Wertheimer, E. | bioRxiv Science

bioRxiv2026

TUMOR-PRE-ADIPOCYTE CROSSTALK SUSTAINS BREAST CANCER GROWTH VIA RET SIGNALLING

Adipose tissue is the dominant stromal component of the breast, yet whether breast tumors exploit adipocyte plasticity to support cancer growth remains unclear. Here, we show that breast tumors actively disturb adipocyte differentiation, generating an immature tumor-adjacent adipose niche enriched in pre-adipocytes that directly promotes tumor progression. In human breast cancer samples, adipocytes located near tumors acquire a pre-adipocyte-like state. Functional studies demonstrate that pre-adipocytes enhance tumor cell proliferation both in vivo and in vitro. Mechanistically, we identify tumor-intrinsic RET signaling as a key regulator of this interaction. The RET receptor is a clinically relevant target expressed in breast cancer. RET drives a PDGF-B-dependent paracrine program that maintains pre-adipocytes in the tumor milieu. In turn, pre-adipocytes provide RET ligands that reinforce oncogenic signaling in tumor cells. Disruption of the RET-PDGF-B axis limits tumor progression. Together, our findings reveal an active tumor-driven mechanism by which breast tumors regulate adipocyte linage states to sustain growth and identify a novel targetable pathway controlling tumor- adipose tissue communication.

cancer biology↗

bioRxiv2024

Modeling Decision-Making Under Uncertainty with Qualitative Outcomes

Modeling decision-making under uncertainty typically relies on quantitative outcomes. Many decisions, however, are qualitative in nature, posing problems for traditional models. Here, we aimed to model uncertainty attitudes in decisions with qualitative outcomes. Participants made choices between certain outcomes and the chance for more favorable outcomes in quantitative (monetary) and qualitative (medical) modalities. Using computational modeling, we estimated the values participants assigned to qualitative outcomes and compared uncertainty attitudes across domains. Our model provided a good fit for the data, including quantitative estimates for qualitative outcomes. The model outperformed a utility function in quantitative decisions. Additionally, we found an association between ambiguity attitudes across domains. Results were replicated in an independent sample. We demonstrate the ability to extract quantitative measures from qualitative outcomes, leading to better estimation of subjective values. This allows for the characterization of individual behavior traits under a wide range of conditions. Author SummaryIn the current study, we explored how people make decisions when the outcomes arent easily measured in numbers, such as in medical choices. Traditional mathematical models, which rely on numerical data, often fall short in these situations, leading to a gap in understanding how people evaluate these qualitative outcomes. Using hierarchical Bayesian modeling, we developed a model that bridges this gap by translating qualitative outcomes into individualized quantitative values, enabling us to better understand the underlying decision-making processes. Our model not only provides a better fit to real-world data than existing models with qualitative or quantitative outcomes but also allows for meaningful comparisons of how people handle uncertainty across different decision-making scenarios. This approach opens new doors for studying decision-making in areas where traditional methods struggle, offering a more nuanced view of human behavior in complex situations.

neuroscience↗