Grail has patented a method to determine a subject’s cancer class by analyzing cell-free DNA molecules from a biological sample. By identifying relative copy numbers at genomic locations and applying them to a machine learning model, the technology can accurately classify cancer types. GlobalData’s report on Grail gives a 360-degree view of the company including its patenting strategy. Buy the report here.

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According to GlobalData’s company profile on Grail, was a key innovation area identified from patents.

Determining cancer class using cell-free dna sequencing

Source: United States Patent and Trademark Office (USPTO). Credit: Grail LLC

A recently granted patent (Publication Number: US11929148B2) outlines a method for determining the cancer class of a subject by analyzing cell-free DNA molecules in a biological sample. The process involves extracting these molecules, sequencing them based on size-selected criteria, identifying relative copy numbers at various genomic locations, and applying this data to a machine learning model trained to determine the cancer class based on the genomic information. The model is specifically trained with a labeled training dataset to accurately classify whether the subject has cancer, the stage or type of cancer, or even the prognosis for the disease.

Furthermore, the method detailed in the patent involves specific thresholds for DNA molecule lengths, sequencing reads, and genomic locations to ensure accurate classification. By utilizing a machine learning model, the process can provide a high degree of confidence in determining the cancer class of a subject based on the identified relative copy numbers. The patent also highlights the importance of removing certain genomic locations with high variance in relative copy number to enhance the accuracy of the classification process. Overall, this innovative method offers a promising approach to cancer classification using cell-free DNA analysis and machine learning techniques, potentially revolutionizing how cancer diagnoses and prognoses are determined in the future.

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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.