Patent Application
20220064737
The instant application contains a Sequence Listing which has been electronically submitted in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Feb. 3, 2020, is named 50251-851_601_SL.txt and is 16,952,822 bytes in size.
Hematopoietic stem cells (HSCs) and hematopoietic progenitor cells (HPCs) divide to produce blood cells by a continuous regeneration process. As the cells divide, they are prone to accumulating mutations that generally do not affect function. About 3-5% of normal individuals above the age of 50 and approximately 10% of people aged 70 to 80 are determined to have clonal hematopoiesis of indeterminate potential (CHIP) defined by the presence of low-level mutations in the peripheral blood in clinically normal individuals.
Some mutations confer advantages in self-renewal, proliferation or both, resulting in clonal expansion of the cells comprising the mutations in question. Although these mutations are not necessarily indicative of a hematological disease, the accumulation of mutations during clonal expansion can, eventually, lead to a disease state (e.g., cancer). Having clonal hematopoiesis has been linked to a more than 10-fold increased risk of developing a blood cancer. Detection of the clonal hematopoiesis can therefore allow an early detection of cancer, which in turn allows for earlier treatment and therefore a greater chance for survival. Differentiation of CHIP from other hematological disorders, such as leukemia, multiple myeloma, and lymphoma, further enables proper treatments and prophylactic activities.
Recent sequencing studies have identified a set of recurrent mutations in several types of hematological malignancies (see, e.g., Mardis E R et al. The New England Journal of Medicine 2009; Bejar R et al. The New England Journal of Medicine 2011; Papaemmanuil E et al. The New England Journal of Medicine 2011; and Walter et al. Leukemia 2011). However, the frequency of these somatic mutations in the general population is unknown.
Accordingly, a cost-effective method of accurately detecting various hematological disorders by detecting differentially methylated regions has not yet been available.
Provided herein are compositions comprising a plurality of different bait oligonucleotides, wherein the plurality of different bait oligonucleotides are configured to collectively hybridize to DNA molecules derived from at least 100 target genomic regions, wherein each genomic region of the at least 100 target genomic regions is differentially methylated in at least a first hematological disorder or hematological cancer relative to another hematological disorder or non-hematological cancer type, wherein the first hematological disorder and the another hematological disorder are selected from leukemia, lymphoid neoplasms (e.g., lymphoma), multiple myeloma, and a myeloid neoplasm.
In some embodiments, the plurality of bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20%, at least 25% or at least 50% of the target genomic regions of any one of Lists 1-8. In some embodiments, the plurality of bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20%, at least 25% or at least 50% of the target genomic regions of Lists 1-8. In some embodiments, the plurality of bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of Lists 1 or 8. In some embodiments, the DNA molecules are derived from at least 25%, 30%, 40%, 50%, 60%, 70%, or 80% of the target genomic regions of Lists 1 or 8. In some embodiments, the plurality of bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% the target genomic regions of any one of Lists 2-4. In some embodiments, the plurality of bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of Lists 2-4. In some embodiments, the plurality of bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 25%, 30%, 40%, 50%, 60%, 70%, or 80% of the target genomic regions of Lists 2-4. In some embodiments, the plurality of bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of any one of Lists 5-7. In some embodiments, the plurality of bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of Lists 5-7. In some embodiments, the plurality of bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 25%, 30%, 40%, 50%, 60%, 70%, or 80% of the target genomic regions of Lists 5-7. In some embodiments, the first hematological disorder and the another hematological disorder are selected from lymphoid neoplasm, multiple myeloma, and myeloid neoplasm.
Also provided herein are compositions comprising a plurality of different bait oligonucleotides configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of any one of Lists 1-7. In some embodiments, the plurality of bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of Lists 1 or 8. In some embodiments, the DNA molecules are derived from at least 25%, 30%, 40%, 50%, 60%, 70%, or 80% of the target genomic regions of Lists 1 or 8. In some embodiments, the plurality of bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% the target genomic regions of any one of Lists 2-4. In some embodiments, the plurality of bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of Lists 2-4. In some embodiments, the plurality of bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of any one of Lists 5-7. In some embodiments, the plurality of bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of Lists 5-7.
Also provided herein are compositions provided above, wherein the plurality of different bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of List 2. In some embodiments, the DNA molecules are derived from at least 25%, 30%, 40%, 50%, 60%, 70%, or 80% of the target genomic regions of List 2.
Also provided herein are compositions provided above, wherein the plurality of different bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of List 3. In some embodiments, the DNA molecules are derived from at least 25%, 30%, 40%, 50%, 60%, 70%, or 80% of the target genomic regions of List 3.
Also provided herein are compositions provided above, wherein the plurality of different bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of List 4. In some embodiments, the DNA molecules are derived from at least 25%, 30%, 40%, 50%, 60%, 70%, or 80% of the target genomic regions of List 4.
Also provided herein are compositions provided above, wherein the plurality of different bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of List 5. In some embodiments, the DNA molecules are derived from at least 25%, 30%, 40%, 50%, 60%, 70%, or 80% of the target genomic regions of List 5.
Also provided herein are compositions provided above, wherein the plurality of different bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of List 6. In some embodiments, the DNA molecules are derived from at least 25%, 30%, 40%, 50%, 60%, 70%, or 80% of the target genomic regions of List 6.
Also provided herein are compositions provided above, wherein the plurality of different bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of List 7. In some embodiments, the DNA molecules are derived from at least 25%, 30%, 40%, 50%, 60%, 70%, or 80% of the target genomic regions of List 7.
Also provided herein are compositions provided above, wherein the plurality of different bait oligonucleotides are configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of List 8. In some embodiments, the DNA molecules are derived from at least 25%, 30%, 40%, 50%, 60%, 70%, or 80% of the target genomic regions of List 8.
Also provided herein are compositions provided above, wherein the total size of the of the target genomic regions is less than 2000 kb, less than 1500 kb, less than 1200 kb, less than 1000 kb, less than 500 kb, or less than 300 kb.
Also provided herein are compositions provided above, wherein the DNA molecules are converted cfDNA fragments. In some embodiments, the target genomic regions are hypermethylated regions, hypomethylated regions, or binary regions that can be either hypermethylated or hypomethylated, as indicated in the sequence listing. In some embodiments, the bait oligonucleotides are configured to hybridize to hypermethylated converted DNA molecules, hypomethylated converted DNA molecules, or both hypermethylated and hypomethylated converted DNA molecules derived from each targeted genomic region, as indicated in the sequence listing.
Also provided herein are compositions provided above, wherein the bait oligonucleotides are each conjugated to an affinity moiety. In some embodiments, the affinity moiety is biotin. In some embodiments, the bait oligonucleotides are each conjugated to a solid surface. In some embodiments, the solid surface is a microarray or chip.
Also provided herein are compositions provided above, wherein the bait oligonucleotides each have a length of 45 to 300 nucleotide bases, 75-200 nucleotide bases, 100-150 nucleotide bases, or about 120 nucleotide bases.
Also provided herein are compositions provided above, wherein the bait oligonucleotides comprise a plurality of sets of two or more bait oligonucleotides, wherein each bait oligonucleotide within a set of bait oligonucleotides is configured to bind to converted DNA molecules derived from the same target genomic region. In some embodiments, each set of bait oligonucleotides comprises 1 or more pairs of a first bait oligonucleotide and a second bait oligonucleotide, each bait oligonucleotide comprises a 5′ end and a 3′ end, a sequence of at least X nucleotide bases at the 3′ end of the first bait oligonucleotide is identical to a sequence of X nucleotide bases at the 5′ end the second bait oligonucleotide, and X is at least 25, 30, 35, 40, 45, 50, 60, 70, 75 or 100. In some embodiments, the first bait oligonucleotide comprises a sequence of at least 31, 40, 50 or 60 nucleotide bases that does not overlap a sequence of the second bait oligonucleotide.
Also provided herein are compositions provided above, wherein the at least 100 target regions comprises at least 200, at least 500, at least 1000, at least 1500, at least 2000, at least 3000, at least 4000, at least 5000, at least 8000, at least 10,000, at least 15,000, or at least 20,000 genomic regions genomic regions.
Also provided herein are compositions provided above, further comprising converted cfDNA from a test subject.
Also provided herein are compositions provided above, wherein the cfDNA from the test subject is converted by a process comprising treatment with bisulfite or a cytosine deaminase.
Also provided herein are methods of enriching converted cfDNA fragments informative of a type of hematological disorder, the method comprising: contacting the bait oligonucleotide composition provided above with DNA derived from a test subject, and enriching the sample for cfDNA corresponding to genomic regions associated with the type of cancer by hybridization capture.
Also provided herein are methods for obtaining sequence information informative of a presence or absence of a type of hematological disorder, a method comprising enriching converted DNA from a test subject by contacting the DNA with a bait oligonucleotide composition provided above, and sequencing the enriched converted DNA.
Also provided herein are methods for determining that a test subject has a type of hematological disorder (HD), a method comprising capturing cfDNA fragments from the test subject with a bait oligonucleotide composition provided above, sequencing the captured cfDNA fragments, and applying a trained classifier to the cfDNA sequences to determine that the test subject has the type of HD. Also provided herein are methods for determining that a test subject has a type of hematological disorder (HD), a method comprising capturing cfDNA fragments from the test subject with a bait oligonucleotide composition provided above, detecting the captured cfDNA fragments by DNA microarray, and applying a trained classifier to the DNA fragments hybridized to the DNA microarray to determine that the test subject has the type of HD.
In some embodiments, the trained classifier determines a presence or absence of cancer and, if the classifier determines a presence of cancer, the classifier determines a cancer type. In some embodiments, the cancer type is selected from the group consisting of uterine cancer, upper GI squamous cancer, all other upper GI cancers, thyroid cancer, sarcoma, urothelial renal cancer, all other renal cancers, prostate cancer, pancreatic cancer, ovarian cancer, neuroendocrine cancer, multiple myeloma, melanoma, lymphoma, small cell lung cancer, lung adenocarcinoma, all other lung cancers, leukemia, hepatobiliary carcinoma, hepatobiliary biliary, head and neck cancer, colorectal cancer, cervical cancer, breast cancer, bladder cancer, and anorectal cancer. In some embodiments, the cancer type is selected from the group consisting of anal cancer, bladder cancer, colorectal cancer, esophageal cancer, head and neck cancer, liver/bile-duct cancer, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, plasma cell neoplasm, and stomach cancer. In some embodiments, the cancer type is selected from the group consisting of thyroid cancer, melanoma, sarcoma, myeloid neoplasm, renal cancer, prostate cancer, breast cancer, uterine cancer, ovarian cancer, bladder cancer, urothelial cancer, cervical cancer, anorectal cancer, head & neck cancer, colorectal cancer, liver cancer, bile duct cancer, pancreatic cancer gallbladder cancer, upper GI cancer, multiple myeloma, lymphoid neoplasm, and lung cancer. In some embodiments, the cancer type is a HD and the HD is selected from the group consisting of CHIP, leukemia, lymphoid neoplasms (e.g., lymphoma), multiple myeloma, and a myeloid neoplasm. In some embodiments, the type of hematological disorder is selected from lymphoid neoplasm, multiple myeloma, and myeloid neoplasm. In some embodiments, the trained classifier is a mixture model classifier. In some embodiments, the classifier was trained on converted DNA sequences derived from at least 1000, at least 2000, or at least 4000 target genomic regions selected from any one of Lists 1-8. In some embodiments, the trained classifier determines the presence or absence of cancer or a cancer type by: (i) generating a set of features for the sample, wherein each feature in the set of features comprises a numerical value; (ii) inputting the set of features into the classifier, wherein the classifier comprises a multinomial classifier; (iii) based on the set of features, determining, at the classifier, a set of probability scores, wherein the set of probability scores comprises one probability score per cancer type class and per non-cancer type class; and (iv) thresholding the set of probability scores based on one or more values determined during training of the classifier to determine a final cancer classification of the sample. In some embodiments, the set of features comprises a set of binarized features. In some embodiments, the numerical value comprises a single binary value. In some embodiments, the multinomial classifier comprises a multinomial logistic regression ensemble trained to predict a source tissue for the cancer. In some embodiments, the method further comprises determining the final cancer classification based on a top-two probability score differential relative to a minimum value, wherein the minimum value corresponds to a predefined percentage of training cancer samples that had been assigned the correct cancer type as their highest score during training of the classifier. In some embodiments, (i) in accordance with a determination that the top-two probability score differential exceeds the minimum value, assign a cancer label corresponding to the highest probability score determined by the classifier as the final cancer classification; and (ii) in accordance with a determination that the top-two probability score differential does not exceed the minimum value, assigning an indeterminate cancer label as the final cancer classification. In some embodiments, the type of hematological disorder is selected from CHIP, leukemia, lymphoid neoplasms (e.g., lymphoma), multiple myeloma, and a myeloid neoplasm. In some embodiments, the type of hematological disorder is selected from lymphoid neoplasm, multiple myeloma, and myeloid neoplasm. In some embodiments, the subject is determined to have a cancer and the specificity is at least 0.990. In some embodiments, the ratio of the likelihood of accurately determining a hematological disorder to the likelihood of inaccurately determining a solid tumor is at least 25:1 or at least 50:1. In some embodiments, the ratio of the likelihood of accurately determining a hematological disorder to the likelihood of inaccurately determining a hematological disorder is at least 8, at least 12:1, or at least 16:1. In some embodiments, the likelihood of accurately determining a cancer type is at least 80%, at least 85%, or at least 89%. In some embodiments, the cancer is a stage I cancer and the likelihood of accurately determining a cancer type is at least 65%, at least 70%, at least 75%, or at least 80%. In some embodiments, the cancer is a stage II cancer and the likelihood of accurately determining a cancer type is at least 75%, at least 80%, at least 85%, or at least 90%. In some embodiments, the cancer is a stage III cancer or a stage IV cancer and the likelihood of accurately determining a cancer type is at least 85% or at least 90%. In some embodiments, the sensitivity for multiple myeloma is at least 55%, at least 65%, at least 75% or at least 85%. In some embodiments, the sensitivity for stage I multiple myeloma is at least 60%, at least 65%, or at least 70%. In some embodiments, the sensitivity for stage II multiple myeloma is at least 60%, at least 75%, or at least 85%. In some embodiments, the bait oligonucleotide composition is configured to hybridize to cfDNA derived from target genomic regions of list 3 or list 6. In some embodiments, the sensitivity for lymphoid neoplasm is at least 55%, at least 60%, at least 65% or at least 70%. In some embodiments, the sensitivity for stage I lymphoid neoplasm is at least 30%. In some embodiments, the sensitivity for stage II lymphoid neoplasm is at least 65%, at least 75%, at least 85% or at least 90%. In some embodiments, the bait oligonucleotide composition is configured to hybridize to cfDNA derived from target genomic regions of list 2 or list 5.
Also provided herein are hematological disorder (HD) assay panels, comprising: at least 500 pairs of probes, wherein each pair of the at least 500 pairs comprise two probes configured to overlap each other by an overlapping sequence, wherein the overlapping sequence comprises a sequence of at least 30-nucleotides, and wherein the at least 30-nucleotide sequence is configured to hybridize to a converted cfDNA molecule corresponding to, or derived from one or more of genomic regions, wherein each of the genomic regions comprises at least five methylation sites, and wherein the at least five methylation sites have an abnormal methylation pattern in HD samples. In some embodiments, each probe of the of the at least 5 pairs of probes comprises a non-overlapping sequence of at least 31 nucleotides. In some embodiments, the converted cfDNA molecules comprise cfDNA molecules treated to covert unmethylated C (cytosine) to U (uracil). In some embodiments, each of the at least 500 pairs of probes is conjugated to a non-nucleotide affinity moiety. In some embodiments, the non-nucleotide affinity moiety is a biotin moiety. In some embodiments, the HD samples are from subjects having a hematological disorder selected from the group consisting of CHIP, leukemia, multiple myeloma, and lymphoma. In some embodiments, the abnormal methylation pattern has at least a threshold p-value rarity in the HD samples. In some embodiments, each of the probes is designed to have sequence homology or sequence complementarity with less than 20 off-target genomic regions. In some embodiments, the less than 20 off-target genomic regions are identified using a k-mer seeding strategy. In some embodiments, the less than 20 off-target genomic regions are identified using k-mer seeding strategy combined to local alignment at seed locations. In some embodiments, the HD assay panel comprises at least 1,000, 2,000, 5,000, 10,000, 50,000, 100,000, 150,000, 200,000, or 250,000 probes. In some embodiments, the at least 500 pairs of probes together comprise at least 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 120,000, 140,000, 160,000, 180,000, 200,000, 240,000, 260,000, 280,000, 300,000, 320,000, 400,000, 450,000, 500,000, 550,000, 600,000, 650,000, 700,000, 750,000, 800,000, 850,000, 900,000, 1 million, 1.5 million, 2 million, 2.5 million, 3 million, 3.5 million, 4 million, 4.5 million, or 5 million nucleotides. In some embodiments, of the probes comprises at least 50, 75, 100, or 120 nucleotides. In some embodiments, each of the probes comprises less than 300, 250, 200, or 150 nucleotides. In some embodiments, each of the probes comprises 100-150 nucleotides. In some embodiments, each of the probes comprises less than 20, 15, 10, 8, or 6 methylation sites. In some embodiments, at least 80, 85, 90, 92, 95, or 98% of the at least five methylation sites are either methylated or unmethylated in the HD samples. In some embodiments, at least 3%, 5%, 10%, 15%, or 20% of the probes comprise no G (Guanine). In some embodiments, each of the probes comprise multiple binding sites to the methylation sites of the converted cfDNA molecule, wherein at least 80, 85, 90, 92, 95, or 98% of the multiple binding sites comprise exclusively either CpG or CpA. In some embodiments, each of the probes is configured to have sequence homology or sequence complementarity with less than 15, 10 or 8 off-target genomic regions. In some embodiments, at least 30% of the genomic regions are in exons or introns. In some embodiments, at least 15% of the genomic regions are in exons. In some embodiments, at least 20% of the genomic regions are in exons. In some embodiments, less than 10% of the genomic regions are in intergenic regions. In some embodiments, the genomic regions are selected from List 1. In some embodiments, the genomic regions comprise at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or 100% of the genomic regions in List 1. In some embodiments, the genomic regions comprise at least 100, 200, 300, 400, 500, 1,000, 5000, 10,000, 15,000, 16,000, 17,000, 18,000, 19,000, 20,000, 21,000 or 23,000 genomic regions in List 1.
Also provided herein are methods of detecting a hematological disorder (HD), comprising: receiving a sample comprising a plurality of cfDNA molecules; treating the plurality of cfDNA molecules to convert unmethylated C (cytosine) to U (uracil), thereby obtaining a plurality of converted cfDNA molecules; applying the HD assay panel of any one of the above embodiments to the plurality of converted cfDNA molecules, thereby enriching a subset of the converted cfDNA molecules; and sequencing the enriched subset of the converted cfDNA molecule, thereby providing a set of sequence reads. In some embodiments, the method further comprises the step of: determining a health condition by evaluating the set of sequence reads, wherein the health condition is a presence or absence of a hematological disorder; a stage of a hematological disorder; a presence or absence of a type of blood cancer; or a presence or absence of at least 1, 2, or 3 different types of hematological disorders. In some embodiments, the sample comprising a plurality of cfDNA molecules was obtained from a human subject. In some embodiments, the hematological disorder is selected from the group consisting of: lymphoid neoplasm, multiple myeloma, and myeloid neoplasm.
Also provided herein are methods for detecting a hematological disorder (HD), comprising the steps of: obtaining a set of sequence reads by sequencing a set of nucleic acid fragments from a subject, wherein each of the nucleic acid fragments corresponds to or is derived from a plurality of genomic regions selected from any one of Lists 1-8; for each of the sequence reads, determining methylation status at a plurality of CpG sites; and detecting a hematological disorder of the subject by evaluating the methylation status for the sequence reads, wherein the hematological disorder detected comprises one or more of: (i) a presence or absence of a hematological disorder; (ii) a stage of a hematological disorder; (iii) a presence or absence of a type of blood cancer; and (iv) a presence or absence of at least 1, 2, or 3 different types of hematological disorders. In some embodiments, the plurality of genomic regions comprises at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 100% of the genomic regions of List 1. In some embodiments, the plurality of genomic regions comprises 100, 200, 300, 400, 500, 1,000, 5000, 10,000, 15,000, 16,000, 17,000, 18,000, 19,000, 20,000, 21,000 or 23,000 of the genomic regions of any one of Lists 1-8.
Also provided herein are methods of designing a hematological disorder (HD) assay panel comprising the steps of: identifying a plurality of genomic regions, wherein each of the plurality of genomic regions (i) comprises at least 30 nucleotides, and (ii) comprises at least five methylation sites, selecting a subset of the genomic regions, wherein the selection is made when cfDNA molecules corresponding to, or derived from each of the genomic regions in HD samples have an abnormal methylation pattern, wherein the abnormal methylation pattern comprises at least five methylation sites either hypomethylated or hypermethylated, and designing an HD assay panel comprising a plurality of probes, wherein each of the probes is configured to hybridize to a converted cfDNA molecule corresponding to, or derived from one or more of the subset of the genomic regions. In some embodiments, the converted cfDNA molecules comprise cfDNA molecules treated to convert unmethylated cytosines to uracils.
Also provided herein are hematological disorder (HD) assay panels comprising a plurality of probes, wherein each of the plurality of probes is configured to hybridize to a converted cfDNA molecule corresponding to one or more of the genomic regions in List 1. In some embodiments, the converted cfDNA molecules comprise cfDNA molecules treated to convert unmethylated cytosines to uracils. In some embodiments, the plurality of probes is configured to hybridize to a plurality of converted ctDNA molecules corresponding to or derived from at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90%, 95% or 100% of the genomic regions of any one of Lists 1-8. In some embodiments, the plurality of probes is configured to hybridize to a plurality of converted cfDNA molecules corresponding to or derived from at least 100, 200, 300, 400, 500, 1,000, 5000, 10,000, 15,000, 16,000, 17,000, 18,000, 19,000, 20,000, 21,000 or 23,000 genomic regions of any one of Lists 1-8. In some embodiments, at least 3%, 5%, 10%, 15%, or 20% of the probes comprise no G (Guanine). In some embodiments, each of the probes comprise multiple binding sites to methylation sites of the converted cfDNA molecule, wherein at least 80, 85, 90, 92, 95, or 98% of the multiple binding sites comprise exclusively either CpG or CpA. In some embodiments, each of the probes is conjugated to a non-nucleotide affinity moiety. In some embodiments, the non-nucleotide affinity moiety is a biotin moiety.
All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.
The novel features of the disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings of which:
Unless defined otherwise, all technical and scientific terms used herein have the meaning commonly understood by a person skilled in the art to which this description belongs. As used herein, the following terms have the meanings ascribed to them below.
As used herein any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, thereby providing a framework for various possibilities of described embodiments to function together.
As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).
In addition, use of the “a” or “an” are employed to describe elements and components of the embodiments herein. This is done merely for convenience and to give a general sense of the description. This description should be read to include one or at least one and the singular also includes the plural unless it is obvious that it is meant otherwise.
As used herein, ranges and amounts can be expressed as “about” a particular value or range. About also includes the exact amount. Hence “about 5 μg” means “about 5 μg” and also “5 μg.” Generally, the term “about” includes an amount that would be expected to be within experimental error. In some embodiments, “about” refers to the number or value recited, “+” or “−” 20%, 10%, or 5% of the number or value. Additionally, ranges recited herein are understood to be shorthand for all of the values within the range, inclusive of the recited endpoints. For example, a range of 1 to 50 is understood to include any number, combination of numbers, or sub-range from the group consisting of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, and 50.
The term “hematological disorder” or “HD” as used herein refers to a disorder which primarily affecting the blood, selected from the group consisting of CHIP, leukemia, lymphoid neoplasms (e.g. lymphoma), multiple myeloma, and myeloid neoplasm.
The term “methylation” as used herein refers to a process by which a methyl group is added to a DNA molecule. For example, a hydrogen atom on the pyrimidine ring of a cytosine base can be converted to a methyl group, forming 5-methylcytosine. The term also refers to a process by which a hydroxymethyl group is added to a DNA molecule, for example by oxidation of a methyl group on the pyrimidine ring of a cytosine base. Methylation and hydroxymethylation tend to occur at dinucleotides of cytosine and guanine referred to herein as “CpG sites.”
The term “methylation” can also refer to the methylation status of a CpG site. A CpG site with a 5-methylcytosine moiety is methylated. A CpG site with a hydrogen atom on the pyrimidine ring of the cytosine base is unmethylated.
In such embodiments, the wet laboratory assay used to detect methylation may vary from those described herein as is well known in the art.
The term “methylation site” as used herein refers to a region of a DNA molecule where a methyl group can be added. “CpG” sites are the most common methylation site, but methylation sites are not limited to CpG sites. For example, DNA methylation may occur in cytosines in CHG and CHH, where H is adenine, cytosine or thymine. Cytosine methylation in the form of 5-hydroxymethylcytosine may also assessed (see, e.g., WO 2010/037001 and WO 2011/127136, which are incorporated herein by reference), and features thereof, using the methods and procedures disclosed herein.
The term “CpG site” as used herein refers to a region of a DNA molecule where a cytosine nucleotide is followed by a guanine nucleotide in the linear sequence of bases along its 5′ to 3′ direction. “CpG” is a shorthand for 5′-C-phosphate-G-3′ that is cytosine and guanine separated by only one phosphate group. Cytosines in CpG dinucleotides can be methylated to form 5-methylcytosine.
The term “CpG detection site” as used herein refers to a region in a probe that is configured to hybridize to a CpG site of a target DNA molecule. The CpG site on the target DNA molecule can comprise cytosine and guanine separated by one phosphate group, where cytosine is methylated or unmethylated. The CpG site on the target DNA molecule can comprise uracil and guanine separated by one phosphate group, where the uracil is generated by the conversion of unmethylated cytosine.
The term “UpG” is a shorthand for 5′-U-phosphate-G-3′ that is uracil and guanine separated by only one phosphate group. UpG can be generated by a bisulfite treatment of a DNA that converts unmethylated cytosines to uracils. Cytosines can be converted to uracils by other methods known in the art, such as chemical modification, synthesis, or enzymatic conversion.
The term “hypomethylated” or “hypermethylated” as used herein refers to a methylation status of a DNA molecule containing multiple CpG sites (e.g., more than 3, 4, 5, 6, 7, 8, 9, 10, etc.) where a high percentage of the CpG sites (e.g., more than 80%, 85%, 90%, or 95%, or any other percentage within the range of 50%-100%) are unmethylated or methylated, respectively.
The terms “methylation state vector” or “methylation status vector” as used herein refers to a vector comprising multiple elements, where each element indicates the methylation status of a methylation site in a DNA molecule comprising multiple methylation sites, in the order they appear from 5′ to 3′ in the DNA molecule. For example, <Mx, Mx+1, Mx+2>, <Mx, Mx+1, Ux+2>, . . . , <Ux, Ux+1, Ux+2> can be methylation vectors for DNA molecules comprising three methylation sites, where M represents a methylated methylation site and U represents an unmethylated methylation site.
The term “abnormal methylation pattern” or “anomalous methylation pattern” as used herein refers to the methylation pattern of a DNA molecule or a methylation state vector that is expected to be found in a sample less frequently than a threshold value. In one embodiment provided herein, the expectedness of finding a specific methylation state vector in a healthy control group comprising healthy individuals is represented by a p-value. A low p-value score generally corresponds to a methylation state vector which is relatively unexpected in comparison to other methylation state vectors within samples from healthy individuals. A high p-value score generally corresponds to a methylation state vector which is relatively more expected in comparison to other methylation state vectors found in samples from healthy individuals in the healthy control group. A methylation state vector having a p-value lower than a threshold value (e.g., 0.1, 0.01, 0.001, 0.0001, etc.) can be defined as an abnormal/anomalous methylation pattern. Various methods known in the art can be used to calculate a p-value or expectedness of a methylation pattern or a methylation state vector. Exemplary methods provided herein involve use of a Markov chain probability that assumes methylation statuses of CpG sites to be dependent on methylation statuses of neighboring CpG sites. Alternate methods provided herein calculate the expectedness of observing a specific methylation state vector in healthy individuals by utilizing a mixture model including multiple mixture components, each being an independent-sites model where methylation at each CpG site is assumed to be independent of methylation statuses at other CpG sites.
The term “HD sample” as used herein refers to a sample comprising genomic DNAs from an individual diagnosed with a hematological disorder. The genomic DNAs can be, but are not limited to, cfDNA fragments or chromosomal DNAs from a subject with a hematological disorder. The genomic DNAs can be sequenced (or otherwise detected) and their methylation status can be assessed by methods known in the art, for example, bisulfite sequencing. When genomic sequences are obtained from public database (e.g., The Cancer Genome Atlas (TCGA)) or experimentally obtained by sequencing a genome of an individual diagnosed with a hematological disorder, HD sample can refer to genomic DNAs or cfDNA fragments having the genomic sequences. The term “HD samples” as a plural refers to samples comprising genomic DNAs from multiple individuals, each individual has been diagnosed with a hematological disorder. In various embodiments, HD samples from more than 10, 20, 50, 100, 200, 300, 500, 1,000, 2,000, 5,000, 10,000, 20,000, 40,000, 50,000, or more individuals diagnosed with a hematological disorder are used.
The term “non-HD sample” or “healthy sample” as used herein refers to a sample comprising genomic DNAs from an individual not diagnosed with a hematological disorder. The genomic DNAs can be, but are not limited to, cfDNA fragments or chromosomal DNAs from a subject without a hematological disorder (e.g., a healthy subject). The genomic DNAs can be sequenced (or otherwise detected) and their methylation status can be assessed by methods known in the art, for example, bisulfite sequencing. When genomic sequences are obtained from public database (e.g., The Cancer Genome Atlas (TCGA)) or experimentally obtained by sequencing a genome of an individual without a hematological disorder, non-HD sample can refer to genomic DNAs or cfDNA fragments having the genomic sequences. The term “non-HD samples” as a plural refers to samples comprising genomic DNAs from multiple individuals, each individual is without a hematological disorder. In various embodiments, healthy samples from more than 10, 20, 50, 100, 200, 300, 500, 1,000, 2,000, 5,000, 10,000, 20,000, 40,000, 50,000, or more individuals without a hematological disorder are used.
The term “training sample” as used herein refers to a sample used to train a classifier described herein and/or to select one or more genomic regions for hematological disorder detection. The training samples can comprise genomic DNAs or a modification there of, from one or more healthy subjects and from one or more subjects having a hematological disorder. The genomic DNAs can be, but are not limited to, cfDNA fragments or chromosomal DNAs. The genomic DNAs can be sequenced (or otherwise detected) and their methylation status can be assessed by methods known in the art, for example, bisulfite sequencing. When genomic sequences are obtained from public database (e.g., The Cancer Genome Atlas (TCGA)) or experimentally obtained by sequencing a genome of an individual, a training sample can refer to genomic DNAs or cfDNA fragments having the genomic sequences.
The term “test sample” as used herein refers to a sample from a subject, whose health condition was, has been or will be tested using a classifier and/or an assay panel described herein. The test sample can comprise genomic DNAs or a modification there of. The genomic DNAs can be, but are not limited to, cfDNA fragments or chromosomal DNAs.
The term “target genomic region” as used herein refers to a region in a genome selected for analysis in test samples. An assay panel is generated with probes designed to hybridize to (and optionally pull down) nucleic acid fragments derived from the target genomic region or a fragment thereof. A nucleic acid fragment derived from the target genomic region refers to a nucleic acid fragment generated by degradation, cleavage, bisulfite conversion, or other processing of the DNA from the target genomic region.
Various target genomic regions are described according to their chromosomal location in the sequence listing filed herewith. Chromosomal DNA is double-stranded, so a target genomic region includes two DNA strands: one with the sequence provided in the listing and a second that is a reverse complement to the sequence in the listing. Probes can be designed to hybridize to one or both sequences. Optionally, probes hybridize to converted sequences resulting from, for example, treatment with sodium bisulfite.
The term “off-target genomic region” as used herein refers to a region in a genome which has not been selected for analysis in test samples, but has sufficient homology to a target genomic region to potentially be bound and pulled down by a probe designed to target the target genomic region. In one embodiment, an off-target genomic region is a genomic region that aligns to a probe along at least 45 bp with at least a 90% match rate.
The terms “converted DNA molecules,” “converted cfDNA molecules,” and “modified fragment obtained from processing of the cfDNA molecules” refer to DNA molecules obtained by processing DNA or cfDNA molecules in a sample for the purpose of differentiating a methylated nucleotide and an unmethylated nucleotide in the DNA or cfDNA molecules. For example, in one embodiment, the sample can be treated with bisulfite ion (e.g., using sodium bisulfite), as is well-known in the art, to convert unmethylated cytosines (“C”) to uracils (“U”). In another embodiment, the conversion of unmethylated cytosines to uracils is accomplished using an enzymatic conversion reaction, for example, using a cytidine deaminase (such as APOBEC). After treatment, converted DNA molecules or cfDNA molecules include additional uracils which are not present in the original cfDNA sample. Replication by DNA polymerase of a DNA strand comprising a uracil results in addition of an adenine to the nascent complementary strand instead of the guanine normally added as the complement to a cytosine or methylcytosine.
The terms “cell free nucleic acid,” “cell free DNA,” or “cfDNA” refers to nucleic acid fragments that circulate in an individual's body (e.g., bloodstream) and originate from one or more healthy cells and/or from one or more HD cells (i.e., cells from a subject having a hematological disorder). Additionally, cfDNA may come from other sources such as viruses, fetuses, etc.
The term “circulating tumor DNA” or “ctDNA” refers to nucleic acid fragments that originate from tumor cells, which may be released into an individual's bloodstream as result of biological processes such as apoptosis or necrosis of dying cells or actively released by viable tumor cells.
The term “fragment” as used herein can refer to a fragment of a nucleic acid molecule. For example, in one embodiment, a fragment can refer to a cfDNA molecule in a blood or plasma sample, or a cfDNA molecule that has been extracted from a blood or plasma sample. An amplification product of a cfDNA molecule may also be referred to as a “fragment.” In another embodiment, the term “fragment” refers to a sequence read, or set of sequence reads, that have been processed for subsequent analysis (e.g., for in machine-learning based classification), as described herein. For example, as is well known in the art, raw sequence reads can be aligned to a reference genome and matching paired end sequence reads assembled into a longer fragment for subsequent analysis.
The term “individual” refers to a human individual. The term “healthy individual” refers to an individual presumed not to have a hematological disorder.
The term “subject” refers to an individual whose DNA is being analyzed. A subject may be a test subject whose DNA is be evaluated using a targeted panel as described herein to evaluate whether the person has a hematological disorder or another disease. A subject may also be part of a control group known not to have a hematological disorder or another disease. A subject may also be part of a hematological disorder or other disease group known to have a hematological disorder or another disease. Control and cancer/disease groups may be used to assist in designing or validating the targeted panel.
The term “sequence reads” as used herein refers to nucleotide sequences reads from a sample. Sequence reads can be obtained through various methods provided herein or as known in the art.
The term “sequencing depth” as used herein refers to the count of the number of times a given target nucleic acid within a sample has been sequenced (e.g., the count of sequence reads at a given target region). Increasing sequencing depth can reduce required amounts of nucleic acids required to assess a disease state (e.g., hematological disease state).
The term “tissue of origin” or “TOO” as used herein refers to the organ, organ group, body region or cell type that a hematological disease arises or originates from. The identification of a tissue of origin or cancer cell type typically allows for identification of the most appropriate next steps in the care continuum of cancer to further detect, diagnose, stage and decide on treatment.
The term “transition” generally refers to changes in base composition from one purine to another purine, or from one pyrimidine to another pyrimidine. For instance, the following changes are transitions: C→U, U→C, G→A, A→G, C→T, and T→C.
“An entirety of probes” of a panel or bait set or “an entirety of polynucleotide-containing probes” of a panel or bait set generally refers to all of the probes delivered with a specified panel or bait set. For instance, in some embodiments, a panel or bait set may include both (1) probes having features specified herein (e.g., probes for binding to cell-free DNA fragments corresponding to or derived from genomic regions set forth herein in one or more Lists) and (2) additional probes that do not contain such feature(s). The entirety of probes of a panel generally refers to all probes delivered with the panel or bait set, including such probes that do not contain the specified feature(s).
In a first aspect, the present description provides an HD assay panel comprising a plurality of probes or a plurality of probe pairs. The assay panels described herein can alternatively be referred to as bait sets or as compositions comprising bait oligonucleotides. The probes can be polynucleotide-containing probes that are specifically designed to target one or more nucleic acid molecules corresponding to, or derived from genomic regions differentially methylated between HD and non-HD samples, between different HD types, between CHIP and other HD samples, between different cancer tissue of origin (TOO) types, or between samples of different stages of HD. In some embodiments, the target genomic regions (or nucleic acid molecules derived therefrom) are selected to maximize classification accuracy, subject to a size budget (which is determined by sequencing budget and desired depth of sequencing).
The HD assay panel's design and utility is generally described in
Exemplary methodology for designing a hematological disorder assay panel is generally described in
The analytics system may then select target genomic regions based on methylation patterns of nucleic acid fragments. One approach considers pairwise distinguishability between pairs of outcomes for regions (or more specifically for CpG sites within regions). Another approach considers distinguishability for regions (or more specifically for CpG sites within regions) when considering each outcome against the remaining outcomes. From the selected target genomic regions with high distinguishability power, the analytics system may design probes to target fragments from the selected genomic regions. The analytics system may generate variable sizes of the hematological disorder assay panel, e.g., where a small sized hematological disorder assay panel includes probes targeting the most informative genomic regions, a medium sized hematological disorder assay panel includes probes from the small sized hematological disorder assay panel and additional probes targeting a second tier of informative genomic regions, and a large sized hematological disorder assay panel includes probes from the small-sized and the medium-sized hematological disorder assay panels along with even more probes targeting a third tier of informative genomic regions. With data obtained such hematological disorder assay panels (e.g., the methylation status on nucleic acids derived from the hematological disorder assay panels), the analytics system may train classifiers with various classification techniques to predict a sample's likelihood of having a particular outcome or state, e.g., hematological disorder, other disorder, other disease, etc.
In some embodiments, the HD assay panel comprises at least 500 pairs of probes, wherein each pair of the at least 500 pairs comprises two probes configured to overlap each other by an overlapping sequence, wherein the overlapping sequence comprises at least 30-nucleotides, and wherein each probe is configured to hybridize to a converted DNA (e.g., a cfDNA) molecule corresponding to one or more genomic regions. In some embodiments, each of the genomic regions comprises at least five methylation sites, and wherein the at least five methylation sites have an abnormal methylation pattern in HD samples or a different methylation pattern between samples of a different HD. For example, in one embodiment, the at least five methylation sites are differentially methylated between HD and non-HD samples, between different HD types, between CHIP and other HD samples, between blood cancer and solid cancer, between different cancer tissue of origin (TOO) types, or between samples of different stages of HD. In some embodiments, each pair of probes comprises a first probe and a second probe, wherein the second probe differs from the first probe. The second probe can overlap with the first probe by an overlapping sequence that is at least 30, at least 40, at least 50, or at least 60 nucleotides in length.
The target genomic regions can be selected from any one of Lists 1-8 (TABLE 1). In some embodiments, the HD assay panel comprises a plurality of probes, wherein each of the plurality of probes is configured to hybridize to a converted cfDNA molecule corresponding to one or more of the genomic regions in any one of Lists 1-8. In some embodiments, the plurality of different bait oligonucleotides is configured to hybridize to DNA molecules derived from at least 20% of the target genomic regions of any one of Lists 1-8. In some embodiments, the plurality of different bait oligonucleotides is configured to hybridize to DNA molecules derived from at least 30%, 40%, 50%, 60%, 70%, or 80% of the target genomic regions of any one of Lists 1-8. For example, the plurality of different bait oligonucleotides can be configured to hybridize to DNA molecules derived from at least 20%, 30%, 40%, 50%, 60%, 70%, or 80% of the target genomic regions of Lists 2-4, or from at least 20%, 30%, 40%, 50%, 60%, 70%, or 80% of the target genomic regions of Lists 5-7.
The target genomic regions can be selected from List 1. The target genomic regions can be selected from List 2. In some embodiments, a method for detecting lymphoid neoplasm comprises evaluating the methylation status for sequencing reads derived from the target genomic regions of List 2. The target genomic regions can be selected from List 3. In some embodiments, a method for detecting multiple myeloma comprises evaluating the methylation status for sequencing reads derived from the target genomic regions of List 3. The target genomic regions can be selected from List 4. In some embodiments, a method for detecting myeloid neoplasm comprises evaluating the methylation status for sequencing reads derived from the target genomic regions of List 4. The target genomic regions can be selected from List 5. In some embodiments, a method for detecting lymphoid neoplasm comprises evaluating the methylation status for sequencing reads derived from the target genomic regions of List 5. The target genomic regions can be selected from List 6. In some embodiments, a method for detecting multiple myeloma comprises evaluating the methylation status for sequencing reads derived from the target genomic regions of List 6. The target genomic regions can be selected from List 7. In some embodiments, a method for detecting myeloid neoplasm comprises evaluating the methylation status for sequencing reads derived from the target genomic regions of List 7. The target genomic regions can be selected from List 8. In some embodiments, a method for detecting myeloid neoplasm comprises evaluating the methylation status for sequencing reads derived from the target genomic regions of List 8. In some embodiments, the genomic regions can be selected from two or more, three or more, four or more, five or more, six or more, of Lists 1-8.
Since the probes are configured to hybridize to a converted DNA or cfDNA molecule corresponding to, or derived from, one or more genomic regions, the probes can have a sequence different from the targeted genomic region. For example, a DNA containing unmethylated CpG site will be converted to include UpG instead of CpG because unmethylated cytosines are converted to uracils by a conversion reaction (e.g., bisulfite treatment). As a result, a probe is configured to hybridize to a sequence including UpG instead of a naturally existing unmethylated CpG. Accordingly, a complementary site in the probe to the unmethylation site can comprise CpA instead of CpG, and some probes targeting a hypomethylated site where all methylation sites are unmethylated can have no guanine (G) bases. In some embodiments, at least 3%, 5%, 10%, 15%, or 20% of the probes comprise no CpG sequences.
The HD assay panel can be used to detect the presence or absence of HD generally and/or provide an HD classification such as an HD type or a stage of HD. In some embodiments, the HD assay panel can be used to provide a cancer classification such as cancer type, stage of cancer such as non-cancer, cancer stage I, cancer stage II, cancer stage III, or cancer stage IV. The panel may include probes targeting nucleic acids derived genomic regions differentially methylated between HD and non-HD samples, between different HD types, between CHIP and other HD samples, between different cancer tissue of origin (TOO) types, or between samples of different stages of HD. For example, in some embodiments, an HD assay panel is designed to enrich nucleic acids derived from differentially methylated genomic regions based on bisulfite sequencing data generated from the cfDNA from HD and non-HD individuals.
Each probe, probe pair, or probe set can be designed to target nucleic acid fragments corresponding to or derived from one or more target genomic regions. The target genomic regions are selected based on several criteria designed to increase selective enriching of informative nucleic acid fragments while decreasing noise and non-specific bindings.
In one example, a panel can include probes that can selectively hybridize (i.e., bind to) and optionally enrich cfDNA fragments that are differentially methylated in HD samples. In this case, sequence from the enriched fragments can provide information relevant to detection of HD. Furthermore, the probes are designed to target genomic regions that are determined to have an abnormal methylation pattern in HD samples, or in sample from a specific type of HD. In one embodiment, probes are designed to target genomic regions determined to be hypermethylated or hypomethylated in certain HDs, or cancer tissue of origins, to provide additional selectivity and specificity of the detection. In some embodiments, a panel comprises probes targeting hypomethylated fragments. In some embodiments, a panel comprises probes targeting hypermethylated fragments. In some embodiments, a panel comprises both a first set of probes targeting hypermethylated fragments and a second set of probes targeting hypomethylated fragments. (
In a second example, genomic regions can be selected when the genomic regions give rise to anomalously methylated DNA molecules in HD samples or samples with known HD types (e.g., CHIP, blood cancer). For example, as described herein, a Markov model trained on a set of non-HD samples can be used to identify genomic regions that give rise to anomalously methylated DNA molecules (i.e., DNA molecules having a methylation pattern below a p-value threshold).
Each of the probes can target a genomic region comprising at least 30 bp, 35 bp, 40 bp, 45 bp, 50 bp, 60 bp, 70 bp, 80 bp, 90 bp, 100 bp or more. In some embodiments, the genomic regions can be selected to have less than 30, 25, 20, 15, 12, 10, 8, or 6 methylation sites.
The genomic regions can be selected when at least 80, 85, 90, 92, 95, or 98% of the at least five methylation (e.g., CpG) sites within the region are either methylated or unmethylated in non-HD or HD samples, samples of a specific type of HD (e.g., samples of CHIP or cancer samples from a tissue of origin (TOO)) or samples of a specific stage of HD.
Genomic regions may be further filtered to select only those that are likely to be informative based on their methylation patterns, for example, CpG sites that are differentially methylated between HD and non-HD samples (e.g., abnormally methylated or unmethylated in HD versus non-HD), between different HD types, between CHIP and other HD samples, or between samples of different stages of HD. For the selection, calculation can be performed with respect to each CpG or a plurality of CpG sites. For example, a first count is determined that is the number of HD-containing samples (HD_count) that include a fragment overlapping that CpG, and a second count is determined that is the number of total samples containing fragments overlapping that CpG site (total). Genomic regions can be selected based on criteria positively correlated to the number of HD-containing samples (HD_count) that include a fragment indicative of HD overlapping that CpG site, and inversely correlated with the number of total samples containing fragments indicative of HD overlapping that CpG site (total). In one embodiment, the number of non-HD samples (nnon-HD) and the number of HD samples (nHD) having a fragment overlapping a CpG site are counted. Then the probability that a sample is HD is estimated, for example as (nHD+1)/(nHD+nnon-HD+2).
CpG sites scored by this metric are ranked and greedily added to a panel until the panel size budget is exhausted. The process of selecting genomic regions indicative of HD is further detailed herein.
Different target regions may be selected depending on whether the assay is intended to be a pan-HD assay or a single-HD assay, or what kind of flexibility is desired. A panel for detecting a specific HD type can be designed using a similar process. In this embodiment, for each HD type, and for each CpG site, the information gain is computed to determine whether to include a probe targeting that CpG site. The information gain may be computed for samples with a given HD compared to all other samples. For example, consider two random variables, “AF” and “CT”. “AF” is a binary variable that indicates whether there is an abnormal fragment overlapping a particular CpG site in a particular sample (yes or no). “CT” is a binary random variable indicating whether the HD is of a particular type (e.g., CHIP, leukemia, lymphoid neoplasms (e.g. lymphoma), multiple myeloma, and myeloid neoplasm). One can compute the mutual information with respect to “CT” given “AF.” That is, how many bits of information about the HD type (e.g., CHIP vs. blood cancer) are gained if one knows whether there is an anomalous fragment overlapping a particular CpG site. This can be used to rank CpG's based on how CHIP-specific they are. This procedure is repeated for a plurality of HD types. If a particular region is differentially methylated only in CHIP (and not blood cancer), CpG's in that region would tend to have high information gains for CHIP. For each HD type, CpG sites are ranked by this information gain metric, and then greedily added to a panel until the size budget for that HD type is exhausted.
Further filtration can be performed to select probes with high specificity for enrichment (i.e., high binding efficiency) of nucleic acids derived from targeted genomic regions. Probes can be filtered to reduce non-specific binding (or off-target binding) to nucleic acids derived from non-targeted genomic regions. For example, probes can be filtered to select only those probes having less than a set threshold of off-target binding events. In one embodiment, probes can be aligned to a reference genome (e.g., a human reference genome) to select probes that align to less than a set threshold of regions across the genome. For example, probes can be selected that align to less than 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9 or 8 off-target regions across the reference genome. In other cases, filtration is performed to remove genomic regions when the sequence of the target genomic regions appears more than 5, 10, 15, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34 or 35 times in a genome. Further filtration can be performed to select target genomic regions when a probe sequence, or a set of probe sequences that are 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% homologous to the target genomic regions, appear less than 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9 or 8 times in a reference genome, or to remove target genomic regions when the probe sequence, or a set of probe sequences designed to enrich for the targeted genomic region are 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% homologous to the target genomic regions, appear more than 5, 10, 15, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34 or 35 times in a reference genome. This is for excluding repetitive probes that can pull down off-target fragments, which are not desired and can impact assay efficiency.
In some embodiments, a fragment-probe overlap of at least 45 bp was demonstrated to be effective for achieving a non-negligible amount of pulldown (though as one of skill in the art would appreciate this number can very) as provided in Example 1. In some embodiments, more than a 10% mismatch rate between the probe and fragment sequences in the region of overlap is sufficient to greatly disrupt binding, and thus pulldown efficiency. Therefore, sequences that can align to the probe along at least 45 bp with at least a 90% match rate can be candidates for off-target pulldown. Thus, in one embodiment, the number of such regions are scored. The best probes have a score of 1, meaning they match in only one place (the intended target region). Probes with an intermediate score (say, less than 5 or 10) may in some instances be accepted, and in some instances any probes above a particular score are discarded. Other cutoff values can be used for specific samples.
Once the probes hybridize and capture DNA fragments corresponding to, or derived from a target genomic region, the hybridized probe-DNA fragment intermediates are pulled down (or isolated), and the targeted DNA is amplified and its methylation status is determined by, for example, sequencing or hybridization to a microarray, etc. The sequence read provides information relevant for detection of HD. For this end, a panel is designed to include a plurality of probes that can capture fragments that can together provide information relevant to detection of HD. In some embodiments, a panel includes at least 500, 1,000, 2,000, 2,500, 5,000, 6,000, 7,500, 10,000, 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 110,000 or 120,000 pairs of probes. In other embodiments, a panel includes at least 1,000, 2,000, 5,000, 10,000, 50,000, 100,000, 150,000, 200,000, 250,000, 300,000, 400,000, 500,000, 550,000, 600,000, 700,000, or 800,000 probes. The plurality of probes together can comprise at least 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 120,000, 140,000, 160,000, 180,000, 200,000, 240,000, 260,000, 280,000, 300,000, 320,000, 400,000, 450,000, 500,000, 550,000, 600,000, 650,000, 700,000, 750,000, 800,000, 850,000, 900,000, 1 million, 1.5 million, 2 million, 2.5 million, 3 million, 3.5 million, 4 million, 4.5 million, or 5 million nucleotides.
The selected target genomic regions can be located in various positions in a genome, including but not limited to exons, introns, intergenic regions, and other parts. In some embodiments, probes targeting non-human genomic regions, such as those targeting viral genomic regions, can be added.
In some instances, primers may be used to specifically amplify targets/biomarkers of interest (e.g., by PCR), thereby enriching the sample for desired targets/biomarkers (optionally without hybridization capture). For example, forward and reverse primers can be prepared for each genomic region of interest and used to amplify fragments that correspond to or are derived from the desired genomic region. Thus, while the present disclosure pays particular attention to HD assay panels and bait sets for hybridization capture, the disclosure is broad enough to encompass other methods for enrichment of cell-free DNA. Accordingly, a skilled artisan, with the benefit of this disclosure, will recognize that methods analogous to those described herein in connection with hybridization capture can alternatively be accomplished by replacing hybridization capture with some other enrichment strategy, such as PCR amplification of cell-free DNA fragments that correspond with genomic regions of interest. In some embodiments, bisulfite padlock probe capture is used to enrich regions of interest, such as is described in Zhang et al. (US 2016/0340740). In some embodiments, additional or alternative methods are used for enrichment (e.g., non-targeted enrichment) such as reduced representation bisulfite sequencing, methylation restriction enzyme sequencing, methylation DNA immunoprecipitation sequencing, methyl-CpG-binding domain protein sequencing, methyl DNA capture sequencing, or microdroplet PCR.
The HD assay panel provided herein is a panel including a set of hybridization probes (also referred to herein as “probes”) designed to, during enrichment, target and pull down nucleic acid fragments of interest for the assay. In some embodiments, the probes are designed to hybridize and enrich DNA or cfDNA molecules from HD samples that have been treated to convert unmethylated cytosines (C) to uracils (U). In other embodiments, the probes are designed to hybridize and enrich DNA or cfDNA molecules from a specific type of HD that has been treated to convert unmethylated cytosines (C) to uracils (U). The probes can be designed to anneal (or hybridize) to a target (complementary) strand of DNA or RNA. The target strand can be the “positive” strand (e.g., the strand transcribed into mRNA, and subsequently translated into a protein) or the complementary “negative” strand. In a particular embodiment, an HD assay panel may include sets of two probes, one probe targeting the positive strand and the other probe targeting the negative strand of a target genomic region.
For each target genomic region, four possible probe sequences can be designed. DNA molecules corresponding to, or derived from, each target region is double-stranded, as such, a probe or probe set can target either the “positive” or forward strand or its reverse complement (the “negative” strand). Additionally, in some embodiments, the probes or probe sets are designed to enrich DNA molecules or fragments that have been treated to convert unmethylated cytosines (C) to uracils (U). Because the probes or probe sets are designed to enrich DNA molecules corresponding to, or derived from the targeted regions after conversion, the probe's sequence can be designed to enrich DNA molecules of fragments where unmethylated C's have been converted to U's (by utilizing A's in place of G's at sites that are unmethylated cytosines in DNA molecules or fragments corresponding to, or derived from, the targeted region). In one embodiment, probes are designed to bind to, or hybridize to, DNA molecules or fragments from genomic regions known to contain HD-specific methylation patterns (e.g., hypermethylated or hypomethylated DNA molecules), thereby enriching (or detecting) HD-specific DNA molecules or fragments. Targeting genomic regions, or HD-specific methylation patterns, can be advantageous allowing one to specifically enrich for DNA molecules or fragments identified as informative for pan-HD or a specific type of HD, and thus, lowering detection needs and costs (e.g., lowering sequencing costs). In other embodiments, two probe sequences can be designed per a target genomic region (one for each DNA strand).
In still other cases, probes are designed to enrich for all DNA molecules or fragments corresponding to, or derived from a targeted region (i.e., regardless of strand or methylation status). This might be because the HD methylation status is not highly methylated or unmethylated, or because the probes are designed to target small mutations or other variations rather than methylation changes, with these other variations similarly indicative of the presence or absence of an HD or the presence or absence of a specific HD. In that case, all four possible probe sequences can be included per a target genomic region.
In some embodiments, some probes are designed to detect variants and mutations indicative to the presence or absence of an HD or the presence or absence of a specific HD. Such probes are designed to enrich DNA molecules or fragments corresponding to or derived from a targeted region that can include such variants or mutations. Some of the variants or mutations can be one or more loci known to be associated with or suspected of being associated with CHIP or another HD. Some of the variants or mutations can be one or more loci identified to be indicative of CHIP or other HD by methods described in 4.5.
The probes can range in length from 10 s, 100 s, 200 s, or 300 s of base pairs. The probes can comprise at least 50, 75, 100, or 120 nucleotides. The probes can comprise less than 300, 250, 200, or 150 nucleotides. In an embodiment, the probes comprise 100-150 nucleotides. In one embodiment, the probes comprise 120 nucleotides.
In some embodiments, the probes are designed in a “2× tiled” fashion to cover overlapping portions of a target region. Each probe optionally overlaps in coverage at least partially with another probe in the library. In such embodiments, the panel contains multiple pairs of probes, with each probe in a pair overlapping the other by at least 25, 30, 35, 40, 45, 50, 60, 70, 75 or 100 nucleotides. In some embodiments, the overlapping sequence can be designed to be complementary to a target genomic region (or cfDNA derived therefrom) or to be complementary to a sequence with homology to a target region or cfDNA. Thus, in some embodiments, at least two probes are complementary to the same sequence within a target genomic region, and a nucleotide fragment corresponding to or derived from the target genomic region can be bound and pulled down by at least one of the probes. Other levels of tiling are possible, such as 3× tiling, 4× tiling, etc., wherein each nucleotide in a target region can bind to more than two probes.
In one embodiment, each base in a target genomic region is overlapped by exactly two probes, as illustrated in
For example, a 100 bp cfDNA fragment comprising a 30 nt target genomic region can be guaranteed to have at least 65 bp overlap with at least one of the overlapping probes. Other levels of tiling are possible. For example, to increase target size and add more probes in a panel, probes can be designed to expand a 30 bp target region by at least 70 bp, 65 bp, 60 bp, 55 bp, or 50 bp. To capture any fragment that overlaps the target region at all (even if by only 1 bp), the probes can be designed to extend past the ends of the target region on either side.
The probes are designed to analyze methylation status of target genomic regions (e.g., of the human or another organism) that are suspected to correlate with the presence or absence of HD generally, presence or absence of certain types of HD, HD stage, or presence or absence of other types of diseases (e.g., other types of cancer such as solid cancer).
Furthermore, the probes are designed to effectively hybridize to (or bind to) and optionally pull down cfDNA fragments containing a target genomic region. In some embodiments, the probes are designed to cover overlapping portions of a target region, so that each probe is “tiled” in coverage such that each probe overlaps in coverage at least partially with another probe in the library. In such embodiments, the panel contains multiple pairs of probes, where each pair comprises at least two probes overlapping each other by an overlapping sequence of at least 25, 30, 35, 40, 45, 50, 60, 70, 75 or 100 nucleotides. In some embodiments, the overlapping sequence can be designed to have sequence homology with or to be complementary to a target genomic region (or a converted version thereof), thus a nucleotide fragment derived from or corresponding to the target genomic region can be bound and optionally pulled down by at least one of the probes.
In one embodiment, the smallest target genomic region is 30 bp. When a new target region is added to the panel (based on the greedy selection as described above), the new target region of 30 bp can be centered on a specific CpG site of interest. Then, it is checked whether each edge of this new target is close enough to other targets such that they can be merged. This is based on a “merge distance” parameter which can be 200 bp by default but can be tuned. This allows close but distinct target regions to be enriched with overlapping probes. Depending on whether close enough targets exist to the left or right of the new target, the new target can be merged with nothing (increasing the number of panel targets by one), merged with just one target either to the left or the right (not changing the number of panel targets), or merged with existing targets both to the left and right (reducing the number of panel targets by one).
In another aspect, methods of selecting target genomic regions for detecting HD and/or a specific type or stage of HD are provided. The targeted genomic regions can be used to design and manufacture probes for an HD assay panel. Methylation status of DNA or cfDNA molecules corresponding to, or derived from, the target genomic regions can be screened using the HD assay panel. Alternative methods, for example by WGBS or other methods known in the art, can be also implemented to detect methylation status of DNA molecules or fragments corresponding to, or derived from, the target genomic regions.
In step 105, a nucleic acid sample (DNA or RNA) is extracted from a subject. In the present disclosure, DNA and RNA may be used interchangeably unless otherwise indicated. That is, the embodiments described herein may be applicable to both DNA and RNA types of nucleic acid sequences. However, the examples described herein may focus on DNA for purposes of clarity and explanation. The sample may be any subset of the human genome, including the whole genome. The sample may include blood, plasma, serum, urine, fecal, saliva, other types of bodily fluids, or any combination thereof. In some embodiments, methods for drawing a blood sample (e.g., syringe or finger prick) may be less invasive than procedures for obtaining a tissue biopsy, which may require surgery. The extracted sample may comprise cfDNA and/or ctDNA. For healthy individuals, the human body may naturally clear out cfDNA and other cellular debris. If a subject has a cancer or disease, cfDNA and/or ctDNA in an extracted sample may be present at a level sufficient to detect the hematological disorder.
In step 110, the cfDNA fragments are treated to convert unmethylated cytosines to uracils. In one embodiment, the method uses a bisulfite treatment of the DNA which converts the unmethylated cytosines to uracils without converting the methylated cytosines. For example, a commercial kit such as the EZ DNA Methylation™—Gold, EZ DNA Methylation™—Direct or an EZ DNA Methylation™—Lightning kit (available from Zymo Research Corp (Irvine, Calif.)) is used for the bisulfite conversion. In another embodiment, the conversion of unmethylated cytosines to uracils is accomplished using an enzymatic reaction. For example, the conversion can use a commercially available kit for conversion of unmethylated cytosines to uracils, such as APOBEC-Seq (NEBiolabs, Ipswich, Mass.).
In step 115, a sequencing library is prepared. In a first step, a ssDNA adapter is added to the 3′-OH end of a bisulfite-converted ssDNA molecule using a ssDNA ligation reaction. In one embodiment, the ssDNA ligation reaction uses CircLigase II (Epicentre) to ligate the ssDNA adapter to the 3′-OH end of a bisulfite-converted ssDNA molecule, wherein the 5′-end of the adapter is phosphorylated and the bisulfite-converted ssDNA has been dephosphorylated (i.e., the 3′ end has a hydroxyl group). In another embodiment, the ssDNA ligation reaction uses Thermostable 5′ AppDNA/RNA ligase (available from New England BioLabs (Ipswich, Mass.)) to ligate the ssDNA adapter to the 3′-OH end of a bisulfite-converted ssDNA molecule. In this example, the first UMI adapter is adenylated at the 5′-end and blocked at the 3′-end. In another embodiment, the ssDNA ligation reaction uses a T4 RNA ligase (available from New England BioLabs) to ligate the ssDNA adapter to the 3′-OH end of a bisulfite-converted ssDNA molecule. In a second step, a second strand DNA is synthesized in an extension reaction. For example, an extension primer, that hybridizes to a primer sequence included in the ssDNA adapter, is used in a primer extension reaction to form a double-stranded bisulfite-converted DNA molecule. Optionally, in one embodiment, the extension reaction uses an enzyme that is able to read through uracil residues in the bisulfite-converted template strand. Optionally, in a third step, a dsDNA adapter is added to the double-stranded bisulfite-converted DNA molecule. Finally, the double-stranded bisulfite-converted DNA is amplified to add sequencing adapters. For example, PCR amplification using a forward primer that includes a P5 sequence and a reverse primer that includes a P7 sequence is used to add P5 and P7 sequences to the bisulfite-converted DNA. Optionally, during library preparation, unique molecular identifiers (UMI) may be added to the nucleic acid molecules (e.g., DNA molecules) through adapter ligation. The UMIs are short nucleic acid sequences (e.g., 4-10 base pairs) that are added to ends of DNA fragments during adapter ligation. In some embodiments, UMIs are degenerate base pairs that serve as a unique tag that can be used to identify sequence reads originating from a specific DNA fragment. During PCR amplification following adapter ligation, the UMIs are replicated along with the attached DNA fragment, which provides a way to identify sequence reads that came from the same original fragment in downstream analysis.
In step 120, targeted DNA sequences may be enriched from the library. This is used, for example, where a targeted panel assay is being performed on the samples. During enrichment, hybridization probes (also referred to herein as “probes”) are used to target, and pull down, nucleic acid fragments informative for the presence or absence of HD (or disease), HD status, or an HD classification (e.g., HD type or tissue of origin). For a given workflow, the probes may be designed to anneal (or hybridize) to a target (complementary) strand of DNA or RNA. The target strand may be the “positive” strand (e.g., the strand transcribed into mRNA, and subsequently translated into a protein) or the complementary “negative” strand. The probes may range in length from 10 s, 100 s, or 1000 s of base pairs. Moreover, the probes may cover overlapping portions of a target region.
After a hybridization step 120, the hybridized nucleic acid fragments are captured and may also be amplified using PCR (enrichment 125). For example, the target sequences can be enriched to obtain enriched sequences that can be subsequently sequenced. In general, any known method in the art can be used to isolate, and enrich for, probe-hybridized target nucleic acids. For example, as is well known in the art, a biotin moiety can be added to the 5′-end of the probes (i.e., biotinylated) to facilitate isolation of target nucleic acids hybridized to probes using a streptavidin-coated surface (e.g., streptavidin-coated beads).
In step 130, sequence reads are generated from the enriched DNA sequences, e.g., enriched sequences. Sequence data may be acquired from the enriched DNA sequences by known means in the art. For example, the method may include next generation sequencing (NGS) techniques including synthesis technology (Illumina), pyrosequencing (454 Life Sciences), ion semiconductor technology (Ion Torrent sequencing), single-molecule real-time sequencing (Pacific Biosciences), sequencing by ligation (SOLiD sequencing), nanopore sequencing (Oxford Nanopore Technologies), or paired-end sequencing. In some embodiments, massively parallel sequencing is performed using sequencing-by-synthesis with reversible dye terminators. In other embodiments, as would be readily understood by one of skill in the art, any known means for detecting nucleic acids and determining methylations status can be used. For example, sequences can be detected, and methylation status determined, using known methylation-aware sequencing (see e.g., WO 2014/043763), a DNA microarray (e.g., with labeled probes adhered or conjugated to a solid surface or DNA array chip), etc.
In step 140, methylation state vectors are generated from the sequence reads. To do so, a sequence read is aligned to a reference genome. The reference genome helps provide the context as to what position in a human genome the fragment cfDNA originates from. In a simplified example, the sequence read is aligned such that the three CpG sites correlate to CpG sites 23, 24, and 25 (arbitrary reference identifiers used for convenience of description). After alignment, there is information both on methylation status of all CpG sites on the cfDNA fragment and which position in the human genome the CpG sites map to. With the methylation status and location, a methylation state vector may be generated for the fragment cfDNA.
With each fragment's methylation state vector, the analytics system subdivides 310 the methylation state vector into strings of CpG sites. In one embodiment, the analytics system subdivides 310 the methylation state vector such that the resulting strings are all less than a given length. For example, a methylation state vector of length 11 may be subdivided into strings of length less than or equal to 3 would result in 9 strings of length 3, 10 strings of length 2, and 11 strings of length 1. In another example, a methylation state vector of length 7 being subdivided into strings of length less than or equal to 4 would result in 4 strings of length 4, 5 strings of length 3, 6 strings of length 2, and 7 strings of length 1. If a methylation state vector is shorter than or the same length as the specified string length, then the methylation state vector may be converted into a single string containing all of the CpG sites of the vector.
The analytics system tallies 320 the strings by counting, for each possible CpG site and possibility of methylation states in the vector, the number of strings present in the control group having the specified CpG site as the first CpG site in the string and having that possibility of methylation states. For example, at a given CpG site and considering string lengths of 3, there are 2{circumflex over ( )}3 or 8 possible string configurations. At that given CpG site, for each of the 8 possible string configurations, the analytics system tallies 320 how many occurrences of each methylation state vector possibility come up in the control group. Continuing this example, this may involve tallying the following quantities: <Mx, Mx+1, Mx+2>, <Mx, Mx+1, Ux+2>, . . . , <Ux, Ux+1, Ux+2> for each starting CpG site x in the reference genome. The analytics system creates 330 the data structure storing the tallied counts for each starting CpG site and string possibility.
There are several benefits to setting an upper limit on string length. First, depending on the maximum length for a string, the size of the data structure created by the analytics system can dramatically increase in size. For instance, maximum string length of 4 means that every CpG site has at the very least 2{circumflex over ( )}4 numbers to tally for strings of length 4. Increasing the maximum string length to 5 means that every CpG site has an additional 2{circumflex over ( )}4 or 16 numbers to tally, doubling the numbers to tally (and computer memory required) compared to the prior string length. Reducing string size helps keep the data structure creation and performance (e.g., use for later accessing as described below), in terms of computational and storage, reasonable. Second, a statistical consideration to limiting the maximum string length is to avoid overfitting downstream models that use the string counts. If long strings of CpG sites do not, biologically, have a strong effect on the outcome (e.g., predictions of anomalousness that predictive of the presence of HD), calculating probabilities based on large strings of CpG sites can be problematic as it requires a significant amount of data that may not be available, and thus would be too sparse for a model to perform appropriately. For example, calculating a probability of anomalousness/HD conditioned on the prior 100 CpG sites would require counts of strings in the data structure of length 100, ideally some matching exactly the prior 100 methylation states. If only sparse counts of strings of length 100 are available, there will be insufficient data to determine whether a given string of length of 100 in a test sample is anomalous or not.
Once the data structure has been created, the analytics system may seek to validate 340 the data structure and/or any downstream models making use of the data structure. One type of validation checks consistency within the control group's data structure. For example, if there are any outlier subjects, samples, and/or fragments within a control group, then the analytics system may perform various calculations to determine whether to exclude any fragments from one of those categories. In a representative example, the healthy control group may contain a sample that is undiagnosed but has an HD such that the sample contains anomalously methylated fragments. This first type of validation ensures that potential HD samples are removed from the healthy control group so as to not affect the control group's purity.
A second type of validation checks the probabilistic model used to calculate p-values with the counts from the data structure itself (i.e., from the healthy control group). A process for p-value calculation is described below in conjunction with
A third type of validation uses a healthy set of validation samples separate from those used to build the data structure, which tests if the data structure is properly built and the model works. An example process for carrying out this type of validation is described below in conjunction with
A fourth type of validation tests with samples from a non-healthy validation group. The analytics system calculates p-values and builds the CDF for the non-healthy validation group. With a non-healthy validation group, the analytics system expects to see the CDF(x)>x for at least some samples or, stated differently, the converse of what was expected in the second type of validation and the third type of validation with the healthy control group and the healthy validation group. If the fourth type of validation fails, then this is indicative that the model is not appropriately identifying the anomalousness that it was designed to identify.
The analytics system takes the validation group and generates 100 a set of methylation state vectors as described in
Anomalously methylated fragments having abnormal methylation patterns in HD samples, subject with a specific type of HD, or subjects with another known disease state, are selected as target genomic regions, according to an embodiment as outlined in
For a given methylation state vector, the analytics system enumerates 410 all possibilities of methylation state vectors having the same starting CpG site and same length (i.e., set of CpG sites) in the methylation state vector. As each methylation state may be methylated or unmethylated there are only two possible states at each CpG site, and thus the count of distinct possibilities of methylation state vectors depends on a power of 2, such that a methylation state vector of length n would be associated with 2n possibilities of methylation state vectors.
The analytics system calculates 420 the probability of observing each possibility of methylation state vector for the identified starting CpG site/methylation state vector length by accessing the healthy control group data structure. In one embodiment, calculating the probability of observing a given possibility uses a Markov chain probability to model the joint probability calculation which will be described in greater detail with respect to
The analytics system calculates 430 a p-value score for the methylation state vector using the calculated probabilities for each possibility. In one embodiment, this includes identifying the calculated probability corresponding to the possibility that matches the methylation state vector in question. Specifically, this is the possibility having the same set of CpG sites, or similarly the same starting CpG site and length as the methylation state vector. The analytics system sums the calculated probabilities of any possibilities having probabilities less than or equal to the identified probability to generate the p-value score.
This p-value represents the probability of observing the methylation state vector of the fragment or other methylation state vectors even less probable in the healthy control group. A low p-value score, thereby, generally corresponds to a methylation state vector which is rare in a healthy subject, and which causes the fragment to be labeled abnormally methylated, relative to the healthy control group. A high p-value score generally relates to a methylation state vector is expected to be present, in a relative sense, in a healthy subject. If the healthy control group is a non-HD group, for example, a low p-value indicates that the fragment is abnormally methylated relative to the non-HD group, and therefore possibly indicative of the presence of HD in the test subject.
As above, the analytics system calculates p-value scores for each of a plurality of methylation state vectors, each representing a cfDNA fragment in the test sample. To identify which of the fragments are abnormally methylated, the analytics system may filter 440 the set of methylation state vectors based on their p-value scores. In one embodiment, filtering is performed by comparing the p-values scores against a threshold and keeping only those fragments below the threshold. This threshold p-value score could be on the order of 0.1, 0.01, 0.001, 0.0001, or similar.
The analytics system calculates 420 probabilities 515 for the enumerated possibilities of methylation state vectors. As methylation is conditionally dependent on methylation status of nearby CpG sites, one way to calculate the probability of observing a given methylation state vector possibility is to use Markov chain model. Generally, a methylation state vector such as <S1, S2, . . . , Sn>, where S denotes the methylation state whether methylated (denoted as M), unmethylated (denoted as U), or indeterminate (denoted as I), has a joint probability that can be expanded using the chain rule of probabilities as:
P(<S1,S2, . . . ,Sn>)=P(Sn|S1, . . . ,Sn-1>)*P(Sn-1|S1, . . . ,Sn-2>)* . . . *P(S2|S1)*P(S1) (1)
Markov chain model can be used to make the calculation of the conditional probabilities of each possibility more efficient. In one embodiment, the analytics system selects a Markov chain order k which corresponds to how many prior CpG sites in the vector (or window) to consider in the conditional probability calculation, such that the conditional probability is modeled as P(Sn|Si, . . . , Sn-1)˜P(Sn|Sn-k-2, . . . , Sn-1).
To calculate each Markov modeled probability for a possibility of methylation state vector, the analytics system accesses the control group's data structure, specifically the counts of various strings of CpG sites and states. To calculate P(Mn|Sn-k-2, . . . , Sn-1), the analytics system takes a ratio of the stored count of the number of strings from the data structure matching <Sn-k-2, . . . , Sn-1, Mn> divided by the sum of the stored count of the number of strings from the data structure matching <Sn-k-2, . . . , Sn-1, Mn> and <Sn-k-2, . . . , Sn-1, Un>. Thus, P(Mn|Sn-k-2, . . . , Sn-1), is calculated ratio having the form:
The calculation may additionally implement a smoothing of the counts by applying a prior distribution. In one embodiment, the prior distribution is a uniform prior as in Laplace smoothing. As an example of this, a constant is added to the numerator and another constant (e.g., twice the constant in the numerator) is added to the denominator of the above equation. In other embodiments, an algorithmic technique such as Knesser-Ney smoothing is used.
In the illustration, the above denoted formulas are applied to the test methylation state vector 505 covering sites 23-26. Once the calculated probabilities 515 are completed, the analytics system calculates 430 a p-value score 525 that sums the probabilities that are less than or equal to the probability of possibility of methylation state vector matching the test methylation state vector 505.
In one embodiment, the computational burden of calculating probabilities and/or p-value scores may be further reduced by caching at least some calculations. For example, the analytic system may cache in transitory or persistent memory calculations of probabilities for possibilities of methylation state vectors (or windows thereof). If other fragments have the same CpG sites, caching the possibility probabilities allows for efficient calculation of p-value scores without needing to re-calculate the underlying possibility probabilities. Equivalently, the analytics system may calculate p-value scores for each of the possibilities of methylation state vectors associated with a set of CpG sites from vector (or window thereof). The analytics system may cache the p-value scores for use in determining the p-value scores of other fragments including the same CpG sites. Generally, the p-value scores of possibilities of methylation state vectors having the same CpG sites may be used to determine the p-value score of a different one of the possibilities from the same set of CpG sites.
In one embodiment, the analytics system uses 435 a sliding window to determine possibilities of methylation state vectors and calculate p-values. Rather than enumerating possibilities and calculating p-values for entire methylation state vectors, the analytics system enumerates possibilities and calculates p-values for only a window of sequential CpG sites, where the window is shorter in length (of CpG sites) than at least some fragments (otherwise, the window would serve no purpose). The window length may be static, user determined, dynamic, or otherwise selected.
In calculating p-values for a methylation state vector larger than the window, the window identifies the sequential set of CpG sites from the vector within the window starting from the first CpG site in the vector. The analytic system calculates a p-value score for the window including the first CpG site. The analytics system then “slides” the window to the second CpG site in the vector, and calculates another p-value score for the second window. Thus, for a window size l and methylation vector length m, each methylation state vector will generate m−l+1 p-value scores. After completing the p-value calculations for each portion of the vector, the lowest p-value score from all sliding windows is taken as the overall p-value score for the methylation state vector. In another embodiment, the analytics system aggregates the p-value scores for the methylation state vectors to generate an overall p-value score.
Using the sliding window helps to reduce the number of enumerated possibilities of methylation state vectors and their corresponding probability calculations that would otherwise need to be performed. Example probability calculations are shown in
The analytics system identifies 450 DNA fragments indicative of HD from the filtered set of anomalously methylated fragments.
According to a first method, the analytics system may identify DNA fragments that are deemed hypomethylated or hypermethylated as fragments indicative of HD from the filtered set of anomalously methylated fragments. Hypomethylated and hypermethylated fragments can be defined as fragments of a certain length of CpG sites (e.g., more than 3, 4, 5, 6, 7, 8, 9, 10, etc.) with a high percentage of methylated CpG sites (e.g., more than 80%, 85%, 90%, or 95%, or any other percentage within the range of 50%-100%) or a high percentage of unmethylated CpG sites (e.g., more than 80%, 85%, 90%, or 95%, or any other percentage within the range of 50%-100%).
According to a second method, the analytics system identifies fragments indicative of HD utilizing probabilistic models of methylation patterns fitted to each HD type and non-HD type. The analytics system calculates log-likelihood ratios for a sample using DNA fragments in the genomic regions considering the various HD types with the fitted probabilistic models for each HD type and non-HD type. The analytics system may determine a DNA fragment to be indicative of HD based on whether at least one of the log-likelihood ratios considered against the various HD types is above a threshold value.
In one embodiment of partitioning the genome, the analytics system partitions the genome into regions by multiple stages. In a first stage, the analytics system separates the genome into blocks of CpG sites. Each block is defined when there is a separation between two adjacent CpG sites that exceeds some threshold, e.g., greater than 200 bp, 300 bp, 400 bp, 500 bp, 600 bp, 700 bp, 800 bp, 900 bp, or 1,000 bp. From each block, the analytics system subdivides at a second stage each block into regions of a certain length, e.g., 500 bp, 600 bp, 700 bp, 800 bp, 900 bp, 1,000 bp, 1,100 bp, 1,200 bp, 1,300 bp, 1,400 bp, or 1,500 bp. The analytics system may further overlap adjacent regions by a percentage of the length, e.g., 10%, 20%, 30%, 40%, 50%, or 60%.
The analytics system analyzes sequence reads derived from DNA fragments for each region. The analytics system may process samples from tissue and/or high-signal cfDNA. High-signal cfDNA samples may be determined by a binary classification model, by HD stage, or by another metric.
For each HD type and non-HD, the analytics system fits a separate probabilistic model for fragments. In one example, each probabilistic model is mixture model comprising a combination of a plurality of mixture components with each mixture component being an independent-sites model where methylation at each CpG site is assumed to be independent of methylation statuses at other CpG sites.
In alternate embodiments, calculation is performed with respect to each CpG site. Specifically, a first count is determined that is the number of HD samples (HD_count) that include an anomalously methylated DNA fragment overlapping that CpG, and a second count is determined that is the total number of samples containing fragments overlapping that CpG (total) in the set. Genomic regions can be selected based on the numbers, for example, based on criteria positively correlated to the number of HD samples (HD_count) that include a DNA fragment overlapping that CpG, and inversely correlated to the total number of samples containing fragments overlapping that CpG (total) in the set.
The analytics system can further calculate log-likelihood ratios (“R”) for a fragment indicating a likelihood of the fragment being indicative of HD considering the various HD types with the fitted probabilistic models for each HD type and non-HD type. The two probabilities may be taken from probabilistic models fitted for each of the HD types and the non-HD type, the probabilistic models defined to calculate a likelihood of observing a methylation pattern on a fragment given each of the HD types and the non-HD type. For example, the probabilistic models may be defined fitted for each of the HD types and the non-HD type.
The analytics system identifies 460 genomic regions indicative of HD. To identify these informative regions, the analytics system calculates an information gain for each genomic region or more specifically each CpG site that describes an ability to distinguish between various outcomes.
A method for identifying genomic regions capable of distinguishing between HD type and non-HD type utilizes a trained classification model that can be applied on the set of anomalously methylated DNA molecules or fragments corresponding to, or derived from an HD or non-HD group. The trained classification model can be trained to identify any condition of interest that can be identified from the methylation state vectors.
In one embodiment, the trained classification model is a binary classifier trained based on methylation states for cfDNA fragments or genomic sequences obtained from a subject cohort with HD or a specific type of HD, and a healthy subject cohort without HD, and is then used to classify a test subject probability of having HD, or not having HD, based on anomalously methylation state vectors. In other embodiments, different classifiers may be trained using subject cohorts known to have particular HD (e.g., CHIP, leukemia, etc.); or known to have different stages of particular HD (e.g., CHIP, cancer stage I, II, III, or IV). In these embodiments, different classifiers may be trained using sequence reads obtained from samples enriched for tumor cells from subject cohorts known to have particular blood cancer (e.g., leukemia, lymphoid neoplasms (e.g. lymphoma), multiple myeloma, and myeloid neoplasm, etc.). Each genomic region's ability to distinguish between HD type and non-HD type in the classification model is used to rank the genomic regions from most informative to least informative in classification performance. The analytics system may identify genomic regions from the ranking according to information gain in classification between non-HD type and HD type.
Computing Information Gain from Hypomethylated and Hypermethylated Fragments Indicative of HD
With fragments indicative of HD, the analytics system may train a classifier according to a process 600 illustrated in
The analytics system determines 610, for each methylation state vector, whether the methylation state vector is indicative of HD. Here, fragments indicative of HD may be defined as hypermethylated or hypomethylated fragments determined if at least some number of CpG sites have a particular state (methylated or unmethylated, respectively) and/or have a threshold percentage of sites that are the particular state (again, methylated or unmethylated, respectively). In one example, cfDNA fragments are identified as hypomethylated or hypermethylated, respectively, if the fragment overlaps at least 5 CpG sites, and at least 80%, 90%, or 100% of its CpG sites are methylated or at least 80%, 90%, or 100% are unmethylated.
In an alternate embodiment, the analytics system considers portions of the methylation state vector and determines whether the portion is hypomethylated or hypermethylated, and may distinguish that portion to be hypomethylated or hypermethylated. This alternative resolves missing methylation state vectors which are large in size but contain at least one region of dense hypomethylation or hypermethylation. This process of defining hypomethylation and hypermethylation can be applied in step 450 of
In one embodiment, the process generates 620 a hypomethylation score (Phypo) and a hypermethylation score (Phyper) per CpG site in the genome. To generate either score at a given CpG site, the classifier takes four counts at that CpG site—(1) count of (methylations state) vectors of the HD set labeled hypomethylated that overlap the CpG site; (2) count of vectors of the HD set labeled hypermethylated that overlap the CpG site; (3) count of vectors of the non-HD set labeled hypomethylated that overlap the CpG site; and (4) count of vectors of the non-HD set labeled hypermethylated that overlap the CpG site. Additionally, the process may normalize these counts for each group to account for variance in group size between the non-HD group and the HD group. In alternative embodiments wherein fragments indicative of HD are more generally used, the scores may be more broadly defined as counts of fragments indicative of HD at each genomic region and/or CpG site.
In one embodiment, to generate 620 the hypomethylation score at a given CpG site, the process takes a ratio of (1) over (1) summed with (3). Similarly, the hypermethylation score is calculated by taking a ratio of (2) over (2) and (4). Additionally, these ratios may be calculated with an additional smoothing technique as discussed above. The hypomethylation score and the hypermethylation score relate to an estimate of HD probability given the presence of hypomethylation or hypermethylation of fragments from the HD set.
The analytics system generates 630 an aggregate hypomethylation score and an aggregate hypermethylation score for each anomalous methylation state vector. The aggregate hyper and hypo methylation scores are determined based on the hyper and hypo methylation scores of the CpG sites in the methylation state vector. In one embodiment, the aggregate hyper and hypo methylation scores are assigned as the largest hyper and hypo methylation scores of the sites in each state vector, respectively. However, in alternate embodiments, the aggregate scores could be based on means, medians, or other calculations that use the hyper/hypo methylation scores of the sites in each vector.
The analytics system ranks 640 all of that subject's methylation state vectors by their aggregate hypomethylation score and by their aggregate hypermethylation score, resulting in two rankings per subject. The process selects aggregate hypomethylation scores from the hypomethylation ranking and aggregate hypermethylation scores from the hypermethylation ranking. With the selected scores, the classifier generates 650 a single feature vector for each subject. In one embodiment, the scores selected from either ranking are selected with a fixed order that is the same for each generated feature vector for each subject in each of the training groups. As an example, in one embodiment the classifier selects the first, the second, the fourth, and the eighth aggregate hyper methylation score, and similarly for each aggregate hypo methylation score, from each ranking and writes those scores in the feature vector for that subject.
The analytics system trains 660 a binary classifier to distinguish feature vectors between the HD and non-HD training groups. Generally, any one of a number of classification techniques may be used. In one embodiment the classifier is a non-linear classifier. In a specific embodiment, the classifier is a non-linear classifier utilizing a L2-regularized kernel logistic regression with a Gaussian radial basis function (RBF) kernel.
Specifically, in one embodiment, the number of non-HD samples or different HD type(s) (nother) and the number of HD samples or HD type(s) (nHD) having an anomalously methylated fragment overlapping a CpG site are counted. Then the probability that a sample is HD is estimated by a score (“S”) that positively correlates to nHD and inversely correlated to nm. The score can be calculated using the equation: (nHD+1)/(nHD+nother+2) or (nHD)/(nHD+nother). The analytics system computes 670 an information gain for each HD type and for each genomic region or CpG site to determine whether the genomic region or CpG site is indicative of HD. The information gain is computed for training samples with a given HD type compared to all other samples. For example, two random variables ‘anomalous fragment’ (‘AF’) and ‘HD type’ (‘CT’) are used. In on embodiment, AF is a binary variable indicating whether there is an anomalous fragment overlapping a given CpG site in a given samples as determined for the anomaly score/feature vector above. CT is a random variable indicating whether the HD is of a particular type. The analytics system computes the mutual information with respect to CT given AF. That is, how many bits of information about the HD type are gained if it is known whether there is an anomalous fragment overlapping a particular CpG site.
For a given HD type, the analytics system uses this information to rank CpG sites based on how HD specific they are. This procedure is repeated for all HD types under consideration. If a particular region is commonly anomalously methylated in training samples of a given HD but not in training samples of other HD types or in healthy training samples, then CpG sites overlapped by those anomalous fragments will tend to have high information gains for the given HD type. The ranked CpG sites for each HD type are greedily added (selected) to a selected set of CpG sites based on their rank for use in the HD classifier.
Computing Pairwise Information Gain from Fragments Indicative of HD Identified from Probabilistic Models
With fragments indicative of HD identified according to the method described herein, the analytics may identify genomic regions according to the process 680 in
The analytics system calculates 695 an informative score for each region describing that region's ability to distinguish between each pair of HD types. For each pair of distinct HD types, the analytics system may specify one type as a positive type and the other as a negative type. In one embodiment, a region's ability to distinguish between the positive type and the negative type is based on mutual information, calculated using the estimated fraction of cfDNA samples of the positive type and of the negative type for which the feature would be expected to be non-zero in the final assay, i.e., at least one fragment of that tier that would be sequenced in a targeted methylation assay. Those fractions are estimated using the observed rates at which the feature occurs in healthy cfDNA, and in high-signal cfDNA and/or tumor samples of each HD type. For example, if a feature occurs frequently in healthy cfDNA, then it will also be estimated to occur frequently in cfDNA of any HD type, and would likely result in a low informative score. The analytics system may choose a certain number of regions for each pair of HD types from the ranking, e.g., 1024.
In additional embodiments, the analytics system further identifies predominantly hypermethylated or hypomethylated regions from the ranking of regions. The analytics system may load the set of fragments in the positive type(s) for a region that was identified as informative. The analytics system, from the loaded fragments, evaluates whether the loaded fragments are predominantly hypermethylated or hypomethylated. If the loaded fragments are predominately hypermethylated or hypomethylated, the analytics system may select probes corresponding to the predominant methylation pattern. If the loaded fragments are not predominantly hypermethylated or hypomethylated, the analytics system may use a mixture of probes for targeting both hypermethylation and hypomethylation. The analytics system may further identify a minimal set of CpG sites that overlap more than some percentage of the fragments.
In other embodiments, the analytics system, after ranking the regions based on informative scores, labels each region with the lowest informative ranking across all pairs of HD types. For example, if a region was the 10th-most-informative region for distinguishing breast from lung, and the 5th-most-informative for distinguishing breast from colorectal, then it would be given an overall label of “5”. The analytics system may design probes starting with the lowest-labeled regions while adding regions to the panel, e.g., until the panel's size budget has been exhausted.
In some embodiments, probes targeting selected genomic regions are further filtered 475 based on the number of their off-target regions. This is for screening probes that pull down too many cfDNA fragments corresponding to, or derived from, off-target genomic regions. Exclusion of probes having many off-target regions can be valuable by decreasing off-target rates and increasing target coverage for a given amount of sequencing.
An off-target genomic region is a genomic region that has sufficient homology to a target genomic region, such that DNA molecules or fragments derived from off-target genomic regions are hybridized to and pulled down by a probe designed to hybridize to a target genomic region. An off-target genomic region can be a genomic region (or a converted sequence of that same region) that aligns to a probe along at least 35 bp, 40 bp, 45 bp, 50 bp, 60 bp, 70 bp, or 80 bp with at least an 80%, 85%, 90%, 95%, or 97% match rate. In one embodiment, an off-target genomic region is a genomic region (or a converted sequence of that same region) that aligns to a probe along at least 45 bp with at least a 90% match rate. Various methods known in the art can be adopted to screen off-target genomic regions.
Exhaustively searching the genome to find all off-target genomic regions can be computationally challenging. In one embodiment, a k-mer seeding strategy (which can allow one or more mismatches) is combined to local alignment at the seed locations. In this case, exhaustive searching of good alignments can be guaranteed based on k-mer length, number of mismatches allowed, and number of k-mer seed hits at a particular location. This requires doing dynamic programing local alignment at a large number of locations, so this approach is highly optimized to use vector CPU instructions (e.g., AVX2, AVX512) and also can be parallelized across many cores within a machine and also across many machines connected by a network. A person of ordinary skill will recognize that modifications and variations of this approach can be implemented for the purpose of identifying off-target genomic regions.
In some embodiments, probes having sequence homology with off-target genomic regions, or DNA molecules corresponding to, or derived from off-target genomic regions comprising more than a threshold number are excluded (or filtered) from the panel. For example, probes having sequence homology with off-target genomic regions, or DNA molecules corresponding to, or derived from off-target genomic regions from more than 30, more than 25, more than 20, more than 18, more than 15, more than 12, more than 10, or more than 5 off-target regions are excluded.
In some embodiments, probes are divided into 2, 3, 4, 5, 6, or more separate groups depending on the numbers of off-target regions. For example, probes having sequence homology with no off-target regions or DNA molecules corresponding to, or derived from off-target regions are assigned to high-quality group, probes having sequence homology with 1-18 off-target regions or DNA molecules corresponding to, or derived from 1-18 off-target regions, are assigned to low-quality group, and probes having sequence homology with more than 19 off-target regions or DNA molecules corresponding to, or derived from 19 off-target regions, are assigned to poor-quality group. Other cut-off values can be used for the grouping.
In some embodiments, probes in the lowest quality group are excluded. In some embodiments, probes in groups other than the highest-quality group are excluded. In some embodiments, separate panels are made for the probes in each group. In some embodiments, all the probes are put on the same panel, but separate analysis is performed based on the assigned groups.
In some embodiments, a panel comprises a larger number of high-quality probes than the number of probes in lower groups. In some embodiments, a panel comprises a smaller number of poor-quality probes than the number of probes in other group. In some embodiments, more than 95%, 90%, 85%, 80%, 75%, or 70% of probes in a panel are high-quality probes. In some embodiments, less than 35%, 30%, 20%, 10%, 5%, 4%, 3%, 2% or 1% of the probes in a panel are low-quality probes. In some embodiments, less than 5%, 4%, 3%, 2% or 1% of the probes in a panel are poor-quality probes. In some embodiments, no poor-quality probes are included in a panel.
In some embodiments, probes having below 50%, below 40%, below 30%, below 20%, below 10% or below 5% are excluded. In some embodiments, probes having above 30%, above 40%, above 50%, above 60%, above 70%, above 80%, or above 90% are selectively included in a panel.
In yet another aspect, methods of using an HD assay panel are provided. The methods can comprise steps of treating DNA molecules or fragments to convert unmethylated cytosines to uracils (e.g., using bisulfite treatment), applying an HD panel (as described herein) to the converted DNA molecules or fragments, enriching a subset of converted DNA molecules or fragments that hybridize (or bind) to the probes in the panel, and detecting the nucleic acid sequence and determining the methylation status thereof, for example, by sequencing the enriched cfDNA fragments. In some embodiments, the sequence reads can be compared to a reference genome (e.g., a human reference genome), allowing for identification of methylation states at a plurality of CpG sites within the DNA molecules or fragments and thus provide information relevant to detecting a hematological disorder (HD). While the present disclosure pays particular attention to sequencing based approaches for detecting nucleic acids and determining methylation status thereof (via sequence reads), the disclosure is broad enough to encompass other methods for detecting nucleic acids and determining methylation status thereof (such as other methylation-aware sequencing approaches (e.g., as described in WO 2014/043763, which is incorporated herein by reference), DNA microarrays (e.g., with labeled probes adhered or conjugated to a solid surface or DNA array chip), etc.
In some embodiments, the sequence reads may be aligned to a reference genome using known methods in the art to determine alignment position information. The alignment position information may indicate a beginning position and an end position of a region in the reference genome that corresponds to a beginning nucleotide base and end nucleotide base of a given sequence read. Alignment position information may also include sequence read length, which can be determined from the beginning position and end position. A region in the reference genome may be associated with a gene or a segment of a gene.
In various embodiments, a sequence read is comprised of a read pair denoted as R1 and R2. For example, the first read R1 may be sequenced from a first end of a nucleic acid fragment whereas the second read R2 may be sequenced from the second end of the nucleic acid fragment. Therefore, nucleotide base pairs of the first read R1 and second read R2 may be aligned consistently (e.g., in opposite orientations) with nucleotide bases of the reference genome. Alignment position information derived from the read pair R1 and R2 may include a beginning position in the reference genome that corresponds to an end of a first read (e.g., R1) and an end position in the reference genome that corresponds to an end of a second read (e.g., R2). In other words, the beginning position and end position in the reference genome represent the likely location within the reference genome to which the nucleic acid fragment corresponds. An output file having SAM (sequence alignment map) format or BAM (binary alignment map) format may be generated and output for further analysis.
From the sequence reads, the location and methylation state for each of CpG site may be determined based on alignment to a reference genome. Further, a methylation state vector for each fragment may be generated specifying a location of the fragment in the reference genome (e.g., as specified by the position of the first CpG site in each fragment, or another similar metric), a number of CpG sites in the fragment, and the methylation state of each CpG site in the fragment whether methylated (e.g., denoted as M), unmethylated (e.g., denoted as U), or indeterminate (e.g., denoted as I). The methylation state vectors may be stored in temporary or persistent computer memory for later use and processing. Further, duplicate reads or duplicate methylation state vectors from a single subject may be removed. In an additional embodiment, it may be determined that a certain fragment has one or more CpG sites that have an indeterminate methylation status. Such fragments may be excluded from later processing or selectively included where downstream data model accounts for such indeterminate methylation statuses.
After conversion, a sequencing library 130 is prepared and sequenced 140 generating a sequence read 142. The analytics system aligns 150 the sequence read 142 to a reference genome 144. The reference genome 144 provides the context as to what position in a human genome the fragment cfDNA originates from. In this simplified example, the analytics system aligns 150 the sequence read such that the three CpG sites correlate to CpG sites 23, 24, and 25 (arbitrary reference identifiers used for convenience of description). The analytics system thus generates information both on methylation status of all CpG sites on the cfDNA fragment 112 and which to position in the human genome the CpG sites map. As shown, the CpG sites on sequence read 142 which were methylated are read as cytosines. In this example, the cytosine's appear in the sequence read 142 only in the first and third CpG site which allows one to infer that the first and third CpG sites in the original cfDNA fragment were methylated. The second CpG site is read as a thymine (U is converted to T during the sequencing process), and thus, one can infer that the second CpG site was unmethylated in the original cfDNA fragment. With these two pieces of information, the methylation status and location, the analytics system generates 160 a methylation state vector 152 for the fragment cfDNA 112. In this example, the resulting methylation state vector 152 is <M23, U24, M25>, wherein M corresponds to a methylated CpG site, U corresponds to an unmethylated CpG site, and the subscript numbers correspond to positions of each CpG site in the reference genome.
Sequence reads obtained by the methods provided herein are further processed by automated algorithms. For example, the analytics system is used to receive sequencing data from a sequencer and perform various aspects of processing as described herein. The analytics system can be one of a personal computer (PC), a desktop computer, a laptop computer, a notebook, a tablet PC, a mobile device. A computing device can be communicatively coupled to the sequencer through a wireless, wired, or a combination of wireless and wired communication technologies. Generally, the computing device is configured with a processor and memory storing computer instructions that, when executed by the processor, cause the processor to perform steps as described in the remainder of this document. Generally, the amount of genetic data and data derived therefrom is sufficiently large, and the amount of computational power required so great, so as to be impossible to be performed on paper or by the human mind alone.
The clinical interpretation of methylation status of targeted genomic regions is a process that includes classifying the clinical effect of each or a combination of the methylation status and reporting the results in ways that are meaningful to a medical professional. The clinical interpretation can be based on comparison of the sequence reads with database specific to HD or non-HD subjects, and/or based on numbers and types of the cfDNA fragments having HD-specific methylation patterns identified from a sample.
In some embodiments, targeted genomic regions are ranked or classified based on their likeness to be differentially methylated in HD samples, and the ranks or classifications are used in the interpretation process. The ranks and classifications can include (1) the type of clinical effect, (2) the strength of evidence of the effect, and (3) the size of the effect. Various methods for clinical analysis and interpretation of genome data can be adopted for analysis of the sequence reads. In some other embodiments, the clinical interpretation of the methylation states of such differentially methylated regions can be based on machine learning approaches that interpret a current sample based on a classification or regression method that was trained using the methylation states of such differentially methylated regions from samples from HD and non-HD patients with known HD status, HD type, HD stage, etc.
The clinically meaning information can include the presence or absence of HD generally, presence or absence of certain types of HDs, HD stage, or presence or absence of other types of diseases. In some embodiments, the information relates to a presence or absence of one or more hematological disorders, selected from the group consisting of CHIP, leukemia, lymphoid neoplasms (e.g. lymphoma), multiple myeloma, and myeloid neoplasm. In some embodiments, the information relates to a presence or absence of one or more hematological disorders, selected from the group consisting of lymphoid neoplasm, multiple myeloma, and myeloid neoplasm. In some embodiments, the samples are not cancerous and are from subjects having white blood cell clonal expansion or no hematological disorder.
To train an HD type classifier, the analytics system obtains a plurality of training samples each having a set of hypomethylated and hypermethylated fragments indicative of HD, e.g., identified via step 450 in the process 400, and a label of the training sample's HD type. The analytics system determines, for each training sample, a feature vector based on the set of hypomethylated and hypermethylated fragments indicative of HD. The analytics system calculates an anomaly score for each CpG site in the targeted genomic regions. In one embodiment, the analytics system defines the anomaly score for the feature vector as a binary scoring based on whether there is a hypomethylated or hypermethylated fragment from the set that encompasses the CpG site. Once all anomaly scores are determined for a training sample, the analytics system determines the feature vector as a vector of elements including, for each element, one of the anomaly scores associated with one of the CpG sites. The analytics system may normalize the anomaly scores of the feature vector based on a coverage of the sample, i.e., a median or average sequencing depth over all CpG sites.
With the feature vectors of the training samples, the analytics system can train the HD classifier. In one embodiment, the analytics system trains a binary HD classifier to distinguish between the labels, HD and non-HD, based on the feature vectors of the training samples. In this embodiment, the classifier outputs a prediction score indicating the likelihood of the presence or absence of HD. In another embodiment, the analytics system trains a multiclass HD classifier to distinguish between many HD types. In this multiclass HD classifier embodiment, the HD classifier is trained to determine an HD prediction that comprises a prediction value for each of the HD types being classified for. The prediction values may correspond to a likelihood that a given sample has each of the HD types. For example, the HD classifier returns an HD prediction including a prediction value for CHIP, leukemia, lymphoid neoplasms (e.g. lymphoma), multiple myeloma, myeloid neoplasm, or any combination thereof. For example, the HD classifier may return an HD prediction for a test sample including a prediction score for CHIP, leukemia, lymphoid neoplasms (e.g. lymphoma), multiple myeloma, myeloid neoplasm, or any combination thereof. In either embodiment, the analytics system trains the HD classifier by inputting sets of training samples with their feature vectors into the HD classifier and adjusting classification parameters so that a function of the classifier accurately relates the training feature vectors to their corresponding label. The analytics system may group the training samples into sets of one or more training samples for iterative batch training of the HD classifier. After inputting all sets of training samples including their training feature vectors and adjusting the classification parameters, the HD classifier is sufficiently trained to label test samples according to their feature vector within some margin of error. The analytics system may train the HD classifier according to any one of a number of methods. As an example, the binary HD classifier may be a L2-regularized logistic regression classifier that is trained using a log-loss function. As another example, the multi-HD classifier may be a multinomial logistic regression. In practice either type of HD classifier may be trained using other techniques. These techniques are numerous including potential use of kernel methods, machine learning algorithms such as multilayer neural networks, etc. In particular, methods as described in PCT/US2019/022122 and U.S. patent application Ser. No. 16/352,602 which are incorporated by reference in their entireties herein can be used for various embodiments.
During deployment, the analytics system obtains a test sample from a subject of unknown HD type. The analytics system processes the test sample to achieve a set of hypomethylated and hypermethylated fragments indicative of HD. The analytics system defines a test feature vector in a similar process as described for the training samples. The analytics system then inputs the test feature vector into the trained HD classifier to yield an HD prediction, e.g., binary prediction (HD or non-HD) or multiclass HD prediction (prediction score for each of a plurality of HD types).
In some examples, the assay panel described herein can be used with a hematological disorder classifier that predicts a disease state for a sample, such as a hematological disorder or non-hematological disorder prediction, and/or an indeterminate prediction. In some examples, the hematological disorder classifier can generate features based on sequence reads by taking into account methylated or unmethylated fragments of DNA at certain genomic areas of interest. For instance, if the hematological disorder classifier determines that a methylation pattern at a fragment resembles that of a certain hematological disorder, then the hematological disorder classifier can set a feature for that fragment as 1, and otherwise if no such fragment is present, then the feature can be set as 0. In this way, the hematological disorder classifier can produce a set of binary features (merely by way of example, 30,000 features) for each sample. Further, in some examples, all or a portion of the set of binary features for a sample can be input into the hematological disorder classifier to provide a set of probability scores, such as one probability score per hematological disorder class and for a non-hematological disorder class. Furthermore, in some examples, the hematological disorder classifier can incorporate or otherwise be used in conjunction with thresholding to determine whether a sample is to be called as hematological disorder or non-hematological disorder, and/or indeterminate thresholding to reflect confidence in a specific hematological disorder call. Such methods are described further below.
To train the hematological disorder classifier, the analytics system (e.g., analytics system 800) can obtain a set of training samples. In some examples, each training sample includes fragment file(s) (e.g., file containing sequence read data), a label corresponding to a type of hematological disorder or non-hematological disorder status of the sample, and/or sex of the individual of the sample. The analytics system can utilize the training set to train the hematological disorder classifier to predict the disease state of the sample.
In some examples, for training, the analytics system divides the genome (e.g., whole genome) or a subset of the genome (e.g., targeted methylation regions) into regions. Merely by way of example, portions of the genome can be separated into “blocks” of CpGs, whereby a new block begins whenever there is a separation between nearest-neighbor CpGs is at least a minimum separation distance (e.g., at least 500 bp). Further, in some examples, each block can be divided into 1000 bp regions and positioned such that neighboring regions have a certain amount (e.g., 50% or 500 bp) of overlap.
Furthermore, in some examples, the analytics system can split the training set into K subsets or folds to be used in a K-fold cross-validation. In some examples, the folds can be balanced for hematological disorder/non-hematological disorder status, cancer stage, age (e.g., grouped in 10 yr buckets), and/or smoking status. In some examples, the training set is split into 5 folds, whereby 5 separate classifiers are trained, in each case training on ⅘ of the training samples and using the remaining ⅕ for validation.
During training with the training set, the analytics system can, for each hematological disorder (and for healthy cfDNA), fit a probabilistic model to the fragments deriving from the samples of that type. As used herein a “probabilistic model” is any mathematical model capable of assigning a probability to a sequence read based on methylation status at one or more sites on the read. During training, the analytics system fits sequence reads derived from one or more samples from subjects having a known disease and can be used to determine sequence reads probabilities indicative of a disease state utilizing methylation information or methylation state vectors. In particular, in some cases, the analytics system determines observed rates of methylation for each CpG site within a sequence read. The rate of methylation represents a fraction or percentage of base pairs that are methylated within a CpG site. The trained probabilistic model can be parameterized by products of the rates of methylation. In general, any known probabilistic model for assigning probabilities to sequence reads from a sample can be used. For example, the probabilistic model can be a binomial model, in which every site (e.g., CpG site) on a nucleic acid fragment is assigned a probability of methylation, or an independent sites model, in which each CpG's methylation is specified by a distinct methylation probability with methylation at one site assumed to be independent of methylation at one or more other sites on the nucleic acid fragment.
In some examples, the probabilistic model is a Markov model, in which the probability of methylation at each CpG site is dependent on the methylation state at some number of preceding CpG sites in the sequence read, or nucleic acid molecule from which the sequence read is derived. See, e.g., U.S. patent application Ser. No. 16/352,602, entitled “Anomalous Fragment Detection and Classification,” and filed Mar. 13, 2019, which is incorporated by reference in its entirety herein and can be used for various embodiments.
In some examples, the probabilistic model is a “mixture model” fitted using a mixture of components from underlying models. For example, in some embodiments, the mixture components can be determined using multiple independent sites models, where methylation (e.g., rates of methylation) at each CpG site is assumed to be independent of methylation at other CpG sites. Utilizing an independent sites model, the probability assigned to a sequence read, or the nucleic acid molecule from which it derives, is the product of the methylation probability at each CpG site where the sequence read is methylated and one minus the methylation probability at each CpG site where the sequence read is unmethylated. In accordance with this example, the analytics system determines rates of methylation of each of the mixture components. The mixture model is parameterized by a sum of the mixture components each associated with a product of the rates of methylation. A probabilistic model Pr of n mixture components can be represented as:
For an input fragment, mi∈{0, 1} represents the fragment's observed methylation status at position i of a reference genome, with 0 indicating unmethylation and 1 indicating methylation. A fractional assignment to each mixture component k is fk, where fk≥0 and Σk=1n fk=1. The probability of methylation at position i in a CpG site of mixture component k is βki. Thus, the probability of unmethylation is 1−βki. The number of mixture components n can be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.
In some examples, the analytics system fits the probabilistic model using maximum-likelihood estimation to identify a set of parameters {βki, fk} that maximizes the log-likelihood of all fragments deriving from a disease state, subject to a regularization penalty applied to each methylation probability with regularization strength r. The maximized quantity for N total fragments can be represented as:
In some examples, the analytics system performs fits separately for each hematological disorder and for healthy cfDNA. As one of skill in the art would appreciate, other means can be used to fit the probabilistic models or to identify parameters that maximize the log-likelihood of all sequence reads derived from the reference samples. For example, in some examples, Bayesian fitting (using e.g., Markov chain Monte Carlo), in which each parameter is not assigned a single value but instead is associated to a distribution, is used. In some examples, gradient-based optimization, in which the gradient of the likelihood (or log-likelihood) with respect to the parameter values is used to step through parameter space towards an optimum, is used. In still some examples, expectation-maximization, in which a set of latent parameters (such as identities of the mixture component from which each fragment is derived) are set to their expected values under the previous model parameters, and then the model's parameters are assigned to maximize the likelihood conditional on the assumed values of those latent variables. The two-step process is then repeated until convergence.
Further, in some examples, the analytics system can generate features for each sample in the training set. For example, for each sample (regardless of label), in each region, for each hematological disorder, for each fragment, the analytics system can evaluate the log-likelihood ratio R with the fitted probabilistic models according to:
Next, for each sample, for each region, for each hematological disorder, for each of a set of “tier” values, the analytics system can count the number of fragments with Rhematological disorder> tier and assign those counts as non-negative integer-valued features. For example, the tiers include threshold values of 1, 2, 3, 4, 5, 6, 7, 8, and 9, resulting in each region hosting 9 features per hematological disorder.
In some examples, the analytics system can select certain features for inclusion in a feature vector for each sample. For example, for each pair of distinct hematological disorder, the analytics system can specify one type as the “positive type” and the other as the “negative type” and rank the features by their ability to distinguish those types. In some cases, the ranking is based on mutual information calculated by the analytics system. For example, the mutual information can be calculated using the estimated fraction of samples of the positive type and negative type (e.g., hematological disorders A and B) for which the feature is expected to be nonzero in a resulting assay. For instance, if a feature occurs frequently in healthy cfDNA, the analytics system determines the feature is unlikely to occur frequently in cfDNA associated with various types of hematological disorder. Consequently, the feature can be a weak measure in distinguishing between disease states. In calculating mutual information I, the variable X is a certain feature (e.g., binary) and variable Y represents a disease state, e.g., hematological disorders A or B:
The joint probability mass function of X and Y is p(x, y) and the marginal probability mass functions are p(x) and p(y). The analytics system can assume that feature absence is uninformative and either disease state is equally likely a priori, for example, p(Y=A)=p(Y=B)=0.5. The probability of observing (e.g., in cfDNA) a given binary feature of hematological disorder A is represented by p(1|A), where fA is the probability of observing the feature in ctDNA samples from tumor (or high-signal cfDNA samples) associated with hematological disorder A, and fH is the probability of observing the feature in a healthy or non-hematological disorder cfDNA sample.
In some examples, only features corresponding to the positive type are included in the ranking, and only when those features' predicted rate of occurrence is greater in the positive type than in the negative type. For example, if “liver” is the positive type and “breast” is the negative type, then only “liver_x” features are considered, and only if their estimated occurrence in liver cfDNA is greater than their estimated occurrence in breast cfDNA. Further, in some examples, for each region, for each hematological disorder pair (including non-hematological disorder as a negative type), the analytics system keeps only the best performing tier. Further, in some examples, the analytics system transforms feature values by binarization, whereby any feature value greater than 0 is set to 1, such that all features are either 0 or 1.
In some examples, the analytics system trains a multinomial logistic regression classifier on the training data for a fold, and generates predictions for the held-out data. For example, for each of the K folds, one logistic regression can be trained for each combination of hyperparameters. Such hyperparameters can include L2 penalty and/or topK (e.g., the number of high-ranking regions to keep per tissue type pair (including non-hematological disorder), as ranked by the mutual information procedure outlined above). For each set of hyperparameters, performance is evaluated on the cross-validated predictions of the full training set, and the set of hyperparameters with the best performance is selected for retraining on the full training set. In some examples, the analytics system uses log-loss as a performance metric, whereby the log-loss is calculated by taking the negative logarithm of the prediction for the correct label for each sample, and then summing over samples (i.e. a perfect prediction of 1.0 for the correct label would give a log-loss of 0).
To generate predictions for a new sample, feature values are calculated using the same method described above, but restricted to features (region/positive class combinations) selected under the chosen topK value. Generated features are then used to create a prediction using the logistic regression model trained above.
In some examples, the analytics trains a two-stage classifier. For example, the analytics system trains a binary hematological disorder classifier to distinguish between the labels, hematological disorder and non-hematological disorder, based on the feature vectors of the training samples. In this case, the binary classifier outputs a prediction score indicating the likelihood of the presence or absence of hematological disorder. In another example, the analytics system trains a multiclass hematological disorder classifier to distinguish between many hematological disorders. In this multiclass hematological disorder classifier, the hematological disorder classifier is trained to determine a hematological disorder prediction that comprises a prediction value for each of the hematological disorders being classified for. The prediction values can correspond to a likelihood that a given sample has each of the hematological disorders. For example, the hematological disorder classifier returns a hematological disorder prediction including a prediction value for CHIP, leukemia, lymphoid neoplasms (e.g., lymphoma), multiple myeloma, a myeloid neoplasm, and non-hematological disorder. For example, the hematological disorder classifier may return a hematological disorder prediction for a test sample including a prediction score for CHIP, leukemia, lymphoid neoplasms (e.g., lymphoma), multiple myeloma, a myeloid neoplasm, and/or non-hematological disorder.
The analytics system can train the hematological disorder classifier according to any one of a number of methods. As an example, the binary hematological disorder classifier may be a L2-regularized logistic regression classifier that is trained using a log-loss function. As another example, the multi-hematological disorder classifier may be a multinomial logistic regression. In practice either type of hematological disorder classifier may be trained using other techniques. These techniques are numerous including potential use of kernel methods, machine learning algorithms such as multilayer neural networks, etc. In particular, methods as described in PCT/US2019/022122 and U.S. patent application Ser. No. 16/352,602 which are incorporated by reference in their entireties herein can be used for various embodiments.
In various embodiments, the sequencer 820 receives an enriched nucleic acid sample 810. As shown in
In some embodiments, the sequencer 820 is communicatively coupled with the analytics system 800. The analytics system 800 includes some number of computing devices used for processing the sequence reads for various applications such as assessing methylation status at one or more CpG sites, variant calling or quality control. The sequencer 820 may provide the sequence reads in a BAM file format to the analytics system 800. The analytics system 800 can be communicatively coupled to the sequencer 820 through a wireless, wired, or a combination of wireless and wired communication technologies. Generally, the analytics system 800 is configured with a processor and non-transitory computer-readable storage medium storing computer instructions that, when executed by the processor, cause the processor to process the sequence reads or to perform one or more steps of any of the methods or processes disclosed herein.
In some embodiments, the sequence reads may be aligned to a reference genome using known methods in the art to determine alignment position information. Alignment position may generally describe a beginning position and an end position of a region in the reference genome that corresponds to a beginning nucleotide based and an end nucleotide base of a given sequence read. Corresponding to methylation sequencing, the alignment position information may be generalized to indicate a first CpG site and a last CpG site included in the sequence read according to the alignment to the reference genome. The alignment position information may further indicate methylation statuses and locations of all CpG sites in a given sequence read. A region in the reference genome may be associated with a gene or a segment of a gene; as such, the analytics system 800 may label a sequence read with one or more genes that align to the sequence read. In one embodiment, fragment length (or size) is determined from the beginning and end positions.
In various embodiments, for example when a paired-end sequencing process is used, a sequence read is comprised of a read pair denoted as R_1 and R_2. For example, the first read R_1 may be sequenced from a first end of a double-stranded DNA (dsDNA) molecule whereas the second read R_2 may be sequenced from the second end of the double-stranded DNA (dsDNA). Therefore, nucleotide base pairs of the first read R_1 and second read R_2 may be aligned consistently (e.g., in opposite orientations) with nucleotide bases of the reference genome. Alignment position information derived from the read pair R_1 and R_2 may include a beginning position in the reference genome that corresponds to an end of a first read (e.g., R_1) and an end position in the reference genome that corresponds to an end of a second read (e.g., R_2). In other words, the beginning position and end position in the reference genome represent the likely location within the reference genome to which the nucleic acid fragment corresponds. In one embodiment, the read pair R_1 and R_2 can be assembled into a fragment, and the fragment used for subsequent analysis and/or classification. An output file having SAM (sequence alignment map) format or BAM (binary) format may be generated and output for further analysis.
Referring now to
The sequence processor 840 generates methylation state vectors for fragments from a sample. At each CpG site on a fragment, the sequence processor 840 generates a methylation state vector for each fragment specifying a location of the fragment in the reference genome, a number of CpG sites in the fragment, and the methylation state of each CpG site in the fragment whether methylated, unmethylated, or indeterminate via the process 300 of
Further, multiple different models 850 may be stored in the model database 855 or retrieved for use with test samples. In one example, a model is a trained hematological disorder classifier for determining a hematological disorder prediction for a test sample using a feature vector derived from anomalous fragments. The training and use of the hematological disorder classifier is discussed elsewhere herein. The analytics system 800 may train the one or more models 850 and store various trained parameters in the parameter database 865. The analytics system 800 stores the models 850 along with functions in the model database 855.
During inference, the score engine 860 uses the one or more models 850 to return outputs. The score engine 860 accesses the models 850 in the model database 855 along with trained parameters from the parameter database 865. According to each model, the score engine receives an appropriate input for the model and calculates an output based on the received input, the parameters, and a function of each model relating the input and the output. In some use cases, the score engine 860 further calculates metrics correlating to a confidence in the calculated outputs from the model. In other use cases, the score engine 860 calculates other intermediary values for use in the model.
The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the present description, and are not intended to limit the scope of what the inventors regard as their description nor are they intended to represent that the experiments below are all or the only experiments performed. Efforts have been made to ensure accuracy with respect to numbers used (e.g., amounts, temperature, etc.) but some experimental errors and deviations should be accounted for.
To test how much overlap between a cfDNA fragment and a probe is required to achieve a non-negligible amount of pulldown, various lengths of overlaps were tested using panels designed to include three different types of probes (V1D3, V1D4, V1E2) having various overlaps with 175 bp target DNA fragments specific to each probe. Tested overlaps ranged between Obp and 120 bp. Samples comprising 175 bp target DNA fragments were applied to the panel and washed, and then DNA fragments bound to the probes were collected. The amounts of the collected DNA fragments were measured and the amounts were plotted as densities over the sizes of overlaps as provided in
There was no significant binding and pull down of target DNA fragments when there were less than 45 bp of overlaps. These results suggest that a fragment-probe overlap of at least 45 bp is generally required to achieve a non-negligible amount of pulldown although this number can vary depending on the assay conditions.
Furthermore, it has been suggested that more than a 10% mismatch rate between the probe and fragment sequences in the region of overlap is sufficient to greatly disrupt binding, and thus pulldown efficiency. Therefore, sequences that can align to the probe along at least 45 bp with at least a 90% match rate are candidates for off-target pulldown.
Thus, we have performed an exhaustive searching of all genomic regions having 45 bp alignments with 90%+ match rate (i.e., off-target regions) for each probe. Specifically, we combined a k-mer seeding strategy (which can allow one or more mismatches) with local alignment at the seed locations. This guaranteed not missing any good alignments based on k-mer length, number of mismatches allowed, and number of k-mer seed hits at a particular location. This involves performing dynamic programing local alignment at a large number of locations, so the implementation was optimized to use vector CPU instructions (e.g., AVX2, AVX512) and parallelized across many cores within a machine and also across many machines connected by a network. This allows exhaustive search which is valuable in designing a high-performance panel (i.e., low off-target rate and high target coverage for a given amount of sequencing).
Following the exhaustive searching, each probe was scored based on the number of off-target regions. The best probes have a score of 1, meaning they match in only one place (high Q). Probes with a low score between 2-19 hits (low Q) were accepted but probes with a poor score more than 20 hits (poor Q) were discarded. Other cutoff values can be used for specific samples.
Numbers of high quality, low quality, and poor quality probes were then counted among probes targeting hypermethylated genomic regions or hypomethylated genomic regions.
Hematological disorders: A HD panel was designed to detect different types of hematological disorders including CHIP, leukemia, multiple myeloma, and lymphoma.
Samples used for genomic region selection: Sample from different sources were used for selection of target genomic regions. They include (1) cell-enriched disseminated tumor cells (DTC) from cancers of different types, (2) bone marrow mononuclear cell (PBMC) samples from patients with leukemia, lymphoma, or multiple myeloma, (3) peripheral blood mononuclear cell (PBMC) samples from patients with leukemia, lymphoma, or multiple myeloma, (4) genomic DNA from FFPE tissue blocks of cancer samples, or (5) genomic DNA from white blood cells, or (7) cfDNA samples from more than 1800 individuals.
Region selection (based on methylation status): For target selection, fragments having abnormal methylation patterns in samples with various hematological disorders were selected as using one or more method as described herein. Us of these methods allowed identification of low noise regions as putative targets. Among the low noise regions, fragments most informative in discriminating disease types were ranked and selected.
Specifically, in some embodiments, when WGBS data were used, fragment sequences in the database were filtered based on p-value using a distribution in healthy control individuals, and only fragments with p<0.001 were retained, as described herein. In some cases, the selected cfDNAs were further filtered to retain only those that were at least 90% methylated or 90% unmethylated. Next, for each CpG site in the selected fragments, the numbers of samples with a hematological disorder or healthy control samples were counted that include fragments overlapping that CpG site. Specifically, P (hematological disorder | overlapping fragment) for each CpG was calculated and genomic sites with high P values were selected as general disorder targets. By design, the selected fragments had very low noise (i.e., few healthy control fragments overlapping).
To find targets specific to a hematological disorder, similar selection processes were performed. CpG sites were ranked based on their information gain, comparing (i) between the numbers of samples of a specific hematological disorder and other samples, wherein other samples including both healthy control samples and samples of a different hematological disorder, (ii) between the numbers of samples of a specific hematological disorder and healthy, control samples, and/or (iii) between the numbers of samples of a specific hematological disorder and a different hematological disorder that include fragments overlapping that CpG site. The process was applied to each of the hematological disorders and the comparison was done for all pairwise combinations for the hematological disorders as illustrated in
Target genomic regions selected as described in this section are listed in TABLE 1. The target genomic regions of Lists 2-4 contain subsets of the methylation sites of the target genomic regions of Lists 5-7, respectively. Likewise, the target genomic regions of List 8 contain a subset of the methylation sites of the target genomic regions of List 1.
To maximize performance, the predictive cancer models described in this Example were trained using sequence data obtained from a plurality of samples from known cancer types and non-cancers from both CCGA sub-studies (CCGA1 and CCGA22), a plurality of tissue samples for known cancers obtained from CCGA1, and a plurality of non-cancer samples from the STRIVE study (See Clinical Trail.gov Identifier: NCT03085888 (//clinicaltrials.gov/ct2/show/NCT03085888)). The STRIVE study is a prospective, multi-center, observational cohort study to validate an assay for the early detection of breast cancer and other invasive cancers, from which additional non-cancer training samples were obtained to train the classifier described herein. The known cancer types included from the CCGA sample set included the following: breast, lung, prostate, colorectal, renal, uterine, pancreas, esophageal, lymphoma, head and neck, ovarian, hepatobiliary, melanoma, cervical, multiple myeloma, leukemia, thyroid, bladder, gastric, and anorectal. As such, a model can be a multi-cancer model (or a multi-cancer classifier) for detecting one or more, two or more, three or more, four or more, five or more, ten or more, or 20 or more different types of cancer.
The classifier performance data shown below was reported out for a locked classifier trained on cancer and non-cancer samples obtained from CCGA2, a CCGA sub-study, and on non-cancer samples from STRIVE. The individuals in the CCGA2 sub-study were different from the individuals in the CCGA1 sub-study whose cfDNA was used to select target genomes. From the CCGA2 study, blood samples were collected from individuals diagnosed with untreated cancer (including 20 tumor types and all stages of cancer) and healthy individuals with no cancer diagnosis (controls). For STRIVE, blood samples were collected from women within 28 days of their screening mammogram. Cell-free DNA (cfDNA) was extracted from each sample and treated with bisulfite to convert unmethylated cytosines to uracils. The bisulfite treated cfDNA was enriched for informative cfDNA molecules using hybridization probes designed to enrich bisulfite-converted nucleic acids derived from each of a plurality of targeted genomic regions in an assay panel comprising all of the genomic regions of Lists 1-8. The enriched bisulfite-converted nucleic acid molecules were sequenced using paired-end sequencing on an Illumina platform (San Diego, Calif.) to obtain a set of sequence reads for each of the training samples, and the resulting read pairs were aligned to the reference genome, assembled into fragments, and methylated and unmethylated CpG sites identified.
For each cancer type (including non-cancer) a probabilistic mixture model was trained and utilized to assign a probability to each fragment from each cancer and non-cancer sample based on how likely it was that the fragment would be observed in a given sample type.
Briefly, for each sample type (cancer and non-cancer samples), for each region (where each region was used as-is if less than 1 kb, or else subdivided into 1 kb regions in length with a 50% overlap (e.g., 500 base pairs overlap) between adjacent regions), a probabilistic model was fit to the fragments derived from the training samples for each type of cancer and non-cancer. The probabilistic model trained for each sample type was a mixture model, where each of three mixture components was an independent-sites model in which methylation at each CpG is assumed to be independent of methylation at other CpGs. Fragments were excluded from the model if: they had a p-value (from a non-cancer Markov model) greater than 0.01; were marked as duplicate fragments; the fragments had a bag size of greater than 1 (for targeted methylation samples only); they did not cover at least one CpG site; or if the fragment was greater than 1000 bases in length. Retained training fragments were assigned to a region if they overlapped at least one CpG from that region. If a fragment overlapped CpGs in multiple regions, it was assigned to all of them.
Each probabilistic model was fit using maximum-likelihood estimation to identify a set of parameters that maximized the log-likelihood of all fragments deriving from each sample type, subject to a regularization penalty.
Specifically, in each classification region, a set of probabilistic models were trained, one for each training label (i.e., one for each cancer type and one for non-cancer). Each model took the form of a Bernoulli mixture model with three components. Mathematically,
Pr(fragment|{βki,fk})=Σk=1nfkΠiβkim
where n is the number of mixture components, set to 3; mi∈{0, 1} is the fragment's observed methylation at position i; fk is the fractional assignment to component k (with fk≥0 and Σfk=1); and βki is the methylation fraction in component k at CpG i. The product over i included only those positions for which a methylation state could be identified from the sequencing. Maximum-likelihood values of the parameters {fk, βki} of each model were estimated by using the rprop algorithm (e.g., the rprop algorithm as described in Riedmiller M, Braun H. RPROP—A Fast Adaptive Learning Algorithm. Proceedings of the International Symposium on Computer and Information Science VII, 1992) to maximize the total log-likelihood of the fragments of one training label, subject to a regularization penalty on βki that took the form of a beta-distributed prior. Mathematically, the maximized quantity was
Σj ln(Pr(fragmentj|{βki,fk}))+Σk,ir ln(βki(1−βki)) (2)
where r is the regularization strength, which was set to 1.
Once the probabilistic models were trained, a set of numerical features was computed for each sample. Specifically, features were extracted for each fragment from each training sample, for each cancer type and non-cancer sample, in each region. The extracted features were the tallies of outlier fragments (i.e., anomalously methylated fragments), which were defined as those whose log-likelihood under a first cancer model exceeded the log-likelihood under a second cancer model or non-cancer model by at least a threshold tier value. Outlier fragments were tallied separately for each genomic region, sample model (i.e., cancer type), and tier (for tiers 1, 2, 3, 4, 5, 6, 7, 8, and 9), yielding 9 features per region for each sample type. In this way, each feature was defined by three properties: a genomic region; a “positive” cancer type label (excluding non-cancer); and the tier value selected from the set {1, 2, 3, 4, 5, 6, 7, 8, 9}. The numerical value of each feature was defined as the number of fragments in that region such that
where the probabilities were defined by equation (1) using the maximum-likelihood-estimated parameter values corresponding to the “positive” cancer type (in the numerator of the logarithm) or to non-cancer (in the denominator).
For each set of pairwise features, the features were ranked using mutual information based on their ability to distinguish the first cancer type (which defined the log-likelihood model from which the feature was derived) from the second cancer type or non-cancer. Specifically, two ranked lists of features were compiled for each unique pair of class labels: one with the first label assigned as the “positive” and the second as the “negative”, and the other with the positive/negative assignment swapped (with the exception of the “non-cancer” label, which was only permitted as the negative label). For each of these ranked lists, only features whose positive cancer type label (as in equation (3)) matched the positive label under consideration were included in the ranking. For each such feature, the fraction of training samples with non-zero feature value was calculated separately for the positive and negative labels. Features for which this fraction was greater in the positive label were ranked by their mutual information with respect to that pair of class labels.
The top ranked 256 features from each pairwise comparison were identified and added to the final feature set for each cancer type and non-cancer. To avoid redundancy, if more than one feature was selected from the same positive type and genomic region (i.e., for multiple negative types), only the one assigned the lowest (most informative) rank for its cancer type pair was retained, breaking ties by choosing the higher tier value. The features in the final feature set for each sample (cancer type and non-cancer) were binarized (any feature value greater than 0 was set to 1, so that all features were either 0 or 1).
The training samples were then divided into distinct 5-fold cross-validation training sets, and a two-stage classifier was trained for each fold, in each case training on ⅘ of the training samples and using the remaining ⅕ for validation.
In the first stage of training, a binary (two-class) logistic regression model for detecting the presence of cancer was trained to discriminate the cancer samples (regardless of TOO) from non-cancer. When training this binary classifier, a sample weight was assigned to the male non-cancer samples to counteract sex-imbalance in the training set. For each sample, the binary classifier outputs a prediction score indicating the likelihood of a presence or absence of cancer.
In the second stage of training, a parallel multi-class logistic regression model for determining cancer tissue of origin was trained with TOO as the target label. Only the cancer samples that received a score above the 95th percentile of the non-cancer samples in the first stage classifier were included in the training of this multi-class classifier. For each cancer sample used in training the multi-class classifier, the multi-class classifier outputs prediction values for the cancer types being classified, where each prediction value is a likelihood that the given sample has a certain cancer type. For example, the cancer classifier can return a cancer prediction for a test sample including a prediction score for breast cancer, a prediction score for lung cancer, and/or a prediction score for no cancer.
Both binary and multi-class classifiers were trained by stochastic gradient descent with mini-batches, and in each case, training was stopped early when the performance on the validation fold (assessed by cross-entropy loss) began to degrade. For predicting on samples outside of the training set, in each stage, the scores assigned by the five cross-validated classifiers were averaged. Scores assigned to sex-inappropriate cancer types were set to zero, with the remaining values renormalized to sum to one.
Scores assigned to the validation folds within the training set were retained for use in assigning cutoff values (thresholds) to target certain performance metrics. In particular, the probability scores assigned to the training set non-cancer samples were used to define thresholds corresponding to particular specificity levels. For example, for a desired specificity target of 99.4%, the threshold was set at the 99.4th percentile of the cross-validated cancer detection probability scores assigned to the non-cancer samples in the training set. Training samples with a probability score that exceeded a threshold were called as positive for cancer.
Subsequently, for each training sample determined to be positive for cancer, a TOO or cancer type assessment was made from the multiclass classifier. First, the multi-class logistic regression classifier assigned a set of probability scores, one for each prospective cancer type, to each sample. Next, the confidence of these scores was assessed as the difference between the highest and second-highest scores assigned by the multi-class classifier for each sample. Then, the cross-validated training set scores were used to identify the lowest threshold value such that of the cancer samples in the training set with top-two score differential exceeding the threshold, 90% had been assigned the correct TOO label as their highest score. In this way, the scores assigned to the validation folds during training were further used to determine a second threshold for distinguishing between confident and indeterminate TOO calls.
At prediction time, samples receiving a score from the binary (first-stage) classifier below the predefined specificity threshold were assigned a “non-cancer” label. For the remaining samples, those whose top-two TOO-score differential from the second-stage classifier was below the second predefined threshold were assigned the “indeterminate cancer” label. The remaining samples were assigned the cancer label to which the TOO classifier assigned the highest score.
The discriminatory value of the target genomic regions of Lists 2-4 was evaluated by testing the ability of a cancer classifier to detect 3 different hematological disorders according to the methylation status of these target genomic regions. Performance was evaluated over a set of 1,532 cancer samples and 1,521 non-cancer samples that were not used to train the classifier, as shown in TABLE 2. For each sample, differentially methylated cfDNA was enriched using a bait set comprising all of the target genomic regions of Lists 1-8. The classifier was then constrained to provide cancer determinations based only on the methylation status of the target genomic regions of the List being evaluated.
Results from the classifier performance analysis for Lists 2-4 are presented in TABLES 2-3. TABLE 2 shows the accuracy of determining a hematological disorder by a classifier considering the methylation status of the target genomic regions of Lists 2, 3 or 4. TABLE 3 shows the sensitivity with a specificity of 0.990 for detecting different stages of the three hematological disorders by a classifier that utilizes only the methylation markers of the corresponding List.
Results from the classifier performance analysis for List 8 and additional results for Lists 2-4 and 8 are presented in TABLES 5-8. An exemplary receiver operator curve (ROC) generated by a trained classifier is shown in
Once a determination of cancer is made, the classifier assigns the cancer to one of twenty distinct cancer types. The accuracy of these determinations with a specificity of 0.990 is presented in various formats. TABLE 5 shows true positives, false positives, and false negatives as scored based on the methylation status of lists of target genomic regions optimized for the detection of specific hematological disorders or random subsets of a list optimized for the detection of all hematological disorders. A true positive occurs when the presence of cancer is detected and the classifier accurately determines that the sample came from a subject with a hematological disorder. A false positive occurs for samples from individuals diagnosed with a solid tumor when presence of cancer is detected and inaccurately determined to be a hematological disorder. A false negative occurs when the sample came from an individual diagnosed with a hematological disorder but the classifier inaccurately determines that the sample came from an individual with a solid tumor. False negatives were very rare for Lists 2-4 and 8. Approximately 5-10% of the samples were false negatives. This might occur because Lists 2-4 and 8 do not include some markers that would aid in accurately determining that a cancer was a solid tumor.
The accuracy of cancer detection based upon the methylation status of target genomic regions in Lists 2-4 and 8 is evaluated for various stages of cancer in TABLE 6. When cancer is detected, a cancer type is assigned from one of twenty possible classes of cancer types. The accuracy of cancer type determination is presented in TABLE 7. The cancer type determination results are for the accuracy of determining all twenty cancer types, even though the lists of target genomic regions were optimized to detect a hematological disorders.
The results in TABLES 6-7 are segregated for various stages of cancer. Cancer detection and cancer type determination were more accurate for samples from individuals diagnosed with later stages of cancer. This was expected because late stage tumors shed more cfDNA. Nevertheless, the accuracy of detecting cancer and assigning a cancer type for early stage cancers is remarkably high. Furthermore, classification accuracy was reasonable accurate with only 50% or even 25% of the target genomic regions of List 8 (all hematological disorders).
The sensitivity at a specificity of 0.990 for detecting stages I-IV hematological disorders by a classifier acting on the methylation status of target genomic regions in Lists 2-4 or random subsets of List 8 is presented in TABLE 8. For example, when the false positive rate for detecting cancer is limited to 1%, a classifier considering the methylation status of the target genomic regions of List 3 (optimized for multiple myeloma), detected multiple myeloma in 70% (7 out of 10) of the samples collected from individuals diagnosed with stage I multiple myeloma. Likewise, when the false positive rate for detecting cancer is limited to 1%, a classifier considering the methylation status of the target genomic regions of List 2 (optimized for lymphoid neoplasm), detected lymphoid neoplasm in 93% (25 out of 27) of the samples collected from individuals diagnosed with stage II lymphoid neoplasm. Furthermore, the sensitivity for HD based upon the methylation status of random 50% and 25% subsets of the target genomic regions of List 8 were essentially identical (with the exception of stage I lymphoid neoplasm), indicating that a substantial fraction of the target genomic regions of List 8 contribute to accurate HD determinations by the classifier.
Blood samples are collected from a group of individuals previously diagnosed with a hematological disorder (“test group”), and other groups of individuals without a hematological disorder or diagnosed with a different type of hematological disorder (“other group”). cfDNA fragments are extracted from the blood samples and treated with bisulfite to convert unmethylated cytosines to uracils. The cancer assay panel described herein was applied to the bisulfite treated samples. Unbound cfDNA fragments are washed and cfDNA fragments bound to the probes are collected. The collected cfDNA fragments are amplified and sequenced. The sequence reads confirm that the probes specifically enrich cfDNA fragments having methylation patterns indicative of a hematological disorder and samples from the test group include significantly more of the differentially methylated cfDNA fragments compared to the other group.
While preferred embodiments of the present disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the disclosure. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed in practicing the disclosure. It is intended that the following claims define the scope of the disclosure and that methods and structures within the scope of these claims and their equivalents be covered thereby.
This is a continuation application of International Patent Application No. PCT/US2020/016673, filed Feb. 4, 2020, which claims benefit of U.S. Provisional Application No. 62/801,556, filed Feb. 5, 2019; 62/801,556, filed Feb. 5, 2019; U.S. Provisional Application No. 62/801,561, filed Feb. 5, 2019; U.S. Provisional Application No. 62/965,327, filed Jan. 24, 2020; U.S. Provisional Application No. 62/965,342, filed Jan. 24, 2020; and PCT International Application No. PCT/US2020/015082, filed Jan. 24, 2020, which applications are incorporated herein by reference in their entireties.
| Number | Date | Country | |
|---|---|---|---|
| 62801556 | Feb 2019 | US | |
| 62801561 | Feb 2019 | US | |
| 62965327 | Jan 2020 | US | |
| 62965342 | Jan 2020 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/US2020/016673 | Feb 2020 | US |
| Child | 17393609 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/US2020/015082 | Jan 2020 | US |
| Child | PCT/US2020/016673 | US |
