Freenome Holdings, Inc.

John St John

Adam Drake

Greg Hogan

Steven Kothen-hill

Rui Yang

David Weinberg

Michael Pearson

Methods and systems provided herein address current limitations of bisulfite-based methylation sequencing by improving the quality and accuracy of nucleic acid methylation sequencing and uses thereof for detection of disease. Methods that include minimally-destructive conversion methods for methylation sequencing as well as specialized UMI adapters provide for improved quality of sequencing libraries and sequencing information. Greater accuracy and more complete methylation-state information permits higher quality feature generation for use in machine learning models and classifier generation.

Methods and systems for high-depth sequencing of methylated nucleic acid

The present disclosure provides methods and systems for screening or detecting a colorectal cancer or following colorectal disease progression that may be applied to cell-free nucleic acids such as cell-free DNA. The method may use detection of methylation signals within a single sequencing read in identified genomic regions as input features to train a machine learning model and generate a classifier useful for stratifying populations of individuals. The method may comprise extracting DNA from a cell-free sample obtained from a subject, converting the DNA for methylation sequencing, generating sequencing reads, and detecting colon proliferative cell disorder-associated signals in the sequencing information and training a machine learning model to provide a discriminator capable of distinguishing groups in a subject population such as healthy, cancer or distinguishing disease subtype or stage. The method may be used for, e.g., predicting, prognosticating, and/or monitoring response to treatment, tumor load, relapse, or colorectal cancer development.

Methods and systems for detecting colorectal cancer via nucleic acid methylation analysis

Growing community of inventors

Brisbane, CA, United States of America

John St John