DETAILED ACTION
Applicant's response, filed 10/27/2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
Claim 8 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 8 recites the limitation "The computer-implemented method of claim 32" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
Response to Amendment
In view of applicant’s amendments to the claims previous rejections under 35 U.S.C. 101 have been reviewed, and withdrawn.
Response to Arguments
Applicant’s arguments, see pages 1-2 of the Remarks, filed 10/27/2025, with respect to the rejection of claims under 35 U.S.C. 101 have been fully considered and are persuasive. The rejections of claims 1-23, 26-29, and 32 has been withdrawn in view of amendments that recite a practical application, specifically a particular treatment.
Claim Rejections - 35 USC § 102
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 102 have been reviewed, and withdrawn.
Response to Arguments
Applicant’s arguments, see pages 2-3 of the Remarks, filed 10/27/2025, with respect to rejections under 35 U.S.C. 102 have been fully considered and are persuasive. The rejections of claims 1, 5, 7-21, 23, 26, 29, and 32 has been withdrawn.
Claim Rejections - 35 USC § 103
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 103 have been reviewed, updated, and provided below.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 2, 5-9, 11-17, 23, 32, 35-44 are rejected under 35 U.S.C. 103 as being unpatentable over Namsaraev et al. (US 20180237863 A1), Bagaev et al. (US 20180358132 A1), Ma et al. (Nucleic Acids Research (2014) 3515-3528), Saare et al. (Clinical epigenetics (2016) 1-10), Yuen et al. (European Journal of Human Genetics (2010) 1006-1012), Gao et al. (BMC genomics (2014) 1-14), Apte et al. (US 20170308669 A1), Decruze et al. (International Journal of Gynecological Cancer (2007) 964-978), and Odigboegwu et al. (Frontiers in cardiovascular medicine (2018) 1-4).
Claim 32 is directed to a method of using sequencing data from a plurality of tissues to filter and compare an observed methylation profile with a pre-defined profile to determine whether a person has a specified medical condition and output the results.
Claims 35-38 are directed to the method of claim 32 but reiterate the limitations provided in steps (i)-(iv) of the conclusion of claim 32.
Namsaraev et al. teaches in paragraph [0004] “In some embodiments, the method comprises obtaining a first biological sample from the subject, wherein the first biological sample comprises cell-free nucleic acid from the subject” and paragraph [0443] “The biological sample includes a mixture of cell-free DNA molecules from a plurality of tissues types that includes a first tissue type”, which reads on obtaining, by a computing system, initial sequence data that describes sequences of an initial set of nucleic acids from a biological sample of a person. Namsaraev et al. teaches in paragraph [0549] “For example, one or more filtering criteria can be used to increase the specificity” and in paragraph [0014] “In some embodiments, the method comprises comparing the first amount to a first cutoff threshold. In some embodiments, the method comprises comparing the second amount to a second cutoff threshold.”, which reads on filtering, by the computing system, the initial sequence data to identify a target subset of sequences from the initial sequence data that correspond to a pre-defined set of genomic regions. Namsaraev et al. teaches in paragraph [0222] “plasma DNA molecules originating from genes that are hypermethylated only in cancer cells can show hypermethylation in plasma of a cancer patient when compared with plasma DNA molecules originating from the same genes but in a sample of a healthy control”, which reads on comparing, by the computing system, an observed methylation profile of the target subset of sequences to a pre-defined methylation profile to determine whether the person has a specified medical condition, wherein the person is deemed to have the specified medical condition wherein the person is deemed to have the specified medical condition if a difference between the observed methylation profile of the second subset of sequences and the predicted methylation profile of the second subset of sequences matches a predefined pattern of deviation for the specified medical condition. Namsaraev et al. teaches in paragraph [0157] “A “preferred end” (or “recurrent ending position”) can refer to an end that is more highly represented or prevalent (e.g., as measured by a rate) in a biological sample having a physiological or pathological (disease) state (e.g., cancer) than a biological sample not having such a state or than at different time points or stages of the same pathological or physiological state, e.g., before or after”.
Ma et al. teaches in the abstract “Differences in methylation across tissues are critical to cell differentiation and are key to understanding the role of epigenetics in complex diseases. In this investigation, we found that locus-specific methylation differences between tissues are highly consistent across individuals. We developed a novel statistical model to predict locus-specific methylation in target tissue based on methylation in surrogate tissue”, which in view of Namsaraev et al. teachings in paragraph [0016] “In some embodiments, the method comprises outputting a report. In some embodiments, the method comprises outputting a report, and the report is indicative of tumor in the subject… In some embodiments, the first amount of the tumor-derived DNA corresponds to a methylation status of the tumor-derived DNA” and in paragraph [0773] “Any of the data mentioned herein can be output from one component to another component and can be output to the user”, reads on a second subset of sequences that correspond to a second pre-defined set of genomic regions; generating a predicted methylation profile of the second subset of sequences based on an observed methylation profile of the first subset of sequences and a predicted methylation profile of the second subset of sequences that was predicted based on an observed methylation profile of the first subset of sequences.
Bagaev et al. teaches in paragraph [0007], “Correctly selecting one or more effective therapies for a subject (e.g., a patient) with cancer or determining the effectiveness of a treatment can be crucial for the survival and overall wellbeing of that subject. Advances in identifying effective therapies and understanding their effectiveness or otherwise aiding in personalized care of patients with cancer are needed”, paragraph [0073] “Generally, techniques described herein provide for improvements over conventional computer-implemented techniques for analysis of medical data such as evaluation of expression data (e.g., RNA expression data) and determining whether one or more therapies (e.g., targeted therapies, radiotherapies, and/or immunotherapies) will be effective in treating the subject”, which in view of the methylation profiling and treatment suggestions from Namsaraev et al. reads on initiating a treatment of the person for the specified medical condition based on whether the person is determined to have the specified medical condition, the treatment including administering a therapeutic agent to the person at a dosage level or a dosage interval that is set based on a methylation level of a set of genomic loci associated with the specified medical condition, wherein:(i) the specified medical condition is a cancer and the therapeutic agent comprises at least one of an immunotherapy or a chemotherapy.
Gao et al. teaches in the abstract “We hypothesized that changes in gene methylation is involved in the prenatal maturation of the intestine and its response to the first days of formula feeding, potentially leading to NEC in preterm pigs used as models for preterm infants. In the 4 d-old formula-fed preterm pigs, four genes associated with intestinal metabolism (CYP2W1, GPR146, TOP1MT, CEND1) showed significant hyper-methylation in their promoter CGIs, and thus, down-regulated transcription. Methylation-driven down-regulation of such genes may predispose the immature intestine to later metabolic dysfunctions and severe NEC lesions. Pre- and postnatal changes in intestinal DNA methylation may contribute to high NEC sensitivity in preterm neonates. Optimizing gene methylation changes via environmental stimuli (e.g. diet, nutrition, gut microbiota), may help to make immature newborn infants more resistant to gut dysfunctions, both short and long term”.
Apte et al. teaches in paragraph [0031] “The treatment system 230 of the system 200 functions to promote one or more treatments to a user (e.g., a subject; a care provider facilitating provision of the treatment; etc.) for treating an antibiotics-associated condition (e.g., reducing the risk of the condition; modifying a microbiome pharmacogenomics profile of a user towards a state susceptible to treatments for an antibiotics-treatable condition, etc.)”, reading on (iii) the specified medical condition is necrotizing enterocolitis and the therapeutic agent comprises an antibiotic therapy.
Saare et al. teaches in the abstract “Alterations in endometrial DNA methylation profile have been proposed as one potential mechanism initiating the development of endometriosis…Comparison of cycle phase- and endometriosis-specific methylation profile changes revealed that 13 out of 28 endometriosis-specific DMRs were present in both datasets…The results of our study accentuate the importance of considering normal cyclic epigenetic changes in studies investigating endometrium-related disease-specific methylation patterns”.
Decruze et al. teaches in the abstract “Endometrial cancer is a hormone-dependent malignancy, and the majority has a precursor phase of endometrial hyperplasia. Histologic subtypes have been recognized with differing natural history. The relationship between hormone response, histology, and molecular profile is not established, but the relevant biology is summarized… We conclude that hormone receptor assessments should be carried out in all patients entered on clinical trials and may aid clinical management in selected cases. Receptor-negative status should not be an absolute contraindication to hormone intervention. Integration of hormone treatment with conventional chemotherapy and growth factor–targeted therapy needs to be explored”, reading on (ii) the specified medical condition is endometriosis and the therapeutic agent comprises a hormone therapy.
Yuen et al. teaches in the abstract “Preeclampsia and intrauterine growth restriction (IUGR) are two of the most common adverse pregnancy outcomes, but their underlying causes are mostly unknown. Although multiple studies have investigated gene expression changes in these disorders, few studies have examined epigenetic changes. Analysis of the DNA methylation pattern associated with such pregnancies provides an alternative approach to identifying cellular changes involved in these disorders Thirty-four loci were hypomethylated (false discovery rate <10% and methylation difference >10%) in the early-onset preeclamptic placentas while no and only five differentially methylated loci were found in late-onset preeclamptic and IUGR placentas, respectively. Hypomethylation of 4 loci in EOPET was further confirmed by bisulfite pyrosequencing of 26 independent placental samples. The promoter of TIMP3 was confirmed to be significantly hypomethylated in EOPET placentas (P=0.00001). Our results suggest that gene-specific hypomethylation may be a common phenomenon in EOPET placentas, and that TIMP3 could serve as a potential prenatal diagnostic marker for EOPET”.
Odigboegwu et al. teaches in the abstract “In this review article, we review the antihypertensive drugs currently being used to treat patients with PE and the advantages or disadvantages of using these drugs during pregnancy”, reading on (iv) the specified medical condition is preeclampsia and the therapeutic agent comprises an anti-hypertensive medication.
It would have been obvious at the time of first filing to modify the teachings of Namsaraev et al. for predicting via methylation profiles, disease progression as well as treatments and their efficacy, with the teachings from Bagaev et al., Gao et al., Yuen et al., Decruze et al., Saare et al., Apte et al., and Odigboegwu et al. for treating the various diseases specified with the treatments specified, as the latter seven merely prescribe/describe well-known treatments for the specified disease and associate hyper/hypomethylation with the diseases. Additionally, it would have been obvious to combine them with the teachings from Ma et al. for the prediction of methylation profiles as Ma et al. states “Differences in methylation across tissues are critical to cell differentiation and are key to understanding the role of epigenetics in complex diseases…We found that our method can greatly improve accuracy of cross-tissue prediction at CpG sites that are variable in the target tissue”. One would have had a reasonable expectation of success given that only the methods of Ma et al. and Namsaraev et al. would need to be combined, the other four are merely prescriptions/associations, and Ma et al. is using the same data on the same disease that Namsaraev et al. is using for the similar purposes. Therefore, it would have been obvious to a person skilled in the art to have modified the teachings of each and to be successful.
Claim 2 is directed to the method of claim 32 but further specifies identifying genomic regions, selecting an initial set of nucleic acids, and enriching a filtered subset through the discarding of sequences.
With respect to claim 2, Namsaraev et al. teaches in paragraph [0547] “The specificity in identifying a cancer genotype (e.g., including a cancer-specific mutation) and any tests using such genotypes (e.g., use of mutational load to determine a level of cancer) can be improved by applying filtering criteria to loci where one or more sequence reads having a mutation have been aligned. As an example for cancer, high specificity can be achieved by scoring a genetic or genomic signature as positive only when there is high confidence that it is cancer associated. This can be achieved by minimizing the number of sequencing and alignment errors that may be misidentified as a mutation, e.g., by comparing to the genomic profile of a group of healthy controls, and/or may be achieved by comparing with the person's own constitutional DNA and/or may be achieved by comparing with the person's genomic profile at an earlier time”.
It would have been obvious at the time of invention to a person skilled within the art that the use of a filter would equate to the use of an enrichment as both would result in an increased proportion of sequences who match the set of genomic regions in question. One would have a reasonable expectation of success given that Namsaraev et al. has outlined the method and both filtering and enrichment are well-understood, routine and conventional activities within the art. Therefore, it would have been obvious to one with ordinary skill in the art to incorporate the teaching into the method and to be successful.
Claim 5 is directed to the method of claim 1 but further specifies that the biological sample comprise plasma, and the initial set of nucleic acids comprise cell-free DNA in the plasma.
Namsaraev et al. teaches in paragraph [0004] “In some embodiments, the method comprises obtaining a first biological sample from the subject, wherein the first biological sample comprises cell-free nucleic acid from the subject”, which reads on wherein the biological sample comprises plasma, and the initial set of nucleic acids comprises cell-free DNA in the plasma.
Claim 6 is directed to the method of claim 32 but further specifies identifying reference methylomes, determining proportions of said references at CpG sites, generating predictions of methylation levels, comparing the predictions and determining if a medical condition likely exists.
Ma et al. teaches in the abstract “We developed a novel statistical model to predict locus-specific methylation in target tissue based on methylation in surrogate tissue… An extended model with multiple CpGs further improved performance. Our results suggest that large-scale epidemiology studies using easy-to-access surrogate tissues (e.g. blood) could be recalibrated to improve understanding of epigenetics in hard-to-access tissues (e.g. atrium) and might enable non-invasive disease screening using epigenetic profiles”, reading on identifying a set of reference component methylomes; determining a proportion of the reference component methylomes at a reference set of CpG sites in the initial set or filtered subset of nucleic acids; generating predictions of methylation levels at a target set of CpG sites in the initial set or filtered subset of nucleic acids; comparing the predictions of methylation levels at the target set of CpG sites to observed methylation levels; and determining whether the person likely has or does not have the specified medical condition based on the comparisons.
Claim 7 is directed to the method of claim 1 but further specifies that the biological sample comprise a stool sample or cerebrospinal fluid.
Namsaraev et al. teaches in paragraph [0139] “The term “sample,” “biological sample” or “patient sample” can include any tissue or material derived from a living or dead subject. A biological sample can be a cell-free sample… A biological sample can be a bodily fluid, such as blood, plasma, serum, urine, vaginal fluid, fluid from a hydrocele (e.g., of the testis), vaginal flushing fluids, pleural fluid, ascitic fluid, cerebrospinal fluid, saliva, sweat, tears, sputum, bronchoalveolar lavage fluid, discharge fluid from the nipple, aspiration fluid from different parts of the body (e.g., thyroid, breast), etc. A sample can be a stool sample”, which reads on wherein the biological sample comprises a stool sample or cerebrospinal fluid.
Claim 8 is directed to the method of claim 1 but further specifies that a copy number profile for the filtered subset of nucleic acids be determined and processed along with the methylation profile to determine the likelihood of the subject having the specified medical condition.
Namsaraev et al. teaches in paragraph [0004] “In some embodiments, the method comprises screening for the tumor based on performing a first assay comprising measuring a copy number of the cell-free nucleic acid”, which reads on determining, by the computing system, a copy number profile for the filtered subset of nucleic acids from the biological sample. Namsaraev et al. teaches in paragraph [0002] “The sensitivity of a test can refer to the likelihood that a subject that is positive for a condition tests positive for the condition. The specificity of a test can refer to the likelihood that a subject that is negative for a condition tests negative for that condition”, in paragraph [0132] “Additional approaches for characterizing diagnostic utility include using likelihood functions, odds ratios, information theory, predictive values, calibration (including goodness-of-fit), and reclassification measurements”, and in paragraph [0157] “A “preferred end” (or “recurrent ending position”) can refer to an end that is more highly represented or prevalent (e.g., as measured by a rate) in a biological sample having a physiological or pathological (disease) state (e.g., cancer) than a biological sample not having such a state or than at different time points or stages of the same pathological or physiological state, e.g., before or after treatment. A preferred end can have an increased likelihood or probability for being detected in the relevant physiological or pathological state relative to other states. The increased probability can be compared between the pathological state and a non-pathological state, for example in patients with and without a cancer and quantified as likelihood ratio or relative probability”, reading on processing, by the computing system, the copy number profile along with the methylation profile for the filtered subset of nucleic acids to determine the likelihood that the person has the specified medical condition.
Claim 9 is directed to the method of claim 1 but further specifies that the initial set of nucleic acids are to be treated to facilitate detection of methylated sites prior to sequencing.
Namsaraev et al. teaches in paragraph [0016] “In some embodiments, measuring the first amount of the tumor derived DNA comprises using methylation-aware sequencing to detect the methylation status of tumor-derived DNA in the biological sample”, which reads on wherein the initial set of nucleic acids were treated to facilitate detection of methylated sites before sequencing.
Claim 10 is directed to the method of claim 1 but further specifies that the medical condition be one selected from the specified group.
Namsaraev et al. teaches in paragraph [0008] “In some embodiments, the pathology is selected from the group consisting of bladder cancer, bone cancer, a brain tumor, breast cancer, cervical cancer, colorectal cancer, esophageal cancer, gastrointestinal cancer, hematopoietic malignancy, leukemia, liver cancer, lung cancer, lymphoma, myeloma, nasal cancer, nasopharyngeal cancer, oral cancer, oropharyngeal cancer, ovarian cancer, prostate cancer, sarcoma, stomach cancer, and thyroid cancer”, which reads on wherein the specified medical condition is ovarian cancer, endometriosis, necrotizing enterocolitis, fetal aneuploidy, preeclampsia, or a brain condition.
Claim 11 is directed to the method of claim 1 but further specifies that the methylation profile indicates a methylation level for the genomic loci.
Namsaraev et al. teaches in paragraph [0740] “The sequencing cam be methylation-aware sequencing. Methylation-aware sequencing can refer to any sequencing method in which the methylation status at various genomic locations is determined”, reading on wherein the methylation profile for the filtered subset of nucleic acids indicates, for each of a plurality of genomic loci, a methylation level of the locus.
Claim 12 is directed to the method of claim 1 but further specifies that the genomic loci be one of those specified is group provided.
Namsaraev et al. teaches in paragraph [0151] “The “methylation index” for each genomic site (e.g., a CpG site) can refer to the proportion of sequence reads showing methylation at the site over the total number of reads covering that site”, which reads on wherein the genomic loci is a CpG site, CpG island, differentially methylated region (DMR), promoter region, enhancer region, or CpG island shore.
Claim 13 is directed to the method of claim 1 but further specifies that determining the likelihood comprise determining a probability that the person has the specified medical condition.
Namsaraev et al. teaches in paragraph [0157] “A preferred end can have an increased likelihood or probability for being detected in the relevant physiological or pathological state relative to other states. The increased probability can be compared between the pathological state and a non-pathological state, for example in patients with and without a cancer and quantified as likelihood ratio or relative probability”, which reads on wherein determining the likelihood that the person has the specified medical condition comprises determining a probability that the person has the specified medical condition.
Claim 14 is directed to the method of claim 1 but further specifies that the likelihood that a person has the specified medical condition comprise a binary indication.
Namsaraev et al. teaches in paragraph [0162] “The classification can be binary (e.g., positive or negative) or have more levels of classification (e.g., a scale from 1 to 10 or 0 to 1). The terms “cutoff” and “threshold” can refer to predetermined numbers used in an operation” and in paragraph [0252] “After a catalog of cell-free DNA preferred ends is established for any physiological or pathological state, targeted or non-targeted methods can be used to detect their presence in cell-free DNA samples, e.g. plasma, or other individuals to determine a classification of the other tested individuals having a similar health, physiologic or disease state”, which reads on wherein determining the likelihood that the person has the specified medical condition comprises generating a binary indication that the person either likely has the specified medical condition or likely does not have the specified medical condition.
Claim 15 is directed to the method of claim 1 but further specifies that processing the methylation profile comprise inputting said profile to a machine learning model and obtaining a likelihood or value from which a likelihood can be derived.
Namsaraev et al. teaches in paragraph [0002] “The sensitivity of a test can refer to the likelihood that a subject that is positive for a condition tests positive for the condition. The specificity of a test can refer to the likelihood that a subject that is negative for a condition tests negative for that condition”, in paragraph [0132] “Additional approaches for characterizing diagnostic utility include using likelihood functions, odds ratios, information theory, predictive values, calibration (including goodness-of-fit), and reclassification measurements”, and in paragraph [0157] “A preferred end can have an increased likelihood or probability for being detected in the relevant physiological or pathological state relative to other states. The increased probability can be compared between the pathological state and a non-pathological state, for example in patients with and without a cancer and quantified as likelihood ratio or relative probability”, which reads on wherein processing the methylation profile comprises providing data representing the methylation profile as input to a machine-learning model, and obtaining the likelihood, or a value from which the likelihood is derived, as an output of the machine-learning model.
Claim 16 is directed to the method of claim 15 and thus claim 1, but further specifies that the machine learning model comprise at least one of those specified within the group provided.
Namsaraev et al. teaches in paragraph [0548] “a Bayesian-based approach can be used, as well as a classification or decision tree based approach”, which reads on wherein the machine-learning model comprises at least one of a classifier, an artificial neural network, a support vector machine, a decision tree, or a regression model.
Claim 17 is directed to the method of claim 15 and thus claim 1, but further specifies that the machine learning model defines a reference methylation profile against which the subjects methylation profiles are compared.
Namsaraev et al. teaches in paragraph [0222] “plasma DNA molecules originating from genes that are hypermethylated only in cancer cells can show hypermethylation in plasma of a cancer patient when compared with plasma DNA molecules originating from the same genes but in a sample of a healthy control”, which reads on wherein the machine-learning model defines reference or predicted methylation profiles against which the methylation profile for the filtered subset are compared to determine the likelihood that the person has the specified medical condition.
Claim 18 is directed to the method of claim 1 but further specifies that the likelihood determined be used to assess whether to perform additional diagnostics.
Namsaraev et al. teaches in paragraph [0430] “The generic common ends can also be detected for the purpose of screening for the sign of any disease (such as a general health screen). Positive findings for such a test can serve as an alert to visit a medical practitioner for more detailed investigation”, which reads on wherein the determined likelihood that the person has the specified medical condition is used by a medical provider to assess whether to perform additional diagnostic testing on the person.
Claim 19 is directed to the method of claim 1 but further specifies that the likelihood determined be used to diagnose or treat the subject for the specified condition.
Namsaraev et al. teaches in paragraph [0430] “The generic common ends can also be detected for the purpose of screening for the sign of any disease (such as a general health screen). Positive findings for such a test can serve as an alert to visit a medical practitioner for more detailed investigation”, which reads on wherein the determined likelihood that the person has the specified medical condition is used by a medical provider to at least one of diagnose the person or treat the person for the specified medical condition.
Claim 20 is directed to the method of claim 1 but further specifies that the outputting of the indication of the likelihood comprise one of the specified methods.
Namsaraev et al. teaches in paragraph [0748] “Any of the data mentioned herein can be output from one component to another component and can be output to the user” and in paragraph [0779] “A report is outputted indicating if the subject from which the sample was obtained has nasopharyngeal cancer”, which reads on wherein outputting the indication of the likelihood that the person has the specified medical condition comprises at least one of presenting the indication on an electronic display, audibly playing the indication through a speaker, storing the indication in a memory of a computing system for subsequent retrieval, or transmitting the indication in an electronic message to one or more users.
Claim 21 is directed to the method of claim 1 but further specifies that enriching the target nucleic acids results in a fraction of the target acids that is greater in the filtered subset than the initial set.
Namsaraev et al. teaches in paragraph [0014] “In some embodiments, the method further comprises enriching the sample for the plurality of cell-free nucleic acid molecules”, which reads on wherein enriching the target nucleic acids in the filtered subset comprises generating the filtered subset so that a fraction of the target nucleic acids that occur in the filtered subset is greater than a fraction of the target nucleic acids that occur in the initial set of nucleic acids.
Claim 23 is directed to the method of claim 1 but further specifies that the filtered subset comprises both target and non-target nucleic acids.
Namsaraev et al. teaches in paragraph [0015] “In some aspects, the present disclosure provides a method of analyzing a biological sample including a mixture of cell-free nucleic acid molecules to determine a level of pathology in a subject from which the biological sample is obtained, the mixture including nucleic acid molecules from the subject and potentially nucleic acid molecules from a pathogen”, which reads on wherein the filtered subset comprises the target nucleic acids and non-targeted nucleic acids.
Claim 26 is directed to a method of using sequencing data from a plurality of tissues to filter and create two subsets from which two distinct methylation profiles can be derived, and then compare the two profiles to determine whether a person has a specified medical condition, and then output the results.
Namsaraev et al. teaches in paragraph [0004] “In some embodiments, the method comprises obtaining a first biological sample from the subject, wherein the first biological sample comprises cell-free nucleic acid from the subject” and in paragraph [0443] “The biological sample includes a mixture of cell-free DNA molecules from a plurality of tissues types that includes a first tissue type”, which reads on obtaining, by a computing system, initial sequence data that describes sequences of an initial set of nucleic acids from a biological sample of a person, the initial set of nucleic acids including nucleic acids originating from a plurality of different tissues of the person. Namsaraev et al. teaches in paragraph [0144] ““Cancer-associated changes” or “cancer-specific changes” can include cancer-derived mutations (including single nucleotide mutations, deletions or insertions of nucleotides, deletions of genetic or chromosomal segments, translocations, inversions), amplification of genes, virus-associated sequences (e.g., viral episomes, viral insertions, viral DNA that is infected into a cell and subsequently released by the cell, and circulating or cell-free viral DNA), aberrant methylation profiles or tumor-specific methylation signatures” and in paragraph [0740] “As for other filtering criteria, the comparison can be used strictly or as a score. Regardless, the methylation status not being methylated can provide a higher likelihood of discarding the sequence read than the methylation status being methylated”, which reads on filtering, by the computing system, the initial sequence data to identify a first subset of sequences from the initial sequence data that correspond to a first pre-defined set of genomic regions. Namsaraev et al. teaches in paragraph [0227] “Plasma methylation density values beyond, for example lower than, a defined cutoff based on the reference values can be used to assess if a subject's plasma has tumor DNA or not. To detect the presence of hypomethylated circulating tumor DNA, the cutoff can be defined as lower than the 5th or 1st percentiles of the values of the control population” and in paragraph [0740] “As for other filtering criteria, the comparison can be used strictly or as a score. Regardless, the methylation status not being methylated can provide a higher likelihood of discarding the sequence read than the methylation status being methylated”, which reads on filtering, by the computing system, the initial sequence data to identify a second subset of sequences from the initial sequence data that correspond to a second pre-defined set of genomic regions. Namsaraev et al. teaches in paragraph [0002] “The sensitivity of a test can refer to the likelihood that a subject that is positive for a condition tests positive for the condition. The specificity of a test can refer to the likelihood that a subject that is negative for a condition tests negative for that condition”, in paragraph [0132] “Additional approaches for characterizing diagnostic utility include using likelihood functions, odds ratios, information theory, predictive values, calibration (including goodness-of-fit), and reclassification measurements”, and in paragraph [0157] “A preferred end can have an increased likelihood or probability for being detected in the relevant physiological or pathological state relative to other states. The increased probability can be compared between the pathological state and a non-pathological state, for example in patients with and without a cancer and quantified as likelihood ratio or relative probability”, which reads on processing, by the computing system, data that includes an observed methylation profile of the first subset of sequences to generate a predicted methylation profile of the second subset of sequences. Namsaraev et al. teaches in paragraph [0222] “plasma DNA molecules originating from genes that are hypermethylated only in cancer cells can show hypermethylation in plasma of a cancer patient when compared with plasma DNA molecules originating from the same genes but in a sample of a healthy control”, which reads on comparing, by the computing system, an observed methylation profile of the second subset of sequences to the predicted methylation profile of the second subset of sequences to determine whether the person has a specified medical condition, wherein the person is deemed to have the specified medical condition if a difference between the observed methylation profile of the second subset of sequences and the predicted methylation profile of the second subset of sequences meets a minimum difference criterion. Finally, Namsaraev et al. teaches in paragraph [0016] “In some embodiments, the method comprises outputting a report. In some embodiments, the method comprises outputting a report, and the report is indicative of tumor in the subject” and in paragraph [0773] “Any of the data mentioned herein can be output from one component to another component and can be output to the user”, which reads on outputting, by the computing system, an indication of whether the person was determined to have the specified medical condition.
Claim 29 is directed to the method of claim 26 but further specifies that the medical condition be one of those specified.
Namsaraev et al. teaches in paragraph [0008] “In some embodiments, the pathology is selected from the group consisting of bladder cancer, bone cancer, a brain tumor, breast cancer, cervical cancer, colorectal cancer, esophageal cancer, gastrointestinal cancer, hematopoietic malignancy, leukemia, liver cancer, lung cancer, lymphoma, myeloma, nasal cancer, nasopharyngeal cancer, oral cancer, oropharyngeal cancer, ovarian cancer, prostate cancer, sarcoma, stomach cancer, and thyroid cancer”, which reads on wherein the specified medical condition is preeclampsia, endometriosis, ovarian cancer, necrotizing enterocolitis, or a brain condition.
Claim 39 is directed to a method for determining whether a mammal has a specified medical condition using epigenetic profiles and initiating a treatment.
Claims 40-44 are directed to the method of claim 39 but reiterate the limitations provided in steps (i)-(iv) of the conclusion of claim 39.
Namsaraev et al. teaches in paragraph [0004] “In some embodiments, the method comprises obtaining a first biological sample from the subject, wherein the first biological sample comprises cell-free nucleic acid from the subject” and paragraph [0443] “The biological sample includes a mixture of cell-free DNA molecules from a plurality of tissues types that includes a first tissue type”, in paragraph [0549] “For example, one or more filtering criteria can be used to increase the specificity” and in paragraph [0014] “In some embodiments, the method comprises comparing the first amount to a first cutoff threshold. In some embodiments, the method comprises comparing the second amount to a second cutoff threshold”, in paragraph [0222] “plasma DNA molecules originating from genes that are hypermethylated only in cancer cells can show hypermethylation in plasma of a cancer patient when compared with plasma DNA molecules originating from the same genes but in a sample of a healthy control”, and in paragraph [0157] “A “preferred end” (or “recurrent ending position”) can refer to an end that is more highly represented or prevalent (e.g., as measured by a rate) in a biological sample having a physiological or pathological (disease) state (e.g., cancer) than a biological sample not having such a state or than at different time points or stages of the same pathological or physiological state, e.g., before or after”.
Ma et al. teaches in the abstract “Differences in methylation across tissues are critical to cell differentiation and are key to understanding the role of epigenetics in complex diseases. In this investigation, we found that locus-specific methylation differences between tissues are highly consistent across individuals. We developed a novel statistical model to predict locus-specific methylation in target tissue based on methylation in surrogate tissue”, which in view of Namsaraev et al. teachings in paragraph [0016] “In some embodiments, the method comprises outputting a report. In some embodiments, the method comprises outputting a report, and the report is indicative of tumor in the subject… In some embodiments, the first amount of the tumor-derived DNA corresponds to a methylation status of the tumor-derived DNA” and in paragraph [0773] “Any of the data mentioned herein can be output from one component to another component and can be output to the user”, reads on determining whether a mammal has a specified medical condition based on comparison of an observed epigenetics profile of a second subset of sequences of nucleic acids of the mammal to a predicted epigenetics profile of a second subset of sequences of nucleic acids of the mammal, the predicted epigenetics profile of the second subset of sequences predicted based on an observed epigenetics profile of a first subset of sequences of nucleic acids of the mammal, the first subset of sequences corresponding to a first set of genomic regions and the second subset of sequences corresponding to a different second set of genomic regions.
Bagaev et al. teaches in paragraph [0007], “Correctly selecting one or more effective therapies for a subject (e.g., a patient) with cancer or determining the effectiveness of a treatment can be crucial for the survival and overall wellbeing of that subject. Advances in identifying effective therapies and understanding their effectiveness or otherwise aiding in personalized care of patients with cancer are needed”, paragraph [0073] “Generally, techniques described herein provide for improvements over conventional computer-implemented techniques for analysis of medical data such as evaluation of expression data (e.g., RNA expression data) and determining whether one or more therapies (e.g., targeted therapies, radiotherapies, and/or immunotherapies) will be effective in treating the subject”, which in view of the methylation profiling and treatment suggestions from Namsaraev et al. reads on initiating a treatment of the person for the specified medical condition based on whether the person is determined to have the specified medical condition, the treatment including administering a therapeutic agent to the person at a dosage level or a dosage interval that is set based on a methylation level of a set of genomic loci associated with the specified medical condition, wherein:(i) the specified medical condition is a cancer and the therapeutic agent comprises at least one of an immunotherapy or a chemotherapy.
Gao et al. teaches in the abstract “We hypothesized that changes in gene methylation is involved in the prenatal maturation of the intestine and its response to the first days of formula feeding, potentially leading to NEC in preterm pigs used as models for preterm infants. In the 4 d-old formula-fed preterm pigs, four genes associated with intestinal metabolism (CYP2W1, GPR146, TOP1MT, CEND1) showed significant hyper-methylation in their promoter CGIs, and thus, down-regulated transcription. Methylation-driven down-regulation of such genes may predispose the immature intestine to later metabolic dysfunctions and severe NEC lesions. Pre- and postnatal changes in intestinal DNA methylation may contribute to high NEC sensitivity in preterm neonates. Optimizing gene methylation changes via environmental stimuli (e.g. diet, nutrition, gut microbiota), may help to make immature newborn infants more resistant to gut dysfunctions, both short and long term”.
Apte et al. teaches in paragraph [0031] “The treatment system 230 of the system 200 functions to promote one or more treatments to a user (e.g., a subject; a care provider facilitating provision of the treatment; etc.) for treating an antibiotics-associated condition (e.g., reducing the risk of the condition; modifying a microbiome pharmacogenomics profile of a user towards a state susceptible to treatments for an antibiotics-treatable condition, etc.)”, reading on (iii) the specified medical condition is necrotizing enterocolitis and the therapeutic agent comprises an antibiotic therapy.
Saare et al. teaches in the abstract “Alterations in endometrial DNA methylation profile have been proposed as one potential mechanism initiating the development of endometriosis…Comparison of cycle phase- and endometriosis-specific methylation profile changes revealed that 13 out of 28 endometriosis-specific DMRs were present in both datasets…The results of our study accentuate the importance of considering normal cyclic epigenetic changes in studies investigating endometrium-related disease-specific methylation patterns”.
Decruze et al. teaches in the abstract “Endometrial cancer is a hormone-dependent malignancy, and the majority has a precursor phase of endometrial hyperplasia. Histologic subtypes have been recognized with differing natural history. The relationship between hormone response, histology, and molecular profile is not established, but the relevant biology is summarized… We conclude that hormone receptor assessments should be carried out in all patients entered on clinical trials and may aid clinical management in selected cases. Receptor-negative status should not be an absolute contraindication to hormone intervention. Integration of hormone treatment with conventional chemotherapy and growth factor–targeted therapy needs to be explored”, reading on (ii) the specified medical condition is endometriosis and the therapeutic agent comprises a hormone therapy.
Yuen et al. teaches in the abstract “Preeclampsia and intrauterine growth restriction (IUGR) are two of the most common adverse pregnancy outcomes, but their underlying causes are mostly unknown. Although multiple studies have investigated gene expression changes in these disorders, few studies have examined epigenetic changes. Analysis of the DNA methylation pattern associated with such pregnancies provides an alternative approach to identifying cellular changes involved in these disorders Thirty-four loci were hypomethylated (false discovery rate <10% and methylation difference >10%) in the early-onset preeclamptic placentas while no and only five differentially methylated loci were found in late-onset preeclamptic and IUGR placentas, respectively. Hypomethylation of 4 loci in EOPET was further confirmed by bisulfite pyrosequencing of 26 independent placental samples. The promoter of TIMP3 was confirmed to be significantly hypomethylated in EOPET placentas (P=0.00001). Our results suggest that gene-specific hypomethylation may be a common phenomenon in EOPET placentas, and that TIMP3 could serve as a potential prenatal diagnostic marker for EOPET”.
Odigboegwu et al. teaches in the abstract “In this review article, we review the antihypertensive drugs currently being used to treat patients with PE and the advantages or disadvantages of using these drugs during pregnancy”, reading on (iv) the specified medical condition is preeclampsia and the therapeutic agent comprises an anti-hypertensive medication.
Claims 3-4, 22 are rejected under 35 U.S.C. 103 as being unpatentable over Namsaraev et al. (US 20180237863 A1), Bagaev et al. (US 20180358132 A1), Apte et al. (US 20170308669 A1), Ma et al. (Nucleic Acids Research (2014) 3515-3528), Saare et al. (Clinical epigenetics (2016) 1-10), Yuen et al. (European Journal of Human Genetics (2010) 1006-1012), Gao et al. (BMC genomics (2014) 1-14), Decruze et al. (International Journal of Gynecological Cancer (2007) 964-978), and Odigboegwu et al. (Frontiers in cardiovascular medicine (2018) 1-4) as applied to claims 2, 5-21, 23, 26, 29, 32, 35-44 above, and further in view of Talens et al. (The FASEB Journal (2010) 3135-3144).
Claim 3 is directed to the method of claim 32 but further specifies the comparison of methylation stability through the use of a methylation characteristic or copy number characteristic.
Namsaraev et al., Bagaev et al., Apte et al., Ma et al., Decruze et al., and Odigboegwu et al. teach the method of claim 32 as described above.
Talens et al. teaches in the abstract “We characterized features of DNA methylation at 16 candidate loci, 8 of which were imprinted, in DNA samples from the Netherlands Twin Register biobank. Except for unmethylated or fully methylated sites, CpG methylation varied considerably in a sample of 30 unrelated individuals”, on page 3138, column 1, paragraph 3 “The difference between DNA methylation at two time points was calculated per individual for each CpG unit as methylation of the old sample minus methylation of the new sample. Missing values were excluded pairwise. Spearman’s rank correlation coefficient (p) was used to calculate the correlation between the two time points”, which in view of the teachings from Namsaraev et al., Bagaev et al., Apte et al., Ma et al., Decruze et al., and Odigboegwu et al. reads on the first subset of the pre-defined set of genomic regions are defined based on the regions in the first subset exhibiting a minimum level of stability with respect to at least one of a methylation characteristic or a copy number characteristic in a population of individuals.
It would have been obvious at the time of first filing to have modified the teachings of Namsaraev et al., Bagaev et al., Apte et al., Ma et al., Decruze et al., and Odigboegwu et al. for the method of claim 32 with the teachings of Talens et al. for the use of specific subsets of loci from CpG sites to calculate methylation characteristics that could be used to identify disease, as the latter states in the abstract “Except for unmethylated or fully methylated sites, CpG methylation varied considerably in a sample of 30 unrelated individuals”, suggesting a possible link to disease states. One would have had a reasonable expectation of success given that Talens et al. is merely deriving a methylation characteristic, which could be calculated if an entire methylation profile is being generated, and restricting the number of sites to examine; one being an additional calculation and the other merely a filtering step. Therefore, it would have been obvious to a person skilled in the art to have modified the teachings of each and to have been successful.
Claim 4 is directed to the method of claim 3 and thus claim 32, but further specifies the use of minimum difference with respect to the methylation characteristic.
Namsaraev et al., Bagaev et al., Apte et al., Ma et al., Decruze et al., and Odigboegwu et al. teach the method of claim 32 as described above.
Talens et al. teaches in the abstract “We characterized features of DNA methylation at 16 candidate loci, 8 of which were imprinted, in DNA samples from the Netherlands Twin Register biobank. Except for unmethylated or fully methylated sites, CpG methylation varied considerably in a sample of 30 unrelated individuals”, on page 3138, column 1, paragraph 3 “The difference between DNA methylation at two time points was calculated per individual for each CpG unit as methylation of the old sample minus methylation of the new sample. Missing values were excluded pairwise. Spearman’s rank correlation coefficient (p) was used to calculate the correlation between the two time points”. While Talens et al. does not explicitly teach the use of a minimum, or threshold, it would be obvious to a person skilled in the art that if “CpG methylation varied considerably” in similar sites between individuals the use of thresholds would be necessary to establish a baseline variation from a pathogenic variation level, which would merely be a problem of optimization.
Claim 22 is directed to the method of claim 32 but further specifies that the subset of sequences consists exclusively of sequences from a set of target nucleic acids.
Namsaraev et al., Bagaev et al., Apte et al., Ma et al., Decruze et al., and Odigboegwu et al. teach the method of claim 32 as described above.
Talens et al. teaches in the abstract “We characterized features of DNA methylation at 16 candidate loci, 8 of which were imprinted, in DNA samples from the Netherlands Twin Register biobank”, reading on the first subset of sequences consists exclusively of sequences for a set of target nucleic acids.
Response to Arguments
Applicant's arguments filed 10/27/2025 have been fully considered but they are not persuasive. Applicant asserts on page 2 of the Remarks filed 10/27/2025 that the amended claim now recites limitations not disclosed within the previously cited prior art. Examiner agrees, however a new search has provided new art which does read on said limitations.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 2-9, 11-17, 32, and 35-44 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-31 of U.S. Patent No. 12009061 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because they teach the same method of using methylation profile information to identify differential expression and link that to the same diseases and propose the same treatment.
Application: 17/602,553
U.S. Patent No. 12009061 B2
Claim 32: A method, comprising: obtaining initial sequence data that describes sequences of an initial set of nucleic acids from a biological sample of a person; filtering the initial sequence data to identify a first subset of sequences that correspond to a first pre-defined set of genomic regions and (ii) a second subset of sequences that correspond to a second pre-defined set of genomic regions; generating a predicted methylation profile of the second subset of sequences based on an observed methylation profile of the first subset of sequences; comparing an observed methylation profile of the second subset of sequences to a predicted methylation profile of the second subset of sequences that was predicted based on an observed methylation profile of the first subset of sequences to determine whether the person has a specified medical condition, wherein the person is deemed to have the specified medical condition if a difference between the observed methylation profile of the second subset of sequences and the predicted methylation profile of the second subset of sequences matches a predefined pattern of deviation for the specified medical condition initiating a treatment of the person for the specified medical condition based on whether the person is determined to have the specified medical condition, the treatment including administering a therapeutic agent to the person at a dosage level or a dosage interval that is set based on a methylation level of a set of genomic loci associated with the specified medical condition, wherein:(i) the specified medical condition is a cancer and the therapeutic agent comprises at least one of an immunotherapy or a chemotherapy, (ii) the specified medical condition is endometriosis and the therapeutic agent comprises a hormone therapy,(iii) the specified medical condition is necrotizing enterocolitis and the therapeutic agent comprises an antibiotic therapy, or (iv) the specified medical condition is preeclampsia and the therapeutic agent comprises an anti-hypertensive medication.
Claim 35: The method of claim 32, wherein the specified medical condition is cancer and the therapeutic agent comprises at least one of the immunotherapy or the chemotherapy.
Claim 36: The method of claim 32, wherein the specified medical condition is endometriosis and the therapeutic agent comprises the hormone therapy.
Claim 37: The method of claim 32, wherein the specified medical condition is necrotizing enterocolitis and the therapeutic agent comprises the antibiotic therapy.
Claim 38: The method of claim 32, wherein the specified medical condition is preeclampsia and the therapeutic agent comprises the anti-hypertensive medication.
Claim 1: A method, comprising: obtaining initial sequence data that describes sequences of an initial set of nucleic acids from a biological sample of a person, the initial set of nucleic acids including nucleic acids originating from a plurality of different tissues of the person; filtering the initial sequence data to identify a first subset of nucleic acids whose sequences correspond to sequences in a first set of genomic regions and a second subset of nucleic acids whose sequences correspond to sequences in a second set of genomic regions; determining an observed methylation profile for the first subset of nucleic acids of the person and an observed methylation profile for the second subset of nucleic acids of the person; determining a predicted methylation profile for the second subset of nucleic acids of the person based on the observed methylation profile for the first subset of nucleic acids of the person; determining a likelihood that the person has a specified medical condition, including (i) determining a difference between the predicted methylation profile for the second subset of nucleic acids of the person and the observed methylation profile for the second subset of nucleic acids of the person and (ii) determining whether the difference matches a predefined pattern of deviation for the specified medical condition; and initiating a treatment of the person for the specified medical condition based on the determined likelihood that the person has the specified medical condition, the treatment including administering a therapeutic agent to the person at a dosage level or a dosage interval that is set based on a methylation level of a set of genomic loci associated with the specified medical condition, wherein (i) the specified medical condition is a cancer and the therapeutic agent comprises at least one of an immunotherapy or a chemotherapy, (ii) the specified medical condition is endometriosis and the therapeutic agent is a hormone therapy, (iii) the specified medical condition is necrotizing enterocolitis and the therapeutic agent is an antibiotic therapy, or (iv) the specified medical condition is preeclampsia and the therapeutic agent is an anti-hypertensive medication.
Claim 21: The method of claim 1, wherein the specified medical condition is the cancer.
Claim 22: The method of claim 21, wherein the cancer is ovarian cancer.
Claim 23: The method of claim 1, wherein the specified medical condition is endometriosis.
Claim 24: The method of claim 1, wherein the specified medical condition is necrotizing enterocolitis.
Claim 25: The method of claim 1, wherein the specified medical condition is preeclampsia.
Claim 2: The method of claim 32 further comprising: identifying a pre-defined set of genomic regions; and selecting target nucleic acids from the initial set of nucleic acids by comparing nucleic acid sequences from the initial set of nucleic acids to sequences from the pre- defined set of genomic regions; wherein enriching the target nucleic acids in the filtered subset comprises discarding nucleic acid sequences from the initial sequence data that are not among the sequences from the pre-defined set of genomic regions, while retaining nucleic acid sequences from the initial sequence data that are among the sequences from the pre-defined set of genomic regions.
Claim 2: The method of claim 1, wherein filtering the initial sequence data to identify the first subset of nucleic acids comprises: comparing nucleic acid sequences from the initial set of nucleic acids to the sequences in the first set of genomic regions; discarding nucleic acid sequences from the initial sequence data that are not determined to match the sequences in the first set of genomic regions; and retaining nucleic acid sequences from the initial sequence data that are determined to match the sequences in the first set of genomic regions.
Claim 3: The method of claim 32 wherein at least the first subset of the pre-defined set of genomic regions are defined based on the regions in the first subset exhibiting a minimum level of stability with respect to at least one of a methylation characteristic or a copy number characteristic in a population of individuals.
Claim 3: The method of claim 2, wherein at least a first subset of the first set of genomic regions are defined based on the regions in the first subset exhibiting at least a minimum level of stability with respect to at least one of the methylation characteristic or the copy number characteristic in a population of individuals.
Claim 4: The method of claim 32 wherein a second subset of the pre-defined set of genomic regions are defined based on the regions in the second subset exhibiting at least a minimum difference with respect to the methylation characteristic or the copy number characteristic between individuals who have the specified medical condition and individuals who do not have the specified medical condition.
Claim 4: The method of claim 3, wherein the second set of genomic regions are defined based on the regions in the second set exhibiting at least a minimum difference with respect to the methylation characteristic or the copy number characteristic between individuals who have the specified medical condition and individuals who do not have the specified medical condition.
Claim 5: The method of claim 32 wherein the biological sample comprises plasma, and the initial set of nucleic acids comprises cell-free DNA in the plasma.
Claim 5: The method of claim 1, wherein the biological sample comprises plasma, and the initial set of nucleic acids comprises cell-free DNA in the plasma.
Claim 6: The method of claim 32 further comprising: identifying a set of reference component methylomes;determining a proportion of the reference component methylomes at a reference set of CpG sites in the initial set or filtered subset of nucleic acids;generating predictions of methylation levels at a target set of CpG sites in the initial set or filtered subset of nucleic acids;comparing the predictions of methylation levels at the target set of CpG sites to observed methylation levels; anddetermining whether the person likely has or does not have the specified medical condition based on the comparisons.
Claim 6: The method of claim 1, comprising: identifying a set of reference component methylomes in the initial set of nucleic acids and a filtered subset of nucleic acids; determining a proportion of the reference component methylomes at a reference set of CpG sites in the initial set or the filtered subset of nucleic acids; generating predictions of methylation levels at a target set of CpG sites in the initial set or the filtered subset of nucleic acids; comparing the predictions of methylation levels at the target set of CpG sites to observed methylation levels; and determining whether the person likely has or does not have the specified medical condition based on the comparison.
Claim 7: The method of claim 32 wherein the biological sample comprises a stool sample or cerebrospinal fluid.
Claim 7: The method of claim 1, wherein the biological sample comprises a stool sample or cerebrospinal fluid.
Claim 8: The computer-implemented method of claim 32 further comprising: determining a copy number profile for the first subset of sequences; and processing the copy number profile and the observed methylation profile for the first subset of sequences to determine the likelihood that the person has the specified medical condition.
Claim 8: The method of claim 1, further comprising: determining a copy number profile for a filtered subset of nucleic acids from the biological sample; and processing the copy number profile a methylation profile for the filtered subset of nucleic acids to determine the likelihood that the person has the specified medical condition.
Claim 9: The method of claim 32 wherein the initial set of nucleic acids were treated to facilitate detection of methylated sites before sequencing.
Claim 9: The method of claim 1, wherein the initial set of nucleic acids were treated to facilitate detection of methylated sites before sequencing.
Claim 11: The method of claim 32 wherein the methylation profile for the first subset of sequences indicates, for each of a plurality of genomic loci, a methylation level of the locus.
Claim 10: The method of claim 1, wherein the observed methylation profile for the first or second subsets of sequences indicates, for each of a plurality of genomic loci, a methylation level of the locus.
Claim 12: The method of claim 32 wherein the genomic loci is a CpG site, CpG island, differentially methylated region (DMR), promoter region, enhancer region, or CpG island shore.
Claim 11: The method of claim 1, wherein the genomic loci is a CpG site, CpG island, differentially methylated region (DMR), promoter region, enhancer region, or CpG island shore.
Claim 13: The method of claim 32 wherein determining the likelihood that the person has the specified medical condition comprises determining a probability that the person has the specified medical condition.
Claim 12: The method of claim 1, wherein determining the likelihood that the person has the specified medical condition comprises determining a probability that the person has the specified medical condition.
Claim 14: The method of claim 32 wherein determining the likelihood that the person has the specified medical condition comprises generating a binary indication that the person either likely has the specified medical condition or likely does not have the specified medical condition.
Claim 13: The method of claim 1, wherein determining the likelihood that the person has the specified medical condition comprises generating a binary indication that the person either likely has the specified medical condition or likely does not have the specified medical condition.
Claim 15: The method of claim 32 comprising providing data representing the observed methylation profile of the first subset of sequences as input to a machine-learning model, and obtaining the likelihood, or a value from which the likelihood is derived, as an output of the machine-learning model.
Claim 14: The method of claim 1, comprising providing data representing the observed methylation profile for the second subset of nucleic acids as input to a machine-learning model.
Claim 16: The method of claim 15 wherein the machine-learning model comprises at least one of a classifier, an artificial neural network, a support vector machine, a decision tree, or a regression model.
Claim 15: The method of claim 14, wherein the machine-learning model comprises at least one of a classifier, an artificial neural network, a support vector machine, a decision tree, or a regression model.
Claim 17: The method of claim 15 wherein the machine-learning model defines reference or predicted methylation profiles against which the observed methylation profile for the first subset of sequences are compared as a further basis for determining the likelihood that the person has the specified medical condition.
Claim 17: The method of claim 1, wherein correlating the difference between the predicted methylation profile for the second subset of nucleic acids and the observed methylation profile for the second subset of nucleic acids with the likelihood that the person has the specified medical condition comprises comparing the difference to a minimum difference criterion.
Claim 39: A method, comprising: determining whether a mammal has a specified medical condition based on comparison of an observed epigenetics profile of a second subset of sequences of nucleic acids of the mammal to a predicted epigenetics profile of a second subset of sequences of nucleic acids of the mammal, the predicted epigenetics profile of the second subset of sequences predicted based on an observed epigenetics profile of a first subset of sequences of nucleic acids of the mammal, the first subset of sequences corresponding to a first set of genomic regions and the second subset of sequences corresponding to a different second set of genomic regions; and initiating a treatment of the mammal for the specified medical condition based on whether the mammal is determined to have the specified medical condition, the treatment including administering a therapeutic agent to the person at a dosage level or a dosage interval that is set based on a epigenetics level of a set of genomic loci associated with the specified medical condition, wherein:(i) the specified medical condition is a cancer and the therapeutic agent comprises at least one of an immunotherapy or a chemotherapy,(ii) the specified medical condition is endometriosis and the therapeutic agent comprises a hormone therapy,(iii) the specified medical condition is necrotizing enterocolitis and the therapeutic agent comprises an antibiotic therapy, or (iv) the specified medical condition is preeclampsia and the therapeutic agent comprises an anti-hypertensive medication.
Claim 26: A method, comprising: obtaining initial sequence data that describes sequences of an initial set of nucleic acids from a biological sample of a person, the initial set of nucleic acids including nucleic acids originating from a plurality of different tissues of the person; filtering the initial sequence data to identify a first subset of sequences from the initial sequence data that correspond to a first pre-defined set of genomic regions; filtering the initial sequence data to identify a second subset of sequences from the initial sequence data that correspond to a second pre-defined set of genomic regions; processing data that includes an observed methylation profile of the first subset of sequences to generate a predicted methylation profile of the second subset of sequences; determining a difference between an observed methylation profile of the second subset of sequences and the predicted methylation profile of the second subset of sequences; determining whether the difference between the observed methylation profile of the second subset of sequences and the predicted methylation profile of the second subset of sequences matches a predefined pattern of deviation for a specified medical condition, wherein the person is deemed to have the specified medical condition if the difference between the observed methylation profile of the second subset of sequences and the predicted methylation profile of the second subset of sequences matches the predefined pattern of deviation for the specified medical condition; and initiating a treatment of the person for the specified medical condition based on the determined likelihood that the person has the specified medical condition, the treatment including administering a therapeutic agent to the person at a dosage level or a dosage interval that is set based on a methylation level of a set of genomic loci associated with the specified medical condition, wherein (i) the specified medical condition is a cancer and the therapeutic agent comprises at least one of an immunotherapy or a chemotherapy, (ii) the specified medical condition is endometriosis and the therapeutic agent is a hormone therapy, (iii) the specified medical condition is necrotizing enterocolitis and the therapeutic agent is an antibiotic therapy, or (iv) the specified medical condition is preeclampsia and the therapeutic agent is an anti-hypertensive medication.
Claim 40: The method of claim 39, wherein the specified medical condition is cancer and the therapeutic agent comprises at least one of the immunotherapy or the chemotherapy.
Claim 41: The method of claim 39, wherein the specified medical condition is endometriosis and the therapeutic agent comprises the hormone therapy.
Claim 42: The method of claim 39, wherein the specified medical condition is necrotizing enterocolitis and the therapeutic agent comprises the antibiotic therapy.
Claim 43: The method of claim 39, wherein the specified medical condition is preeclampsia and the therapeutic agent comprises the anti-hypertensive medication.
Claim 44: The method of claim 39, wherein the epigenetics profiles comprise methylation profiles of the first and second subset of sequences, respectively.
Conclusion
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/K.N.A./
Examiner, Art Unit 1687
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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