Dynamic transcription factor activity and networks during ErbB2 breast oncogenesis and targeted therapy

Weiss, Michael; Bernabé, Beatriz Peñalver; Shin, Sue; Asztalos, Szilárd; Dubbury, Sara; Mui, Michael D.; Bellis, Abigail D.; Bluver, Dennis A.; Tonetti, Debra A.; Sáez-Rodríguez, Julio; Broadbelt, Linda J.; Jeruss, Jacqueline S.; Shea, LD

doi:10.1039/c4ib00086b

Cited by 12 publications

(26 citation statements)

References 66 publications

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“…To observe this complex interplay of activated TFrs in response to alterations of physical and chemical stimuli in the cellular microenvironment, we generated dynamic TFr activity networks from the acquired data using a method termed NTRACER ( N etworks for TRACER) 40 .…”

Section: Resultsmentioning

confidence: 99%

“…TFrs consisted of lentiviral constructs carrying plasmids designed so that each contained a consensus binding sequence for a particular TF in the enhancer region upstream of a minimal TATA-box promoter driving expression of the reporter gene firefly luciferase 38–40 . Consensus sequences (File S1) were identified from a combination of available databases (e.g., TRANSFAC), commercial sources (e.g., Promega, Madison, WI, USA) and previous reports 38–40, 43 .…”

Section: Methodsmentioning

confidence: 99%

“…Consensus sequences (File S1) were identified from a combination of available databases (e.g., TRANSFAC), commercial sources (e.g., Promega, Madison, WI, USA) and previous reports 38–40, 43 . Lentiviruses were produced using the human embryonic kidney (HEK) 293T cell line (ATCC, Manassas, VA, USA) and third-generation packaging system 44 .…”

Section: Methodsmentioning

confidence: 99%

“…The dynamic network was originally modeled as a three-level Boolean paradigm 40 . Herein, we present an improved methodology to avoid data discretization.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, we have expanded the libraries of available dynamic inference methods, we have automated the determination of the shortest paths between each TFr and the applied extracellular stimuli, and we have allowed the inclusion of self-loops, where a single TFr was allowed to act on itself from the TRACER experimental data only. Solely new modifications added to NTRACER since our original publication 40 are discussed here.…”

Section: Methodsmentioning

confidence: 99%

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Dynamic transcription factor activity networks in response to independently altered mechanical and adhesive microenvironmental cues

et al. 2016

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Multiple aspects of the local extracellular environment profoundly affect cell phenotype and function. Physical and chemical cues in the environment trigger intracellular signaling cascades that ultimately activate transcription factors (TFs) – powerful regulators of the cell phenotype. TRACER (TRanscriptional Activity CEll aRrays) was employed for large-scale, dynamic quantification of TF activity in human fibroblasts cultured on hydrogels with a controlled elastic modulus and integrin ligand density. We identified three groups of TFs: responders to alterations in ligand density alone, substrate stiffness or both. Dynamic networks of regulatory TFs were constructed computationally and revealed distinct TF activity levels, directionality (i.e., activation or inhibition), and dynamics for adhesive and mechanical cues. Moreover, TRACER networks predicted conserved hubs of TF activity across multiple cell types, which are significantly altered in clinical fibrotic tissues. Our approach captures the distinct and overlapping effects of adhesive and mechanical stimuli, identifying conserved signaling mechanisms in normal and disease states.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The dynamic network was originally modeled as a three-level Boolean paradigm 40 . Herein, we present an improved methodology to avoid data discretization.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Dynamic transcription factor activity networks in response to independently altered mechanical and adhesive microenvironmental cues

et al. 2016

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Biomaterial Scaffolds as Pre‐metastatic Niche Mimics Systemically Alter the Primary Tumor and Tumor Microenvironment

Aguado

Hartfield

Bushnell

et al. 2018

Adv Healthcare Materials

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Primary tumor (PT) immune cells and pre-metastatic niche (PMN) sites are critical to metastasis. Recently, synthetic biomaterial scaffolds used as PMN mimics are shown to capture both immune and metastatic tumor cells. Herein, studies are performed to investigate whether the scaffold-mediated redirection of immune and tumor cells would alter the primary tumor microenvironment (TME). Transcriptomic analysis of PT cells from scaffold-implanted and mock-surgery mice identifies differentially regulated pathways relevant to invasion and metastasis progression. Transcriptomic differences are hypothesized to result from scaffold-mediated modulations of immune cell trafficking and phenotype in the TME. Culturing tumor cells with conditioned media generated from PT immune cells of scaffold-implanted mice decrease invasion in vitro more than two-fold relative to mock surgery controls and reduce activity of invasion-promoting transcription factors. Secretomic characterization of the conditioned media delineates interactions between immune cells in the TME and tumor cells, showing an increase in the pan-metastasis inhibitor decorin and a concomitant decrease in invasion-promoting chemokine (C-C motif) ligand 2 (CCL2) in scaffold-implanted mice. Flow cytometric and transcriptomic profiling of PT immune cells identify phenotypically distinct tumor-associated macrophages (TAMs) in scaffold-implanted mice, which may contribute to an invasion-suppressive TME. Taken together, this study demonstrates biomaterial scaffolds systemically influence metastatic progression through manipulation of the TME.

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Systems analysis of dynamic transcription factor activity identifies targets for treatment in Olaparib resistant cancer cells

Decker

Hobson

Zhang

et al. 2017

Biotech & Bioengineering

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The development of resistance to targeted therapeutics is a challenging issue for the treatment of cancer. Cancers that have mutations in BRCA, a DNA repair protein, have been treated with poly (ADP-ribose) polymerase (PARP) inhibitors, which target a second DNA repair mechanism with the aim of inducing synthetic lethality. While these inhibitors have shown promise clinically, the development of resistance can limit their effectiveness as a therapy. This study investigated mechanisms of resistance in BRCA-mutated cancer cells (HCC1937) to Olaparib (AZD2281) using TRACER, a technique for measuring dynamics of transcription factor (TF) activity in living cells. TF activity was monitored in the parental HCC1937 cell line and two distinct resistant cell lines, one with restored wild-type BRCA1 and one with acquired resistance independent of BRCA1 for 48 hours during treatment with Olaparib. Partial least squares discriminant analysis (PLSDA) was used to categorize the three cell types based on TF activity, and network analysis was used to investigate the mechanism of early response to Olaparib in the study cells. NOTCH signaling was identified as a common pathway linked to resistance in both Olaparib-resistant cell types. Western blotting confirmed upregulation of NOTCH protein, and sensitivity to Olaparib was restored through co-treatment with a gamma secretase inhibitor. The identification of NOTCH signaling as a common pathway contributing to PARP inhibitor resistance by TRACER indicates the efficacy of transcription factor dynamics in identifying targets for intervention in treatment-resistant cancer and provides a new method for determining effective strategies for directed chemotherapy.

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Dynamic transcription factor activity and networks during ErbB2 breast oncogenesis and targeted therapy

Cited by 12 publications

References 66 publications

Dynamic transcription factor activity networks in response to independently altered mechanical and adhesive microenvironmental cues

Dynamic transcription factor activity networks in response to independently altered mechanical and adhesive microenvironmental cues

Biomaterial Scaffolds as Pre‐metastatic Niche Mimics Systemically Alter the Primary Tumor and Tumor Microenvironment

Systems analysis of dynamic transcription factor activity identifies targets for treatment in Olaparib resistant cancer cells

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