Allelica Partners with Taiwanese Biobanks to Perform Breast Cancer Polygenic Risk Score Analysis

Allelica has partnered with SP BioMED in Taiwan to perform polygenic risk score analysis on a population of known breast cancer cases

Allelica Partners with Taiwanese Biobanks to Perform Breast Cancer Polygenic Risk Score Analysis
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Allelica, the leading provider of polygenic risk score analysis technology, announced that it has joined forces with the Taiwanese precision medicine provider, SP BioMED, to perform a polygenic risk score (PRS) study for breast cancer. SP BioMED will run PRS analysis using Allelica’s PREDICT module to calculate PRS for breast cancer on known breast cancer cases in their local biobanks.

The objective of the study is to determine the best-performing genotyping technology for accurate, scalable, and cost-effective genome-wide data generation for downstream applications such as PRS calculation in Taiwan’s population. The study builds on Allelica’s recent efforts to empower local institutions to build capacity to provide clinical-grade PRS analysis and reporting.

“Breast cancer is the most common cause of cancer in Taiwanese women, so understanding the role that genetics plays in this disease risk is critical to identifying women at high risk,” said Allelica CSO, George Busby. “Generating accurate genetic data at low cost is key to enabling healthcare systems to benefit from the enormous potential of genomic medicine. Working together with local providers like SP BioMED to identify the most appropriate technology to drive the development of better medicine across diverse populations is one of Allelica’s top priorities.”

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The study will analyze 856 Taiwanese breast cancer samples genotyped by SP BioMED and associated laboratories using three different technologies: the Thermo Fisher Axiom Genome-Wide TPM 2.0 Array Plate (Taiwan Precision Medicine, Version 2.0), Whole Genome Sequencing (WGS) at 30X coverage and WGS at 2X coverage.

Raw sequencing data from each of the technologies will be analyzed with Allelica’s PREDICT module which performs genome-wide imputation, genetic ancestry assessment, calculation and reporting of Allelica’s breast cancer PRS compared to genetic ancestry matched reference distributions. Allelica’s PREDICT Module, a software tool for the calculation and reporting of multiple PRS, is compatible with microarray technology with genome-wide coverage, and WGS as low as 2X.

The breast cancer PRS calculated in the study is Allelica’s proprietary PRS for breast cancer which has been clinically validated in multiple ancestries. Allelica’s PRS for breast cancer has been bench-marked against the leading scores and outperforms them in crucial metrics including AUC, OR per SD and calibration.

The project is being performed together with SP BioMED, led by Dr. Shih-Feng Tsai of the National Health Research Institutes Taiwan. SP BioMED has provided enabling genomic technology to a network of laboratories in Taiwan focused on offering clinical genetic services to local healthcare providers.

Their services are targeted to patients with chronic diseases such as cancer and cardiovascular disease, health-conscientious individuals who take an active interest in their personal risk profiles, and participants of biobanks studies for diseases such as chronic kidney disease, coronary artery disease, congestive heart failure, and cancers.

“Predicting genetic risk of chronic diseases, including cancer, is essential for realizing the goal of early detection and precision prevention of common diseases, such as breast cancer,” said Dr. Shih-Feng Tsai. “By collaborating with Allelica, we can adopt their PRS technology in Taiwan to implement genomic medicine in clinical practice.”

The samples being run in the study will also be part of Allelica’s Federated Learning Initiative. The Federated Learning Initiative, founded by Allelica, enables Allelica’s partners and collaborators to contribute to the development of better, more inclusive PRS models without the need to share data. The objective of the Initiative is to develop PRS models which perform equally well across individuals, regardless of their ancestry or ethnicity.