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Allelica Wants Polygenic Risk Scores to Become Routine in Heart Disease Care


CHICAGO – Investigators from bioinformatics startup Allelica have demonstrated that polygenic risk scores can help deliver a more accurate picture of coronary artery disease (CAD) risk than the accepted method of looking only at low-density lipoprotein (LDL).

In a research letter published in Circulation last week, they showed that in a European cohort, CAD risk from LDL is modified by polygenic risk.

While further research and validation are necessary, the finding could potentially upend conventional wisdom about cardiovascular disease prevention and lead to changes in standards of care, the company suggested.

In the paper, the Allelica team pulled a dataset of 15,433 cases of coronary artery disease from the UK Biobank, ran the company's polygenic risk score algorithm, then considered phenotypic data from the cohort, which includes mostly people of European origin. The researchers found that polygenic risk modified overall risk of coronary artery disease in about 10 percent of subjects, suggesting that those with high polygenic risk scores in particular could benefit from statins and other drugs that lower LDL cholesterol.

"LDL cholesterol doesn't affect everyone the same," said Allelica CEO Giordano Bottà, the study's corresponding author. "What is considered to be an average LDL cholesterol [level] is not average for everyone," he said, specifically mentioning those with familial hypercholesterolemia. For this subgroup, anyone with LDL above 190 milligrams per deciliter should be a candidate for a statin.

"Even those persons that we identified with average LDL but high [polygenic] risk, potentially they should take statins as people with hypercholesterolemia do," Bottà said.

The Circulation research letter hinted that polygenic risk scores could eventually become part of the standard of care for assessing susceptibility to heart attacks. However, Allelica worked with a predominately white study pool, so further research is necessary on more diverse populations, according to Bottà.

"An important next step will be understanding how these results generalize in other prospective cohorts and in populations of different ancestries," the authors wrote in their research letter. "Because PRS can be quantified from a young age, it can provide information to mitigate the elevated lifetime risk that even moderate [LDL cholesterol] exposure causes in individuals with high [coronary artery disease] PRS."

Allelica, which just relocated its headquarters to New York from Rome as a result of a $1.75 million seed funding round announced last week, has developed a software engine called Discover that, according to Bottà, can "improve the predictive power of each PRS present in the polygenic score catalog," a curated compendium of risk scores that he likened to a genome-wide association study catalog.

Discover arrives at its polygenic risk scores through a novel method that employs a stacked clumping and thresholding (SCT) algorithm. "SCT is able to learn new single nucleotide polymorphism (SNP) weights using genome-wide association study summary statistics and a validation dataset, as opposed to other methods that can only identify optimal hyperparameters," the Circulation article explained.

Bottà said that Allelica's software allows healthcare systems and genetics laboratories to perform their own polygenic risk score analysis independently. It is optimized for Illumina iScan microarrays, but the technology works with any low-coverage, whole-genome sequence.

This kind of microarray is as low-cost as direct-to-consumer genetic test. Allelica can in fact use raw data from the likes of 23andme and Ancestry, though the company will not be offering its own DTC testing.

"PRS provides the highest value in a clinical setting with physicians playing a key role in planning a preventative strategy," Bottà said. "We really want to go [directly] to the clinic because now physicians can use this information effectively."

Users upload raw sequencing or array data to the Discover software-as-a-service platform, which then runs the analysis pipeline, including quality control and risk adjustment based on each person's ancestry, to produce a normalized polygenic risk score that a physician can act on. Bottà said that the risk adjustment allows for individual scores to fit into shared distributions to refine the scoring system.

"We [want to] let everyone understand that the polygenic risk score must not be considered in isolation. You need to integrate the additional risk factors because that is just a piece of the puzzle," he said.

Bottà said that the Discover platform can identify who should be prioritized for risk score analysis. "Ideally, we want to genotype everyone, but we know that there are some categories of people where the cost benefit is higher," he said.

A pooled cohort equation can assess cardiovascular risk based on traditional risk factors to ascertain intermediate risk, specifically mentioning the American College of Cardiology's Atherosclerotic Cardiovascular Disease Risk Estimator Plus, which screens for 10-year risk of heart attack based on phenotypic factors such as smoking, diabetes, and cholesterol that come from medical records. However, adding a polygenic risk score provides a more precise assessment, putting patients that otherwise would be at intermediate risk in higher or lower categories based on their genomes.

Stanford Medicine is doing just that with another software vendor, UK-based Genomics plc.

"It is all about then guiding the therapeutic intervention," Bottà said. "Physicians asked to have a tool that can guide them to prescribe … interventions in a data-driven fashion."

It will take some time to integrate the recent findings into clinical decision support systems and, ultimately, into clinical practice, but Allelica, founded in 2018, has begun some pilots with healthcare systems. Bottà declined to name any right now, citing nondisclosure agreements.

"In the US, we are assessing how polygenic risk scores should be integrated and the best way to communicate and return the results," he said, hinting at forthcoming news about integration with an electronic health records system.

Bottà said that the company is also looking to build cost-effectiveness into its software tool.

With the recent funding, Allelica just hired a health economist, as well as a senior VP for commercial operations with experience in value-based healthcare. According to Bottà, these hires should help the company better explain the importance of using polygenic risk scores at scale not only to improve patient outcomes, but for health systems and payors to save money.

He said that Allelica will soon release a study demonstrating the efficacy of its polygenic risk scores in preventing heart disease and saving money for healthcare systems.

"Genetics analysis has always been seen as something expensive, something that is not for everyone," Bottà said. "Here we are proposing that it is for everyone, because you can really add value."

The company does not have any pilots underway with payors, but will be pursuing such collaborations in the future, according to Bottà.

Specialty medical societies and disease-specific advocacy groups will be key to validation and acceptance of Allelica's polygenic risk scores. Bottà said that the firm is working on this with the American Heart Association — the publisher of Circulation — and several other undisclosed organizations, but said it will take more evidence before these findings lead to changes in the standard of care.

"They really want to see that this is feasible at scale," both scientifically and economically, Bottà said.

Allelica is also studying polygenic risk scores for breast cancer and prostate cancer. Bottà said that the company has "improved the predictive power of the PRS" for breast cancer, as described in a 2018 consensus statement published in the American Journal of Human Genetics.

He said that the company has integrated polygenic risk scores into models that consider multiple risk factors, including phenotypic data from EHRs. "We can get the data that's needed to build an absolute risk model," Bottà said. "We provide the risk over 10 years, over five years, or a lifetime risk according to their guidelines."

Then, Allelica needs to match risk assessments with parameters needed for care guidelines from groups like the American Heart Association. "We provide the genetic risk that is steady for your entire life, but then [adjusted] to the overall risk based on what the risk factors are that you have at that particular point of time," he said.

Allelica's team includes CSO and Cofounder George Busby, who left his position as senior research associate in translational genomics at the University of Oxford's Big Data Institute in January to join the company full time. CTO and Cofounder Paolo Di Domenico is a longtime software engineer.

"We realized that the business model of centralizing the analysis in only one lab is an old business model and doesn't really provide the value that the healthcare system needs and wants," according to Bottà. He said that health systems can genotype patients once at a central lab, but then Allelica can provide individual departments like oncology or cardiology with polygenic risk scores specific to each specialty.

"We wanted to empower the healthcare system to run the analysis inside and also to be independent," he said.