What question did this study set out to answer?

The central aim is to develop an adaptive AI framework to optimize treatment sequencing in triple-negative breast cancer by simulating tumor microenvironment changes.

March 3, 2026Open Access

PP042 Personalized therapy sequencing in triple-negative breast cancer using microenvironment-guided reinforcement learning and spatial transcriptomic feedback

Key Points

The central aim is to develop an adaptive AI framework to optimize treatment sequencing in triple-negative breast cancer by simulating tumor microenvironment changes.
Developed a multi-agent reinforcement learning framework
Integrated histopathologic data with spatial transcriptomic feedback
Simulated therapy-guided tumor microenvironment progression
Adaptive sequencing improved therapy outcomes compared to static models
Better performance in addressing therapeutic resistance in TNBC
Effective integration of spatial interactions between tumor components

Abstract

Triple-negative breast cancer (TNBC) often develops resistance to systemic therapies due to dynamic remodeling of the tumor microenvironment (TME). Despite its clinical relevance, treatment selection models typically assume a static TME and fail to account for evolving spatial interactions between immune, stromal, and malignant components. This study aims to develop an adaptive AI-based framework using multi-agent reinforcement learning (MARL) to simulate therapy-guided TME progression and optimize treatment sequencing in TNBC by integrating histopathologic and spatial transcriptomic data.

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Cite This Study

Prihantini et al. (Sun,) studied this question.

synapsesocial.com/papers/69a67dd6f353c071a6f09e40 https://doi.org/https://doi.org/10.1016/j.esmoop.2025.105960

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