The CCE welcomes collaboration opportunities with other groups working to advance our understanding of tumor evolution. Our current efforts center on somatic tumor evolution and evolution of resistance, and we are always eager to explore new ideas and partnerships across a wide range of cancer types and data modalities. Please email us if you are interested in collaborating with us. Below is a list of ongoing research areas and collaborative projects at the CCE.

Role and Timing of Chromosomal Instability During Tumorigenesis

Cancer progression follows common evolutionary principles in which genomic instability fuels subclonal diversification and therapeutic resistance. Our lab investigates these dynamics through an integrative approach that combines mathematical modeling, single-cell genomics, and close experimental collaboration. A central focus is disentangling both the timing and fitness consequences of key chromosomal events such as whole genome doubling, copy-number alterations, and broader chromosomal-instability that reshape a tumor’s evolutionary trajectory. We develop computational frameworks to model growth and mutation accumulation across diverse cancer types, estimating how specific genomic changes accelerate clonal expansion and downstream heterogeneity. By coupling in silico models with in vitro systems and patient-derived data, we aim to pinpoint which alterations mark critical, pan-cancer evolutionary transitions and how their timing influences cancer growth, metastatic potential, or treatment response. Additionally we are interested in the role of tetraploidy and the quantifying the effect of WGD events on tumor fitness and the rate of adaptation to therapy since WGDs provide a mechanism for further instability but also a buffer against additional downstream lesions. Ultimately, this quantitative roadmap of CIN-driven evolution seeks to reveal early, targetable vulnerabilities that can inform intervention strategies across the cancer spectrum.

Evolutionary Dynamics of Mutation Accumulation in Cell Populations

Cancer progression is driven by the accumulation of genetic and epigenetic alterations that emerge through somatic evolution. As these changes accumulate, they confer selective advantages that allow certain clones to expand, persist, or outcompete others within the tissue environment. Understanding the dynamics of this process requires models that account not just for individual mutations, but for how populations of cells interact, adapt, and diversify over time. Our lab investigates these evolutionary dynamics by integrating experimental systems—including high-complexity cellular barcoding and in vitro evolution assays—with quantitative modeling frameworks such as branching processes and stochastic simulation alongside statistical and machine learning methods for inference. We use these tools to estimate mutation rates, infer the fitness effects of emerging subclones, and characterize how microenvironmental context shapes selection. By combining lineage tracing, single-cell genomics, and mathematical modeling, we aim to uncover the principles that govern how tumors evolve from heterogeneous populations and identify key transitions in their evolutionary trajectory.

Methods for Estimating Cell Dynamics and Drug Response

Understanding drug response can help us elucidate mechanisms of action and can act as an important step in preclinical models. We develop models of cell division, growth, cycling, and dynamics to create richer metrics for understanding cell response beyond classical viability estimates. These methods allow us to quantify the impact of drug concentration on specific mechanisms of cell growth such as the rate of splitting, death, or cell cycle progression. Our toolkit provides a robust and flexible framework for statistical modeling of the concentration-response of cell dynamics with the goal of better comparing compounds, revealing mechanism-specific vulnerabilities, guiding the design of downstream experiments and combination strategies, and finding associations with expression towards creating better biomarkers to predict drug response.

Optimal Scheduling of Cancer Therapy to Delay Resistance

Resistance to anticancer therapy remains one of the most formidable challenges in oncology. Even when initial responses are strong, tumors often relapse as resistant clones emerge—either through de novo mutations or the expansion of pre-existing subpopulations. Our research focuses on understanding and modeling the evolutionary dynamics that drive resistance, with the goal of designing treatment strategies that extend therapeutic response and delay or prevent relapse. We develop mathematical models that integrate tumor heterogeneity, pharmacokinetics, drug interactions, and cell population dynamics to simulate how treatment regimens influence clonal evolution and resistance over time. These models allow us to identify schedules that minimize the risk of resistance emergence by accounting for both mechanistic drug effects and patient variability. Our work spans both the development of frameworks to estimate cellular dynamics from experimental data and the design of computational platforms for optimizing combination therapies. By coupling these models with real-world pharmacologic and genomic data, we aim to create personalized and robust dosing strategies that better align treatment with the underlying biology of resistance. Our models have been applied to optimize schedules for combination therapies such as osimertinib/dacomitinib in EGFR-mutant lung cancer and palbociclib/fulvestrant in ER+ breast cancer, including informing dose selection in ongoing clinical trials. These collaborations underscore the potential for mechanistic modeling to guide treatment design in both preclinical and clinical settings.