State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, China.
Department of Neurosurgery, Center for Integrated Oncology (CIO), University Hospital Bonn, Bonn, Germany.
Front Immunol. 2021 Oct 4;12:751530. doi: 10.3389/fimmu.2021.751530. eCollection 2021.
Cancer heterogeneity is a major challenge in clinical practice, and to some extent, the varying combinations of different cell types and their cross-talk with tumor cells that modulate the tumor microenvironment (TME) are thought to be responsible. Despite recent methodological advances in cancer, a reliable and robust model that could effectively investigate heterogeneity with direct prognostic/diagnostic clinical application remained elusive.
To investigate cancer heterogeneity, we took advantage of single-cell transcriptome data and constructed the first indication- and cell type-specific reference gene expression profile (RGEP) for breast cancer (BC) that can accurately predict the cellular infiltration. By utilizing the BC-specific RGEP combined with a proven deconvolution model (LinDeconSeq), we were able to determine the intrinsic gene expression of 15 cell types in BC tissues. Besides identifying significant differences in cellular proportions between molecular subtypes, we also evaluated the varying degree of immune cell infiltration (basal-like subtype: highest; Her2 subtype: lowest) across all available TCGA-BRCA cohorts. By converting the cellular proportions into functional gene sets, we further developed a 24 functional gene set-based prognostic model that can effectively discriminate the overall survival ( = 5.9 × 10, = 1091, TCGA-BRCA cohort) and therapeutic response (chemotherapy and immunotherapy) ( = 6.5 × 10, = 348, IMvigor210 cohort) in the tumor patients.
Herein, we have developed a highly reliable BC-RGEP that adequately annotates different cell types and estimates the cellular infiltration. Of importance, the functional gene set-based prognostic model that we have introduced here showed a great ability to screen patients based on their therapeutic response. On a broader perspective, we provide a perspective to generate similar models in other cancer types to identify shared factors that drives cancer heterogeneity.
癌症异质性是临床实践中的一个主要挑战，在某种程度上，不同细胞类型的不同组合及其与肿瘤细胞的相互作用，调节肿瘤微环境（TME）被认为是其原因。尽管癌症的最近方法学进展，一种可靠和强大的模型，能够有效地研究异质性与直接预后/诊断的临床应用仍然难以捉摸。
为了研究癌症异质性，我们利用单细胞转录组数据，构建了第一个用于乳腺癌（BC）的具有指示性和细胞类型特异性的参考基因表达谱（RGEP），该谱可以准确预测细胞浸润。通过利用 BC 特异性 RGEP 结合已证明的去卷积模型（LinDeconSeq），我们能够确定 BC 组织中 15 种细胞类型的内在基因表达。除了确定分子亚型之间细胞比例的显著差异外，我们还评估了所有可用 TCGA-BRCA 队列中免疫细胞浸润的不同程度（基底样亚型：最高；Her2 亚型：最低）。通过将细胞比例转化为功能基因集，我们进一步开发了一个基于 24 个功能基因集的预后模型，该模型可以有效地区分肿瘤患者的总生存率（=5.9×10，=1091，TCGA-BRCA 队列）和治疗反应（化疗和免疫治疗）（=6.5×10，=348，IMvigor210 队列）。
在这里，我们开发了一种高度可靠的 BC-RGEP，它充分注释了不同的细胞类型，并估计了细胞浸润。重要的是，我们在这里介绍的基于功能基因集的预后模型具有根据治疗反应筛选患者的强大能力。从更广泛的角度来看，我们提供了一种在其他癌症类型中生成类似模型的视角，以识别驱动癌症异质性的共同因素。
