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Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
From bench to bedside: Single-cell analysis for cancer immunotherapy
Emily F Davis-Marcisak1, Atul Deshpande2, Genevieve L Stein-O'Brien1
1McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins School of Medicine, 550 N Broadway, Suite 1101E, Baltimore, MD 21205, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 1650 Orleans Street, Room 485, Baltimore, MD 21287, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Immunotherapy Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Single-cell technologies offer powerful insights into cancer's complexity and therapeutic responses. This review covers essential sample processing and computational analysis for advancing cancer immunotherapy research.
Area of Science:
- Oncology
- Immunology
- Biotechnology
Background:
- Single-cell technologies are revolutionizing cancer research by enabling detailed molecular profiling of individual cells within tumors.
- Understanding tumor heterogeneity, microenvironment, and cell state dynamics is crucial for predicting therapeutic outcomes, especially in immunotherapy.
- Translational research applying these technologies to clinical samples presents significant technical challenges.
Purpose of the Study:
- To review current methodologies in sample processing and computational analysis for single-cell studies in translational cancer immunotherapy.
- To highlight the importance of multi-disciplinary collaboration for successful implementation of single-cell analysis in clinical settings.
- To provide insights into overcoming technical hurdles in generating high-quality data from clinical samples.
Main Methods:
- Review of existing literature on single-cell technologies, sample preparation techniques, and computational analysis pipelines.
- Focus on methods applicable to clinical samples for cancer immunotherapy research.
- Emphasis on data quality control and interpretation strategies.
Main Results:
- Single-cell technologies provide unprecedented resolution for dissecting tumor complexity.
- Effective sample processing and robust computational analysis are critical for reliable data interpretation.
- Identifying predictors of immunotherapy response necessitates rigorous, coordinated approaches.
Conclusions:
- Single-cell analysis holds immense potential for advancing precision medicine in cancer immunotherapy.
- Standardization of sample processing and computational methods is key to unlocking this potential.
- Integrated, multi-disciplinary efforts are essential for translating single-cell discoveries into clinical practice.
