What are the characteristics of cancer cells that can metastasize to the brain?

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What are the characteristics of cancer cells that can metastasize to the brain?

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“Cell”: What are the characteristics of cancer cells that can metastasize to the brain?

Brain metastases (BrM) refer to a disease in which tumor cells originating in other parts of the body metastasize to the brain, causing tumors to grow in the brain. Brain metastases from lung cancer are the most common, followed by breast cancer and melanoma.

According to statistics, the probability of developing brain metastases in cancer patients is as high as 20%-40% [1]. Even with active comprehensive treatment, the median survival rates of patients with brain metastases at 2 and 5 years are only 8.1% and 2.4%. [2].

Unfortunately, little is known in the medical community about the molecular mechanisms by which tumor cells migrate and colonize the brain , as well as a lack of detailed information on the cellular composition and functional status of brain metastases, which hinders researchers from studying brain metastases. Research in depth.

Recently, the team of Prof. Hugo Gonzalez from the University of California, San Francisco, by single-cell sequencing (scRNA-seq) of brain metastases tissue, combined with mass cytometry (CyTOF) and other technologies, revealed that brain metastases cells have proliferation and inflammation as a function of proliferation and inflammation.

There are two main cell states, and the relationship between immune cells in the matrix and the cell microenvironment is summarized and analyzed, which is helpful for the understanding of the characteristics of brain metastases and their interaction with the cell microenvironment . The related results were published in ” Cell” magazine [3].

The researchers collected primary tumors as melanoma (n=3), breast cancer (n=3), lung cancer (n=3), ovarian cancer (n=2), colorectal cancer (n=1), kidney cancer Brain metastases from 15 patients (n=1), adult rhabdomyosarcoma (n=1), and unidentified primary cancer (n=1) were sequenced by single-cell sequencing, and 11 of them were sequenced based on immunological tables. mass spectrometry analysis.

Finally , a total of 80,377 single-cell transcripts were captured , and cells were annotated by evaluating marker expression, chromosomal aberrations, copy number variations, etc., and labeled as 49,488 brain metastases cells (MTCs) and 30,889 brain metastases-related non-malignant stromal cells .

Interestingly, tumor stromal cells from different patients formed multiple aggregated clusters in the dimensionality reduction map (UMAP), while brain metastases cells from each patient formed independent clusters, and the difference between each case was different. There was no overlap, suggesting a high degree of interpatient heterogeneity of brain metastases (Figure 1).

▲ Figure 1 scRNA-seq and CyTOF were performed on brain metastases from 15 patients, dimensionality reduction was performed by UMAP, and cells were annotated

The researchers first analyzed the stromal cells in the tumors and found that they were mainly composed of vascular cells (endothelial and parietal cells), inflammatory immune cells and mesenchymal progenitor cells , and identified 20 distinct clusters based on the expression of marker genes. (figure 2).

▲ Figure 2 Stromal cells in tumors: endothelial cells (EC-1, EC-2 and EC-3 clusters) with marker genes CLDN5 and PECAM1, parietal cells (PC) and vascular smooth muscle cells with marker genes RGS5 and ACTA2 ( vSMCs, PC-1, PC-2, PC-3 clusters), mesenchymal stromal cell-like cells (MSC-like-1 and MSC-like-2) with marker genes ISLR and CTHRC1, with marker genes CD3D and IL7R T cells (T:CD8+:EM, T:CD4+:CM1, T:CD4+:cm2, Tregs and T:Cm), B cells with marker genes JCHAIN ​​and MZB1 (B-c1 and B-c2), with marker genes Metastasis-associated macrophages with genes AIF1 and LYZ (MAMs: APOE+ and MAMs: S100A8+), dendritic cells (DC, cDC2: CD1C+/CLEC10A+) with marker genes CD1C and CLEC10A, and asterisks with marker genes GFAP and S100B Glial cells

Vascular cells in the stroma constitute the blood-tumor interface (BTI) and are closely related to chemotherapy resistance in patients with brain metastases.

According to scRNA-seq results, vascular endothelial cells are mainly divided into three clusters (EC-1, EC-2 and EC-3), among which EC-3 is arterial-like endothelial cells (marker genes are EFNB2 and GJA5), EC-2 For vein-like endothelial cells (marker genes ACKR1 and NR2F2), EC-1 is in between.

The EC-1 cluster cells expressed genes related to processes such as angiogenesis and collagen deposition, while the EC-2 cluster expressed genes related to hypoxia, inflammation and antigen presentation .

In addition, we detected six multispecific ATP-binding cassettes (ABCs) in endothelial cells, including ABCB1 (i.e., multidrug resistance protein 1, MDR) and ABCG2, both transporters of normal blood-brain barrier A key molecule in venous-like (EC-2 cluster) endothelial cells was significantly overexpressed in areas where endothelial cells converged (Fig. 3).

▲ Figure 3 Six multispecific ATP-binding cassettes (ABC) were detected in endothelial cells, and ABCB1 and ABCG2 were significantly overexpressed in the area where vein-like (EC-2 cluster, red) endothelial cells converged

The researchers then analyzed the characteristics of infiltrating immune cells in brain metastases and found that the immune cells in brain metastases were mainly T cells and macrophages, among which T cells showed higher heterogeneity , mainly composed of five Cluster composition: CD8+ effector memory T cells (T:CD8+:EM cluster, representing CD8+ effector memory T cells, and so on below) , central memory T cells (T:CD4+:CM1 cluster, T:CD4+:CM2 cluster and T:CD4+:CM2 cluster CM clusters) and regulatory T cells (Tregs clusters) .

These cells have different immune states and functions. For example, the T:CD8+:EM cluster is rich in cytotoxic molecules (such as GZMA and IFNG), and the T:CD4+:CM cluster represents a high expression of immunomodulatory markers such as LTB and IL32. The T:CM clusters expressing low levels of CD4 and CD8A, although also expressing LTB and IL32, are also enriched for interferon-related genes such as ISG15 and MX1.

To better understand these T cell state and functional transitions, researchers used a nonlinear dimensionality reduction technique to capture the geometric and functional states in high-dimensional data [4]. This analysis showed that the five T-cell clusters were ordered by diffusive component 1 (DC1) fraction.

Among the top 50 genes positively correlated with DC1, there are molecules related to lymphocyte activation (such as CORO1A and LCK), molecules related to chemotaxis and migration (such as CCR7 and CXCR4), and these top 50 DC1 related genes There was a strong correlation between the expression of and the T cell activation gene cluster (GO: 0042110), suggesting that differences in T cell activation status drive T cell diversity. (Figure 4)

▲ Fig. 4 The five T cell clusters are ordered according to the diffusion component 1 (DC1) score, and there is a strong correlation between the expression of the top 50 DC1-related genes and the T cell activation gene cluster (GO: 0042110).

To understand the role of each T cell state in relation to the cellular microenvironment, the researchers assessed the expression of genes related to the regulatory microenvironment and metabolism, and found that interferon signaling-related genes dominated the T:Cm cluster and were associated with high T cell activation. Relevant depletion signatures were enriched in the T:CD8+:EM and T:CD4+:CM clusters, whereas those T-cell clusters with lower DC1 scores (T:CD4+:cm2, Treg and T:Cm) showed a strong absence of depletion. functional characteristics.

Furthermore, this phenotypic transition from activated T cells to nonfunctional T cells coincides with a shift in cellular metabolism from predominantly glycolysis and the TCA cycle to predominantly lipid metabolism , revealing that T cells Cell functional state (from activated/depleted to non-functioning), associated with concomitant metabolic and microenvironmental reprogramming.

Finally, the researchers also studied the main body of brain metastases-tumor cells. Through the method of non-negative matrix factorization (NMFS) [5], two types of potential tumor cell subsets were identified, and the external data sets and The same phenomenon was found in animal models of brain metastases: a group of genes related to proliferation and pre-mRNA splicing were highly expressed, and another group of genes related to stress, inflammation, translation and extracellular matrix deposition was highly expressed . The generation of these two types of cell populations is likely related to the extracellular matrix.

The study also has certain limitations, due to the lack of matching data on extracranial primary tumors, it is impossible to study the differences and associations between brain metastases and primary tumors.

Overall, this study provides a comprehensive analysis of cell types and cellular functional status in brain metastases , deepening the understanding of tumor cell-intrinsic properties and cellular An understanding of the interplay of microenvironmental properties in tumor brain metastases will aid in the development of future therapies targeting brain metastases.

references

1.Kamp MA, Slotty PJ, Cornelius JF, Steiger HJ, Rapp M, Sabel M: The impact of cerebral metastases growth pattern on neurosurgical treatment. Neurosurg Rev 2018, 41(1):77-86.

2.Achrol AS, Rennert RC, Anders C, Soffietti R, Ahluwalia MS, Nayak L, Peters S, Arvold ND, Harsh GR, Steeg PS et al: Brain metastases. Nat Rev Dis Primers 2019, 5(1):5.

3.Gonzalez H, Mei W, Robles I, Hagerling C, Allen BM, Hauge Okholm TL, Nanjaraj A, Verbeek T, Kalavacherla S, van Gogh M et al: Cellular architecture of human brain metastases. Cell 2022, 185 (4): 729-745 e720.

4.Haghverdi L, Buettner F, Theis FJ: Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 2015, 31(18):2989-2998.

5.Puram SV, Tirosh I, Parikh AS, Patel AP, Yizhak K, Gillespie S, Rodman C, Luo CL, Mroz EA, Emerick KS et al: Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer. Cell 2017, 171(7):1611-1624 e1624.

What are the characteristics of cancer cells that can metastasize to the brain?

(source:internet, reference only)

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