DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 3-5, 11, 14, 16, 19-21, 23-25, 27-28, 33-36, and 40-42 are cancelled.
Claims 1-2, 6-10, 12-13, 15, 17-18, 22, 26, 29-32, and 37-39 are currently pending and under exam herein.
Claims 1-2, 6-10, 12-13, 15, 17-18, 22, 26, 29-32, and 37-39 are rejected.
Claims 6, 10 and 29 are objected to.
Priority
The instant application claims benefits to provisional application no. 63/316, 277 filed on March 3, 2022. Domestic priority benefit is acknowledged. Thus, the effective filing date of claims 1-2, 6-10, 12-13, 15, 17-18, 22, 26, 29-32, and 37-39 is March 3, 2022.
Information Disclosure Statement
The information disclosure statement (IDS) was filed on 07/05/2023. All references in the IDS have been considered by the examiner and attached in this office action.
Drawings
The Drawings filed on 03/01/2023 are accepted.
Specification
The Specification filed on 03/01/2023 are accepted.
Claim Objections
Claim 6 is objected to because of the following informalities: “a plurality of individual not having the disease” should be “a plurality of individuals not having the disease”. Appropriate correction is required.
Claim 10 is objected to because the acronym “CHALM” should be fully spelled out. Appropriate correction is required.
Claim 29 is objected to because the acronym “EM-seq” should be fully spelled out. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 39 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 39 recites the limitation "the disease". There is insufficient antecedent basis for this limitation in the claim.
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 1-2, 6-10, 12-13, 15, 17-18, 22, 26, 29-32, and 37-39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea/law of nature/natural phenomenon:
Claim 1 recites analyzing data to determine an epigenetic signature, wherein the epigenetic signature comprises features obtained from two or more of the following profiles: a methylation profile comprising information derived from one or more methylation sites, a nucleosome dynamics profile comprising information from one or more of a nucleosome positional information, nucleosome occupancy, or nucleosome fuzziness, or a fragmentation profile comprising information derived from read distribution in one or more base length windows (Abstract Idea: Mental Process and/or Mathematical Process). The observation, evaluation, and comparison of biological data profiles in order to generate a signature is a process that in the simplest sense can be carried out in the human mind. In addition, the process of analyzing data in the broadest sense would also involve the use of mathematical comparisons and operations, which may constitute a mathematical process.
Claim 2 recites extracting features from sequencing data, wherein the features include information from two or more of the following profiles: a methylation profile comprising information derived from one or more methylation sites, a nucleosome dynamics profile comprising information from one or more of a nucleosome positional information, nucleosome occupancy, or nucleosome fuzziness, or a fragmentation profile comprising information derived from read distribution in one or more base length windows (Abstract Idea: Mental Process and/or Mathematical Process). The observation, evaluation, and comparison of biological data profiles in order to extra details is a process that in the simplest sense can be carried out in the human mind. In addition, the process of comparing data to extra features in the broadest sense would also involve the use of mathematical comparisons and operations, which may constitute a mathematical process. Claim 2 also recites analyzing the features using a machine learning model to generate an epigenetic signature based on the features (Abstract Idea: Mathematical Concept and/or Mental Process. A machine learning model in the broadest sense can be a linear regression or logistic regression, which is a mere mathematical algorithm. In addition, similar to above, the process of analyzing and comparing biological data to extra differential signatures in the simplest sense, can also be carried out in the human mind or with pen and paper.
Claim 6 recites extracting features from sequencing data, wherein the features include information from two or more of the following profiles: a methylation profile comprising information derived from one or more methylation sites, a nucleosome dynamics profile comprising information from one or more of a nucleosome positional information, nucleosome occupancy, or nucleosome fuzziness, or a fragmentation profile comprising information derived from read distribution in one or more base length windows (Abstract Idea: Mental Process and/or Mathematical Process). The observation, evaluation, and comparison of biological data profiles in order to extra details is a process that in the simplest sense can be carried out in the human mind. In addition, the process of comparing data to extra features in the broadest sense would also involve the use of mathematical comparisons and operations, which may constitute a mathematical process. Claim 6 also recites training a machine learning model with the extracted features to identify a disease epigenetic signature based (Abstract Idea: Mathematical Concept and/or Mental Process. A machine learning model in the broadest sense can be a linear regression or logistic regression, which is a mere mathematical algorithm. In addition, similar to above, the process of analyzing and comparing biological data to extra differential signatures of disease in the simplest sense, can also be carried out in the human mind or with pen and paper.
Claim 7 recites that the methylation profile utilizes information from at least one or more of the listed methylation sites (Abstract Idea: Mental Process). This limitation further specifies the type data extracted for the methylation profile and is part of the abstract idea of analyzing the data obtained.
Claim 8 recites that the one or more methylation cites comprise one or more gene promoter region (Abstract Idea: Mental Process). Again, this limitation is merely specifying the extract data for the methylation profile is part of the abstract idea of analyzing the data obtained.
Claim 9 recites that the methylation profile comprises quantitative information from at least one of the methylation sites (Abstract Idea: Mental Process). This limitation is merely specifying the extract data for the methylation profile is part of the abstract idea of analyzing the data obtained.
Claim 10 recites that the quantitative information in the methylation profile is based on a β-value or a CHALM ratio (Abstract Idea: Mathematical Concept and/or Mental Process). The process of calculating the β-value or CHAML ratio utilizes mathematical operations, and in the simplest sense can also be done in the human mind.
Claim 12 recites that the nucleosome dynamics information is based on a nucleosome at a genomic locus (Abstract Idea This limitation further specifies the type data extracted for the nucleosome profile and is part of the abstract idea of analyzing the data obtained.
Claim 13 recites that the nucleosome positional information is based on a window protection score (Abstract Idea: Mathematical Concept and/or Mental Process). The process of calculating the score in the broadest sense utilizes mathematical operations, and in the simplest sense can also be done in the human mind.
Claim 15 recites that the nucleosome occupancy is based on a frequency a nucleosome occupies a genomic region (Abstract Idea: Mathematical Concept and/or Mental Process). The process of calculating the frequency in the broadest sense utilizes mathematical operations, and in the simplest sense can also be done in the human mind.
Claim 17 recites that the nucleosome fuzziness is based on a deviation of a nucleosome position from a prefer nucleosome position (Abstract Idea: Mathematical Concept and/or Mental Process). The process of calculating the deviation in the broadest sense utilizes mathematical operations, and in the simplest sense can also be done in the human mind.
Claim 18 recites that the fragmentation profile is based on a base length window from 30-250 bases (Abstract Idea: Mental Process). This limitation further specifies the type data extracted for the fragmentation profile and is part of the abstract idea of analyzing the data obtained.
Claim 22 recites that the epigenetic signatures is derived from: a methylation and nucleosome profile, a methylation and fragmentation profile, a nucleosome and fragmentation profile, or a methylation, nucleosome, and fragment profile (Abstract Idea: Mental Process). This limitation further specifies the type data extracted and analyzed for the epigenetic signature and is part of the abstract idea of analyzing the data obtained.
Claim 26 recites that the nucleosome dynamics profile is based on information derived from nucleosome positional information, nucleosome occupancy, nucleosome fuzziness or a combination thereof (Abstract Idea: Mental Process). Again, this limitation further specifies the type data extracted for the nucleosome profile and is part of the abstract idea of analyzing the data obtained.
These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. While claims 2 and 6 recite performing some aspects of the analysis with a “machine learning model”, there are no additional limitations that indicate that this model requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-2, 6-10, 12-13, 15, 17-18, 22, 26, 29-32, and 37-39 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to collect input data using known techniques. Specifically, the claims recite the following additional elements:
Claim 2 recites receiving sequencing data obtained from performing a non-disruptive methylation sequencing technique on a sample. This additional element is a mere data gathering step that constitutes as an insignificant extra-solution activity which does not integrate the judicial exception into a practical application. Claim 2 also recites inputting extracted features into a machine learning model, which again is mere data gathering and manipulation. Lastly claim 2 recites outputting the data in the form of a generated epigenetic signature, which is mere data outputting under insignificant extra-solution activity.
Claim 6 recites receiving sequencing data obtained from performing a non-disruptive methylation sequencing technique on samples. This additional element is a mere data gathering step that constitutes as an insignificant extra-solution activity which does not integrate the judicial exception into a practical application. Claim 6 also recites inputting extracted features with labels of whether or not the sample has a disease into a machine learning model, which again is mere data gathering and manipulation. Lastly claim 6 recites outputting the data in the form of a generated epigenetic signature, which is mere data outputting under insignificant extra-solution activity.
Claim 29 recites that the non-disruptive methylation sequencing technique is EM-seq technique. This limitation is a mere data gathering step, and constitutes as insignificant extra-solution activity.
Claim 30 recites that the non-disruptive methylation sequencing is performed on a targeted genetic location. Again, this limitation is merely specifying the data gathering step, and constitutes as insignificant extra-solution activity.
Claim 31 recites performing the non-disruptive methylation sequencing, which makes it a data gathering step.
Claim 32 recites that the data from the non-disruptive methylation sequencing includes a plurality of sequence reads. This is merely expanding on the input data type, and still part of the data gathering step.
Claim 37 recites that the sample is a cell-free DNA sample, which is merely another data gathering component.
Claim 39 recites that the disease is cancer, which is again merely specifying the sample data type, and part of the data gathering component.
There are no limitations that indicate that the claimed machine learning model or the formats of the provided data require anything other than generic computing systems and sequencing techniques. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. As such, claims 1-2, 6-10, 12-13, 15, 17-18, 22, 26, 29-32, and 37-39 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic computing environment and collecting biological data for analysis with known and common sequencing methods. For example, the specification of the instant application paragraph [0045] provides various examples of non-disruptive methylation sequencing including EM-sequencing along with citations to art which demonstrate that non-disruptive methylation sequencing is a well-establish concept in the field. In addition, paragraph [0050] of the specification states that suitable sequencing techniques useful for non-disruptive methylation sequencing techniques are well known in the art and paragraph [0065] states that the one or more methylation sites can be any of those provided in the Illumina Infinium HumanMethylation450 BeadChip (450K), a kit that is commercially available and public. Paragraph [0075], [0076], and [0079] respectively states that methods for determining WPS (nucleosome positional information), tools for determining nucleosome occupancy information, and comprehensive bioinformatics pipeline designed for dynamic nucleosome analysis at single-nucleotide resolution, are known in the art. As discussed above, there are no additional limitations to indicate that the claimed machine learning model requires anything other than generic computer components in order to carry out the recited abstract idea in the claims, and no additional limitations to indicate that the data gathering components utilize anything other than commercial and known methods. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Therefore, the additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-2, 6-10, 12-13, 15, 17-18, 22, 26, 29-32, and 37-39 are not patent eligible.
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.
Claims 1-2, 6-10, 18, 22, 30-32, 37 and 39 rejected under 35 U.S.C. 102(a)(1) as being anticipated by Siejka-Zielinska et al. (Science Advances Vol 7 Issue 36 eabh0534, Published Sep 1, 2021), herein after known as Siejka. The claim limitations of the instant application are italicized below.
With respect to claim 1, Siejka teaches the development of a cell-free DNA TET-assisted pyridine borane sequencing (cfTAPS) method to provide multimodal information about epigenetic and genetic features for early identification of carcinomas (pg. 1 Abstract, a method of determining an epigenetic signature). Siejka starts by collecting circulating cell free DNA (cfDNA) from 30 noncancer control patients, 8 pancreatitis patients, 24 Pancreatic Ductal Adenocarcinoma (PDAC) patients, 21 hepatocellular carcinoma (HCC) patients, and 4 cirrhosis patients (pg. 7 left col para 3, from a sample obtained from an individual). Siejka then performs cfTAPS on the samples, which is a mild, bi-sulfite-free method for base resolution direct DNA methylation sequencing (pg. 1 Abstract and pg. 8 left col para 3, the method comprising analyzing data obtained from a non-disruptive methylation sequencing technique performed on the sample obtained from the individual to determine the epigenetic signature). Then, Siejka processes the raw sequenced reads and uses the bismark_methylation_extractor to extract methylation data from the sequencing data (pg. 8 left col para 5, a methylation profile comprising information derived from one or more methylation sites). In addition, Siejka calculated fragmentation profiles for the samples as well in R with Samtools (pg. 9 left col para 3, a fragmentation profile comprising information derived from read distribution one or more base length windows). Based on these data, Siejka finds differential DNA methylation patterns and fragmentation patterns in patients with cancer and control patients without cancer (pg. 4 right col para 1 and pg. 5 right col para 1, wherein the epigenetic signature comprises features obtained from two or more of the following profiles: a methylation profile … or a fragmentation profile).
Regarding claim 2, Siejka teaches the development of a cell-free DNA TET-assisted pyridine borane sequencing (cfTAPS) method to provide multimodal information about epigenetic and genetic features for early identification of carcinomas (pg. 1 Abstract, a method of generating an epigenetic signature). Siejka starts by collecting circulating cell free DNA (cfDNA) from 30 noncancer control patients, 8 pancreatitis patients, 24 Pancreatic Ductal Adenocarcinoma (PDAC) patients, 21 hepatocellular carcinoma (HCC) patients, and 4 cirrhosis patients (pg. 7 left col para 3, from a sample obtained from an individual). Siejka then performs cfTAPS on the samples, which is a mild, bi-sulfite-free method for base resolution direct DNA methylation sequencing (pg. 1 Abstract and pg. 8 left col para 3, the method comprising; receiving sequencing data obtained from a non-disruptive methylation sequencing technique performed on the sample obtained from the individual). Then, Siejka processes the raw sequenced reads and uses the bismark_methylation_extractor to extract methylation data from the sequencing data (pg. 8 left col para 5, a methylation profile comprising information derived from one or more methylation sites). In addition, Siejka calculated fragmentation profiles for the samples as well in R with Samtools (pg. 9 left col para 3, a fragmentation profile comprising information derived from read distribution one or more base length windows). Based on these data, Siejka finds differential DNA methylation patterns and fragmentation patterns in patients with cancer and control patients without cancer (pg. 4 right col para 1 and pg. 5 right col para 1, extracting features from the sequencing data, wherein the features include information from two or more of the following profiles: a methylation profile … or a fragmentation profile). Siejka does this by inputting the methylation profiles, fragmentation profiles, and another tissue contribution profile into supervised machine learning models (pg. 4 left col para 3 and pg. 9 left col para 6, inputting the extracted features into a machine learning model). Siejka states that the models were able to capture cancer type-specific methylation changes, and cfDNA fragmentations signatures that can be used to distinguish cancer samples from noncancer controls with high accuracy (pg. 4 left col para 3 and pg. 5 right col para 1, analyzing the features using the machine learning model to generate the epigenetic signature based on a plurality of the features; and outputting the generated epigenetic signature).
Concerning claim 6, Siejka teaches the development of a cell-free DNA TET-assisted pyridine borane sequencing (cfTAPS) method to provide multimodal information about epigenetic and genetic features for early identification of carcinomas (pg. 1 Abstract, a method of identifying a disease epigenetic signature indicative of an individual having a disease). Siejka starts by collecting circulating cell free DNA (cfDNA) from 30 noncancer control patients, 8 pancreatitis patients, 24 Pancreatic Ductal Adenocarcinoma (PDAC) patients, 21 hepatocellular carcinoma (HCC) patients, and 4 cirrhosis patients for sequencing (pg. 7 left col para 3, receiving sequencing data from a plurality of individuals having the disease and a plurality of individual not having the disease). Siejka then performs cfTAPS on the samples, which is a mild, bi-sulfite-free method for base resolution direct DNA methylation sequencing (pg. 1 Abstract and pg. 8 left col para 3, wherein the sequencing data is obtained from a non-disruptive methylation sequencing technique performed on the sample obtained from the individual). Then, Siejka processes the raw sequenced reads and uses the bismark_methylation_extractor to extract methylation data from the sequencing data (pg. 8 left col para 5, a methylation profile comprising information derived from one or more methylation sites). In addition, Siejka calculated fragmentation profiles for the samples as well in R with Samtools (pg. 9 left col para 3, a fragmentation profile comprising information derived from read distribution one or more base length windows). Based on these data, Siejka finds differential DNA methylation patterns and fragmentation patterns in patients with cancer and control patients without cancer (pg. 4 right col para 1 and pg. 5 right col para 1, extracting features from the sequencing data, wherein the features include information from two or more of the following profiles: a methylation profile … or a fragmentation profile). Siejka does this by inputting the methylation profiles, fragmentation profiles, and another tissue contribution profile, each associated with cancer/noncancer types into supervised machine learning models (pg. 4 left col para 3 and pg. 9 left col para 6, inputting the extracted features into a machine learning model, wherein the extracted features from each of the plurality of individuals are embedded with an associated classification of the individual having the disease or not having the disease). Siejka states that the models were trained and able to capture cancer type-specific methylation changes, and cfDNA fragmentations signatures that can be used to distinguish cancer samples from noncancer controls with high accuracy (pg. 4 left col para 3 and pg. 5 right col para 1, training the machine learning model using the extracted features to identify the disease epigenetic signature; and outputting the disease epigenetic signature).
With respect to claim 7, Siejka teaches the use of whole-genome DNA methylation profiling, and analysis of global methylation levels of cfDNA in cancer and control samples (pg. 3 left col para 6 and right col para 2). Although Siejka does not explicitly state all of the CpG methylation sites that they looked at, a whole-genome DNA methylation profiling would inherently include at least one or methylation sites stated in claim 7 (wherein each of the one or more methylation sites of the methylation profile are selected from the group consisting of cg18081940, cg23089825, cg16395183, cg19811148, cg07790615, cg20996351, cg04977528, cg24465685, cg20428713, cg13678973, cg25339566, cg16596317, cg23786625, cg11328303, cg19578660, cg02272851, cg10298052, cg13585930, cg23575688, cg12394201, cg08149193, cg18854419, cg07603330, cg10658542, cg13099890, cg22302985, cg13596497, cg14507533, cg25366582, cg22396555, cg10566012, cg05168229, cg10795666, cg25078444, cg16038120, cg23883632, cg18380808, cg13615592, cg00250422, cg19691260, cg16558770, cg15681853, cg03397724, cg10514097, cg06674117, cg16047279, cg12127472, cg08843809, cg08697732, cg06384763, cg04203646, cg17112426, cg08278741, cg14587524, cg26087117, cg18320766, cg08063125, cg10004780, cg18921980, cg02514318, cg20002504, cg18897632, cg15313459, cg19370054, cg16564824, cg02631468, cg01471196, cg23770904, cg18412834, cg24080247, cg11549874, cg13155421, cg19442495, cg22536150, cg05413061, cg23346462, cg09477895, cg13605674, cg13314965, cg09417547, cg00181669, cg23967169, cg10237419, cg21077559, cg27600205, cg19755714, cg18797590, cg00699993, cg06485940, cg27661394, cg00939495, cg11036833, cg23915769, cg07224726, cg02022733, cg03640756, cg15361590, cg04598517, cg06782035, cg13954457, cg25482900, cg20952257, cg14062050, cg01881524, cg11538641, cg11387340, cg05389236, cg19419054, cg10575547, cg17240815, cg24772267, cg00920327, cg00772257, cg26253500, cg23244488, cg22778435, cg26065247, cg02088996, cg19868631, cg22280038, cg07803375, cg20230721, cg03333330, cg21517947, cg10406295, cg05166490, cg07739205, cg20980783, cg06617456, cg01568998, cg13407456, cg23758305, cg20675505, cg07585876, cg03734437, and cg13410764).
Regarding claim 8, Siejka discloses that they investigated the predictive potential of cfDNA methylation in enhancer and promoter regions for HCC and PDAC (pg. 4 left col para 3, wherein the one or more methylation sites of the methylation profile comprise one or more gene promoter region methylation sites).
Concerning claim 9, Siejka teaches the use of whole-genome DNA methylation profiling, and analysis of global methylation levels of cfDNA in cancer and control samples (pg. 3 left col para 6 and right col para 2). Although Siejka does not explicitly state all of the CpG methylation sites that they looked at, a whole-genome DNA methylation profiling would inherently include quantitative information from at least one or methylation sites (wherein the methylation profile comprises quantitative information from at least one of the one or more methylation sites).
With respect to claim 10, Siejka teaches that methylation levels were calculated by using the number of methylated CpGs divided by the number of total CpGs sequenced, which results in a value that represents ratio of methylated sites over total sites (pg. 8 left col para 6 - right col para 1, wherein the quantitative information is based on a β-value from the at least one methylation site).
Regarding claim 18, Siejka discloses that they looked at fragments patterns in the whole genome, listing how cancer patients have a higher frequency of fragments below 150 base pairs (bp), in the range of short from 70-150 bp (pg. 5 left col para 2 and pg. 4 Fig. 3C, wherein the fragmentation profile is based on one or more base length windows occupying the range of 30 to 250 bases in length).
Concerning claim 22, Siejka teaches the use of a methylation profile and fragmentation profile to find differential DNA methylation patterns and fragmentation patterns in patients with cancer and control patients without cancer (pg. 4 right col para 1 and pg. 5 right col para 1, wherein the epigenetic signature comprises features from: … ii) the methylation profile and the fragmentation profile).
With respect to claim 30, Siejka discusses the use of targeted cfTAPS with the differentiated methylation biomarkers to reduce sequencing costs (pg. 7 left col para 2, wherein the non-disruptive methylation sequencing technique is performed based on targeted genetic locations).
Regarding claim 31, Siejka teaches that the cfTAPS was performed on all the samples, and that cfTAPS is a mild, bi-sulfite-free sequencing method for base resolution direct DNA methylation sequencing (pg. 1 Abstract and pg. 8 left col para 3, further comprising performing the non-disruptive methylation sequencing technique).
Concerning claim 32, Siejka discloses that after performing cfTAPS, the libraries of raw sequenced reads were processed with trim:galore and the clean reads were aligned to the human reference genome (pg. 8 left col para 4, wherein the data obtained from the non-disruptive methylation sequencing technique comprises a plurality of sequence reads).
With respect to claim 37, Siejka teaches that cell free DNA samples were extracted from the plasma of patients (pg. 7 right col para 3, wherein the sample is a cell-DNA sample).
Regarding claim 39, Siejka discloses that cfTAPS is utilized for accurate and early identification of cancers like HCC and PDAC (pg. 1 Abstract, wherein the disease is a cancer).
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 12-13, 15, 17, 26, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Siejka as applied to claim 1-2, 6, 7-10, 18, 22, 30-32, 37 and 39 above, and further in view of Erger et al. (Genome Medicine Vol 12:54, Published June 12, 2020), herein after known as Erger. The claim limitations of the instant claim are italicized below.
Although Siejka does discuss the possible use nucleosome data (nucleosome positioning) within the data analysis (pg. 7 left col para 1), Siejka does not explicitly use a nucleosome dynamic profile comprising information derived from any one or more of (a) nucleosome positional information; (b) nucleosome occupancy; or (c) nucleosome fuzziness. However, the use of nucleosome information in addition to methylation data to determine differential epigenetic signatures relevant to cancer has been well known in the art before the effective filing date of the instant application, as demonstrated by Erger. In addition, Siejka chose to use cell free DNA TET-assisted pyridine borane sequencing (cfTAPS) for identification of cancer epigenetics. Hence, Siejka does not discuss the use of EM-sequencing techniques. However, the use of EM-sequencing for cancer epigenetic detection was already well known in the art before the effective filing date of the instant application as demonstrated by Erger.
With respect to claim 12, Erger teaches the use of nucleosome foot printing analysis (pg. 4 right col para 2). Specifically, Erger obtains nucleosome information for a given genomic region based on the transcription start site (TSS) coordinates from the UCSC Table Browser (pg. 4 right col para 2, wherein the nucleosome dynamics information is based on a nucleosome at a genomic locus).
Regarding claim 13, Erger teaches the use window protection scores for nucleosome positional information (pg. 4 right col para 2, wherein the nucleosome positional information is based on a window protection score (WPS)).
Concerning claim 15, Erger teaches the use nucleosome occupancy and specifically how fragment length peak at ~170 base pairs (bp) in blood-derived cfDNA is evident, as well as an additional fragment length periodicity of 10.5bp (pg. 17 of Supplementary Fig. S17). The former feature is called the nucleosomal peak and represents the length of DNA wrapped around a single nucleosome including the adjacent linker DNA, the latter periodicity represents the rotation of the DNA double helix along its longitudinal axis when wrapped around the nucleosome (pg. 17 of Supplementary Fig. S17, wherein the nucleosome occupancy is based on the frequency a nucleosome occupies a genomic region).
With respect to claim 17, Erger teaches the calculation of nucleosome organization strength by taking the sum of the reads at the preferred center (the green area) and subtracting the sum of reads in the flanking region (the red area) (pg. 6 Fig. 3, wherein the nucleosome fuzziness is based on the deviation of a nucleosome position from a prefer nucleosome position).
Regarding claim 26, Erger teaches the use of nucleosome occupancy for epigenetic cfDNA profiling in cancer (pg. 1 Abstract, wherein the nucleosome dynamics profile comprises information derived from nucleosome positional information, nucleosome occupancy, nucleosome fuzziness, or a combination thereof).
With respect to claim 29, Erger teaches the use of non-disruptive enzymatic methyl sequencing, specifically the commercial NEB EM-seq protocol, for comprehensive and inexpensive epigenetic cfDNA profiling in cancer (pg. 1 Abstract and pg. 2 right col para 6, wherein the non- disruptive methylation sequencing technique is an EM-seq technique).
It would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention to utilize the nucleosome information gathered from Erger within the data analysis model of Siejka to improve the predictive power of the early cancer detection. One of ordinary skill in the art would have been motivated to incorporate the nucleosome information into the data analysis model in order to increase the accuracy and sensitivity of detection. As stated in Siejka, the incorporation of other genetic and epigenetic information such as nucleosome positioning would further improve sensitivity and ability to detect outliers within highly heterogenous disease cohorts (pg. 7 left col para 1). In addition, one of skill in the art before the effective filing date of the claimed invention would have a reasonable expectation of success at incorporating the nucleosome information of Erger within the data analysis model of Siejka as both analyze and process cell free DNA for the same field of early cancer and differential epigenetic detection.
Furthermore, it would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention to utilize the commercial enzymatic methyl-sequencing technique of Erger as a substitution to the cfTAPS method of Siejka to maximize DNA preservation and data reproducibility, as stated in Erger (pg. 1 Abstract and pg. 11 left col para 3). One of ordinary skill in the art would have been motivated to incorporate the enzymatic methyl sequencing technique into the pipeline of Siejka due to its commercial availability, preservation of cfDNA fragmentation information, and precise quantification even with small amounts of DNA, which is also stated in Erger (pg. 1 Abstract and pg. 2 left col para 3). In addition, one of skill in the art before the effective filing date of the claimed invention would have a reasonable expectation of success at incorporating the EM sequencing within the pipeline of Siejka as both EM-sequencing and cfTAPS operate on cell-free DNA to process similar methylation information for detection of cancer epigenetic signatures. Both are non-disruptive methylation sequencing techniques that achieve predictable sequencing results that can be utilize in a machine learning model for detection of cancer epigenetics, as demonstrated by Erger and Siejka.
Conclusion
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/W.Y./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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