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Updated: Jul 15, 2026

Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional 3D Model
Published on: June 11, 2014
Adding New Dimensions to 3D Cancer Models
Kevan Chu1,2, Lukas E Dow1,2,3,4
1Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York.
Abstract:
Understanding patient-specific responses to anticancer therapies and how individual tumors interact with their tumor microenvironment (TME) is a challenging task. To measure the impact of the TME on diverse and clinically relevant treatments, Ramos Zapatero and colleagues coupled patient-derived organoid (PDO) and cancer-associated fibroblast (CAF) cocultures with high-throughput mass cytometry-based assessment of cell state. Using a newly developed "Trellis" algorithm enabled integration and analysis of highly complex, multidimensional treatment response data. This work showed that tumor cell response to chemotherapy was associated with both intrinsic and nonintrinsic signaling states, whereby proliferative rate, growth factor signaling, and CAFs interaction influenced chemoprotection. Furthermore, the work suggests a potential role for the TME in promoting lineage plasticity associated with drug resistance. In all, the pipeline described provides a blueprint for exploring the intricate interplay of factors influencing cancer treatment response.
Insights
This study reveals how the tumor microenvironment (TME) impacts cancer drug response. Patient-derived organoids and a new algorithm show TME factors influence chemotherapy effectiveness and drug resistance.
Area of Science:
- Oncology
- Cancer Biology
- Immunology
Background:
- Understanding patient-specific responses to anticancer therapies is crucial.
- The tumor microenvironment (TME) significantly influences treatment efficacy.
- Interactions between tumor cells and their microenvironment remain complex.
Purpose of the Study:
- To investigate the impact of the TME on diverse anticancer treatments.
- To develop a method for analyzing complex, multidimensional treatment response data.
- To identify factors within the TME that affect chemotherapy response and drug resistance.
Main Methods:
- Coupled patient-derived organoids (PDOs) and cancer-associated fibroblast (CAF) cocultures.
- Employed high-throughput mass cytometry for cell state assessment.
- Developed and utilized a novel "Trellis" algorithm for data integration and analysis.
Main Results:
- Tumor cell response to chemotherapy is linked to intrinsic and non-intrinsic signaling.
- Proliferative rate, growth factor signaling, and CAF interactions modulate chemoprotection.
- The TME may promote lineage plasticity, contributing to drug resistance.
Conclusions:
- The developed pipeline offers a framework for studying TME influences on cancer treatment.
- This approach can elucidate the complex interplay of factors affecting therapeutic outcomes.
- Findings highlight the TME's role in chemotherapy response and acquired drug resistance.
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