BALTIMORE, July 25, 2025: In a groundbreaking development akin to weather forecast models, researchers have unveiled a new method to predict cellular activity in tissues over time. This innovative software, which merges genomics technologies with computational modeling, aims to foresee changes in cell behavior, such as intercellular communication that may lead to cancer proliferation.
The study, co-led by the University of Maryland School of Medicine’s (UMSOM) Institute for Genome Sciences (IGS), was published online on July 25 in the journal Cell. It represents the culmination of a multi-year, multi-laboratory project at the intersection of software development and collaborative bench and clinical research. This pioneering research could eventually pave the way for computer programs that assist in determining optimal cancer treatments by essentially creating a “digital twin” of the patient.
Revolutionizing Cancer Treatment with Digital Twins
“Although standard biomedical research has made immeasurable strides in characterizing cellular ecosystems with genomics technologies, the result is still a single snapshot in time—rather than showing how diseases, like cancer, can arise from communication between the cells,” said Jeanette Johnson, PhD, a Postdoc Fellow at IGS and co-first author of the study. “Cancer is controlled or enabled by the immune system, which is highly individualized; this complexity makes it difficult to make predictions from human cancer data to a specific patient.”
The uniqueness of this research lies in its use of a plain-language “hypothesis grammar.” This approach employs common language as a bridge between biological systems and computational models, simulating cellular behavior in tissue.
The Role of Hypothesis Grammar
Paul Macklin, PhD, Professor of Intelligence Systems Engineering at Indiana University, led the team that developed this grammar to describe cell behavior. The grammar allows scientists to use simple English sentences to construct digital representations of multicellular biological systems, enabling the development of computational models for complex diseases like cancer.
“As much as this new ‘grammar’ enables communication between biology and code, it also enables communication between scientists from different disciplines to leverage this modeling paradigm in their research,” stated Daniel Bergman, PhD, a scientist at IGS and Assistant Professor of Pharmacology and Physiology at UMSOM, who co-led the study with Dr. Johnson.
Applications in Breast and Pancreatic Cancer
Dr. Bergman and his colleagues at IGS combined this grammar with genomic data from real patient samples to study breast and pancreatic cancer, utilizing technologies such as spatial transcriptomics. In breast cancer, the IGS team modeled an effect where the immune system fails to curtail tumor cell growth, instead promoting invasion and cancer spread. They adapted this computational modeling framework to simulate a real-world immunotherapy clinical trial for pancreatic cancer.
Using genomics data from untreated pancreatic cancer tissue samples, the model predicted varied responses to immunotherapy among virtual “patients,” highlighting the importance of cellular ecosystems in precision oncology. Pancreatic cancer, notoriously difficult to treat, is often surrounded by dense fibroblast structures. The team used spatial genomics technology to further explore fibroblast-tumor cell communication, tracking tumor growth and progression from real patient tissue.
“What makes these models so exciting to me as someone who studies immunology is that they can be informed, initialized, and built upon using both laboratory and human genomics data,” said Dr. Johnson.
Implications for Future Research and Clinical Trials
Dr. Elana J. Fertig, Director of IGS and a lead author on the study, emphasized the transformative potential of this approach. “Ever since my transition from weather prediction to computation, I believed we could apply the same principles across biological systems to create predictive cancer models. Adapting this approach to genomics technologies gives us a virtual cell laboratory for conducting in silico experiments,” she explained.
Dr. Fertig described the research as “a tapestry of team science,” with additional validation of the computational models from clinical collaborators at Johns Hopkins University and Oregon Health Sciences University. The project received funding from the National Foundation for Cancer Research.
The new grammar is open source, allowing the scientific community to benefit from it. “By making this tool accessible, we are providing a path forward to standardize such models and make them generally accepted,” said Dr. Bergman. Genevieve Stein-O’Brien, PhD, led researchers in applying this approach to a neuroscience example, simulating brain layer development.
“With this work from IGS, we have a new framework for biological research, enabling digital twins and virtual clinical trials in cancer and beyond,” said Mark T. Gladwin, MD, Vice President for Medical Affairs at the University of Maryland, Baltimore.
The announcement comes as the scientific community continues to seek innovative solutions for complex diseases like cancer. As this research progresses, it holds the promise of revolutionizing personalized medicine and providing new insights into the intricate dynamics of cellular ecosystems.
