Transcriptome- and proteome-oriented identification of dysregulated eIF4G, STAT3, and Hippo pathways altered by PIK3CA H1047R in HER2/ER-positive breast cancer

STAT3: Key targets of growth-promoting receptor positive breast cancer

Breast cancer has become the malignant tumor with the first incidence and the second mortality among female cancers. Most female breast cancers belong to luminal-type breast cancer and HER2-positive breast cancer. These breast cancer cells all have different driving genes, which constantly promote the proliferation and metastasis of breast cancer cells. Signal transducer and activator of transcription 3 (STAT3) is an important breast cancer-related gene, which can promote the progress of breast cancer. It has been proved in clinical and basic research that over-expressed and constitutively activated STAT3 is involved in the progress, proliferation, metastasis and chemotherapy resistance of breast cancer. STAT3 is an important key target in luminal-type breast cancer and HER2-positive cancer, which has an important impact on the curative effect of related treatments. In breast cancer, the activation of STAT3 will change the spatial position of STAT3 protein and cause different phenotypic changes of breast cancer cells. In the current basic research and clinical research, small molecule inhibitors activated by targeting STAT3 can effectively treat breast cancer, and enhance the efficacy level of related treatment methods for luminal-type and HER2-positive breast cancers.

Insight into the differential toxicity of PFOA and PFBA based on a 3D-cultured MDA-MB-231 cell model

Perfluoroalkyl substances (PFASs) are a category of high-concerned emerging contaminants which are suspected to correlate with various human adverse health outcomes including tumors. It is also a question whether short-chain PFASs are qualified alternatives under the regulation of long-chain PFASs. In this study, a three-dimensional (3D) culture system based on Gelatin methacrylate (GelMA) hydrogel matrix was used to investigate the impacts of 120-h perfluorooctanoic acid (PFOA) and perfluorobutanoic acid (PFBA) exposure of MDA-MB-231 cells. The results showed that PFOA exposure promoted the proliferation, migration, and invasion of MDA-MB-231 cells in an environmentally relevant concentration range (0.1 to 10 μM), exhibiting a clear malignant-promoting risk. In contrast, PFBA only showed a trend to induce non-invasive cell migration. Hippo/YAP signaling pathway was identified as the contributor to the differences between the two PFASs. PFOA but PFBA reduced YAP phosphorylation and increased the nuclear content of YAP, which further facilitated abundant key factors of epithelial-mesenchymal transition (EMT) process. Our results provided a new idea for the carcinogenicity of PFOA using a 3D-based paradigm. Although the effects by PFBA were much milder than PFOA in the current test duration, the cell model suitable for longer exposure is still necessary to better assess the safety of alternative short-chain PFASs.

Research progress of omics technology in the field of tumor resistance: From singleomics to multiomics combination application

Drug resistance is the main obstacle in the treatment of many cancers. It is of great clinical significance to study the mechanism of drug resistance and find new targets. Multi-omics mainly includes genomics, epigenomics, transcriptomics, proteomics, metabolomics, and radiomics. In recent years, the research of tumor resistance has made rapid development, which has significantly accelerated the discovery of new targets.

PRER: A patient representation with pairwise relative expression of proteins on biological networks

Changes in protein and gene expression levels are often used as features in predictive modeling such as survival prediction. A common strategy to aggregate information contained in individual proteins is to integrate the expression levels with the biological networks. In this work, we propose a novel patient representation where we integrate proteins’ expression levels with the protein-protein interaction (PPI) networks: Patient representation with PRER (Pairwise Relative Expressions with Random walks). PRER captures the dysregulation patterns of proteins based on the neighborhood of a protein in the PPI network. Specifically, PRER computes a feature vector for a patient by comparing the source protein’s expression level with other proteins’ levels that are within its neighborhood. The neighborhood of the source protein is derived by biased random-walk strategy on the network. We test PRER’s performance in survival prediction task in 10 different cancers using random forest survival models. PRER yields a statistically significant predictive performance in 9 out of 10 cancers when compared to the same model trained with features based on individual protein expressions. Furthermore, we identified the pairs of proteins that their interactions are predictive of patient survival but their individual expression levels are not. The set of identified relations provides a valuable collection of protein biomarkers with high prognostic value. PRER can be used for other complex diseases and prediction tasks that use molecular expression profiles as input. PRER is freely available at: https://github.com/hikuru/PRER.

Cancer remains to be one of the most prevalent and challenging diseases to treat. Cancer is a complex disease with several disrupted molecular mechanisms at play. The protein expression level is a fundamental indicator of how the molecular mechanisms are altered in each tumor. Predicting patient survival based on the changes is essential for understanding the cancer mechanisms and arriving at patient-specific treatment plans. For this task, existing machine learning models are used, such as random survival forest, which requires a feature-based representation of each patient based on her tumors. Most of these models use the individual molecular quantities of the tumors. However, cancer is a complex disease in which molecular mechanisms are dysregulated in various ways. In this work, we present a new patient representation scheme in which we integrate each tumor’s protein expression levels with their neighboring proteins’ expression levels in a protein-protein interaction network to capture patient-specific dysregulation patterns. Our results suggest that proteins’ relative expressions are more predictive than their individual expressions. We also analyze which of the protein interactions are more predictive of patient survival. The identified set of important protein interactions can be potentially used for cancer prognosis.

A systems biology approach to discovering pathway signaling dysregulation in metastasis

Total metastatic burden is the primary cause of death for many cancer patients. While the process of metastasis has been studied widely, much remains to be understood. Moreover, few agents have been developed that specifically target the major steps of the metastatic cascade. Many individual genes and pathways have been implicated in metastasis but a holistic view of how these interact and cooperate to regulate and execute the process remains somewhat rudimentary. It is unclear whether all of the signaling features that regulate and execute metastasis are yet fully understood. Novel features of a complex system such as metastasis can often be discovered by taking a systems-based approach. We introduce the concepts of systems modeling and define some of the central challenges facing the application of a multidisciplinary systems-based approach to understanding metastasis and finding actionable targets therein. These challenges include appreciating the unique properties of the high-dimensional omics data often used for modeling, limitations in knowledge of the system (metastasis), tumor heterogeneity and sampling bias, and some of the issues key to understanding critical features of molecular signaling in the context of metastasis. We also provide a brief introduction to integrative modeling that focuses on both the nodes and edges of molecular signaling networks. Finally, we offer some observations on future directions as they relate to developing a systems-based model of the metastatic cascade.

Transcriptomic insight into salinomycin mechanisms in breast cancer cell lines: synergistic effects with dasatinib and induction of estrogen receptor β

BACKGROUND

Tumors are heterogeneous in nature, composed of different cell populations with various mutations and/or phenotypes. Using a single drug to encounter cancer progression is generally ineffective. To improve the treatment outcome, multiple drugs of distinctive mechanisms but complementary anticancer activities (combination therapy) are often used to enhance antitumor efficacy and minimize the risk of acquiring drug resistance. We report here the synergistic effects of salinomycin (a polyether antibiotic) and dasatinib (a Src kinase inhibitor).

METHODS

Functionally, both drugs induce cell cycle arrest, intracellular reactive oxygen species (iROS) production, and apoptosis. We rationalized that an overlapping of the drug activities should offer an enhanced anticancer effect, either through vertical inhibition of the Src-STAT3 axis or horizontal suppression of multiple pathways. We determined the toxicity induced by the drug combination and studied the kinetics of iROS production by fluorescence imaging and flow cytometry. Using genomic and proteomic techniques, including RNA-sequencing (RNA-seq), reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and Western Blot, we subsequently identified the responsible pathways that contributed to the synergistic effects of the drug combination.

RESULTS

Compared to either drug alone, the drug combination showed enhanced potency against MDA-MB-468, MDA-MB-231, and MCF-7 human breast cancer (BC) cell lines and tumor spheroids. The drug combination induces both iROS generation and apoptosis in a time-dependent manner, following a 2-step kinetic profile. RNA-seq data revealed that the drug combination exhibited synergism through horizontal suppression of multiple pathways, possibly through a promotion of cell cycle arrest at the G1/S phase via the estrogen-mediated S-phase entry pathway, and partially via the BRCA1 and DNA damage response pathway.

CONCLUSION

Transcriptomic analyses revealed for the first time, that the estrogen-mediated S-phase entry pathway partially contributed to the synergistic effect of the drug combination. More importantly, our studies led to the discoveries of new potential therapeutic targets, such as E2F2, as well as a novel drug-induced targeting of estrogen receptor β (ESR2) approach for triple-negative breast cancer treatment, currently lacking of targeted therapies.

The Role of the Hippo Pathway in Breast Cancer Carcinogenesis, Prognosis, and Treatment: A Systematic Review

Background

The Hippo pathway is a developmental pathway recently discovered in Drosophila melanogaster; in mammals it normally controls organ development and wound healing. Hippo signaling is deregulated in breast cancer (BC). MST1/2 and LATS1/2 kinases are the upstream molecular elements of Hippo signaling which phosphorylate and regulate the two effectors of Hippo signaling, YAP1 and TAZ cotranscriptional activators. The two molecular effectors of the Hippo pathway facilitate their activity through TEAD transcription factors. Several molecular pathways with known oncogenic functions cross-talk with the Hippo pathway.

Methods

A systematic review studying the correlation of the Hippo pathway with BC tumorigenesis, prognosis, and treatment was performed.

Results

Recent literature highlights the critical role of Hippo signaling in a wide spectrum of biological mechanisms in BC.

Discussion

The Hippo pathway has a crucial position in BC molecular biology, cellular behavior, and response to treatment. Targeting the Hippo pathway could potentially improve the prognosis and outcome of BC patients.

Current Coverage of the mTOR Pathway by Next-Generation Sequencing Oncology Panels

The mTOR pathway is in the process of establishing itself as a key access-point of novel oncological drugs and targeted therapies. This is also reflected by the growing number of mTOR pathway genes included in commercially available next-generation sequencing (NGS) oncology panels. This review summarizes the portfolio of medium sized diagnostic, as well as research destined NGS panels and their coverage of the mTOR pathway, including 16 DNA-based panels and the current gene list of Foundation One as a major reference entity. In addition, we give an overview of interesting, mTOR-associated somatic mutations that are not yet incorporated. Especially eukaryotic translation initiation factors (eIFs), a group of mTOR downstream proteins, are on the rise as far as diagnostics and drug targeting in precision medicine are concerned. This review aims to raise awareness for the true coverage of NGS panels, which should be valuable in selecting the ideal platform for diagnostics and research.

In silico polypharmacology of natural products

Natural products with polypharmacological profiles have demonstrated promise as novel therapeutics for various complex diseases, including cancer. Currently, many gaps exist in our knowledge of which compounds interact with which targets, and experimentally testing all possible interactions is infeasible. Recent advances and developments of systems pharmacology and computational (in silico) approaches provide powerful tools for exploring the polypharmacological profiles of natural products. In this review, we introduce recent progresses and advances of computational tools and systems pharmacology approaches for identifying drug targets of natural products by focusing on the development of targeted cancer therapy. We survey the polypharmacological and systems immunology profiles of five representative natural products that are being considered as cancer therapies. We summarize various chemoinformatics, bioinformatics and systems biology resources for reconstructing drug-target networks of natural products. We then review currently available computational approaches and tools for prediction of drug-target interactions by focusing on five domains: target-based, ligand-based, chemogenomics-based, network-based and omics-based systems biology approaches. In addition, we describe a practical example of the application of systems pharmacology approaches by integrating the polypharmacology of natural products and large-scale cancer genomics data for the development of precision oncology under the systems biology framework. Finally, we highlight the promise of cancer immunotherapies and combination therapies that target tumor ecosystems (e.g. clones or 'selfish' sub-clones) via exploiting the immunological and inflammatory 'side' effects of natural products in the cancer post-genomics era.