Shuo C Li

20210327535, Oct 21, 2021

Aug 22, 2019

17/269983

- Oakland CA, US
- Los Angeles CA, US
Shuo LI - Los Angeles CA, US
Chun-Chi LIU - Los Angeles CA, US
Xiaohui NI - Los Angeles CA, US

G16B 20/10
G16B 30/00
G16B 40/30

The present disclosure provides methods and systems for detecting or inferring levels of Copy Number Variants (CNVs) in cell-free nucleic acid samples to detect or assess cancer and prenatal diseases. Cell-free nucleic acid methylation sequencing data may be utilized to distinguish tumor-derived or fetal-derived sequencing reads from normal cfDNA sequencing reads. Each cell-free nucleic acid sequencing read (e.g., containing tumor or fetal methylation markers) may be classified as corresponding to a tumor/fetal-derived or a normal-plasma cell-free nucleic acid, based on the methylation cfDNA sequencing data (e.g., obtained using Bisulfite sequencing or bisulfite-free sequencing methods) and tumor/fetal methylation markers. Next, a profile of the tumor/fetal-derived sequencing read counts may be constructed and then normalized. The CNV status (e.g., gain or loss) of each genomic region may be inferred, and a diagnosis or prognosis can be made based on a subjects inferred CNV profile.

20210125683, Apr 29, 2021

Sep 14, 2018

16/647339

- Oakland CA, US
Shuo LI - Los Angeles CA, US
Wenyuan LI - Los Angeles CA, US

THE REGENTS OF THE UNIVERSITY OF CALIFORNIA - Oakland CA

G16B 20/20
G16B 40/20

The present disclosure provides a probabilistic model for accurate and sensitive somatic single nucleotide variant (SNV) detection in cell-free nucleic acid samples comprising a set of sequence data. A joint genotype may be determined for each locus in the set of sequence data, and germline mutations may be intrinsically removed. A set of filtrations can be applied to eliminate low quality somatic variant calls. Further, a global tumor cell-free deoxyribonucleic acid (cfDNA) fraction and overlapping read mates can be considered, thereby enabling accurate SNV detection and variant allele frequency estimation from samples with low tumor cfDNA fraction. A sensitive early detection of minimal residual disease (MRD) is designed by using the probabilistic model and the machine learning model for distinguishing true variants from sequencing errors.