Non-invasive cancer detection methods
AssigneeIllumina, Inc.
InventorsMahdi Golkaram, Frixos Papadopoulos, Fan Song, Raakhee Vijayaraghavan, Li Liu, Jennifer LoCoco, Chen Zhao
Life of this patent
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Abstract
Method of detecting a cancer and identifying a tissue of origin of cfDNA in a subject involve obtaining a sample from a subject, collecting cfDNA from the sample, generating sequence libraries of ctDNA fragments, analyzing the sequence libraries of the ctDNA fragments, and classifying the sample as having ctDNA fragments from a healthy tissue or a cancer tissue.
Claims23
A method of detecting a cancer in a subject, the method comprising:
- obtaining a sample from a subject;
- isolating cfDNA from the sample;
- generating sequence libraries of ctDNA fragments from the isolated cfDNA;
- analyzing the sequence libraries of the ctDNA fragments to identify a tissue of origin of the ctDNA,
- thereby detecting the cancer in the subject.
A method of identifying a tissue of origin of cfDNA in a subject, the method comprising:
- obtaining a sample from a subject;
- isolating cfDNA from the sample;
- generating sequence libraries of ctDNA fragments from the cfDNA;
- aligning paired-end reads of the ctDNA fragments;
- analyzing 5′end 4-mer motifs, window protection score (WPS), and genome wide fragmentation length distribution (GWFLD) of the ctDNA fragments;
- generating independent data models for 5′end 4-mer motifs, WPS, and GWFLD;
- generating an ensemble model of the independent models from the independent data models using a machine learning process;
- and
- classifying the sample as comprising ctDNA fragments from a healthy tissue or a cancer tissue based on the ensemble model.
A method of determining a presence of cancer in a patient, the method comprising:
- isolating cfDNA from samples from a plurality of subjects;
- generating sequence libraries of ctDNA fragments from the isolated cfDNA;
- aligning paired-end reads of the ctDNA fragments;
- analyzing 5′end 4-mer motifs, window protection score (WPS), and genome wide fragmentation length distribution (GWFLD) of the ctDNA fragments;
- generating independent models for 5′end 4-mer motifs, WPS, and GWFLD;
- generating an ensemble model of the independent models using a machine learning process from the independent models;
- applying the ensemble model to sequence libraries of ctDNA fragments for a sample obtained from the patient;
- and
- determining whether the patient has cancer based on the application of the ensemble model.
The method of any one of the foregoing claims, wherein the sample is blood.
The method of any one of the foregoing claims, wherein the cancer is one or more of lung cancer, liver cancer, renal cancer, breast cancer, glioma, or colorectal cancer.
The method of any one of the foregoing claims, wherein generating sequence libraries of ctDNA fragments comprises enriching, isolating, and sequencing a subset of genes or genomic region of interest.
The method of any one of the foregoing claims, wherein enriching, isolating, and sequencing a subset of genes or regions of a genome comprises capturing a subset of genes or genomic region of interest by hybridization of ctDNA fragments to probes that are specific for the subset of genes or genomic region of interest.
Dependencies are inferred from the claim text.