Related Experiment Video
Updated: Jan 13, 2026

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Multi-omics profiling uncovers immune-molecular clusters with distinct chemo-immunotherapeutic vulnerabilities in a
Olivier Castellanet1, Jean Monatte1, Nathan Corvaisier1
1Aix Marseille Univ, CNRS, INSERM, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Marseille, France.
Background:
Triple-negative breast cancer (TNBC) is a highly aggressive and heterogeneous breast cancer subtype with limited treatment options. Predicting patient response to chemo-immunotherapy remains challenging, highlighting the need for robust stratification strategies.
Methods:
We performed a multi-parametric analysis combining histological, genomic, transcriptomic, proteomic, and immune profiling in the immunocompetent MMTV-R26Met TNBC mouse model and compared outcomes with patient data from human TNBC cohorts and TNBC tumor microarray. To enable therapeutic testing and functional validation, we established syngeneic grafts from primary tumors and used them to evaluate combined chemotherapy (epirubicin) and anti-PD-1 immunotherapy.
Results:
Multi-parametric analysis of TNBC heterogeneity modeled by the MMTV-R26Met mice identified four distinct TNBC clusters, defined by unique intrinsic (molecular/genomic) and extrinsic (immune) features, which closely parallel patient subtypes, including rare metaplastic forms, and correlate with clinical outcomes. Both intrinsic and immune hallmarks of primary tumors were conserved across serial syngeneic transplantations, confirming the translational value of this preclinical platform. Treatment assessments indicated cluster-specific therapeutic vulnerabilities associated with molecular and immune traits. Specifically, whereas chemo-immunotherapy is beneficial to neutrophil-enriched tumors, immunotherapy alone appears to be more effective in macrophage-enriched tumors. Our findings indicate that TNBC treatment response is shaped by the interplay between tumor-intrinsic and immune features.
Conclusion:
Our study provides a robust preclinical platform for precision immuno-oncology, enabling stratification of TNBC patients for tailored onco-immunotherapies.
Insights
Researchers developed a preclinical model for triple-negative breast cancer (TNBC) to predict patient response to chemo-immunotherapy. This model identified distinct TNBC subtypes, guiding tailored treatment strategies for better outcomes.
Area of Science:
- Oncology
- Immunology
- Genomics
Background:
- Triple-negative breast cancer (TNBC) is aggressive and heterogeneous, presenting treatment challenges.
- Predicting patient response to chemo-immunotherapy requires effective stratification methods.
Purpose of the Study:
- To develop a robust preclinical platform for precision immuno-oncology in TNBC.
- To stratify TNBC patients for tailored onco-immunotherapies based on intrinsic and immune features.
Main Methods:
- Multi-parametric analysis (histological, genomic, transcriptomic, proteomic, immune profiling) in a TNBC mouse model.
- Comparison with human TNBC patient data and tumor microarrays.
- Syngeneic graft establishment for therapeutic testing of chemotherapy and anti-PD-1 immunotherapy.
Main Results:
- Identified four distinct TNBC clusters mirroring patient subtypes and clinical outcomes.
- Demonstrated conservation of tumor hallmarks across serial transplantations, validating the preclinical platform.
- Revealed cluster-specific treatment vulnerabilities: chemo-immunotherapy for neutrophil-enriched tumors, immunotherapy alone for macrophage-enriched tumors.
Conclusions:
- The study establishes a valuable preclinical platform for precision immuno-oncology in TNBC.
- This platform enables stratification of TNBC patients for personalized immunotherapy selection.

