Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Interventional Institute of Zhengzhou University, Zhengzhou, China.
Front Immunol. 2022 Jan 27;13:828330. doi: 10.3389/fimmu.2022.828330. eCollection 2022.
Stemness refers to the capacities of self-renewal and repopulation, which contributes to the progression, relapse, and drug resistance of colorectal cancer (CRC). Mounting evidence has established the links between cancer stemness and intratumoral heterogeneity across cancer. Currently, the intertumoral heterogeneity of cancer stemness remains elusive in CRC.
This study enrolled four CRC datasets, two immunotherapy datasets, and a clinical in-house cohort. Non-negative matrix factorization (NMF) was performed to decipher the heterogeneity of cancer stemness. Multiple machine learning algorithms were applied to develop a nine-gene stemness cluster predictor. The clinical outcomes, multi-omics landscape, potential mechanisms, and immune features of the stemness clusters were further explored.
Based on 26 published stemness signatures derived by alternative approaches, we decipher two heterogeneous clusters, low stemness cluster 1 (C1) and high stemness cluster 2 (C2). C2 possessed a higher proportion of advanced tumors and displayed worse overall survival and relapse-free survival compared with C1. The MSI-H and CMS1 tumors tended to enrich in C1, and the mesenchymal subtype CMS4 was the prevalent subtype of C2. Subsequently, we developed a nine-gene stemness cluster predictor, which robustly validated and reproduced our stemness clusters in three independent datasets and an in-house cohort. C1 also displayed a generally superior mutational burden, and C2 possessed a higher burden of copy number deletion. Further investigations suggested that C1 enriched numerous proliferation-related biological processes and abundant immune infiltration, while C2 was significantly associated with mesenchyme development and differentiation. Given results derived from three algorithms and two immunotherapeutic cohorts, we observed C1 could benefit more from immunotherapy. For patients with C2, we constructed a ridge regression model and further identified nine latent therapeutic agents, which might improve their clinical outcomes.
This study proposed two stemness clusters with stratified prognosis, multi-omics landscape, potential mechanisms, and treatment options. Current work not only provided new insights into the heterogeneity of cancer stemness, but also shed light on optimizing decision-making in immunotherapy and chemotherapy.
干性是指自我更新和再殖的能力，这有助于结直肠癌（CRC）的进展、复发和耐药。越来越多的证据已经确定了癌症干性与癌症内部异质性之间的联系。目前，CRC 中癌症干性的肿瘤间异质性仍然难以捉摸。
本研究纳入了四个 CRC 数据集、两个免疫治疗数据集和一个临床内部队列。采用非负矩阵分解（NMF）来破译癌症干性的异质性。应用多种机器学习算法开发了一个九基因干性簇预测器。进一步探讨了干性簇的临床结局、多组学景观、潜在机制和免疫特征。
基于通过替代方法获得的 26 个已发表的干性特征，我们破译了两个异质性簇，低干性簇 1（C1）和高干性簇 2（C2）。C2 中晚期肿瘤的比例更高，与 C1 相比，总生存期和无复发生存期更差。MSI-H 和 CMS1 肿瘤倾向于富集在 C1 中，而间质亚型 CMS4 是 C2 的主要亚型。随后，我们开发了一个九基因干性簇预测器，该预测器在三个独立数据集和一个内部队列中稳健地验证和再现了我们的干性簇。C1 也表现出较高的突变负担，而 C2 具有较高的拷贝数缺失负担。进一步的研究表明，C1 富集了许多与增殖相关的生物学过程和丰富的免疫浸润，而 C2 与间质发育和分化显著相关。基于三个算法和两个免疫治疗队列的结果，我们观察到 C1 可能从免疫治疗中获益更多。对于 C2 患者，我们构建了一个岭回归模型，并进一步确定了九个潜在的治疗药物，这可能改善他们的临床结局。
本研究提出了两个具有分层预后、多组学景观、潜在机制和治疗选择的干性簇。目前的工作不仅为癌症干性的异质性提供了新的见解，也为免疫治疗和化疗的决策优化提供了思路。
