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 .
Status of the Claims
Claims 1-10, 13, 14, 16-19, 23, 32, 62 are currently pending and under exam herein.
Claims 1-10, 13, 14, 16-19, 23, 32, 62 are rejected.
Priority
The instant application claims priority from provisional 63/392,980 filed on 7/28/2022. Thus, the effective filing date of the instant application is 7/28/2022.
Drawings
The Drawings filed on 7/28/2023 were considered.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 09/19/2023 and 01/30/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
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.
Claims 1-10, 13, 14, 16-19, 23, 32, 62 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-10, 13, 14, 16-19, 23, 32, 62 are directed to a methylation-based age prediction as feature for cancer classification
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if
so, then it is determined in Prong Two whether the recited judicial exception is integrated into a
practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception,
examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a
law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
for each genomic region of a plurality of genomic regions, identifying nucleic acid fragments from the plurality having genomic locations overlapping the genomic region, and (mental process)
calculating, for the genomic region, an indicativeness score representing a correlation between chronological age and methylation patterns, and calculated based on chronological ages of individuals from whom the identified nucleic acid fragments are derived and methylation patterns of identified nucleic acid fragments; (mathematical concept)
generating a feature set comprising one or more genomic regions of the plurality of genomic regions, the one or more genomic regions in the feature set having indicativeness scores above a threshold; and . (mathematical concept and/or mental process)
training a machine-learned age-prediction model to determine a predicted chronological age of a tested individual from whom a test sample is derived, the training based on methylation patterns of nucleic acid fragments in the plurality of training samples overlapping the one or more genomic regions in the feature set. (mathematical concept)
Dependent claim 2 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
training a linear regression for each genomic region of the feature set based on the methylation patterns of the nucleic acid fragments overlapping each genomic region from training samples of the plurality labelled as non-cancer; (mathematical concept)
Dependent claim 3 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
applying the trained age-prediction model to determine a predicted chronological age of the test subject from whom the test sample was derived based on methylation patterns of the additional nucleic acid fragments overlapping the one or more genomic regions in the feature set; (mathematical concept)
calculating an age residual as a difference between the labelled chronological age and the predicted chronological age of the test subject; and (mathematical concept)
determining that the test sample has a strong likelihood for presence of cancer in response to determining that the age residual is above a residual threshold. (mental process and/or mathematical concept)
Dependent claim 4 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
applying the trained age-prediction model to a second plurality of training samples identified as non-cancer to determine a predicted age for each of the second plurality of training samples; (mathematical concept)
calculating an age residual for each of the second plurality of training samples by comparing the predicted age to a labelled chronological age of the second plurality of training samples; and (mathematical concept)
identifying the residual threshold based on the calculated age residuals for the second plurality of training samples, wherein at least a majority of the calculated age residuals for the second plurality of training samples satisfy the residual threshold. (mental process and/or mathematical concept)
Dependent claim 5 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
in response to determining that the test sample has the strong likelihood for presence of cancer: filtering the methylation patterns of the plurality of additional nucleic acid fragments with p-value filtering to identify a set of anomalous methylation patterns; (mental process and/or mathematical concept)
generating a feature vector for the test sample based on the age residual and the set of anomalous methylation patterns; and (mental process and/or mathematical concept)
determining a cancer prediction for the test sample by inputting the feature vector into a trained cancer classifier. (mental process and/or mathematical concept)
Dependent claim 6 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the cancer prediction is a binary prediction between presence and absence of cancer or another disease state. (mathematical concept)
Dependent claim 7 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the cancer prediction is a multiclass prediction between a plurality of cancer types. (mathematical concept)
Dependent claim 8 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the cancer prediction is a multiclass prediction between a plurality of disease states. (mathematical concept)
Dependent claim 9 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
determining a presence of cancer in the test sample using a secondary machine-learned cancer classifier, the secondary cancer classifier configured to receive as input the predicted chronological age of the subject and methylation patterns of the plurality of additional nucleic acid fragments and output a prediction of the presence of cancer in the test sample. (mathematical concept)
Dependent claim 10 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the secondary machine-learned cancer classifier is further configured to receive as input clinical information and genetic background of the subject and output the prediction of the presence of cancer in the test sample. (mathematical concept)
Dependent claim 13 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the indicativeness score is determined by training a linear regression to regress chronological age from methylation density of non-cancer training samples, wherein methylation density is calculated as a percentage of nucleic acid fragments having genomic locations which overlap a particular genomic region having a methylated state in that particular genomic region (mathematical concept)
Dependent claim 14 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the machine-learned age-prediction model comprises a multivariate regression. (mathematical concept)
Dependent claim 16 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the machine-learned age-prediction model receives as input a methylation density corresponding to each of the genomic regions in the feature set. (mathematical concept)
Dependent claim 17 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein a number of the one or more genomic regions in the feature set is selected from a range of 5-10,000. (mathematical concept)
Dependent claim 23 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein each training sample is labeled with a sex or a smoking status of the individual from whom the training sample is derived, and comprising: (mathematical concept and/or mathematical process)
calculating, for the genomic region, an additional indicativeness score representing a correlation between sex or smoking status and methylation patterns, and (mathematical concept)
training a machine-learned characteristic prediction model to determine a predicted sex or smoking status of a tested individual from whom a test sample is derived. (mathematical concept)
Independent claim 32 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
for each genomic region of a plurality of genomic regions, identify nucleic acid fragments from the plurality having genomic locations overlapping the genomic region, and (mental process)
calculate, for the genomic region, an indicativeness score representing a correlation between chronological age and methylation patterns, and calculated based on chronological ages of individuals from whom the identified nucleic acid fragments are derived and methylation patterns of identified nucleic acid fragments; (mathematical concept)
generate a feature set comprising one or more genomic regions of the plurality of genomic regions, the one or more genomic regions in the feature set having indicativeness scores above a threshold; and (mathematical concept and/or mental process)
train a machine-learned age-prediction model to determine a predicted chronological age of a tested individual from whom a test sample is derived, the training based on methylation patterns of nucleic acid fragments in the plurality of training samples overlapping the one or more genomic regions in the feature set. (mathematical concept)
Independent claim 63 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
for each genomic region of a plurality of genomic regions, identify nucleic acid fragments from the plurality having genomic locations overlapping the genomic region, and (mental process)
calculate, for the genomic region, an indicativeness score representing a correlation between chronological age and methylation patterns, and calculated based on chronological ages of individuals from whom the identified nucleic acid fragments are derived and methylation patterns of identified nucleic acid fragments; ; (mathematical concept)
generate a feature set comprising one or more genomic regions of the plurality of genomic regions, the one or more genomic regions in the feature set having indicativeness scores above a threshold; and (mathematical concept and/or mental process)
train a machine-learned age-prediction model to determine a predicted chronological age of a tested individual from whom a test sample is derived, the training based on methylation patterns of nucleic acid fragments in the plurality of training samples overlapping the one or more genomic regions in the feature set. . (mathematical concept)
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-10, 13, 14, 16-19, 23, 32, 62 recite an abstract idea as the dependent claims will inherit the abstract ideas from the independent claims.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further
examination is performed that analyzes if the claim recites additional elements that when examined as a
whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that
integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception
in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements
are analyzed to determine if the abstract idea is integrated into a practical application (MPEP
2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract
idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
The additional element in independent claim 1 includes:
A method comprising:
obtaining a plurality of training samples, each training sample: comprising a plurality of nucleic acid fragments, each of the plurality of nucleic acid fragments having a genomic location overlapping at least one genomic region of a plurality of genomic regions, and labelled with a chronological age of an individual from whom the training sample is derived;
sequencing the plurality of nucleic acid fragments for each training sample to identify a methylation pattern for each nucleic acid fragment;
The additional element in dependent claim 2 includes:
obtaining a plurality of additional training samples, each additional training sample: comprising a plurality of additional nucleic acid fragments having additional genomic locations overlapping at least one genomic region of the plurality of genomic regions, labelled with a chronological age of an individual from whom the additional training sample was derived, and labelled as non-cancer or cancer based on a previous determination of cancer presence in the additional training sample;
sequencing the plurality of additional nucleic acid fragments to identify a methylation pattern for each additional nucleic acid fragment;
The additional element in dependent claim 3 includes:
obtaining a test sample, the test sample comprising a plurality of additional nucleic acid fragments and labelled with a chronological age of a test subject from whom the test sample is derived;
sequencing the plurality of additional nucleic acid fragments for the test sample to identify methylation patterns for the additional nucleic acid fragments of the plurality;
The additional element in dependent claim 18 includes:
wherein sequencing the nucleic acid fragments comprises whole genome bisulfite sequencing (WGBS).
The additional element in dependent claim 19 includes:
wherein sequencing the nucleic acid fragments comprises targeted sequencing.
The additional element in independent claim 32 includes:
A non-transitory computer readable storage medium comprising computer program instructions that, when executed by one or more processors, cause the one or more processors to:
obtain a plurality of training samples, each training sample: comprising a plurality of nucleic acid fragments, each of the plurality of nucleic acid fragments having a genomic location overlapping at least one genomic region of a plurality of genomic regions, and labelled with a chronological age of an individual from whom the training sample is derived;
sequence the plurality of nucleic acid fragments for each training sample to identify a methylation pattern for each nucleic acid fragment;
The additional element in independent claim 63 includes:
A system comprising: one or more processors; a non-transitory computer readable storage medium storing computer program instructions that, when executed by the one or more processors, cause the one or more processors to:
obtain a plurality of training samples, each training sample: comprising a plurality of nucleic acid fragments, each of the plurality of nucleic acid fragments having a genomic location overlapping at least one genomic region of a plurality of genomic regions, and labelled with a chronological age of an individual from whom the training sample is derived;
sequence the plurality of nucleic acid fragments for each training sample to identify a methylation pattern for each nucleic acid fragment;
The additional elements of obtaining a plurality of training samples, each training sample: comprising a plurality of nucleic acid fragments, each of the plurality of nucleic acid fragments having a genomic location overlapping at least one genomic region of a plurality of genomic regions, and labelled with a chronological age of an individual from whom the training sample is derived (claim 1), sequencing the plurality of nucleic acid fragments for each training sample to identify a methylation pattern for each nucleic acid fragment (Claim 1), obtaining a plurality of additional training samples, each additional training sample: comprising a plurality of additional nucleic acid fragments having additional genomic locations overlapping at least one genomic region of the plurality of genomic regions, labelled with a chronological age of an individual from whom the additional training sample was derived, and labelled as non-cancer or cancer based on a previous determination of cancer presence in the additional training sample; (claim 20, sequencing the plurality of additional nucleic acid fragments to identify a methylation pattern for each additional nucleic acid fragment; (Claim 2), obtaining a test sample, the test sample comprising a plurality of additional nucleic acid fragments and labelled with a chronological age of a test subject from whom the test sample is derived; (claim 3), sequencing the plurality of additional nucleic acid fragments for the test sample to identify methylation patterns for the additional nucleic acid fragments of the plurality; (Claim 3), wherein sequencing the nucleic acid fragments comprises whole genome bisulfite sequencing (WGBS) (Claim 18), wherein sequencing the nucleic acid fragments comprises targeted sequencing. (Claim 19), obtain a plurality of training samples, each training sample: comprising a plurality of nucleic acid fragments, each of the plurality of nucleic acid fragments having a genomic location overlapping at least one genomic region of a plurality of genomic regions, and labelled with a chronological age of an individual from whom the training sample is derived; (Claim 32), sequence the plurality of nucleic acid fragments for each training sample to identify a methylation pattern for each nucleic acid fragment; (Claim 32), obtain a plurality of training samples, each training sample: comprising a plurality of nucleic acid fragments, each of the plurality of nucleic acid fragments having a genomic location overlapping at least one genomic region of a plurality of genomic regions, and labelled with a chronological age of an individual from whom the training sample is derived; (Claim 63), sequence the plurality of nucleic acid fragments for each training sample to identify a methylation pattern for each nucleic acid fragment; (Claim 63) are insignificant extra-solution activity that are part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)).
The additional elements of A method comprising (Claim 1, A non-transitory computer readable storage medium comprising computer program instructions that, when executed by one or more processors, cause the one or more processors to: (Claim 32), A system comprising: one or more processors; a non-transitory computer readable storage medium storing computer program instructions that, when executed by the one or more processors, cause the one or more processors to: (Claim 63) fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
The additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-10, 13, 14, 16-19, 23, 32, 62 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-10, 13, 14, 16-19, 23, 32, 62 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
The additional elements recited in claims 1-10, 13, 14, 16-19, 23, 32, 62 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of obtaining a plurality of training samples, each training sample: comprising a plurality of nucleic acid fragments, each of the plurality of nucleic acid fragments having a genomic location overlapping at least one genomic region of a plurality of genomic regions, and labelled with a chronological age of an individual from whom the training sample is derived (claim 1), sequencing the plurality of nucleic acid fragments for each training sample to identify a methylation pattern for each nucleic acid fragment (Claim 1), obtaining a plurality of additional training samples, each additional training sample: comprising a plurality of additional nucleic acid fragments having additional genomic locations overlapping at least one genomic region of the plurality of genomic regions, labelled with a chronological age of an individual from whom the additional training sample was derived, and labelled as non-cancer or cancer based on a previous determination of cancer presence in the additional training sample; (claim 20, sequencing the plurality of additional nucleic acid fragments to identify a methylation pattern for each additional nucleic acid fragment; (Claim 2), obtaining a test sample, the test sample comprising a plurality of additional nucleic acid fragments and labelled with a chronological age of a test subject from whom the test sample is derived; (claim 3), sequencing the plurality of additional nucleic acid fragments for the test sample to identify methylation patterns for the additional nucleic acid fragments of the plurality; (Claim 3), wherein sequencing the nucleic acid fragments comprises whole genome bisulfite sequencing (WGBS) (Claim 18), wherein sequencing the nucleic acid fragments comprises targeted sequencing. (Claim 19), obtain a plurality of training samples, each training sample: comprising a plurality of nucleic acid fragments, each of the plurality of nucleic acid fragments having a genomic location overlapping at least one genomic region of a plurality of genomic regions, and labelled with a chronological age of an individual from whom the training sample is derived; (Claim 32), sequence the plurality of nucleic acid fragments for each training sample to identify a methylation pattern for each nucleic acid fragment; (Claim 32), obtain a plurality of training samples, each training sample: comprising a plurality of nucleic acid fragments, each of the plurality of nucleic acid fragments having a genomic location overlapping at least one genomic region of a plurality of genomic regions, and labelled with a chronological age of an individual from whom the training sample is derived; (Claim 63), sequence the plurality of nucleic acid fragments for each training sample to identify a methylation pattern for each nucleic acid fragment; (Claim 63) are conventional and part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)). Evidence for US20190287652A1 which uses whole genome bisulfite sequencing and Vidaki et al. which discusses different sequencing techniques and how to determine methylation on DNA.
The additional elements of A method comprising (Claim 1), A non-transitory computer readable storage medium comprising computer program instructions that, when executed by one or more processors, cause the one or more processors to: (Claim 32), A system comprising: one or more processors; a non-transitory computer readable storage medium storing computer program instructions that, when executed by the one or more processors, cause the one or more processors to: (Claim 63) are conventional fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
When taken alone, all additional elements in claims 1-10, 13, 14, 16-19, 23, 32, 62 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-10, 13, 14, 16-19, 23, 32, 62 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Claim Rejections - 35 USC § 103
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.
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 1-10, 13, 14, 16-19, 23, 32, 62, are rejected under 35 U.S.C. 103 as being unpatentable over Vidaki et al. (Vidaki et al. DNA Methylation-Based Forensic Age Prediction Using Artificial Neural Networks and next Generation Sequencing. Forensic Science International: Genetics 2017, 28, 225–236.) in further view of Levine et al. (Levine, M. E.; Hosgood, H. D.; Chen, B.; Absher, D.; Assimes, T.; Horvath, S. DNA Methylation Age of Blood Predicts Future Onset of Lung Cancer in the Women’s Health Initiative. Aging, 2015, 7, 690–700.) in view of US20190287652A1 in further view of Bollepalli et al. (Bollepalli et al. EpiSmokEr: A robust classifier to determine smoking status from DNA methylation data, Arxiv, December 6, 2018). The italicized text corresponds to the instant claim limitations.
With respect to the limitations of Claims 1, 2, 3, 13, 14, 16, 17, 19, 32, 63, Vidaki et al. teachesGenome-wide profiling has led to a more comprehensive understanding of gene regulation epigenetic mechanisms. Illumina’s Human Methylation BeadChip technology is one of the most commonly used genome-wide methylation platforms that allows for simultaneous measurement of the methylation status of 27,578 (27 K chip) or 482,421 (450 K chip) CpG sites in the genome at single nucleotide resolution. Thousands of samples have been assayed using this platform in the literature and researchers have made some of these genome-wide methylation data available in online databases such as the National Center for Biotechnology Information Gene Expression Omnibus (GEO).
In order to build the age prediction model, data from a total of 1156 whole blood samples were collected from individuals aged between 2 and 90 years old and from various ethnic backgrounds (mean age = 44) from seven genome-wide DNA methylation studies summarised in Table S1/ Additionally, it is an obvious step to acquire the data in the same method as the original people did. (Materials and methods, paragraphs 1-2, A method comprising: obtaining a plurality of training samples, each training sample: comprising a plurality of nucleic acid fragments, each of the plurality of nucleic acid fragments having a genomic location overlapping at least one genomic region of a plurality of genomic regions, and labelled with a chronological age of an individual from whom the training sample is derived; sequencing the plurality of nucleic acid fragments for each training sample to identify a methylation pattern for each nucleic acid fragment; sequencing the plurality of nucleic acid fragments for each training sample to identify a methylation pattern for each nucleic acid fragment; (Claim 1) The ability to accurately predict age regardless of the tissue type would be very advantageous in criminal investigations where the identification of the tissue source of a sample is often challenging. Even if the purpose of this study was to identify age-associated CpG sites in blood, the ability to apply a potential model in other tissues with similar accuracy would save both time and resources. In an attempt to select more robust age-associated differentially-methylated markers across tissues, the study by Horvath [48] was chosen as the most appropriate. The author built an age prediction model applicable in various tissues using a total of 353 markers, which were categorised by a coefficient value (ranging from −1.719 to 3.067) that relates the CpG sites to a transformed version of age. In order to cover all potential correlations with age and maximise the chance of selecting suitable markers, 45 CpG sites from the 353 marker pool included in Hovarth’s model were selected, specifically this included those displaying the highest (positive/negative) coefficients (Table S3). Their chromosomal location was confirmed using the Ensembl genome browser; most are located within or near a gene. While it has previously been demonstrated that the ELOVL2 marker can be a good predictor of chronological age in blood [24], there is an absence of genome-wide data for the relevant CpG sites, hence these sites could not be included in this study. (2.2. Selection of potential age-associated CpG sites, identifying nucleic acid fragments from the plurality having genomic locations overlapping the genomic region, and (claim 1) generating a feature set comprising one or more genomic regions of the plurality of genomic regions, the one or more genomic regions in the feature set having indicativeness scores above a threshold; and (Claim 1) Using publicly available DNA methylation databases, normalised beta values for the selected 45 CpG sites were gathered for a total of 1156 whole blood samples from individuals 2–90 years old. Methylation fractions (zero to one) were compared against the actual age of each individual in order to investigate potential correlation between methylation levels and age (example graphs for 16 out of 45 CpGs are presented in Fig. 1). As expected, some CpG sites showed greater variation than others; for example, cg07455279 (NDUFA3) demonstrated the largest methylation range (difference between the lowest and highest detected methylation value for each marker) (0.815) while cg05442902 (P2RXL1) usually showed low methylation levels (<0.387). In general, the methylation of certain CpG sites such as cg19761273 (CSNK1D), cg01511567 (SSRP1), cg07158339 (FXN) and cg05442902 (P2RXL1) was clearly decreasing with advancing age, while others, cg20692569 (FZD9), cg04528819 (KLF14), cg04084157 (VGF) and cg22736354 (NHLRC1) to name but a few, were increasingly methylated over time. These observations align with the age relationship that Horvath reported in his study (3.1. Age-associated DNA methylation changes in blood, calculating, for the genomic region, an indicativeness score representing a correlation between chronological age and methylation patterns, and calculated based on chronological ages of individuals from whom the identified nucleic acid fragments are derived and methylation patterns of identified nucleic acid fragments (Claim 1) The ability to estimate the age of the donor from recovered biological material at a crime scene can be of substantial value in forensic investigations. Aging can be complex and is associated with various molecular modifications in cells that accumulate over a person’s lifetime including epigenetic patterns. The aim of this study was to use age-specific DNA methylation patterns to generate an accurate model for the prediction of chronological age using data from whole blood. In total, 45 age-associated CpG sites were selected based on their reported age coefficients in a previous extensive study and investigated using publicly available methylation data obtained from 1156 whole blood samples (aged 2–90 years) analysed with Illumina’s genome-wide methylation platforms (27 K/450 K). Applying stepwise regression for variable selection, 23 of these CpG sites were identified that could significantly contribute to age prediction modelling and multiple regression analysis carried out with these markers provided an accurate prediction of age (R2 = 0.92, mean absolute error (MAE) = 4.6 years). However, applying machine learning, and more specifically a generalised regression neural network model, the age prediction significantly improved (R2 = 0.96) with a MAE = 3.3 years for the training set and 4.4 years for a blind test set of 231 cases. The machine learning approach used 16 CpG sites, located in 16 different genomic regions, with the top 3 predictors of age belonged to the genes NHLRC1, SCGN and CSNK1D. The proposed model was further tested using independent cohorts of 53 monozygotic twins (MAE = 7.1 years) and a cohort of 1011 disease state individuals (MAE = 7.2 years). Furthermore, we highlighted the age markers’ potential applicability in samples other than blood by predicting age with similar accuracy in 265 saliva samples (R2 = 0.96) with a MAE = 3.2 years (training set) and 4.0 years (blind test). In an attempt to create a sensitive and accurate age prediction test, a next generation sequencing (NGS)-based method able to quantify the methylation status of the selected 16 CpG sites was developed using the Illumina MiSeq® platform. The method was validated using DNA standards of known methylation levels and the age prediction accuracy has been initially assessed in a set of 46 whole blood samples. Although the resulted prediction accuracy using the NGS data was lower compared to the original model (MAE = 7.5 years), it is expected that future optimization of our strategy to account for technical variation as well as increasing the sample size will improve both the prediction accuracy and reproducibility. (ABSTRACT) Following multivariate, linear regression and ANN analysis, we identified an epigenetic aging signature based on the methylation status of a total of 16 CpG sites, training a machine-learned age-prediction model to determine a predicted chronological age of a tested individual from whom a test sample is derived, the training based on methylation patterns of nucleic acid fragments in the plurality of training samples overlapping the one or more genomic regions in the feature set.(claim 1) A non-transitory computer readable storage medium comprising computer program instructions that, when executed by one or more processors, cause the one or more processors to:
obtain a plurality of training samples, each training sample: comprising a plurality of nucleic acid fragments, each of the plurality of nucleic acid fragments having a genomic location overlapping at least one genomic region of a plurality of genomic regions, and labelled with a chronological age of an individual from whom the training sample is derived; sequence the plurality of nucleic acid fragments for each training sample to identify a methylation pattern for each nucleic acid fragment; for each genomic region of a plurality of genomic regions, identify nucleic acid fragments from the plurality having genomic locations overlapping the genomic region, and calculate, for the genomic region, an indicativeness score representing a correlation between chronological age and methylation patterns, and calculated based on chronological ages of individuals from whom the identified nucleic acid fragments are derived and methylation patterns of identified nucleic acid fragments; generate a feature set comprising one or more genomic regions of the plurality of genomic regions, the one or more genomic regions in the feature set having indicativeness scores above a threshold; and train a machine-learned age-prediction model to determine a predicted chronological age of a tested individual from whom a test sample is derived, the training based on methylation patterns of nucleic acid fragments in the plurality of training samples overlapping the one or more genomic regions in the feature set. (Claim 32), A system comprising: one or more processors;a non-transitory computer readable storage medium storing computer program instructions that, when executed by the one or more processors, cause the one or more processors to: obtain a plurality of training samples, each training sample: comprising a plurality of nucleic acid fragments, each of the plurality of nucleic acid fragments having a genomic location overlapping at least one genomic region of a plurality of genomic regions, and labelled with a chronological age of an individual from whom the training sample is derived; sequence the plurality of nucleic acid fragments for each training sample to identify a methylation pattern for each nucleic acid fragment; for each genomic region of a plurality of genomic regions, identify nucleic acid fragments from the plurality having genomic locations overlapping the genomic region, and calculate, for the genomic region, an indicativeness score representing a correlation between chronological age and methylation patterns, and calculated based on chronological ages of individuals from whom the identified nucleic acid fragments are derived and methylation patterns of identified nucleic acid fragments; generate a feature set comprising one or more genomic regions of the plurality of genomic regions, the one or more genomic regions in the feature set having indicativeness scores above a threshold; and train a machine-learned age-prediction model to determine a predicted chronological age of a tested individual from whom a test sample is derived, the training based on methylation patterns of nucleic acid fragments in the plurality of training samples overlapping the one or more genomic regions in the feature set. (claim 63) training a linear regression to regress chronological age from methylation density of non-cancer training samples, wherein methylation density is calculated as a percentage of nucleic acid fragments having genomic locations which overlap a particular genomic region having a methylated state in that particular genomic region. (Claim 13) wherein the machine-learned age-prediction model comprises a multivariate regression. (Claim 14) wherein the machine-learned age-prediction model receives as input a methylation density corresponding to each of the genomic regions in the feature set. (Claim 16) wherein a number of the one or more genomic regions in the feature set is selected from a range of 5-10,000. (Claim 17) wherein sequencing the nucleic acid fragments comprises targeted sequencing. (Claim 19) With respect to the limitations of Claims 2, 3, Vidaki et al. also teaches It is important to bear in mind that, in contrast with a medical setting, information regarding possible disease status is not available when trying to predict chronological age from an unknown bloodstain or sample during a criminal investigation. Consequently, it is important to build a robust age prediction model containing DNA methylation markers that would not show differential methylation patterns due to disease states. However, this might be extremely challenging to do. Therefore, although Horvath has already reported that the predicted age from cancer tissues correlated poorly with patient age in his study [48], we aimed to investigate a set of diseased samples and the effect on age prediction. For this purpose, seven datasets including diseased samples were analysed in an attempt to further validate the proposed age prediction model (Table S2). Fig. S2 shows the predicted vs. chronological age for all 1011 samples; combining all diseases together, a correlation of 0.74 and a mean absolute error of 7.18 years was obtained. However, when analysing separately samples suffering from blood vs. non-blood related diseases it becomes evident that the error is much higher for blood related diseases (error = 12.74 years). This is of course expected since the methylation data were gathered by analysing whole blood samples and therefore the potential effect is direct. In more detail, the obtained mean absolute errors for each disease were as follows: type I diabetes – 8.63 years, anaemia – 14.38 years, bone marrow disorders (including leukaemia) – 11.09 years, ovarian cancer – 7.45 years, breast cancer – 6.77 years and schizophrenia – 5.03 years. (3.5. Effect of disease state on age predictions
)The prior art previously mentions how disease and cancer can play a roll in affecting methylation. They even chose steps to prevent it by choosing the optimal genomic regions. It was done in a single step with multiple diseases instead of multiple steps. Both applicant and prior art are just doing routine optimization to find the best sites. The prior arts goal was to have good age prediction regardless of known cancer status which is exactly what applicant is doing. Examiner finds it as routine optimization to perform it in multiple steps instead of 1. It would lead to the same conclusion of the same chosen regions. The entire knowledge is taught within the prior art. They also establish a difference in aging based on cancer and Levine et al. correlates this with identifying cancer. In re Kulling, 897 F.2d 1147, 1149, 14 USPQ2d 1056, 1058 (Fed. Cir. 1990)(Claimed amount of wash solution was found to be unpatentable as a matter of routine optimization in the pertinent art, further supported by the prior art disclosure of the need to avoid undue amounts of wash solution); and In re Geisler, 116 F.3d 1465, 1470, 43 USPQ2d 1362, 1366 (Fed. Cir. 1997)(Claims were unpatentable because appellants failed to submit evidence of criticality to demonstrate that that the wear resistance of the protective layer in the claimed thickness range of 50-100 Angstroms was "unexpectedly good"); Smith v. Nichols, 88 U.S. 112, 118-19 (1874) (a change in form, proportions, or degree "will not sustain a patent"); In re Williams, 36 F.2d 436, 438, 4 USPQ 237 (CCPA 1929) ("It is a settled principle of law that a mere carrying forward of an original patented conception involving only change of form, proportions, or degree, or the substitution of equivalents doing the same thing as the original invention, by substantially the same means, is not such an invention as will sustain a patent, even though the changes of the kind may produce better results than prior inventions."). See also KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416, 82 USPQ2d 1385, 1395 (2007) (identifying "the need for caution in granting a patent based on the combination of elements found in the prior art." training a linear regression for each genomic region of the feature set based on the methylation patterns of the nucleic acid fragments overlapping each genomic region from training samples of the plurality labelled as non-cancer; obtaining a plurality of additional training samples, each additional training sample: comprising a plurality of additional nucleic acid fragments having additional genomic locations overlapping at least one genomic region of the plurality of genomic regions, labelled with a chronological age of an individual from whom the additional training sample was derived, and labelled as non-cancer or cancer based on a previous determination of cancer presence in the additional training sample; sequencing the plurality of additional nucleic acid fragments to identify a methylation pattern for each additional nucleic acid fragment; for each genomic region of the plurality: applying the linear regression to methylation patterns of nucleic acid fragments of the plurality of additional training samples to determine a predicted chronological age of the individual from whom the additional training sample was derived, calculating age residuals for each additional training sample as a difference between its predicted chronological age and its labelled chronological age, and comparing age residuals of the additional training samples labelled as cancer to age residuals of the additional training samples labelled as non-cancer; and generating a reduced feature set from the feature set based on the comparison of age residuals, wherein the reduced feature set comprises a lesser number of genomic regions than the feature set, and the reduced feature set is used to train the machine-learned age-prediction model. (Claim 2) further comprising:obtaining a test sample, the test sample comprising a plurality of additional nucleic acid fragments and labelled with a chronological age of a test subject from whom the test sample is derived; sequencing the plurality of additional nucleic acid fragments for the test sample to identify methylation patterns for the additional nucleic acid fragments of the plurality; applying the trained age-prediction model to determine a predicted chronological age of the test subject from whom the test sample was derived based on methylation patterns of the additional nucleic acid fragments overlapping the one or more genomic regions in the feature set; calculating an age residual as a difference between the labelled chronological age and the predicted chronological age of the test subject; and (Claim 3) wherein the residual threshold is determined by:applying the trained age-prediction model to a second plurality of training samples identified as non-cancer to determine a predicted age for each of the second plurality of training samples; calculating an age residual for each of the second plurality of training samples by comparing the predicted age to a labelled chronological age of the second plurality of training samples; and identifying the residual threshold based on the calculated age residuals for the second plurality of training samples, wherein at least a majority of the calculated age residuals for the second plurality of training samples satisfy the residual threshold.(Claim 4)
Vidaki et al. does not explicitly teach
determining that the test sample has a strong likelihood for presence of cancer in response to determining that the age residual is above a residual threshold (Claim 3)
wherein the secondary machine-learned cancer classifier is further configured to receive as input clinical information and genetic background of the subject and output the prediction of the presence of cancer in the test sample. (Claim 10)
wherein the residual threshold is determined by:applying the trained age-prediction model to a second plurality of training samples identified as non-cancer to determine a predicted age for each of the second plurality of training samples; calculating an age residual for each of the second plurality of training samples by comparing the predicted age to a labelled chronological age of the second plurality of training samples; and identifying the residual threshold based on the calculated age residuals for the second plurality of training samples, wherein at least a majority of the calculated age residuals for the second plurality of training samples satisfy the residual threshold. (Claim 4)
further comprising:in response to determining that the test sample has the strong likelihood for presence of cancer: filtering the methylation patterns of the plurality of additional nucleic acid fragments with p-value filtering to identify a set of anomalous methylation patterns; generating a feature vector for the test sample based on the age residual and the set of anomalous methylation patterns; and determining a cancer prediction for the test sample by inputting the feature vector into a trained cancer classifier. (Claim 5)
wherein the cancer prediction is a binary prediction between presence and absence of cancer or another disease state. (Claim 6)
, wherein the cancer prediction is a multiclass prediction between a plurality of cancer types. (Claim 7),
wherein the cancer prediction is a multiclass prediction between a plurality of disease states (Claim 8)
determining a presence of cancer in the test sample using a secondary machine-learned cancer classifier, the secondary cancer classifier configured to receive as input the predicted chronological age of the subject and methylation patterns of the plurality of additional nucleic acid fragments and output a prediction of the presence of cancer in the test sample.(Claim 9)
wherein each training sample is labeled with a sex or a smoking status of the individual from whom the training sample is derived, and comprising: calculating, for the genomic region, an additional indicativeness score representing a correlation between sex or smoking status and methylation patterns, and training a machine-learned characteristic prediction model to determine a predicted sex or smoking status of a tested individual from whom a test sample is derived. (Claim 23)
With respect to the limitations of Claims, 3, 10, Levine et al. teaches Three curves were calculated, varying the level of baseline IEAA (standardized) so that it equaled −1, 0, and 1, respectively. As shown in Figure 2 having a standardized IEAA level equal to one greatly increased the likelihood of developing lung cancer over twenty years of follow-up. For instance, after ten years, only about 5% of individuals in the negative age acceleration group (IEAA=−1) were predicted to develop lung cancer, and after twenty-years the number was only predicted to rise to about 10%. In the average age acceleration group (IEAA = 0), about 12%, and 25% of individuals were predicted to develop lung cancer after ten and twenty years, respectively. However, for women with positive age acceleration (IEAA = 1), it was predicted that after ten years almost 25% would develop lung cancer, and after twenty years, over half would have developed lung cancer. This establishes the principal if predicted age is off of epigenetic age then there is a strong likely hood of cancer. (IEAA predicts lung cancer incidence) adjusting for age, race/ethnicity, CHD status, pack-years and smoking status (never, former, current)—and stratifying by 1) 10-year age groups (50–59, 60–69, 70–79) and 2) smoking status. Based on these models, we calculated predictive probabilities for 10-year incidence of lung cancer (Statistical analysis, determining that the test sample has a strong likelihood for presence of cancer in response to determining that the age residual is above a residual threshold (Claim 3) wherein the secondary machine-learned cancer classifier is further configured to receive as input clinical information and genetic background of the subject and output the prediction of the presence of cancer in the test sample. (Claim 10)
With respect to the limitations of Claims 4, 5, 6, 7, 8, 9, US20190287652A1 teaches An analytics system creates a data structure counting strings of methylation vectors from a healthy control group. The analytics system enumerates possibilities of methylation state vectors given a sample fragment from a subject, and calculates probabilities for all possibilities with a Markov chain probability. The analytics system generates a p-value score for the subject's test methylation state vector by summing the calculated probabilities that are less than or equal to the calculated probability of the possibility matching the test methylation state vector. The analytics system determines the test methylation state vector to be anomalously methylated compared to the healthy control group if the p-value score is below a threshold score. With a number of such sample fragments, the analytics system can filter the sample fragments based on each p-value score. The analytics system can run a classification model on the filtered set to predict whether the subject has cancer. (abstract) the method further comprises: applying the sample state vector to a classifier, trained with a cancer set of training fragments from one or more subjects with cancer and a non-cancer set of training fragments from one or more subjects without cancer, wherein the classifier can be used to determine whether the sample fragment is from a subject with cancer. (Specification) According to aspects of the invention, the methods and systems of the present invention can be trained to detect or classify multiple cancer indications. For example, the methods, systems and classifiers of the present invention can be used to detect the presence of one or more, two or more, three or more, five or more, ten or more, fifteen or more, or twenty or more different types of cancer. Examples of cancers that can be detected using the methods, systems and classifiers of the present invention include carcinoma, lymphoma, blastoma, sarcoma, and leukemia or lymphoid malignancies. More particular examples of such cancers include, but are not limited to, squamous cell cancer (e.g., epithelial squamous cell cancer), skin carcinoma, melanoma, lung cancer, including small-cell lung cancer, non-small cell lung cancer (“NSCLC”), adenocarcinoma of the lung and squamous carcinoma of the lung, cancer of the peritoneum, gastric or stomach cancer including gastrointestinal cancer, pancreatic cancer (e.g., pancreatic ductal adenocarcinoma), cervical cancer, ovarian cancer (e.g., high grade serous ovarian carcinoma), liver cancer (e.g., hepatocellular carcinoma (HCC)), hepatoma, hepatic carcinoma, bladder cancer (e.g., urothelial bladder cancer), testicular (germ cell tumor) cancer, breast cancer (e.g., HER2 positive, HER2 negative, and triple negative breast cancer), brain cancer (e.g., astrocytoma, glioma (e.g., glioblastoma)), colon cancer, rectal cancer, colorectal cancer, endometrial or uterine carcinoma, salivary gland carcinoma, kidney or renal cancer (e.g., renal cell carcinoma, nephroblastoma or Wilms' tumor), prostate cancer, vulval cancer, thyroid cancer, anal carcinoma, penile carcinoma, head and neck cancer, esophageal carcinoma, and nasopharyngeal carcinoma (NPC). Additional examples of cancers include, without limitation, retinoblastoma, thecoma, arrhenoblastoma, hematologic malignancies, including but not limited to non-Hodgkin's lymphoma (NHL), multiple myeloma and acute hematologic malignancies, endometriosis, fibrosarcoma, choriocarcinoma, laryngeal carcinomas, Kaposi's sarcoma, Schwannoma, oligodendroglioma, neuroblastomas, rhabdomyosarcoma, osteogenic sarcoma, leiomyosarcoma, and urinary tract carcinomas. Also Sensitivity and specificity were estimated from classifiers; each classifier corrected for or suppressed assay-specific interfering biological signals (eg, CH, hematologic conditions, age-related alterations). Non-cancer cases were used to estimate specificity after correcting for interfering signal. (Specification): wherein the residual threshold is determined by:applying the trained age-prediction model to a second plurality of training samples identified as non-cancer to determine a predicted age for each of the second plurality of training samples; calculating an age residual for each of the second plurality of training samples by comparing the predicted age to a labelled chronological age of the second plurality of training samples; and identifying the residual threshold based on the calculated age residuals for the second plurality of training samples, wherein at least a majority of the calculated age residuals for the second plurality of training samples satisfy the residual threshold. (Claim 4) further comprising:in response to determining that the test sample has the strong likelihood for presence of cancer: filtering the methylation patterns of the plurality of additional nucleic acid fragments with p-value filtering to identify a set of anomalous methylation patterns; generating a feature vector for the test sample based on the age residual and the set of anomalous methylation patterns; and determining a cancer prediction for the test sample by inputting the feature vector into a trained cancer classifier. (Claim 5) wherein the cancer prediction is a binary prediction between presence and absence of cancer or another disease state. (Claim 6), wherein the cancer prediction is a multiclass prediction between a plurality of cancer types. (Claim 7), wherein the cancer prediction is a multiclass prediction between a plurality of disease states (Claim 8) determining a presence of cancer in the test sample using a secondary machine-learned cancer classifier, the secondary cancer classifier configured to receive as input the predicted chronological age of the subject and methylation patterns of the plurality of additional nucleic acid fragments and output a prediction of the presence of cancer in the test sample.(Claim 9)
With respect to the limitations of Claims 23, Bollepalli teaches building a machine learning model that uses methylation and sex as inputs to determine if the person is a smoker (Results, wherein each training sample is labeled with a sex or a smoking status of the individual from whom the training sample is derived, and comprising: calculating, for the genomic region, an additional indicativeness score representing a correlation between sex or smoking status and methylation patterns, and training a machine-learned characteristic prediction model to determine a predicted sex or smoking status of a tested individual from whom a test sample is derived. (Claim 23)
A person of ordinary skill in the art would be motivated to combine Vidaki et al. as it teaches a machine learning method that uses DNA/RNA methylation to predict aging. As well as acquiring the data necessary to do so. US20190287652A1 teaches filtering the methylation patterns of the plurality of additional nucleic acid fragments with p-value filtering to identify a set of anomalous methylation patterns to improve classification as wells as the use of WGBS sequencing. Levine et al. provides the knowledge of using epigenetic age based on methylated DNA differences with patients actual age as an indicator for cancer. Bollepalli teaches the link between methylation, sex and smoking. Each piece of art all deals with the Analysis of DNA/RNA methylation. Each method works independently and the way each method work is not being changed when combined therefore there is a reasonable expectation of success that it will work when put together.
Conclusion
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/C.H.B./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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