Personalized tumor combination therapy optimization using the single‑cell transcriptome

Genome Medicine, 2023 · DOI: https://doi.org/10.1186/s13073-023-01256-6 · Published: December 2, 2023

Simple Explanation

This research introduces comboSC, a computational tool designed to optimize personalized cancer combination therapy using single-cell transcriptomes. ComboSC stratifies patient samples based on their personalized immune microenvironment using single-cell RNA sequencing. The tool identifies synergistic drug combinations to boost immunotherapy and prioritizes them for clinical use through bipartition graph optimization.

Study Duration

Not specified

Participants

119 tumor samples from 15 cancer types

Evidence Level

Proof-of-concept study

Key Findings

1
ComboSC can predict potential drug combinations for experimental validation and clinical usage using the single-cell transcriptome.
2
The application of comboSC to 119 tumor samples from 15 cancer types demonstrated its generalized utility in combination therapy optimization.
3
The study validates predicted drug combinations with literature evidence, clinical trial data, and in-vivo samples.

Research Summary

The study introduces comboSC, a computational prototype for personalized cancer combination therapy optimization using single-cell transcriptomes. ComboSC stratifies individual patient samples based on their personalized immune microenvironment and identifies synergistic drug combinations. The tool was applied to 119 tumor samples from 15 cancer types and validated using literature, clinical trial data, and in-vivo samples, showing its potential to accelerate personalized tumor treatment.

Practical Implications

Personalized Cancer Treatment

ComboSC facilitates personalized tumor treatment by reducing screening time from a large drug combination space, saving valuable treatment time for individual patients.

Drug Discovery

The tool can be used to predict potential drug combinations for further experimental validation and clinical usage, accelerating the drug discovery process.

Clinical Application

A user-friendly web server of comboSC is available for both clinical and research users, making it accessible for practical applications.

Study Limitations

1
Limited dataset sizes for rare cancer types may affect the accuracy of predictions for these cancers.
2
The analysis only considers four immune exhaustion trajectories, which may oversimplify complex immune exhaustion mechanisms.
3
The tool neglects the simultaneous effect of targeted drugs on both tumor and immune cells.

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