Effective personalized therapy for breast cancer based on predictions of cell signaling pathway activation from gene expression analysis

Systems Biology and Experimental Model Systems of Cancer

Over the past decade, we have witnessed an increasing number of large-scale studies that have provided multi-omics data by high-throughput sequencing approaches. This has particularly helped with identifying key (epi)genetic alterations in cancers. Importantly, aberrations that lead to the activation of signaling networks through the disruption of normal cellular homeostasis is seen both in cancer cells and also in the neighboring tumor microenvironment. Cancer systems biology approaches have enabled the efficient integration of experimental data with computational algorithms and the implementation of actionable targeted therapies, as the exceptions, for the treatment of cancer. Comprehensive multi-omics data obtained through the sequencing of tumor samples and experimental model systems will be important in implementing novel cancer systems biology approaches and increasing their efficacy for tailoring novel personalized treatment modalities in cancer. In this review, we discuss emerging cancer systems biology approaches based on multi-omics data derived from bulk and single-cell genomics studies in addition to existing experimental model systems that play a critical role in understanding (epi)genetic heterogeneity and therapy resistance in cancer.

Current trends and opportunities in targeting p21 activated kinase-1(PAK1) for therapeutic management of breast cancers

Breast cancer is the most frequently diagnosed cancer in women worldwide. Identifying reliable biomarkers and druggable molecular targets pose to be a significant quest in breast cancer research. p21-activated kinase 1 (PAK1) is a serine/threonine kinase that direct cell motility, cytoskeletal remodelling, and has been shown to function as a downstream regulator for various cancer signalling cascades that promote cell proliferation, apoptosis deregulation and hasten mitotic abnormalities, resulting in tumor formation and progression. The heterogeneity and acquired drug resistance are important factors that challenge the treatment of breast cancer. p21-activated kinase 1 signalling is crucial for activation of the Ras/RAF/MEK/ERK, PI3K/Akt/mTOR and Wnt signalling cascades which regulate cell survival, cell cycle progression, differentiation, and proliferation. A study involving proteogenomics analysis on breast cancer tissues showed the PAK1 as outlier kinase. In addition to this, few outlier molecules were identified specific to subtypes of breast cancer. A few substrates of PAK1 in breast cancer are already known. In this paper, we have discussed a similar approach called Kinase Interacting Substrate Screening (KISS) for the identification of novel oncogenic substrates of p21-activated kinase specific to subtypes of breast cancer. Such high throughput approaches are expected to accelerate the process of identifying novel drug targets and biomarkers.

How to Choose a Mouse Model of Breast Cancer, a Genomic Perspective

Human breast cancer is a heterogeneous disease with numerous subtypes that have been defined through immunohistological, histological, and gene expression patterns. The diversity of breast cancer has made the study of its various underlying causes complex. To facilitate the examination of particular facets of breast cancer, mouse models have been generated, ranging from carcinogen induced models to genetically engineered mice. While mouse models have been generated to mimic the initiating event, including p53 loss, BRCA loss, or overexpression of HER2 / Neu / erbB2, other genomic events are often not well characterized. However, these secondary genetic events are often critical to the mouse tumor evolution, subtype, and outcome, just as they are in human breast cancer. As such, these other genomic events are a critical component of what models are chosen to study specific subtypes of human breast cancer. Here we review the genomic analyses that have been completed for various genetically engineered mouse models, how they compare to human breast cancer, and detail how this information can be used in choosing a mouse model for analysis.

Mesenchymal stem cell‑derived extracellular vesicles promote the in vitro proliferation and migration of breast cancer cells through the activation of the ERK pathway

Mesenchymal stem cells (MSCs) have been demonstrated to be involved in tumor progression and the modulation of the tumor microenvironment, partly through their secretome. Extracellular vesicles (EVs) are membranous nanovesicles secreted by multiple types of cells and have been demonstrated to mediate intercellular communication in both physiological and pathological conditions. However, numerous questions still remain regarding the underlying mechanisms and functional consequences of these interactions. The purpose of this study was to investigate the effects of human umbilical cord mesenchymal stem cell‑derived EVs (hUC‑MSC‑EVs) on the proliferation, migration and invasion of human breast cancer cells. We successfully generated and identified hUC‑MSCs and hUC‑MSC‑EVs which were used in this study. The results revealed that treatment of the MDA‑MB‑231 and MCF‑7 human breast cancer cells with medium containing hUC‑MSC‑EVs significantly enhanced the proliferation, migration and invasion of the cells in vitro. Treatment of the cells with medium containing hUC‑MSC‑EVs also reduced E‑cadherin expression and increased N‑cadherin expression, thus promoting the epithelial‑mesenchymal transition (EMT) of the breast cancer cells. Treatment of the breast cancer cells with extracellular signal‑regulated kinase (ERK) inhibitor prior to the interaction with hUC‑MSC‑EVs significantly reversed the enhanced proliferation, migration and invasion, as well as the EMT of the breast cancer cells induced by the hUC‑MSC‑EVs. On the whole, these data indicate that hUC‑MSC‑EVs promote the invasive and migratory potential of breast cancer cells through the induction of EMT via the ERK pathway, leading to malignant tumor progression and metastasis. Taken together, the findings of this study suggest that targeting pathways to reverse EMT may lead to the development of novel therapeutic approaches with which to combat breast cancer.

Activating Transcription Factor 4 Modulates TGFβ-Induced Aggressiveness in Triple-Negative Breast Cancer via SMAD2/3/4 and mTORC2 Signaling

Purpose: On the basis of the identified stress-independent cellular functions of activating transcription factor 4 (ATF4), we reported enhanced ATF4 levels in MCF10A cells treated with TGFβ1. ATF4 is overexpressed in patients with triple-negative breast cancer (TNBC), but its impact on patient survival and the underlying mechanisms remain unknown. We aimed to determine ATF4 effects on patients with breast cancer survival and TNBC aggressiveness, and the relationships between TGFβ and ATF4. Defining the signaling pathways may help us identify a cell signaling-tailored gene signature.Experimental Design: Patient survival data were determined by Kaplan-Meier analysis. Relationship between TGFβ and ATF4, their effects on aggressiveness (tumor proliferation, metastasis, and stemness), and the underlying pathways were analyzed in three TNBC cell lines and in vivo using patient-derived xenografts (PDX).Results: ATF4 overexpression correlated with TNBC patient survival decrease and a SMAD-dependent crosstalk between ATF4 and TGFβ was identified. ATF4 expression inhibition reduced migration, invasiveness, mammosphere-forming efficiency, proliferation, epithelial-mesenchymal transition, and antiapoptotic and stemness marker levels. In PDX models, ATF4 silencing decreased metastases, tumor growth, and relapse after chemotherapy. ATF4 was shown to be active downstream of SMAD2/3/4 and mTORC2, regulating TGFβ/SMAD and mTOR/RAC1-RHOA pathways independently of stress. We defined an eight-gene signature with prognostic potential, altered in 45% of 2,509 patients with breast cancer.Conclusions: ATF4 may represent a valuable prognostic biomarker and therapeutic target in patients with TNBC, and we identified a cell signaling pathway-based gene signature that may contribute to the development of combinatorial targeted therapies for breast cancer. Clin Cancer Res; 24(22); 5697-709. ©2018 AACR.

Using gene expression data to direct breast cancer therapy: evidence from a preclinical trial

The heterogeneity both within and between breast cancers presents a significant clinical challenge for both diagnosis and therapy. This heterogeneity is present at all levels of analysis in breast cancer, ranging from genomic to metabolomic. A function of this heterogeneity is that numerous signaling networks are activated, and while treatment of one arm may be initially effective, this allows the tumor to be poised to evolve a resistance mechanism. Here we review the classification of breast cancers and discuss therapy of hormone positive, HER2 positive, and triple negative breast cancers. Model systems for breast cancer are examined allowing for a preclinical trial using a personalized medicine approach to be tested. This preclinical trial was based solely on cell signaling pathway activation and effectively and specifically blocked tumor growth in a preclinical model system.

Triple-negative breast cancer and the potential for targeted therapy

Breast cancer is composed of several well-recognized subtypes including estrogen receptor, progesterone receptor and HER2 triple-negative breast cancer (TNBC). Without available targeted therapy options, standard of care for TNBC remains chemotherapy. It is of interest to note that TNBC tumors generally have better responses to chemotherapy compared with other subtypes. However, patients without complete response account for approximately 80% of TNBC. Mounting evidence suggests significant heterogeneity within the TNBC subtype, and studies have focused on genetic targets with high rates of altered expression. Recent studies suggest clear possibilities for benefits from targeted therapy in TNBC. In this review, we summarize studies of targeted therapy, including within mouse models, and discuss their applications in the development of combinatorial treatments to treat TNBC.