DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-15 are pending.
Priority
This application is a CON of application no. 16/705769, filed 12/06/2019, which is a CON of PCT US18/36963, filed 06/11/2018, which claims benefit of 62/517,571, filed 06/09/2017. The instant application has the effective filing date of 09 June 2017.
Information Disclosure Statement
No information disclosure statement (IDS) has been filed in the instant application. Applicants are reminded of their duty to disclose all information known to them to be material to patentability as defined in 37 C.F.R. 1.56.
Drawings
The drawings, submitted on 04/25/2023, are accepted 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 1-15 are rejected under U.S.C 101 because the claimed invention is directed to abstract ideas and natural phenomena without significantly more, as detailed in the analysis below.
Eligibility Step 1: Subject matter eligibility evaluation in accordance with MPEP § 2106:
Claims 1-15 are directed to a statutory category (method).
Therefore, in accordance with MPEP § 2106.03 all claims have patent eligible subject matter.
[Eligibility Step 1: YES]
Eligibility Step 2A: This step determines whether a claim is directed to a judicial exception in accordance with MPEP § 2106.
Eligibility Step 2A -- Prong One: Limitations are analyzed to determine if the claims recite any concepts that could equate to a judicial exception (i.e. abstract idea, law of nature, or natural phenomenon). Possible judicial exceptions are explored below.
Recitations of Judicial Exceptions:
Claims 1-2 and 15: constructing at least one first sequencing library from the first plurality of cfDNA fragments; determining genomic locations of the first fragment endpoints within a reference genome for at least some of the first plurality of cfDNA fragments as a function of the sequences: e. determining a first vector comprising the number of first fragment endpoints observed at each genomic location; g. constructing at least one second sequencing library from the second plurality of cfDNA fragments; determining genomic locations of the second fragment endpoints within the reference genome for at least some of the second plurality of cfDNA fragments as a function of the sequences; j. determining a second vector comprising the number of second fragment endpoints observed at each genomic location; k. linking the first vector and. the second vector; defining a mathematical function to segregate distributions of quantities from the linked vectors into a first group and a second group, the first group comprising genomic coordinates 1, with lesser difference to the mathematical function and the second group comprising genomic coordinates with greater difference to the mathematical function; and m. identifying one or more sentinel endpoints as members of the second group. (mathematical concept, mental process)
Claim 2 (only): diagnosing a disease or physiological condition in a subject in need thereof wherein the at least one first physiological state is a healthy state and the at least one second physiological state is a disease state, comprising; diagnosing the disease or physiological condition in the subject if the number of sentinel endpoints in the subject vector is above a threshold value. (mathematical concept, mental process, natural phenomena)
Claim 3: wherein some of the isolated cfDNA are filtered to retain cfDNA having a length between an upper bound and a lower bound. (mental process)
Claim 4: wherein the upper bound is 200, 190, 180, 170, 160, 150, 140, 130, 120, 110, 100, 90, 80, 70, 60, or 50 base pairs and the lower bound is 20, 25, 30, 35, 36, 40, 45, 50, 60, 70, 80, 90, 100, 110, or 120 base pairs. (mental process)
Claim 8: further comprising providing a report, with scores. (mathematical concept , mental process)
Claim 9: further comprising recommending treatment for the diagnosed disease or physiological condition in the subject (mental process)
Claim 10: wherein the disease or physiological condition is selected from the group consisting of cancer, normal pregnancy, complications of pregnancy, myocardial infarction, inflammatory bowel disease, systemic autoimmune disease, localized autoimmune disease, allotransplantation with rejection, allotransplantation without rejection, stroke, and localized tissue damage. (mental process)
Claim 11: wherein the cancer is colorectal or ovarian cancer (mental process)
Claim 13: further comprising filtering sentinel endpoints based upon proximity to one or more genomic annotations. (mental process)
Claim 14: wherein the one or more genomic annotations comprises or consists of transcription start sites (TSSs). (mental process)
Claim 15 (only): determining that a disease or physiological condition in a subject has an increased burden, severity, or clinical stage, wherein the at least one first physiological state is a disease state or physiological condition and the at least one second physiological state is the disease state or physiological condition with an increased burden, severity, or clinical stage,
comparing the subject vector to the sentinel endpoints; g. identifying the burden, severity, or clinical stage of the disease or physiological condition as having an increased burden, severity, or clinical stage if the number of sentinel endpoints in the subject vector has more sentinel endpoints than a threshold value. (mathematical concept, mental process)
Step 2A – Prong One Analysis:
Analysis techniques such as selecting, filtering, comparing, and identifying data from other groups, requiring nothing more than the human mind and pen/paper, read on observations, evaluations, judgments, and opinions, and fall under the mental process grouping of abstract ideas. Limitations that merely provide additional information regarding the data being analyzed in this manner are similarly categorized (claims 4, 11, and 14).
Analysis techniques such as scoring, thresholding, vectorizations, and computing mathematical functions recite mathematical calculations and relationships that fall under the mathematical concept grouping of abstract ideas.
Analysis techniques that make correlations between aberrations in isolated DNA fragments and disease risk read on natural phenomena. See Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1375, 118 USPQ2d 1541, 1545 (Fed. Cir. 2016) and Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1361, 123 USPQ2d 1081, 1087 (Fed. Cir. 2017).
Therefore, the claims are found to recite judicial exceptions.
[Eligibility Step 2A – Prong One: YES]
Eligibility Step 2A – Prong Two: 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. If the claim contains no additional claim elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)). Additional elements are recited, categorized, and analyzed below.
Data Gathering Elements:
Claims 1-2 and 15: isolating cell-free DNA (cfDNA) from biological sample(s) from one or more subjects with at least one first physiological state, the isolated. cfDNA comprising a first plurality of cfDNA fragments;
sequencing first fragment endpoints of the first plurality of cfDNA fragments;
isolating cfDNA from biological sample(s) from one or more subjects with at least one second physiological state, the isolated cfDNA comprising a second plurality of cfDNA fragments; sequencing second fragment endpoints of the second plurality of cfDNA fragments;
Claim 5: wherein a subset of any of the isolated cfDNA is targeted to a genomic location.
Claim 6: wherein the genomic location comprises one or more genomic annotations.
Claim 7: wherein the one or more genomic annotations comprises or consists of transcription start sites (TSSs).
Claim 12: wherein the biological sample comprises or consists of whole blood, peripheral blood plasma, urine, or cerebral spinal fluid.
Step 2A – Prong Two Analysis:
The elements merely gather data to obtain information to be manipulated by the judicial exceptions. As such they act as mere data gathering activities, classified as insignificant extra-solution activity per MPEP 2106.05 (g).
As such, the additional elements, when viewed separately and in the context of a whole claimed invention, do not integrate the judicial exceptions into practical application.
[Eligibility Step 2A – Prong Two: NO]
Eligibility Step 2B: Claim elements are probed for inventive concept equating to significantly more than the judicial exception (MPEP 2106.04(II)).
Step 2B Analysis:
Sequencing and isolating cell-free DNA fragments from a biological sample is found well-understood, routine, and conventional per Sequenom, 788 F.3d at 1377-78, 115 USPQ2d at 1157); Cleveland Clinic Foundation 859 F.3d at 1362, 123 USPQ2d at 1088 (Fed. Cir. 2017); Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546; and Gahan et al. (Springer; Vol. 5; p. 13, 114-139; 2014), which is a book excerpt, that reviews the topics of isolating cfDNA fragments and analyzing transcription start sites.
As such, the additional elements are further found to lack inventive concept.
[Eligibility Step 2B: NO]
Therefore, claims 1-15 are directed to judicial exceptions without significantly more and are rejected under 35 U.S.C 101.
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 1-2 and 5-14 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over Abdueva (2019/0287645, which claims priority to provisional applications filed 04/24/2017 and 12/21/2016) in view of Lo et al. (2017/0024513).
Claim 1 is directed to a method that includes: a. isolating cell-free DNA (cfDNA) from biological sample(s) from one or more subjects with at least one first physiological state, the isolated cfDNA comprising a first plurality of cfDNA fragments; b. constructing at least one first sequencing library from the first plurality of cfDNA fragments; c. sequencing first fragment endpoints of the first plurality of cfDNA fragments; d. determining genomic locations of the first fragment endpoints within a reference genome for at least some of the first plurality of cfDNA fragments as a function of the sequences:
Abdueva describes using cell-free DNA sequence information to assess fragmentome profiles, representative of a tissue of origin, disease, or progression [abstract].
Abdueva teaches isolating cell-free DNA (cfDNA) fragments from circulating blood plasma, which carry a footprint of tumor somatic variation when isolated from subjects with cancer [0327]; obtaining sequence information representative of the cell-free DNA fragments and performing a multi-parametric analysis on a plurality of data sets using the sequence information to generate a multi-parametric model representative of the cell-free DNA fragments [0017]; in which the data may include the end position of sequenced DNA fragments or the number of unique sequenced DNA fragments that cover a mappable position [0018]; and each sequence read is mapped to a set of a plurality of reference sequences from the human genome, which obtains base positions [0190].
Claim 1 is further directed to e. determining a first vector comprising the number of first fragment endpoints observed at each genomic location; f. isolating cfDNA from biological sample(s) from one or more subjects with at least one second physiological state, the isolated cfDNA comprising a second plurality of cfDNA fragments; g. constructing at least one second sequencing library from the second plurality of cfDNA fragments; and h. sequencing second fragment endpoints of the second plurality of cfDNA fragments.
Abdueva teaches the multi-parametric analysis may comprise generating a distribution plot of the number of fragments (e.g., the function value y) associated with each input vector, wherein each xi is an independent variable across the sequencing read data, such as a mappable base position [0191]; wherein the training data includes isolated [0327] cfDNA fragments from a first class and second class [0085]; wherein the first and second classes are selected from: having a cancer and not having the cancer [0086]; and the multi-parametric model may comprise three data sets [0245]; such as sequenced fragment end positions [0246].
Claim 1 is further directed to i. determining genomic locations of the second fragment endpoints within the reference genome for at least some of the second plurality of cfDNA fragments as a function of the sequences; j. determining a second vector comprising the number of second fragment endpoints observed at each genomic location; k. linking the first vector and. the second vector; defining a mathematical function to segregate distributions of quantities from the linked vectors into a first group and a second group, the first group comprising genomic coordinates, with lesser difference to the mathematical function and the second group comprising genomic coordinates with greater difference to the mathematical function; and m. identifying one or more sentinel endpoints as members of the second group.
Abdueva teaches in a multi-parametric model to detect cancer, each sequence read was further mapped to a set of a plurality of reference sequences from the human genome [0205]; generating a distribution plot of the number of fragments (e.g., the function value y) associated with each input vector, wherein each xi is an independent variable across the sequencing read data, such as a mappable base position [0191]; plotting the minor allele fraction (MAF) of each healthy control subject without cancer against the MAF of each subject with cancer [0205]. Abdueva teaches among this multi-parametric model, it was observed that cancer subjects with high maximum MAF (e.g., denoted by red circles) tend to have higher values for centered median 10 bp fragment size an lower values for exon-normalized 10 bp fragment start coverage compared to healthy controls [0205]; and a multi-parametric model was thereby performed on cfDNA samples from subjects to detect cancer in these subjects [0205].
Abueva further teaches other examples of uni-parametric models include, but are not limited to, a 2-D analysis on a 2-D starting position distribution, on a 2-D ending position distribution, or on a 2-D fragment length distribution [0185].
Claim 2 is directed to performing steps (a)-(e) of claim 1; diagnosing a disease or physiological condition in a subject in need thereof wherein the at least one first physiological state is a healthy state; the at least one second physiological state is a disease state; and the number of sentinel endpoints in the subject vector is above a threshold value.
Abdueva further teaches a bivariate normal or bivariate t-distribution model P(x) is built to obtain a probability of a particular fragment coming from a non-malignant cell; and if the probability p is below a threshold £, then such a fragment is considered to be anomalous [0337].
Claim 5 is directed to a subset of any of the isolated cfDNA is targeted to a genomic location.
Abdueva teaches mapping a multi-parametric distribution of cfDNA molecules to one or more selected genomic loci [0085].
Claim 6 is directed to the genomic location including one or more genomic annotations. Claim 7 is directed to one or more genomic annotations including transcription start sites (TSSs).
Abdueva teaches the models may be used in a panel configuration to selectively enrich regions (e.g., fragmentome profile associated regions) and ensure a high number of reads spanning a particular mutation or important chromatin-centered events like transcription start sites (TSSs) [0237]; and examining TSS areas where methylation repression can be inferred from nucleosomal occupancy [0254].
Claim 8 is directed to providing a report, that includes scores.
Abdueva teaches outputting a likelihood of an abnormal state class of a dataset based on an input dataset and values indicating a quantitative measure of one or more features derived from fragmentome profiling [0081]; generating a heat map and across genomic locations to visualize a single gene (e.g., KRAS) across a large number of clinical samples as shown in FIG. 6 [0213]; and in some embodiments, at least one of the nucleosomal occupancy profiles is associated with one or more assessments selected from the group consisting of: tumor indication, early detection of cancer, tumor type, tumor severity, tumor aggressiveness, tumor resistance to treatment, tumor clonality, tumor druggability, tumor progression, and plasma dysregulation score [0028].
Claim 9 is directed to recommending treatment for the diagnosed disease or physiological condition in the subject.
Abdueva teaches the present disclosure provides various uses of cell-free nucleic acids (e.g., DNA or RNA); and such uses include detecting, monitoring and determining treatment for a subject having or suspected of having a health condition, such as a disease (e.g., cancer) [0004].
Claim 10 is directed to the disease or physiological condition including one of the following: cancer, normal pregnancy, complications of pregnancy, myocardial infarction, inflammatory bowel disease, systemic autoimmune disease, localized autoimmune disease, allotransplantation with rejection, allotransplantation without rejection, stroke, and localized tissue damage.
Abdueva teaches the subject is having or suspected of having a health condition, such as a disease, such as cancer [0004]; and multi-parametric analysis patterns can include associations of peak heights relating to a phenotype of cohorts, such as those diagnosed with a condition of a cardiovascular condition, infection, inflammation, auto-immune disorder, cancer, diagnosed with a specific type of cancer, diagnosed with a specific stage of cancer [0201].
Claim 11 is directed to the cancer including colorectal cancer or ovarian cancer.
Abdueva teaches in some instances, a cohort comprises individuals having a specific type of cancer, such as breast, colorectal, pancreatic, prostate, melanoma, lung or liver [0235].
Claim 12 is directed to the biological sample including whole blood, peripheral blood plasma, urine, or cerebral spinal fluid.
Abdueva teaches circulating cell-free DNA (cfDNA) may be predominantly short DNA fragments shed from dying tissue cells into bodily fluids such as peripheral blood plasma or serum [0158]; and the biological sample can comprise, for example, a bodily fluid or a solid tissue sample, in which bodily fluids include, for example, blood, serum, plasma, tumor cells, saliva, urine, lymphatic fluid, prostatic fluid, seminal fluid, milk, sputum, stool, tears, and derivatives of these [0139].
Claim 13 is directed to filtering sentinel endpoints based on proximity to one or more genomic annotations.
Abdueva teaches one or more multiple filtering techniques may be applied to the multi-parametric distribution data, either prior to arriving at the calculated plasma deregulation metric or after the plasma deregulation metric is established [0206]; and filtering techniques may also involve removing assay-specific noise via subtraction of pre-defined fragment start coverage associated with assay biases, such as enrichment-related biases associated with targeted capture [0206].
Claim 14 is directed to the one or more genomic annotations including transcription start sites (TSSs).
Abdueva teaches as a result, discrimination between mono-nucleosomal and di-nucleosomal nature of DNA fragments may aid in identifying and determining underlying regulation around transcription start sites (TSS), e.g., in cases of alternative TSS promoter usage, as shown in FIG. 1C [0172], which illustrates variation in transcription start sites (TSS) by the presence of dinucleosomal complex in malignant (late stage lung cancer) versus normal samples [0094].
Abdueva does not explicitly teach step (m), identifying one or more endpoints as members of the group comprising genomic coordinates with greater difference to the mathematical function (claim 1).
Lo et al. describes methods of analyzing cell-free DNA fragmentation patterns.
Lo et al. teaches there are cell-free DNA ending positions that commonly occur across individuals of different physiological states or disease states [0078]; a preferred end can be considered relevant for a physiological or disease state when it has a high likelihood or probability for being detected in that physiological or pathological state; a preferred end is of a certain probability more likely to be detected in the relevant physiological or pathological state than in other states; because the probability of detecting a preferred end in a relevant physiological or disease state is higher, such preferred or recurrent ends (or ending positions) would be seen in more than one individual with that same physiological or disease state[0082]; and preferred ending positions for the particular tissue type can be identified, and a relative abundance of cell-free DNA molecules ending on the preferred ending positions can be used to provide the classification of the proportional contribution. In another example, an amplitude in a fragmentation pattern (e.g., number of cell-free DNA molecules ending at a genomic position) in a region specific to the particular tissue type can be used [0003].
Therefore, Abdueva teaches classifying physiological states based on sequenced cfDNA fragments at end positions. Lo et al. provides one of ordinary skill in the art with sufficient motivation to further identify these end position fragments as being associated with the physiological condition as they increase the confidence of the predicted classification. As such, it would be obvious to one of ordinary skill in the art to identify the sentinel end fragments with a reasonable expectation of success in cfDNA fragmentation profiling analysis.
Claims 3-4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Abdueva (20190287645), in view of Lo et al. (2017/0024513) as applied to claims 1-2 and 5-14 previously, and in view of Snyder et al. (Cell; Volume 164: 57-68; 2016).
Abdueva teaches a method of using cell-free DNA fragment endpoint analysis to distinguish between physiological states and conditions.
Claim 3 is directed to filtering some of the isolated cfDNA into a length between an upper and lower. Claim 4 is directed to the upper bound is 200, 190, 180, 170, 160, 150, 140, 130, 120, 110, 100, 90, 80, 70, 60, or 50 base pairs and the lower bound is 20, 25, 30, 35, 36, 40, 45, 50, 60, 70, 80, 90, 100, 110, or 120 base pairs.
Abdueva further teaches the cell-free DNA may be in the form of short fragments, most of which are less than 200 base pairs in length [0243]; and the fragments can be assayed in various ways such as by sequencing cfDNA fragments or separating cfDNA fragments by size and quantifying them [0307].
Abdueva does not explicitly teach that the fragment lengths must have an upper and lower bound.
Snyder et al. describes using isolated and sequenced cell-free DNA to generate nucleosome occupancy maps.
Snyder et al. teaches a method that can be applied to non-invasively classify cancers at time of diagnosis by matching the epigenetic signature of cfDNA fragmentation patterns against reference datasets corresponding to diverse cancer types (page 10, column 1); and fragment length of cfDNA in single-stranded sequencing library preparation that include highly enriched short fragments of 50–120 bp (page 4, fig. 1d).
Claim 15 is directed to performing steps (a)-(e) of claim 1; comparing the subject vector to the sentinel endpoints; and identifying the burden, severity, or clinical stage of the disease or physiological condition as having an increased burden, severity, or clinical stage if the number of sentinel endpoints in the subject vector has more sentinel endpoints than a threshold value.
Abdueva teaches in some embodiments, the distribution score is indicative of a mutation burden of the genetic aberration [0010]; an assessment may be selected from the group consisting of: indication, tumor type, tumor severity, tumor aggressiveness, tumor resistance to treatment, and tumor clonality [0265]; and other multi-parametric distributions may be obtained from a group selected from (a) subjects with a tissue specific cancer, or (b) subjects with a particular stage of cancer [0008].
Abdueva does not teach comparing the subject vector to the sentinel endpoints.
However, Abdueva teaches the distribution score comprises values indicating one or more of a number of the DNA fragments with dinucleosomal protection and a number of the DNA fragments with mononucleosomal protection [0010].
Snyder et al. further teaches developing a window protection score (WPS) to quantify the expectation that cfDNA fragment endpoints should cluster adjacent to NCP boundaries, while also being depleted on the NCP itself (page 3, column 2); and whether the predominant local positions of nucleosomes in tissue contributing to cfDNA could be inferred from the distribution of aligned fragment endpoints (page 3, column 1)
Snyder et al. further teaches a model in which cfDNA fragments with a dinucleotide composition are preferentially protected from nuclease cleavage by association with proteins (page 3, column 1); and defining the windowed protection score (WPS) of a window of size k as the number of molecules spanning the window minus those with an endpoint within the window (page 12, column 1).
Therefore, Abdueva teaches a method of filtering the cfDNA fragments by size and further teaches thar most cfDNA fragments are less than 200 base pairs long; and considering dinucleosomal protection within its analysis. Snyder et al. teaches a method of using cfDNA fragment analysis to classify physiological states, in the form of cancer types, using a filtered library of fragments within the range of 50-120bp; and accounting for dinucleosomal protection via comparing the analysis result with fragment endpoints. As such, it would be obvious to one of ordinary skill in the art to apply the fragment length range and protection scoring technique of Snyder et al. to the length filtering and scoring method of Abdueva with an expectation of predictable results an improved system of using cfDNA fragmentation profiling for the classification of physiological states and conditions.
Conclusion
No claims are currently allowed.
Correspondence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Milana Thompson whose telephone number is (571)272-8740. The examiner can normally be reached Monday - Friday, 9:00-6:00 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at (571) 272-1113. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.K.T./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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