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.

The Many Microenvironments of Ovarian Cancer.

High-grade serous ovarian cancer (HGSOC) is the most common and deadly subtype of ovarian cancer as it is commonly diagnosed after substantial metastasis has already occurred. The past two decades have been an active era in HGSOC research, with new information on the origin and genomic signature of the tumor cell. Additionally, studies have begun to characterize changes in the HGSOC microenvironment and examine the impact of these changes on tumor progression and response to therapies. While this knowledge may provide valuable insight into better prognosis and treatments for HGSOCs, its collection, synthesis, and application are complicated by the number of unique microenvironments in the disease-the initiating site (fallopian tube), first metastasis (ovary), distal metastases (peritoneum), and recurrent/platinum-resistant setting. Here, we review the state of our understanding of these diverse sites and highlight remaining questions.

Engineering Approaches to Study Cellular Decision Making.

In their native environment, cells are immersed in a complex milieu of biochemical and biophysical cues. These cues may include growth factors, the extracellular matrix, cell-cell contacts, stiffness, and topography, and they are responsible for regulating cellular behaviors such as adhesion, proliferation, migration, apoptosis, and differentiation. The decision-making process used to convert these extracellular inputs into actions is highly complex and sensitive to changes both in the type of individual cue (e.g., growth factor dose/level, timing) and in how these individual cues are combined (e.g., homotypic/heterotypic combinations). In this review, we highlight recent advances in the development of engineering-based approaches to study the cellular decision-making process. Specifically, we discuss the use of biomaterial platforms that enable controlled and tailored delivery of individual and combined cues, as well as the application of computational modeling to analyses of the complex cellular decision-making networks.

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.

Immobilized epidermal growth factor stimulates persistent, directed keratinocyte migration via activation of PLCγ1.

Epidermal growth factor (EGF) is a critical element in dermal repair, but EGF-containing wound dressings have not been successful clinically. However, these dressings have delivered only soluble EGF, and the native environment provides both soluble and matrix-bound EGF. To address our hypothesis that tethered EGF can stimulate cell behaviors not achievable with soluble EGF, we examined single-cell movement and signaling in human immortalized HaCaT keratinocytes treated with soluble or immobilized EGF. Although both EGF treatments increased collective sheet displacement and individual cell speed, only cells treated with immobilized EGF exhibited directed migration, as well as 2-fold greater persistence compared with soluble EGF. Immunofluorescence showed altered EGF receptor (EGFR) trafficking, where EGFR remained membrane-localized in the immobilized EGF condition. Cells treated with soluble EGF demonstrated higher phosphorylated ERK1/2, and cells on immobilized EGF exhibited higher pPLCγ1, which was localized at the leading edge. Treatment with U0126 inhibited migration in both conditions, demonstrating that ERK1/2 activity was necessary but not responsible for the observed differences. In contrast, PLCγ1 inhibition with U73122 significantly decreased persistence on immobilized EGF. Combined, these results suggest that immobilized EGF increases collective keratinocyte displacement via an increase in single-cell migration persistence resulting from altered EGFR trafficking and PLCγ1 activation.-Kim, C. S., Mitchell, I. P., Desotell, A. W., Kreeger, P. K., Masters, K. S. Immobilized epidermal growth factor stimulates persistent, directed keratinocyte migration via activation of PLCγ1.

A kinetic model identifies phosphorylated estrogen receptor-α (ERα) as a critical regulator of ERα dynamics in breast cancer.

Receptor levels are a key mechanism by which cells regulate their response to stimuli. The levels of estrogen receptor-α (ERα) impact breast cancer cell proliferation and are used to predict prognosis and sensitivity to endocrine therapy. Despite the clinical application of this information, it remains unclear how different cellular processes interact as a system to control ERα levels. To address this question, experimental results from the ERα-positive human breast cancer cell line (MCF-7) treated with 17-ß-estradiol or vehicle control were used to develop a mass-action kinetic model of ERα regulation. Model analysis determined that RNA dynamics could be captured through phosphorylated ERα (pERα)-dependent feedback on transcription. Experimental analysis confirmed that pERα-S118 binds to the estrogen receptor-1 (ESR1) promoter, suggesting that pERα can feedback on ESR1 transcription. Protein dynamics required a separate mechanism in which the degradation rate for pERα was 8.3-fold higher than nonphosphorylated ERα. Using a model with both mechanisms, the root mean square error was 0.078. Sensitivity analysis of this combined model determined that while multiple mechanisms regulate ERα levels, pERα-dependent feedback elicited the strongest effect. Combined, our computational and experimental results identify phosphorylation of ERα as a critical decision point that coordinates the cellular circuitry to regulate ERα levels.

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, NRG1ß, 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, NRG1ß, 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.

The role of Piezo1 mechanotransduction in high-grade serous ovarian cancer: Insights from an in vitro model of collective detachment.

Slowing peritoneal spread in high-grade serous ovarian cancer (HGSOC) would improve patient prognosis and quality of life. HGSOC spreads when single cells and spheroids detach, float through the peritoneal fluid and take over new sites, with spheroids thought to be more aggressive than single cells. Using our in vitro model of spheroid collective detachment, we determine that increased substrate stiffness led to the detachment of more spheroids. We identified a mechanism where Piezo1 activity increased MMP-1/MMP-10, decreased collagen I and fibronectin, and increased spheroid detachment. Piezo1 expression was confirmed in omental masses from patients with stage III/IV HGSOC. Using OV90 and CRISPR-modified PIEZO1-/- OV90 in a mouse xenograft model, we determined that while both genotypes efficiently took over the omentum, loss of Piezo1 significantly decreased ascitic volume, tumor spheroids in the ascites, and the number of macroscopic tumors in the mesentery. These results support that slowing collective detachment may benefit patients and identify Piezo1 as a potential therapeutic target.

Analysis of cancer signaling networks by systems biology to develop therapies.

Cancer is a complex and heterogeneous disease, demonstrating variations with respect to tumor types and between individual tumors. This heterogeneity has complicated the search for 'magic bullets'-individual genes or pathways that could be targeted and have beneficial effects for large numbers of patients. Instead, recent studies suggest that cancer can be more effectively analyzed through the use of systems biology techniques that examine multiple pathways and account for interactions between these pathways. In this review, we outline the various ways in which systems biology can be utilized to translate high-throughput data into a signaling network and then computationally analyze how cells make decisions based on the information flow through this network. We then discuss recent studies utilizing network-level analysis to reveal therapeutic targets, predict which tumors will be sensitive to existing drugs, and develop combinatorial therapies that target multiple pathways, demonstrating the potential for systems biology to revolutionize cancer therapy.

Model of collective detachment in high-grade serous ovarian cancer demonstrates that tumor spheroids produce ECM to support metastatic processes.

High-grade serous ovarian cancer (HGSOC) metastasizes through transcoelomic spread, with both single cells and spheroids of tumor cells observed in patient ascites. These spheroids may form through single cells that detach and aggregate (Sph-SC) or through collective detachment (Sph-CD). We developed an in vitro model to generate and separate Sph-SC from Sph-CD to enable study of Sph-CD in disease progression. In vitro-generated Sph-CD and spheroids isolated from ascites were similar in size (mean diameter 51 vs 55 µm, p > 0.05) and incorporated multiple ECM proteins. Using the in vitro model, nascent protein labeling, and qRT-PCR, we determined that ECM was produced after detachment. As fibronectin plays a key role in many cell adhesion events, we confirmed that inhibiting RGD-based adhesion or fibronectin assembly reduced Sph-CD-mesothelial adhesion strength under shear stress. Our model will enable future studies to determine factors that favor formation of Sph-CD, as well as allow investigators to manipulate Sph-CD to better study their effects on HGSOC progression.

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

Identifying the optimal treatment strategy for cancer is an important challenge, particularly for complex diseases like epithelial ovarian cancer (EOC) that are prone to recurrence. In this study we developed a quantitative, multivariate model to predict the extent of ovarian cancer cell death following treatment with an ErbB inhibitor (canertinib, CI-1033). A partial least squares regression model related the levels of ErbB receptors and ligands at the time of treatment to sensitivity to CI-1033. In this way, the model mimics the clinical problem by incorporating only information that would be available at the time of drug treatment. The full model was able to fit the training set data and was predictive. Model analysis demonstrated the importance of including both ligand and receptor levels in this approach, consistent with reports of the role of ErbB autocrine loops in EOC. A reduced multi-protein model was able to predict CI-1033 sensitivity of six distinct EOC cell lines derived from the three subtypes of EOC, suggesting that quantitatively characterizing the ErbB network could be used to broadly predict EOC response to CI-1033. Ultimately, this systems biology approach examining multiple proteins has the potential to uncover multivariate functions to identify subsets of tumors that are most likely to respond to a targeted therapy.

Bioengineering Approaches to Improve Gynecological Cancer Outcomes.

Gynecological cancers are diagnosed in over a million females worldwide, with ovarian, endometrial (uterine), and cervical the most common. Here, we highlight recent progress by bioengineers to improve screening and diagnosis for these diseases, including potential point-of-care approaches. We provide particular attention to the use of tissue engineering, biomaterials, microfluidics, and organoids to identify mechanisms regulating disease progression and predict therapeutic responses. We also highlight opportunities for engineers to address the racial/ethnic/geographic disparities that continue to impact gynecological cancer outcomes. A challenge to improve outcomes for all gynecological cancers will be to expand the diversity of patients included in basic/clinical research to better capture the confounding effects of social/economic variables on disease progression.

Processing and Analysis of Ascites.

The accumulation of peritoneal fluid, referred to as ascites, is common in ovarian cancer. This fluid is a complex mixture that may include cells as well as a diverse array of cytokines and growth factors. Here we describe a comprehensive method to process ascites to maximize data collection. The cellular fraction and fluid are first separated by centrifugation. The fluid can be frozen for later analysis of soluble factors or for use in in vitro experiments. The cellular fraction can be processed to analyze its composition or stored for future use.

A Subset of Secreted Proteins in Ascites Can Predict Platinum-Free Interval in Ovarian Cancer.

The time between the last cycle of chemotherapy and recurrence, the platinum-free interval (PFI), predicts overall survival in high-grade serous ovarian cancer (HGSOC). To identify secreted proteins associated with a shorter PFI, we utilized machine learning to predict the PFI from ascites composition. Ascites from stage III/IV HGSOC patients treated with neoadjuvant chemotherapy (NACT) or primary debulking surgery (PDS) were screened for secreted proteins and Lasso regression models were built to predict the PFI. Through regularization techniques, the number of analytes used in each model was reduced; to minimize overfitting, we utilized an analysis of model robustness. This resulted in models with 26 analytes and a root-mean-square error (RMSE) of 19 days for the NACT cohort and 16 analytes and an RMSE of 7 days for the PDS cohort. High concentrations of MMP-2 and EMMPRIN correlated with a shorter PFI in the NACT patients, whereas high concentrations of uPA Urokinase and MMP-3 correlated with a shorter PFI in PDS patients. Our results suggest that the analysis of ascites may be useful for outcome prediction and identified factors in the tumor microenvironment that may lead to worse outcomes. Our approach to tuning for model stability, rather than only model accuracy, may be applicable to other biomarker discovery tasks.

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/ß 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.

Multispectral Staining and Analysis of Extracellular Matrix.

Multiplexed immunofluorescent (IF) techniques enable the detection of multiple antigens within the same sample and are therefore useful in situations where samples are rare or small in size. Similar to standard IF, multiplexed IF yields information on both the location and relative amount of detected antigens. While this method has been used primarily to detail cell phenotypes, we have recently adapted it to profile the extracellular matrix (ECM), which provides technical challenges due to autofluorescence and spatial overlap. This chapter details the planning, execution, optimization, and troubleshooting to use multiplexed IF to profile the ECM of human fallopian tube tissue.

Multi-modal Profiling of the Extracellular Matrix of Human Fallopian Tubes and Serous Tubal Intraepithelial Carcinomas.

Recent evidence supports the fimbriae of the fallopian tube as one origin site for high-grade serous ovarian cancer (HGSOC). The progression of many solid tumors is accompanied by changes in the microenvironment, including alterations of the extracellular matrix (ECM). Therefore, we sought to determine the ECM composition of the benign fallopian tube and changes associated with serous tubal intraepithelial carcinomas (STICs), precursors of HGSOC. The ECM composition of benign human fallopian tube was first defined from a meta-analysis of published proteomic datasets that identified 190 ECM proteins. We then conducted de novo proteomics using ECM enrichment and identified 88 proteins, 7 of which were not identified in prior studies (COL2A1, COL4A5, COL16A1, elastin, LAMA5, annexin A2, and PAI1). To enable future in vitro studies, we investigated the levels and localization of ECM components included in tissue-engineered models (type I, III, and IV collagens, fibronectin, laminin, versican, perlecan, and hyaluronic acid) using multispectral immunohistochemical staining of fimbriae from patients with benign conditions or STICs. Quantification revealed an increase in stromal fibronectin and a decrease in epithelial versican in STICs. Our results provide an in-depth picture of the ECM in the benign fallopian tube and identified ECM changes that accompany STIC formation. (J Histochem Cytochem XX: XXX-XXX, XXXX).

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.