A team of researchers at the University of California, Irvine, has now used mathematical modeling and experiments in mice to identify a possible answer. Their study, published in Cancer Research, suggests that the rate at which regulatory T cells, or Tregs, enter a tumor may be a critical determinant of whether PD-1 immunotherapy produces durable control or eventual resistance. Tregs are specialized immune cells that normally prevent excessive or misdirected immune reactions, protecting healthy tissues from autoimmune damage. Inside a tumor, however, their suppressive properties can be exploited by cancer. By limiting the activity of cancer-killing immune cells, Tregs may help malignant cells survive even after immunotherapy has removed one of the tumor’s most important defenses.

The researchers focused on the complex cellular contest taking place within the tumor microenvironment. Effector T cells patrol tissues, identify abnormal cells and destroy them through direct cellular attacks and the release of toxic molecules. Tumors can interfere with this process through several mechanisms, including the display of PD-L1, a surface protein that binds to the PD-1 receptor on effector T cells. This interaction functions as an immune “brake,” reducing T-cell activity and allowing cancer cells to evade destruction. PD-1 blockade drugs interrupt the PD-1–PD-L1 connection, effectively releasing that brake. The treatment can revive exhausted effector T cells and restore their ability to attack. But the same biological intervention may also intensify or preserve Treg-mediated suppression, creating a previously underappreciated route through which the tumor can recover.

Rather than examining possible resistance mechanisms one at a time, the UC Irvine team built a mathematical model that represented the major interactions among tumor cells, effector T cells, Tregs and the PD-1 pathway. The equations were based on findings accumulated over decades of cancer biology and immunology research. They described how immune cells multiply, migrate into tumors, become activated or suppressed, and influence the growth or elimination of malignant cells. The model was then compared with experimental data from mice bearing melanoma tumors. By repeatedly refining the parameters until the simulations reproduced observed biological outcomes, the researchers created a computational framework intended to capture both the average behavior of the disease and the variability found among individual animals.

That variability was essential to the next stage of the investigation. Once the model had been validated, the team generated 342 virtual mice with melanoma. Each simulated animal received a different combination of biological characteristics, such as the growth behavior of tumor cells, the abundance of immune cells, their rates of activation and their ability to migrate through tumor tissue. This approach allowed the researchers to explore a broad range of plausible immune environments without having to perform a separate experiment for every possible combination. The virtual population was then treated with simulated PD-1 blockade, and the researchers compared the characteristics of animals that achieved favorable responses with those that eventually experienced tumor regrowth.

More than 30 biological parameters were included in the analysis, but one variable repeatedly separated the two groups: the speed of Treg infiltration into the tumor. The model indicated that tumors receiving Tregs rapidly were more likely to resist or escape PD-1 blockade, while slower Treg entry was associated with improved treatment responses. This finding does not mean that Tregs are the only cause of resistance, or that every patient with a high level of Treg activity will fail to respond. Instead, it identifies the rate of Treg influx as a potentially powerful control point in the dynamic system that determines whether immune pressure remains strong enough to suppress cancer. “The mathematical analysis pointed directly to one variable,” said Rachel Sousa, the study’s first author. “It indicated that the rate of Treg infiltration into the tumor was the critical factor.”

The team next tested that prediction in living animals. Researchers engineered mice whose Tregs were less efficient at migrating into tumors while leaving the rest of the immune system intact. These animals were then treated with PD-1 blockade immunotherapy. The combination of reduced Treg infiltration and checkpoint inhibition substantially outperformed PD-1 blockade alone. In mice whose tumors were not completely eradicated, the combined intervention slowed tumor growth and nearly doubled survival duration. The experiment provided an important test of the model because it did not merely show that Tregs were present in resistant tumors; it examined whether changing their movement into the tumor could alter the outcome of therapy. The agreement between the simulated prediction and the mouse experiments suggests that Treg trafficking may be a more actionable target than simply measuring the total number of immune cells within a tumor.

The findings also help explain why earlier efforts to suppress Tregs have been difficult to translate into effective treatments. Tregs are not inherently harmful: throughout the body, they prevent uncontrolled inflammation and protect healthy organs from immune attack. Broadly eliminating them could therefore produce dangerous autoimmune or inflammatory side effects, while also damaging beneficial immune responses. The UC Irvine study points instead toward a more selective strategy, in which the movement or activity of tumor-protective Tregs is disrupted specifically within the cancer microenvironment. Such an approach could potentially be paired with PD-1 blockade, preserving the immune system’s protective functions elsewhere while preventing Tregs from rebuilding the suppressive conditions that allow a tumor to return.

The researchers emphasize that the work is not an immediately available treatment for patients, and the results in mice must be tested through further preclinical studies and, eventually, carefully designed clinical trials. Nevertheless, the study illustrates how mathematical oncology can accelerate the search for therapeutic targets. Conventional research often evaluates one proposed mechanism after another, with each experiment requiring substantial time, biological material and funding. A validated computational model can screen many mechanisms and treatment combinations before laboratory teams commit to large-scale experiments. Francesco Marangoni, one of the study’s senior investigators, said the project brought mathematics and biology together so that each discipline could inform the other. John Lowengrub, the other senior investigator, said the model not only forecast biological outcomes but also identified a potentially overlooked target for improving cancer therapy.

The model may ultimately prove useful beyond melanoma and beyond PD-1 blockade. Because it represents the relationships among tumor growth, immune-cell recruitment, immune suppression and treatment response, researchers can adapt it to examine other immunotherapies or combinations of drugs. It could also help determine which patients are most likely to benefit from interventions aimed at Treg migration, provided that equivalent biological markers can be identified in human tumors. The broader message is that resistance to cancer therapy may not arise from a single mutation or a single immune defect, but from the changing balance of cells moving through the tumor over time. By revealing how one rate of cellular movement can influence that balance, the UC Irvine study offers a potential roadmap for making immunotherapy more durable—and demonstrates how computer-generated disease models can help turn the enormous complexity of cancer biology into testable treatment strategies.

Subject of Research: Regulatory T-cell infiltration as a determinant of acquired resistance to PD-1 immunotherapy in melanoma.

Article Title: Mathematical and Mouse Models Identify Regulatory T Cell Influx as A Key Determinant of Acquired Resistance to PD-1 Immunotherapy

News Publication Date: Aug. 20, 2026

Web References: https://news.uci.edu/ ; https://aacrjournals.org/cancerres/article/doi/10.1158/0008-5472.CAN-25-5784

References: Cancer Research, “Mathematical and Mouse Models Identify Regulatory T Cell Influx as A Key Determinant of Acquired Resistance to PD-1 Immunotherapy.”

Keywords: cancer immunotherapy, melanoma, PD-1 blockade, PD-L1, regulatory T cells, Tregs, tumor microenvironment, immunotherapy resistance, mathematical modeling, computational oncology, effector T cells, cancer research

Over the past five years, the Quantitative Biology Fellows program has affirmed the critical importance of integrating robust computational skills with biological insight. These investigators are navigating the difficult terrain of cancer biology by deploying innovative theoretical frameworks alongside empirical evidence to decode intricate cellular mechanisms. They benefit from a unique funding structure, which provides $240,000 over three years and pairs postdoctoral scientists with dual mentors—an established computational scientist and a cancer biologist. This model fosters cross-disciplinary mentorship that is vital for the synthesis of quantitative and experimental approaches, enabling groundbreaking discoveries at the intersection of “wet” lab and “dry” lab research spheres.

One fellow, Dr. Simone Bruno at the Dana-Farber Cancer Institute, is focusing her work on triple-negative breast cancer (TNBC), one of the most aggressive and therapeutically challenging subtypes of breast cancer. Dr. Bruno’s research centers on the dynamics of chromatin—the structural arrangement of DNA and its regulatory proteins—and how directed alterations in this architecture influence cancer growth and resistance to therapies. Utilizing Bayesian inference to parameterize mathematical models that describe chromatin modification circuits, she intends to integrate these insights with pharmacokinetic and pharmacodynamic drug models. This composite computational framework aims to dissect the multifaceted mechanisms driving TNBC progression and resistance, potentially revealing novel intervention points to improve patient outcomes. Importantly, although TNBC serves as the model system, the methodologies developed here have broader applicability to diverse cancer types where chromatin remodeling is a pivotal factor.

At Memorial Sloan Kettering Cancer Center, Dr. Paul C. Klauser is pioneering computational protein design to overcome longstanding challenges in radiopharmaceutical development. Radiopharmaceuticals, which combine radioactive elements with targeting molecules, have transformed oncologic diagnostics and therapy but remain limited by the inefficiency of traditional chelators that bind radiometals. Dr. Klauser employs state-of-the-art diffusion models such as RFdiffusion to generate thousands of candidate protein scaffolds optimized for metal binding. These backbones are further refined using tools like ProteinMPNN and AlphaFold 3 to ensure structural stability and affinity for metals like copper, manganese, and lutetium. By engineering protein-based chelators capable of fusing with therapeutic antibodies, his computational methodology could vastly enhance the precision and efficacy of radiometal-based imaging and treatments, with a focus on HER2-positive gastric cancer yet far-reaching implications across cancers amenable to radiopharmaceutical interventions.

The adaptive immune response within tumor microenvironments is another frontier explored by Dr. Sohyeon Park at UCLA. Macrophages, specialized immune cells, exhibit “immune memory,” modifying their behavior based on previous antigen encounters, which can either inhibit or promote tumor progression. Despite recognition of this plasticity, the epigenetic and structural genomic basis of macrophage memory remains elusive. Dr. Park combines bulk Hi-C genomic data with machine learning-driven 3D chromosome reconstruction and deep learning image analysis to model how chromatin topology governs gene expression in macrophages. By quantifying spatial relationships between nuclear speckles and mRNA distribution, she seeks to mathematically characterize transcriptional regulation influenced by prior stimulation. This integrative computational and experimental approach aspires to unlock strategies for reprogramming macrophage memory, potentially tipping the balance toward enhanced anti-tumor immunity.

At the University of Texas Southwestern Medical Center, Dr. Ruoyu Wang addresses the enigmatic genomic “dark matter” of non-coding regions, which harbor regulatory elements vital to gene expression control and are frequently mutated in cancer. His innovative application of deep generative AI models to single-molecule regulatory genomics enables probabilistic exploration of chromatin state landscapes at DNA sequence resolution. By training these models on high-throughput genomic datasets, Dr. Wang’s framework can generate diverse hypothetical configurations of chromatin that reflect functional variability. This capability paves the way for high-fidelity annotation of the cancer regulatory genome, offering unprecedented granularity for discerning mutations that drive oncogenesis and identifying potential therapeutic targets within non-coding DNA.

The sophisticated temporal and spatial dynamics of gene regulation in cancer cells are the focus of Dr. Aaron Zweig’s work at the New York Genome Center. Employing stochastic differential equations to model gene expression trajectories over time, his computational pipeline incorporates provably identifiable linear and shallow neural networks optimized via adjoint differentiation techniques. Concurrently, spatial interactions among clustered transcriptomic data are analyzed through graph neural networks and self-attention mechanisms applied to latent gene embeddings derived from variational autoencoders integrating multi-modal RNA sequencing data. This approach uniquely captures both temporal variations and spatial heterogeneity in gene regulation, with particular relevance to acute myeloid leukemia (AML), where understanding transcriptional evolution could illuminate “precursor” cellular states and inform transplant immunotherapy strategies to minimize host tissue damage.

The Damon Runyon Cancer Research Foundation’s commitment to fostering such innovative quantitative research stems from its recognition that complex cancers demand equally complex and nuanced investigative tools. By supporting interdisciplinary collaborations that merge experimental oncology with computational modeling, Damon Runyon emphasizes the indispensable role quantitative biology plays in the era of personalized medicine. Through its intense selectivity—funding fewer than 10% of applicants—the Foundation ensures that only the most promising, visionary scientists gain support, promoting a culture of excellence that has historically propelled myriad breakthroughs, including multiple Nobel laureates.

The stories of these five fellows highlight how increasingly sophisticated computational methodologies are reshaping cancer research paradigms. From mathematical models simulating chromatin dynamics, deep learning–based structural genomics, protein engineering for radiotherapy, to complex neural network architectures capturing temporal-spatial gene regulation, these approaches exemplify the essential integration of quantitative rigor and biological insight. Their work stands as a testament to the transformative potential inherent in bridging computation and cancer biology—a synergy poised to deliver new therapeutic breakthroughs and precision interventions that could dramatically improve patient survival and quality of life.

As computational power and machine learning algorithms continue to evolve, the scientific community anticipates that such integrative frameworks will become standard tools within oncologic research. These fellows not only push the boundaries of knowledge but also exemplify the future of cancer research, where data-driven models and experimental validation go hand-in-hand to conquer one of medicine’s most formidable challenges. Their innovative projects reaffirm the belief that understanding cancer’s complexity at the molecular and cellular levels necessitates the convergence of diverse expertise, setting a new standard for collaborative science.

Subject of Research:

Cancer biology, computational biology, quantitative biology, chromatin dynamics, radiopharmaceutical design, immune cell epigenetics, regulatory genomics, machine learning, mathematical modeling.

Article Title:

Damon Runyon Names New Quantitative Biology Fellows Driving Computational Innovation in Cancer Research

News Publication Date:

Information not provided.

Web References:

http://damonrunyon.org

Keywords:

Cancer, Breast cancer, Quantitative analysis, Data analysis, Computational biology, Mathematical biology, Gene regulation, Mutation, Macrophages, Bioinformatics, Numerical analysis, Comparative analysis