MS09 - ONCO-07

Dynamical modeling of cell-state transitions in cancer therapy resistance (Part 2)

Friday, July 18 at 3:50pm

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Note: this minisymposia has multiple sessions. The other session is: and Part-1.

Mohit Kumar Jolly (Indian Institute of Science), Sarthak Sahoo (Indian Institute of Science)

Description:

The ability of cancer cells to dynamically alter their cell-state/phenotype in response to stress, including existing therapies, poses a major impediment to effective cancer treatment. Latest experimental advancements have enabled a better characterization of such cell-state transitions and driven the development of mathematical models that can both offer mechanistic understanding and suggest new potent strategies (combinatorial, sequential, adaptive) for clinical management. This proposed mini-symposium brings together 8 experts across diverse academic backgrounds (oncology, mathematics, engineering), geography (Sweden, India, USA, Canada), career stages (from senior PhD students to full Professors) and genders (4 men, 4 women) to present their latest exciting work in this direction. These experts will discuss how an iterative interdisciplinary crosstalk among multi-scale mathematical models and quantitative experimental and clinical data has unraveled diverse regulatory mechanisms (transcriptional, epigenetic, signalling) contribute to cell-state transitions and consequent heterogeneity in a cell population, and its implications in mediating drug tolerance or resistance for multiple existing therapies, and eventually suggesting new therapeutic strategies that can overcome current clinical challenges.

David P Cook

Ottawa Hospital Research Institute

"Phenotypic constraints in ovarian cancer - a new perspective on targeted therapy"

High-grade serous ovarian cancer (HGSC) remains the most lethal gynecological malignancy, with a five-year survival rate below 50%. Despite the adoption of PARP inhibitors for patients with BRCA1/2 mutations (20% of cases), clinical management has remained unchanged for decades. The complex genetic landscape of HGSC has not revealed opportunities for effective targeted therapies as seen in other cancer types. To address this challenge, we conducted a meta-analysis of single-cell RNA sequencing data from 471 tumor samples, coupled with spatial transcriptomics using the 10x Genomics Xenium platform. Our analysis revealed three recurrent malignant epithelial phenotypes ('epitypes') that mirror fallopian tube lineages: SecA, SecB, and Cil. These epitypes emerge through non-genetic plasticity and serve distinct functions in disease progression—SecA cells showing high proliferation while SecB cells predominate in metastatic sites and post-chemotherapy samples. Each exhibits unique regulatory patterns and microenvironment interactions. Our findings suggest that developmental regulatory networks constrain malignant phenotypes, creating opportunities for phenotype-targeted therapeutics independent of genetic alterations. Targeting cellular plasticity could restrict tumours' adaptive capabilities, potentially enhancing treatment response and immune recognition in this challenging cancer.

Jill Gallaher

Moffitt Cancer Center

"Dynamic evolvability during tumor growth and treatment"

Drug resistance is an ongoing problem for maintaining a treatment response in advanced cancers, which are often more heterogeneous and evolvable. There are benefits for cells to be evolvable, e.g. to easily respond to large shifts in the microenvironment with large heritable shifts in traits, like allowing metastases to survive a new environment and thrive even during treatment. However, evolvability may also be a detriment. With too much deviation from the parental phenotype, cells lose important functions necessary to survive. So, is there an optimal rate of evolvability for tumors to grow and survive treatment that can be exploited therapeutically? We use an off-lattice agent-based model to investigate how the rate of change through proliferation-resistance phenotype space affects tumor growth and response to treatment. During growth, proliferation is selected for, but more evolvability leads to more heterogeneity and faster recurrence under treatment. When evolvability can evolve without constraints, faster evolvability changes will lead to faster recurrence. When evolvability is costly, tumor survival depends on the rate and jump size of heritable changes to transiently lose proliferation fitness selected for during growth and gain resistance for survival. We consider how to design treatment strategies based on a tumor’s evolvability dynamics.

Cordelia McGehee

Mayo Clinic

" Chemotherapy dosing as a driver of population evolution in models of intra-tumoral cell-cell competition in cancer"

Despite ongoing therapeutic advances in the treatment of cancer, many advanced solid tumors recur after initial therapy. Minimizing the emergence of drug resistance is a central problem in cancer pharmacology. Dose and dose schedule of chemotherapy administration has traditionally followed the maximum tolerated dose principle which aims to quickly eradicate the tumor while minimizing drug toxicity for the patient. In a clonal drug-sensitive cell population, using the highest dose of drug and achieving maximum tumor killing is a logical strategy. However, when a pre-existing drug-resistant cell population resides within a cancer cell population, the rapid elimination of drug sensitive cells has been hypothesized to lead to proliferation of the resistant cell population. In such cases, an alternative dosing paradigm coined adaptive therapy has been proposed to maintain the sensitive cell population in a tumor and thus prevent unchecked proliferation of the drug-resistant cells. In this talk, we use a model of cellular competition to mathematically explore two distinct paradigms of adaptive therapy dosing: continuous dose modulation versus intermittent high dose therapy. We compare these regimens to standard dosing schemes to explore how dose and dose schedule can drive cellular population evolution.

Russell C Rockne

Beckman Research Institute, City of Hope

"State-transitions at the single cell and system levels in chronic and acute myeloid leukemia"

In this presentation, I will discuss experimental data and mathematical models used to study state transitions in chronic and acute myeloid leukemia (CML and AML). Our experimental approach involves inducible and constitutively activated mouse models of CML and AML, which are monitored longitudinally through blood sampling and RNA sequencing. The mathematical models employed are stochastic differential equations and their corresponding probability density functions. By integrating experimental data with these mathematical models, and iteratively validating the models while generating new hypotheses, we have demonstrated that state transitions can be detected at very early stages of disease initiation. Furthermore, these transitions can be used to predict responses to chemotherapy and tyrosine kinase inhibitor (TKI) therapies. We explore how state-transitions can be used to characterize and quantify resistance to therapy through analysis of gene programs within cell types over time.

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Annual Meeting for the Society for Mathematical Biology, 2025.