Impact of extracellular matrix stiffness on genomic heterogeneity in MYCN-amplified neuroblastoma cell line

López-Carrasco, Amparo; Martín-Vañó, Susana; Burgos‐Panadero, Rebeca; Monferrer, Ezequiel; Berbegall, Ana P.; Fernández-Blanco, Beatriz; Navarro, Samuel; Noguera, Rosa

doi:10.21203/rs.3.rs-59368/v2

Cited by 3 publications

(4 citation statements)

References 76 publications

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“…Cellular stresses caused by 2D cell culture induces increased chromosomal instability, which is rescued in 3D model systems and is dependent on integrins [147]. Additionally, cancer cells cultured using stiff hydrogels have increased chromosomal instability [148]. The composition of the extracellular matrix, which determines microenvironmental characteristics of the tumor, such as stiffness, is mediated by cancer-associated fibroblasts (CAFs) [149].…”

Section: Modulation Of the Microenvironment To Escape Immune Clearancementioning

confidence: 99%

When breaks get hot: inflammatory signaling in BRCA1/2-mutant cancers

Vugt

Parkes

2022

Trends in Cancer

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Genomic instability and inflammation are intricately connected hallmark features of cancer. DNA repair defects due to BRCA1/2 mutation instigate immune signaling through the cGAS/STING pathway. The subsequent inflammatory signaling provides both tumor-suppressive as well as tumor-promoting traits. To prevent clearance by the immune system, genomically instable cancer cells need to adapt to escape immune surveillance. Currently, it is unclear how genomically unstable cancers, including BRCA1/2-mutant tumors, are rewired to escape immune clearance. Here, we summarize the mechanisms by which genomic instability triggers inflammatory signaling and describe adaptive mechanisms by which cancer cells can 'fly under the radar' of the immune system. Additionally, we discuss how therapeutic activation of the immune system may improve treatment of genomically instable cancers. BRCA1/2 and genomic instability in cancer

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Section: Modulation Of the Microenvironment To Escape Immune Clearancementioning

confidence: 99%

When breaks get hot: inflammatory signaling in BRCA1/2-mutant cancers

Vugt

Parkes

2022

Trends in Cancer

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“…Further, as before, ρ > 0 is the ECM degradation rate, and µ v ≥ 0 is the ECM remodelling rate. Finally, as the mutation rate from cancer cells subpopulation c 1 into subpopulation c 2 , Q (v, t), is dependent on both time and the ECM density levels [33], we adopt for this the modelling proposed [3,15], and so mathematically we formalise this as…”

Section: Extended Tumour Invasion Model With Two Cancer Cells Subpopulationsmentioning

confidence: 99%

Inverse Reconstruction of Cell Proliferation Laws in Cancer Invasion Modelling

Alwuthaynani

Trucu²

2021

Math Appl Sci Eng

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The process of local cancer cell invasion of the surrounding tissue is key for the overall tumour growth and spread within the human body, the past 3 decades witnessing intense mathematical modelling efforts in these regards. However, for a deep understanding of the cancer invasion process these modelling studies require robust data assimilation approaches. While being of crucial importance in assimilating potential clinical data, the inverse problems approaches in cancer modelling are still in their early stages, with questions regarding the retrieval of the characteristics of tumour cells motility, cells mutations, and cells population proliferation, remaining widely open. This study deals with the identification and reconstruction of the usually unknown cancer cell proliferation law in cancer modelling from macroscopic tumour snapshot data collected at some later stage in the tumour evolution. Considering two basic tumour configurations, associated with the case of one cancer cells population and two cancer cells subpopulations that exercise their dynamics within the extracellular matrix, we combine Tikhonov regularisation and gaussian mollification approaches with finite element and finite differences approximations to reconstruct the proliferation laws for each of these sub-populations from both exact and noisy measurements. Our inverse problem formulation is accompanied by numerical examples for the reconstruction of several proliferation laws used in cancer growth modelling.

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“…The mutation law Q(•, •) is considerd here unknown due to either unknown dependance on the primary cell population c 1 , or unknown dependance on the ECM v, or unknown dependance on both primary tumour cell population and ECM. In this study we investigate three assumptions related to this mutation term, namely: (i) mutation depends linearly on the density of primary tumour cells; (ii) mutation depends linearly on the density of primary tumour cells, and nonlinearly on the ECM density [22]; (iii) mutation law is very general and depends autonomously on the primary tumour and ECM. Thus mathematically, these cases correspond to three inverse problems that seek to identify the unknown mutation law Q(•, •) in the following three situations:…”

Section: Mathematical Model For Two Local Cancer Cell Sub-populationsmentioning

confidence: 99%

“…Experimental studies have shown that some changes in the extracellular matrix (ECM) can correlate with sustained cell proliferative signalling and an increased risk of developing cancer [28]. Other studies have shown that culturing cells for long times in stiff hydrogels can lead to the subclonal selection of genomic aberrations in cells [21], thus suggesting that the ECM properties could impact the mutation status of cells in solid tumours. Other studies suggested that the maintenance of cells at high density in the absence of proliferation leads to an increase in mutagenesis following cell division [18].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Mutation Laws in Heterogeneous Tumours with Local and Nonlocal Dynamics

Alwuthaynani¹,

Eftimie²,

Trucu³

2021

Preprint

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Cancer cell mutations occur when cells undergo multiple cell divisions, and these mutations can be spontaneous or environmentally-induced. The mechanisms that promote and sustain these mutations are still not fully understood. This study deals with the identification (or reconstruction) of the usually unknown cancer cell mutation law, which lead to the transformation of a primary tumour cell population into a secondary, more aggressive cell population. We focus on local and nonlocal mathematical models for cell dynamics and movement, and identify these mutation laws from macroscopic tumour snapshot data collected at some later stage in the tumour evolution. In a local cancer invasion model, we first reconstruct the mutation law when we assume that the mutations depend only on the surrounding cancer cells (i.e., the ECM plays no role in mutations). Second, we assume that the mutations depend on the ECM only, and we reconstruct the mutation law in this case. Third, we reconstruct the mutation when we assume that there is no prior knowledge about the mutations. Finally, for the nonlocal cancer invasion model, we reconstruct the mutation law that depends on the cancer cells and on the ECM. For these numerical reconstructions, our approximations are based on the finite difference method combined with the finite elements method. As the inverse problem is ill-posed, we use the Tikhonov regularisation technique in order to regularise the solution. Stability of the solution is examined by adding additive noise into the measurements.

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Impact of extracellular matrix stiffness on genomic heterogeneity in MYCN-amplified neuroblastoma cell line

Cited by 3 publications

References 76 publications

When breaks get hot: inflammatory signaling in BRCA1/2-mutant cancers

When breaks get hot: inflammatory signaling in BRCA1/2-mutant cancers

Inverse Reconstruction of Cell Proliferation Laws in Cancer Invasion Modelling

Reconstruction of Mutation Laws in Heterogeneous Tumours with Local and Nonlocal Dynamics

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