Gondal Mahnoor N, Cieslik Marcin, Chinnaiyan Arul M
Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA.
Sci Data. 2025 Jan 22;12(1):139. doi: 10.1038/s41597-025-04381-6.
Immune checkpoint blockade (ICB) therapies have emerged as a promising avenue for the treatment of various cancers. Despite their success, the efficacy of these treatments is variable across patients and cancer types. Numerous single-cell RNA-sequencing (scRNA-seq) studies have been conducted to unravel cell-specific responses to ICB treatment. However, these studies are limited in their sample sizes and require advanced coding skills for exploration. Here, we have compiled eight scRNA-seq datasets from nine cancer types, encompassing 223 patients, 90,270 cancer cells, and 265,671 other cell types. This compilation forms a unique resource tailored to investigate how cancer cells respond to ICB treatment across cancer types. We meticulously curated, quality-checked, pre-processed, and analyzed the data, ensuring easy access for researchers. Moreover, we designed a user-friendly interface for seamless exploration. By sharing the code and data for creating these interfaces, we aim to assist fellow researchers. These resources offer valuable support to those interested in leveraging and exploring single-cell datasets across diverse cancer types, facilitating a comprehensive understanding of ICB responses.
免疫检查点阻断（ICB）疗法已成为治疗各种癌症的一条有前景的途径。尽管取得了成功，但这些治疗方法的疗效在不同患者和癌症类型之间存在差异。已经进行了大量的单细胞RNA测序（scRNA-seq）研究，以揭示细胞对ICB治疗的特异性反应。然而，这些研究的样本量有限，并且需要先进的编码技能来进行探索。在这里，我们汇集了来自九种癌症类型的八个scRNA-seq数据集，涵盖223名患者、90270个癌细胞和265671个其他细胞类型。这一汇集形成了一个独特的资源，专门用于研究癌细胞如何跨癌症类型对ICB治疗作出反应。我们精心策划、质量检查、预处理和分析了数据，确保研究人员能够轻松获取。此外，我们设计了一个用户友好的界面，以便进行无缝探索。通过共享创建这些界面的代码和数据，我们旨在帮助其他研究人员。这些资源为那些有兴趣利用和探索跨多种癌症类型的单细胞数据集的人提供了宝贵的支持，有助于全面了解ICB反应。
