Decoding and Debugging Biological Programs for a Healthier Future

Newswise — Alessandro Vasciaveo, PhD, has fond memories of being introduced to computers and programming through his father’s Apple II.

“I believe that everyone should do something they are passionate about, and my passion for computers started while growing up and learning to write computer programs to solve complex tasks,” said Vasciaveo, who joined Sanford Burnham Prebys as an assistant professor in computational biology and artificial intelligence in fall 2024.

As a young adult, he deliberated between pursuing computer engineering or exploring a more direct path to societal impact as a physician. Ultimately, Vasciaveo leaned into his enthusiasm for computation. His journey as a student and scientist-in-training took him across Italy, Germany and the United States. Along the way, his commitment to contributing to health improvement remained a constant.

Vasciaveo now uses his training and experience as a scientist and engineer to advance knowledge of human biology through research. He has learned how to apply his expertise in computer science and computational biology toward the identification of novel treatments and cures for diseases.

“Similar to opening and dissecting a computer to see how all the components connect and interact, I want to reverse-engineer the biological machines that are our cells,” said Vasciaveo.

“We need to figure out what programs healthy cells normally follow and identify when something goes wrong — like a glitch in a computer program — that might lead to disease. And finding the right treatment is somewhat like how computer scientists fix bugs in software to make it work properly again.”  

Prior to joining the institute, Vasciaveo used similar approaches to study conditions ranging from cancer to neurodegenerative diseases as an associate research scientist at Columbia University in New York City. He will largely focus on cancer research at Sanford Burnham Prebys, drawing on his experience with numerous projects in the field, including one that demonstrated how a computer algorithm may be able to identify treatments for individual patients affected by cancer.

In that effort, scientists developed a strategy called OncoLoop that combines experimental data and computer algorithms to predict which drugs would likely be most effective in the clinic. They began by identifying the cellular programs of individual patients’ tumors that were most responsible for causing cancerous behaviors and matching them with mouse models of prostate cancer to find a pair to perform preliminary tests before proposing effective drugs for clinical use.

“With thousands of gene mutations found to be associated across many cancer types, we focused on a much smaller set of master regulator proteins that we’ve shown to be most culpable for cancerous programs on an individual patient basis,” explained Vasciaveo.

Once a match was established, the team used computational analyses to predict which drug would be most effective against the identified master regulator proteins, then tested the medication on the patient’s mouse model counterpart.

“We found that three out of four of the predicted drugs induced highly significant inhibition of tumor growth in the mice,” Vasciaveo said. “We believe this OncoLoop strategy has the potential to improve our ability to quickly find and test what will be the best drugs for patients, and that it can be applied far beyond prostate cancer.”

The findings from the OncoLoop project were published in Cancer Discovery in 2023.

Vasciaveo also contributed to research that uncovered a possible treatment approach for a deadly form of pediatric cancer.

“Patients whose neuroblastoma tumors have amplification of the MYCN gene face a standard of care that is grueling and can have long-lasting implications for growth and development,” said Vasciaveo.

There are no effective targeted therapies for this aggressive subtype of neuroblastoma, in large part because the product of the MYCN gene is considered an “undruggable” protein that lacks architecture on its surface capable of binding with small molecules — the chemical building blocks of most drugs.

Vasciaveo and his collaborators compared the ability of many chemical compounds to interfere with a set of 10 core proteins determined to drive MYCN amplified tumors. The scientists found that one of the most disruptive compounds was isopomiferin, part of a group of chemicals common in the plant kingdom. The work was published in Cell Chemical Biology in April 2024.

“In order to continue exploring our findings for potential therapies, I want my team here at Sanford Burnham Prebys to discover new knowledge about basic mechanisms of biology,” said Vasciaveo.

“At Columbia, in one project focused on cell-to-cell communication in the small intestine, we felt like detectives chasing down leads because we kept finding unexpected results.”

The team discovered that cells believed to be the gut’s main source of stem cells had been misidentified for more than 15 years. Instead, the scientists found that the gut’s real stem cells — responsible for regenerating the lining of our intestines nearly twice per week — reside in another location entirely, manufacture different proteins and react to a distinct set of signals. These findings were published in Cell in June 2024, along with a summary penned by Yale scientists.

“This discovery was particularly meaningful to me because it resulted from a genuine team effort between computational and experimental scientists, and due to the collaborative scientific journey of learning and exploration that brought it to fruition,” added Vasciaveo.

Understanding the true identity of stem cells and characterizing their biological programs has enormous implications for regenerative medicine and cancer.  It may help scientists, including stem cell biologists and regenerative medicine researchers in the Sanford Burnham Prebys Development, Aging and Regeneration Program, find ways to coax less active stem cells into action, which may help tissues recover after injury and promote healthier aging.

Vasciaveo’s vision centers on leveraging massive datasets from single-cell biology and advanced machine learning to uncover critical insights into how cells communicate and function in healthy and diseased states, and to identify key players of disease programs that can serve as precise therapeutic targets.

This approach will allow for more precise treatments tailored to each patient’s unique biology, pushing the boundaries of traditional medicine. These efforts have the potential to significantly accelerate the process of identifying new therapies for challenging diseases. Beyond cancer, Vasciaveo envisions applications in regenerative medicine, such as unlocking ways to activate stem cells for improved tissue repair and healthier aging.

Sanford Burnham Prebys offers the perfect environment for this ambitious work, Vasciaveo said, highlighting its strong focus on AI-driven research and interdisciplinary collaboration, as well as fostering new collaborations around drug discovery with the Conrad Prebys Center for Chemical Genomics.

“This is a place where computational and experimental scientists can work together seamlessly, and that’s key for achieving breakthroughs in drug discovery and regenerative medicine,” he said.

“I see my lab as a hub for innovation where we’re working to deepen our understanding of biology and contribute to advancements in health care,” said Vasciaveo. “We’re creating opportunities for scientists who want to be part of this exciting journey where computation meets biology to solve real-world problems.”