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Updated: Sep 15, 2025
Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
Published on: December 9, 2016
Deciphering cancer therapy resistance via patient-level single-cell transcriptomics with CellResDB
Tianyuan Liu1,2, Huiyuan Qiao3, Liping Ren4
1Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
Abstract:
Cancer therapy resistance remains a major challenge, with limited resources available for systematically studying its underlying mechanisms at the patient level. The existing databases are either restricted to bulk RNA-seq data, lack single-cell resolution, or provide limited clinical annotations, making them insufficient for in-depth exploration of the tumor microenvironment (TME) dynamics in therapy resistance. To bridge this gap, we present CellResDB, a patient-derived platform comprising nearly 4.7 million cells from 1391 patient samples across 24 cancer types. CellResDB provides comprehensive annotations of TME features linked to therapy resistance. To enhance accessibility, we include an intelligent robot, CellResDB-Robot, which facilitates intuitive data retrieval and analysis. In summary, CellResDB represents a valuable resource for cancer therapy and provides an experimental protocol for applying large language models (LLMs) within the biomedical database. CellResDB is freely available at https://cellknowledge.com.cn/cellresponse .
Insights
CellResDB is a new database offering insights into cancer therapy resistance by analyzing millions of cells from diverse patient samples. This resource aids in understanding tumor microenvironment dynamics and improving cancer treatments.
Area of Science:
- Oncology
- Bioinformatics
- Immunology
Background:
- Cancer therapy resistance is a significant clinical challenge.
- Existing databases lack single-cell resolution and comprehensive clinical data for studying resistance mechanisms.
- Understanding the tumor microenvironment (TME) is crucial for overcoming therapy resistance.
Purpose of the Study:
- To develop a comprehensive, patient-derived database for studying cancer therapy resistance.
- To provide high-resolution single-cell data with extensive TME annotations linked to treatment outcomes.
- To create an accessible platform for researchers to explore resistance mechanisms.
Main Methods:
- Compiled a large-scale dataset of nearly 4.7 million cells from 1391 patient samples across 24 cancer types.
- Integrated single-cell RNA sequencing data with detailed clinical and TME annotations.
- Developed an intelligent robot (CellResDB-Robot) for intuitive data retrieval and analysis.
Main Results:
- Established CellResDB, a unique resource detailing TME features associated with cancer therapy resistance.
- The database enables in-depth analysis of cellular heterogeneity and its role in treatment failure.
- Demonstrated the utility of LLMs in biomedical database applications.
Conclusions:
- CellResDB serves as a valuable resource for advancing cancer therapy research.
- The platform facilitates the systematic investigation of patient-level resistance mechanisms.
- CellResDB promotes a deeper understanding of TME dynamics in therapeutic contexts.
