Methods and systems for refining copy number variation in a liquid biopsy assay

Patent metadata

Field	Value
Publication number	US-11211144-B2
Application number	US-202117179279-A
Country	US
Kind code	B2
Filing date	Feb 18, 2021
Priority date	Feb 18, 2020
Publication date	Dec 28, 2021
Grant date	Dec 28, 2021

Abstract

Methods, systems, and software are provided for validating a copy number variation in a test subject. A first dataset is obtained comprising bin-level sequence ratios, segment-level sequence ratios and segment-level measures of dispersion. Bins representing regions of a human reference genome are determined from sequencing cell-free nucleic acids in a liquid biopsy sample and reference samples. Segments encompass subsets of adjacent bins, where segment-level sequence ratios and measures of dispersion are determined using bin-level sequence ratios. A copy number status annotation for a segment is validated by applying the first dataset to a plurality of filters comprising a measure of central tendency bin-level sequence ratio filter, a confidence filter, and a measure of central tendency-plus-deviation bin-level sequence ratio filter. When a filter is fired, the copy number status annotation of the segment is rejected; and when no filter is fired, the copy number status annotation of the segment is validated.

First claim

What is claimed is:

1. A method of validating a copy number variation status for a test subject, the method comprising:
at a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors:
(A) obtaining, from a first sequencing reaction, a corresponding sequence of each cell-free DNA fragment in a first plurality of cell-free DNA fragments in a liquid biopsy sample of the test subject, thereby obtaining a first plurality of at least 100,000 sequence reads;
(B) aligning each respective sequence read in the first plurality of at least 100,000 sequence reads to a reference sequence for the species of the subject; and
(C) determining:
(1) a plurality of at least 1000 bin-level sequence ratios using the plurality of at least 100,000 sequence reads, each respective bin-level sequence ratio in the plurality of at least 1000 bin-level sequence ratios corresponding to a respective bin in a plurality of at least 1000 bins, wherein:
each respective bin in the plurality of at least 1000 bins represents a corresponding region of a reference genome for the species of the subject,
the plurality of at least 1000 bins collectively covers at least 50 Mb of the genome for the species of the test subject, and
each respective bin-level sequence ratio in the plurality of at least 1000 bin-level sequence ratios is determined from a comparison between (i) a proportion of sequence reads in the first plurality of at least 100,000 sequence reads that map to the corresponding bin in the plurality of at least 1000 bins, and (ii) a proportion of sequence reads for one or more reference samples that map to the corresponding bin in the plurality of at least 1000 bins:

(2) a plurality of segment-level sequence ratios using the plurality of at least 1000 bin-level sequence ratios, each respective segment-level sequence ratio in the plurality of segment-level sequence ratios corresponding to a segment in a plurality of segments, wherein:
each respective segment in the plurality of segments represents a corresponding region of the reference genome for the species of the subject encompassing a subset of adjacent bins in the plurality of bins, and
each respective segment-level sequence ratio in the plurality of segment-level sequence ratios is determined from a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of adjacent bins encompassed by the respective segment; and

(3) a plurality of segment-level measures of dispersion using the plurality of at least 1000 bin-level sequence ratio, each respective segment-level measure of dispersion in the plurality of segment-level measures of dispersion corresponding to a respective segment in the plurality of segments, wherein:
each respective segment-level measure of dispersion in the plurality of segment-level measures of dispersion is determined from the plurality of bin-level sequence ratios corresponding to the subset of adjacent bins encompassed by the respective segment,

wherein the copy number variation status of a respective segment in the plurality of segments is validated when a plurality of conditions are satisfied, the plurality of conditions comprising:
(1) that a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment satisfies one or more bin-level sequence ratio thresholds;
(2) that the segment-level measure of dispersion corresponding to the respective segment satisfies a confidence threshold; and
(3) that a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment satisfies one or more measure of central tendency-plus-deviation bin-level sequence ratio thresholds, wherein the one or more measure of central tendency-plus-deviation bin-level copy ratio thresholds are derived from (i) a measure of central tendency of the bin-level sequence ratios corresponding to the plurality of bins that map to the same chromosome of the reference genome for the species of the subject as the respective segment, and (ii) a measure of dispersion across the bin-level sequence ratios corresponding to the plurality of bins that map to the same chromosome of the reference genome for the species of the subject as the respective segment.

2. The method of claim 1 , wherein the test subject is a patient with a cancer.

3. The method of claim 2 , wherein:
the respective segment in the plurality of segments comprises a target gene; and
the method further comprises treating the patient with the cancer by:
determining whether the copy number variation of the target gene is a focal copy number variation by validating the copy number variation status of the respective segment, thereby determining whether the patient has an aggressive form of the cancer associated with a focal copy number variation of the target gene;
administering, when the patient has the aggressive form of cancer associated with focal copy number variation of the target gene, a first therapy for the aggressive form of the cancer to the patient; and
administering, when the patient does not have the aggressive form of cancer associated with focal copy number variation of the target gene a second therapy for a less aggressive form of the cancer to the patient.

4. The method of claim 3 , wherein the first therapy is selected from the group consisting of:
crizotinib for treatment of ovarian cancer, cervical cancer, chromophobe renal cell carcinoma, liver cancer, endocrine tumor, oropharyngeal cancer, retinoblastoma, biliary cancer, adrenal cancer, breast cancer, melanoma, non-clear cell renal cell carcinoma, tumor of unknown origin, kidney cancer, bladder cancer, gastric cancer, bone cancer, non-small cell lung cancer, thymoma, prostate cancer, clear cell renal cell carcinoma, skin cancer, thyroid cancer, sarcoma, testicular cancer, head and neck cancer, head and neck squamous cell carcinoma, meningioma, peritoneal cancer, endometrial cancer, pancreatic cancer, or esophageal cancer when the target gene is MET;
crizotinib for treatment of neural cancer, brain cancer, glioblastoma, or low-grade glioma when the target gene is MET;
crizotinib or osimertinib for treatment of non-small cell lung cancer when the target gene is MET;
crizotinib or savolitinib for treatment of chromophobe renal cell carcinoma, non-clear cell renal cell carcinoma, kidney cancer, clear cell renal cell carcinoma when the target gene is MET;
panitumumab and cabozantinib co-therapy, cetuximab, or panitumumab for treatment of colorectal cancer when the target gene is MET;
cetuximab, panitumumab, gefitinib, or lapatinib for treatment of ovarian cancer, cervical cancer, chromophobe renal cell carcinoma, liver cancer, endocrine tumor, oropharyngeal cancer, retinoblastoma, biliary cancer, adrenal cancer, breast cancer, melanoma, non-clear cell renal cell carcinoma, tumor of unknown origin, kidney cancer, bladder cancer, bone cancer, non-small cell lung cancer, thymoma, prostate cancer, clear cell renal cell carcinoma, skin cancer, thyroid cancer, sarcoma, testicular cancer, head and neck cancer, meningioma, peritoneal cancer, endometrial cancer, pancreatic cancer, or small cell lung cancer when the target gene is EGFR;
depatuxizumab for treatment of brain cancer or glioblastoma when the target gene is EGFR;
cetuximab or panitumumab for treatment of colorectal cancer, gastric cancer, or esophageal cancer when the target gene is EGFR;
cetuximab, panitumumab, gefitinib, or lapatinib for treatment of head and neck squamous cell carcinoma when the target gene is EGFR;
trastuzumab, lapatinib and trastuzumab co-therapy, cetuximab, or panitumumab for treatment of colorectal cancer when the target gen

Assignees

• Tempus Labs Inc

Inventors

• Zhu Wei
• Tell Robert
• Driessen Terri M

Classifications

• G16B40/30

  Unsupervised data analysis · CPC title

• G16B30/10

  Sequence alignment; Homology search · CPC title

• G16B20/10 Primary

  Ploidy or copy number detection · CPC title

• G16B40/20

  Supervised data analysis · CPC title

• G16B40/00

  ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding · CPC title