1.Tumor ecosystems are comprised of cancer cells, infiltrating immune cells, stromal cells, and other cell types together with non-cellular tissue components.
腫瘤生態(tài)系統(tǒng)由癌細胞、浸潤免疫細胞、基質細胞和其他細胞類型以及非細胞組織成分組成。
Tumor ecosystems are further shaped by cellular relationships and strategies targeting relationships that promote tumor development hold considerable promise.
腫瘤生態(tài)系統(tǒng)進一步受到細胞關系的影響，而以促進腫瘤發(fā)展的關系為目標的策略具有相當大的前景。
Each tumor ecosystem was composed of tumor cells with varying phenotypic abnormalities, and tumor cell phenotypes associated with therapy resistance were abundant.
每個腫瘤生態(tài)系統(tǒng)都由不同表型異常的腫瘤細胞組成，與治療耐藥相關的腫瘤細胞表型是豐富的。
腫瘤生態(tài)系統(tǒng)的概況
Phenotypic Abnormalities and Tumor Individuality Are Linked to Features of Poor Prognosis.
Phenotypic abnormality describes the extent of tumor cell phenotypic deviation from nontumor epithelial cells. ( To describe phenotypic abnormalities, we trained an artificial neural network (autoencoder) (Goodfellow et al., 2016; Hinton and Salakhutdinov, 2006) with multidimensional single-cell data from the juxta-tumoral samples).
Tumor individuality quantifies the similarity of tumors based on cell phenotypes. (To assess the individuality of tumor ecosystems, we applied a graph-based approach to the epithelial cell data from all samples).
Tumor richness represents the number of different co-existing tumor cell phenotypes within an ecosystem. (To explore the concept of tumor richness, we calculated the frequency of each epithelial cell cluster per sample and reported the number of clusters above 1%.).
To identify clusters and cluster combinations with the power to distinguish a given group from all other samples, we employed a random forest classifier.[1]
2.CSCs are active architects of their microenvironment and drive interactions with other tumor components, such as immune cells, cancer-associated fibroblasts and differentiated cells, blood vessels, and other extracellular cues to engineer a sustainable niche. We also highlight considerations for modeling this dynamic tumor ecology and discuss potential therapeutic strategies for targeting these multifaceted interactions.
癌癥干細胞是其微環(huán)境的活躍構筑者，并驅動與其他腫瘤成分的相互作用，如免疫細胞、癌癥相關的成纖維細胞和分化細胞、血管和其他細胞外因素，來打造一個可持續(xù)的生態(tài)環(huán)境。我們還強調了對這種動態(tài)腫瘤生態(tài)建模的考慮，并討論了針對這些多方面相互作用的潛在治療策略。
CSCs evade killing by immune cells through a variety of mechanisms. For example, CD133+ glioblastoma CSCs and ABCB5+ melanoma CSCs downregulate MHC class I molecules (human HLA-A, HLA-B, and HLA-C) to escape from T cell attack. Glioblastoma CSCs also decrease the expression of low molecular weight protein (LMP) and transporter associated with antigen processing (TAP) to reduce the capacity of antigen processing and presenting pathways.
癌癥干細胞通過多種機制逃避免疫細胞的殺傷。例如，CD133+膠質母細胞瘤CSCs和ABCB5+黑色素瘤CSCs下調MHC I類分子（人類HLA-A、HLA-B和HLA-C）以逃避T細胞攻擊。膠質母細胞瘤CSCs還降低了低分子量蛋白（LMP）和與抗原處理相關的轉運體（TAP）的表達，從而降低了抗原處理和呈現(xiàn)途徑的能力。[2]
3.Conversely, malignant cells varied within and between tumors in their expression of signatures related to cell cycle, stress, hypoxia, epithelial differentiation, and partial epithelial-tomesenchymal transition (p-EMT).
相反，惡性細胞在腫瘤內(nèi)部和腫瘤之間表達與細胞周期、應激、缺氧、上皮分化和部分上皮-間充質轉化(p-EMT)相關的信號有差異。
By integrating single-cell transcriptomes with bulk expression profiles for hundreds of tumors, we refined HNSCC subtypes by their malignant and stromal composition, and established p-EMT as an independent predictor of nodal metastasis, grade, and adverse pathologic features.
通過整合數(shù)百種腫瘤的單細胞轉錄組和體表達譜，我們根據(jù)其惡性和基質成分對HNSCC亞型進行了提純，并建立了p-EMT作為淋巴結轉移、分級和不良病理特征的獨立預測因子。
Single-cell profiles of non-malignant cells highlighted the composition of the TME. We partitioned the 3,363 non-malignant cells to eight main clusters by their expression states (Figures 2A). We annotated clusters by the expression of known marker genes as T-cells, B/plasma cells, macrophages, dendritic cells, mast cells, endothelial cells, fibroblasts, and myocytes.
非惡性細胞的單細胞特征突出了TME的組成。我們將3363個非惡性細胞按其表達狀態(tài)劃分為8個主要簇(圖2A)。我們通過已知標記基因t細胞、B/漿細胞、巨噬細胞、樹突狀細胞、肥大細胞、內(nèi)皮細胞、成纖維細胞和肌細胞的表達來標記簇。[3]
[1] WAGNER J, RAPSOMANIKI M A, CHEVRIER S, et al. A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer [J]. Cell, 2019, 177(5): 1330-45 e18.
[2] PRAGER B C, XIE Q, BAO S, et al. Cancer Stem Cells: The Architects of the Tumor Ecosystem [J]. Cell stem cell, 2019, 24(1): 41-53.
[3] PURAM S V, TIROSH I, PARIKH A S, et al. Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer [J]. Cell, 2017, 171(7): 1611-24 e24.
