A multivariate model of ErbB network composition predicts ovarian cancer cell response to canertinib

Rational design of HER2-targeted combination therapies to reverse drug resistance in fibroblast-protected HER2+ breast cancer cells

Introduction

Fibroblasts, an abundant cell type in the breast tumor microenvironment, interact with cancer cells and orchestrate tumor progression and drug resistance. However, the mechanisms by which fibroblast-derived factors impact drug sensitivity remain poorly understood. Here, we develop rational combination therapies that are informed by proteomic profiling to overcome fibroblast-mediated therapeutic resistance in HER2+ breast cancer cells.

Methods

Drug sensitivity to the HER2 kinase inhibitor lapatinib was characterized under conditions of monoculture and exposure to breast fibroblast-conditioned medium. Protein expression was measured using reverse phase protein arrays. Candidate targets for combination therapy were identified using differential expression and multivariate regression modeling. Follow-up experiments were performed to evaluate the effects of HER2 kinase combination therapies in fibroblast-protected cancer cell lines and fibroblasts.

Results

Compared to monoculture, fibroblast-conditioned medium increased the expression of plasminogen activator inhibitor-1 (PAI1) and cell cycle regulator polo like kinase 1 (PLK1) in lapatinib-treated breast cancer cells. Combination therapy of lapatinib with inhibitors targeting either PAI1 or PLK1, eliminated fibroblast-protected cancer cells, under both conditions of direct coculture with fibroblasts and protection by fibroblast-conditioned medium. Analysis of publicly available, clinical transcriptomic datasets revealed that HER2-targeted therapy fails to suppress PLK1 expression in stroma-rich HER2+ breast tumors and that high PAI1 gene expression associates with high stroma density. Furthermore, we showed that an epigenetics-directed approach using a bromodomain and extraterminal inhibitor to globally target fibroblast-induced proteomic adaptions in cancer cells, also restored lapatinib sensitivity.

Conclusions

Our data-driven framework of proteomic profiling in breast cancer cells identified the proteolytic degradation regulator PAI1 and the cell cycle regulator PLK1 as predictors of fibroblast-mediated treatment resistance. Combination therapies targeting HER2 kinase and these fibroblast-induced signaling adaptations eliminates fibroblast-protected HER2+ breast cancer cells.

Cellular context alters EGF-induced ERK dynamics and reveals potential crosstalk with GDF-15

Cellular signaling dynamics are sensitive to differences in ligand identity, levels, and temporal patterns. These signaling patterns are also impacted by the larger context that the cell experiences (i.e., stimuli such as media formulation or substrate stiffness that are constant in an experiment exploring a particular variable but may differ between independent experiments which explore that variable) although the reason for different dynamics is not always obvious. Here, we compared extracellular-regulated kinase (ERK) signaling in response to epidermal growth factor treatment of human mammary epithelial cells cultures in either well culture or a microfluidic device. Using a single-cell ERK kinase translocation reporter, we observed extended ERK activation in well culture and only transient activity in microfluidic culture. The activity in microfluidic culture resembled that of the control condition, suggesting that shear stress led to the early activity and a loss of autocrine factors dampened extended signaling. Through experimental analysis we identified growth differentiation factor-15 as a candidate factor that led to extended ERK activation through a protein kinase C-α/β dependent pathway. Our results demonstrate that context impacts ERK dynamics and that comparison of distinct contexts can be used to elucidate new aspects of the cell signaling network.

Topological defects in the mesothelium suppress ovarian cancer cell clearance

We investigated an in vitro model for mesothelial clearance, wherein ovarian cancer cells invade into a layer of mesothelial cells, resulting in mesothelial retraction combined with cancer cell disaggregation and spreading. Prior to the addition of tumor cells, the mesothelial cells had an elongated morphology, causing them to align with their neighbors into well-ordered domains. Flaws in this alignment, which occur at topological defects, have been associated with altered cell density, motion, and forces. Here, we identified topological defects in the mesothelial layer and showed how they affected local cell density by producing a net flow of cells inward or outward, depending on the defect type. At locations of net inward flow, mesothelial clearance was impeded. Hence, the collective behavior of the mesothelial cells, as governed by the topological defects, affected tumor cell clearance and spreading. Importantly, our findings were consistent across multiple ovarian cancer cell types, suggesting a new physical mechanism that could impact ovarian cancer metastasis.

Ovarian cancer cells direct monocyte differentiation through a non-canonical pathway

BACKGROUND

Alternatively-activated macrophages (AAMs), an anti-inflammatory macrophage subpopulation, have been implicated in the progression of high grade serous ovarian carcinoma (HGSOC). Increased levels of AAMs are correlated with poor HGSOC survival rates, and AAMs increase the attachment and spread of HGSOC cells in vitro. However, the mechanism by which monocytes in the HGSOC tumor microenvironment are differentiated and polarized to AAMs remains unknown.

METHODS

Using an in vitro co-culture device, we cultured naïve, primary human monocytes with a panel of five HGSOC cell lines over the course of 7 days. An empirical Bayesian statistical method, EBSeq, was used to couple RNA-seq with observed monocyte-derived cell phenotype to explore which HGSOC-derived soluble factors supported differentiation to CD68+ macrophages and subsequent polarization towards CD163+ AAMs. Pathways of interest were interrogated using small molecule inhibitors, neutralizing antibodies, and CRISPR knockout cell lines.

RESULTS

HGSOC cell lines displayed a wide range of abilities to generate AAMs from naïve monocytes. Much of this variation appeared to result from differential ability to generate CD68+ macrophages, as most CD68+ cells were also CD163+. Differences in tumor cell potential to generate macrophages was not due to a MCSF-dependent mechanism, nor variance in established pro-AAM factors. TGFα was implicated as a potential signaling molecule produced by tumor cells that could induce macrophage differentiation, which was validated using a CRISPR knockout of TGFA in the OVCAR5 cell line.

CONCLUSIONS

HGSOC production of TGFα drives monocytes to differentiate into macrophages, representing a central arm of the mechanism by which AAMs are generated in the tumor microenvironment.

Tumor cell sensitivity to vemurafenib can be predicted from protein expression in a BRAF-V600E basket trial setting

BACKGROUND

Genetics-based basket trials have emerged to test targeted therapeutics across multiple cancer types. However, while vemurafenib is FDA-approved for BRAF-V600E melanomas, the non-melanoma basket trial was unsuccessful, suggesting mutation status is insufficient to predict response. We hypothesized that proteomic data would complement mutation status to identify vemurafenib-sensitive tumors and effective co-treatments for BRAF-V600E tumors with inherent resistance.

METHODS

Reverse Phase Proteomic Array (RPPA, MD Anderson Cell Lines Project), RNAseq (Cancer Cell Line Encyclopedia) and vemurafenib sensitivity (Cancer Therapeutic Response Portal) data for BRAF-V600E cancer cell lines were curated. Linear and nonlinear regression models using RPPA protein or RNAseq were evaluated and compared based on their ability to predict BRAF-V600E cell line sensitivity (area under the dose response curve). Accuracies of all models were evaluated using hold-out testing. CausalPath software was used to identify protein-protein interaction networks that could explain differential protein expression in resistant cells. Human examination of features employed by the model, the identified protein interaction networks, and model simulation suggested anti-ErbB co-therapy would counter intrinsic resistance to vemurafenib. To validate this potential co-therapy, cell lines were treated with vemurafenib and dacomitinib (a pan-ErbB inhibitor) and the number of viable cells was measured.

RESULTS

Orthogonal partial least squares (O-PLS) predicted vemurafenib sensitivity with greater accuracy in both melanoma and non-melanoma BRAF-V600E cell lines than other leading machine learning methods, specifically Random Forests, Support Vector Regression (linear and quadratic kernels) and LASSO-penalized regression. Additionally, use of transcriptomic in place of proteomic data weakened model performance. Model analysis revealed that resistant lines had elevated expression and activation of ErbB receptors, suggesting ErbB inhibition could improve vemurafenib response. As predicted, experimental evaluation of vemurafenib plus dacomitinb demonstrated improved efficacy relative to monotherapies.

CONCLUSIONS

Combined, our results support that inclusion of proteomics can predict drug response and identify co-therapies in a basket setting.

Alternatively-Activated Macrophages Upregulate Mesothelial Expression of P-Selectin to Enhance Adhesion of Ovarian Cancer Cells

Peritoneal metastasis of high-grade serous ovarian cancer (HGSOC) occurs when tumor cells suspended in ascites adhere to mesothelial cells. Despite the strong relationship between metastatic burden and prognosis in HGSOC, there are currently no therapies specifically targeting the metastatic process. We utilized a coculture model and multivariate analysis to examine how interactions between tumor cells, mesothelial cells, and alternatively-activated macrophages (AAM) influence the adhesion of tumor cells to mesothelial cells. We found that AAM-secreted MIP-1β activates CCR5/PI3K signaling in mesothelial cells, resulting in expression of P-selectin on the mesothelial cell surface. Tumor cells attached to this de novo P-selectin through CD24, resulting in increased tumor cell adhesion in static conditions and rolling underflow. C57/BL6 mice treated with MIP-1β exhibited increased P-selectin expression on mesothelial cells lining peritoneal tissues, which enhanced CaOV3 adhesion ex vivo and ID8 adhesion in vivo Analysis of samples from patients with HGSOC confirmed increased MIP-1β and P-selectin, suggesting that this novel multicellular mechanism could be targeted to slow or stop metastasis in HGSOC by repurposing anti-CCR5 and P-selectin therapies developed for other indications.Significance: This study reports novel insights on the peritoneal dissemination occurring during progression of ovarian cancer and has potential for therapeutic intervention.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/13/3560/F1.large.jpg Cancer Res; 78(13); 3560-73. ©2018 AACR.

M2 macrophages induce ovarian cancer cell proliferation via a heparin binding epidermal growth factor/matrix metalloproteinase 9 intercellular feedback loop

In ovarian cancer, a high ratio of anti-inflammatory M2 to pro-inflammatory M1 macrophages correlates with poor patient prognosis. The mechanisms driving poor tumor outcome as a result of the presence of M2 macrophages in the tumor microenvironment remain unclear and are challenging to study with current techniques. Therefore, in this study we utilized a micro-culture device previously developed by our lab to model concentrated paracrine signaling in order to address our hypothesis that interactions between M2 macrophages and ovarian cancer cells induce tumor cell proliferation. Using the micro-culture device, we determined that co-culture with M2-differentiated primary macrophages or THP-1 increased OVCA433 proliferation by 10-12%. This effect was eliminated with epidermal growth factor receptor (EGFR) or heparin-bound epidermal growth factor (HB-EGF) neutralizing antibodies and HBEGF expression in peripheral blood mononuclear cells from ovarian cancer patients was 9-fold higher than healthy individuals, suggesting a role for HB-EGF in tumor progression. However, addition of HB-EGF at levels secreted by macrophages or macrophage-conditioned media did not induce proliferation to the same extent, indicating a role for other factors in this process. Matrix metalloproteinase-9, MMP-9, which cleaves membrane-bound HB-EGF, was elevated in co-culture and its inhibition decreased proliferation. Utilizing inhibitors and siRNA against MMP9 in each population, we determined that macrophage-secreted MMP-9 released HB-EGF from macrophages, which increased MMP9 in OVCA433, resulting in a positive feedback loop to drive HB-EGF release and increase proliferation in co-culture. Identification of multi-cellular interactions such as this may provide insight into how to most effectively control ovarian cancer progression.

Human epidermal growth factor receptor targeted inhibitors for the treatment of ovarian cancer

Ovarian cancer is the second most lethal gynecological cancer worldwide and while most patients respond to initial therapy, they often relapse with resistant disease. Human epidermal growth factor receptors (especially HER1/EGFR and HER2/ERBB2) are involved in disease progression; hence, strategies to inhibit their action could prove advantageous in ovarian cancer patients, especially in patients resistant to first line therapy. Monoclonal antibodies and tyrosine kinase inhibitors are two classes of drugs that act on these receptors. They have demonstrated valuable antitumor activity in multiple cancers and their possible use in ovarian cancer continues to be studied. In this review, we discuss the human epidermal growth factor receptor family; review emerging clinical studies on monoclonal antibodies and tyrosine kinase inhibitors targeting these receptors in ovarian cancer patients; and propose future research possibilities in this area.

Mechanistic modeling quantifies the influence of tumor growth kinetics on the response to anti-angiogenic treatment

Tumors exploit angiogenesis, the formation of new blood vessels from pre-existing vasculature, in order to obtain nutrients required for continued growth and proliferation. Targeting factors that regulate angiogenesis, including the potent promoter vascular endothelial growth factor (VEGF), is therefore an attractive strategy for inhibiting tumor growth. Computational modeling can be used to identify tumor-specific properties that influence the response to anti-angiogenic strategies. Here, we build on our previous systems biology model of VEGF transport and kinetics in tumor-bearing mice to include a tumor compartment whose volume depends on the “angiogenic signal” produced when VEGF binds to its receptors on tumor endothelial cells. We trained and validated the model using published in vivo measurements of xenograft tumor volume, producing a model that accurately predicts the tumor’s response to anti-angiogenic treatment. We applied the model to investigate how tumor growth kinetics influence the response to anti-angiogenic treatment targeting VEGF. Based on multivariate regression analysis, we found that certain intrinsic kinetic parameters that characterize the growth of tumors could successfully predict response to anti-VEGF treatment, the reduction in tumor volume. Lastly, we use the trained model to predict the response to anti-VEGF therapy for tumors expressing different levels of VEGF receptors. The model predicts that certain tumors are more sensitive to treatment than others, and the response to treatment shows a nonlinear dependence on the VEGF receptor expression. Overall, this model is a useful tool for predicting how tumors will respond to anti-VEGF treatment, and it complements pre-clinical in vivo mouse studies.

One hallmark of cancer is angiogenesis, the formation of new blood capillaries from pre-existing vessels. Angiogenesis promotes tumor growth by enabling the tumor to obtain oxygen and nutrients from the surrounding microenvironment. Cancer drugs that inhibit angiogenesis ("anti-angiogenic therapies") have focused on inhibiting proteins that promote the growth of new blood vessels. The response to anti-angiogenic therapy is highly variable, and some tumors do not respond at all. Therefore, identifying a biomarker that predicts how specific tumors will respond would be extremely valuable. This work uses a computational model of tumor-bearing mice to investigate the response to anti-angiogenic treatment that targets the potent promoter of angiogenesis, vascular endothelial growth factor (VEGF), and how the response is influenced by tumor growth kinetics. We show that certain properties of tumor growth can be used to predict how much the tumor volume will be reduced upon administration of an anti-VEGF drug. This work identifies tumor growth parameters that may be reliable biomarkers for predicting how tumors will respond to anti-VEGF therapy. Our computational model generates novel, testable hypotheses and nicely complements pre-clinical studies of anti-angiogenic therapeutics.

Partial Least Squares Regression Models for the Analysis of Kinase Signaling

Partial least squares regression (PLSR) is a data-driven modeling approach that can be used to analyze multivariate relationships between kinase networks and cellular decisions or patient outcomes. In PLSR, a linear model relating an X matrix of dependent variables and a Y matrix of independent variables is generated by extracting the factors with the strongest covariation. While the identified relationship is correlative, PLSR models can be used to generate quantitative predictions for new conditions or perturbations to the network, allowing for mechanisms to be identified. This chapter will provide a brief explanation of PLSR and provide an instructive example to demonstrate the use of PLSR to analyze kinase signaling.

Hierarchy of cellular decisions in collective behavior: Implications for wound healing

Collective processes such as wound re-epithelialization result from the integration of individual cellular decisions. To determine which individual cell behaviors represent the most promising targets to engineer re-epithelialization, we examined collective and individual responses of HaCaT keratinocytes seeded upon polyacrylamide gels of three stiffnesses (1, 30, and 100 kPa) and treated with a range of epidermal growth factor (EGF) doses. Wound closure was found to increase with substrate stiffness, but was responsive to EGF treatment only above a stiffness threshold. Individual cell behaviors were used to create a partial least squares regression model to predict the hierarchy of factors driving wound closure. Unexpectedly, cell area and persistence were found to have the strongest correlation to the observed differences in wound closure. Meanwhile, the model predicted a relatively weak correlation between wound closure with proliferation, and the unexpectedly minor input from proliferation was successfully tested with inhibition by aphidicolin. Combined, these results suggest that the poor clinical results for growth factor-based therapies for chronic wounds may result from a disconnect between the individual cellular behaviors targeted in these approaches and the resulting collective response. Additionally, the stiffness-dependency of EGF sensitivity suggests that therapies matched to microenvironmental characteristics will be more efficacious.

High-grade serous ovarian cancer cell lines exhibit heterogeneous responses to growth factor stimulation

Background

The factors driving the onset and progression of ovarian cancer are not well understood. Recent reports have identified cell lines that are representative of the genomic pattern of high-grade serous ovarian cancer (HGSOC), in which greater than 90 % of tumors have a mutation in TP53. However, many of these representative cell lines have not been widely used so it is unclear if these cell lines capture the variability that is characteristic of the disease.

Methods

We investigated six TP53-mutant HGSOC cell lines (Caov3, Caov4, OV90, OVCA432, OVCAR3, and OVCAR4) for migration, MMP2 expression, proliferation, and VEGF secretion, behaviors that play critical roles in tumor progression. In addition to comparing baseline variation between the cell lines, we determined how these behaviors changed in response to four growth factors implicated in ovarian cancer progression: HB-EGF, NRG1β, IGF1, and HGF.

Results

Baseline levels of each behavior varied across the cell lines and this variation was comparable to that seen in tumors. All four growth factors impacted cell proliferation or VEGF secretion, and HB-EGF, NRG1β, and HGF impacted wound closure or MMP2 expression in at least two cell lines. Growth factor-induced responses demonstrated substantial heterogeneity, with cell lines sensitive to all four growth factors, a subset of the growth factors, or none of the growth factors, depending on the response of interest. Principal component analysis demonstrated that the data clustered together based on cell line rather than growth factor identity, suggesting that response is dependent on intrinsic qualities of the tumor cell rather than the growth factor.

Conclusions

Significant variation was seen among the cell lines, consistent with the heterogeneity of HGSOC.

Electronic supplementary material

The online version of this article (doi:10.1186/s12935-015-0263-4) contains supplementary material, which is available to authorized users.

Analysis of the quantitative balance between insulin-like growth factor (IGF)-1 ligand, receptor, and binding protein levels to predict cell sensitivity and therapeutic efficacy

Background

The insulin-like growth factor (IGF) system impacts cell proliferation and is highly activated in ovarian cancer. While an attractive therapeutic target, the IGF system is complex with two receptors (IGF1R, IGF2R), two ligands (IGF1, IGF2), and at least six high affinity IGF-binding proteins (IGFBPs) that regulate the bioavailability of IGF ligands. We hypothesized that a quantitative balance between these different network components regulated cell response.

Results

OVCAR5, an immortalized ovarian cancer cell line, were found to be sensitive to IGF1, with the dose of IGF1 (i.e., the total mass of IGF1 available) a more reliable predictor of cell response than ligand concentration. The applied dose of IGF1 was depleted by both cell-secreted IGFBPs and endocytic trafficking, with IGFBPs sequestering up to 90% of the available ligand. To explore how different variables (i.e., IGF1, IGFBPs, and IGF1R levels) impacted cell response, a mass-action steady-state model was developed. Examination of the model revealed that the level of IGF1-IGF1R complexes per cell was directly proportional to the extent of proliferation induced by IGF1. Model analysis suggested, and experimental results confirmed, that IGFBPs present during IGF1 treatment significantly decreased IGF1-mediated proliferation. We utilized this model to assess the efficacy of IGF1 and IGF1R antibodies against different network compositions and determined that IGF1R antibodies were more globally effective due to the receptor-limited state of the network.

Conclusions

Changes that affect IGF1R occupancy have predictable effects on IGF1-induced proliferation and our model captured these effects. Analysis of this model suggests that IGF1R antibodies will be more effective than IGF1 antibodies, although the difference was minimal in conditions with low levels of IGF1 and IGFBPs. Examining how different components of the IGF system influence cell response will be critical to improve our understanding of the IGF signaling network in ovarian cancer.

Synergistic Chemoimmunotherapy of Epithelial Ovarian Cancer Using ErbB-Retargeted T Cells Combined with Carboplatin

Epithelial ovarian cancer (EOC) remains the most lethal gynecologic malignancy, underscoring the need for better therapies. Adoptive immunotherapy using genetically targeted T cells represents a promising new treatment for hematologic malignancies. However, solid tumors impose additional obstacles, including the lack of suitable targets for safe systemic therapy and the need to achieve effective T cell homing to sites of disease. Because EOC undergoes transcœlomic metastasis, both of these challenges may be circumvented by T cell administration to the peritoneal cavity. In this study, we describe such an immunotherapeutic approach for EOC, in which human T cells were targeted against the extended ErbB family, using a chimeric Ag receptor named T1E28z. T1E28z was coexpressed with a chimeric cytokine receptor named 4αβ (combination termed T4), enabling the selective ex vivo expansion of engineered T cells using IL-4. Unlike control T cells, T4(+) T cells from healthy donors and patients with EOC were activated by and destroyed ErbB(+) EOC tumor cell lines and autologous tumor cultures. In vivo antitumor activity was demonstrated in mice bearing established luciferase-expressing SKOV-3 EOC xenografts. Tumor regression was accompanied by mild toxicity, manifested by weight loss. Although efficacy was transient, therapeutic response could be prolonged by repeated T cell administration. Furthermore, prior treatment with noncytotoxic doses of carboplatin sensitized SKOV-3 tumors to T4 immunotherapy, promoting enhanced disease regression using lower doses of T4(+) T cells. By combining these approaches, we demonstrate that repeated administration of carboplatin followed by T4(+) T cells achieved optimum therapeutic benefit in the absence of significant toxicity, even in mice with advanced tumor burdens.

Using partial least squares regression to analyze cellular response data

This Teaching Resource provides lecture notes, slides, and a problem set for a lecture introducing the mathematical concepts and interpretation of partial least squares regression (PLSR) that were part of a course entitled "Systems Biology: Mammalian Signaling Networks." PLSR is a multivariate regression technique commonly applied to analyze relationships between signaling or transcriptional data and cellular behavior.

Bioinformatics and variability in drug response: a protein structural perspective

Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk-benefit profile for many drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein-drug interactions and, ultimately, drug response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions. Next, we describe the impact of coding mutations on protein structures and drug response. Finally, we highlight tools for analysing protein structures and protein-drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants.