Towards Equitable Blood Disease Gene Mutation Testing: SCHD26 Oral Abstract Presenter Dr. Rafaella Litvin

A new AI-driven approach to detecting Clonal Hematopoiesis of Indeterminate Potential (CHIP) could transform early cancer risk screening. Presented at SCHD26, this research highlights how low-cost blood smear analysis may expand access and reduce disparities in genetic testing.

Advancing Cancer Health Equity at SCHD26

At the Summit on Cancer Health Disparities (SCHD26), first authors of selected abstracts took to the stage to present their research to an audience of oncology peers, colleagues, and mentors from across the globe. In the conference’s oral abstract session, the next generation of disparities researchers charted a path towards equity grounded in rigorous empirical study.

Understanding CHIP and Its Impact on Health Risks

Dr. Rafaella Litvin, Hematology and Oncology Fellow at Cleveland Clinic, presented her abstract “Deep Learning Model for Prediction of Mutational Profiles Associated with Clonal Hematopoiesis of Indeterminate Potential (CHIP) from Peripheral Blood Smears.” Dr. Litvin’s co-authors include Dr. Jacob Scott, Dr. Arda Durmaz, Dr. Marko Velimirovic, Dr. Joy Nakitandwe, Dr. Abhay Singh, Dr. David Bosler, Dr. Abhinav Vayal Veettil, and Payton Clark.

Dr. Litvin’s work focuses on a condition called Clonal Hematopoiesis of Indeterminate Potential (CHIP). People with CHIP are significantly more likely to develop blood cancers, heart disease, stroke, and other serious conditions; it is also more prevalent in Black patients, who are simultaneously more likely to develop the most serious complications. The genetic detection test for CHIP is hugely expensive, which is where Dr. Litvin’s team comes in: they are developing a low-cost test derived from a blood smear. While they’re still in the early stages, results are promising. If the model is successful, CHIP testing can lead to lifesaving early detection, especially in underserved communities.

A Breakthrough Approach: AI-Powered Blood Smear Testing

If the team is able to identify CHIP, there are resources that exist to help mitigate the risk that accompanies the mutation.

“There are already CHIP clinics in the country where we're working on cardiovascular risk modification to try to minimize the impact of CHIP,” Dr. Litvin said in her SCHD26 presentation. “There are multiple trials looking at the inflammasome and trying to limit the inflammation caused by these mutations and limit the impact of cardiovascular risk. And there's also trials, ongoing targets and specific mutations such as IH1, to try to prevent the progression to a hematologic model.”

The challenge, then, is identifying CHIP in the first place. Dr. Litvin’s intervention is simple, cheap, and able to be integrated into a preexisting routine procedure—meaning CHIP detection will skyrocket, especially in those communities least able to access the prohibitively expensive test that is currently the only approved option.

Why Early CHIP Detection Matters

“We know that the true prevalence among black patients and other minorities is likely underestimated,” Dr. Litvin said. “From several studies, these patients are less likely to be evaluated in genetics formats, less likely to get NGF oncology, and right now the only way they detect CHIP is incidentally. We know that lower income patients have worse survival when they do develop hematologic malignancies… There's no protocol. People are just diagnosed incidentally.”

Frequently Asked Questions About CHIP and Early Detection

Q1. What is Clonal Hematopoiesis of Indeterminate Potential (CHIP)?

CHIP is a condition where blood cells acquire genetic mutations that increase the risk of blood cancers, cardiovascular disease, and stroke.

Q2. Why is CHIP testing important?

Early detection allows clinicians to monitor patients and implement interventions to reduce the risk of serious complications.

Q3. Why is current CHIP testing limited?

Standard detection relies on expensive genetic sequencing, making it less accessible to low-income and underserved populations.

Q4. How does AI improve CHIP detection?

AI models can analyze routine blood smears to predict mutation profiles, offering a faster and more affordable screening method.

Q5. Who benefits most from this new approach?

Underserved communities, including minority and low-income populations, who currently face barriers to genetic testing access.