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 .
Information Disclosure Statement
The IDS filed 9/17/2024 has been considered by the Examiner.
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
This application list references in the specification (pages 96-106).
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Priority of US application 62/666,574 filed 05/03/2018 is acknowledged.
Claim Status
Claims 1-30 are cancelled.
Claims 31-50 are pending and are examined on the merits.
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.
Claims 31-50 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential steps, such omission amounting to a gap between the steps. See MPEP § 2172.01. The omitted steps are: both claims 31 and 38 start with “a biological sample” including “a mixture of cell-free DNA molecules from a plurality of tissue types”, which is interpreted as biological materials of cell-free DNA molecules as a mixture. Then starting from the first functional step, till end of both claims, every single step requires DNA sequences for analyzing. It is not clear the relationship between the subject analyzed in every functional steps and the cell-free DNA molecules as a mixture of a biological sample.
Consequently, the first functional steps in both claims 31 and 38 are unclear regarding “identifying a first set of genomic positions that …” We don’t know identifying such genomic positions from where, or what.
Apparently, a DNA sequencing step is missing. Applicant can amend the claims 31 and 38 to add a sequencing step, or delete the biological sample and mixture, simply reciting something like “acquiring cell-free DNA sequence data that corresponding to a biological sample of tissue mixture…”
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 31-50 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 31-37 and 50 are to a method of analyzing a biological sample, with a series functional steps, so a process.
Claims 38-49 are to another method of analyzing a biological sample, with a series functional steps, so another process.
Step 2A Prong One: Identification of Abstract Ideas
The claims recite:
To determine a classification of a proportional contribution of the first tissue type in the mixture,
This step recites “to determine” something in a general way, it reads on a judgement or decision-making activity. Hence this step equates to an abstract idea of mental processes.
Identifying a first set of genomic positions that have a specified distance from a center of one or more tissue-specific open chromatin regions corresponding to the first tissue type;
This step recites identifying a first set of genomic positions that have a specified distance from a center of one or more tissue-specific open chromatin regions, which will require the comparing of a specified distance (a first set of genomic positions to a center of one or more tissue-specific open chromatin regions). Hence this step equates to an abstract idea of mathematical concepts.
Analyzing a first plurality of cell-free DNA molecules from the biological sample of a subject,
This step recites “analyzing” something in a general way, it reads on a data analysis in a general way. Hence this step equates to an abstract idea of mental processes.
Determining a genomic position in a reference genome corresponding to both ends of the cell-free DNA molecule;
This step recites determining a genomic position in a reference genome, which will require the comparing of both ends and the reference genome position. Hence this step equates to an abstract idea of mathematical concepts.
Classifying one end as an upstream end and another end as a downstream end based on which end has a lower value for the genomic position;
This step recites a judgement or decision-making activity (classifying … as …) which will require the comparing of numbers (has a lower value for the genomic position). Hence this step equates to an abstract idea of mental processes and mathematical concepts.
Determining that a first number of the first plurality of cell-free DNA molecules have an upstream end at one of the first set of genomic positions;
This step recites determining that at least one of the cfDNA has an upstream end at one of the first set of genomic positions, which requires comparing of numbers (one end vs the first set of genomic positions). Comparing two numbers can be achieved in human mind. Hence this step equates to an abstract idea of mental processes.
Determining that a second number of the first plurality of cell-free DNA molecules have a downstream end at one of the first set of genomic positions;
This step recites determining that at least one of the cfDNA has a downstream end at one of the first set of genomic positions, which requires comparing of numbers (one end vs first set of genomic positions). Comparing two numbers can be achieved in human mind. Hence this step equates to an abstract idea of mental processes.
Computing a separation value between the first number and the second number;
This step recites comparing of numbers explicitly. Hence this step equates to an abstract idea of mathematical concepts.
Determining the classification of the proportional contribution of the first tissue type by comparing the separation value to one or more calibration values determined from one or more calibration samples whose proportional contributions of the first tissue type are known.
This step recites a judgement or decision-making activity (determining the classification of the proportional contribution) which will require the comparing of numbers (the separation value to one or more calibration values). Hence this step equates to an abstract idea of mental processes and mathematical concepts.
Step 2A Prong Two: Consideration of Practical Application
The claims result in a process of determining the classification of the proportional contribution of the first tissue type by comparing the separation value to one or more calibration values determined from one or more calibration samples whose proportional contributions of the first tissue type are known, which reads on an abstract idea of mental processes and mathematical concepts. The claims do not recite any additional elements that integrate the abstract idea/judicial exception into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method does not recite "additional elements" that are not limitations drawn to an abstract idea.
The claims do not include additional elements that are sufficient to amount of significantly more than the judicial exception.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 31, 33-36, 38-43. 45-48 and 50 are rejected under 35 U.S.C. 103 as being unpatentable over Snyder et al. ("Cell-free DNA comprises an in vivo nucleosome footprint that informs its tissues-of-origin." Cell 164.1 (2016): 57-68. Newly cited), and further in view of Sun et al. ("Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments.” Proceedings of the National Academy of Sciences 112.40 (2015): E5503-E5512. Newly cited).
Claim 31 is interpreted as a method to analyze a biological sample. Regarding claim 31, Snyder provides (page 57, section “Summary”) “by deep sequencing cell-free DNA (cfDNA), isolated from circulating blood plasma, we generated maps of genome-wide in vivo nucleosome occupancy and found that short cfDNA fragments harbor footprints of transcription factors. The cfDNA nucleosome occupancies correlate well with the nuclear architecture, gene structure, and expression observed in cells, suggesting that they could inform the cell type of origin” and “We build on this observation to show how nucleosome footprints can be used to infer cell types contributing to cfDNA in pathological states such as cancer”, which teaches cell types contributing (aka “tissue contributors” because the cell types are classified by tissue types) to cfDNA mixtures.
Snyder provides (page 57, section “Summary”) “infer cell types contributing to cfDNA”, which teaches to determine tissue type contribution in the cfDNA mixture.
Snyder provides (page 58, col 2, 1st para) “DHS (Examiner: DNase I hypersensitivity, DHS sites are experimentally detected open chromatin regions. They are parts of the genome where chromatin is less compact, so the DNA is more accessible) sites, e.g., consistent with the repositioning of nucleosomes flanking a distal regulatory element (Figure 2C)”, which links the cfDNA nucleosome/endpoint patterns to DHS/open chromatin. Also suggest the “distance from a center”, because in Fig. 2C (page 60) it is the DHS peaks/summits that are used in counting distance. Exact “specified distance from center” is not explicit in Snyder but it is obvious/routine once DHS peaks/summits are used.
Snyder provides (page 58, col 2, 1st para) “cfDNA fragment endpoints should cluster adjacent to NCP (Examiner: nucleosome core particle) boundaries, while also being depleted on the NCP itself. To quantify this, we developed a windowed protection score (WPS), which is the number of DNA fragments completely spanning a 120 bp window centered at a given genomic coordinate minus the number of fragments with an endpoint within that same window (Figure 2A)”, which suggests a genomic position in a reference genome corresponding to both ends of the cell-free DNA molecules.
Snyder provides (page 58, col 1 col 2 connection sentence) “we next asked whether the predominant local positions of nucleosomes in tissue(s) contributing to cfDNA could be inferred from the distribution of aligned fragment endpoints”, which suggests classifying one end as an upstream and another end as a downstream end. Because once both endpoints are mapped to a reference genome, lower-coordinate/higher-coordinate classification is an obvious operation.
Snyder provides (page 58, col 2, 1st para) “the number of DNA fragments completely spanning a 120 bp window centered at a given genomic coordinate minus the number of fragments with an endpoint within that same window (Figure 2A)”, which teaches endpoint counting by genomic windows. The “upstream end” and the “downstream end” specific counting would be obvious.
Snyder provides (page 58, col 2, 1st para) “we developed a windowed protection score (WPS), which is the number of DNA fragments completely spanning a 120 bp window centered at a given genomic coordinate minus the number of fragments with an endpoint within that same window (Figure 2A)”, which teaches quantitative WPS using spanning fragments minus endpoints. It is obvious that the difference or the ratio between two endpoint classes would be a “separation value”.
Snyder does not determining tissue contribution.
Sun provides (page E5503, section “Significance”) “we obtained a bird’s eye view of the identities and contributions of these tissues to the circulating DNA pool. The tissue contributors and their relative proportions are identified by a bioinformatics deconvolution process that draws reference from DNA methylation signatures representative of each tissue type. We validated this approach in pregnant women, cancer patients, and transplant recipients. This method also allows one to identify the tissue of origin of genomic aberrations observed in plasma DNA” and (page E5508, col 2, 2nd para lines 4-6) “the DNA mixture experiment showed that the conceptual framework of this approach is sound (Figs. 2 and 3), which teaches calibration/ reference comparison for tissue.
Regarding claim 33, Snyder provides (page 58, col 2, 1st para) “we developed a windowed protection score (WPS), which is the number of DNA fragments completely spanning a 120 bp window centered at a given genomic coordinate minus the number of fragments with an endpoint within that same window (Figure 2A)”, which teaches WPS as a subtraction. It would be obvious to use a ratio as an alternative for quantification.
Regarding claim 34, Snyder provides (page 58, col 2, 1st para) “120 bp window centered at a given genomic coordinate minus the number of fragments with an endpoint within that same window (Figure 2A)”, which teaches a specified distance includes a range of distance.
Regarding claim 35, Snyder provides (page 58, col 2, 1st para) “the WPS correlates with genomic features such as DHS sites, e.g., consistent with the repositioning of nucleosomes flanking a distal regulatory element (Figure 2C)”, which suggests symmetric or flanking ranges around open chromatin center.
Regarding claim 36, Snyder provides (page 58, col 2, 1st para) “the WPS correlates with genomic features such as DHS sites, e.g., consistent with the repositioning of nucleosomes flanking a distal regulatory element (Figure 2C)”, which suggests optimization once upstream/downstream phasing is considered.
Claim 38 is a variation of claim 31, with one step further to determine a classification of whether a pathology exists for the first tissue type in the mixture.
Regarding claim 38, Snyder provides (page 57, section “Summary”) “by deep sequencing cell-free DNA (cfDNA), isolated from circulating blood plasma, we generated maps of genome-wide in vivo nucleosome occupancy and found that short cfDNA fragments harbor footprints of transcription factors. The cfDNA nucleosome occupancies correlate well with the nuclear architecture, gene structure, and expression observed in cells, suggesting that they could inform the cell type of origin. Nucleosome spacing inferred from cfDNA in healthy individuals correlates most strongly with epigenetic features of lymphoid and myeloid cells, consistent with hematopoietic cell death as the normal source of cfDNA. We build on this observation to show how nucleosome footprints can be used to infer cell types contributing to cfDNA in pathological states such as cancer”, which teaches cfDNA mixtures and tissue contributors.
Snyder provides (page 57, section “Summary”) “The cfDNA nucleosome occupancies correlate well with the nuclear architecture, gene structure, and expression observed in cells, suggesting that they could inform the cell type of origin. Nucleosome spacing inferred from cfDNA in healthy individuals correlates most strongly with epigenetic features of lymphoid and myeloid cells” and “we build on this observation to show how nucleosome footprints can be used to infer cell types contributing to cfDNA in pathological states such as cancer”, which teaches tissue contribution inference, and classification whether pathology exists.
Snyder provides (page 58, col 2, 1st para) “DHS (Examiner: DNase I hypersensitivity) sites, e.g., consistent with the repositioning of nucleosomes flanking a distal regulatory element (Figure 2C)”, which links the cfDNA nucleosome/endpoint patterns to DHS/open chromatin. Also suggest the “distance from a center”, because in Fig. 2C (page 60) it is the DHS peaks/summits that are used in counting distance.
Snyder provides (page 58, col 2, 1st para) “cfDNA fragment endpoints should cluster adjacent to NCP (Examiner: nucleosome core particle) boundaries, while also being depleted on the NCP itself. To quantify this, we developed a windowed protection score (WPS), which is the number of DNA fragments completely spanning a 120 bp window centered at a given genomic coordinate minus the number of fragments with an endpoint within that same window (Figure 2A)”, which suggests a genomic position in a reference genome corresponding to both ends of the cell-free DNA molecules.
Snyder provides (page 58, col 1 col 2 connection sentence) “we next asked whether the predominant local positions of nucleosomes in tissue(s) contributing to cfDNA could be inferred from the distribution of aligned fragment endpoints”, which suggests classifying one end as an upstream and another end as a downstream end. Because once both endpoints are mapped to a reference genome, lower-coordinate/higher-coordinate classification is an obvious operation.
Snyder provides (page 58, col 2, 1st para) “the number of DNA fragments completely spanning a 120 bp window centered at a given genomic coordinate minus the number of fragments with an endpoint within that same window (Figure 2A)”, which teaches endpoint counting by genomic windows. Orientation-specific counting would be obvious.
Snyder provides (page 58, col 2, 1st para) “we developed a windowed protection score (WPS), which is the number of DNA fragments completely spanning a 120 bp window centered at a given genomic coordinate minus the number of fragments with an endpoint within that same window (Figure 2A)”, which teaches quantitative WPS using spanning fragments minus endpoints. It is obvious that difference/ration between two endpoint classes is a predictable variation of endpoint-derived scores.
Snyder does not determining tissue contribution.
Sun provides (page E5503, section “Significance”) “we obtained a bird’s eye view of the identities and contributions of these tissues to the circulating DNA pool. The tissue contributors and their relative proportions are identified by a bioinformatics deconvolution process that draws reference from DNA methylation signatures representative of each tissue type. We validated this approach in pregnant women, cancer patients, and transplant recipients. This method also allows one to identify the tissue of origin of genomic aberrations observed in plasma DNA” and (page E5508, col 2, 2nd para lines 4-6) “the DNA mixture experiment showed that the conceptual framework of this approach is sound (Figs. 2 and 3), which teaches calibration/ reference comparison for tissue.
Regarding claim 39 and 40, Snyder does not teach reference values. Sun provides (page E5505, col 1, last para) “Genome-wide bisulfite sequencing was performed in 29 HCC patients and 32 control subjects without cancer” and (E5512, col 1, last para) “the sequenced read density of the test case was compared with the values of the reference control subjects”, which teaches reference values from control samples without the pathology.
Regarding claim 41, Snyder provides (page 57, section “Summary”) “The cfDNA nucleosome occupancies correlate well with the nuclear architecture, gene structure, and expression observed in cells, suggesting that they could inform the cell type of origin. Nucleosome spacing inferred from cfDNA in healthy individuals correlates most strongly with epigenetic features of lymphoid and myeloid cells” and “we build on this observation to show how nucleosome footprints can be used to infer cell types contributing to cfDNA in pathological states such as cancer”, which suggests pathology is abnormally high fractional concentration of cfDNA from first tissue.
Regarding claim 42, Snyder does not teaches validating organ transplantation. Sun provides (page E5505, col 1, 2nd para) “we performed plasma DNA tissue mapping for four liver transplant recipients and three bone marrow transplant recipients (Table S3)”, which teaches validating organ transplantation.
Regarding claim 43, Snyder provides (page 57, section “Summary”) “we build on this observation to show how nucleosome footprints can be used to infer cell types contributing to cfDNA in pathological states such as cancer”, which teaches the pathology is cancer of the first tissue.
Regarding claim 45, Snyder provides (page 58, col 2, 1st para) “we developed a windowed protection score (WPS), which is the number of DNA fragments completely spanning a 120 bp window centered at a given genomic coordinate minus the number of fragments with an endpoint within that same window (Figure 2A)”, which teaches the separation value includes a difference.
Regarding claim 46, Snyder provides (page 58, col 2, 1st para) “we developed a windowed protection score (WPS), which is the number of DNA fragments completely spanning a 120 bp window centered at a given genomic coordinate minus the number of fragments with an endpoint within that same window (Figure 2A)”, which teaches a range of distances.
Regarding claim 47, Snyder provides (page 58, col 2, 1st para) “we developed a windowed protection score (WPS), which is the number of DNA fragments completely spanning a 120 bp window centered at a given genomic coordinate minus the number of fragments with an endpoint within that same window (Figure 2A)”, which suggests a first range before center and second after center.
Regarding claim 48, Snyder provides (page 58, col 2, 1st para) “the WPS correlates with the locations of nucleosomes within strongly positioned arrays, as mapped by other groups with in vitro methods or ancient DNA (Figure 2B). At other sites, the WPS correlates with genomic features such as DHS sites, e.g., consistent with the repositioning of nucleosomes flanking a distal regulatory element (Figure 2C)”, which teaches a first contribution in first manner, and a second in second manner.
Regarding claim 50, Snyder does not teach a calibration function fit to calibration points. Sun provides (page E5504, col 2, 2nd para) “blood cells (18), the liver (13, 19), and the placenta during pregnancy (15, 16) are known to be major contributors of circulating nucleic acids. We therefore tested the deconvolution algorithm by using DNA mixtures of varying percentage contributions (denoted as input DNA in Fig. 2) of buffy coat DNA, placenta DNA, and liver DNA. The buffy coat DNA was obtained from a 40-y-old healthy nonpregnant woman. The placenta DNA was obtained following the delivery of a healthy female baby at 38 wk of gestation. The liver DNA was obtained from the nonneoplastic liver tissues adjacent to a hepatocellular carcinoma (HCC) at resection from a 57-y-old female subject. As can be seen in Figs. 2 and 3, the percentage contributions measured by the sequencing and deconvolution analysis correlated well with those of the input DNA mixtures”, which teaches a calibration function fit to calibration points comprising known proportional contributions and measured relative abundances.
It would have been prima facie obvious to combine Snyder’s cfDNA endpoint/nucleosome-footprint tissue-of-origin analysis with Sun’s teaching using reference tissue to profile and determine contribution data so to infer proportional tissue contribution in prenatal, cancer, and transplantation assessments. Because Snyder expressly connects cfDNA nucleosome spacing and endpoint patterns with DHS/open chromatin/regulatory features.
One would reasonably expect success as Snyder already demonstrated that cfDNA nucleosome footprints and endpoint patterns correlated with tissue/cell identity and cancer tissue-of-origin, and Sun demonstrated that plasma DNA tissue contribution can be quantitatively inferred in clinically relevant mixture.
Claims 32 and 44 are rejected under 35 U.S.C. 103 as being unpatentable over Snyder and Sun, as applied to claims 31, 33-36, 38-43. 45-48 and 50 above, and further in view of Kundaje et al.: ("Integrative analysis of 111 reference human epigenomes." Nature 518.7539 (2015): 317. Newly cited).
Regarding claims 32 and 44, neither Snyder nor Sun teaches at least 500 tissue-specific open chromatin regions. Kundaje provides (page 2, section “Abstract”) “the NIH Roadmap Epigenomics Consortium generated the largest collection to-date of human epigenomes for primary cells and tissues” and (page 25, penultimate para) “3,516,964 DNase enriched regions across epigenomes”, which teaches over 500 tissue-specific open chromatin regions for primary cells and tissues.
It would have been prima facie obvious to combine the combined pipeline of Snyder and Sun which analysis cfDNA endpoint/nucleosome-footprint for tissue-of-origin with the reference contribution data so to infer proportional tissue contribution in prenatal, cancer, and transplantation assessments, with Kundaje’s teaching of large scale epigenomic research on open chromatin regions. Because Snyder expressly connects cfDNA nucleosome spacing and endpoint patterns with DHS/open chromatin/regulatory features.
One would reasonably expect success as the combined pipeline of Snyder and Sun already use plasma DNA tissue contribution data to infer proportional tissue contribution in prenatal, cancer, and transplantation assessments. Kundaje’s large scale epigenomic research on open chromatin regions will enhance the reference data that is already used by Snyder and Sun. this is a classic example of:
“Combining prior art elements according to known methods to yield predictable results” (MPEP §2143.III.(A)).
Conclusion
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/GL/
Patent Examiner
Art Unit 1686
/Anna Skibinsky/
Primary Examiner, AU 1635
Prosecution Insights