Immune Environment and Osteosarcoma

Heymann, Marie-Françoise; Heymann, Dominique

doi:10.5772/67448

Cited by 6 publications

(9 citation statements)

References 91 publications

(88 reference statements)

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“…On the other hand, activated M2-like macrophages exhibited low antitumour activity, and growth of tumour cells was only reduced when the anti-EGFR antibody cetuximab was co-administered to cancer cells in a process involving antibody-dependent cell-mediated phagocytosis [ 179 ]. The main role of liposomal mifamurtide may be inducing the expression of proinflammatory cytokines that lead to an M1 macrophage response [ 180 ].…”

Section: Nanomedicines To Modulate the Tme And Icdmentioning

confidence: 99%

Liposomal Formulations to Modulate the Tumour Microenvironment and Antitumour Immune Response

Gilabert-Oriol¹,

Ryan²,

Leung

et al. 2018

IJMS

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Tumours are complex systems of genetically diverse malignant cells that proliferate in the presence of a heterogeneous microenvironment consisting of host derived microvasculature, stromal, and immune cells. The components of the tumour microenvironment (TME) communicate with each other and with cancer cells, to regulate cellular processes that can inhibit, as well as enhance, tumour growth. Therapeutic strategies have been developed to modulate the TME and cancer-associated immune response. However, modulating compounds are often insoluble (aqueous solubility of less than 1 mg/mL) and have suboptimal pharmacokinetics that prevent therapeutically relevant drug concentrations from reaching the appropriate sites within the tumour. Nanomedicines and, in particular, liposomal formulations of relevant drug candidates, define clinically meaningful drug delivery systems that have the potential to ensure that the right drug candidate is delivered to the right area within tumours at the right time. Following encapsulation in liposomes, drug candidates often display extended plasma half-lives, higher plasma concentrations and may accumulate directly in the tumour tissue. Liposomes can normalise the tumour blood vessel structure and enhance the immunogenicity of tumour cell death; relatively unrecognised impacts associated with using liposomal formulations. This review describes liposomal formulations that affect components of the TME. A focus is placed on formulations which are approved for use in the clinic. The concept of tumour immunogenicity, and how liposomes may enhance radiation and chemotherapy-induced immunogenic cell death (ICD), is discussed. Liposomes are currently an indispensable tool in the treatment of cancer, and their contribution to cancer therapy may gain even further importance by incorporating modulators of the TME and the cancer-associated immune response.

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Section: Nanomedicines To Modulate the Tme And Icdmentioning

confidence: 99%

Liposomal Formulations to Modulate the Tumour Microenvironment and Antitumour Immune Response

Gilabert-Oriol¹,

Ryan²,

Leung

et al. 2018

IJMS

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“…In general, we found that high levels of CD8 T cells and M1 Macrophages are associated with good prognosis, while a high level of M0 Macrophages correlates with poor prognosis in osteosarcoma. These results make sense because CD8 T cells are known to kill cancer cells directly [ 47 , 48 ] and M1 Macrophages exhibit anti-tumor effects by producing cytokines that inhibit osteosarcoma growth [ 49 ]. These facts could also explain the observed differences between the outcomes of patients in clusters 2 and 3.…”

Section: Discussionmentioning

confidence: 99%

Immune classification of osteosarcoma

Shahriyari

2021

MBE

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Tumor immune microenvironment has been shown to be important in predicting the tumor progression and the outcome of treatments. This work aims to identify different immune patterns in osteosarcoma and their clinical characteristics. We use the latest and best performing deconvolution method, CIBERSORTx, to obtain the relative abundance of 22 immune cells. Then we cluster patients based on their estimated immune abundance and study the characteristics of these clusters, along with the relationship between immune infiltration and outcome of patients. We find that abundance of CD8 T cells, NK cells and M1 Macrophages have a positive association with prognosis, while abundance of γδ T cells, Mast cells, M0 Macrophages and Dendritic cells have a negative association with prognosis. Accordingly, the cluster with the lowest proportion of CD8 T cells, M1 Macrophages and highest proportion of M0 Macrophages has the worst outcome among clusters. By grouping patients with similar immune patterns, we are also able to suggest treatments that are specific to the tumor microenvironment.

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“…TGF-

and IL-10 are secreted by helper T cells, M2 macrophages, and cancer cells [ 32 , 33 , 35 , 65 , 66 , 67 ]. IL-4 and IL-13 are secreted by helper T cells and M2 macrophages [ 32 , 65 , 68 ]. Thus, we model the dynamics of

as:

…”

Section: Methodsmentioning

confidence: 99%

“…

consists of IL-6 and IL-17, where IL-6 is produced by M1 macrophages, helper T cells, and cancer cells [ 33 , 36 , 65 , 67 , 69 ], and IL-17 is produced by helper T cells [ 32 ]. The corresponding equation for

is:

…”

Section: Methodsmentioning

confidence: 99%

“…We use the quasi-equilibrium state assumption on the other cytokines and estimate them to be proportional to the number of cells that produce them. IL-12 and IL-23 are both secreted by M1 macrophages and dendritic cells [ 29 , 32 , 65 , 66 , 67 , 78 ]; therefore, we model the concentration of these cytokines as:

where

, and

are constants.…”

Section: Methodsmentioning

confidence: 99%

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Data-Driven Mathematical Model of Osteosarcoma

Kirshtein

et al. 2021

Cancers

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As the immune system has a significant role in tumor progression, in this paper, we develop a data-driven mathematical model to study the interactions between immune cells and the osteosarcoma microenvironment. Osteosarcoma tumors are divided into three clusters based on their relative abundance of immune cells as estimated from their gene expression profiles. We then analyze the tumor progression and effects of the immune system on cancer growth in each cluster. Cluster 3, which had approximately the same number of naive and M2 macrophages, had the slowest tumor growth, and cluster 2, with the highest population of naive macrophages, had the highest cancer population at the steady states. We also found that the fastest growth of cancer occurred when the anti-tumor immune cells and cytokines, including dendritic cells, helper T cells, cytotoxic cells, and IFN-γ, switched from increasing to decreasing, while the dynamics of regulatory T cells switched from decreasing to increasing. Importantly, the most impactful immune parameters on the number of cancer and total cells were the activation and decay rates of the macrophages and regulatory T cells for all clusters. This work presents the first osteosarcoma progression model, which can be later extended to investigate the effectiveness of various osteosarcoma treatments.

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Immune Environment and Osteosarcoma

Cited by 6 publications

References 91 publications

Liposomal Formulations to Modulate the Tumour Microenvironment and Antitumour Immune Response

Liposomal Formulations to Modulate the Tumour Microenvironment and Antitumour Immune Response

Immune classification of osteosarcoma

Data-Driven Mathematical Model of Osteosarcoma

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