Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future

Area of Science:

• Molecular Biology
• Immunology
• Bioinformatics
• Drug Discovery

Background:

• Tumor cells interact with immune cells within the tumor microenvironment (TME), influencing immune evasion and cancer progression.
• Accurate characterization and quantification of TME components are crucial for advancing cancer research and therapeutic strategies.
• The advent of single-cell and spatial transcriptomics has spurred the development of novel computational methods for TME analysis.

Purpose of the Study:

• To review and compare emerging computational strategies for deconvoluting and subtyping the complex cellular composition of the TME.
• To discuss downstream subtyping analyses that leverage single-cell and spatial transcriptomic data.
• To emphasize the importance of integrating diverse datasets for a comprehensive understanding of the dynamic TME.

Main Methods:

• Review of computational methods for TME deconvolution and subtyping.
• Comparison of different analytical approaches based on transcriptomic data (bulk, single-cell, spatial).
• Discussion of integrative analysis strategies incorporating epigenetics and T-cell receptor sequencing.

Main Results:

• Multiple computational strategies exist for deconvoluting and subtyping TME components from transcriptomic data.
• Downstream analyses are essential for interpreting the functional roles of identified cell populations.
• No single method is sufficient; integrative approaches are necessary for comprehensive TME characterization.

Conclusions:

• Computational deconvolution and subtyping of the TME are rapidly evolving fields.
• Integrating transcriptomic data with epigenetics and TCR sequencing provides deeper insights into TME dynamics.
• Further development and validation of integrative computational methods are critical for advancing cancer immunology and drug discovery.