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DiagMMR
DiagMMR

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PREDICT Module
PREDICT Module

0 review

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Solutions

Description

Product Description:

DiagMMR is designed to be a predictive diagnostic tool that enables preventive follow-up and counselling for affected mutation carriers who are at high risk for developing Lynch syndrome related cancers.

While current Lynch syndrome diagnosis relies heavily on tumor studies, the quantitative DiagMMR method allows carrier diagnosis based on a minimally invasive tissue sample before the person has developed any cancer, without the knowledge and details of an inherited mutation.

The inherent challenges to variant interpretation in genetic testing can be avoided and the conclusive and actionable results removes the burden of uncertainty from both the patients and the treating medical professionals.

About LS CancerDiag:
LS CancerDiag Ltd is committed to reducing cancer mortality rates with a low-cost, simple diagnostic method that detects an inherited cancer-causing condition prior to cancer. Their vision is to see DiagMMR™ as a new global testing standard in LS diagnostics. Lynch syndrome is the main cause of hereditary colorectal cancers and it also causes a broad spectrum of other tumors. With a significant proportion of the world’s population carrying the Lynch syndrome mutation, there are potentially tens of millions under a severe risk for being affected by LS. Their groundbreaking DiagMMR™ assay is an easy functional test that can be used for diagnosis of MMR deficiency i.e. Lynch syndrome (previously known as Hereditary Non­Polyposis Colorectal Cancer; HNPCC). Their mission is to save lives through an innovation that dramatically simplifies diagnostics of Lynch syndrome by delivering predictive and accurate results with a fast, unique and cost-efficient method globally.
Product Description:
Allelica’s PREDICT module is a resource for clinical laboratories and healthcare providers to generate personalized PRS reports for common diseases. The end-to-end solution starts with patient genotype data generated from microarray or lcWGS technology. Users can then choose from a range of diseases to report on, including coronary artery disease, breast cancer, and Alzheimer’s disease. In just a few hours, the platform applies Allelica’s predictive analytics to calculate individual genetic risk scores for the diseases of interest. The technology accounts for a patient’s ancestry by comparing the risk scores to a distribution matched to the individual’s ancestry. The resulting report communicates the individual’s genetic risk of disease in a concise and comprehensible document.
About Allelica:

Allelica has created a secure and trusted platform for Polygenic Risk Score analysis and reporting to equip health systems and clinical laboratories with cutting-edge tools to reduce the impact of common disease through genomic medicine. 

All processes performed on the Allelica platform are fully HIPAA and GDPR-compliant. ISO 13485 and ISO 9001 certifications guarantee that Allelica applies medical device quality management processes every step of the way.

Allelica is working with leading health systems and genetics laboratories in the United States and across Europe to advance disease prevention through widened application of PRS in healthcare.

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Clients

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Use Cases

Description:

LYNCH SYNDROME

1 in 279 people have Lynch syndrome, i.e. inherited mismatch repair (MMR) deficiency, making it the most common cause of hereditary cancers.

INHERITED DEFECT

Lynch syndrome (LS) is inherited with 50% probability within affected families. Each single year, more than 2 million new cancer cases should be tested for LS.

INDIVIDUAL RISK

Individuals with inherited MMR deficiency have an up to 80% risk of developing cancer and at a significantly younger age than the general population.

INNOVATIVE SOLUTION

DiagMMR is a simple and accurate test to reliably detect MMR deficiency, supporting the early detection of Lynch syndrome, enabling preventive care and helping save lives.

Pediatric use cases:

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EHR Integrations

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Differentiators

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Company Details

Founded in 2013

Founded in 2018

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