“‘They are synthetic patients,’ Suzanne Tamang, PhD, says of the AI-created population. ‘It was derived from real veterans, although it’s synthetic. It’s a new method, based on generative AI, and it allows us to bring in environmental data to explore different ways to link it to population health.’”

Tamang’s population health approach considers how the interplay of clinical, social, and environmental factors influences health outcomes. Her research using air pollution data from the U.S. could help clinicians mitigate flares caused by factors like wildfire smoke. 

The synthetic population, encompassing a diverse range of demographics and geographic locations across the U.S., not only enhances the generalizability of her findings but also maintains patient privacy. This innovative method supports the development of predictive models that could significantly improve healthcare responses to environmental triggers.

Tamang’s work advances autoimmune research by translating complex AI technologies into practical tools that improve access to quality health care for vulnerable populations. Her pioneering efforts aim to empower health systems with data to better anticipate and mitigate the impacts of environmental factors on health outcomes.

“Our work aims to unite various experts to tackle these significant challenges by providing them with comprehensive datasets,” she says. “This approach marks a critical advancement in our ongoing projects within the new climate and health space.”

“We have such a unique opportunity here with this pandemic that I’ll spend the rest of my career studying how COVID-19 triggers autoimmunity and how we can treat it.”

– PJ Utz, MD

When the pandemic began, Stanford and other institutions proactively started to collect samples, enroll patients, and study the disease as it evolved. 

“We were essentially building the new immunological frontier while the airplane was being built,” Utz explains. This proactive approach has allowed researchers to capture unprecedented data on the immune responses triggered by COVID-19.

Building on the knowledge gained from past pandemics, today’s researchers, equipped with unparalleled clinical samples and molecular tools, are now able to conduct detailed studies of viral effects on the immune system and advance theories about post-viral autoimmune effects that were previously speculative.

“We have such a unique opportunity here with this pandemic,” Utz says, “that I’ll spend the rest of my career studying how COVID-19 triggers autoimmunity and how we can treat it.”

“Our research not only advances our understanding of chronic pain and autoimmune diseases but also transforms patient care through personalized medicine and predictive modeling, shaping future public health strategies.”

– Titilola Falasinnu, PhD

The Falasinnu Lab is composed of epidemiologists, clinicians, pain scientists, and informaticists who use large datasets and advanced computational methods to understand the complex nature of pain in patients with autoimmune rheumatic diseases, such as lupus. They are developing predictive models to forecast pain episodes in patients to enable personalized medicine and treatment plans. 

Their research has illuminated overlapping factors that influence chronic pain, paving the way for interventions that address both biological and psychosocial aspects of pain management. The aim is to improve patient quality of life and reduce reliance on long-term opioid therapies, which carry significant health risks.

“Our research not only advances our understanding of chronic pain and autoimmune diseases but also transforms patient care through personalized medicine and predictive modeling, shaping future public health strategies,” she says.

Similar data-driven methods are used to address the challenges faced by cancer patients experiencing depression. Hernandez-Boussard and her team have been studying depression after a cancer diagnosis. By applying machine learning, a branch of artificial intelligence (AI) that uses algorithms and statistical models to make data-based predictions, they have identified features associated with depression in these patients. One significant finding was the association between loneliness and depression following a cancer diagnosis. “If we can identify recent losses in a patient’s life, like a divorce or the death of a loved one, we can better predict and manage their risk of depression,” explains Hernandez-Boussard. 

Integrating data from a variety of sources, her team crafts comprehensive profiles of patients. This holistic approach allows for targeted interventions that address not only the clinical symptoms of depression but also the social and emotional factors at play. For example, if a patient is flagged for significant loneliness following a divorce, the healthcare team can proactively connect them with support groups, counseling services, or community resources. This not only helps to mitigate their risk of depression but fosters a more supportive and responsive care environment.

“The advances in this field over the last 10 years have been truly mind-blowing,” says Fakhri. “Seeing the success of these drugs, and just how dynamic the field has been, made me want to keep working on CLL.”

Broad Clinical Trial Options

In 2022, Fakhri became the director of the CLL clinical trial portfolio at Stanford after the passing of Steven Coutre, MD, who had established the clinical research program in the Division of Hematology. Since joining, she has launched five new clinical trials for patients with CLL. She is also hard at work to open clinical trials benefiting patients with Richter’s transformation – a condition in which CLL transforms to a more aggressive lymphoma with currently very poor outcomes. Those trials, she says, range from testing new front-line options for patients who have new CLL diagnoses to comparing treatments for people whose recurrent cancers are not responding to newer targeted agents in the field.

“One of my priorities at Stanford is making sure that we always have trials in both of these settings,” says Fakhri. “Despite all the advances in CLL, there are still a subset of patients with high-risk features that need new treatment options, and we want to meet their needs.”

About 88% of patients newly diagnosed with CLL will survive for at least five years, according to the latest data from the National Cancer Institute. That represents a large increase from the 70% to 75% five-year survival rate in the 1990s and early 2000s. But patients who have recurrence of their cancer, even years later, often fare less well – that is one of the populations Fakhri hopes to help with new clinical trials.

Fakhri adds that the most effective CLL drugs have come out of a detailed molecular understanding of how CLL impacts cells, and this kind of basic research must continue. To that end, she is collaborating with Stanford scientists including Sydney Lu, MD, PhD, who studies the underlying biology of CLL and other cancers. Lu and Fakhri are studying the implications of a gene mutation, known as SF3B1, that is found in about one in 10 CLL cancers and is associated with worse outcomes for patients. If they can understand the molecular consequences of SF3B1, Fakhri says, they may be able to develop new drugs to counteract the mutation or develop a better understanding of the clinical behavior and response to different therapies in patients harboring the SF3B1 mutation.