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Related Experiment Video

Updated: Aug 6, 2025

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

Published on: March 8, 2022

Integration of transcriptomics data into agent-based models of solid tumor metastasis.

Jimmy Retzlaff^1,2,3, Xin Lai^1,2,3,4, Carola Berking^1,2,3

¹Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

Computational and Structural Biotechnology Journal

|March 21, 2023

Summary

This summary is machine-generated.

This study integrates transcriptomics data into multi-scale cancer models to understand therapy resistance. The workflow identifies key pathways and simulation parameters driving resistance in melanoma.

Keywords:

Immunotherapy Melanoma Multi-scale modelling Systems biology

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Area of Science:

Computational biology
Cancer research
Systems biology

Background:

Cancer research increasingly uses high-throughput transcriptomics for profiling patient samples.
Transcriptomics reveals gene signatures linked to cancer aggressiveness and therapy resistance.
Omics data alone lacks spatiotemporal insights into tumor progression.

Purpose of the Study:

To present a computational workflow for integrating transcriptomics data into multi-scale cancer models.
To link transcriptomics-derived pathways to agent-based model parameters for studying cancer.
To identify mechanisms of therapy resistance using computational modeling.

Main Methods:

Transcriptomics analysis to identify differentially regulated pathways in therapy responders and non-responders.
Linking identified pathways to agent-based model parameters.
Global and local sensitivity analysis via systematic model simulations to assess parameter relevance in therapy resistance.
Application to a de novo agent-based model of melanoma micrometastasis with immune cell interactions.

Main Results:

The workflow successfully integrates transcriptomics data into a hybrid, multi-scale cancer model.
Sensitivity analysis identified key parameters influencing therapy resistance.
The model application revealed three distinct scenarios of therapy resistance in melanoma.

Conclusions:

The presented workflow enables the integration of transcriptomics data into multi-scale cancer models.
This approach provides insights into the spatiotemporal dynamics of tumor progression and therapy resistance.
The methodology can be applied to identify and understand mechanisms of therapeutic failure in various cancers.