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 .
Applicant’s election without traverse of Species C, wherein the information is both epigenetic information AND sequence read information, in the reply filed on 9/16/2025 is acknowledged.
Claims 1, 5-20, 27 and 28 are pending and under examination to the extent they read on the elected species. Claims 2-4, 21-26 and 29-108 have been canceled.
This application claims priority to PCT/US20/19957, filed 2/26/2020, and claims priority to two US provisional applications. The effective filing date for the pending claims is 2/27/2019. The examiner has reviewed all the PCT-related documentation.
This application has published as US 2022/0028494 A1.
The IDS filed 10/14/2021 and 5/3/2022 have each been entered and considered.
The drawings are objected to because Figures 11-13, comprising screenshots, are blurry, with very small type or other features that are difficult or impossible to discern, even upon enlargement. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The Sequence Listing and associated papers have been entered.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation (BRI) using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art.
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, 5-20, 27 and 28 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Applicant is directed to MPEP 2106 and the Federal Register notice (FR89, no 137 (7/17/2024) p 58128-58138) for the most current and complete guidelines in the analysis of patent- eligible subject matter. The current MPEP is the primary source for the USPTO’s patent eligibility guidance.
With respect to step (1): YES. The claims are drawn to statutory categories: computer-implemented processes.
With respect to step (2A) (1): YES. The claims recite an abstract idea, law of nature and/or natural phenomenon. The claims recite an abstract idea of determining the cellular origin of a type of DNA in a sample, through mathematic and statistical processes including distributions, estimation of fractions, aggregation and classification (See MPEP 2106.07(a)). The claims also embrace the natural law describing the naturally occurring correlations between genetic or epigenetic information and a phenotype of “origin”, a genotype/ phenotype relationship. (MPEP 2106.04). The claims explicitly recite elements that, individually and in combination, constitute one or more judicial exceptions (JE).
Mathematic concepts, Mental Processes or Elements in Addition (EIA) in the claim(s) include:
1. (Original) A method of determining a cellular origin of at least a subset of deoxyribonucleic acid (DNA) molecules from a cell-free DNA (cfDNA) sample obtained from a subject at least partially using a computer, the method comprising:
(EIA: preamble stating the goal of the method, and the source of the sample: an element of data gathering)
(a) identifying one or more sets of DNA molecules of unknown cellular origin from the cfDNA sample that each comprise one or more member DNA molecules that each comprise at least one genomic region in common with one another from sequence information obtained from the cfDNA sample;
(Mental process, in a computing environment, or using a computer as a tool, of comparing sequence read information to identify “regions in common” which are steps of comparison, matching and judgement. [0074, 0125])
(b) determining a distribution of one or more properties among the one or more member DNA molecules within each of the one or more sets of DNA molecules from epigenetic information and/or the sequence information obtained from the cfDNA sample to generate one or more distribution sets, which properties are selected from the group consisting of: a length of a given DNA molecule, an offset of a midpoint of a given DNA molecule from a midpoint of the at least one genomic region of the given DNA molecule, and an epigenetic status or pattern exhibited by a given DNA molecule;
(Mathematic concept of creating mathematic distributions which describe a property. [0016-0018, 0116-0120, 00130] “[0130] Method 200 also includes comparing (typically using a computer) the distribution of the properties … or a statistical transformation of one or more components of the distribution…”)
(c) estimating a fraction of member DNA molecules, if any, within each of the one or more distribution sets that originate from a targeted cellular origin to generate a fraction estimate for each of the one or more distribution sets for the cfDNA sample;
(Mathematic concept of calculating or estimating fractions that describe a property: origination from a targeted cellular origin. [0017-0018, 00115, 00119-00120, 00127] “an estimate of Θi,j”)
(d) aggregating the fraction estimates for the cfDNA sample to generate a sample classification score for the cfDNA sample; and,
(Mathematic concept of adding, multiplying, or otherwise combining the fractional estimates; [00123] one type of aggregation is transformation into a z-score, or calculating the means of the z-scores.)
(e) classifying the cfDNA sample as comprising DNA molecules from cells of the targeted cellular origin when the sample classification score for the cfDNA sample exceeds a reference classification score, thereby determining the cellular origin of at least the subset of DNA molecules from the cfDNA sample obtained from the subject.
(Mental process of comparing one score value to a reference score, and judging whether one exceeds the other. Alternatively, a mathematic concept of one data value being less than or greater than another. [00128])
5. (Previously Presented) The method of claim 1, wherein the genomic regions comprise one or more regions of differential chromatin organization between at least two cell types.
(Mental concept modification, defining “genomic regions”.)
6. (Previously Presented) The method of claim 1, wherein the genomic regions comprise one or more transcriptional factor binding regions, one or more distal regulatory elements (DREs), one or more repetitive elements, one or more intron-exon junctions, and/or one or more transcriptional start sites (TSSs).
(Mental concept modification, defining “genomic regions”.)
7. (Original) The method of claim 6, wherein the one or more transcriptional factor binding regions comprise one or more CTCF binding regions.
(Mental concept modification, defining “genomic regions”.)
8. (Previously Presented) The method of claim 1, wherein the epigenetic loci comprise one or more methylation sites, one or more acetylation sites, one or more ubiquitylation sites, one or more phosphorylation sites, one or more sumoylation sites, one or more ribosylation sites, one or more citrullination sites, one or more histone post-translational modification sites, and/or one or more histone variant sites.
(Mathematic concept modification, defining “epigenetic loci” used to calculate the distribution in the mathematic concept.)
9. (Previously Presented) The method of claim 8, wherein the epigenetic information comprises a methylation status of the one or more methylation sites, an acetylation status the one or more acetylation sites, a ubiquitylation status of the one or more ubiquitylation sites, a phosphorylation status of the one or more phosphorylation sites, a sumoylation status of the one or more sumoylation sites, a ribosylation status of the one or more ribosylation sites, a citrullination status of the one or more citrullination sites, a histone post-translational modification status of the one or more histone post-translational modification sites, and/or a histone variant status of the one or more histone variant sites.
(Mathematic concept modification, defining “epigenetic loci” used to calculate the distribution in the mathematic concept.)
10. (Previously Presented) The method of claim 1, wherein the epigenetic pattern comprises one or more of: a methylation pattern, an acetylation pattern, a ubiquitylation pattern, a phosphorylation pattern, a sumoylation pattern, a ribosylation pattern, a citrullination pattern, a histone post-translational modification pattern, and/or a histone variant pattern.
(Mathematic concept modification, defining “epigenetic pattern” used to calculate the distribution in the mathematic concept.)
11. (Original) The method of 10, wherein the methylation pattern comprises a 5- methylcytosine (5mC) pattern and/or a 5-hydroxymethylcytosine (5hmC) pattern.
(Mathematic concept modification, defining “methylation pattern” used to calculate the distribution in the mathematic concept.)
12. (Previously Presented) The method of claim 1, wherein the cellular origin of the subset of DNA molecules or the targeted cellular origin comprises a tumor cell.
(Mental process modification, specifying the phenotype of the cellular origin)
13. (Previously Presented) The method of claim 1, wherein the cellular origin of the subset of DNA molecules or the targeted cellular origin comprises a non-tumor cell.
(Mental process modification, specifying the phenotype of the cellular origin)
14. (Previously Presented) The method of claim 1, wherein the cellular origin of the subset of DNA molecules or the targeted cellular origin comprises a fetal cell.
(Mental process modification, specifying the phenotype of the cellular origin)
15. (Previously Presented) The method of claim 1, wherein the cellular origin of the subset of DNA molecules or the targeted cellular origin comprises a maternal cell.
(Mental process modification, specifying the phenotype of the cellular origin)
16. (Previously Presented) The method of claim 1, wherein the cellular origin of the subset of DNA molecules or the targeted cellular origin comprises a cell from a transplant donor subject.
(Mental process modification, specifying the phenotype of the cellular origin)
17. (Previously Presented) The method of claim 1, wherein the cellular origin of the subset of DNA molecules or the targeted cellular origin comprises a cell from a transplant recipient subject.
(Mental process modification, specifying the phenotype of the cellular origin)
18. (Previously Presented) The method of claim 1, wherein the cellular origin of the subset of DNA molecules or the targeted cellular origin comprises a non-diseased cell.
(Mental process modification, specifying the phenotype of the cellular origin)
19. (Previously Presented) The method of claim 1, wherein the cellular origin of the subset of DNA molecules comprises a diseased cell, thereby diagnosing a disease in the subject.
(Mental process modification, specifying the phenotype of the cellular origin)
20. (Previously Presented) The method of claim 1, further comprising administering one or more therapies to the subject to treat the disease in the subject.
(EIA, a generic treatment step)
27. (Previously Presented) The method of claim 1, comprising estimating a maximum likelihood that a fraction of DNA molecules in a given distribution set originates from the targeted cellular origin, using the equations of:
PNG
media_image1.png
74
660
media_image1.png
Greyscale
where Pr is probability, Θ is the fraction of DNA molecules in the given distribution set that originate from the targeted cellular origin, ML is the maximum likelihood, D is a collection of DNA molecules {d1, d2,..., dN} from a test sample, n is a given DNA molecule in the given distribution set, dn is a set of observed variables that represent observed fragmentomics and epigenetic information, zn is a latent/hidden variable that represents a targeted or normal cell of origin, and Θ is a set of parameters that are estimated from control genomic regions on a targeted panel or from a reference set of cfDNA samples with DNA molecules from normal cells and cfDNA samples with DNA molecules from targeted cells.
(Mathematic concept modification spelling out how the estimations or ML calculations are performed)
28. (Original) The method of claim 27, wherein dn = (xn, yn, kn, qn),where n is the given DNA molecule in the given distribution set, xn is an offset of a midpoint of the given DNA molecule from a center of the genomic region of that given DNA molecule, yn is a length of the given DNA molecule, kn is a number of CpG sites in the given DNA molecule, and qn is a methyl binding domain (MBD) partition of the given DNA molecule.
(Mathematic concept modification spelling out how the estimations or ML calculations are performed)
Natural law embraced by the claim(s): The claims embrace the naturally occurring correlations between changes in cfDNA information and a phenotype: a cell of origin, or disease. This correlation exists whether or not it is measured.
With respect to step 2A (2): NO. The claims were examined further to determine whether they integrated any JE into a practical application (MPEP 2106.04(d)). The claimed additional elements are analyzed alone, or in combination to determine if the JE is integrated into a practical application (MPEP 2106.05(a-c, e, f and h)).
Claim(s) 1 recite(s) the additional non-abstract element(s) of data gathering, or a description of the data gathered.
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantec Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Claim(s) 20 recite(s) the additional non-abstract element (EIA) of a treatment or prophylaxis:
The identified treatment step fails to integrate the JE into a practical application, as the step does not “affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition” see (MPEP 2106.04(d)(2)).
Claim(s) 1 recite(s) the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof.
The EIA do not provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a general-purpose computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Dependent claim(s) 5-19, 27, 28 recite(s) an abstract limitation to the JE reciting additional mathematic concepts, or mental processes. Additional abstract limitations cannot provide a practical application of the JE as they are a part of that JE.
In combination, the limitations of data gathering, for the purpose of carrying out the JE, using a general-purpose computer merely provide extra-solution activity, and fail to integrate the JE into a practical application.
With respect to step 2B: NO. The claims recite a JE, do not integrate that JE into a practical application, and thus are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). The additional elements were considered individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to claim(s) 1: The limitation(s) identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception.
The claims require epigenetic and/or sequence information from cfDNA samples.
Snyder et al (2016) receive epigenetic and sequence read information from cfDNA samples.
Li et al (2018) receive epigenetic and sequence read information from cfDNA samples.
Guo et al (2017) receive epigenetic and sequence read information from cfDNA samples.
Kang et al (2017) receive epigenetic and sequence read information from cfDNA samples.
These elements meet the BRI of the identified data gathering limitations. As such, the prior art recognizes that this data gathering element is routine, well understood and conventional in the art (as in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook).
In the specification at [00243] it is disclosed that the steps identified as data gathering can be met using publicly available data, from public databases, such as ENCODE.
Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not effect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
With respect to claim(s) 1: the limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception.
Each of Snyder, Li, Kang and Guo disclose computer systems or computing elements which meet the BRI of the claimed computer system or computer system elements, comprising input, output/ display, a processor, and memory.
As such, the prior art recognizes that these computing elements are routine, well understood and conventional in the art.
The specification, at [00227-00240] discloses the use of routine general-purpose computers for carrying out the invention, and/or the use of commercially available computer system elements.
These elements do not improve the functioning of the computer itself, or comprise an improvement to any other technical field (Trading Technologies Int’l v IBG, TLI Communications). They do not require or set forth a particular machine (Ultramercial v. Hulu, LLC., Alice Corp. Pty. Ltd v. CLS Bank Int’l), they do not effect a transformation of matter, nor do they provide an unconventional step. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook, Versata Development Group v. SAP America).
Dependent claim(s) 5-19, 27, 28 each recite a limitation requiring additional mathematic concepts or mental processes. Additional abstract limitations cannot provide significantly more than the JE as they are a part of that JE (MPEP 2106.05).
In combination, the data gathering steps providing the information required to be acted upon by the JE, performed in a generic computer or generic computing environment fail to rise to the level of significantly more than that JE. The data gathering steps provide the data for the JE, which is carried out by the general-purpose computers. No non-routine step or element has clearly been identified.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 5-7, 12-13, 18-19 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Snyder et al. (2016).
Snyder et al. (2016) Cell-free DNA comprises an in vivo nucleosome footprint that informs its tissues of origin. Cell, vol 164, p57-68 and some supplemental material.
Snyder is directed to determining a tissue or cell of origin, for cell-free DNA, using sequence information, epigenetic information, gene expression information, and attributes of chromatin structure. (abstract).
With respect to claim 1 and “(a) identifying one or more sets of DNA molecules of unknown cellular origin from the cfDNA sample that each comprise one or more member DNA molecules that each comprise at least one genomic region in common with one another from sequence information obtained from the cfDNA sample;”
Snyder obtains cfDNA samples from healthy and cancer patients. The cfDNA samples are sequenced to about 95x coverage (p58) and a single stranded library was generated to about 30x coverage. Fragment length distributions are created (Fig 1b). Cancer patient cfDNA samples were obtained, and sequenced (p63: “small cell lung cancer, a squamous cell lung cancer, a colorectal adenocarcinoma, a hepatocellular carcinoma, and a ductal carcinoma in situ breast cancer”). Reference cell library data was obtained for comparison. To identify subsets of cfDNA having corresponding genomic regions, the cfDNA were mapped to human genome reference GRC37h. (supplemental information).
With respect to claim 1 and “(b) determining a distribution of one or more properties among the one or more member DNA molecules within each of the one or more sets of DNA molecules from epigenetic information and/or the sequence information obtained from the cfDNA sample to generate one or more distribution sets, which properties are selected from the group consisting of: a length of a given DNA molecule, an offset of a midpoint of a given DNA molecule from a midpoint of the at least one genomic region of the given DNA molecule, and an epigenetic status or pattern exhibited by a given DNA molecule;”
Snyder generates fragment length distributions, as well as epigenetic status/pattern distributions. The fragment length distributions are shown, for example at Fig 1b. The epigenetic status or pattern is nucleosome occupation of a binding site. Fig 2, Fig 3, Fig 4 all illustrate epigenetic pattern/status distributions. Offset is also addressed by Snyder, such as at p58, supplemental Fig s2D pS3.
With respect to claim 1 and “(c) estimating a fraction of member DNA molecules, if any, within each of the one or more distribution sets that originate from a targeted cellular origin to generate a fraction estimate for each of the one or more distribution sets for the cfDNA sample;”
Snyder estimates fractions of DNA molecules that originate from a specific cellular origin, for example at Fig 5.
With respect to claim 1 and “(d) aggregating the fraction estimates for the cfDNA sample to generate a sample classification score for the cfDNA sample; and,”
Snyder aggregates fraction estimates as set forth in the integrated supplemental material at p67, and the additional supplemental experimental procedures.
With respect to claim 1 and “(e) classifying the cfDNA sample as comprising DNA molecules from cells of the targeted cellular origin when the sample classification score for the cfDNA sample exceeds a reference classification score, thereby determining the cellular origin of at least the subset of DNA molecules from the cfDNA sample obtained from the subject.”
Snyder classifies the cellular origin of at least one subset of DNA molecules, as set forth in Fig 5.
The analyses of Snyder are all performed at least in part using a computer. As such, claim 1 is anticipated.
With respect to claim 5, Snyder investigates genomic regions having differential chromatin structures, at least at p58, col 2, last paragraph.
With respect to claims 6-7, the genomic regions of Snyder comprise at least one of TF binding sites, DRE’s and/or TSS and Snyder specifically studies CTCF binding regions (p60), throughout.
With respect to claims 12-13, 18-19, Snyder identifies tumor and non-tumor cells, as well as healthy or diseased cells.
Claim(s) 1, 8-13, 18-19, 27-28 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Kang et al (2017; PTO-1449).
Kang et al. (2017) CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA. Genome Biology, vol 18: 53, 12 pages. PTO-1449.
Kang et al are directed to the computer-program CancerLocator, to identify the cellular/tissue origin of cfDNA in a cf sample. Kang utilizes methylation data, and sequence read data, to identify the origin of cancer cells.
With respect to claim 1 and “(a) identifying one or more sets of DNA molecules of unknown cellular origin from the cfDNA sample that each comprise one or more member DNA molecules that each comprise at least one genomic region in common with one another from sequence information obtained from the cfDNA sample;”
Kang obtains reference healthy and cancer cell sequence and methylation data from TCGA databases. (p2, fig 1) The cancer types were from breast, colon, kidney, liver and lung (p3). The cfDNA were grouped based on genomic regions in common (methods. P7-8) such as CpG cluster regions (p8, Building features). The cfDNA is mapped to HG19 human reference genome, to identify groups which have regions in common (p8, WGBS processing). The last paragraph of p7 discloses methylation aware sequencing, and DNA sequencing with 150bp paired read ends. Methylation level information is obtained, p8.
With respect to claim 1 and “(b) determining a distribution of one or more properties among the one or more member DNA molecules within each of the one or more sets of DNA molecules from epigenetic information and/or the sequence information obtained from the cfDNA sample to generate one or more distribution sets, which properties are selected from the group consisting of: a length of a given DNA molecule, an offset of a midpoint of a given DNA molecule from a midpoint of the at least one genomic region of the given DNA molecule, and an epigenetic status or pattern exhibited by a given DNA molecule;”
Kang constructs distributions representing methylation levels of CpG clusters, as in Fig 1, the discussion at page 3, and the methods section p8. Figure 2 illustrates the difference between the methylation level distribution of normal plasma cfDNA, and solid tumor tissue type t methylation level distributions.
With respect to claim 1 and “(c) estimating a fraction of member DNA molecules, if any, within each of the one or more distribution sets that originate from a targeted cellular origin to generate a fraction estimate for each of the one or more distribution sets for the cfDNA sample;”
Kang provides an estimated fraction of cellular origins for the cfDNA sample, as set forth in Fig 2, related to tumor burden. Normal is furthest to the left, while the two more to the right represent ctDNA fraction. See also methods at pages 8-9, and Fig 6.
With respect to claim 1 and “(d) aggregating the fraction estimates for the cfDNA sample to generate a sample classification score for the cfDNA sample; and,”
Aggregation is a part of the performance evaluation testing of Kang, p10.
With respect to claim 1 and “(e) classifying the cfDNA sample as comprising DNA molecules from cells of the targeted cellular origin when the sample classification score for the cfDNA sample exceeds a reference classification score, thereby determining the cellular origin of at least the subset of DNA molecules from the cfDNA sample obtained from the subject.”
Kang is able to predict the tissue or cell of origin, using their MLE model (p2, conclusions). “Finally, given the genome-wide methylation profile derived from the cfDNA sample of an unknown patient, CancerLocator uses the informative features to estimate the fraction of ctDNAs in the plasma and the likelihood that the detected ctDNAs come from each tumor type. Based on those likelihoods, CancerLocator makes the final decision on whether the patient has tumors and, if yes, the locations of the primary tumor.” P2.
The methods of Kang are performed at least in part on a computer. As such, claim 1 is anticipated.
With respect to claim 8-10, methylation patterns, methylation data, methylation distributions are all provided by Kang.
With respect to claim 11, Kang uses a commercial kit which identifies 5-methylcytosines, p7 EZ-DNA-Methylation-GOLD kit (Zymo research).
With respect to claims 12-13, 18-19, Kang identifies cancer and non-cancer cells of origin, as well as healthy or diseased cells of origin.
With respect to claims 27-28, Kang provides maximum likelihood estimations at pages 3, 9.
Claim(s) 1, 8-13, 18-20, 27-28 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Li (2018; PTO-1449).
Li et al. (2018) CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data. Nucleic Acids Research, vol 46, no 15, e89, 11 pages, and some supplementary material.
Li is directed to probabilistic modeling of joint methylation states in adjacent CpG sites on individual sequencing reads, to detect trace amounts of tumor cfDNA in a cell-free plasma sample. CancerDetector showed high accuracy in detection of ctDNA, and predicting tumor fraction, and had consistent results in predicting tumor size, and survival outcome (abstract).
With respect to claim 1 and “(a) identifying one or more sets of DNA molecules of unknown cellular origin from the cfDNA sample that each comprise one or more member DNA molecules that each comprise at least one genomic region in common with one another from sequence information obtained from the cfDNA sample;”
Li obtains DNA methylation data from liver tumor samples, matched plasma samples (cell-free) and matched normal tissues from TCGA. Li also performed methylation sequencing on plasma cfDNA samples, obtaining both methylation information and sequence read information (p2, overview). The reads were mapped into genomic regions of selected liver-cancer-specific markers, or to hg19 to select subsets of the sequence reads that have genomic regions in common (p2, overview, p5 processing WGBS data). Li notes that deep sequence coverage was not necessarily required for CancerDetector to be effective. Methylation levels at various positions were also collected. See also p5, methods.
With respect to claim 1 and “(b) determining a distribution of one or more properties among the one or more member DNA molecules within each of the one or more sets of DNA molecules from epigenetic information and/or the sequence information obtained from the cfDNA sample to generate one or more distribution sets, which properties are selected from the group consisting of: a length of a given DNA molecule, an offset of a midpoint of a given DNA molecule from a midpoint of the at least one genomic region of the given DNA molecule, and an epigenetic status or pattern exhibited by a given DNA molecule;”
Li determines the presence and level of methylation patterns at cancer-specific, or normal-associated markers, which are epigenetic status/ patterns (Figure 2, p3 step 1). These methylation levels and patterns are studied using distributions, modeled as Beta distributions. The parameters of the Beta distributions are determined by either “method of moments” or maximum likelihood (p4, step 2, Fig 3. See also p6, Results, col 1, and supplemental information S1, S2)
With respect to claim 1 and “(c) estimating a fraction of member DNA molecules, if any, within each of the one or more distribution sets that originate from a targeted cellular origin to generate a fraction estimate for each of the one or more distribution sets for the cfDNA sample;”
Li provides estimations of tumor-derived cfDNA fractions beginning at p4, col 2. cfDNA are classified into either the Tumor class (T) or Normal class (N). Methylation patterns, and methylation sequencing data are analyzed. “For a read that is aligned to the region of marker k, we assume that it can come from one of two classes with the class-specific likelihood P(r|mc k), where mc k is the methylation pattern of class c. Let θ be the tumor-derived cfDNA fraction, so the fraction of normal cfDNA is 1 − θ. We want to estimate θ by maximizing the log-likelihood log P(R|θ,M). This is a maximum likelihood estimation problem.”
With respect to claim 1 and “(d) aggregating the fraction estimates for the cfDNA sample to generate a sample classification score for the cfDNA sample; and,”
Li removes confounding markers from the data, then combines the fractional estimates for each marker (p5, col 1). Using simulated data, “it can be observed that CancerDetector can (i) detect tumor cfDNAs with a low tumor fraction (θ = 1%) at low sequencing coverage (2×), and (ii) improve the detection limit from 1% to 0.3% when increasing the sequencing coverages (5× and 10×)” (P6, col 2 and Fig 4). On nonsimulated data, CancerDetector also outperformed CancerLocator (Fig 5, p7). “Summarizing the performance comparison using the Area Under Curve (AUC), our method can achieve an AUC of 0.990 averaged over 10 runs with standard deviation 0.004 for all real samples and an average AUC of 0.988 with standard deviation 0.005 for early-stage cancer patients;” (p7 col 2).
With respect to claim 1 and “(e) classifying the cfDNA sample as comprising DNA molecules from cells of the targeted cellular origin when the sample classification score for the cfDNA sample exceeds a reference classification score, thereby determining the cellular origin of at least the subset of DNA molecules from the cfDNA sample obtained from the subject.”
Li determines whether the sample comprises ctDNA, and the predicted tumor burden. The process was able to distinguish healthy samples from cancer samples, as well as differentiating HBV carriers from cancer patients. Li is able to correlate predicted tumor fraction to tumor size, and is useful in monitoring progression and anti-cancer treatments (p8 col 1, Fig 6). Li notes that the higher the tumor fraction theta in plasma cfDNAs, the more likely an individual is to have or develop cancer. Li directly sets forth that the method should be applied to large numbers of non-cancer samples to solidify the threshold, so that “any individual with cfDNA tumor fraction theta>theta threshold may be predicted as a cancer carrier.”
The analyses of Li are all carried out using computers, as such claim 1 is anticipated.
With respect to claims 8-11, Li analyses differential methylation patterns and levels, which are 5-mc or 5-hmc.
With respect to claims 12-13, 18-19, the origins of the cfDNA are from normal patients, cancer patients, and/or patients with a disease other than cancer (HBV).
With respect to claim 20, treatment of a patient with anti-cancer treatments is discussed and CancerDetector is suitable for monitoring said patients.
With respect to claims 27-28 the MLE method of Li appears to be the same as the instant method, where “M” replaces the Θ.
Claim(s) 1, 5-15, 18-19, 27-28 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Guo (2017).
Guo et al. (2017) Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA. Nature Genetics, vol 49, no 4, p635-645, and some supplemental information.
With respect to claim 1 and “(a) identifying one or more sets of DNA molecules of unknown cellular origin from the cfDNA sample that each comprise one or more member DNA molecules that each comprise at least one genomic region in common with one another from sequence information obtained from the cfDNA sample;”
Guo obtains primary tissues, tissues from cancer patients, and plasma samples from healthy patients and cancer patients (Online Methods, p9/10). Sequence read data and methylation sequencing data were generated. The reads were processed then mapped to a reference genome to identify reads mapping to the same genomic regions. Guo also obtains haplotypic information. (Fig 2).
With respect to claim 1 and “(b) determining a distribution of one or more properties among the one or more member DNA molecules within each of the one or more sets of DNA molecules from epigenetic information and/or the sequence information obtained from the cfDNA sample to generate one or more distribution sets, which properties are selected from the group consisting of: a length of a given DNA molecule, an offset of a midpoint of a given DNA molecule from a midpoint of the at least one genomic region of the given DNA molecule, and an epigenetic status or pattern exhibited by a given DNA molecule;”
Guo generates distributions of methylation levels of informative methylation blocks, as set forth in Fig 4, and p640. These are epigenetic status or patterns. See also supplemental figure 11.
With respect to claim 1 and “(c) estimating a fraction of member DNA molecules, if any, within each of the one or more distribution sets that originate from a targeted cellular origin to generate a fraction estimate for each of the one or more distribution sets for the cfDNA sample;”
The MHL distributions were used to estimate tumor fractions, as shown at Fig 4, and p640. Estimated tumor fractions “positively correlated with normalized cfDNA yields from the patients with cancer…” (p640). In the online methods sections, fragment length distributions and CpG ratios were analyzed (p9/10). Further in the online methods sections, MHB distributions from plasma samples from healthy individuals, colorectal cancer patients and lung cancer patients were determined (p10/10).
With respect to claim 1 and “(d) aggregating the fraction estimates for the cfDNA sample to generate a sample classification score for the cfDNA sample; and,”
MHB scores were aggregated, as set forth in the online methods section, p (10/10). See also supplemental figure 9.
With respect to claim 1 and “(e) classifying the cfDNA sample as comprising DNA molecules from cells of the targeted cellular origin when the sample classification score for the cfDNA sample exceeds a reference classification score, thereby determining the cellular origin of at least the subset of DNA molecules from the cfDNA sample obtained from the subject.”
Guo uses healthy or matched reference scores as comparisons for the MHB score, to identify the presence of cells from cancer (online methods, p 10/10). See supplemental fig 13.
All the analysis of Guo is performed on computers, at least in part, thus claim 1 is anticipated.
With respect to claim 5, Guo addresses genomic regions that have differing chromatin organizations at p 636, “co-localization of MHB with known regulatory elements.”
With respect to claims 6-7, Fig 1 of Guo shows genomic regions that comprise a variety of genomic regions, including 5’, 3’ UTR, promoters, introns, exons, enhancers, downstream elements and intergenic genomic regions. See also supplemental figure 1.
With respect to claims 8-11, Guo is directed to methylation status, level, and pattern, and the methylation is 5-mc or 5-hmc.
With respect to claims 12-13, 18-19 Guo is directed to identifying healthy or tumor (cancer) related origins.
With respect to claims 14-15, Guo notes that methylation is often used to discriminate between fetal and maternal genomes in the introduction.
With respect to claims 27-28, the MLE method of Guo appears to be the same as that being claimed. (Fig 1, and online methods, p 10/10).
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.
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.
Claim(s) 14-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (2018) as applied to claims 1, 8-13, 18-20, 27-28 above, in view of Sun (2015; PTO-1449).
Li et al. (2018) CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data. Nucleic Acids Research, vol 46, no 15, e89, 11 pages, and some supplementary material.
Sun et al. (2015) Plasma DNA tissue mapping by genome wide methylation sequencing for noninvasive prenatal, cancer and transplantation assessments. PNAS vol 112 (40) e5503-e5512.
Li is directed to probabilistic modeling of joint methylation states in adjacent CpG sites on individual sequencing reads, to detect trace amounts of tumor cfDNA in a cell-free plasma sample. CancerDetector showed high accuracy in detection of ctDNA, and predicting tumor fraction, and had consistent results in predicting tumor size, and survival outcome (abstract).
With respect to claim 1 and “(a) identifying one or more sets of DNA molecules of unknown cellular origin from the cfDNA sample that each comprise one or more member DNA molecules that each comprise at least one genomic region in common with one another from sequence information obtained from the cfDNA sample;”
Li obtains DNA methylation data from liver tumor samples, matched plasma samples (cell-free) and matched normal tissues from TCGA. Li also performed methylation sequencing on plasma cfDNA samples, obtaining both methylation information and sequence read information (p2, overview). The reads were mapped into genomic regions of selected liver-cancer-specific markers, or to hg19 to select subsets of the sequence reads that have genomic regions in common (p2, overview, p5 processing WGBS data). Li notes that deep sequence coverage was not necessarily required for CancerDetector to be effective. Methylation levels at various positions were also collected. See also p5, methods.
With respect to claim 1 and “(b) determining a distribution of one or more properties among the one or more member DNA molecules within each of the one or more sets of DNA molecules from epigenetic information and/or the sequence information obtained from the cfDNA sample to generate one or more distribution sets, which properties are selected from the group consisting of: a length of a given DNA molecule, an offset of a midpoint of a given DNA molecule from a midpoint of the at least one genomic region of the given DNA molecule, and an epigenetic status or pattern exhibited by a given DNA molecule;”
Li determines the presence and level of methylation patterns at cancer-specific, or normal-associated markers, which are epigenetic status/ patterns (Figure 2, p3 step 1). These methylation levels and patterns are studied using distributions, modeled as Beta distributions. The parameters of the Beta distributions are determined by either “method of moments” or maximum likelihood (p4, step 2, Fig 3. See also p6, Results, col 1, and supplemental information S1, S2)
With respect to claim 1 and “(c) estimating a fraction of member DNA molecules, if any, within each of the one or more distribution sets that originate from a targeted cellular origin to generate a fraction estimate for each of the one or more distribution sets for the cfDNA sample;”
Li provides estimations of tumor-derived cfDNA fractions beginning at p4, col 2. cfDNA are classified into either the Tumor class (T) or Normal class (N). Methylation patterns, and methylation sequencing data are analyzed. “For a read that is aligned to the region of marker k, we assume that it can come from one of two classes with the class-specific likelihood P(r|mc k), where mc k is the methylation pattern of class c. Let θ be the tumor-derived cfDNA fraction, so the fraction of normal cfDNA is 1 − θ. We want to estimate θ by maximizing the log-likelihood log P(R|θ,M). This is a maximum likelihood estimation problem.”
With respect to claim 1 and “(d) aggregating the fraction estimates for the cfDNA sample to generate a sample classification score for the cfDNA sample; and,”
Li removes confounding markers from the data, then combines the fractional estimates for each marker (p5, col 1). Using simulated data, “it can be observed that CancerDetector can (i) detect tumor cfDNAs with a low tumor fraction (θ = 1%) at low sequencing coverage (2×), and (ii) improve the detection limit from 1% to 0.3% when increasing the sequencing coverages (5× and 10×)” (P6, col 2 and Fig 4). On nonsimulated data, CancerDetector also outperformed CancerLocator (Fig 5, p7). “Summarizing the performance comparison using the Area Under Curve (AUC), our method can achieve an AUC of 0.990 averaged over 10 runs with standard deviation 0.004 for all real samples and an average AUC of 0.988 with standard deviation 0.005 for early-stage cancer patients;” (p7 col 2).
With respect to claim 1 and “(e) classifying the cfDNA sample as comprising DNA molecules from cells of the targeted cellular origin when the sample classification