ArticleScientific data2025
Integrated cancer cell-specific single-cell RNA-seq datasets of immune checkpoint blockade-treated patients.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- ELISA (Embedding-Linked Interactive Single-cell Agent): an interpretable hybrid generative Artificial Intelligence agent for expression-grounded discovery in single-cell genomics.Briefings in bioinformatics · 2026Article
- Modulation of tumor-derived exosomes and reprogramming of cancer-associated fibroblasts for colorectal cancer therapy.Molecular cancer · 2026Article
- MHC class I on target cells regulates CD4Nature immunology · 2026Article
- Conventional type-1 DC density is associated with checkpoint inhibitor response across multiple types of cancer.The Journal of clinical investigation · 2026Article
- Dissecting molecular heterogeneity in primary gastric cancer by IGFBP7-related analysis.Journal of translational medicine · 2026Article
- Advances in targeting KRAS mutations: A promising approach for the treatment of non‑small cell lung cancer (Review).Oncology reports · 2026Review
- scChat: A Large Language Model-Powered Co-Pilot for Contextualized Single-Cell RNA Sequencing Analysis.AIChE journal. American Institute of Chemical Engineers · 2026Article
- Haruka Resolves Perturbation Response Heterogeneity in Spatial Cell Niches.bioRxiv : the preprint server for biology · 2025Article
- CDK12/13 inactivation triggers STING-mediated antitumor immunity in preclinical models.The Journal of clinical investigation · 2025Article
- Incorporating hierarchical information into multiple instance learning for patient phenotype prediction with single-cell RNA-sequencing data.Bioinformatics (Oxford, England) · 2025Article
- Uncovering gene and cellular signatures of immune checkpoint response via machine learning and single-cell RNA-seq.NPJ precision oncology · 2025Article
- Single-Cell Transcriptomic Approaches for Decoding Non-Coding RNA Mechanisms in Colorectal Cancer.Non-coding RNA · 2025Review
- Integrated cancer cell-specific single-cell RNA-seq datasets of immune checkpoint blockade-treated patients.Scientific data · 2025Article
- A Systematic Overview of Single-Cell Transcriptomics Databases, their Use cases, and Limitations.ArXiv · 2024Article
- A systematic overview of single-cell transcriptomics databases, their use cases, and limitations.Frontiers in bioinformatics · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.