1.Tumor ecosystems are comprised of cancer cells, infiltrating immune cells, stromal cells, and other cell types together with non-cellular tissue components.

肿瘤生态系统由癌细胞、浸润免疫细胞、基质细胞和其他细胞类型以及非细胞组织成分组成。

Tumor ecosystems are further shaped by cellular relationships and strategies targeting relationships that promote tumor development hold considerable promise.

肿瘤生态系统进一步受到细胞关系的影响，而以促进肿瘤发展的关系为目标的策略具有相当大的前景。

Each tumor ecosystem was composed of tumor cells with varying phenotypic abnormalities, and tumor cell phenotypes associated with therapy resistance were abundant.

每个肿瘤生态系统都由不同表型异常的肿瘤细胞组成，与治疗耐药相关的肿瘤细胞表型是丰富的。

肿瘤生态系统的概况

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.

癌症干细胞是其微环境的活跃构筑者，并驱动与其他肿瘤成分的相互作用，如免疫细胞、癌症相关的成纤维细胞和分化细胞、血管和其他细胞外因素，来打造一个可持续的生态环境。我们还强调了对这种动态肿瘤生态建模的考虑，并讨论了针对这些多方面相互作用的潜在治疗策略。

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.

癌症干细胞通过多种机制逃避免疫细胞的杀伤。例如，CD133+胶质母细胞瘤CSCs和ABCB5+黑色素瘤CSCs下调MHC I类分子（人类HLA-A、HLA-B和HLA-C）以逃避T细胞攻击。胶质母细胞瘤CSCs还降低了低分子量蛋白（LMP）和与抗原处理相关的转运体（TAP）的表达，从而降低了抗原处理和呈现途径的能力。[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，恶性细胞在肿瘤内部和肿瘤之间表达与细胞周期、应激、缺氧、上皮分化和部分上皮-间充质转化(p-EMT)相关的信号有差异。

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.

通过整合数百种肿瘤的单细胞转录组和体表达谱，我们根据其恶性和基质成分对HNSCC亚型进行了提纯，并建立了p-EMT作为淋巴结转移、分级和不良病理特征的独立预测因子。

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.

非恶性细胞的单细胞特征突出了TME的组成。我们将3363个非恶性细胞按其表达状态划分为8个主要簇(图2A)。我们通过已知标记基因t细胞、B/浆细胞、巨噬细胞、树突状细胞、肥大细胞、内皮细胞、成纤维细胞和肌细胞的表达来标记簇。[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.