Lay Abstract
Desmoid tumors vary widely in behavior—some regress or remain stable, while others grow aggressively—but we currently lack tools to predict their course. To address this, a pilot study analyzed gene activity in individual cells from patient tumors and developed a novel method to identify cells carrying hallmark mutations in CTNNB1 or APC, allowing for precise distinction between tumor-specific signals and those from surrounding cells. Building on this work, the proposed study has three goals: (1) apply the improved approach to existing patient samples to identify gene activity patterns linked to tumor behavior and share these data through an interactive online portal; (2) recreate and manipulate these gene programs in laboratory-grown tumor models to test their roles in driving progression or regression; and (3) investigate how neighboring blood vessel and immune cells influence tumor behavior. Together, these efforts aim to uncover the biological drivers of desmoid tumor variability and lay the groundwork for targeted therapies and more personalized care strategies.
Scientific Abstract
Desmoid tumors, driven predominantly by CTNNB1 or APC mutations, display diverse clinical behaviors ranging from spontaneous regression to aggressive growth, yet the biological basis for this variability remains unclear. This project builds on a pilot study that used single-nucleus RNA sequencing with mutation annotation to identify transcriptional signatures unique to CTNNB1-mutant cells and developed a targeted amplification protocol to improve detection of these mutant populations. Preliminary findings suggest distinct transcriptional profiles and cellular compositions between regressing and progressing tumors. The central hypothesis is that transcriptional programs linked to progression or regression can be modeled and perturbed in vitro. Aim 1 applies enhanced genotyping and transcriptomic analysis across desmoid tumors to identify gene programs and microenvironmental features associated with clinical outcomes, with data shared through a public portal. Aim 2 tests key tumor-intrinsic hypotheses, including a profibrotic COL25A1-driven loop and a potential antifibrotic role for CD44. Aim 3 focuses on the tumor microenvironment, using computational and coculture approaches to identify supportive or inhibitory niches involving immune and endothelial cells. Together, this research will define the transcriptional and cellular mechanisms that drive desmoid tumor behavior and enable predictive and therapeutic advances using patient-derived models.
