In an era where the fusion of computational science and biology is revolutionizing cancer research, the Damon Runyon Cancer Research Foundation has spotlighted five early-career scientists who are reshaping the landscape of quantitative biology. These newly named Quantitative Biology Fellows embody the cutting edge of interdisciplinary cancer research, employing advanced computational methods to unravel some of the most complex biological phenomena underpinning cancer development, progression, and therapy resistance. Each fellow harnesses a blend of mathematical modeling, machine learning, and experimental data to approach cancer biology from a fresh, quantitatively driven perspective, underscoring the essential role of computational biology in modern precision medicine.
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
