Ayesha N. Shajahan-Haq

MYC regulates the unfolded protein response and glucose and glutamine uptake in endocrine resistant breast cancer

BackgroundAbout 70% of all breast cancers are estrogen receptor alpha positive (ER+) and are treated with antiestrogens. However, 50% of ER + tumors develop resistance to these drugs (endocrine resistance). In endocrine resistant cells, an adaptive pathway called the unfolded protein response (UPR) is elevated that allows cells to tolerate stress more efficiently than in sensitive cells. While the precise mechanism remains unclear, the UPR can trigger both pro-survival and pro-death outcomes that depend on the nature and magnitude of the stress. In this study, we identified MYC, an oncoprotein that is upregulated in endocrine resistant breast cancer, as a regulator of the UPR in glucose-deprived conditions.MethodsER+ human breast cancer cell lines (LCC1, LCC1, LY2 and LCC9) and rat mammary tumors were used to confirm upregulation of MYC in endocrine resistance. To evaluate functional relevance of proteins, siRNA-mediated inhibition or small molecule inhibitors were used. Cell density/number was evaluated with crystal violet assay; cell cycle and apoptosis were measured by flow cytometry. Relative quantification of glutamine metabolites were determined by mass spectrometry. Signaling molecules of the UPR, apoptosis or autophagy pathways were investigated by western blotting.ResultsIncreased MYC function in resistant cells correlated with increased dependency on glutamine and glucose for survival. Inhibition of MYC reduced cell growth and uptake of both glucose and glutamine in resistant cells. Interestingly, in glucose-deprived conditions, glutamine induced apoptosis and necrosis, arrested autophagy, and triggered the unfolded protein response (UPR) though GRP78-IRE1α with two possible outcomes: (i) inhibition of cell growth by JNK activation in most cells and, (ii) promotion of cell growth by spliced XBP1 in the minority of cells. These disparate effects are regulated, at different signaling junctions, by MYC more robustly in resistant cells.ConclusionsEndocrine resistant cells overexpress MYC and are better adapted to withstand periods of glucose deprivation and can use glutamine in the short term to maintain adequate metabolism to support cell survival. Our findings reveal a unique role for MYC in regulating cell fate through the UPR, and suggest that targeting glutamine metabolism may be a novel strategy in endocrine resistant breast cancer.

Application of metabolomics in drug resistant breast cancer research.

The metabolic profiles of breast cancer cells are different from normal mammary epithelial cells. Breast cancer cells that gain resistance to therapeutic interventions can reprogram their endogenous metabolism in order to adapt and proliferate despite high oxidative stress and hypoxic conditions. Drug resistance in breast cancer, regardless of subgroups, is a major clinical setback. Although recent advances in genomics and proteomics research has given us a glimpse into the heterogeneity that exists even within subgroups, the ability to precisely predict a tumor’s response to therapy remains elusive. Metabolomics as a quantitative, high through put technology offers promise towards devising new strategies to establish predictive, diagnostic and prognostic markers of breast cancer. Along with other “omics” technologies that include genomics, transcriptomics, and proteomics, metabolomics fits into the puzzle of a comprehensive systems biology approach to understand drug resistance in breast cancer. In this review, we highlight the challenges facing successful therapeutic treatment of breast cancer and the innovative approaches that metabolomics offers to better understand drug resistance in cancer.

Modelling the effect of GRP78 on anti-oestrogen sensitivity and resistance in breast cancer

Understanding the origins of resistance to anti-oestrogen drugs is of critical importance to many breast cancer patients. Recent experiments show that knockdown of GRP78, a key gene in the unfolded protein response (UPR), can re-sensitize resistant cells to anti-oestrogens, and overexpression of GRP78 in sensitive cells can cause them to become resistant. These results appear to arise from the operation and interaction of three cellular systems: the UPR, autophagy and apoptosis. To determine whether our current mechanistic understanding of these systems is sufficient to explain the experimental results, we built a mathematical model of the three systems and their interactions. We show that the model is capable of reproducing previously published experimental results and some new data gathered specifically for this paper. The model provides us with a tool to better understand the interactions that bring about anti-oestrogen resistance and the effects of GRP78 on both sensitive and resistant breast cancer cells.

Caveolin-1 regulates cancer cell metabolism via scavenging Nrf2 and suppressing MnSOD-driven glycolysis

Aerobic glycolysis is an indispensable component of aggressive cancer cell metabolism. It also distinguishes cancer cells from most healthy cell types in the body. Particularly for this reason, targeting the metabolism to improve treatment outcomes has long been perceived as a potentially valuable strategy. In practice, however, our limited knowledge of why and how metabolic reprogramming occurs has prevented progress towards therapeutic interventions that exploit the metabolic peculiarities of tumors. We recently described that in breast cancer, MnSOD upregulation is both necessary and sufficient to activate glycolysis. Here, we focused on determining the molecular mechanisms of MnSOD upregulation. We found that Caveolin-1 (Cav-1) is a central component of this mechanism due to its suppressive effects of NF-E2-related factor 2 (Nrf2), a transcription factor upstream of MnSOD. In transformed MCF10A(Er/Src) cells, Cav-1 loss preceded the activation of Nrf2 and its induction of MnSOD expression. Consistently, with previous observations, MnSOD expression secondary to Nrf2 activation led to an increase in the glycolytic rate dependent on mtH2O2 production and the activation of AMPK. Moreover, rescue of Cav-1 expression in a breast cancer cell line (MCF7) suppressed Nrf2 and reduced MnSOD expression. Experimental data were reinforced by epidemiologic nested case-control studies showing that Cav-1 and MnSOD are inversely expressed in cases of invasive ductal carcinoma, with low Cav-1 and high MnSOD expression being associated with lower 5-year survival rates and molecular subtypes with poorest prognosis.

Interferon Regulatory Factor-1 Signaling Regulates the Switch between Autophagy and Apoptosis to Determine Breast Cancer Cell Fate

Interferon regulatory factor-1 (IRF1) is a tumor suppressor that regulates cell fate in several cell types. Here, we report an inverse correlation in expression of nuclear IRF1 and the autophagy regulator ATG7 in human breast cancer cells that directly affects their cell fate. In mice harboring mutant Atg7, nuclear IRF1 was increased in mammary tumors, spleen, and kidney. Mechanistic investigations identified ATG7 and the cell death modulator beclin-1 (BECN1) as negative regulators of IRF1. Silencing ATG7 or BECN1 caused estrogen receptor-α to exit the nucleus at the time when IRF1 nuclear localization occurred. Conversely, silencing IRF1 promoted autophagy by increasing BECN1 and blunting IGF1 receptor and mTOR survival signaling. Loss of IRF1 promoted resistance to antiestrogens, whereas combined silencing of ATG7 and IRF1 restored sensitivity to these agents. Using a mathematical model to prompt signaling hypotheses, we developed evidence that ATG7 silencing could resensitize IRF1-attenuated cells to apoptosis through mechanisms that involve other estrogen-regulated genes. Overall, our work shows how inhibiting the autophagy proteins ATG7 and BECN1 can regulate IRF1-dependent and -independent signaling pathways in ways that engender a new therapeutic strategy to attack breast cancer.

MYC-Driven Pathways in Breast Cancer Subtypes

The transcription factor MYC (MYC proto-oncogene, bHLH transcription factor) is an essential signaling hub in multiple cellular processes that sustain growth of many types of cancers. MYC regulates expression of RNA, both protein and non-coding, that control central metabolic pathways, cell death, proliferation, differentiation, stress pathways, and mechanisms of drug resistance. Activation of MYC has been widely reported in breast cancer progression. Breast cancer is a complex heterogeneous disease and treatment options are primarily guided by histological and biochemical evaluations of the tumors. Based on biochemical markers, three main breast cancer categories are ER+ (estrogen receptor alpha positive), HER2+ (human epidermal growth factor receptor 2 positive), and TNBC (triple-negative breast cancer; estrogen receptor negative, progesterone receptor negative, HER2 negative). MYC is elevated in TNBC compared with other cancer subtypes. Interestingly, MYC-driven pathways are further elevated in aggressive breast cancer cells and tumors that display drug resistant phenotype. Identification of MYC target genes is essential in isolating signaling pathways that drive tumor development. In this review, we address the role of MYC in the three major breast cancer subtypes and highlight the most promising leads to target MYC functions.

Breast cancer cell obatoclax response characterization using passivated-electrode insulator-based dielectrophoresis

Inherent electrical properties of cells can be beneficial to characterize different cell lines and their response to experimental drugs. This paper presents a novel method to characterize the response of breast cancer cells to drug stimuli through use of off‐chip passivated‐electrode insulator‐based dielectrophoresis (OπDEP) and the application of AC electric fields. This work is the first to demonstrate the ability of OπDEP to differentiate between two closely related breast cancer cell lines, LCC1 and LCC9 while assessing their drug sensitivity to an experimental anti‐cancer agent, Obatoclax. Although both cell lines are derivatives of estrogen‐responsive MCF‐7 breast cancer cells, growth of LCC1 is estrogen independent and anti‐estrogen responsive, while LCC9 is both estrogen‐independent and anti‐estrogen resistant. Under the same operating conditions, LCC1 and LCC9 had different DEP profiles. LCC1 cells had a trapping onset (crossover) frequency of 700 kHz and trapping efficiencies between 30–40%, while LCC9 cells had a lower crossover frequency (100 kHz) and showed higher trapping efficiencies of 40–60%. When exposed to the Obatoclax, both cell lines exhibited dose‐dependent shifts in DEP crossover frequency and trapping efficiency. Here, DEP results supplemented with cell morphology and proliferation assays help us to understand the response of these breast cancer cells to Obatoclax.

Systems Approaches to Cancer Biology

Cancer systems biology aims to understand cancer as an integrated system of genes, proteins, networks, and interactions rather than an entity of isolated molecular and cellular components. The inaugural Systems Approaches to Cancer Biology Conference, cosponsored by the Association of Early Career Cancer Systems Biologists and the National Cancer Institute of the NIH, focused on the interdisciplinary field of cancer systems biology and the challenging cancer questions that are best addressed through the combination of experimental and computational analyses. Attendees found that elucidating the many molecular features of cancer inevitably reveals new forms of complexity and concluded that ensuring the reproducibility and impact of cancer systems biology studies will require widespread method and data sharing and, ultimately, the translation of important findings to the clinic. Cancer Res; 76(23); 6774-7. ©2016 AACR.

BMRF-Net: a software tool for identification of protein interaction subnetworks by a bagging Markov random field-based method

UNLABELLED Identification of protein interaction subnetworks is an important step to help us understand complex molecular mechanisms in cancer. In this paper, we develop a BMRF-Net package, implemented in Java and C++, to identify protein interaction subnetworks based on a bagging Markov random field (BMRF) framework. By integrating gene expression data and protein-protein interaction data, this software tool can be used to identify biologically meaningful subnetworks. A user friendly graphic user interface is developed as a Cytoscape plugin for the BMRF-Net software to deal with the input/output interface. The detailed structure of the identified networks can be visualized in Cytoscape conveniently. The BMRF-Net package has been applied to breast cancer data to identify significant subnetworks related to breast cancer recurrence. AVAILABILITY AND IMPLEMENTATION The BMRF-Net package is available at http://sourceforge.net/projects/bmrfcjava/. The package is tested under Ubuntu 12.04 (64-bit), Java 7, glibc 2.15 and Cytoscape 3.1.0.

EGR1 regulates cellular metabolism and survival in endocrine resistant breast cancer

About 70% of all breast cancers are estrogen receptor alpha positive (ER+; ESR1). Many are treated with antiestrogens. Unfortunately, de novo and acquired resistance to antiestrogens is common but the underlying mechanisms remain unclear. Since growth of cancer cells is dependent on adequate energy and metabolites, the metabolomic profile of endocrine resistant breast cancers likely contains features that are deterministic of cell fate. Thus, we integrated data from metabolomic and transcriptomic analyses of ER+ MCF7-derived breast cancer cells that are antiestrogen sensitive (LCC1) or resistant (LCC9) that resulted in a gene-metabolite network associated with EGR1 (early growth response 1). In human ER+ breast tumors treated with endocrine therapy, higher EGR1 expression was associated with a more favorable prognosis. Mechanistic studies showed that knockdown of EGR1 inhibited cell growth in both cells and EGR1 overexpression did not affect antiestrogen sensitivity. Comparing metabolite profiles in LCC9 cells following perturbation of EGR1 showed interruption of lipid metabolism. Tolfenamic acid, an anti-inflammatory drug, decreased EGR1 protein levels and synergized with antiestrogens in inhibiting cell proliferation in LCC9 cells. Collectively, these findings indicate that EGR1 is an important regulator of breast cancer cell metabolism and is a promising target to prevent or reverse endocrine resistance.About 70% of all breast cancers are estrogen receptor alpha positive (ER+; ESR1). Many are treated with antiestrogens. Unfortunately, de novo and acquired resistance to antiestrogens is common but the underlying mechanisms remain unclear. Since growth of cancer cells is dependent on adequate energy and metabolites, the metabolomic profile of endocrine resistant breast cancers likely contains features that are deterministic of cell fate. Thus, we integrated data from metabolomic and transcriptomic analyses of ER+ MCF7-derived breast cancer cells that are antiestrogen sensitive (LCC1) or resistant (LCC9) that resulted in a gene-metabolite network associated with EGR1 (early growth response 1). In human ER+ breast tumors treated with endocrine therapy, higher EGR1 expression was associated with a more favorable prognosis. Mechanistic studies showed that knockdown of EGR1 inhibited cell growth in both cells and EGR1 overexpression did not affect antiestrogen sensitivity. Comparing metabolite profiles in LCC9 cells following perturbation of EGR1 showed interruption of lipid metabolism. Tolfenamic acid, an anti-inflammatory drug, decreased EGR1 protein levels and synergized with antiestrogens in inhibiting cell proliferation in LCC9 cells. Collectively, these findings indicate that EGR1 is an important regulator of breast cancer cell metabolism and is a promising target to prevent or reverse endocrine resistance.