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Tumor Progression

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Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer

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Divergent evolutionary trajectories in transplanted tumor models.

Carlos Villacorta-Martin¹, Amanda J Craig¹, Augusto Villanueva^1,2

¹Liver Cancer Research Program, Division of Liver Diseases, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

Nature Genetics

|October 28, 2017

Summary

Human tumor models used in personalized medicine may not accurately represent primary tumors, especially in later stages. This study highlights potential limitations of using late-passage models for cancer research.

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Area of Science:

Oncology
Translational Medicine
Cancer Biology

Background:

Human-derived tumor models are crucial for personalized medicine.
These models aim to mimic primary tumors for drug testing and research.
Concerns exist regarding the fidelity of these models to the original tumor.

Purpose of the Study:

To evaluate the representativeness of human-derived tumor models compared to primary tumors.
To investigate the impact of passage number on model accuracy.
To inform the use of tumor models in personalized cancer therapy.

Main Methods:

Comparison of molecular and cellular characteristics between primary tumors and their corresponding in vitro models.
Analysis across different passage numbers of cultured tumor models.
Utilizing techniques such as genomics, transcriptomics, and histopathology.

Main Results:

Significant divergence in key biological features between late-passage tumor models and primary tumors.
Early-passage models demonstrated higher similarity to the original tumor tissue.
Genetic and phenotypic drift observed with increasing passages.

Conclusions:

Late-passage human-derived tumor models may not accurately reflect the complexity of primary tumors.
Careful consideration of passage number is essential for reliable preclinical research.
Findings suggest a need for optimized protocols in developing and utilizing tumor models for personalized medicine.