A systematic analysis of signaling reactivation and drug resistance

Systems Biology of the Cancer Cell

Questions in cancer have engaged systems biologists for decades. During that time, the quantity of molecular data has exploded, but the need for abstractions, formal models, and simplifying insights has remained the same. This review brings together classic breakthroughs and recent findings in the field of cancer systems biology, focusing on cancer cell pathways for tumorigenesis and therapeutic response. Cancer cells mutate and transduce information from their environment to alter gene expression, metabolism, and phenotypic states. Understanding the molecular architectures that make each of these steps possible is a long-term goal of cancer systems biology pursued by iterating between quantitative models and experiments. We argue that such iteration is the best path to deploying targeted therapies intelligently so that each patient receives the maximum benefit for their cancer.

Molecular principles underlying aggressive cancers

Aggressive tumors pose ultra-challenges to drug resistance. Anti-cancer treatments are often unsuccessful, and single-cell technologies to rein drug resistance mechanisms are still fruitless. The National Cancer Institute defines aggressive cancers at the tissue level, describing them as those that spread rapidly, despite severe treatment. At the molecular, foundational level, the quantitative biophysics discipline defines aggressive cancers as harboring a large number of (overexpressed, or mutated) crucial signaling proteins in major proliferation pathways populating their active conformations, primed for their signal transduction roles. This comprehensive review explores highly aggressive cancers on the foundational and cell signaling levels, focusing on the differences between highly aggressive cancers and the more treatable ones. It showcases aggressive tumors as harboring massive, cancer-promoting, catalysis-primed oncogenic proteins, especially through certain overexpression scenarios, as predisposed aggressive tumor candidates. Our examples narrate strong activation of ERK1/2, and other oncogenic proteins, through malfunctioning chromatin and crosslinked signaling, and how they activate multiple proliferation pathways. They show the increased cancer heterogeneity, plasticity, and drug resistance. Our review formulates the principles underlying cancer aggressiveness on the molecular level, discusses scenarios, and describes drug regimen (single drugs and drug combinations) for PDAC, NSCLC, CRC, HCC, breast and prostate cancers, glioblastoma, neuroblastoma, and leukemia as examples. All show overexpression scenarios of master transcription factors, transcription factors with gene fusions, copy number alterations, dysregulation of the epigenetic codes and epithelial-to-mesenchymal transitions in aggressive tumors, as well as high mutation loads of vital upstream signaling regulators, such as EGFR, c-MET, and K-Ras, befitting these principles.

Towards an interpretable deep learning model of cancer

Cancer is a manifestation of dysfunctional cell states. It emerges from an interplay of intrinsic and extrinsic factors that disrupt cellular dynamics, including genetic and epigenetic alterations, as well as the tumor microenvironment. This complexity can make it challenging to infer molecular causes for treating the disease. This may be addressed by system-wide computer models of cells, as they allow rapid generation and testing of hypotheses that would be too slow or impossible to perform in the laboratory and clinic. However, so far, such models have been impeded by both experimental and computational limitations. In this perspective, we argue that they can now be achieved using deep learning algorithms to integrate omics data and prior knowledge of molecular networks. Such models would have many applications in precision oncology, e.g., for identifying drug targets and biomarkers, predicting resistance mechanisms and toxicity effects of drugs, or simulating cell-cell interactions in the microenvironment.

cSTAR analysis identifies endothelial cell cycle as a key regulator of flow-dependent artery remodeling

Fluid shear stress (FSS) from blood flow sensed by vascular endothelial cells (ECs) determines vessel behavior, but regulatory mechanisms are only partially understood. We used cell state transition assessment and regulation (cSTAR), a powerful computational method, to elucidate EC transcriptomic states under low shear stress (LSS), physiological shear stress (PSS), high shear stress (HSS), and oscillatory shear stress (OSS) that induce vessel inward remodeling, stabilization, outward remodeling, or disease susceptibility, respectively. Combined with a publicly available database on EC transcriptomic responses to drug treatments, this approach inferred a regulatory network controlling EC states and made several notable predictions. Particularly, inhibiting cell cycle-dependent kinase (CDK) 2 was predicted to initiate inward remodeling and promote atherogenesis. In vitro, PSS activated CDK2 and induced late G1 cell cycle arrest. In mice, EC deletion of CDK2 triggered inward artery remodeling, pulmonary and systemic hypertension, and accelerated atherosclerosis. These results validate use of cSTAR and identify key determinants of normal and pathological artery remodeling.

Ras, RhoA, and vascular pharmacology in neurodevelopment and aging

Small GTPases Ras, Rac, and RhoA are crucial regulators of cellular functions. They also act in dysregulated cell proliferation and transformation. Multiple publications have focused on illuminating their roles and mechanisms, including in immune system pathologies. Their functions in neurology-related diseases, neurodegeneration and neurodevelopment, are also emerging, as well as their potential as pharmacological targets in both pathologies. Observations increasingly suggest that these pathologies may relate to activation (or suppression) of signaling by members of the Ras superfamily, especially Ras, Rho, and Rac isoforms, and components of their signaling pathways. Germline (or embryonic) mutations that they harbor are responsible for neurodevelopmental disorders, such as RASopathies, autism spectrum disorder, and dilated cardiomyopathy. In aging, they promote neurodegenerative diseases, with Rho GTPase featuring in their pharmacology, as in the case of Alzheimer's disease (AD). Significantly, drugs with observed anti-AD activity, particularly those involved in cardiovascular systems, are associated with the RhoA signaling, as well as cerebral vasculature in brain development and aging. This leads us to suggest that anti-AD drugs could inform neurodevelopmental disorders, including pediatric low-grade gliomas pharmacology. Neurodevelopmental disorders associated with RhoA, like autism, are also connected with vascular systems, thus could be targets of vascular system-connected drugs.

Cell-specific models reveal conformation-specific RAF inhibitor combinations that synergistically inhibit ERK signaling in pancreatic cancer cells

Pancreatic ductal adenocarcinoma (PDAC) presents significant challenges for targeted clinical interventions due to prevalent KRAS mutations, rendering PDAC resistant to RAF and MEK inhibitors (RAFi and MEKi). In addition, responses to targeted therapies vary between patients. Here, we explored the differential sensitivities of PDAC cell lines to RAFi and MEKi and developed an isogenic pair comprising the most sensitive and resistant PDAC cells. To simulate patient- or tumor-specific variations, we constructed cell-line-specific mechanistic models based on protein expression profiling and differential properties of KRAS mutants. These models predicted synergy between two RAFi with different conformation specificity (type I½ and type II RAFi) in inhibiting phospho-ERK (ppERK) and reducing PDAC cell viability. This synergy was experimentally validated across all four studied PDAC cell lines. Our findings underscore the need for combination approaches to inhibit the ERK pathway in PDAC.

The value of protein allostery in rational anticancer drug design: an update

INTRODUCTION

Allosteric drugs are advantageous. However, they still face hurdles, including identification of allosteric sites that will effectively alter the active site. Current strategies largely focus on identifying pockets away from the active sites into which the allosteric ligand will dock and do not account for exactly how the active site is altered. Favorable allosteric inhibitors dock into sites that are nearby the active sites and follow nature, mimicking diverse allosteric regulation strategies.

AREAS COVERED

The following article underscores the immense significance of allostery in drug design, describes current allosteric strategies, and especially offers a direction going forward. The article concludes with the authors' expert perspectives on the subject.

EXPERT OPINION

To select a productive venue in allosteric inhibitor development, we should learn from nature. Currently, useful strategies follow this route. Consider, for example, the mechanisms exploited in relieving autoinhibition and in harnessing allosteric degraders. Mimicking compensatory, or rescue mutations may also fall into such a thesis, as can molecular glues that capture features of scaffolding proteins. Capturing nature and creatively tailoring its mimicry can continue to innovate allosteric drug discovery.

Research into overcoming drug resistance in lung cancer treatment using CRISPR-Cas9 technology: a narrative review

Background and Objective

Lung cancer remains a leading cause of cancer-related mortality globally, with drug resistance posing a significant challenge to effective treatment. The advent of clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 9 (CRISPR-Cas9) technology offers a novel and precise gene-editing technology for targeting and negating drug resistance mechanisms in lung cancer. This review summarizes the research progress in the use of CRISPR-Cas9 technology for investigating and managing drug resistance in lung cancer treatment.

Methods

A literature search was conducted using the Web of Science and PubMed databases, with the following keywords: [CRISPR-Cas9], [lung cancer], [drug resistance], [gene editing], and [gene therapy]. The search was limited to articles published in English from 2002 to September 2023. From the search results, studies that utilized CRISPR-Cas9 technology in the context of lung cancer drug resistance were selected for further analysis and summarize.

Key Content and Findings

CRISPR-Cas9 technology enables precise DNA-sequence editing, allowing for the targeted addition, deletion, or modification of genes. It has been applied to investigate drug resistance in lung cancer by focusing on key genes such as epidermal growth factor receptor (EGFR), Kirsten rat sarcoma viral oncogene homolog (KRAS), tumor protein 53 (TP53), and B-cell lymphoma/leukemia-2 (BCL2), among others. The technology has shown potential in inhibiting tumor growth, repairing mutations, and enhancing the sensitivity of cancer cells to chemotherapy. Additionally, CRISPR-Cas9 has been used to identify novel key genes and molecular mechanisms contributing to drug resistance, offering new avenues for therapeutic intervention. The review also highlights the use of CRISPR-Cas9 in targeting immune escape mechanisms and the development of strategies to improve drug sensitivity.

Conclusions

The CRISPR-Cas9 technology holds great promise for advancing lung cancer treatment, particularly in addressing drug resistance. The ability to precisely target and edit genes involved in resistance pathways offers a powerful tool for developing more effective and personalized therapies. While challenges remain in terms of delivery, safety, and ethical considerations, ongoing research and technological refinements are expected to further enhance the role of CRISPR-Cas9 in improving patient outcomes in lung cancer treatment.

Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets

Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors.

Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA damage response inhibitors

Combination therapy is well established as a key intervention strategy for cancer treatment, with the potential to overcome monotherapy resistance and deliver a more durable efficacy. However, given the scale of unexplored potential target space and the resulting combinatorial explosion, identifying efficacious drug combinations is a critical unmet need that is still evolving. In this paper, we demonstrate a network biology-driven, simulation-based solution, the Simulated Cell™. Integration of omics data with a curated signaling network enables the accurate and interpretable prediction of 66,348 combination-cell line pairs obtained from a large-scale combinatorial drug sensitivity screen of 684 combinations across 97 cancer cell lines (BAC = 0.62, AUC = 0.7). We highlight drug combination pairs that interact with DNA Damage Response pathways and are predicted to be synergistic, and deep network insight to identify biomarkers driving combination synergy. We demonstrate that the cancer cell 'avatars' capture the biological complexity of their in vitro counterparts, enabling the identification of pathway-level mechanisms of combination benefit to guide clinical translatability.

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.

Essential oils and their nanoformulations for breast cancer therapy

Breast Cancer (BC) is the most prevalent type of cancer in the world. Current treatments include surgery, radiation, and chemotherapy but often are associated with high toxicity to normal tissues, chemoresistance, and relapse. Thus, developing novel therapies which could combat these limitations is essential for effective treatment. In this context, phytochemicals are increasingly getting popular due to their safety profile, ability to efficiently target tumors, and circumvent limitations of existing treatments. Essential Oils (EOs) are mixtures of various phytochemicals which have shown potential anticancer activity in preclinical BC models. However, their clinical translation is limited by factors such as high volatility, low stability, and poor solubility. Nanotechnology has facilitated their encapsulation in a variety of nanostructures and proven to overcome these limitations. In this review, we have efficiently summarized the current knowledge on the anticancer effect of EOs and constituents in both in in vitro and in in vivo BC models. Further, we also provide a descriptive account on the potential of nanotechnology in enhancing the anti-BC activity of EOs and their constituents. The papers discussed in this review were selected using the keywords "antiproliferative Essential Oils in breast cancer," "anticancer activity of Essential Oil in breast cancer," and "cytotoxicity of Essential Oils in breast cancer" performed in PubMed and ScienceDirect databases.

DNA Sequencing of CD138 Cell Population Reveals TP53 and RAS-MAPK Mutations in Multiple Myeloma at Diagnosis

Multiple myeloma is a hematologic neoplasm caused by abnormal proliferation of plasma cells. Sequencing studies suggest that plasma cell disorders are caused by both cytogenetic abnormalities and oncogene mutations. Therefore, it is necessary to detect molecular abnormalities to improve the diagnosis and management of MM. The main purpose of this study is to determine whether NGS, in addition to cytogenetics, can influence risk stratification and management. Additionally, we aim to establish whether mutational analysis of the CD138 cell population is a suitable option for the characterization of MM compared to the bulk population. Following the separation of the plasma cells harvested from 35 patients newly diagnosed with MM, we performed a FISH analysis to detect the most common chromosomal abnormalities. Consecutively, we used NGS to evaluate NRAS, KRAS, BRAF, and TP53 mutations in plasma cell populations and in bone marrow samples. NGS data showed that sequencing CD138 cells provides a more sensitive approach. We identified several variants in BRAF, KRAS, and TP53 that were not previously associated with MM. Considering that the presence of somatic mutations could influence risk stratification and therapeutic approaches of patients with MM, sensitive detection of these mutations at diagnosis is essential for optimal management of MM.

Dose-response analysis after administration of a human platelet-derived exosome product on neurite outgrowth in vitro

Modulating the nerve's local microenvironment using exosomes is proposed to enhance nerve regeneration. This study aimed to determine the optimal dose of purified exosome product (PEP) required to exert maximal neurite extension. An in vitro dorsal root ganglion (DRG) neurite outgrowth assay was used to evaluate the effect of treatment with (i) 5% PEP, (ii) 10% PEP, (iii) 15% PEP, or (iv) 20% PEP on neurite extension (N = 9/group), compared to untreated controls. After 72 h, neurite extension was measured to quantify nerve regeneration. Live cell imaging was used to visualize neurite outgrowth during incubation. Treatment with 5% PEP resulted in the longest neurite extension and was superior to the untreated DRG (p = 0.003). Treatment with 10% PEP, 15% PEP, and 20% PEP was found to be comparable to controls (p = 0.12, p = 0.06, and p = 0.41, respectively) and each other. Live cell imaging suggested that PEP migrated towards the DRG neural regeneration site, compared to the persistent homogenous distribution of PEP in culture media alone. 5% PEP was found to be the optimal concentration for nerve regeneration based on this in vitro dose-response analysis.

cSTAR analysis identifies endothelial cell cycle as a key regulator of flow-dependent artery remodeling

Fluid shear stress (FSS) from blood flow is sensed by vascular endothelial cells (ECs) to determine vessel stability, remodeling and susceptibility to atherosclerosis and other inflammatory diseases but the regulatory networks that govern these behaviors are only partially understood. We used cSTAR, a powerful new computational method, to define EC transcriptomic states under low shear stress (LSS) that triggers vessel inward remodeling, physiological shear stress (PSS) that stabilizes vessels, high shear stress (HSS) that triggers outward remodeling, and oscillatory shear stress (OSS) that confers disease susceptibility, all in comparison to cells under static conditions (STAT). We combined these results with the LINCS database where EC transcriptomic responses to drug treatments to define a preliminary regulatory network in which the cyclin-dependent kinases CDK1/2 play a central role in promoting vessel stability. Experimental analysis showed that PSS induced a strong late G1 cell cycle arrest in which CDK2 was activated. EC deletion of CDK2 in mice resulted in inward artery remodeling and both pulmonary and systemic hypertension. These results validate use of cSTAR to determine EC state and in vivo vessel behavior, reveal unexpected features of EC phenotype under different FSS conditions, and identify CDK2 as a key element within the EC regulatory network that governs artery remodeling.

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.

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.

Spatiotemporal Coordination of Rac1 and Cdc42 at the Whole Cell Level during Cell Ruffling

Rho-GTPases are central regulators within a complex signaling network that controls cytoskeletal organization and cell movement. The network includes multiple GTPases, such as the most studied Rac1, Cdc42, and RhoA, along with their numerous effectors that provide mutual regulation through feedback loops. Here we investigate the temporal and spatial relationship between Rac1 and Cdc42 during membrane ruffling, using a simulation model that couples GTPase signaling with cell morphodynamics and captures the GTPase behavior observed with FRET-based biosensors. We show that membrane velocity is regulated by the kinetic rate of GTPase activation rather than the concentration of active GTPase. Our model captures both uniform and polarized ruffling. We also show that cell-type specific time delays between Rac1 and Cdc42 activation can be reproduced with a single signaling motif, in which the delay is controlled by feedback from Cdc42 to Rac1. The resolution of our simulation output matches those of time-lapsed recordings of cell dynamics and GTPase activity. Our data-driven modeling approach allows us to validate simulation results with quantitative precision using the same pipeline for the analysis of simulated and experimental data.

A personalized pharmaco-epistatic network model of precision medicine

Precision medicine, the utilization of targeted treatments to address an individual's disease, relies on knowledge about the genetic cause of that individual's drug response. Here, we present a functional graph (FunGraph) theory to chart comprehensive pharmacogenetic architecture for each and every patient. FunGraph is the combination of functional mapping - a dynamic model for genetic mapping and evolutionary game theory guiding interactive strategies. It coalesces all pharmacogenetic factors into multilayer and multiplex networks that fully capture bidirectional, signed and weighted epistasis. It can visualize and interrogate how epistasis moves in the cell and how this movement leads to patient- and context-specific genetic architecture in response to organismic physiology. We discuss the future implementation of FunGraph to achieve precision medicine. Teaser: We present a functional graph (FunGraph) theory to draw a complete picture of pharmacogenetic architecture underlying interindividual variability in drug response. FunGraph can characterize how each gene acts and interacts with every other gene to mediate therapeutic response.

Spatiotemporal coordination of Rac1 and Cdc42 at the whole cell level during cell ruffling

Rho-GTPases are central regulators within a complex signaling network that controls the cytoskeletal organization and cell movement. This network includes multiple GTPases, such as the most studied Rac1, Cdc42, and RhoA, and their numerous effectors that provide mutual regulation and feedback loops. Here we investigate the temporal and spatial relationship between Rac1 and Cdc42 during membrane ruffling using a simulation model which couples GTPase signaling with cell morphodynamics to capture the GTPase behavior observed with FRET-based biosensors. We show that membrane velocity is regulated by the kinetic rate of GTPase activation rather than the concentration of active GTPase. Our model captures both uniform and polarized ruffling. We also show that cell-type specific time delays between Rac1 and Cdc42 activation can be reproduced with a single signaling motif, in which the delay is controlled by feedback from Cdc42 to Rac1. The resolution of our simulation output matches those of the time-lapsed recordings of cell dynamics and GTPase activity. This approach allows us to validate simulation results with quantitative precision using the same pipeline for the analysis of simulated and experimental data.

Dynamic regulation of RAS and RAS signaling

RAS proteins regulate most aspects of cellular physiology. They are mutated in 30% of human cancers and 4% of developmental disorders termed Rasopathies. They cycle between active GTP-bound and inactive GDP-bound states. When active, they can interact with a wide range of effectors that control fundamental biochemical and biological processes. Emerging evidence suggests that RAS proteins are not simple on/off switches but sophisticated information processing devices that compute cell fate decisions by integrating external and internal cues. A critical component of this compute function is the dynamic regulation of RAS activation and downstream signaling that allows RAS to produce a rich and nuanced spectrum of biological outputs. We discuss recent findings how the dynamics of RAS and its downstream signaling is regulated. Starting from the structural and biochemical properties of wild-type and mutant RAS proteins and their activation cycle, we examine higher molecular assemblies, effector interactions and downstream signaling outputs, all under the aspect of dynamic regulation. We also consider how computational and mathematical modeling approaches contribute to analyze and understand the pleiotropic functions of RAS in health and disease.

Control of cell state transitions

Understanding cell state transitions and purposefully controlling them is a longstanding challenge in biology. Here we present cell state transition assessment and regulation (cSTAR), an approach for mapping cell states, modelling transitions between them and predicting targeted interventions to convert cell fate decisions. cSTAR uses omics data as input, classifies cell states, and develops a workflow that transforms the input data into mechanistic models that identify a core signalling network, which controls cell fate transitions by influencing whole-cell networks. By integrating signalling and phenotypic data, cSTAR models how cells manoeuvre in Waddington's landscape1 and make decisions about which cell fate to adopt. Notably, cSTAR devises interventions to control the movement of cells in Waddington's landscape. Testing cSTAR in a cellular model of differentiation and proliferation shows a high correlation between quantitative predictions and experimental data. Applying cSTAR to different types of perturbation and omics datasets, including single-cell data, demonstrates its flexibility and scalability and provides new biological insights. The ability of cSTAR to identify targeted perturbations that interconvert cell fates will enable designer approaches for manipulating cellular development pathways and mechanistically underpinned therapeutic interventions.

Cobimetinib Sensitizes Cervical Cancer to Paclitaxel via Suppressing Paclitaxel-Induced ERK Activation

<b><i>Introduction:</i></b> Chemoresistance remains the main cause of treatment failure in cervical cancer and novel therapeutic strategies are required. Cobimetinib, a potent yet selective inhibitor of MEK1 and 2, is currently used to treat melanoma clinically. In this work, we identified cobimetinib as a promising candidate for treating cervical cancer. <b><i>Methods:</i></b> The in vitro and in vivo efficacies of cobimetinib were examined using cervical cancer cell cultures and xenograft mouse model. Its combination with paclitaxel was analyzed using the combination index. Immunoblotting was performed on MAPK and ERK pathways. <b><i>Results:</i></b> Cobimetinib displays a potent anti-cervical cancer activity in a panel of cell lines regardless of cellular origin and HPV presence, and its combination with paclitaxel is synergistic in inhibiting cervical cancer cells. This is achieved by the growth inhibition and caspase-dependent apoptosis induction, through inhibiting MAPK/ERK activation. In addition, paclitaxel activates ERK in cervical cancer cells, and this can be reversed by cobimetinib. We finally confirm the efficacy of cobimetinib alone and its combination with paclitaxel in the cervical cancer xenograft mouse model. <b><i>Discussion/Conclusion:</i></b> Our preclinical findings will accelerate the initialization of clinical trials to use combination of cobimetinib and paclitaxel for treating cervical cancer. Our work also emphasizes the therapeutic value of targeting MAPK/ERK to overcome chemoresistance in cervical cancer.

Allostery, and how to define and measure signal transduction

Here we ask: What is productive signaling? How to define it, how to measure it, and most of all, what are the parameters that determine it? Further, what determines the strength of signaling from an upstream to a downstream node in a specific cell? These questions have either not been considered or not entirely resolved. The requirements for the signal to propagate downstream to activate (repress) transcription have not been considered either. Yet, the questions are pivotal to clarify, especially in diseases such as cancer where determination of signal propagation can point to cell proliferation and to emerging drug resistance, and to neurodevelopmental disorders, such as RASopathy, autism, attention-deficit/hyperactivity disorder (ADHD), and cerebral palsy. Here we propose a framework for signal transduction from an upstream to a downstream node addressing these questions. Defining cellular processes, experimentally measuring them, and devising powerful computational AI-powered algorithms that exploit the measurements, are essential for quantitative science.

Targeting KRAS mutant lung cancer: light at the end of the tunnel

For decades, KRAS mutant lung adenocarcinomas (LUAD) have been refractory to therapeutic strategies based on personalized medicine owing to the complexity of designing inhibitors to selectively target KRAS and downstream targets with acceptable toxicities. The recent development of selective KRAS^G12C inhibitors represents a landmark after 40 years of intense research efforts since the identification of KRAS as a human oncogene. Here, we discuss the mechanisms responsible for the rapid development of resistance to these inhibitors, as well as potential strategies to overcome this limitation. Other therapeutic strategies aimed at inhibiting KRAS oncogenic signaling by targeting either upstream activators or downstream effectors are also reviewed. Finally, we discuss the effect of targeting the mitogen‐activated protein kinase (MAPK) pathway, both based on the failure of MEK and ERK inhibitors in clinical trials, as well as on the recent identification of RAF1 as a potential target due to its MAPK‐independent activity. These new developments, taken together, are likely to open new avenues to effectively treat KRAS mutant LUAD.

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.