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The success of chimeric antigen receptor (CAR) T cells in blood cancers has intensified efforts to develop CAR T therapies for solid cancers. In the solid tumor microenvironment, CAR T cell trafficking and suppression of cytotoxic killing represent limiting factors for therapeutic efficacy. Here, we present a microwell platform to study CAR T cell interactions with 3D breast tumor spheroids and determine predictors of anti-tumor CAR T cell function. To precisely control antigen sensing, we utilized a switchable adaptor CAR system that covalently attaches to co-administered antibody adaptors and mediates antigen recognition. Following the addition of an anti-HER2 adaptor antibody, primary human CAR T cells exhibited higher infiltration, clustering, and secretion of effector cytokines. By tracking CAR T cell killing in individual spheroids, we showed the suppressive effects of spheroid size and identified the initial CAR T cell to spheroid area ratio as a predictor of cytotoxicity. We demonstrate that larger spheroids exhibit higher hypoxia levels and are infiltrated by CAR T cells with a suppressed activation state, characterized by reduced expression of IFN-γ, TNF-α, and granzyme B. Spatiotemporal analysis revealed lower CAR T cell numbers and cytotoxicity in the spheroid core compared to the periphery. Finally, increasing CAR T cell seeding density resulted in higher CAR T cell infiltration and cancer cell elimination in the spheroid core. Our findings provide new quantitative insight into CAR T cell function within 3D cancer spheroids. Given its miniaturized nature and live imaging capabilities, our microfabricated system holds promise for screening cellular immunotherapies.
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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.
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Cancer-mesothelial cell interactions are critical for multiple solid tumors to colonize the surface of peritoneal organs. Understanding mechanisms of mesothelial barrier dysfunction that impair its protective function is critical for discovering mesothelial-targeted therapies to combat metastatic spread. Here, we utilized a live cell imaging-based assay to elucidate the dynamics of ovarian cancer spheroid transmesothelial migration and mesothelial-generated mechanical forces. Treatment of mesothelial cells with the adenylyl cyclase agonist forskolin strengthens cell-cell junctions, reduces actomyosin fibers, contractility-driven matrix displacements, and cancer spheroid transmigration in a protein kinase A (PKA)-dependent mechanism. We also show that inhibition of the cytoskeletal regulator Rho-associated kinase in mesothelial cells phenocopies the anti-metastatic effects of forskolin. Conversely, upregulation of contractility in mesothelial cells disrupts cell-cell junctions and increases the clearance rates of ovarian cancer spheroids. Our findings demonstrate the critical role of mesothelial cell contractility and mesothelial barrier integrity in regulating metastatic dissemination within the peritoneal microenvironment.
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A high density of macrophages in the ovarian cancer microenvironment is associated with disease progression and poor outcomes. Understanding cancer-macrophage interaction mechanisms that establish this pro-tumorigenic microenvironment is critical for developing macrophage-targeted therapies. Here, 3D microfluidic assays and patient-derived xenografts are utilized to define the role of cancer-derived colony stimulating factor 1 (CSF1) on macrophage infiltration dynamics toward ovarian cancer cells. It is demonstrated that multiple ovarian cancer models promote the infiltration of macrophages into a 3D extracellular matrix in vitro in a cell density-dependent manner. Macrophages exhibit directional migration and increased migration speed under both direct interactions with cancer cells embedded within the matrix and paracrine crosstalk with cancer cells seeded in an independent microchannel. It is also found that platinum-based chemotherapy increases macrophage recruitment and the levels of cancer cell-derived CSF1. Targeting CSF1 signaling under baseline or chemotherapy-treatment conditions reduces the number of infiltrated macrophages. It is further shown that results obtained with the 3D microfluidic model reflect the recruitment profiles of macrophages in patient-derived xenografts in vivo. These findings highlight the role of CSF1 signaling in establishing macrophage-rich ovarian cancer microenvironments, as well as the utility of microfluidic models in recapitulating 3D tumor ecosystems and dissecting cancer-macrophage signaling.
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The success of chimeric antigen receptor (CAR) T cells in blood cancers has intensified efforts to develop CAR T therapies for solid cancers. In the solid tumor microenvironment, CAR T cell trafficking and suppression of cytotoxic killing represent limiting factors for therapeutic efficacy. Here, we present a microwell platform to study CAR T cell interactions with 3D tumor spheroids and determine predictors of anti-tumor CAR T cell function. To precisely control antigen sensing by CAR T cells, we utilized a switchable adaptor CAR system, that instead of directly binding to an antigen of interest, covalently attaches to co-administered antibody adaptors that mediate tumor antigen recognition. Following addition of an anti-HER2 adaptor antibody, primary human CAR T cells exhibited higher infiltration and clustering compared to the no adaptor control. By tracking CAR T cell killing at the individual spheroid level, we showed the suppressive effects of spheroid size and identified the initial CAR T cell : spheroid area ratio as a predictor of cytotoxicity. Spatiotemporal analysis revealed lower CAR T cell numbers and cytotoxicity in the spheroid core compared to the periphery. Finally, increasing CAR T cell seeding density, resulted in higher CAR T cell infiltration and cancer cell elimination in the spheroid core. Our findings provide new quantitative insights into CAR T cell-mediated killing of HER2+ breast tumor cells. Given the miniaturized nature and live imaging capabilities, our microfabricated system holds promise for discovering cell-cell interaction mechanisms that orchestrate antitumor CAR T cell functions and screening cellular immunotherapies in 3D tumor models.
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Macrophages are pivotal in driving breast tumor development, progression, and resistance to treatment, particularly in estrogen receptor-positive (ER+) tumors, where they infiltrate the tumor microenvironment (TME) influenced by cancer cell-secreted factors. By analyzing single-cell RNA-sequencing data from 25 ER+ tumors, we elucidated interactions between cancer cells and macrophages, correlating macrophage density with epithelial cancer cell density. We identified that S100A11, a previously unexplored factor in macrophage-cancer crosstalk, predicts high macrophage density and poor outcomes in ER+ tumors. We found that recombinant S100A11 enhances macrophage infiltration and migration in a dose-dependent manner. Additionally, in 3D models, we showed that S100A11 expression levels in ER+ cancer cells predict macrophage infiltration patterns. Neutralizing S100A11 decreased macrophage recruitment, both in cancer cell lines and in a clinically relevant patient-derived organoid model, underscoring its role as a paracrine regulator of cancer-macrophage interactions in the protumorigenic TME. This study offers novel insights into the interplay between macrophages and cancer cells in ER+ breast tumors, highlighting S100A11 as a potential therapeutic target to modulate the macrophage-rich tumor microenvironment.
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Microfluidic platforms enable more precise control of biological stimuli and environment dimensionality than conventional macroscale cell-based assays; however, long fabrication times and high-cost specialized equipment limit the widespread adoption of microfluidic technologies. Recent improvements in vat photopolymerization three-dimensional (3D) printing technologies such as liquid crystal display (LCD) printing offer rapid prototyping and a cost-effective solution to microfluidic fabrication. Limited information is available about how 3D printing parameters and resin cytocompatibility impact the performance of 3D-printed molds for the fabrication of polydimethylsiloxane (PDMS)-based microfluidic platforms for cellular studies. Using a low-cost, commercially available LCD-based 3D printer, we assessed the cytocompatibility of several resins, optimized fabrication parameters, and characterized the minimum feature size. We evaluated the response to both cytotoxic chemotherapy and targeted kinase therapies in microfluidic devices fabricated using our 3D-printed molds and demonstrated the establishment of flow-based concentration gradients. Furthermore, we monitored real-time cancer cell and fibroblast migration in a 3D matrix environment that was dependent on environmental signals. These results demonstrate how vat photopolymerization LCD-based fabrication can accelerate the prototyping of microfluidic platforms with increased accessibility and resolution for PDMS-based cell culture assays.
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During tumor progression, mechanical abnormalities in the tumor microenvironment (TME) trigger signaling pathways in cells that activate cellular programs, resulting in tumor growth and drug resistance. In this review, we describe mechanisms of action for anti-cancer therapies and mechanotransduction programs that regulate cellular processes, including cell proliferation, apoptosis, survival and phenotype switching. We discuss how the therapeutic response is impacted by the three main mechanical TME abnormalities: high extracellular matrix (ECM) composition and stiffness; interstitial fluid pressure (IFP); and elevated mechanical forces. We also review drugs that normalize these abnormalities or block mechanosensors and mechanotransduction pathways. Finally, we discuss current challenges and perspectives for the development of new strategies targeting mechanically induced drug resistance in the clinic.
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Mecanotransducción Celular , Neoplasias , Humanos , Neoplasias/tratamiento farmacológico , Neoplasias/genética , Neoplasias/metabolismo , Transducción de Señal , Matriz Extracelular/patología , Resistencia a Antineoplásicos , Microambiente TumoralRESUMEN
The metastatic ovarian cancer microenvironment is characterized by an intricate interaction network between cancer cells and host cells. This complex heterotypic cancer-host cell crosstalk results in an environment that promotes cancer cell metastasis and treatment resistance, leading to poor patient prognosis and survival. In this review, we focus on two host cell types found in the ovarian cancer microenvironment: mesothelial cells and tumor-associated macrophages. Mesothelial cells make up the protective lining of organs in the abdominal cavity. Cancer cells attach and invade through the mesothelial monolayer to form metastatic lesions. Crosstalk between mesothelial and cancer cells can contribute to metastatic progression and chemotherapy resistance. Tumor-associated macrophages are the most abundant immune cell type in the ovarian cancer microenvironment with heterogeneous subpopulations exhibiting protumor or antitumor functions. Macrophage reprogramming toward a protumor or antitumor state can be influenced by chemotherapy and communication with cancer cells, resulting in cancer cell invasion and treatment resistance. A better understanding of cancer-mesothelial and cancer-macrophage crosstalk will uncover biomarkers of metastatic progression and therapeutic targets to restore chemotherapy sensitivity.
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Neoplasias Ováricas , Microambiente Tumoral , Humanos , Femenino , Línea Celular Tumoral , Epitelio/metabolismo , Neoplasias Ováricas/tratamiento farmacológico , Macrófagos/metabolismoRESUMEN
Collective cell behavior contributes to all stages of cancer progression. Understanding how collective behavior emerges through cell-cell interactions and decision-making will advance our understanding of cancer biology and provide new therapeutic approaches. Here, we summarize an interdisciplinary discussion on multicellular behavior in cancer, draw lessons from other scientific disciplines, and identify future directions.
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Conducta de Masa , Neoplasias , Humanos , ComunicaciónRESUMEN
The majority of patients with high grade serous ovarian cancer (HGSOC) develop recurrent disease and chemotherapy resistance. To identify drug combinations that would be effective in treatment of chemotherapy resistant disease, we examined the efficacy of drug combinations that target the three antiapoptotic proteins most commonly expressed in HGSOC-BCL2, BCL-XL, and MCL1. Co-inhibition of BCL2 and BCL-XL (ABT-263) with inhibition of MCL1 (S63845) induces potent synergistic cytotoxicity in multiple HGSOC models. Since this drug combination is predicted to be toxic to patients due to the known clinical morbidities of each drug, we developed layer-by-layer nanoparticles (LbL NPs) that co-encapsulate these inhibitors in order to target HGSOC tumor cells and reduce systemic toxicities. We show that the LbL NPs can be designed to have high association with specific ovarian tumor cell types targeted in these studies, thus enabling a more selective uptake when delivered via intraperitoneal injection. Treatment with these LbL NPs displayed better potency than free drugs in vitro and resulted in near-complete elimination of solid tumor metastases of ovarian cancer xenografts. Thus, these results support the exploration of LbL NPs as a strategy to deliver potent drug combinations to recurrent HGSOC. While these findings are described for co-encapsulation of a BCL2/XL and a MCL1 inhibitor, the modular nature of LbL assembly provides flexibility in the range of therapies that can be incorporated, making LbL NPs an adaptable vehicle for delivery of additional combinations of pathway inhibitors and other oncology drugs.
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We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoire of observational data across multiple scales and modalities. In 2020, the US National Cancer Institute, and the US Department of Energy, through a trans-disciplinary research community at the intersection of advanced computing and cancer research, initiated team science collaborative projects to explore the development and implementation of predictive Cancer Patient Digital Twins. Several diverse pilot projects were launched to provide key insights into important features of this emerging landscape and to determine the requirements for the development and adoption of cancer patient digital twins. Projects included exploring approaches to using a large cohort of digital twins to perform deep phenotyping and plan treatments at the individual level, prototyping self-learning digital twin platforms, using adaptive digital twin approaches to monitor treatment response and resistance, developing methods to integrate and fuse data and observations across multiple scales, and personalizing treatment based on cancer type. Collectively these efforts have yielded increased insights into the opportunities and challenges facing cancer patient digital twin approaches and helped define a path forward. Given the rapidly growing interest in patient digital twins, this manuscript provides a valuable early progress report of several CPDT pilot projects commenced in common, their overall aims, early progress, lessons learned and future directions that will increasingly involve the broader research community.
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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.
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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.
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Neoplasias de la Mama , Animales , Neoplasias de la Mama/metabolismo , Línea Celular Tumoral , Citocinas , Modelos Animales de Enfermedad , Femenino , Humanos , Ratones , Microambiente TumoralRESUMEN
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.
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Ecosistema , Neoplasias , Daño del ADN , Humanos , Inmunoterapia , Neoplasias/patología , Microambiente TumoralRESUMEN
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.
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Neoplasias Pancreáticas , Proteínas Quinasas p38 Activadas por Mitógenos , Citoesqueleto de Actina/metabolismo , Movimiento Celular , Proteínas del Citoesqueleto/metabolismo , Humanos , Miosina Tipo II/metabolismo , Neoplasias Pancreáticas/genética , Neoplasias Pancreáticas/patología , Proteómica , Estrés Mecánico , Microambiente Tumoral , Proteínas Quinasas p38 Activadas por Mitógenos/genética , Proteínas Quinasas p38 Activadas por Mitógenos/metabolismo , Proteína de Unión al GTP rac1/genética , Proteína de Unión al GTP rac1/metabolismo , Neoplasias PancreáticasRESUMEN
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.
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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.
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Neoplasias de la Mama , Lapatinib , Apoptosis , Recuento de Células , Chaperón BiP del Retículo Endoplásmico , Humanos , Transducción de SeñalRESUMEN
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 â¼210 clinically relevant proteins in >12,000 cancer cell line samples in response to â¼170 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.
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Antineoplásicos/farmacología , Neoplasias/metabolismo , Mapas de Interacción de Proteínas/efectos de los fármacos , Proteómica/métodos , Antineoplásicos/uso terapéutico , Línea Celular Tumoral , Proliferación Celular/efectos de los fármacos , Supervivencia Celular/efectos de los fármacos , Biología Computacional , Resistencia a Antineoplásicos , Humanos , Terapia Molecular Dirigida , Neoplasias/tratamiento farmacológico , Análisis por Matrices de Proteínas , Interfaz Usuario-ComputadorRESUMEN
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.