Receptor-Driven ERK Pulses Reconfigure MAPK Signaling and Enable Persistence of Drug-Adapted BRAF-Mutant Melanoma Cells

Comparative Analysis of the Effect of the BRAF Inhibitor Dabrafenib in 2D and 3D Cell Culture Models of Human Metastatic Melanoma Cells

BACKGROUND/AIM

Melanoma, a variant of skin cancer, presents the highest mortality rates among all skin cancers. Despite advancements in targeted therapies, immunotherapies, and tissue culture techniques, the absence of an effective early treatment model remains a challenge. This study investigated the impact of dabrafenib on both 2D and 3D cell culture models with distinct molecular profiles.

MATERIALS AND METHODS

We developed a high-throughput workflow enabling drug screening on spheroids. Our approach involved cultivating 2D and 3D cultures derived from normal melanocytes and metastatic melanoma cells, treating them with dabrafenib and conducting viability, aggregation, migration, cell cycle, and apoptosis assays.

RESULTS

Dabrafenib exerted multifaceted influences, particularly on migration at concentrations of 10 and 25 μM. It induced a decrease in cell viability, impeded cellular adhesion to the matrix, inhibited cellular aggregation and spheroid formation, arrested the cell cycle in the G1 phase, and induced apoptosis.

CONCLUSION

These results confirm the therapeutic potential of dabrafenib in treating melanoma with the BRAF V600E mutation and that 3D models are validated models to study the potential of new molecules for therapeutic purposes. Furthermore, our study underscores the relevance of 3D models in simulating physiological in vivo microenvironments, providing insights into varied treatment responses between normal and tumor cells.

Synthetic gene circuit evolution: Insights and opportunities at the mid-scale

Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.

Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events

Understanding the dynamics of intracellular signaling pathways, such as ERK1/2 (ERK) and Akt1/2 (Akt), in the context of cell fate decisions is important for advancing our knowledge of cellular processes and diseases, particularly cancer. While previous studies have established associations between ERK and Akt activities and proliferative cell fate, the heterogeneity of single-cell responses adds complexity to this understanding. This study employed a data-driven approach to address this challenge, developing machine learning models trained on a dataset of growth factor-induced ERK and Akt activity time courses in single cells, to predict cell division events. The most predictive models were developed by applying discrete wavelet transforms (DWTs) to extract low-frequency features from the time courses, followed by using Ensemble Integration, a data integration and predictive modeling framework. The results demonstrated that these models effectively predicted cell division events in MCF10A cells (F-measure=0.524, AUC=0.726). ERK dynamics were found to be more predictive than Akt, but the combination of both measurements further enhanced predictive performance. The ERK model`s performance also generalized to predicting division events in RPE cells, indicating the potential applicability of these models and our data-driven methodology for predicting cell division across different biological contexts. Interpretation of these models suggested that ERK dynamics throughout the cell cycle, rather than immediately after growth factor stimulation, were associated with the likelihood of cell division. Overall, this work contributes insights into the predictive power of intra-cellular signaling dynamics for cell fate decisions, and highlights the potential of machine learning approaches in unraveling complex cellular behaviors.

Feedback between stochastic gene networks and population dynamics enables cellular decision-making

Phenotypic selection occurs when genetically identical cells are subject to different reproductive abilities due to cellular noise. Such noise arises from fluctuations in reactions synthesizing proteins and plays a crucial role in how cells make decisions and respond to stress or drugs. We propose a general stochastic agent-based model for growing populations capturing the feedback between gene expression and cell division dynamics. We devise a finite state projection approach to analyze gene expression and division distributions and infer selection from single-cell data in mother machines and lineage trees. We use the theory to quantify selection in multi-stable gene expression networks and elucidate that the trade-off between phenotypic switching and selection enables robust decision-making essential for synthetic circuits and developmental lineage decisions. Using live-cell data, we demonstrate that combining theory and inference provides quantitative insights into bet-hedging-like response to DNA damage and adaptation during antibiotic exposure in Escherichia coli.

Simulating BRAFV600E-MEK-ERK signalling dynamics in response to vertical inhibition treatment strategies

In vertical inhibition treatment strategies, multiple components of an intracellular pathway are simultaneously inhibited. Vertical inhibition of the BRAFV600E-MEK-ERK signalling pathway is a standard of care for treating BRAFV600E-mutated melanoma where two targeted cancer drugs, a BRAFV600E-inhibitor, and a MEK inhibitor, are administered in combination. Targeted therapies have been linked to early onsets of drug resistance, and thus treatment strategies of higher complexities and lower doses have been proposed as alternatives to current clinical strategies. However, finding optimal complex, low-dose treatment strategies is a challenge, as it is possible to design more treatment strategies than are feasibly testable in experimental settings. To quantitatively address this challenge, we develop a mathematical model of BRAFV600E-MEK-ERK signalling dynamics in response to combinations of the BRAFV600E-inhibitor dabrafenib (DBF), the MEK inhibitor trametinib (TMT), and the ERK-inhibitor SCH772984 (SCH). From a model of the BRAFV600E-MEK-ERK pathway, and a set of molecular-level drug-protein interactions, we extract a system of chemical reactions that is parameterised by in vitro data and converted to a system of ordinary differential equations (ODEs) using the law of mass action. The ODEs are solved numerically to produce simulations of how pathway-component concentrations change over time in response to different treatment strategies, i.e., inhibitor combinations and doses. The model can thus be used to limit the search space for effective treatment strategies that target the BRAFV600E-MEK-ERK pathway and warrant further experimental investigation. The results demonstrate that DBF and DBF-TMT-SCH therapies show marked sensitivity to BRAFV600E concentrations in silico, whilst TMT and SCH monotherapies do not.

A CRISPRi/a screening platform to study cellular nutrient transport in diverse microenvironments

Blocking the import of nutrients essential for cancer cell proliferation represents a therapeutic opportunity, but it is unclear which transporters to target. Here we report a CRISPR interference/activation screening platform to systematically interrogate the contribution of nutrient transporters to support cancer cell proliferation in environments ranging from standard culture media to tumours. We applied this platform to identify the transporters of amino acids in leukaemia cells and found that amino acid transport involves high bidirectional flux dependent on the microenvironment composition. While investigating the role of transporters in cystine starved cells, we uncovered a role for serotonin uptake in preventing ferroptosis. Finally, we identified transporters essential for cell proliferation in subcutaneous tumours and found that levels of glucose and amino acids can restrain proliferation in that environment. This study establishes a framework for systematically identifying critical cellular nutrient transporters, characterizing their function and exploring how the tumour microenvironment impacts cancer metabolism.

Purinergic Ca2+ signaling as a novel mechanism of drug tolerance in BRAF mutant melanoma

Drug tolerance is a major cause of relapse after cancer treatment. In spite of intensive efforts1-9, its molecular basis remains poorly understood, hampering actionable intervention. We report a previously unrecognized signaling mechanism supporting drug tolerance in BRAF-mutant melanoma treated with BRAF inhibitors that could be of general relevance to other cancers. Its key features are cell-intrinsic intracellular Ca2+ signaling initiated by P2X7 receptors (purinergic ligand-gated cation channels), and an enhanced ability for these Ca2+ signals to reactivate ERK1/2 in the drug-tolerant state. Extracellular ATP, virtually ubiquitous in living systems, is the ligand that can initiate Ca2+ spikes via P2X7 channels. ATP is abundant in the tumor microenvironment and is released by dying cells, ironically implicating treatment-initiated cancer cell death as a source of trophic stimuli that leads to ERK reactivation and drug tolerance. Such a mechanism immediately offers an explanation of the inevitable relapse after BRAFi treatment in BRAF-mutant melanoma, and points to actionable strategies to overcome it.

Integrative analysis of bulk and single-cell RNA-seq reveals the molecular characterization of the immune microenvironment and oxidative stress signature in melanoma

Background

The immune microenvironment and oxidative stress of melanoma show significant heterogeneity, which affects tumor growth, invasion and treatment response. Single-cell and bulk RNA-seq data were used to explore the heterogeneity of the immune microenvironment and oxidative stress of melanoma.

Methods

The R package Seurat facilitated the analysis of the single-cell dataset, while Harmony, another R package, was employed for batch effect correction. Cell types were classified using Uniform Manifold Approximation and Projection (UMAP). The Secreted Signaling algorithm from CellChatDB.human was applied to elucidate cell-to-cell communication patterns within the single-cell data. Consensus clustering analysis for the skin cutaneous melanoma (SKCM) samples was executed with the R package ConsensusClusterPlus. To quantify immune infiltrating cells, we utilized CIBERSORT, ESTIMATE, and TIMERxCell algorithms provided by the R package Immuno-Oncology Biological Research (IOBR). Single nucleotide variant (SNV) analysis was conducted using Maftools, an R package specifically designed for this purpose. Subsequently, the expression levels of PXDN and PAPSS2 genes were assessed in melanoma tissues compared to adjacent normal tissues. Furthermore, in vitro experiments were conducted to evaluate the proliferation and reactive oxygen species expression in melanoma cells following transfection with siRNA targeting PXDN and PAPSS2.

Results

Malignant tumor cell populations were reclassified based on a comprehensive single-cell dataset analysis, which yielded six distinct tumor subsets. The specific marker genes identified for these subgroups were then used to interrogate the Cancer Genome Atlas Skin Cutaneous Melanoma (TCGA-SKCM) cohort, derived from bulk RNA sequencing data, resulting in the delineation of two immune molecular subtypes. Notably, patients within the cluster2 (C2) subtype exhibited a significantly more favorable prognosis compared to those in the cluster1 (C1) subtype. An alignment of immune characteristics was observed between the C2 subtype and unique immune functional tumor cell subsets. Genes differentially expressed across these subtypes were subsequently leveraged to construct a predictive risk model. In vitro investigations further revealed elevated expression levels of PXDN and PAPSS2 in melanoma tissue samples. Functional assays indicated that modulation of PXDN and PAPSS2 expression could influence the production of reactive oxygen species (ROS) and the proliferative capacity of melanoma cells.

Conclusion

The constructed six-gene signature can be used as an immune response and an oxidative stress marker to guide the clinical diagnosis and treatment of melanoma.

Investigating Heterogeneous Cell-Cycle Progression Using Single-Cell Imaging Approaches

Investigating cell-cycle progression has been challenging due to the complex interconnectivity of regulatory processes and inherent cell-to-cell heterogeneity, which often require synchronization procedures. However, recent advancements in cell-cycle sensors and single-cell imaging techniques have turned this heterogeneity into an advantage for investigating the molecular mechanisms underlying diverse responses. This has led to significant progress in our understanding of cell-cycle regulation. In this paper, we present a comprehensive live single-cell imaging workflow that leverages cutting-edge live-cell sensors. These advanced single-cell imaging procedures provide promising opportunities for elucidating the molecular mechanisms underpinnings of heterogeneous responses in cell-cycle progression.

A guide to ERK dynamics, part 2: downstream decoding

Signaling by the extracellular signal-regulated kinase (ERK) pathway controls many cellular processes, including cell division, death, and differentiation. In this second installment of a two-part review, we address the question of how the ERK pathway exerts distinct and context-specific effects on multiple processes. We discuss how the dynamics of ERK activity induce selective changes in gene expression programs, with insights from both experiments and computational models. With a focus on single-cell biosensor-based studies, we summarize four major functional modes for ERK signaling in tissues: adjusting the size of cell populations, gradient-based patterning, wave propagation of morphological changes, and diversification of cellular gene expression states. These modes of operation are disrupted in cancer and other related diseases and represent potential targets for therapeutic intervention. By understanding the dynamic mechanisms involved in ERK signaling, there is potential for pharmacological strategies that not only simply inhibit ERK, but also restore functional activity patterns and improve disease outcomes.

A guide to ERK dynamics, part 1: mechanisms and models

Extracellular signal-regulated kinase (ERK) has long been studied as a key driver of both essential cellular processes and disease. A persistent question has been how this single pathway is able to direct multiple cell behaviors, including growth, proliferation, and death. Modern biosensor studies have revealed that the temporal pattern of ERK activity is highly variable and heterogeneous, and critically, that these dynamic differences modulate cell fate. This two-part review discusses the current understanding of dynamic activity in the ERK pathway, how it regulates cellular decisions, and how these cell fates lead to tissue regulation and pathology. In part 1, we cover the optogenetic and live-cell imaging technologies that first revealed the dynamic nature of ERK, as well as current challenges in biosensor data analysis. We also discuss advances in mathematical models for the mechanisms of ERK dynamics, including receptor-level regulation, negative feedback, cooperativity, and paracrine signaling. While hurdles still remain, it is clear that higher temporal and spatial resolution provide mechanistic insights into pathway circuitry. Exciting new algorithms and advanced computational tools enable quantitative measurements of single-cell ERK activation, which in turn inform better models of pathway behavior. However, the fact that current models still cannot fully recapitulate the diversity of ERK responses calls for a deeper understanding of network structure and signal transduction in general.

Sequential activation of E2F via Rb degradation and c-Myc drives resistance to CDK4/6 inhibitors in breast cancer

Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6i) are key therapeutic agents in the management of metastatic hormone-receptor-positive breast cancer. However, the emergence of drug resistance limits their long-term efficacy. Here, we show that breast cancer cells develop CDK4/6i resistance via a sequential two-step process of E2F activation. This process entails retinoblastoma (Rb)-protein degradation, followed by c-Myc-mediated amplification of E2F transcriptional activity. CDK4/6i treatment halts cell proliferation in an Rb-dependent manner but dramatically reduces Rb-protein levels. However, this reduction in Rb levels insufficiently induces E2F activity. To develop CDK4/6i resistance, upregulation or activating mutations in mitogenic or hormone signaling are required to stabilize c-Myc levels, thereby augmenting E2F activity. Our analysis of pre-treatment tumor samples reveals a strong correlation between c-Myc levels, rather than Rb levels, and poor therapeutic outcomes after CDK4/6i treatment. Moreover, we propose that proteasome inhibitors can potentially reverse CDK4/6i resistance by restoring Rb levels.

Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity

Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.

Drug-tolerant persister cells in cancer: the cutting edges and future directions

Drug-tolerant persister (DTP) cell populations were originally discovered in antibiotic-resistant bacterial biofilms. Similar populations with comparable features have since been identified among cancer cells and have been linked with treatment resistance that lacks an underlying genomic alteration. Research over the past decade has improved our understanding of the biological roles of DTP cells in cancer, although clinical knowledge of the role of these cells in treatment resistance remains limited. Nonetheless, targeting this population is anticipated to provide new treatment opportunities. In this Perspective, we aim to provide a clear definition of the DTP phenotype, discuss the underlying characteristics of these cells, their biomarkers and vulnerabilities, and encourage further research on DTP cells that might improve our understanding and enable the development of more effective anticancer therapies.

PI3K signaling through a biochemical systems lens

Following 3 decades of extensive research into PI3K signaling, it is now evidently clear that the underlying network does not equate to a simple ON/OFF switch. This is best illustrated by the multifaceted nature of the many diseases associated with aberrant PI3K signaling, including common cancers, metabolic disease, and rare developmental disorders. However, we are still far from a complete understanding of the fundamental control principles that govern the numerous phenotypic outputs that are elicited by activation of this well-characterized biochemical signaling network, downstream of an equally diverse set of extrinsic inputs. At its core, this is a question on the role of PI3K signaling in cellular information processing and decision making. Here, we review the determinants of accurate encoding and decoding of growth factor signals and discuss outstanding questions in the PI3K signal relay network. We emphasize the importance of quantitative biochemistry, in close integration with advances in single-cell time-resolved signaling measurements and mathematical modeling.

Advances in targeted therapy and immunotherapy for melanoma (Review)

Melanoma is the most aggressive and deadly type of skin cancer and is known for its poor prognosis as soon as metastasis occurs. Since 2011, new and effective therapies for metastatic melanoma have emerged, with US Food and Drug Administration approval of multiple targeted agents, such as V-Raf murine sarcoma viral oncogene homolog B1/mitogen-activated protein kinase kinase inhibitors and multiple immunotherapy agents, such as cytotoxic T lymphocyte-associated protein 4 and anti-programmed cell death protein 1/ligand 1 blockade. Based on insight into the respective advantages of the above two strategies, the present article provided a review of clinical trials of the application of targeted therapy and immunotherapy, as well as novel approaches of their combinations for the treatment of metastatic melanoma in recent years, with a focus on upcoming initiatives to improve the efficacy of these treatment approaches for metastatic melanoma.

A Combination of Conformation-Specific RAF Inhibitors Overcome Drug Resistance Brought about by RAF Overexpression

Cancer cells often adapt to targeted therapies, yet the molecular mechanisms underlying adaptive resistance remain only partially understood. Here, we explore a mechanism of RAS/RAF/MEK/ERK (MAPK) pathway reactivation through the upregulation of RAF isoform (RAFs) abundance. Using computational modeling and in vitro experiments, we show that the upregulation of RAFs changes the concentration range of paradoxical pathway activation upon treatment with conformation-specific RAF inhibitors. Additionally, our data indicate that the signaling output upon loss or downregulation of one RAF isoform can be compensated by overexpression of other RAF isoforms. We furthermore demonstrate that, while single RAF inhibitors cannot efficiently inhibit ERK reactivation caused by RAF overexpression, a combination of two structurally distinct RAF inhibitors synergizes to robustly suppress pathway reactivation.

Multiple cancers escape from multiple MAPK pathway inhibitors and use DNA replication stress signaling to tolerate aberrant cell cycles

Many cancers harbor pro-proliferative mutations of the mitogen-activated protein kinase (MAPK) pathway. In BRAF-driven melanoma cells treated with BRAF inhibitors, subpopulations of cells escape drug-induced quiescence through a nongenetic manner of adaptation and resume slow proliferation. Here, we found that this phenomenon is common to many cancer types driven by EGFR, KRAS, or BRAF mutations in response to multiple, clinically approved MAPK pathway inhibitors. In 2D cultures and 3D spheroid models of various cancer cell lines, a subset of cells escaped drug-induced quiescence within 4 days to resume proliferation. These "escapee" cells exhibited DNA replication deficits, accumulated DNA lesions, and mounted a stress response that depended on the ataxia telangiectasia and RAD3-related (ATR) kinase. We further identified that components of the Fanconi anemia (FA) DNA repair pathway are recruited to sites of mitotic DNA synthesis (MiDAS) in escapee cells, enabling successful completion of cell division. Analysis of patient tumor samples and clinical data correlated disease progression with an increase in DNA replication stress response factors. Our findings suggest that many MAPK pathway-mutant cancers rapidly escape drug action and that suppressing early stress tolerance pathways may achieve more durable clinical responses to MAPK pathway inhibitors.

Melanoma cells with acquired resistance to vemurafenib have decreased autophagic flux and display enhanced ability to transfer resistance

Over the last years, the incidence of melanoma, the deadliest form of skin cancer, has risen significantly. Nearly half of the melanoma patients exhibit the BRAF^V600E mutation. Although the use of BRAF and MEK inhibitors (BRAFi and MEKi) showed an impressive success rate in melanoma patients, durability of response remains an issue because tumor quickly becomes resistant. Here, we generated and characterized Lu1205 and A375 melanoma cells resistant to vemurafenib (BRAFi). Resistant cells (Lu1205R and A375R) exhibit higher IC₅₀ (5-6 fold increase) and phospho-ERK levels and 2-3 times reduced apoptosis than their sensitive parents (Lu1205S and A375S). Moreover, resistant cells are 2-3 times bigger, display a more elongated morphology and have a modulation the migration capacity. Interestingly, pharmacological inhibition of sphingosine kinases, that prevents sphingosine-1-phosphate production, reduces migration of Lu1205R cells by 50 %. In addition, although Lu1205R cells showed increased basal levels of the autophagy markers LC3II and p62, they have decreased autophagosome degradation and autophagy flux. Remarkably, expression of Rab27A and Rab27B, which are involved in the release of extracellular vesicles are dramatically augmented in resistant cells (i.e. 5-7 fold increase). Indeed, conditioned media obtained from Lu1205R cells increased the resistance to vemurafenib of sensitive cells. Hence, these results support that resistance to vemurafenib modulates migration and the autophagic flux and may be transferred to nearby sensitive melanoma cells by factors that are released to the extracellular milieu by resistant cells.

Kinetics of RTK activation determine ERK reactivation and resistance to dual BRAF/MEK inhibition in melanoma

The combination of BRAF and MEK inhibitors (BRAFi/MEKi) has shown promising response rates in treating BRAF-mutant melanoma by inhibiting ERK activation. However, treatment efficacy is limited by the emergence of drug-tolerant persister cells (persisters). Here, we show that the magnitude and duration of receptor tyrosine kinase (RTK) activation determine ERK reactivation and persister development. Our single-cell analysis reveals that only a small subset of melanoma cells exhibits effective RTK and ERK activation and develops persisters, despite uniform external stimuli. The kinetics of RTK activation directly influence ERK signaling dynamics and persister development. These initially rare persisters form major resistant clones through effective RTK-mediated ERK activation. Consequently, limiting RTK signaling suppresses ERK activation and cell proliferation in drug-resistant cells. Our findings provide non-genetic mechanistic insights into the role of heterogeneity in RTK activation kinetics in ERK reactivation and BRAFi/MEKi resistance, suggesting potential strategies for overcoming drug resistance in BRAF-mutant melanoma.

An integrated mechanistic and data-driven computational model predicts cell responses to high- and low-affinity EGFR ligands

The biophysical properties of ligand binding heavily influence the ability of receptors to specify cell fates. Understanding the rules by which ligand binding kinetics impact cell phenotype is challenging, however, because of the coupled information transfers that occur from receptors to downstream signaling effectors and from effectors to phenotypes. Here, we address that issue by developing an integrated mechanistic and data-driven computational modeling platform to predict cell responses to different ligands for the epidermal growth factor receptor (EGFR). Experimental data for model training and validation were generated using MCF7 human breast cancer cells treated with the high- and low-affinity ligands epidermal growth factor (EGF) and epiregulin (EREG), respectively. The integrated model captures the unintuitive, concentration-dependent abilities of EGF and EREG to drive signals and phenotypes differently, even at similar levels of receptor occupancy. For example, the model correctly predicts the dominance of EREG over EGF in driving a cell differentiation phenotype through AKT signaling at intermediate and saturating ligand concentrations and the ability of EGF and EREG to drive a broadly concentration-sensitive migration phenotype through cooperative ERK and AKT signaling. Parameter sensitivity analysis identifies EGFR endocytosis, which is differentially regulated by EGF and EREG, as one of the most important determinants of the alternative phenotypes driven by different ligands. The integrated model provides a new platform to predict how phenotypes are controlled by the earliest biophysical rate processes in signal transduction and may eventually be leveraged to understand receptor signaling system performance depends on cell context.

One-sentence summary

Integrated kinetic and data-driven EGFR signaling model identifies the specific signaling mechanisms that dictate cell responses to EGFR activation by different ligands.

Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools

Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.

Multi-range ERK responses shape the proliferative trajectory of single cells following oncogene induction

Oncogene-induced senescence is a phenomenon in which aberrant oncogene expression causes non-transformed cells to enter a non-proliferative state. Cells undergoing oncogenic induction display phenotypic heterogeneity, with some cells senescing and others remaining proliferative. The causes of heterogeneity remain unclear. We studied the sources of heterogeneity in the responses of human epithelial cells to oncogenic BRAF^V600E expression. We found that a narrow expression range of BRAF^V600E generated a wide range of activities of its downstream effector ERK. In population-level and single-cell assays, ERK activity displayed a non-monotonic relationship to proliferation, with intermediate ERK activities leading to maximal proliferation. We profiled gene expression across a range of ERK activities over time and characterized four distinct ERK response classes, which we propose act in concert to generate the ERK-proliferation response. Altogether, our studies map the input-output relationships between ERK activity and proliferation, elucidating how heterogeneity can be generated during oncogene induction.

Synergistic Targeting of DNA-PK and KIT Signaling Pathways in KIT Mutant Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is the most common and aggressive form of acute leukemia, with a 5-year survival rate of just 24%. Over a third of all AML patients harbor activating mutations in kinases, such as the receptor tyrosine kinases FLT3 (receptor-type tyrosine-protein kinase FLT3) and KIT (mast/stem cell growth factor receptor kit). FLT3 and KIT mutations are associated with poor clinical outcomes and lower remission rates in response to standard-of-care chemotherapy. We have recently identified that the core kinase of the non-homologous end joining DNA repair pathway, DNA-PK (DNA-dependent protein kinase), is activated downstream of FLT3; and targeting DNA-PK sensitized FLT3-mutant AML cells to standard-of-care therapies. Herein, we investigated DNA-PK as a possible therapeutic vulnerability in KIT mutant AML, using isogenic FDC-P1 mouse myeloid progenitor cell lines transduced with oncogenic mutant KIT (V560G and D816V) or vector control. Targeted quantitative phosphoproteomic profiling identified phosphorylation of DNA-PK in the T2599/T2605/S2608/S2610 cluster in KIT mutant cells, indicative of DNA-PK activation. Accordingly, proliferation assays revealed that KIT mutant FDC-P1 cells were more sensitive to the DNA-PK inhibitors M3814 or NU7441, compared with empty vector controls. DNA-PK inhibition combined with inhibition of KIT signaling using the kinase inhibitors dasatinib or ibrutinib, or the protein phosphatase 2A activators FTY720 or AAL(S), led to synergistic cell death. Global phosphoproteomic analysis of KIT-D816V cells revealed that dasatinib and M3814 single-agent treatments inhibited extracellular signal-regulated kinase and AKT (RAC-alpha serine/threonine-protein kinase)/MTOR (serine/threonine-protein kinase mTOR) activity, with greater inhibition of both pathways when used in combination. Combined dasatinib and M3814 treatment also synergistically inhibited phosphorylation of the transcriptional regulators MYC and MYB. This study provides insight into the oncogenic pathways regulated by DNA-PK beyond its canonical role in DNA repair and demonstrates that DNA-PK is a promising therapeutic target for KIT mutant cancers.

Oscillatory ERK Signaling and Morphology Determine Heterogeneity of Breast Cancer Cell Chemotaxis via MEK-ERK and p38-MAPK Signaling Pathways

Chemotaxis, regulated by oscillatory signals, drives critical processes in cancer metastasis. Crucial chemoattractant molecules in breast cancer, CXCL12 and EGF, drive the activation of ERK and Akt. Regulated by feedback and crosstalk mechanisms, oscillatory signals in ERK and Akt control resultant changes in cell morphology and chemotaxis. While commonly studied at the population scale, metastasis arises from small numbers of cells that successfully disseminate, underscoring the need to analyze processes that cancer cells use to connect oscillatory signaling to chemotaxis at single-cell resolution. Furthermore, little is known about how to successfully target fast-migrating cells to block metastasis. We investigated to what extent oscillatory networks in single cells associate with heterogeneous chemotactic responses and how targeted inhibitors block signaling processes in chemotaxis. We integrated live, single-cell imaging with time-dependent data processing to discover oscillatory signal processes defining heterogeneous chemotactic responses. We identified that short ERK and Akt waves, regulated by MEK-ERK and p38-MAPK signaling pathways, determine the heterogeneous random migration of cancer cells. By comparison, long ERK waves and the morphological changes regulated by MEK-ERK signaling, determine heterogeneous directed motion. This study indicates that treatments against chemotaxis in consider must interrupt oscillatory signaling.

Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance

BRAF is prototypical of oncogenes that can be targeted therapeutically and the treatment of BRAF^V600E melanomas with RAF and MEK inhibitors results in rapid tumor regression. However, drug-induced rewiring generates a drug adapted state thought to be involved in acquired resistance and disease recurrence. In this article, we study mechanisms of adaptive rewiring in BRAF^V600E melanoma cells using an energy-based implementation of ordinary differential equation (ODE) modeling in combination with proteomic, transcriptomic and imaging data. We develop a method for causal tracing of ODE models and identify two parallel MAPK reaction channels that are differentially sensitive to RAF and MEK inhibitors due to differences in protein oligomerization and drug binding. We describe how these channels, and timescale separation between immediate-early signaling and transcriptional feedback, create a state in which the RAS-regulated MAPK channel can be activated by growth factors under conditions in which the BRAF^V600E -driven channel is fully inhibited. Further development of the approaches in this article is expected to yield a unified model of adaptive drug resistance in melanoma.

Targeted Quantification of Protein Phosphorylation and Its Contributions towards Mathematical Modeling of Signaling Pathways

Post-translational modifications (PTMs) are key regulatory mechanisms that can control protein function. Of these, phosphorylation is the most common and widely studied. Because of its importance in regulating cell signaling, precise and accurate measurements of protein phosphorylation across wide dynamic ranges are crucial to understanding how signaling pathways function. Although immunological assays are commonly used to detect phosphoproteins, their lack of sensitivity, specificity, and selectivity often make them unreliable for quantitative measurements of complex biological samples. Recent advances in Mass Spectrometry (MS)-based targeted proteomics have made it a more useful approach than immunoassays for studying the dynamics of protein phosphorylation. Selected reaction monitoring (SRM)-also known as multiple reaction monitoring (MRM)-and parallel reaction monitoring (PRM) can quantify relative and absolute abundances of protein phosphorylation in multiplexed fashions targeting specific pathways. In addition, the refinement of these tools by enrichment and fractionation strategies has improved measurement of phosphorylation of low-abundance proteins. The quantitative data generated are particularly useful for building and parameterizing mathematical models of complex phospho-signaling pathways. Potentially, these models can provide a framework for linking analytical measurements of clinical samples to better diagnosis and treatment of disease.

Computational modeling implicates protein scaffolding in p38 regulation of Akt

Cells process environmental cues by activating intracellular signaling pathways with numerous interconnections and opportunities for cross-regulation. We employed a systems biology approach to investigate intersections of kinase p38, a context-dependent tumor suppressor or promoter, with Akt and ERK, two kinases known to promote cell survival, proliferation, and drug resistance in cancer. Using live, single cell microscopy, multiplexed fluorescent reporters of p38, Akt, and ERK activities, and a custom automated image-processing pipeline, we detected marked heterogeneity of signaling outputs in breast cancer cells stimulated with chemokine CXCL12 or epidermal growth factor (EGF). Basal activity of p38 correlated inversely with amplitude of Akt and ERK activation in response to either ligand. Remarkably, small molecule inhibitors of p38 immediately decreased basal activities of Akt and ERK but increased the proportion of cells with high amplitude ligand-induced activation of Akt signaling. To identify mechanisms underlying cross-talk of p38 with Akt signaling, we developed a computational model incorporating subcellular compartmentalization of signaling molecules by scaffold proteins. Dynamics of this model revealed that subcellular scaffolding of Akt accounted for observed regulation by p38. The model also predicted that differences in the amount of scaffold protein in a subcellular compartment captured the observed single cell heterogeneity in signaling. Finally, our model predicted that reduction in kinase signaling can be accomplished by both scaffolding and direct kinase inhibition. However, scaffolding inhibition can potentiate future kinase activity by redistribution of pathway components, potentially amplifying oncogenic signaling. These studies reveal how computational modeling can decipher mechanisms of cross-talk between the p38 and Akt signaling pathways and point to scaffold proteins as central regulators of signaling dynamics and amplitude.

Escape from G1 arrest during acute MEK inhibition drives the acquisition of drug resistance

Mutations and gene amplifications that confer drug resistance emerge frequently during chemotherapy, but their mechanism and timing are poorly understood. Here, we investigate BRAFV600E amplification events that underlie resistance to the MEK inhibitor selumetinib (AZD6244/ARRY-142886) in COLO205 cells, a well-characterized model for reproducible emergence of drug resistance, and show that BRAF amplifications acquired de novo are the primary cause of resistance. Selumetinib causes long-term G1 arrest accompanied by reduced expression of DNA replication and repair genes, but cells stochastically re-enter the cell cycle during treatment despite continued repression of pERK1/2. Most DNA replication and repair genes are re-expressed as cells enter S and G2; however, mRNAs encoding a subset of factors important for error-free replication and chromosome segregation, including TIPIN, PLK2 and PLK3, remain at low abundance. This suggests that DNA replication following escape from G1 arrest in drug is more error prone and provides a potential explanation for the DNA damage observed under long-term RAF-MEK-ERK1/2 pathway inhibition. To test the hypothesis that escape from G1 arrest in drug promotes de novo BRAF amplification, we exploited the combination of palbociclib and selumetinib. Combined treatment with selumetinib and a dose of palbociclib sufficient to reinforce G1 arrest in selumetinib-sensitive cells, but not to impair proliferation of resistant cells, delays the emergence of resistant colonies, meaning that escape from G1 arrest is critical in the formation of resistant clones. Our findings demonstrate that acquisition of MEK inhibitor resistance often occurs through de novo gene amplification and can be suppressed by impeding cell cycle entry in drug.

Mouse Syngeneic Melanoma Model with Human Epidermal Growth Factor Receptor Expression

The development of epidermal growth factor receptor (EGFR)-targeting agents for the treatment of malignant melanoma requires cheap and easy animal tumor models for high-throughput in vivo screening. Thus, the aim of this study was to develop mouse syngeneic melanoma model that expresses human EGFR. Cloudman S91 clone M3 mouse melanoma cells were transduced with lentiviral particles carrying the human EGFR gene followed by a multistep selection process. The resulting M3-EGFR has been tested for EGFR expression and functionality in vitro and in vivo. Radioligand assay confirmed the presence of 13,900 ± 1500 EGF binding sites per cell at a dissociation constant of 5.3 ± 1.4 nM. M3-EGFR demonstrated the ability to bind and internalize specifically and provide the anticipated intracellular nuclear import of three different EGFR-targeted modular nanotransporters designed for specific anti-cancer drug delivery. Introduction of the human EGFR gene did not alter the tumorigenicity of the offspring M3-EGFR cells in host immunocompetent DBA/2J mice. Preservation of the expression of EGFR in vivo was confirmed by immunohistochemistry. To sum up, we successfully developed the first mouse syngeneic melanoma model with preserved in vivo expression of human EGFR.

Circ_0084043-miR-134-5p axis regulates PCDH9 to suppress melanoma

The low survival rates, poor responses, and drug resistance of patients with melanoma make it urgent to find new therapeutic targets. This study investigated whether the circ_0084043-miR-134-5p axis regulates the antitumor effect of protocadherin 9 (PCDH9) in melanoma. Ectopic expression or knock down (KD) of PCDH9 with a lentivirus vector, we explored its effects on the proliferation, invasion, and apoptosis of melanoma and verified its regulatory effect on ras-related C3 botulinum toxin substrate 1 (RAC1), proline-rich tyrosine kinase 2 (Pyk2), Cyclin D1, matrix metalloproteinase 2 (MMP2), and MMP9. We further observed the effect of KD circ_0084043 on the malignant behavior of melanoma and studied whether circ_0084043 sponged miR-134-5p and regulated PCDH9. We found that circ_0084043 was overexpressed in melanoma and associated with the malignant phenotype. PCDH9 was poorly expressed in human melanoma tissues, and overexpression of PCDH9 inhibited melanoma progression. Quantitative real-time PCR and Western blotting results showed that overexpression of PCDH9 could downregulate RAC1, MMP2, and MMP9 and upregulate Pyk2 and Cyclin D1. Circ_0084043 KD inhibited invasion and promoted apoptosis in melanoma cells. Circ_0084043 could sponge miR-134-5p and thus indirectly regulate PCDH9. Furthermore, we discovered that inhibiting circ_0084043 had an anti–PD-Ll effect. In vivo, PCDH9 overexpression inhibited melanoma tumor growth, but PCDH9 KD promoted it. In conclusion, PCDH9, which is regulated by the circ 0084043-miR-134-5p axis, can suppress malignant biological behavior in melanoma and influence the expression levels of Pyk2, RAC1, Cyclin D1, MMP2, and MMP9.

CDK4/6 initiates Rb inactivation and CDK2 activity coordinates cell-cycle commitment and G1/S transition

External signaling controls cell-cycle entry until cells irreversibly commit to the cell cycle to ensure faithful DNA replication. This process is tightly regulated by cyclin-dependent kinases (CDKs) and the retinoblastoma protein (Rb). Here, using live-cell sensors for CDK4/6 and CDK2 activities, we propose that CDK4/6 initiates Rb inactivation and CDK2 activation, which coordinates the timing of cell-cycle commitment and sequential G1/S transition. Our data show that CDK4/6 activation induces Rb inactivation and thereby E2F activation, driving a gradual increase in CDK2 activity. We found that rapid CDK4/6 inhibition can reverse cell-cycle entry until CDK2 activity reaches to high levels. This suggests that high CDK2 activity is required to initiate CDK2-Rb positive feedback and CDK4/6-indpendent cell-cycle progression. Since CDK2 activation also facilitates initiation of DNA replication, the timing of CDK2-Rb positive feedback is coupled with the G1/S transition. Our experiments, which acutely increased CDK2 activity by cyclin E1 overexpression, indicate that cells commit to the cell cycle before triggering DNA replication. Together, our data suggest that CDK4/6 inactivates Rb to begin E2F and CDK2 activation, and high CDK2 activity is necessary and sufficient to generate a bistable switch for Rb phosphorylation before DNA replication. These findings highlight how cells initiate the cell cycle and subsequently commit to the cell cycle before the G1/S transition.

AP-1 transcription factor network explains diverse patterns of cellular plasticity in melanoma cells

Cellular plasticity associated with fluctuations in transcriptional programs allows individual cells in a tumor to adopt heterogeneous differentiation states and switch phenotype during their adaptive responses to therapies. Despite increasing knowledge of such transcriptional programs, the molecular basis of cellular plasticity remains poorly understood. Here, we combine multiplexed transcriptional and protein measurements at population and single-cell levels with multivariate statistical modeling to show that the state of AP-1 transcription factor network plays a unifying role in explaining diverse patterns of plasticity in melanoma. We find that a regulated balance among AP-1 factors cJUN, JUND, FRA2, FRA1, and cFOS determines the intrinsic diversity of differentiation states and adaptive responses to MAPK inhibitors in melanoma cells. Perturbing this balance through genetic depletion of specific AP-1 proteins, or by MAPK inhibitors, shifts cellular heterogeneity in a predictable fashion. Thus, AP-1 may serve as a critical node for manipulating cellular plasticity with potential therapeutic implications.

Integrating knowledge and omics to decipher mechanisms via large-scale models of signaling networks

Signal transduction governs cellular behavior, and its dysregulation often leads to human disease. To understand this process, we can use network models based on prior knowledge, where nodes represent biomolecules, usually proteins, and edges indicate interactions between them. Several computational methods combine untargeted omics data with prior knowledge to estimate the state of signaling networks in specific biological scenarios. Here, we review, compare, and classify recent network approaches according to their characteristics in terms of input omics data, prior knowledge and underlying methodologies. We highlight existing challenges in the field, such as the general lack of ground truth and the limitations of prior knowledge. We also point out new omics developments that may have a profound impact, such as single‐cell proteomics or large‐scale profiling of protein conformational changes. We provide both an introduction for interested users seeking strategies to study cell signaling on a large scale and an update for seasoned modelers.

Identification of cell type-specific correlations between ERK activity and cell viability upon treatment with ERK1/2 inhibitors

Increased MAPK signaling is a hallmark of various cancers and is a central regulator of cell survival. Direct ERK1/2 inhibition is considered a promising approach to avoid ERK1/2 reactivation caused by upstream kinases BRAF, MEK1/2, and KRAS, as well as by receptor tyrosine kinase inhibitors, but the dynamics and selectivity of ERK1/2 inhibitors are much less studied compared with BRAF or MEK inhibitors. Using ERK1/2 and downstream kinase ELK1 reporter cell lines of lung cancer (H1299; NRAS^Q61K), colon cancer (HCT-116; KRAS^G13D), neuroblastoma (SH-SY5Y), and leukemia (U937), we examined the relationship between ERK inhibition and drug-induced toxicity for five ERK inhibitors: SCH772984, ravoxertinib, LY3214996, ulixertinib, and VX-11e, as well as one MEK inhibitor, PD0325901. Comparing cell viability and ERK inhibition revealed different ERK dependencies for these cell lines. We identify several drugs, such as SCH772984 and VX-11e, which induce excessive toxicity not directly related to ERK1/2 inhibition in specific cell lines. We also show that PD0325901, LY3214996, and ulixertinib are prone to ERK1/2 reactivation over time. We distinguished two types of ERK1/2 reactivation: the first could be reversed by adding a fresh dose of inhibitors, while the second persists even after additional treatments. We also showed that cells that became resistant to the MEK1/2 inhibitor PD0325901 due to ERK1/2 reactivation remained sensitive to ERK1/2 inhibitor ulixertinib. Our data indicate that correlation of ERK inhibition with drug-induced toxicity in multiple cell lines may help to find more selective and effective ERK1/2 inhibitors.

Therapy resistance: opportunities created by adaptive responses to targeted therapies in cancer

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.

Optogenetic actuator - ERK biosensor circuits identify MAPK network nodes that shape ERK dynamics

Combining single-cell measurements of ERK activity dynamics with perturbations provides insights into the MAPK network topology. We built circuits consisting of an optogenetic actuator to activate MAPK signaling and an ERK biosensor to measure single-cell ERK dynamics. This allowed us to conduct RNAi screens to investigate the role of 50 MAPK proteins in ERK dynamics. We found that the MAPK network is robust against most node perturbations. We observed that the ERK-RAF and the ERK-RSK2-SOS negative feedback operate simultaneously to regulate ERK dynamics. Bypassing the RSK2-mediated feedback, either by direct optogenetic activation of RAS, or by RSK2 perturbation, sensitized ERK dynamics to further perturbations. Similarly, targeting this feedback in a human ErbB2-dependent oncogenic signaling model increased the efficiency of a MEK inhibitor. The RSK2-mediated feedback is thus important for the ability of the MAPK network to produce consistent ERK outputs, and its perturbation can enhance the efficiency of MAPK inhibitors.

Overcoming differential tumor penetration of BRAF inhibitors using computationally guided combination therapy

BRAF-targeted kinase inhibitors (KIs) are used to treat malignancies including BRAF-mutant non-small cell lung cancer, colorectal cancer, anaplastic thyroid cancer, and, most prominently, melanoma. However, KI selection criteria in patients remain unclear, as are pharmacokinetic/pharmacodynamic (PK/PD) mechanisms that may limit context-dependent efficacy and differentiate related drugs. To address this issue, we imaged mouse models of BRAF-mutant cancers, fluorescent KI tracers, and unlabeled drug to calibrate in silico spatial PK/PD models. Results indicated that drug lipophilicity, plasma clearance, faster target dissociation, and, in particular, high albumin binding could limit dabrafenib action in visceral metastases compared to other KIs. This correlated with retrospective clinical observations. Computational modeling identified a timed strategy for combining dabrafenib and encorafenib to better sustain BRAF inhibition, which showed enhanced efficacy in mice. This study thus offers principles of spatial drug action that may help guide drug development, KI selection, and combination.

High-Content Screening Pipeline for Natural Products Targeting Oncogenic Signaling in Melanoma

The incidence of melanoma, the most fatal dermatological cancer, has dramatically increased over the last few decades. Modern targeted therapy with kinase inhibitors induces potent clinical responses, but drug resistance quickly develops. Combination therapy improves treatment outcomes. Therefore, novel inhibitors targeting aberrant proliferative signaling in melanoma via the MAPK/ERK and PI3K/AKT pathways are urgently needed. Biosensors were combined that report on ERK/AKT activity with image-based high-content screening and HPLC-based activity profiling. An in-house library of 2576 plant extracts was screened on two melanoma cell lines with different oncogenic mutations leading to pathological ERK/AKT activity. Out of 140 plant extract hits, 44 were selected for HPLC activity profiling. Active thymol derivatives and piperamides from Arnica montana and Piper nigrum were identified that inhibited pathological ERK and/or AKT activity. The pipeline used enabled an efficient identification of natural products targeting oncogenic signaling in melanoma.

Sphingolipids control dermal fibroblast heterogeneity

Human cells produce thousands of lipids that change during cell differentiation and can vary across individual cells of the same type. However, we are only starting to characterize the function of these cell-to-cell differences in lipid composition. Here, we measured the lipidomes and transcriptomes of individual human dermal fibroblasts by coupling high-resolution mass spectrometry imaging with single-cell transcriptomics. We found that the cell-to-cell variations of specific lipid metabolic pathways contribute to the establishment of cell states involved in the organization of skin architecture. Sphingolipid composition is shown to define fibroblast subpopulations, with sphingolipid metabolic rewiring driving cell-state transitions. Therefore, cell-to-cell lipid heterogeneity affects the determination of cell states, adding a new regulatory component to the self-organization of multicellular systems.

Plasticity of Extrachromosomal and Intrachromosomal BRAF Amplifications in Overcoming Targeted Therapy Dosage Challenges

Focal amplifications (FA) can mediate targeted therapy resistance in cancer. Understanding the structure and dynamics of FAs is critical for designing treatments that overcome plasticity-mediated resistance. We developed a melanoma model of dual MAPK inhibitor (MAPKi) resistance that bears BRAFV600 amplifications through either extrachromosomal DNA (ecDNA)/double minutes (DM) or intrachromosomal homogenously staining regions (HSR). Cells harboring BRAFV600E FAs displayed mode switching between DMs and HSRs, from both de novo genetic changes and selection of preexisting subpopulations. Plasticity is not exclusive to ecDNAs, as cells harboring HSRs exhibit drug addiction-driven structural loss of BRAF amplicons upon dose reduction. FA mechanisms can couple with kinase domain duplications and alternative splicing to enhance resistance. Drug-responsive amplicon plasticity is observed in the clinic and can involve other MAPK pathway genes, such as RAF1 and NRAS. BRAF FA-mediated dual MAPKi-resistant cells are more sensitive to proferroptotic drugs, extending the spectrum of ferroptosis sensitivity in MAPKi resistance beyond cases of dedifferentiation.

SIGNIFICANCE

Understanding the structure and dynamics of oncogene amplifications is critical for overcoming tumor relapse. BRAF amplifications are highly plastic under MAPKi dosage challenges in melanoma, through involvement of de novo genomic alterations, even in the HSR mode. Moreover, BRAF FA-driven, dual MAPKi-resistant cells extend the spectrum of resistance-linked ferroptosis sensitivity. This article is highlighted in the In This Issue feature, p. 873.

A synthetic gene circuit for imaging-free detection of signaling pulses

Cells employ intracellular signaling pathways to sense and respond to changes in their external environment. In recent years, live-cell biosensors have revealed complex pulsatile dynamics in many pathways, but studies of these signaling dynamics are limited by the necessity of live-cell imaging at high spatiotemporal resolution. Here, we describe an approach to infer pulsatile signaling dynamics from a single measurement in fixed cells using a pulse-detecting gene circuit. We computationally screened for circuits with the capability to selectively detect signaling pulses, revealing an incoherent feedforward topology that robustly performs this computation. We implemented the motif experimentally for the Erk signaling pathway using a single engineered transcription factor and fluorescent protein reporter. Our "recorder of Erk activity dynamics" (READer) responds sensitively to spontaneous and stimulus-driven Erk pulses. READer circuits open the door to permanently labeling transient, dynamic cell populations to elucidate the mechanistic underpinnings and biological consequences of signaling dynamics.

Independent Drug Action in Combination Therapy: Implications for Precision Oncology

Combination therapies are superior to monotherapy for many cancers. This advantage was historically ascribed to the ability of combinations to address tumor heterogeneity, but synergistic interaction is now a common explanation as well as a design criterion for new combinations. We review evidence that independent drug action, described in 1961, explains the efficacy of many practice-changing combination therapies: it provides populations of patients with heterogeneous drug sensitivities multiple chances of benefit from at least one drug. Understanding response heterogeneity could reveal predictive or pharmacodynamic biomarkers for more precise use of existing drugs and realize the benefits of additivity or synergy.

Confocal Imaging of Single-Cell Signaling in Orthotopic Models of Ovarian Cancer

Ovarian cancer (OVCA) is frequently detected at late stages of disease, often with dissemination throughout the peritoneal cavity surface, abdomen, and ascites fluid. Tumor signaling via mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K) pathways can promote OVCA progression and depend on local microenvironmental cues. To better study OVCA in situ within native tissue contexts, here we describe confocal microscopy techniques to image mouse models of intraperitoneal disease at a single-cell resolution. As a proof of principle demonstration, examples are highlighted for simultaneously imaging tumor vascularization, infiltrating and often immunosuppressive immune cells (tumor-associated macrophages), and OVCA kinase activity.

Combination treatment optimization using a pan-cancer pathway model

The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify candidates for novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of heterogeneous cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the drug combinations used in sequential treatment plans—that is, optimized sequences of potentially different drug combinations—providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be adapted to find treatment candidates for other complex diseases than cancers as well, as long as a suitable predictive model is available.

Combination therapies are a promising approach to counter complex diseases such as cancers. Two key difficulties in the design of effective cancer combination therapies are the large number of available drugs and the heterogeneity of cancers which render exhaustive laboratory studies impractical. In recent years, sophisticated signaling pathway models that can predict responses to combination treatments at a molecular level have been developed. This motivates the question of how one can leverage mechanistic models to identify candidates for novel combination treatments. In this paper we propose a combination treatment optimization framework which employs a large-scale pan-cancer pathway model. We formulate treatment optimization problems for single cell lines and heterogeneous populations of cancer cells. We further investigate sequential treatment plans and combine an existing evolutionary algorithm with an efficient Hamiltonian Monte-Carlo based sampling scheme. During extensive simulation studies our approach identified combination therapies which are predicted to be more effective than conventional treatments. We hope that one day in silico experiments will be used to identify a small set of promising treatment candidates which can then form a starting point for laboratory studies, allowing for an efficient use of limited resources and accelerated discovery of effective therapies.

Substratum stiffness regulates Erk signaling dynamics through receptor-level control

The EGFR/Erk pathway is triggered by extracellular ligand stimulation, leading to stimulus-dependent dynamics of pathway activity. Although mechanical properties of the microenvironment also affect Erk activity, their effects on Erk signaling dynamics are poorly understood. Here, we characterize how the stiffness of the underlying substratum affects Erk signaling dynamics in mammary epithelial cells. We find that soft microenvironments attenuate Erk signaling, both at steady state and in response to epidermal growth factor (EGF) stimulation. Optogenetic manipulation at multiple signaling nodes reveals that intracellular signal transmission is largely unaffected by substratum stiffness. Instead, we find that soft microenvironments decrease EGF receptor (EGFR) expression and alter the amount and spatial distribution of EGF binding at cell membranes. Our data demonstrate that the mechanical microenvironment tunes Erk signaling dynamics via receptor-ligand interactions, underscoring how multiple microenvironmental signals are jointly processed through a highly conserved pathway that regulates tissue development, homeostasis, and disease progression.

Analysing and simulating energy-based models in biology using BondGraphTools

Like all physical systems, biological systems are constrained by the laws of physics. However, mathematical models of biochemistry frequently neglect the conservation of energy, leading to unrealistic behaviour. Energy-based models that are consistent with conservation of mass, charge and energy have the potential to aid the understanding of complex interactions between biological components, and are becoming easier to develop with recent advances in experimental measurements and databases. In this paper, we motivate the use of bond graphs (a modelling tool from engineering) for energy-based modelling and introduce, BondGraphTools, a Python library for constructing and analysing bond graph models. We use examples from biochemistry to illustrate how BondGraphTools can be used to automate model construction in systems biology while maintaining consistency with the laws of physics.

WebMaBoSS: A Web Interface for Simulating Boolean Models Stochastically

WebMaBoSS is an easy-to-use web interface for conversion, storage, simulation and analysis of Boolean models that allows to get insight from these models without any specific knowledge of modeling or coding. It relies on an existing software, MaBoSS, which simulates Boolean models using a stochastic approach: it applies continuous time Markov processes over the Boolean network. It was initially built to fill the gap between Boolean and continuous formalisms, i.e., providing semi-quantitative results using a simple representation with a minimum number of parameters to fit. The goal of WebMaBoSS is to simplify the use and the analysis of Boolean models coping with two main issues: 1) the simulation of Boolean models of intracellular processes with MaBoSS, or any modeling tool, may appear as non-intuitive for non-experts; 2) the simulation of already-published models available in current model databases (e.g., Cell Collective, BioModels) may require some extra steps to ensure compatibility with modeling tools such as MaBoSS. With WebMaBoSS, new models can be created or imported directly from existing databases. They can then be simulated, modified and stored in personal folders. Model simulations are performed easily, results visualized interactively, and figures can be exported in a preferred format. Extensive model analyses such as mutant screening or parameter sensitivity can also be performed. For all these tasks, results are stored and can be subsequently filtered to look for specific outputs. This web interface can be accessed at the address: https://maboss.curie.fr/webmaboss/ and deployed locally using docker. This application is open-source under LGPL license, and available at https://github.com/sysbio-curie/WebMaBoSS.

Cell-Cell Communication Networks in Tissue: Toward Quantitatively Linking Structure with Function

Forefront techniques for molecular interrogation of mammalian tissues, such as multiplexed tissue imaging, intravital microscopy, and single-cell RNA sequencing (scRNAseq), can combine to quantify cell-type abundance, co-localization, and global levels of receptors and their ligands. Nonetheless, it remains challenging to translate these various quantities into a more comprehensive understanding of how cell-cell communication networks dynamically operate. Therefore, construction of computational models for network-level functions - including niche-dependent actions, homeostasis, and multi-scale coordination - will be valuable for productively integrating the battery of experimental approaches. Here, we review recent progress in understanding cell-cell communication networks in tissue. Featured examples include ligand-receptor dissection of immunosuppressive and mitogenic signaling in the tumor microenvironment. As a future direction, we highlight an unmet potential to bridge high-level statistical approaches with low-level physicochemical mechanisms.