The extracellular matrix (ECM) is a salient feature of all solid tissues within the body. This complex, acellular entity is composed of hundreds of individual molecules whose assembly, architecture ...and biomechanical properties are critical to controlling the behaviour and phenotype of the different cell types residing within tissues. Cells are the basic unit of life and the core building block of tissues and organs. At their simplest, they follow a set of rules, governed by their genetic code and effected through the complex protein signalling networks that these genes encode. These signalling networks assimilate and process the information received by the cell to control cellular decisions that govern cell fate. The ECM is the biggest provider of external stimuli to cells and as such is responsible for influencing intracellular signalling dynamics. In this review, we discuss the inclusion of ECM as a central regulatory signalling sub‐network in computational models of cellular decision making, with a focus on its role in diseases such as cancer.
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This article is part of a themed section on Translating the Matrix. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.1/issuetoc
Abstract
The tumour stroma, and in particular the extracellular matrix (ECM), is a salient feature of solid tumours that plays a crucial role in shaping their progression. Many desmoplastic tumours ...including breast cancer involve the significant accumulation of type I collagen. However, recently it has become clear that the precise distribution and organisation of matrix molecules such as collagen I is equally as important in the tumour as their abundance. Cancer-associated fibroblasts (CAFs) coexist within breast cancer tissues and play both pro- and anti-tumourigenic roles through remodelling the ECM. Here, using temporal proteomic profiling of decellularized tumours, we interrogate the evolving matrisome during breast cancer progression. We identify 4 key matrisomal clusters, and pinpoint collagen type XII as a critical component that regulates collagen type I organisation. Through combining our proteomics with single-cell transcriptomics, and genetic manipulation models, we show how CAF-secreted collagen XII alters collagen I organisation to create a pro-invasive microenvironment supporting metastatic dissemination. Finally, we show in patient cohorts that collagen XII may represent an indicator of breast cancer patients at high risk of metastatic relapse.
Signaling pathways control cell fate decisions that ultimately determine the behavior of cancer cells. Therefore, the dynamics of pathway activity may contain prognostically relevant information ...different from that contained in the static nature of other types of biomarkers. To investigate this hypothesis, we characterized the network that regulated stress signaling by the c-Jun N-terminal kinase (JNK) pathway in neuroblastoma cells. We generated an experimentally calibrated and validated computational model of this network and used the model to extract prognostic information from neuroblastoma patient-specific simulations of JNK activation. Switch-like JNK activation mediates cell death by apoptosis. An inability to initiate switch-like JNK activation in the simulations was significantly associated with poor overall survival for patients with neuroblastoma with or without MYCN amplification, indicating that patient-specific simulations of JNK activation could stratify patients. Furthermore, our analysis demonstrated that extracting information about a signaling pathway to develop a prognostically useful model requires understanding of not only components and disease-associated changes in the abundance or activity of the components but also how those changes affect pathway dynamics.
As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to ...leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.
The dynamic assembly of multiprotein complexes is a central mechanism of many cell signaling pathways. This process is key to maintaining the spatiotemporal specificity required for an accurate, yet ...adaptive, response to rapidly changing cellular conditions. We describe a technique for the specific isolation and downstream proteomic characterization of any two interacting proteins, to the exclusion of their individual moieties and competing binding partners. We termed the approach bimolecular complementation affinity purification (BiCAP) because it combines the use of conformation-specific nanobodies with a protein-fragment complementation assay with affinity purification. Using BiCAP, we characterized the specific interactome of the epidermal growth factor receptor (EGFR) family member ERBB2 when in the form of a homodimer or when in the form of a heterodimer with either EGFR or ERBB3. We identified dimer-specific interaction patterns for key adaptor proteins and identified a number of previously unknown interacting partners. Functional analysis for one of these newly identified partners revealed a noncanonical mechanism of extracellular signal-regulated kinase (ERK) activation that is specific to the ERBB2:ERBB3 heterodimer and acts through the adaptor protein FAM59A in breast cancer cells.
The oncogenic receptor tyrosine kinase (RTK) ERBB2 is known to dimerize with other EGFR family members, particularly ERBB3, through which it potently activates PI3K signalling. Antibody-mediated ...inhibition of this ERBB2/ERBB3/PI3K axis has been a cornerstone of treatment for ERBB2-amplified breast cancer patients for two decades. However, the lack of response and the rapid onset of relapse in many patients now question the assumption that the ERBB2/ERBB3 heterodimer is the sole relevant effector target of these therapies.
Through a systematic protein-protein interaction screen, we have identified and validated alternative RTKs that interact with ERBB2. Using quantitative readouts of signalling pathway activation and cell proliferation, we have examined their influence upon the mechanism of trastuzumab- and pertuzumab-mediated inhibition of cell growth in ERBB2-amplified breast cancer cell lines and a patient-derived xenograft model.
We now demonstrate that inactivation of ERBB3/PI3K by these therapeutic antibodies is insufficient to inhibit the growth of ERBB2-amplified breast cancer cells. Instead, we show extensive promiscuity between ERBB2 and an array of RTKs from outside of the EGFR family. Paradoxically, pertuzumab also acts as an artificial ligand to promote ERBB2 activation and ERK signalling, through allosteric activation by a subset of these non-canonical RTKs. However, this unexpected activation mechanism also increases the sensitivity of the receptor network to the ERBB2 kinase inhibitor lapatinib, which in combination with pertuzumab, displays a synergistic effect in single-agent resistant cell lines and PDX models.
The interaction of ERBB2 with a number of non-canonical RTKs activates a compensatory signalling response following treatment with pertuzumab, although a counter-intuitive combination of ERBB2 antibody therapy and a kinase inhibitor can overcome this innate therapeutic resistance.
We previously used a pulse-based
assay to unveil targetable signalling pathways associated with innate cisplatin resistance in lung adenocarcinoma (Hastings et al., 2020). Here we advanced this model ...system and identified a non-genetic mechanism of resistance that drives recovery and regrowth in a subset of cells. Using RNAseq and a suite of biosensors to track single cell fates both
and
we identified that early S phase cells have a greater ability to maintain proliferative capacity, which correlated with reduced DNA damage over multiple generations. In contrast, cells in G1, late S or those treated with PARP/RAD51 inhibitors, maintained higher levels of DNA damage and underwent prolonged S/G2 phase arrest and senescence. Combined with our previous work, these data indicate that there is a non-genetic mechanism of resistance in human lung adenocarcinoma that is dependent on the cell cycle stage at the time of cisplatin exposure.
High-risk neuroblastoma is an aggressive childhood cancer that is characterized by high rates of chemoresistance and frequent metastatic relapse. A number of studies have characterized the genetic ...and epigenetic landscape of neuroblastoma, but due to a generally low mutational burden and paucity of actionable mutations, there are few options for applying a comprehensive personalized medicine approach through the use of targeted therapies. Therefore, the use of multi-agent chemotherapy remains the current standard of care for neuroblastoma, which also conceptually limits the opportunities for developing an effective and widely applicable personalized medicine approach for this disease. However, in this review we outline potential approaches for tailoring the use of chemotherapy agents to the specific molecular characteristics of individual tumours by performing patient-specific simulations of drug-induced apoptotic signalling. By incorporating multiple layers of information about tumour-specific aberrations, including expression as well as mutation data, these models have the potential to rationalize the selection of chemotherapeutics contained within multi-agent treatment regimens and ensure the optimum response is achieved for each individual patient.
Each member of the epidermal growth factor receptor (EGFR) family plays a key role in normal development, homeostasis, and a variety of pathophysiological conditions, most notably in cancer. ...According to the prevailing dogma, these four receptor tyrosine kinases (RTKs; EGFR, ERBB2, ERBB3, and ERBB4) function exclusively through the formation of homodimers and heterodimers within the EGFR family. These combinatorial receptor interactions are known to generate increased interactome diversity and therefore influence signaling output, subcellular localization and function of the heterodimer. This molecular plasticity is also thought to play a role in the development of resistance toward targeted cancer therapies aimed at these known oncogenes. Interestingly, many studies now challenge this dogma and suggest that the potential for EGFR family receptors to interact with more distantly related RTKs is much greater than currently appreciated. Here we discuss how the promiscuity of these oncogenic receptors may lead to the formation of many unexpected receptor pairings and the significant implications for the efficiency of many targeted cancer therapies.
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