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ORF overexpression screen data

Supplementary detailed methods

Supplementary Figure 1. Systemic neutrophilia in tumor bearing mice. Supplementary Figure 2. Increased metastasis does not result from increased intravasation or post-extravasation events. Supplementary Figure 3. NK-mediated clearance of B16-F10-GFP+ cells. Supplementary Figure 4. Ly6G+ neutrophils are more abundant in spleens of 4T1 bearing mice. Supplementary Figure 5. Splenocyte conditioned medium activates endothelial cells and induces MMP-9 secretion.

Supplementary Figure S3 describes the effects of distinct MEK inhibitors in combination with BCL-2/XL inhibition and contribution of BCL-2 vs. BCL-XL to apoptotic vulnerability of HGSOC PDX models

Supplementary Table 1 shows the p53 mutation in the PDX models

Most patients with late-stage high-grade serous ovarian cancer (HGSOC) initially respond to chemotherapy but inevitably relapse and develop resistance, highlighting the need for novel therapies to improve patient outcomes. The MEK/ERK pathway is activated in a large subset of HGSOC, making it an attractive therapeutic target. Here, we systematically evaluated the extent of MEK/ERK pathway activation and efficacy of pathway inhibition in a large panel of well-annotated HGSOC patient–derived xenograft models. The vast majority of models were nonresponsive to the MEK inhibitor cobimetinib (GDC-0973) despite effective pathway inhibition. Proteomic analyses of adaptive responses to GDC-0973 revealed that GDC-0973 upregulated the proapoptotic protein BIM, thus priming the cells for apoptosis regulated by BCL2-family proteins. Indeed, combination of both MEK inhibitor and dual BCL-2/XL inhibitor (ABT-263) significantly reduced cell number, increased cell death, and displayed synergy in vitro in most models. In vivo, GDC-0973 and ABT-263 combination was well tolerated and resulted in greater tumor growth inhibition than single agents. Detailed proteomic and correlation analyses identified two subsets of responsive models—those with high BIM at baseline that was increased with MEK inhibition and those with low basal BIM and high pERK levels. Models with low BIM and low pERK were nonresponsive. Our findings demonstrate that combined MEK and BCL-2/XL inhibition has therapeutic activity in HGSOC models and provide a mechanistic rationale for the clinical evaluation of this drug combination as well as the assessment of the extent to which BIM and/or pERK levels predict drug combination effectiveness in chemoresistant HGSOC.

Supplementary Data from Mechanical Stress Signaling in Pancreatic Cancer Cells Triggers p38 MAPK- and JNK-Dependent Cytoskeleton Remodeling and Promotes Cell Migration via Rac1/cdc42/Myosin II

High-grade serous ovarian cancer (HGSOC) is often sensitive to initial treatment with platinum and taxane combination chemotherapy, but most patients relapse with chemotherapy-resistant disease. To systematically identify genes modulating chemotherapy response, we performed pooled functional genomic screens in HGSOC cell lines treated with cisplatin, paclitaxel, or cisplatin plus paclitaxel. Genes in the intrinsic pathway of apoptosis were among the top candidate resistance genes in both gain-of-function and loss-of-function screens. In an open reading frame overexpression screen, followed by a mini-pool secondary screen, anti-apoptotic genes including BCL2L1 (BCL-XL) and BCL2L2 (BCL-W) were associated with chemotherapy resistance. In a CRISPR-Cas9 knockout screen, loss of BCL2L1 decreased cell survival whereas loss of proapoptotic genes promoted resistance. To dissect the role of individual anti-apoptotic proteins in HGSOC chemotherapy response, we evaluated overexpression or inhibition of BCL-2, BCL-XL, BCL-W, and MCL1 in HGSOC cell lines. Overexpression of anti-apoptotic proteins decreased apoptosis and modestly increased cell viability upon cisplatin or paclitaxel treatment. Conversely, specific inhibitors of BCL-XL, MCL1, or BCL-XL/BCL-2, but not BCL-2 alone, enhanced cell death when combined with cisplatin or paclitaxel. Anti-apoptotic protein inhibitors also sensitized HGSOC cells to the poly (ADP-ribose) polymerase inhibitor olaparib. These unbiased screens highlight anti-apoptotic proteins as mediators of chemotherapy resistance in HGSOC, and support inhibition of BCL-XL and MCL1, alone or combined with chemotherapy or targeted agents, in treatment of primary and recurrent HGSOC.Implications:Anti-apoptotic proteins modulate drug resistance in ovarian cancer, and inhibitors of BCL-XL or MCL1 promote cell death in combination with chemotherapy.

Supplementary Figure Legends

Table S4: Somatic Sequence Alterations in parental tumor and xenograft DF101

Table S5: Somatic Sequence Alterations in parental tumor and xenograft DF149

Purpose: Ovarian cancer is the leading cause of death from gynecologic malignancy in the United States, with high rates of recurrence and eventual resistance to cytotoxic chemotherapy. Model systems that allow for accurate and reproducible target discovery and validation are needed to support further drug development in this disease.Experimental Design: Clinically annotated patient-derived xenograft (PDX) models were generated from tumor cells isolated from the ascites or pleural fluid of patients undergoing clinical procedures. Models were characterized by IHC and by molecular analyses. Each PDX was luciferized to allow for reproducible in vivo assessment of intraperitoneal tumor burden by bioluminescence imaging (BLI). Plasma assays for CA125 and human LINE-1 were developed as secondary tests of in vivo disease burden.Results: Fourteen clinically annotated and molecularly characterized luciferized ovarian PDX models were generated. Luciferized PDX models retain fidelity to both the nonluciferized PDX and the original patient tumor, as demonstrated by IHC, array CGH, and targeted and whole-exome sequencing analyses. Models demonstrated diversity in specific genetic alterations and activation of PI3K signaling pathway members. Response of luciferized PDX models to standard-of-care therapy could be reproducibly monitored by BLI or plasma markers.Conclusions: We describe the establishment of a collection of 14 clinically annotated and molecularly characterized luciferized ovarian PDX models in which orthotopic tumor burden in the intraperitoneal space can be followed by standard and reproducible methods. This collection is well suited as a platform for proof-of-concept efficacy and biomarker studies and for validation of novel therapeutic strategies in ovarian cancer. Clin Cancer Res; 23(5); 1263–73. ©2016 AACR.

Table S2: Array CGH characterization of PDX models

Normal cells explore multiple states to survive stresses encountered during development and self-renewal as well as environmental stresses such as starvation, DNA damage, toxins or infection. Cancer cells co-opt normal stress mitigation pathways to survive stresses that accompany tumour initiation, progression, metastasis and immune evasion. Cancer therapies accentuate cancer cell stresses and invoke rapid non-genomic stress mitigation processes that maintain cell viability and thus represent key targetable resistance mechanisms. In this Review, we describe mechanisms by which tumour ecosystems, including cancer cells, immune cells and stroma, adapt to therapeutic stresses and describe three different approaches to exploit stress mitigation processes: (1) interdict stress mitigation to induce cell death; (2) increase stress to induce cellular catastrophe; and (3) exploit emergent vulnerabilities in cancer cells and cells of the tumour microenvironment. We review challenges associated with tumour heterogeneity, prioritizing actionable adaptive responses for optimal therapeutic outcomes, and development of an integrative framework to identify and target vulnerabilities that arise from adaptive responses and engagement of stress mitigation pathways. Finally, we discuss the need to monitor adaptive responses across multiple scales and translation of combination therapies designed to take advantage of adaptive responses and stress mitigation pathways to the clinic.

Advanced or metastatic pancreatic cancer is highly resistant to existing therapies, and new treatments are urgently needed to improve patient outcomes. Current studies focus on alternative treatment approaches that target the abnormal microenvironment of pancreatic tumors and the resulting elevated mechanical stress in the tumor interior. Nevertheless, the underlying mechanisms by which mechanical stress regulates pancreatic cancer metastatic potential remain elusive. Herein, we used a proteomic assay to profile mechanical stress–induced signaling cascades that drive the motility of pancreatic cancer cells. Proteomic analysis, together with selective protein inhibition and siRNA treatments, revealed that mechanical stress enhances cell migration through activation of the p38 MAPK/HSP27 and JNK/c-Jun signaling axes, and activation of the actin cytoskeleton remodelers: Rac1, cdc42, and myosin II. In addition, mechanical stress upregulated transcription factors associated with epithelial-to-mesenchymal transition and stimulated the formation of stress fibers and filopodia. p38 MAPK and JNK inhibition resulted in lower cell proliferation and more effectively blocked cell migration under mechanical stress compared with control conditions. The enhanced tumor cell motility under mechanical stress was potently reduced by cdc42 and Rac1 silencing with no effects on proliferation. Our results highlight the importance of targeting aberrant signaling in cancer cells that have adapted to mechanical stress in the tumor microenvironment, as a novel approach to effectively limit pancreatic cancer cell migration. Implications: Our findings highlight that mechanical stress activated the p38 MAPK and JNK signaling axis and stimulated pancreatic cancer cell migration via upregulation of the actin cytoskeleton remodelers cdc42 and Rac1.

Soft lithography microfluidics offer many benefits over conventional biological assays; however, the impact this field is inhibited by the lack of widespread adoption of this technology in part due to prohibitive cost and fabrication time. Recent improvements in three-dimensional (3D) printing technologies such as digital light processing (DLP) printing offer a cost-effective and rapid prototyping solution to microfluidic fabrication. Limited information is available about how 3D printing parameters and resin cytocompatibility impact the performance of 3D printed molds for fabrication of polydimethylsiloxane (PDMS)-based microfluidics for cellular studies. Using a low-cost, commercially available DLP 3D printer, we assess the cytocompatibility of several resins, optimize printer settings and characterize minimum feature size of our system. We demonstrate the applications of DLP printing for soft lithography microfluidics by developing four assays to characterize cell viability, drug response, establish concentration gradients, and monitor live-cell 3D invasion into a hydrogel.

Ovarian cancer is the second most deadly gynecologic cancer in the United States, and tumorassociated macrophages in the ovarian cancer microenvironment are the most abundant immune cell type and are associated poor survival. Here, we utilize three-dimensional microfluidic assays to investigate the dynamics of macrophage infiltration towards ovarian cancer cells. Experimental results demonstrate that both ovarian cancer cell lines and patient-derived xenograft models promote the infiltration of macrophages into a 3D collagen type I extracellular matrix. Additionally, blocking CSF1 signaling reduced the number of recruited macrophages as well as migration speed, while macrophage recruitment was enhanced by addition of recombinant CSF1. We further demonstrated that results obtained with our microfluidic model are consistent with the recruitment of macrophages in vivo by patient-derived xenograft models, and that a xenograft model with high CSF1 expression showed an enhanced ability to recruit macrophages both in vitro and in vivo. These results highlight the role of CSF1 signaling in ovarian cancer, as well as the utility of microfluidic models in recapitulating the 3D ovarian cancer microenvironment.

Significance Factors stemming from the tumor microenvironment can modulate drug resistance. Therefore, rational approaches to design efficacious targeted therapies are needed. Here, we show that fibroblasts reduce lapatinib sensitivity in a subset of HER2+ breast cancer cells via paracrine signaling that can be reversed by targeting downstream survival programs such as MTOR and antiapoptotic proteins (BCL-XL and MCL-1). Our finding that fibroblasts derived from either primary tumor or metastatic sites limit lapatinib response has important implications for developing treatment strategies that can restore sensitivity in multiple drug-resistant microenvironments. Due to the diversity of microenvironmental factors between these sites, targeting downstream survival programs instead of individual factors released by fibroblasts represents a promising strategy to combat microenvironment complexity., Despite the implementation of multiple HER2-targeted therapies, patients with advanced HER2+ breast cancer ultimately develop drug resistance. Stromal fibroblasts represent an abundant cell type in the tumor microenvironment and have been linked to poor outcomes and drug resistance. Here, we show that fibroblasts counteract the cytotoxic effects of HER2 kinase-targeted therapy in a subset of HER2+ breast cancer cell lines and allow cancer cells to proliferate in the presence of the HER2 kinase inhibitor lapatinib. Fibroblasts from primary breast tumors, normal breast tissue, and lung tissue have similar protective effects on tumor cells via paracrine factors. This fibroblast-mediated reduction in drug sensitivity involves increased expression of antiapoptotic proteins and sustained activation of the PI3K/AKT/MTOR pathway, despite inhibition of the HER2 and the RAS-ERK pathways in tumor cells. HER2 therapy sensitivity is restored in the fibroblast cocultures by combination treatment with inhibitors of MTOR or the antiapoptotic proteins BCL-XL and MCL-1. Expression of activated AKT in tumor cells recapitulates the effects of fibroblasts resulting in sustained MTOR signaling and poor lapatinib response. Lapatinib sensitivity was not altered by fibroblasts in tumor cells that exhibited sustained MTOR signaling due to a strong gain-of-function PI3KCA mutation. These findings indicate that in addition to tumor cell-intrinsic mechanisms that cause constitutive PI3K/AKT/MTOR pathway activation, secreted factors from fibroblasts can maintain this pathway in the context of HER2 inhibition. Our integrated proteomic–phenotypic approach presents a strategy for the discovery of protective mechanisms in fibroblast-rich tumors and the design of rational combination therapies to restore drug sensitivity.

The most common kind of cancer among women is breast cancer. Understanding the tumor microenvironment and the interactions between individual cells and cytokines assists us in arriving at more effective treatments. Here, we develop a data-driven mathematical model to investigate the dynamics of key cell types and cytokines involved in breast cancer development. We use time-course gene expression profiles of a mouse model to estimate the relative abundance of cells and cytokines. We then employ a least-squares optimization method to evaluate the model’s parameters based on the mice data. The resulting dynamics of the cells and cytokines obtained from the optimal set of parameters exhibit a decent agreement between the data and predictions. We perform a sensitivity analysis to identify the crucial parameters of the model and then perform a local bifurcation on them. The results reveal a strong connection between adipocytes, IL6, and the cancer population, suggesting them as potential targets for therapies.

Breast cancer is the most prominent type of cancer among women. Understanding the microenvironment of breast cancer and the interactions between cells and cytokines will lead to better treatment approaches for patients. In this study, we developed a data-driven mathematical model to investigate the dynamics of key cells and cytokines involved in breast cancer development. We used gene expression profiles of tumors to estimate the relative abundance of each immune cell and group patients based on their immune patterns. Dynamical results show the complex interplay between cells and molecules, and sensitivity analysis emphasizes the direct effects of macrophages and adipocytes on cancer cell growth. In addition, we observed the dual effect of IFN-γ on cancer proliferation, either through direct inhibition of cancer cells or by increasing the cytotoxicity of CD8+ T-cells.

New treatments for patients with advanced or metastatic pancreatic cancers are urgently needed due to their resistance to all current therapies. Current studies focus on alternative treatment approaches that target or normalize the abnormal microenvironment of pancreatic tumors, which among others, is responsible for elevated mechanical stress in the tumor interior. Nevertheless, the underlying mechanisms by which mechanical stress regulates pancreatic cancer metastatic potential remain elusive. Herein, we used a large-scale proteomic assay to profile mechanical stress-induced signaling cascades that drive the motility of pancreatic cancer cells. Proteomic analysis, together with selective protein inhibition and siRNA treatments, revealed that mechanical stress enhances cell migration through activation of the p38 MAPK/HSP27 and JNK/c-Jun signaling axes, and activation of the actin cytoskeleton remodelers: Rac1, cdc42, and Myosin II. Our results highlight targeting aberrant signaling in cancer cells that are adapted to the mechanical tumor microenvironment as a novel approach to effectively limit pancreatic cancer cell migration.

High-grade serous ovarian cancer (HGSOC) is often sensitive to initial treatment with platinum and taxane combination chemotherapy, but most patients relapse with chemotherapy-resistant disease. To systematically identify genes modulating chemotherapy response, we performed pooled functional genomic screens in HGSOC cell lines treated with cisplatin, paclitaxel, or cisplatin plus paclitaxel. Genes in the intrinsic pathway of apoptosis were among the top candidate resistance genes in both gain-of-function and loss-of-function screens. In an open reading frame overexpression screen, followed by a mini-pool secondary screen, anti-apoptotic genes including BCL2L1 (BCL-XL) and BCL2L2 (BCL-W) were associated with chemotherapy resistance. In a CRISPR-Cas9 knockout screen, loss of BCL2L1 decreased cell survival whereas loss of proapoptotic genes promoted resistance. To dissect the role of individual anti-apoptotic proteins in HGSOC chemotherapy response, we evaluated overexpression or inhibition of BCL-2, BCL-XL, BCL-W, and MCL1 in HGSOC cell lines. Overexpression of anti-apoptotic proteins decreased apoptosis and modestly increased cell viability upon cisplatin or paclitaxel treatment. Conversely, specific inhibitors of BCL-XL, MCL1, or BCL-XL/BCL-2, but not BCL-2 alone, enhanced cell death when combined with cisplatin or paclitaxel. Anti-apoptotic protein inhibitors also sensitized HGSOC cells to the poly (ADP-ribose) polymerase inhibitor olaparib. These unbiased screens highlight anti-apoptotic proteins as mediators of chemotherapy resistance in HGSOC, and support inhibition of BCL-XL and MCL1, alone or combined with chemotherapy or targeted agents, in treatment of primary and recurrent HGSOC. Implications: Anti-apoptotic proteins modulate drug resistance in ovarian cancer, and inhibitors of BCL-XL or MCL1 promote cell death in combination with chemotherapy.

Most patients with late-stage high-grade serous ovarian cancer (HGSOC) initially respond to chemotherapy but inevitably relapse and develop resistance, highlighting the need for novel therapies to improve patient outcomes. The MEK/ERK pathway is activated in a large subset of HGSOC, making it an attractive therapeutic target. Here, we systematically evaluated the extent of MEK/ERK pathway activation and efficacy of pathway inhibition in a large panel of well-annotated HGSOC patient–derived xenograft models. The vast majority of models were nonresponsive to the MEK inhibitor cobimetinib (GDC-0973) despite effective pathway inhibition. Proteomic analyses of adaptive responses to GDC-0973 revealed that GDC-0973 upregulated the proapoptotic protein BIM, thus priming the cells for apoptosis regulated by BCL2-family proteins. Indeed, combination of both MEK inhibitor and dual BCL-2/XL inhibitor (ABT-263) significantly reduced cell number, increased cell death, and displayed synergy in vitro in most models. In vivo, GDC-0973 and ABT-263 combination was well tolerated and resulted in greater tumor growth inhibition than single agents. Detailed proteomic and correlation analyses identified two subsets of responsive models—those with high BIM at baseline that was increased with MEK inhibition and those with low basal BIM and high pERK levels. Models with low BIM and low pERK were nonresponsive. Our findings demonstrate that combined MEK and BCL-2/XL inhibition has therapeutic activity in HGSOC models and provide a mechanistic rationale for the clinical evaluation of this drug combination as well as the assessment of the extent to which BIM and/or pERK levels predict drug combination effectiveness in chemoresistant HGSOC.

Background: Inflammatory Breast Cancer (IBC) is an aggressive subtype of breast cancer that is associated with an increased likelihood of early metastasis and a poor prognosis, accounting for approximately 10% of all breast cancer mortality. Currently, there is limited availability of cell line and patient-derived xenograft (PDX) models of IBC. As a result, there is an urgent need for additional model systems to facilitate our understanding of this disease. Organoids have been used to grow breast cancer cells with high efficiency, resulting in long-term cultures with genetic and phenotypic stability; however, the extent to which IBC can be modeled using this system is unknown. We undertook an evaluation of organoid culture technology for growing IBC patient samples, with the goal of generating a model of IBC that more faithfully recapitulates the key features of the disease. Methods: Tumor samples (taken from lumpectomy, mastectomy, PDX, or core biopsy samples), were digested to the organoid level using collagenase, and were grown in three dimensional cultures using a basement membrane extract and a fully-defined organoid medium, as described (Sachs et al. Cell 2018). First, we surveyed non-IBC metastatic breast cancer samples to determine the efficiency of growth of multiple breast cancer subtypes under these conditions. Then, we assessed the efficiency of growth of IBC samples taken after treatment, at the time of mastectomy. Co-culture experiments in which IBC organoids were grown in the presence of human endothelial cells were performed in three dimensional culture using a mixture of type I collagen and basement membrane extract. Results: Our success rate for establishing organoid cultures from breast cancer samples in general was 75% (33 out of 44 samples), and for triple-negative breast cancers, the success rate for establishment of organoid cultures was 100% (14 out of 14 samples). Expansion of ER+ tumor organoids (16 of 26 samples) was primarily restricted by a slow growth rate in culture, as compared to triple-negative tumor organoids. We were able to establish 12 breast cancer organoids from core biopsies of multiple metastatic sites, including brain, liver, bone, breast, and skin. We subsequently were able to establish organoid cultures from five out of five IBC tumor samples. Our IBC organoids preserved histopathologic features of the original tumor, and expressed key markers, such as E-cadherin, that are known to be associated with IBC. In addition, our IBC organoids interacted with human endothelial cells under co-culture conditions designed to model lymphovascular invasion, an essential feature of IBC. Conclusion: We have shown that organoid cultures can be established to propagate IBC tumors with high efficiency, preserve key pathologic features of IBC in vitro, and model interactions between tumor cells and the microenvironment. Thus, organoids are useful complements to existing IBC models, and can be used to identify potential therapeutic vulnerabilities associated with this particularly aggressive breast cancer subtype. Citation Format: Rosenbluth JM, Zervantonakis I, Boedicker M, Wagle N, Dillon D, Nakhlis F, Brugge JS, Overmoyer B. Patient-derived organoid models of inflammatory breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-17-01.

The evolution of breast tumors greatly depends on the interaction network among different cell types, including immune cells and cancer cells in the tumor. This study takes advantage of newly collected rich spatio-temporal mouse data to develop a data-driven mathematical model of breast tumors that considers cells’ location and key interactions in the tumor. The results show that cancer cells have a minor presence in the area with the most overall immune cells, and the number of activated immune cells in the tumor is depleted over time when there is no influx of immune cells. Interestingly, in the case of the influx of immune cells, the highest concentrations of both T cells and cancer cells are in the boundary of the tumor, as we use the Robin boundary condition to model the influx of immune cells. In other words, the influx of immune cells causes a dominant outward advection for cancer cells. We also investigate the effect of cells’ diffusion and immune cells’ influx rates in the dynamics of cells in the tumor micro-environment. Sensitivity analyses indicate that cancer cells and adipocytes’ diffusion rates are the most sensitive parameters, followed by influx and diffusion rates of cytotoxic T cells, implying that targeting them is a possible treatment strategy for breast cancer.

A panel of genetically engineered mouse models recapitulates genotype-driven responses to immunotherapy and uncovers a driver of immunotherapy resistance in ovarian cancer.

Single-cell metabolic state analysis reveals cytotoxic T cell activity patterns that are spatially organized in human colorectal tumors.

The ratio of two cancer-associated fibroblast subpopulations with distinct functions affects breast cancer clinical outcomes.

SummaryPerturbation biology is a powerful approach to developing quantitative models of cellular behaviors and gaining mechanistic insights into disease development. In recent years, large-scale resources for phenotypic and mRNA responses of cancer cell lines to perturbations have been generated. However, similar large-scale protein response resources are not available, resulting in a critical knowledge gap for elucidating oncogenic mechanisms and developing effective cancer therapies. Here we generated and compiled perturbed expression profiles of ~210 clinically relevant proteins in >12,000 cancer cell-line samples in response to >150 drug compounds using reverse-phase protein arrays. We show that integrating protein response signals substantially increases the predictive power for drug sensitivity and aids in gaining insights into mechanisms of drug resistance. We build a systematic map of protein-drug connectivity and develop an open-access, user-friendly data portal for community use. Our study provides a valuable information resource for a broad range of quantitative modeling and biomedical applications.HighlightsA large collection of cancer cell line protein responses to drug perturbationsPerturbed protein responses greatly increase predictive power for drug sensitivityBuild a systematic map of protein-drug connectivity based on response profilesDevelop a user-friendly, interactive data portal for community use

The extent of lymphocyte infiltration controls cancer subclone divergence, and a single immune cold region can increase the risk of relapse.

Intercellular mechanisms by which the stromal microenvironment contributes to solid tumor progression and targeted therapy resistance remain poorly understood, presenting significant clinical hurdles. PEAK1 (Pseudopodium-Enriched Atypical Kinase One) is an actin cytoskeleton- and focal adhesion-associated pseudokinase that promotes cell state plasticity and cancer metastasis by mediating growth factor-integrin signaling crosstalk. Here, we determined that stromal PEAK1 expression predicts poor outcomes in HER2-positive breast cancers high in SNAI2 expression and enriched for MSC content. Notably, we identified that mesenchymal stem cells (MSCs) and cancer-associated fibroblasts (CAFs) express high PEAK1 protein levels and MSCs require PEAK1 to potentiate tumorigenesis, lapatinib resistance and metastasis of HER2-positive breast cancer cells. Analysis of PEAK1-dependent secreted factors from MSCs revealed a CCL4-, INHBA- and GDF5-focused network that converged on PI3K/Akt signaling. In this regard, we observed that MSC expression of PEAK1 is required for sustained Akt phosphorylation in neighboring HER2-positive breast cancer cells following lapatinib treatment. Finally, we uncovered a significant correlation between INHBA and PEAK1 expression levels in breast cancer, and that INHBA is an excellent predictor of disease relapse and decreased survival in HER2-positive tumors enriched for PEAK1 expression and MSC content. Importantly, we provide the first evidence that PEAK1 promotes tumorigenic phenotypes through a previously unrecognized SNAI2-PEAK1-INHBA-PI3K/Akt stromal to tumor cell signaling axis. These results establish a new, targetable intercellular mechanism that may be leveraged to improve targeted therapy responses and patient outcomes in breast cancer and other stroma-rich malignancies.

Intercellular mechanisms by which the stromal microenvironment contributes to solid tumor progression and targeted therapy resistance remain poorly understood, presenting significant clinical hurdles. PEAK1 (Pseudopodium-Enriched Atypical Kinase One) is an actin cytoskeleton- and focal adhesion-associated pseudokinase that promotes cell state plasticity and cancer metastasis by mediating growth factor-integrin signaling crosstalk. Here, we determined that stromal PEAK1 expression predicts poor outcomes in HER2-positive breast cancers high in SNAI2 expression and enriched for MSC content. Specifically, we identified that the fibroblastic stroma in HER2-positive breast cancer patient tissue stains positive for both nuclear SNAI2 and cytoplasmic PEAK1. Furthermore, mesenchymal stem cells (MSCs) and cancer-associated fibroblasts (CAFs) express high PEAK1 protein levels and potentiate tumorigenesis, lapatinib resistance and metastasis of HER2-positive breast cancer cells in a PEAK1-dependent manner. Analysis of PEAK1-dependent secreted factors from MSCs revealed INHBA/activin-A as a necessary factor in the conditioned media of PEAK1-expressing MSCs that promotes lapatinib resistance. Single-cell CycIF analysis of MSC-breast cancer cell co-cultures identified enrichment of p-Akthigh/p-gH2AXlow, MCL1high/p-gH2AXlow and GRP78high/VIMhigh breast cancer cell subpopulations by the presence of PEAK1-expressing MSCs and lapatinib treatment. Bioinformatic analyses on a PEAK1-centric stroma-tumor cell gene set and follow-up immunostaining of co-cultures predict targeting antiapoptotic and stress pathways as a means to improve targeted therapy responses and patient outcomes in HER2-positive breast cancer and other stroma-rich malignancies. These data provide the first evidence that PEAK1 promotes tumorigenic phenotypes through a previously unrecognized SNAI2-PEAK1-INHBA stromal cell axis.

Sequential treatment provides therapeutic benefit in pancreatic cancer models.

Human bone-marrow-on-a-chip improves drug studies of hematopoietic cell function and identifies a neutrophil maturation defect in a genetic disorder.

© 2020 American Chemical Society. Nanoparticle surface chemistry is a fundamental engineering parameter that governs tumor-targeting activity. Electrostatic assembly generates controlled polyelectrolyte complexes through the process of adsorption and charge overcompensation utilizing synthetic polyions and natural biomacromolecules; it can yield films with distinctive hydration, charge, and presentation of functional groups. Here, we used electrostatic layer-by-layer (LbL) assembly to screen 10 different surface chemistries for their ability to preferentially target human ovarian cancer in vitro. Our screen identified that poly-l-aspartate, poly-l-glutamate, and hyaluronate-coated LbL nanoparticles have striking specificity for ovarian cancer, while sulfated poly(β-cyclodextrin) nanoparticles target noncancerous stromal cells. We validated top candidates for tumor-homing ability with a murine model of metastatic disease and with patient-derived ovarian cancer spheroids. Nanoparticle surface chemistry also influenced subcellular trafficking, indicating strategies to target the cell membrane, caveolae, and perinuclear vesicles. Our results confirm LbL is a powerful tool to systematically engineer nanoparticles and achieve specific targeting.

The lack of effective chemotherapies for high-grade serous ovarian cancers (HGS-OvCa) has motivated a search for alternative treatment strategies. Here, we present an unbiased systems-approach to interrogate a panel of 14 well-annotated HGS-OvCa patient-derived xenografts for sensitivity to PI3K and PI3K/mTOR inhibitors and uncover cell death vulnerabilities. Proteomic analysis reveals that PI3K/mTOR inhibition in HGS-OvCa patient-derived xenografts induces both pro-apoptotic and anti-apoptotic signaling responses that limit cell killing, but also primes cells for inhibitors of anti-apoptotic proteins. In-depth quantitative analysis of BCL-2 family proteins and other apoptotic regulators, together with computational modeling and selective anti-apoptotic protein inhibitors, uncovers new mechanistic details about apoptotic regulators that are predictive of drug sensitivity (BIM, caspase-3, BCL-XL) and resistance (MCL-1, XIAP). Our systems-approach presents a strategy for systematic analysis of the mechanisms that limit effective tumor cell killing and the identification of apoptotic vulnerabilities to overcome drug resistance in ovarian and other cancers., High-grade serous ovarian cancers (HGS-OvCa) frequently develop chemotherapy resistance. Here, the authors through a systematic analysis of proteomic and drug response data of 14 HGS-OvCa PDXs demonstrate that targeting apoptosis regulators can improve response of these tumors to inhibitors of the PI3K/mTOR pathway.

Endothelial permeability has been extensively investigated in the context of pathologies such as cancer and also in studies of drug delivery from the circulation. Hypoxia is a critical regulator of endothelial cell (EC) behavior and affects the barrier function of endothelial linings, yet its role has been little studied. This paper reveals the effect of hypoxia on the permeability of an EC monolayer by cellular experiments using a microfluidic device and a conventional cell culture dish. Human umbilical vein endothelial cells (HUVECs) were seeded into one microfluidic channel, creating an EC monolayer on each vertical surface of a collagen gel confined to a central chamber. Oxygen tension was regulated to produce normoxic (21% O2) or hypoxic (3% O2) conditions by the supply of gas mixtures of oxygen, carbon dioxide, and nitrogen at predefined ratios into channels fabricated into the device. Permeability of the EC monolayer quantified by analyzing diffusion of fluorescence-labelled dextrans into the collagen gel increases with barrier function loss by 6 hour hypoxic exposure, showing 11-fold and 4-fold increases for 70 kDa and 10 kDa dextrans, respectively, on average. Consistent with this, subsequent immunofluorescent staining and separate western blot analysis of HUVECs on a culture dish demonstrate loose cell–cell adhesion resulting from internalization of VE-cadherin under hypoxia. Thus, hypoxic stress increases endothelial permeability by altering cell–cell junction integrity.

Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.

Cancer cells have genetic alterations that often directly affect intracellular protein signaling processes allowing them to bypass control mechanisms for cell death, growth and division. Cancer drugs targeting these alterations often work initially, but resistance is common. Combinations of targeted drugs may overcome or prevent resistance, but their selection requires context-specific knowledge of signaling pathways including complex interactions such as feedback loops and crosstalk. To infer quantitative pathway models, we collected a rich dataset on a melanoma cell line: Following perturbation with 54 drug combinations, we measured 124 (phospho-)protein levels and phenotypic response (cell growth, apoptosis) in a time series from 10 minutes to 67 hours. From these data, we trained time-resolved mathematical models that capture molecular interactions and the coupling of molecular levels to cellular phenotype, which in turn reveal the main direct or indirect molecular responses to each drug. Systematic model simulations identified novel combinations of drugs predicted to reduce the survival of melanoma cells, with partial experimental verification. This particular application of perturbation biology demonstrates the potential impact of combining time-resolved data with modeling for the discovery of new combinations of cancer drugs., Author summary Data-driven mathematical modeling of biological systems has enormous potential to understand and predict the interplay between molecular and phenotypic response to perturbation, and provides a rational approach to the nomination of therapy. In cancer, intense effort has focused on drugs that specifically target the machinery involved in tumor development, maintenance and response to therapy. Although many drugs have clinical efficacy in a fraction of patients, the response is rarely durable and patients often develop resistance within months. We believe that the robustness and complexity of living cells, as well as inaccurate assumptions about drug specificity in model systems, underlie the inadequacy of single-agent targeted therapy. In this work, we developed a framework to derive mathematical models of biological systems from molecular and phenotypic temporal responses, and test this framework on melanoma cells. These models are computationally executable and can be used to predict drug combinations likely to be effective at slowing growth or killing cancer cells. Our framework has several advantages: (1) drug specificity is learned, not assumed, during model training, (2) training on temporal (not static) response data improves predictive power, (3) data-driven dynamic models have the potential to accurately reflect a cellular system’s behavior in a context-specific manner.

In ovarian cancer, the presence of macrophages in the tumor microenvironment (TME) has been associated with poor clinical outcomes. Understanding mechanisms of macrophage recruitment and signaling with tumor cells in the TME may uncover novel therapeutic targets to block the pro-tumor effects of macrophages and improve patient outcomes. Several tumor secreted factors, such as mCSF, CCL2, IL8, and periostin have been implicated in the recruitment of macrophages to the tumor site. However, previous studies of macrophage recruitment have utilized culture systems that fail to recapitulate TME complexity. Therefore, a 3D physiologically relevant microfluidic platform was used in this work that can accurately control cell-cell interactions. Our microfluidic design incorporates chemokine concentration gradients and allows for the study of the spatiotemporal macrophage infiltration dynamics towards ovarian cancer cells using live cell microscopy. We first studied the abilities of a panel of high grade serous ovarian cancer models comprised of established cancer cell lines (murine ID8 and human CaOV3, OHSAHO, OVCAR8) and patient-derived high-grade serous ovarian cancer xenografts (4 PDX models) to recruit macrophages via microfluidic and collagen-droplet based 3D assays. The presence of tumor cells enhanced macrophage infiltration into collagen 3-fold after 48 hours. We found that ovarian cancer models that efficiently recruited macrophages expressed higher levels of mCSF, CCL2, and IL8 cytokines. Furthermore, tumor cell secreted factors more potently increased macrophage recruitment compared to chemotactic gradients of CCL2 and mCSF, indicating a complex signaling network between tumor cells and macrophages. RAW264.7 murine macrophages additionally induced resistance to cisplatin-induced cell death in ID8 cells proportional to increasing macrophage:tumor cell ratios. We also studied the ability of CSF1R inhibition to block macrophage infiltration. Treatment with BLZ945, a potent CSF1R inhibitor that is evaluated in clinical trials, effectively blocked the recruitment of the RAW264.7 macrophages towards ID8 ovarian cancer cells. We subsequently characterized the attachment and invasion profiles of the human and murine ovarian cancer models in syngeneic and immunocompromised xenografts in vivo. We found that tumor cells attached and invaded the ovaries, abdominal organs, and fatty tissue. In a preliminary analysis, one of the PDX models that efficiently recruited macrophages also formed invasive implants, while another PDX model with poor macrophage infiltration did not exhibit invasive implant formation. This work demonstrates the capability of a microfluidic platform to model macrophage infiltration in ovarian cancer. In ongoing studies using this microfluidic platform we are investigating the effects of chemotherapy-induced changes in the tumor cell secretome on macrophage infiltration and the resulting effects on tumor cell chemotherapy sensitivity. Citation Format: Alexis L. Scott, Oluwadamilola Ayoola, Ioannis K. Zervantonakis. Microfluidic modeling of tumor-macrophage signaling in ovarian cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr PO042.

Perturbation biology is a powerful approach to modeling quantitative cellular behaviors and understanding detailed disease mechanisms. However, large-scale protein response resources of cancer cell lines to perturbations are not available, resulting in a critical knowledge gap. Here we generated and compiled perturbed expression profiles of ~210 clinically relevant proteins in >12,000 cancer cell line samples in response to ~170 drug compounds using reverse-phase protein arrays. We show that integrating perturbed protein response signals provides mechanistic insights into drug resistance, increases the predictive power for drug sensitivity, and helps identify effective drug combinations. We build a systematic map of “protein-drug” connectivity and develop a user-friendly data portal for community use. Our study provides a rich resource to investigate the behaviors of cancer cells and the dependencies of treatment responses, thereby enabling a broad range of biomedical applications.

Overcoming transport barriers to delivery of therapeutic agents in tumors remains a major challenge. Focused ultrasound (FUS), in combination with modern nanomedicine drug formulations, offers the ability to maximize drug transport to tumor tissue while minimizing toxicity to normal tissue. This potential remains unfulfilled due to the limitations of current approaches in accurately assessing and quantifying how FUS modulates drug transport in solid tumors. We developed a novel acoustofluidic platform by integrating a physiologically relevant 3D microfluidic device and a FUS system with a closed-loop controller to study drug transport and assess the response of cancer cells to chemotherapy in real time using live cell microscopy. FUS-induced heating triggered local release of the chemotherapeutic agent doxorubicin from a liposomal carrier and resulted in higher cellular drug uptake in the FUS focal region. This differential drug uptake induced locally confined DNA damage and glioblastoma tumor cell death in the 3D environment. Our study demonstrates the capabilities of acoustofluidics for accurate control of drug release and monitoring of localized cell response in a 3D in vitro tumor model and has important implications for developing novel strategies to deliver therapeutic agents directly to the tumor tissue while sparing healthy tissue.