IP Library › Granted Patent US 12,626,780
Granted Patent B2
US 12,626,780 · App. 16/352,739 · Granted May 12, 2026

Method and system for selecting, managing, and analyzing data of high dimensionality

Inventors: Darya Filippova (Sunnyvale, CA); Anton Valouev (La Canada, CA); Virgil Nicula (Cupertino, CA); Karthik Jagadeesh (San Francisco, CA); M. Cyrus Maher (San Mateo, CA); Matthew H. Larson (San Francisco, CA); Monica Portela dos Santos Pimentel (San Jose, CA); Robert Abe Paine Calef (Redwood City, CA)
Assignee: GRAIL, Inc.
G16B30/10
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Quick Facts
Patent No.
US 12,626,780
App. No.
16/352,739
Granted
May 12, 2026
Kind
B2
Abstract

A system, method and computer program product for analyzing data of high dimensionality (e.g., sequence reads of nucleic acid samples in connection with a disease condition) are provided.

Claims (58)

1 . A method of analyzing sequence reads of nucleic acid samples in connection with a cancer condition, comprising:

receiving, at a data collection component of a computer system, a first set of sequence reads of the nucleic acid samples from each healthy subject in a reference group of healthy subjects, wherein the nucleic acid samples comprise a set of cell-free DNA (cfDNA) fragments and wherein the first set of sequence reads is derived from a DNA sequencing process and wherein the first set of sequence reads are not known to contain one or more mutations indicative of the cancer condition;

aligning, using a processor of the computer system, each sequence read in the first set of sequence reads to regions in a reference genome;

establishing, using the processor and subsequent to the aligning, a low variability filter, wherein the establishing comprises:

identifying, using the processor, numbers of sequence reads aligned to each of the regions in the reference genome across each healthy subject in the reference group of healthy subjects;

deriving, using the processor and based on the identifying, quantity data associated with each of the regions based on the numbers of sequence reads;

calibrating, using the processor, the quantity data for each of the regions;

computing, using the processor, reference quantities for each of the regions based on the calibrated quantity data, wherein the reference quantities comprise at least a first reference quantity and a second reference quantity, wherein the first reference quantity corresponds to an average of the calibrated quantity data and wherein the second reference quantity is a standard deviation of the calibrated quantity data;

determining, using the processor, a difference between the first reference quantity and the second reference quantity for each of the regions;

classifying each of the regions as a high variability region or a low variability region by comparing the difference against a predetermined threshold, wherein the high variability region is defined by the difference being greater than the predetermined threshold and wherein the low variability region is defined by the difference between less than the predetermined threshold;

receiving, at the data collection component, a training set of sequence reads from subjects in a training group, wherein the training set includes a second set of sequence reads of nucleic acid samples from healthy subjects and a third set of sequence reads of nucleic acid samples from cancer subjects who are known to have the cancer condition, wherein the second set of sequence reads of nucleic acid samples are not known to contain the one or more mutations indicative of the cancer condition and wherein the third set of sequence reads of nucleic acid samples do contain the one or more mutations indicative of the cancer condition and wherein the training set of sequence reads from the subjects in the training group comprise cfDNA fragments of varying length;

applying, using the processor, the established low variability filter to the training set of sequence reads;

generating, using the processor and based on the applying, a filtered training set of sequence reads, wherein the generating the filtered training set of sequence reads comprises discarding one or more sequence reads in the training set of sequence reads that are aligned to the high variability region in the reference genome training, using the processor, a machine learning model on the filtered training set of sequence reads;

applying, using the processor, a predictive capability of the trained machine learning model to identify differences between the second set of sequence reads of nucleic acid samples from the healthy subjects and the third set of sequence reads of nucleic acid samples from the cancer subjects.

2 . The method of claim 1 , wherein the cancer condition is a cancer type selected from the group consisting of lung cancer, ovarian cancer, kidney cancer, bladder cancer, hepato-biliary cancer, pancreatic cancer, upper gastrointestinal cancer, sarcoma, breast cancer, liver cancer, prostate cancer, brain cancer, and combinations thereof.

3 . The method of claim 1 , further comprising: performing initial data processing of the first set of sequence reads of nucleic acid samples from each healthy subject in the reference group of healthy subjects based on a fourth set of sequence reads of nucleic acid samples from a baseline group of healthy subjects, wherein the reference group and the baseline group do not overlap, and wherein the initial data processing comprises correction of GC biases or normalization of numbers of sequence reads that align to regions of the reference genome.

4 . The method of claim 1 , further comprising: performing initial data processing of the sequence reads of nucleic acid samples from each subject in the training group based on a fourth set of sequence reads of nucleic acid samples from a baseline group of healthy subjects, wherein the baseline group and the training group do not overlap, and wherein the initial data processing comprises correction of GC biases or normalization of numbers of sequence reads aligned to regions of the reference genome.

5 . The method of claim 1 , wherein the quantity data consists of one quantity corresponding to a total number of sequence reads that align to the low variability region.

6 . The method of claim 1 , wherein the quantity data comprises multiple quantities each corresponding to a subset of the sequence reads that align to the low variability region, wherein each sequence read within a same subset corresponds to nucleic acid samples having a same predetermined fragment size or size range, wherein sequence reads in different subsets correspond to nucleic acid samples having a different fragment size or size range.

7 . The method of claim 1 , wherein the one or more parameters are determined by principal component analysis (PCA).

8 . The method of claim 1 , further comprising: refining the one or more parameters in a multi-fold cross-validation process by dividing the second filtered training set of sequence reads into a filtered training subset and a filtered validation subset.

9 . The method of claim 8 , wherein the filtered training and validation subsets in one fold of the multi-fold cross-validation process are different from another filtered training and validation subset in another fold of the multi-fold cross-validation process.

10 . The method of claim 1 , wherein the high variability region in the reference genome corresponds to a plurality of regions in the reference genome that exhibit variability above the predetermined threshold and wherein each of the plurality of regions has the same size.

11 . The method of claim 1 , wherein the high variability region in the reference genome includes a plurality of high variability regions that correspond to a plurality of regions in the reference genome that exhibit variability above the predetermined threshold and wherein each of the plurality of regions do not have the same size.

12 . The method of claim 1 , wherein the one or more parameters are determined based on a subset of the training set of sequence reads.

13 . The method of claim 1 , wherein the nucleic acid samples from the subjects in the training group comprise cfDNA fragments that are longer than the predetermined threshold length, wherein the predetermined threshold length is less than 160 nucleotides.

14 . The method of claim 13 , wherein the predetermined threshold length is 140 nucleotides or less.

15 . The method of claim 13 , wherein the sequence reads in the training set includes sequence reads of cfDNA fragments in the nucleic acid samples from the subjects in the training group having a length falling between a second threshold length and a third threshold length, wherein: the second threshold length is from 240 to 260 nucleotides, and the third threshold length is from 290 nucleotides to 310 nucleotides.

16 . A computer system comprising: one or more processors; and a non-transitory computer-readable medium including one or more sequences of instructions that, when executed by the one or more processors, cause the processors to

receive, at a data collection component of the computer system, a first set of sequence reads of nucleic acid samples from each healthy subject in a reference group of healthy subjects, wherein the nucleic acid samples comprise cell-free DNA (cfDNA) fragments and wherein the first set of sequence reads is derived from a DNA sequencing process and wherein the first set of sequence reads are not known to contain one or more mutations indicative of a cancer condition;

align, using the one or more processors, each sequence read in the first set of sequence reads to regions in a reference genome;

establish, using the one or more processors and subsequent to the aligning, a low variability filter, wherein the instructions to establish comprise instructions to:

identify, using the one or more processors, numbers of sequence reads aligned to each of the regions in the reference genome across each healthy subject in the reference group of healthy subjects;

derive, using the one or more processors and based on the identifying, quantity data associated with each of the regions based on the numbers of sequence reads;

calibrate, using the one or more processors, the quantity data for each of the regions;

compute, using the one or more processors, reference quantities for each of the regions based on the calibrated quantity data, wherein the reference quantities comprise at least a first reference quantity and a second reference quantity, wherein the first reference quantity corresponds to an average of the calibrated quantity data and wherein the second reference quantity is a standard deviation of the calibrated quantity data;

determine, using the one or more processors, a difference between the first reference quantity and the second reference quantity for each of the regions;

classify, using the one or more processors, each of the regions as a high variability region or a low variability region by comparing the difference against a predetermined threshold, wherein the high variability region is defined by the difference being greater than the predetermined threshold and wherein the low variability region is defined by the difference between less than the predetermined threshold;

receive, at the data collection component, a training set of sequence reads from subjects in a training group, wherein the training set includes a second set of sequence reads of nucleic acid samples from healthy subjects and a third set of sequence reads of nucleic acid samples from cancer subjects who are known to have a cancer condition, wherein the second set of sequence reads of nucleic acid samples are not known to contain the one or more mutations indicative of the cancer condition and wherein the third set of sequence reads of nucleic acid samples do contain the one or more mutations indicative of the cancer condition and wherein the training set of sequence reads from the subjects in the training group comprise cfDNA fragments of varying length;

apply, using the one or more processors, the established low variability filter to the training set of sequence reads;

generate, using the one or more processors and based on the applying, a filtered training set of sequence reads, wherein the instructions to generate the filtered training set of sequence reads comprise instructions to discard one or more sequence reads in the filtered training set of sequence reads that are aligned to the high variability region in the reference genome;

training, using the processor, a machine learning model on the filtered training set of sequence reads;

applying, using the processor, a predictive capability of the trained machine learning model to identify differences between the second set of sequence reads of nucleic acid samples from the healthy subjects and the third set of sequence reads of nucleic acid samples from the cancer subjects.

17 . A non-transitory computer-readable storage medium having stored thereon program code instructions that, when executed by a processor of a computer system, cause the computer system to perform a method comprising:

receiving, at a data collection component of a computer system, a first set of sequence reads of nucleic acid samples from each healthy subject in a reference group of healthy subjects, wherein the nucleic acid samples comprise a first set of cell-free DNA (cfDNA) fragments and wherein the first set of sequence reads is derived from a DNA sequencing process and wherein the first set of sequence reads are not known to contain one or more mutations indicative of a cancer condition;

aligning, using a processor of the computer system, each sequence read in the first set of sequence reads to regions in a reference genome;

establishing, using the processor and subsequent to the aligning, a low variability filter, wherein the establishing comprises:

identifying, using the processor, numbers of sequence reads aligned to each of the regions in the reference genome across each healthy subject in the reference group of healthy subjects;

deriving, using the processor and based on the identifying, quantity data associated with each of the regions based on the numbers of sequence reads;

calibrating, using the processor, the quantity data for each of the regions;

computing, using the processor, reference quantities for each of the regions based on the calibrated quantity data, wherein the reference quantities comprise at least a first reference quantity and a second reference quantity, wherein the first reference quantity corresponds to an average of the calibrated quantity data and wherein the second reference quantity is a standard deviation of the calibrated quantity data;

determining, using the processor, a difference between the first reference quantity and the second reference quantity for each of the regions;

classifying each of the regions as a high variability region or a low variability region by comparing the difference against a predetermined threshold, wherein the high variability region is defined by the difference being greater than the predetermined threshold and wherein the low variability region is defined by the difference between less than the predetermined threshold;

receiving, at the data collection component, a training set of sequence reads from subjects in a training group, wherein the training set includes a second set of sequence reads of nucleic acid samples from healthy subjects and a third set of sequence reads of nucleic acid samples from cancer subjects who are known to have a cancer condition, wherein the second set of sequence reads of nucleic acid samples are not known to contain the one or more mutations indicative of the cancer condition and wherein the third set of sequence reads of nucleic acid samples do contain the one or more mutations indicative of the cancer condition and wherein the training set of sequence reads from the subjects in the training group comprise cfDNA fragments of varying length;

applying, using the processor, the established low variability filter to the training set of sequence reads;

generating, using the processor and based on the applying, a filtered training set of sequence reads, wherein the generating the filtered training set of sequence reads comprises discarding one or more sequence reads in the filtered training set of sequence reads that are aligned to the high variability region in the reference genome;

training, using the processor, a machine learning model on the filtered training set of sequence reads;

applying, using the processor, a predictive capability of the trained machine learning model to identify differences between the second set of sequence reads of nucleic acid samples from the healthy subjects and the third set of sequence reads of nucleic acid samples from the cancer subjects.

Assignments (3)
CHANGE OF NAME Recorded Feb 13, 2025
From: GRAIL, LLC
To: GRAIL, INC.
Reel/Frame 070208/0555 →
MERGER AND CHANGE OF NAME Recorded Oct 13, 2021
From: GRAIL, INC.; SDG OPS, LLC
To: GRAIL, LLC
Reel/Frame 057788/0719 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 19, 2019
From: FILIPPOVA, DARYA; VALOUEV, ANTON; NICULA, VIRGIL; JAGADEESH, KARTHIK; MAHER, M. CYRUS; LARSON, MATTHEW H.; PORTELA DOS SANTOS PIMENTEL, MONICA; CALEF, ROBERT ABE PAINE
To: GRAIL, INC.
Reel/Frame 048942/0137 →
Continuity (2)
Provisional Application 62642461 · Mar 13, 2018
Related Publication 20190287649A1 · Sep 19, 2019
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