and within molecularly and clinically define subtypes (two-sided log-rank test false discovery rate (FDR) <5%). Interestingly, 74.5% (1291/1733) of the models are able to distinguish patients with luminal A cancer from those with luminal B cancer (Fisher’s exact test FDR <5%).

Conclusion

AIPS represents the first tool that would allow an individual breast cancer patient to obtain a thorough knowledge of the molecular processes active in their tumor from only one individual gene expression (N-of-1) profile.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-017-0824-7) contains supplementary material, which is available to authorized users.