DETAILED ACTION
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 Status
Claims 1-29 are currently pending and under examination herein.
Claims 1-29 are rejected.
Claims 1 and 13 are objected to.
Priority
The instant application claims priority as a 371 of PCT/US2022/020222 filed 14 March 2022 and to US Provisional Applications 63160246 filed 12 March 2021 and 63229167 field 04 August 2021. In this action, claims 1-29 are examined as though they had an effective filing date of 12 March 2021. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 24 January 2024 and 23 May 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawing filed on 08 September 2023 are accepted.
Claim Objections
Claims 1 and 13 are objected to because they recite both “data set” and “dataset” when referring to the “reference methylation probability”. For the purposes of examination, there are assumed to be no differences between these terms, as supported by claim 25. The claims should be consistent throughout when referring to the same data.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 4, 8, 16, 20, and 28 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 4 and 16 recite “WGBS”. No explicit definition for WGBS was found within the claims or specification and one of ordinary skill in the art would not be reasonably certain of its definition. The metes and bounds of the limitation are therefore unclear rendering the claim indefinite. The rejection can be overcome by defining acronyms within the claims at their first mention.
Claims 8, 20, and 28 are rejected because they recite “calculating the likelihood”. These claims depend from claims 1, 13, and 25, which recite calculating different likelihoods. Therefore, there is insufficient antecedent basis for these limitation in the claims. The metes and bounds of these limitations are therefore unclear rendering the claims indefinite.
Claims 8 and 28 are rejected because they recite “ultimate predictor of epigenetic age”. The term “ultimate” is a relative term which renders the claim indefinite. The term “ultimate” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For the purposes of examination, the prediction of age is considered the ultimate predictor.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 29 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 29 recites “A computer-readable storage medium” executed by a computer. The claims and specification does not define the computer readable medium as non-transitory. Therefore, the broadest reasonable interpretation encompass transitory medium. Transitory forms of signal transmission (often referred to as "signals per se") are not directed to any of the statutory categories include (see MPEP 2106.03). Therefore, the claim is not directed to a statutory category. This rejection can be overcome by specifying the CRM is non-transitory.
Claims 1-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a natural law without significantly more. In accordance with MPEP 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea or natural law (Step 2A, Prong 1). Claims 1-28 are directed to methods. Although claim 29 is not currently directed to a statutory category of invention (see above), it is included in the subject matter eligibility analysis to facilitate compact prosecution. In the instant application, the claims recite the following limitations that equate to an abstract idea or natural law:
Claim 1 recites the limitations - providing a reference methylation probability data set comprising estimates in the change in average methylation levels with age for each CpG site in a plurality of CpG sites; providing a filtered methylation profile of the single cell comprising a defined number of CpG sites that exhibit the greatest absolute Pearson correlation with an age in the reference methylation probability data set, wherein the CpG sites are those common between the single cell and the reference methylation probability dataset; calculating the likelihood of observing the filtered methylation profile of the single cell for a plurality of ages; and determining the age for which the likelihood is greatest among the ages in the plurality of ages to produce the epigenetic age of the single cell. Based on the broadest reasonable interpretation, providing datasets involving estimating change of averages and correlations, calculating likelihoods, determining maximum likelihoods encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the limitations rely on a natural correlation between changes in DNA methylation and time. This further draws the limitation to a law of nature.
Claim 2 recites the limitation - wherein the reference methylation probability data set comprises the estimates produced using a univariate linear model and training data. Based on the broadest reasonable interpretation, producing estimates with a linear model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 3 recites the limitation – based on the univariate models and the filtered methylation profile, the posterior probability of observing unmethylated or methylated states in a single cell for any given age is computed. Based on the broadest reasonable interpretation, computing the posterior probability encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 4 recites the limitation - wherein the training data are from bulk RRBS, WGBS, or DNAm array profiling of mammalian tissues. This limitation specifies the data provided in the judicial exception of claim 1 and modeling of claim 2. The refined limitation indicated still represents a judicial expectation.
Claim 5 recites the limitation - wherein the single cell has a sparse methylome profile. This limitation specifies the data utilized in the judicial exceptions of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 6 recites the limitation - wherein the single cell has a partial methylome profile compared to the methylome profiles used in the reference methylation probability data set. This limitation specifies the data utilized in the judicial exceptions of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 7 recites the limitation - wherein the step of determining comprises the use of bulk methylation data to train linear regression models that can predict methylation levels given exclusively age as the input. Based on the broadest reasonable interpretation, training a regression model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the limitations rely on a natural correlation between DNA methylation and time. This further draws the limitation to a law of nature.
Claim 8 recites the limitation - using a selected fraction of age-related CpGs and their associated probabilities, calculating the likelihood that a cell comes from a tissue of a certain chronological age and registering the age of maximum likelihood as an ultimate predictor of epigenetic age. Based on the broadest reasonable interpretation, calculating the likelihood encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the limitations rely on a natural correlation between DNA methylation and time. This further draws the limitation to a law of nature.
Claim 9 recites the limitation - wherein the absolute Pearson correlation is at least 0.81. This limitation specifies the data provided in the judicial exception of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 10 recites the limitation - herein the reference methylation probability data set comprises 102, 103, 104, 105, 106, or 107 or more CpG reads. This limitation specifies the data provided in the judicial exception of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 11 recites the limitation - wherein the filtered methylation profile comprises digitized methylation values. This limitation specifies the data utilized in the judicial exceptions of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 12 recites the limitation - wherein the likelihood is computed for every CpG in the filtered methylation profile based on the absolute distance between the observed methylation value and the linear regression estimate at each age step within a wide range. Based on the broadest reasonable interpretation, computing the likelihoods encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the limitations rely on a natural correlation between DNA methylation and time. This further draws the limitation to a law of nature.
Claim 13 recites the limitation - providing a reference methylation probability data set comprising estimates in the change in average methylation levels with age for each CpG site in a plurality of CpG sites, providing a filtered methylation profile of the low-pass sample comprising a defined number of CpG sites that exhibit the greatest absolute Pearson correlation with an age in the reference methylation probability data set, wherein the CpG sites are those common between the low-pass sample and the reference methylation probability dataset, calculating the likelihood of observing the filtered methylation profile of the low-pass sample for a plurality of ages, and determining the age for which the likelihood is greatest among the ages in the plurality of ages to produce the epigenetic age of the low-pass sample. Based on the broadest reasonable interpretation, providing datasets involving estimating change of averages and correlations, calculating likelihoods, determining maximum likelihoods encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the limitations rely on a natural correlation between DNA methylation and time. This further draws the limitation to a law of nature.
Claim 14 recites the limitation - wherein the reference methylation probability data set comprises the estimates produced using a univariate linear model and training data. Based on the broadest reasonable interpretation, producing estimates with a linear model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 15 recites the limitation - based on the univariate models and the filtered methylation profile, the posterior probability of observing unmethylated or methylated states in the low-pass sample for any given age is computed. Based on the broadest reasonable interpretation, computing the posterior probability encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 16 recites the limitation - wherein the training data are from bulk RRBS, WGBS, or DNAm array profiling of mammalian tissues. This limitation specifies the data provided in the judicial exception of claim 13 and modeling of claim 14. The refined limitation indicated still represents a judicial expectation.
Claim 17 recites the limitation - wherein the low-pass sample has a sparse methylome profile. This limitation specifies the data utilized in the judicial exceptions of claim 13. The refined limitation indicated still represents a judicial expectation.
Claim 18 recites the limitation - wherein the low-pass sample has a partial methylome profile compared to the methylome profiles used in the reference methylation probability data set. This limitation specifies the data utilized in the judicial exceptions of claim 13. The refined limitation indicated still represents a judicial expectation.
Claim 19 recites the limitation - wherein the step of determining comprises the use of bulk methylation data to train linear regression models that can predict methylation levels given exclusively age as the input. Based on the broadest reasonable interpretation, training a regression model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the limitations rely on a natural correlation between DNA methylation and time. This further draws the limitation to a law of nature.
Claim 20 recites the limitation - using a selected fraction of age-related CpGs and their associated probabilities, calculating the likelihood that the low- pass sample comes from a tissue of a certain chronological age and registering the age of maximum likelihood as a predictor of the epigenetic age. Based on the broadest reasonable interpretation, calculating the likelihood encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the limitations rely on a natural correlation between DNA methylation and time. This further draws the limitation to a law of nature.
Claim 21 recites the limitation - wherein the absolute Pearson correlation is at least 0.81. This limitation specifies the data provided in the judicial exception of claim 13. The refined limitation indicated still represents a judicial expectation.
Claim 22 recites the limitation - wherein the reference methylation probability data set comprises 102, 103, 104, 105, 106, or 107 or more CpG reads. This limitation specifies the data provided in the judicial exception of claim 13. The refined limitation indicated still represents a judicial expectation.
Claim 23 recites the limitation - wherein the filtered methylation profile comprises digitized methylation values. This limitation specifies the data utilized in the judicial exceptions of claim 13. The refined limitation indicated still represents a judicial expectation.
Claim 24 recites the limitation - wherein the likelihood is computed for every CpG in the filtered methylation profile based on the absolute distance between the observed methylation value and the linear regression estimate at each age step within a wide range. Based on the broadest reasonable interpretation, computing the likelihoods encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the limitations rely on a natural correlation between DNA methylation and time. This further draws the limitation to a law of nature.
Claim 25 recites the limitation - estimating the change in average methylation levels with age for each CpG site using a univariate linear model and training data from bulk RRBS or DNAm array profiling to create a reference methylation probability dataset; isolating common CpG sites between any given single-cell profile and the reference methylation probability dataset; selecting a defined number of CpGs that exhibit the greatest absolute Pearson correlation with age in the reference methylation probability dataset to create a filtered methylation profile of an individual cell; calculating the likelihood of observing this filtered methylation profile of an individual cell at any given age; and determining the age for which this likelihood is maximal, thereby creating an accurate epigenetic age metric in single cells with different and sparse methylome profiles. Based on the broadest reasonable interpretation, estimating a change using a linear model, isolating information based on probabilities, selecting based on a correlation, calculating likelihoods, and determining maximum likelihoods encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the limitations rely on a natural correlation between DNA methylation and time. This further draws the limitation to a law of nature.
Claim 26 recites the limitation - wherein bulk methylation data is used to train linear regression models that can predict methylation levels given exclusively age as the input. Based on the broadest reasonable interpretation, training a regression model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the limitations rely on a natural correlation between DNA methylation and time. This further draws the limitation to a law of nature.
Claim 27 recites the limitation - based on the univariate models and the filtered single cell methylation profile, the posterior probability of observing unmethylated or methylated states in a single cell for any given age is computed. Based on the broadest reasonable interpretation, computing the posterior probability encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 28 recites the limitations - using a selected fraction of age-related CpGs and their associated probabilities, calculating the likelihood that a cell comes from a tissue of a certain chronological age and registering the age of maximum likelihood as an ultimate predictor of epigenetic age. Based on the broadest reasonable interpretation, calculating the likelihood encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the limitations rely on a natural correlation between DNA methylation and time. This further draws the limitation to a law of nature.
Claim 29 recites the limitation - perform the method of claim 1. This recites the judicial exceptions of claims 1 (see above).
These limitations recite concepts of calculating, selecting, and determining information, values, probabilities, correlations and linear models that are so generically recited that they can be practically performed in the human mind as claimed, which falls under the “Mental processes” and “Mathematical concepts” grouping of abstract ideas. A mathematical concept need not be expressed in mathematical symbols, because words used in a claim operating on data to solve a problem can serve the same purpose as a formula (MPEP 2106.04(a)(2)). Additionally, both product claims and process claims may recite mental processes, which can include a claim that requires a computer (MPEP 2106.04(a)(2)). Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, the limitations describe and rely on natural correlations between DNA methylation and time, which fall under natural laws. This is similar to a correlation between the presence of myeloperoxidase in a bodily sample (such as blood or plasma) and cardiovascular disease risk, Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1361, 123 USPQ2d 1081, 1087 (Fed. Cir. 2017), that the courts have identified as a law of nature. As such, claims 1-29 recite an abstract idea and law of nature (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). These judicial exceptions are not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology (MPEP § 2106.04(d)(1)). Rather, the claims provide insignificant extra-solution activity (MPEP § 2106.05(g)) and provide mere instructions to apply a judicial exception (MPEP § 2106.05(f)). Specifically, the claims recite the following additional elements:
Claim 29 recites, computer-readable storage medium, computer-readable code, and a computer.
There are no limitations that indicate that the claimed calculating, selecting, and determining information, values, probabilities, correlations and linear models require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible. There is no indication that these steps are affected by the judicial exception in any way and thus do not integrate the recited judicial exception into a practical application. As such, claims 1-29 are directed to an abstract idea and natural law (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite conventional additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The claims also recite conventional additional elements that represent insignificant extra-solution activities.
As discussed above, there are no additional limitations to indicate that the claimed calculating, selecting, and determining information, values, probabilities, correlations and linear models require anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea or natural law using a generic computer do not render an abstract idea or natural law eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. As specified in MPEP 2106.05(g), extra-solution activities can be understood as incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Insignificant extra-solution activities include mere data gathering, selecting a particular data source or type of data to be manipulated, and displaying information.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, Claims 1-29 are not patent eligible.
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 4, 6-12, 25-26, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Martins et al. (US 20200190568 A1, IDS of 24 January 2024), in view of Paoli‐Iseppi et al. (2018, Molecular Ecology Resources, Vol. 19: 1-15). Italicized text from reference art.
Applicable Claims include:
Claim 1. A method of estimating an epigenetic age of a single cell from a mammalian tissue, the method comprising: (Claim 1.i) providing a reference methylation probability data set comprising estimates in the change in average methylation levels with age for each CpG site in a plurality of CpG sites, (Claim 1.ii) providing a filtered methylation profile of the single cell comprising a defined number of CpG sites that exhibit the greatest absolute Pearson correlation with an age in the reference methylation probability data set, wherein the CpG sites are those common between the single cell and the reference methylation probability dataset, (Claim 1.iii) calculating the likelihood of observing the filtered methylation profile of the single cell for a plurality of ages, and (Claim 1.iv) determining the age for which the likelihood is greatest among the ages in the plurality of ages to produce the epigenetic age of the single cell.
Claim 2. The method of claim 1, wherein the reference methylation probability data set comprises the estimates produced using a univariate linear model and training data.
Claim 4. The method of claim 2, wherein the training data are from bulk RRBS, WGBS, or DNAm array profiling of mammalian tissues.
Claim 6. The method of claim 1, wherein the single cell has a partial methylome profile compared to the methylome profiles used in the reference methylation probability data set.
Claim 7. The method of claim 1, wherein the step of determining comprises the use of bulk methylation data to train linear regression models that can predict methylation levels given exclusively age as the input.
Claim 8. The method of claim 1, further comprising, using a selected fraction of age-related CpGs and their associated probabilities, calculating the likelihood that a cell comes from a tissue of a certain chronological age and registering the age of maximum likelihood as an ultimate predictor of epigenetic age.
Claim 9. The method of claim 1, wherein the absolute Pearson correlation is at least 0.81.
Claim 10. The method of claim 1, wherein the reference methylation probability data set comprises 102, 103, 104, 105, 106, or 107 or more CpG reads.
Claim 11. The method of claim 1, wherein the filtered methylation profile comprises digitized methylation values.
Claim 12. The method of claim 1, wherein the likelihood is computed for every CpG in the filtered methylation profile based on the absolute distance between the observed methylation value and the linear regression estimate at each age step within a wide range.
Claim 25. A method of estimating epigenetic age of single cells in any mammalian tissue comprising (Claim 25.i) estimating the change in average methylation levels with age for each CpG site using a univariate linear model and training data from bulk RRBS or DNAm array profiling to create a reference methylation probability dataset, (Claim 25.ii) isolating common CpG sites between any given single-cell profile and the reference methylation probability dataset, selecting a defined number of CpGs that exhibit the greatest absolute Pearson correlation with age in the reference methylation probability dataset to create a filtered methylation profile of an individual cell, (Claim 25.iii) calculating the likelihood of observing this filtered methylation profile of an individual cell at any given age, and (Claim 25.iv) determining the age for which this likelihood is maximal, thereby creating an accurate epigenetic age metric in single cells with different and sparse methylome profiles.
Claim 26. The method of claim 25, wherein bulk methylation data is used to train linear regression models that can predict methylation levels given exclusively age as the input.
Claim 29. A computer-readable storage medium comprising computer-readable code that, when executed by a computer, causes the computer to perform the method of claim 1.
Regarding Claims 1 and 29, Martins et al. teach (Claim 1.i) providing a reference methylation probability data set comprising estimates in the change in average methylation levels with age for each CpG site in a plurality of CpG sites (Paragraph 0014: a significance of each of the 300 set of probes to serve as biomarkers related to age was validated). Martins et al. teach (Claim 1.ii) providing a filtered methylation profile of the sample comprising a defined number of CpG sites that exhibit the greatest absolute Pearson correlation with an age in the reference methylation probability data set, wherein the CpG sites are those common between the sample and the reference methylation probability dataset (Paragraph 0013: significance of the applied regression models were evaluated by computing Pearson's correlation coefficient; Paragraph 0017: identifying relevant and unique markers from the filtered markers, wherein the identification comprises carrying out correlation to classify each marker based on the association thereof to aging). Martins et al. suggest (Claim 1.iii) calculating the likelihood of observing the filtered methylation profile of the sample for a plurality of ages (Paragraph 0017: balancing the age distribution of samples from which the relevant and unique markers are obtained). The selected markers are used to estimate the age (i.e., the likelihood they indicate the predicted age). Martins et al. teach (Claim 1.iv) determining the age for which the likelihood is greatest among the ages in the plurality of ages to produce the epigenetic age of the sample (Paragraph 0020: calculates the age of the biological sample). Additionally, Martins et al. teach the utilization for single cells (Paragraph 0140: methylation sequencing includes DNA methylation profiling of single cells). Additionally, Martins et al. teach a computer readable media executed by a computer to implement the method (Paragraph 0022: the disclosure relates to computer readable media comprising computer-executable instructions, which, when executed by a processor, cause the processor to carry out a method). Claim 29 recites the method of claim 1 directed to a computer readable medium.
Regarding Claim 2, Martins et al. teach the reference methylation probability data set comprises the estimates produced using a univariate linear model and training data (Paragraph 0012: processing of the raw data of each dataset to generate a set of probes with methylation levels. estimate the importance based on three different methodologies: glmnet-lasso).
Regarding Claim 4, Martins et al. teach the training data are from bulk RRBS, WGBS, or DNAm array profiling of mammalian tissues (Paragraph 0036: methylation array analysis of the genomic DNA).
Regarding Claim 6, Martins et al. teach the single cell has a partial methylome profile compared to the methylome profiles used in the reference methylation probability data set (Paragraph 0012: these datasets were merged, preprocessed, normalized, age-balanced and divided in training subset and testing subsets). Testing dataset was subset of training dataset (i.e. subset is equivalent to partial).
Regarding Claim 7 and 26, The method of claim 1, wherein
Martins et al. teach the step of determining comprises the use of bulk methylation data to train linear regression models that can predict methylation levels given exclusively age as the input (Paragraph 0015: demonstrating a specific and robust association between the markers of the disclosure and age and high prediction accuracy; Paragraph 0033: application of a regression analysis comprising glmnet-lasso; Paragraph 0025: predicting aging or an age-related disease in a subject). Quantifying the relationship between methylation and age is essential to the predictive modeling of Martins et al.
Regarding Claim 8, Martins et al. teach using a selected fraction of age-related CpGs and their associated probabilities, calculating the likelihood that a cell comes from a tissue of a certain chronological age and registering the age of maximum likelihood as an ultimate predictor of epigenetic age (Paragraph 0015: the difference between the chronological age and the predicted age as determined by the disclosure; Paragraph 0020: calculates the age of the biological sample; Paragraph 0038: calculating an age of a tissue specific biological sample). The age predicted is assumed to be the ultimate one (see 112(b) above). Using a selected fraction of age-related CpGs is equivalent to the filtering of claim 1.
Regarding Claim 9, Martins et al. teach the absolute Pearson correlation is at least 0.81 (Paragraph 0015: the correlation coefficient between sample age and methylation status at the external dataset of skin biopsies was about 0.96).
Regarding Claim 10, Martins et al. teach the reference methylation probability data set comprises 102, 103, 104, 105, 106, or 107 or more CpG reads (Paragraph 0012: About 508 samples were used, each sample had more than 450,000 CpG/probes/features).
Regarding Claim 11, Martins et al. teach the filtered methylation profile comprises digitized methylation values (Paragraph 0138: Wherein the “sequence” is provided and/or received in digital form). The methods are on a computer. Therefore, the values are digitized.
Regarding Claim 12, Martins et al. teach the likelihood is computed for every CpG in the filtered methylation profile based on the absolute distance between the observed methylation value and the linear regression estimate at each age step within a wide range (Paragraph 0013: the age-calculating or age-predicting algorithm of the present disclosure was developed. Machine Learning (ML) algorithms were applied, in each case, a 50 fold resampling cross-validation was used for optimization of the tuning parameters. Model prediction errors were computed using mean absolute error (MAE) and/or root mean squared error (RMSE)). Utilizing the absolute distance between the observed methylation value and the regression estimate was part of optimizing the regression analysis and is part of linear regression (i.e. mean squared error (MSE)).
Regarding Claim 25, Martins et al. teach (Claim 25.i) estimating the change in average methylation levels with age for each CpG site using a univariate linear model and training data from bulk RRBS or DNAm array profiling to create a reference methylation probability dataset (Paragraph 0014: a significance of each of the 300 set of probes to serve as biomarkers related to age was validated). Martins et al. teach (Claim 25.ii) isolating common CpG sites between any given single-cell profile and the reference methylation probability dataset, selecting a defined number of CpGs that exhibit the greatest absolute Pearson correlation with age in the reference methylation probability dataset to create a filtered methylation profile of an individual cell (Paragraph 0013: significance of the applied regression models were evaluated by computing Pearson's correlation coefficient; Paragraph 0017: identifying relevant and unique markers from the filtered markers, wherein the identification comprises carrying out correlation to classify each marker based on the association thereof to aging). Martins et al. suggest (Claim 25.iii) calculating the likelihood of observing this filtered methylation profile of an individual cell at any given age (Paragraph 0017: balancing the age distribution of samples from which the relevant and unique markers are obtained). The selected markers are used to estimate the age (i.e., the likelihood they indicate the predicted age). Martins et al. teach (Claim 25.iv) determining the age for which this likelihood is maximal, thereby creating an accurate epigenetic age metric in single cells with different and sparse methylome profiles (Paragraph 0020: calculates the age of the biological sample). Additionally, Martins et al. teach the utilization for single cells (Paragraph 0140: methylation sequencing includes DNA methylation profiling of single cells).
Martins et al. does not explicitly teach calculating the likelihoods between the methylation levels and age (Claim 1.iii and 25.iii). Paoli‐Iseppi et al. teach additional methods of relating methylation and epigenetic age related to Martins et al.
Paoli‐Iseppi et al. teach (Claim 1.iii and 25.iii) calculating the likelihood of observing the filtered methylation profile of a sample for a plurality of ages (Page 4, Column 2, Paragraph 1: Individual markers were inspected visually using simple linear regression, and markers that had an R2 < 0.2 or showed small changes in DNAm range (<15%) were removed). R2 were calculated are related to the likelihood that the methylation profile is a function of the age distribution.
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Paoli‐Iseppi et al. with Martins et al. Paoli‐Iseppi et al. teach methods of methylation investigation which enhanced efficiency that expands it applicability bisulfite sequencing and other conventional techniques (Page 10, Column 2, Paragraph 1: Reduced representation bisulphite sequencing can also be used to quantify CpG DNAm, but does require a higher quantity of initial genomic DNA. Our results now show that the DREAM method can also be used to quantify global DNAm and screen for aDMPs in non-model animals), which is a focus of Martins et al. and the instant application. Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because both are within the same technical field – generating and utilizing methylation data to investigate biological organisms.
Claims 1-29 are rejected under 35 U.S.C. 103 as being unpatentable over Martins et al., in view of Paoli‐Iseppi et al., as applied to claims 1-2, 4, 6-12, 25-26, and 29 above, and in further view of Clark et al. (2018, Nature Communications, Vol. 9: 1-34, with Supplemental Information). Italicized text from reference art.
Applicable Claims include:
Claims 1-2, 4, 6-12, 25-26, and 29 are presented above.
Claim 3. The method of claim 2, wherein, based on the univariate models and the filtered methylation profile, the posterior probability of observing unmethylated or methylated states in a single cell for any given age is computed.
Claim 5. The method of claim 1, wherein the single cell has a sparse methylome profile.
Claim 13. A method of estimating an epigenetic age for a low-pass sample, the method comprising: (Claim 13.i) providing a reference methylation probability data set comprising estimates in the change in average methylation levels with age for each CpG site in a plurality of CpG sites, (Claim 13.ii) providing a filtered methylation profile of the low-pass sample comprising a defined number of CpG sites that exhibit the greatest absolute Pearson correlation with an age in the reference methylation probability data set, wherein the CpG sites are those common between the low-pass sample and the reference methylation probability dataset, (Claim 13.iii) calculating the likelihood of observing the filtered methylation profile of the low-pass sample for a plurality of ages, and (Claim 13.iv) determining the age for which the likelihood is greatest among the ages in the plurality of ages to produce the epigenetic age of the low-pass sample.
Claim 14. The method of claim 13, wherein the reference methylation probability data set comprises the estimates produced using a univariate linear model and training data.
Claim 15. The method of claim 14, wherein, based on the univariate models and the filtered methylation profile, the posterior probability of observing unmethylated or methylated states in the low-pass sample for any given age is computed.
Claim 16. The method of claim 14, wherein the training data are from bulk RRBS, WGBS, or DNAm array profiling of mammalian tissues.
Claim 17. The method of claim 13, wherein the low-pass sample has a sparse methylome profile.
Claim 18. The method of claim 13, wherein the low-pass sample has a partial methylome profile compared to the methylome profiles used in the reference methylation probability data set.
Claim 19. The method of claim 13, wherein the step of determining comprises the use of bulk methylation data to train linear regression models that can predict methylation levels given exclusively age as the input.
Claim 20. The method of claim 13, further comprising, using a selected fraction of age-related CpGs and their associated probabilities, calculating the likelihood that the low- pass sample comes from a tissue of a certain chronological age and registering the age of maximum likelihood as a predictor of the epigenetic age.
Claim 21. The method of claim 13, wherein the absolute Pearson correlation is at least 0.81.
Claim 22. The method of claim 13, wherein the reference methylation probability data set comprises 102, 103, 104, 105, 106, or 107 or more CpG reads.
Claim 23. The method of claim 13, wherein the filtered methylation profile comprises digitized methylation values.
Claim 24. The method of claim 13, wherein the likelihood is computed for every CpG in the filtered methylation profile based on the absolute distance between the observed methylation value and the linear regression estimate at each age step within a wide range.
Claim 27. The method of claim 26, wherein, based on the univariate models and the filtered single cell methylation profile, the posterior probability of observing unmethylated or methylated states in a single cell for any given age is computed.
Claim 28. The method of claim 27, further comprising, using a selected fraction of age-related CpGs and their associated probabilities, calculating the likelihood that a cell comes from a tissue of a certain chronological age and registering the age of maximum likelihood as an ultimate predictor of epigenetic age.
Regarding Claims 1, 13, and 29, Martins et al. teach (Claim 1.i) providing a reference methylation probability data set comprising estimates in the change in average methylation levels with age for each CpG site in a plurality of CpG sites (Paragraph 0014: a significance of each of the 300 set of probes to serve as biomarkers related to age was validated). Martins et al. teach (Claim 1.ii) providing a filtered methylation profile of the sample comprising a defined number of CpG sites that exhibit the greatest absolute Pearson correlation with an age in the reference methylation probability data set, wherein the CpG sites are those common between the sample and the reference methylation probability dataset (Paragraph 0013: significance of the applied regression models were evaluated by computing Pearson's correlation coefficient; Paragraph 0017: identifying relevant and unique markers from the filtered markers, wherein the identification comprises carrying out correlation to classify each marker based on the association thereof to aging). Martins et al. suggest (Claim 1.iii) calculating the likelihood of observing the filtered methylation profile of the sample for a plurality of ages (Paragraph 0017: balancing the age distribution of samples from which the relevant and unique markers are obtained). The selected markers are used to estimate the age (i.e., the likelihood they indicate the predicted age). Martins et al. teach (Claim 1.iv) determining the age for which the likelihood is greatest among the ages in the plurality of ages to produce the epigenetic age of the sample (Paragraph 0020: calculates the age of the biological sample). Additionally, Martins et al. teach the utilization for single cells (Paragraph 0140: methylation sequencing includes DNA methylation profiling of single cells). Additionally, Martins et al. teach a computer readable media executed by a computer to implement the method (Paragraph 0022: the disclosure relates to computer readable media comprising computer-executable instructions, which, when executed by a processor, cause the processor to carry out a method). Claims 13 and 29 recites the method of claim 1 directed to a different sample type and computer readable medium.
Regarding Claim 2 and 14, Martins et al. teach the reference methylation probability data set comprises the estimates produced using a univariate linear model and training data (Paragraph 0012: processing of the raw data of each dataset to generate a set of probes with methylation levels. estimate the importance based on three different methodologies: glmnet-lasso).
Regarding Claim 4 and 16, Martins et al. teach the training data are from bulk RRBS, WGBS, or DNAm array profiling of mammalian tissues (Paragraph 0036: methylation array analysis of the genomic DNA).
Regarding Claim 6 and 18, Martins et al. teach the single cell has a partial methylome profile compared to the methylome profiles used in the reference methylation probability data set (Paragraph 0012: these datasets were merged, preprocessed, normalized, age-balanced and divided in training subset and testing subsets). Testing dataset was subset of training dataset (i.e. subset is equivalent to partial).
Regarding Claim 7, 19, and 26, Martins et al. teach the step of determining comprises the use of bulk methylation data to train linear regression models that can predict methylation levels given exclusively age as the input (Paragraph 0015: demonstrating a specific and robust association between the markers of the disclosure and age and high prediction accuracy; Paragraph 0033: application of a regression analysis comprising glmnet-lasso; Paragraph 0025: predicting aging or an age-related disease in a subject). Quantifying the relationship between methylation and age is essential to the predictive modeling of Martins et al.
Regarding Claim 8, 20, and 28, Martins et al. teach using a selected fraction of age-related CpGs and their associated probabilities, calculating the likelihood that a cell comes from a tissue of a certain chronological age and registering the age of maximum likelihood as an ultimate predictor of epigenetic age (Paragraph 0015: the difference between the chronological age and the predicted age as determined by the disclosure; Paragraph 0020: calculates the age of the biological sample; Paragraph 0038: calculating an age of a tissue specific biological sample). The age predicted is assumed to be the ultimate one (see 112(b) above). Using a selected fraction of age-related CpGs is equivalent to the filtering of claim 1.
Regarding Claim 9 and 21, Martins et al. teach the absolute Pearson correlation is at least 0.81 (Paragraph 0015: the correlation coefficient between sample age and methylation status at the external dataset of skin biopsies was about 0.96).
Regarding Claim 10 and 22, Martins et al. teach the reference methylation probability data set comprises 102, 103, 104, 105, 106, or 107 or more CpG reads (Paragraph 0012: About 508 samples were used, each sample had more than 450,000 CpG/probes/features).
Regarding Claim 11 and 23, Martins et al. teach the filtered methylation profile comprises digitized methylation values (Paragraph 0138: Wherein the “sequence” is provided and/or received in digital form). The methods are on a computer. Therefore, the values are digitized.
Regarding Claim 12 and 24, Martins et al. teach the likelihood is computed for every CpG in the filtered methylation profile based on the absolute distance between the observed methylation value and the linear regression estimate at each age step within a wide range (Paragraph 0013: the age-calculating or age-predicting algorithm of the present disclosure was developed. Machine Learning (ML) algorithms were applied, in each case, a 50 fold resampling cross-validation was used for optimization of the tuning parameters. Model prediction errors were computed using mean absolute error (MAE) and/or root mean squared error (RMSE)). Utilizing the absolute distance between the observed methylation value and the regression estimate was part of optimizing the regression analysis and is part of linear regression (i.e. mean squared error (MSE)).
Regarding Claim 25, Martins et al. teach (Claim 25.i) estimating the change in average methylation levels with age for each CpG site using a univariate linear model and training data from bulk RRBS or DNAm array profiling to create a reference methylation probability dataset (Paragraph 0014: a significance of each of the 300 set of probes to serve as biomarkers related to age was validated). Martins et al. teach (Claim 25.ii) isolating common CpG sites between any given single-cell profile and the reference methylation probability dataset, selecting a defined number of CpGs that exhibit the greatest absolute Pearson correlation with age in the reference methylation probability dataset to create a filtered methylation profile of an individual cell (Paragraph 0013: significance of the applied regression models were evaluated by computing Pearson's correlation coefficient; Paragraph 0017: identifying relevant and unique markers from the filtered markers, wherein the identification comprises carrying out correlation to classify each marker based on the association thereof to aging). Martins et al. suggest (Claim 25.iii) calculating the likelihood of observing this filtered methylation profile of an individual cell at any given age (Paragraph 0017: balancing the age distribution of samples from which the relevant and unique markers are obtained). The selected markers are used to estimate the age (i.e., the likelihood they indicate the predicted age). Martins et al. teach (Claim 25.iv) determining the age for which this likelihood is maximal, thereby creating an accurate epigenetic age metric in single cells with different and sparse methylome profiles (Paragraph 0020: calculates the age of the biological sample). Additionally, Martins et al. teach the utilization for single cells (Paragraph 0140: methylation sequencing includes DNA methylation profiling of single cells).
Martins et al. does not explicitly teach calculating the likelihoods between the methylation levels and age (Claim 1.iii and 25.iii). Martins et al. does not explicitly teach the methods utilizing low pass samples (Claims 13-24). Martins et al. does not explicitly teach posterior probability (Claims 3, 15, and 27). Paoli‐Iseppi et al. teach additional methods of relating methylation and epigenetic age related to Martins et al. (see rational to combine above).
Paoli‐Iseppi et al. teach (Claim 1.iii and 25.iii) calculating the likelihood of observing the filtered methylation profile of the sample for a plurality of ages (Page 4, Column 2, Paragraph 1: Individual markers were inspected visually using simple linear regression, and markers that had an R2 < 0.2 or showed small changes in DNAm range (<15%) were removed). R2 were calculated are related to the likelihood that the methylation profile is a function of the age distribution.
Martins et al. and Paoli‐Iseppi et al. do not explicitly teach the methods utilizing low pass samples (Claims 13-24) or posterior probability (Claims 3, 15, and 27). Clark et al. teach additional methods of relating methylation and epigenetic age related to Martins et al. and Paoli‐Iseppi et al.
Regarding Claims 1, 13, and 29, Clark et al. teach utilizing a low-pass sample (Page 2, Column 2, Paragraph 2: We also compared the methylation coverage to data from our previous BS-seq protocols that did not incorporate a DNA accessibility component, again finding only small differences in coverage, albeit these became more pronounced when down-sampling the total sequence coverage). Based on Paragraph 0044 of the published specification (i.e. modified bisulfite sequencing combined with down sampling).
Regarding Claim 2 and 14, Clark et al. teach the reference methylation probability data set comprises the estimates produced using a univariate linear model and training data (Page 8, Column 1, Paragraph 7: for each cell, we used the fitted values as input features to a regression model with the gene expression levels as the response variable).
Regarding Claim 3, 15, and 27, Clark et al. teach based on the univariate models and the filtered methylation data, the posterior probability of observing unmethylated or methylated states in a single cell is computed (Page 8, Column 1, Paragraph 3: calculated by maximum a posteriori assuming a beta prior distribution).
Regarding Claim 5 and 17, Clark et al. teach the single cell has a sparse methylome profile (Page 17, Supp Fig 7: coverage of scNMT-seq data, which is sparse).
Regarding Claim 11 and 23, Clark et al. teach the filtered methylation profile comprises digitized methylation values (Page 8, Column 1, Paragraph 3: modelled using a binomial model).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Clark et al. with Martins et al. and Paoli‐Iseppi et al. Clark et al. teach their methods enhance and extend studying epigenetic changes through methylation data (Page 2, Column 1, Paragraph 2: Importantly, this implies that the coverage is not influenced by the overall accessibility, so lowly accessible sites will not suffer from increased technical variation compared to highly accessible sites). Clark et al. teach their methods are well suited for studying methylation in single cells (Page 2, Column 1, Paragraph 2: NOMe-seq is particularly attractive for single-cell applications). Clark et al. suggest utilizing their methods to study changes over time (Page 5, Column 2, Paragraph 3: our method can be used to dissect the dynamics of epigenome interactions during a developmental trajectory). The teachings and suggestions of Clark et al. are also major focuses of Martins et al., Paoli‐Iseppi et al., and the instant application. Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because all are within the same technical field – utilizing methylation data to assess epigenetic differences.
Claims 1-29 are rejected under 35 U.S.C. 103 as being unpatentable over Martins et al., in view of Paoli‐Iseppi et al., and in further view of Clark et al., as applied to claims 1-29 above, and in further view of Farlik et al. (2015, Cell Reports, Vol. 10: 1-22). Italicized text from reference art.
Regarding Claims 1-29, these limitations are taught by Martins et al., Paoli‐Iseppi et al., and Clark et al. as above.
Regarding Claims 13-24, Farlik et al. further suggests using shallow sequencing techniques for samples assessing changes epigenetic changes with methylation (Page 3, Column 2, Paragraph 4: We thus concluded that it is more cost-effective to sequence many one-cell and four-cell samples at low coverage rather than sequencing relatively few of these samples to saturation, and we show below that relatively shallow sequencing can be sufficient for analyzing epigenomic cell-state dynamics). Shallow sequencing generates low-pass samples (low read depth).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Farlik et al. with Clark et al., Paoli‐Iseppi et al., and Martins et al. Farlik et al. teach their methods are more cost effective for studying epigenetic changes through methylation data, including for studying single cells (Page 3, Column 2, Paragraph 4: We thus concluded that it is more cost-effective to sequence many one-cell and four-cell samples at low coverage rather than sequencing relatively few of these samples to saturation, and we show below that relatively shallow sequencing can be sufficient for analyzing epigenomic cell-state dynamics). Farlik et al. suggest utilizing their methods to study changes over time (Page 10, Column 1, Paragraph 2: our method is able to place single cells on biologically interpretable time-course trajectories, even in cases where changes in DNA methylation are locus specific and non-linear). The teachings and suggestions of Farlik et al. are also major focuses of Clark et al., Martins et al., Paoli‐Iseppi et al., and the instant application. Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because all are within the same technical field – utilizing methylation data to assess epigenetic differences.
Double Patenting
No double patenting rejection is issued.
Conclusion
No claims are allowed.
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/B.H.E./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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