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 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 1-18 are rejected under 35 U.S.C. 101 because:
The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) “ comparing the quantitative value(s) of the at least one biomarker to a control sample or to a control value; wherein an increase or a decrease in the quantitative value(s) of the at least one biomarker, when compared to the control sample or to the control value, is/are indicative of the subject having an increased risk of dying from prostate cancer.” This judicial exception is not integrated into a practical application because these limitations recite abstract ideas (i.e. invoke a judicial exception) and this judicial exception is not integrated into a practical application because the above cited limitations are both directed to an abstract idea, which could be performed by a mental step or with the use of a black box computer.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additionally recited steps solely recite “determining in a biological sample obtained from the subject a quantitative value of at least one biomarker of the following in the biological sample” and that “ the quantitative value of glycoprotein acetyls quantifies a nuclear magnetic resonance spectroscopy signal that represents the abundance of circulating glycated proteins”. The determining step and quantification are being viewed as simply mere data gathering steps, wherein quantification is done by NMR (which is well known technology, as will be detailed below in the prior art rejections).
Furthermore, it is noted that data gathering to be used in the abstract idea is insignificant extrasolution activity, and not a particular practical application. See MPEP 2106.05(g).
In addition, the examiner notes that office policy (per the July 2015 Interim Eligibility Guidance with regards to USC 101 rejections) states that abstracts ideas may be ideas themselves and that one specific example of an abstract idea is the idea of comparing new and stored information (such comparing the measured levels at least one signature protein) and using rules to identify options (such as whether or not the subject should be diagnosed with a mycobacterium species infection).
In addition, the examiner notes that following the procedure outlined in Mayo
Collab. Svcs. v. Prometheus Labs (SCOTUS) 101 USPQ2d 1961, 132 S. Ct. 1289 (2012) that claim 1 (and therefore their dependent claims as well) would be ineligible since in step 1 it is noted that the claims are directed towards one of the statutory categories (i.e. a method of determining whether a subject is at risk of dying from prostate cancer)), while in step 2a it is noted that the claim is directed to a judicial exception (an abstract idea, as described above as the comparison step) and in step 2b it is noted that the claims do not recite additional elements that amount to significantly more than the judicial exception since the additional measuring step is simply a mere data gathering step.
Therefore claims 1-18 are ineligible.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1, 2, 5, 6, 10-13 and 15-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mehta et al (WO 2019/232512 A1).
Regarding Claim 1, Mehta et al teaches a method for determining whether a subject is at risk of dying from prostate cancer (wherein “the invention provides compositions and methods for glycan analysis for disease detection, diagnosis and prognosis, such as cancer”) (see page 17, lines 13-24);
wherein the method comprises determining in a biological sample obtained (see page 34, lines 21-27 and pages 36, lines 5-9) from the subject a quantitative value of at least one biomarker of the following in the biological sample:
- glycoprotein acetyls,-
a ratio of docosahexaenoic acid to total fatty acids,
- a ratio of linoleic acid to total fatty acids,
- a ratio of monounsaturated fatty acids and/or of oleic acid to total fatty acids,
- a ratio of omega-3 fatty acids to total fatty acids, - a ratio of omega-6 fatty acids to total fatty acids,
- a ratio of saturated fatty acids to total fatty acids,
- fatty acid degree of unsaturation,
- histidine; and comparing the quantitative value(s) of the at least one biomarker (specifically glycans, such as glycoprotein acetyls) to quantitative value(s) of the biomarkers in a control sample or to a control value (see page 29, lines 14-27; page 30, lines 28-32; and page 34, lines 21-27);
wherein an increase or a decrease in the quantitative value(s) of the at least one biomarker, when compared to quantitative value(s) of the biomarkers in the control sample or to the control value, is/are indicative of the subject having an increased risk of dying from prostate cancer (i.e. of prostate cancer being diagnosed and progressing) (see page 50, lines 3-15; page 50, line 27 – page 51, line 10);
wherein the at least one biomarker comprises or is glycoprotein acetyls (see page 12, lines 1-15 and page 49, lines 24-32) , wherein an increase in the quantitative value of glycoprotein acetyls, when compared to quantitative value(s) of the biomarkers in the control sample or to the control value, is indicative of the subject having an increased risk of dying from prostate cancer (i.e. of prostate cancer being diagnosed and progressing) (see page 50, lines 3-15; page 50, line 27 – page 51, line 10); and wherein the quantitative value of glycoprotein acetyls quantifies a nuclear magnetic resonance spectroscopy signal that represents an abundance of circulating glycated proteins (see page 27, lines 20-33 and page 28, line 30 – page 29, line 3).
Furthermore, Mehta et al teaches that “screening biomarkers can find biomarkers that are differentially glycosylated in cancer as well as at various dysplasic stages of the tissue which progresses to cancer. The screened biomarker can be used for cancer screening, risk-assessment, prognosis, disease identification, the diagnosis of disease stages, and the selection of therapeutic targets” (see page 35, lines 25-29).
Regarding Claims 2 and 15-17, Mehta et al teaches that the method comprises determining in the biological sample quantitative values of a plurality of biomarkers (specifically 3, 4 or 5 biomarkers) (see page 34, lines 10-27).
Regarding Claim 5, Mehta et al teaches that the prostate cancer comprises or is malignant neoplasm of prostate (see page 37, lines 23-27).
Regarding Claim 6, Mehta et al teaches that the quantitative value of the at least one biomarker is/are measured using nuclear magnetic resonance spectroscopy (see page 27, lines 20-33 and page 28, line 30 – page 29, line 3).
Regarding Claim 10, Mehta et al teaches that the quantitative value(s) of the biomarkers in control sample or control value represents men without known diagnosis of prostate cancer (i.e. healthy men) (see page 16, lines 21-28 and page 10, lines 28-32).
Regarding Claims 11-13, Mehta et al teaches that the subject may be a man without known diagnosis of prostate cancer (i.e. a seemingly healthy man), a ,man in a general screening setting (wherein Patients with familial history of cancer, and hence a heightened risk of developing the disease, can be tested regularly to monitor their propensity for disease, see page 29, lines 19-20) or a man with a known diagnosis (see page 10, lines 1-15 and page 37, lines 1-10).
Claim(s) 1,2,5,6,10-13 and 15-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Drake et al (WO 2016/036705).
Regarding Claim 1, Drake et al teaches a method for determining whether a subject is at risk of dying from prostate cancer ((i.e. of prostate cancer being diagnosed and progressing) (see abstract, page 4, lines 16-21 and page 10, lines 8-14);
wherein the method comprises determining in a biological sample obtained (see page 20, lines 8-22 and page 42, lines 3-14) from the subject a quantitative value of at least one biomarker of the following in the biological sample:
- glycoprotein acetyls,-
a ratio of docosahexaenoic acid to total fatty acids,
- a ratio of linoleic acid to total fatty acids,
- a ratio of monounsaturated fatty acids and/or of oleic acid to total fatty acids,
- a ratio of omega-3 fatty acids to total fatty acids,
- a ratio of omega-6 fatty acids to total fatty acids,
- a ratio of saturated fatty acids to total fatty acids,
- fatty acid degree of unsaturation,
- histidine; and comparing the quantitative value(s) of the at least one biomarker (specifically glycans, such as glycoprotein acetyls) to quantitative value(s) of the biomarkers in a control sample or to a control value (see page 10, lines 17-30 and page 38, lines 23-29);
wherein an increase or a decrease in the quantitative value(s) of the at least one biomarker, when compared to quantitative value(s) of the biomarkers in the control sample or to the control value, is/are indicative of the subject having an increased risk of dying from prostate cancer (i.e. of prostate cancer being diagnosed and progressing) (see page 10, lines 17-30);
wherein the at least one biomarker comprises or is glycoprotein acetyls (see page 6, lines 6-19) , wherein an increase in the quantitative value of glycoprotein acetyls, when compared to quantitative value(s) of the biomarkers in the control sample or to the control value, is indicative of the subject having an increased risk of dying from prostate cancer (i.e. of prostate cancer being diagnosed and progressing) (see page 10, lines 17-30); and wherein the quantitative value of glycoprotein acetyls quantifies a nuclear magnetic resonance spectroscopy signal that represents an abundance of circulating glycated proteins (see page 22, line 25 – page 23, line 10).
Regarding Claims 2 and 15-17, Drake et al teaches that the method comprises determining in the biological sample quantitative values of a plurality of biomarkers (specifically 3, 4 or 5 biomarkers) (see page 42, lines 8-23).
Regarding Claim 5, Drake et al teaches that the prostate cancer comprises or is malignant neoplasm of prostate (see page 42, lines 3-8).
Regarding Claim 6, Drake et al teaches that the quantitative value of the at least one biomarker is/are measured using nuclear magnetic resonance spectroscopy (see page 22, line 25 – page 23, line 10).
Regarding Claim 10, Drake et al teaches that the quantitative value(s) of the biomarkers in control sample or control value represents men without known diagnosis of prostate cancer (i.e. healthy men) (see page 10, lines 17-30 and page 12, line 31 – page 13, line 7).
Regarding Claims 11-13, Drake et al teaches that the subject may be a man without known diagnosis of prostate cancer (i.e. a seemingly healthy man), a ,man in a general screening setting (wherein Patients with familial history of cancer, and hence a heightened risk of developing the disease, can be tested regularly to monitor their propensity for disease, see page 29, lines 9-10) or a man with a known diagnosis (see page 10, lines 17-30 and page 29, lines 18-33).
Claim Rejections - 35 USC § 103
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.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Mehta et al (or Drake et al) as applied to claim 1 above, and further in view of Wolf et al (WO 2021/186047).
Regarding Claim 3, Mehta et al teaches determining in the biological sample obtained from the subject a quantitative value of glycoprotein acetyls and comparing the quantitative value(s) of the biomarkers to quantitative value(s) of the biomarkers in a control sample or to a control value(s);wherein an increase or a decrease in the quantitative value(s) of the biomarkers, when compared to quantitative value(s) of the biomarkers in the control sample or to the control value, is/are indicative of the subject having an increased risk of dying from prostate cancer (see page 50, lines 3-15; page 50, line 27 – page 51, line 10 and page 49, lines 24-32).
In addition, Drake et al teaches determining in the biological sample obtained from the subject a quantitative value of glycoprotein acetyls and comparing the quantitative value(s) of the biomarkers to quantitative value(s) of the biomarkers in a control sample or to a control value(s);wherein an increase or a decrease in the quantitative value(s) of the biomarkers, when compared to quantitative value(s) of the biomarkers in the control sample or to the control value, is/are indicative of the subject having an increased risk of dying from prostate cancer (see page 10, lines 17-30 and page 38, lines 23-29).
However, neither Mehta et al nor Drake et al discloses that at least one fatty acid measure(s) of the following: the ratio of docosahexaenoic acid to total fatty acids, the ratio of linoleic acid to total fatty acids, the ratio of monounsaturated fatty acids and/or of oleic acid to total fatty acids, the ratio of omega-3 fatty acids to total fatty acids, the ratio of omega-6 fatty acids to total fatty acids, the ratio of saturated fatty acids to total fatty acids, fatty acid degree of unsaturation are also measured and compared.
However, in the analogous art of blood marker analyses with biomarkers, Wolf et al teaches that stronger correlations were found between the microbiome and an inflammatory surrogate (glycoprotein acetyls, GlycA, FIG. 16A), as well as various emerging lipid measures linked to host health, such as HDL and VLDL particle size (HDL-D and VLDL-D, p=0.3 and 0.28 respectively), the lipid content of lipoprotein subfractions (including XL-HDL-L and L-HDL-L, p=0.39 and 0.37 respectively), and circulating polyunsaturated fatty acids (PUFA) fatty acid (omega-6 [FAo)6/FA] and PUFA [PUFA/FA] to total fatty acid ratios, p=0.31 for both) (see [0320]). Accordingly, it would have been obvious to one of ordinary skill in the art to utilize a fatty acid measure/ratio (as taught by Wolf et al) since such ratios show stronger correlations and therefore more effectively predict or diagnosis disorders.
Claims 3, 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Mehta et al (or Drake et al) as applied to claim 1 above, and further in view of Chinnaiyan et al (US PGPub 2011/0151497).
Regarding Claim 3, Mehta et al teaches determining in the biological sample obtained from the subject a quantitative value of glycoprotein acetyls and comparing the quantitative value(s) of the biomarkers to quantitative value(s) of the biomarkers in a control sample or to a control value(s);wherein an increase or a decrease in the quantitative value(s) of the biomarkers, when compared to quantitative value(s) of the biomarkers in the control sample or to the control value, is/are indicative of the subject having an increased risk of dying from prostate cancer (see page 50, lines 3-15; page 50, line 27 – page 51, line 10 and page 49, lines 24-32).
In addition, Drake et al teaches determining in the biological sample obtained from the subject a quantitative value of glycoprotein acetyls and comparing the quantitative value(s) of the biomarkers to quantitative value(s) of the biomarkers in a control sample or to a control value(s);wherein an increase or a decrease in the quantitative value(s) of the biomarkers, when compared to quantitative value(s) of the biomarkers in the control sample or to the control value, is/are indicative of the subject having an increased risk of dying from prostate cancer (see page 10, lines 17-30 and page 38, lines 23-29).
However, neither Mehta et al nor Drake et al discloses that at least one fatty acid measure(s) of the following: the ratio of docosahexaenoic acid to total fatty acids, the ratio of linoleic acid to total fatty acids, the ratio of monounsaturated fatty acids and/or of oleic acid to total fatty acids, the ratio of omega-3 fatty acids to total fatty acids, the ratio of omega-6 fatty acids to total fatty acids, the ratio of saturated fatty acids to total fatty acids, fatty acid degree of unsaturation are also measured and compared.
However, in the analogous art of cancer biomarker detection, Chinnaiyan et al teaches a method of diagnosing prostate cancer, comprising: detecting the level of sarcosine, glutamic acid, glycine and cysteine in a urine sample from a subject; and diagnosing prostate cancer when the levels of sarcosine, glutamic acid, glycine and cysteine are elevated relative to the level in a non-cancerous subject. In some embodiments, the method further comprises the step of detecting the level of one or more metabolites selected from, for example, acetyl glucosamine, kyurenine, uracil, homocysteine, asparagine, glutamic acid, sperminide, spermine, 2-aminoadipic acid, leucine, proline, threonine, maleate, histidine, citrulline, adenosine and inosine (see [0011] and [0108]). In addition, Chinnaiyan et al teaches that in its method of diagnosing prostate cancer by using fatty acid measures and/or ratios as the biomarker used for diagnosing cancer (see [0012]-[0015], [0107] and [0199]). It would have been obvious to one of ordinary skill in the art to utilize a fatty acid measure/ratio (as taught by Chinnaiyan et al) since fatty acid measures are well known cancer specific metabolites which may be detected simultaneously. Furthermore, it would have been obvious to one of ordinary skill in the art to utilize a fatty acid measure/ratio (as taught by Chinnaiyan et al, which discloses that the level of one or more metabolites (e.g., pipecolic acid or fatty acids (including but not limited to myristic acid, palmitic acid, arachidonic acid, stearic acid, lauric acid, oleic acid)) to also aid in the differentiation or characterization of breast cancer.
Regarding Claim 4, Mehta et al teaches determining in the biological sample obtained from the subject a quantitative value of glycoprotein acetyls and comparing the quantitative value(s) of the biomarkers to quantitative value(s) of the biomarkers in a control sample or to a control value(s);wherein an increase or a decrease in the quantitative value(s) of the biomarkers, when compared to quantitative value(s) of the biomarkers in the control sample or to the control value, is/are indicative of the subject having an increased risk of dying from prostate cancer (see page 50, lines 3-15; page 50, line 27 – page 51, line 10 and page 49, lines 24-32).
In addition, Drake et al teaches determining in the biological sample obtained from the subject a quantitative value of glycoprotein acetyls and comparing the quantitative value(s) of the biomarkers to quantitative value(s) of the biomarkers in a control sample or to a control value(s);wherein an increase or a decrease in the quantitative value(s) of the biomarkers, when compared to quantitative value(s) of the biomarkers in the control sample or to the control value, is/are indicative of the subject having an increased risk of dying from prostate cancer (see page 10, lines 17-30 and page 38, lines 23-29).
However, neither Mehta et al nor Drake et al discloses that at least one fatty acid measure(s) of the following: the ratio of docosahexaenoic acid to total fatty acids, the ratio of linoleic acid to total fatty acids, the ratio of monounsaturated fatty acids and/or of oleic acid to total fatty acids, the ratio of omega-3 fatty acids to total fatty acids, the ratio of omega-6 fatty acids to total fatty acids, the ratio of saturated fatty acids to total fatty acids, fatty acid degree of unsaturation as well as the level of histidine are also measured and compared.
However, in the analogous art of cancer biomarker detection, Chinnaiyan et al teaches a method of diagnosing prostate cancer, comprising: detecting the level of sarcosine, glutamic acid, glycine and cysteine in a urine sample from a subject; and diagnosing prostate cancer when the levels of sarcosine, glutamic acid, glycine and cysteine are elevated relative to the level in a non-cancerous subject. In some embodiments, the method further comprises the step of detecting the level of one or more metabolites selected from, for example, acetyl glucosamine, kyurenine, uracil, homocysteine, asparagine, glutamic acid, sperminide, spermine, 2-aminoadipic acid, leucine, proline, threonine, maleate, histidine, citrulline, adenosine and inosine (see [0011] and [0108]). In addition, Chinnaiyan et al teaches that in its method of diagnosing prostate cancer by using fatty acid measures and/or ratios as the biomarker used for diagnosing cancer (see [0012]-[0015], [0107] and [0199]). It would have been obvious to one of ordinary skill in the art to utilize a fatty acid measure/ratio (as taught by Chinnaiyan et al) since fatty acid measures (as well as the use of histidine levels) are well known cancer specific metabolites which may be detected simultaneously. Furthermore, it would have been obvious to one of ordinary skill in the art to utilize a fatty acid measure/ratio (as taught by Chinnaiyan et al, which discloses that the level of one or more metabolites (e.g., pipecolic acid or fatty acids (including but not limited to myristic acid, palmitic acid, arachidonic acid, stearic acid, lauric acid, oleic acid)) to also aid in the differentiation or characterization of breast cancer.
Regarding Claim 14, Mehta et al teaches determining in the biological sample obtained from the subject a quantitative value of glycoprotein acetyls and comparing the quantitative value(s) of the biomarkers to quantitative value(s) of the biomarkers in a control sample or to a control value(s);wherein an increase or a decrease in the quantitative value(s) of the biomarkers, when compared to quantitative value(s) of the biomarkers in the control sample or to the control value, is/are indicative of the subject having an increased risk of dying from prostate cancer (see page 50, lines 3-15; page 50, line 27 – page 51, line 10 and page 49, lines 24-32).
In addition, Drake et al teaches determining in the biological sample obtained from the subject a quantitative value of glycoprotein acetyls and comparing the quantitative value(s) of the biomarkers to quantitative value(s) of the biomarkers in a control sample or to a control value(s);wherein an increase or a decrease in the quantitative value(s) of the biomarkers, when compared to quantitative value(s) of the biomarkers in the control sample or to the control value, is/are indicative of the subject having an increased risk of dying from prostate cancer (see page 10, lines 17-30 and page 38, lines 23-29).
However, neither Mehta et al nor Drake et al discloses that at least one fatty acid measure(s) of the following: - a ratio of docosahexaenoic acid to total fatty acids,- a ratio of linoleic acid to total fatty acids,- a ratio of monounsaturated fatty acids and/or of oleic acid to total fatty acids,- a ratio of omega-3 fatty acids to total fatty acids, - a ratio of omega-6 fatty acids to total fatty acids, - a ratio of saturated fatty acids to total fatty acids,- fatty acid degree of unsaturation,- histidine; and comparing the quantitative value(s) of the biomarkers to quantitative value(s) of the biomarkers in a control sample or to a control value(s); wherein an increase or a decrease in the quantitative value(s) of the biomarkers, when compared to quantitative value(s) of the biomarkers in the control sample or to the control value, is/are indicative of the subject having an increased risk of dying from prostate cancer.
However, in the analogous art of cancer biomarker detection, Chinnaiyan et al teaches a method of diagnosing prostate cancer, comprising: detecting the level of sarcosine, glutamic acid, glycine and cysteine in a urine sample from a subject; and diagnosing prostate cancer when the levels of sarcosine, glutamic acid, glycine and cysteine are elevated relative to the level in a non-cancerous subject. In some embodiments, the method further comprises the step of detecting the level of one or more metabolites selected from, for example, acetyl glucosamine, kyurenine, uracil, homocysteine, asparagine, glutamic acid, sperminide, spermine, 2-aminoadipic acid, leucine, proline, threonine, maleate, histidine, citrulline, adenosine and inosine (see [0011] and [0108]). In addition, Chinnaiyan et al teaches that in its method of diagnosing prostate cancer by using fatty acid measures and ratios as the biomarker used for diagnosing cancer (see [0012]-[0015], [0107] and [0199]). It would have been obvious to one of ordinary skill in the art to utilize a fatty acid measure/ratio (as taught by Chinnaiyan et al) since fatty acid measures are well known cancer specific metabolites which may be detected simultaneously. Furthermore, it would have been obvious to one of ordinary skill in the art to utilize a fatty acid measure/ratio (as taught by Chinnaiyan et al, which discloses that the level of one or more metabolites (e.g., pipecolic acid or fatty acids (including but not limited to myristic acid, palmitic acid, arachidonic acid, stearic acid, lauric acid, oleic acid)) to also aid in the differentiation or characterization of breast cancer.
Claim(s) 7-9 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Mehta et al (or Drake et al) as applied to claim 1 above, and further in view of Pritchard et al (WO 2020242976 A1).
Regarding Claim 7, the combination of Fitzgerald et al and Nicholson et al does not disclose that the method of claim 1 further comprises determining whether the subject is at risk of dying from prostate cancer using a risk score, hazard ratio, odds ratio, and/or predicted absolute risk or relative risk calculated on the basis of the quantitative value(s) of at least one biomarker or of the plurality of the biomarkers.
However, in the analogous art of methods and systems for predicting the risk of a subject developing a polygenic disease, Pritchard et al teaches a method of predicting the risk of an individual developing a polygenic disease or medically relevant trait is provided, the method comprising: a) providing a database comprising correlation data for associations between genetic variants and the disease or medically relevant trait based on genome-wide testing of a population for genetic variants associated with the disease or the medically relevant trait; b) genotyping the individual to determine if the individual has one or more of the genetic variants associated with the disease or the medically relevant phenotypic trait; c) calculating at least one polygenic risk score based on the genetic variants detected in the individual by genotyping, wherein the polygenic risk score (PRS) indicates the risk of the individual developing the disease or the medically relevant trait (see [0004]). In addition, Pritchard et al teaches a computer implemented method for predicting the risk of an individual developing a disease or medically relevant phenotypic trait is provided, the computer performing steps comprising: a) receiving genome sequencing data for an individual; b) identifying variant alleles present in the individual from the genome sequencing data, wherein the individual has a plurality of variant alleles selected from Tables 5-10 and 13; c) calculating at least one polygenic risk score using a database, as described herein, based on the variant alleles present in the individual, wherein the polygenic risk score (PRS) indicates the risk of the individual developing the disease or the medically relevant trait; and d) displaying information regarding the risk of the individual developing the disease or the medically relevant trait.In certain embodiments, the computer implemented method further comprises: a) generating a predictive model using one or more algorithms, wherein the predictive model is based on at least one PRS for a genetic association with a size effect on a clinical biomarker measurement and at least one PRS for a genetic association with the disease or the medically relevant trait; and b) calculating a combined risk score from the predictive model, wherein the combined risk score better predicts the risk of the individual developing the disease or the medically relevant trait than each separate PRS (see [0027]-[0028]). Accordingly, it would have been obvious to one of ordinary skill in the art to utilize a risk score (as taught by Pritchard et al) when measuring biomarkers for the benefit of effectively and more accurately indicating the risk of an individual developing a disease and enabling this determination to be effectively displayed.
Regarding Claims 8-9 and 18, the combination of Mehta et al (or Drake et al) and Pritchard et al teaches that wherein the risk score, hazard ratio, odds ratio, and/or predicted relative risk and/or absolute risk is calculated on the basis of at least one further measure, and wherein the further measure comprises a characteristic of the subject such as age, height, weight, body mass index, race or ethnic group, smoking, and/or family history of mental and/or behavioral disorders of the subject (see [0016]-[0017], [0269] and claim 28 of Pritchard et al).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENNIFER WECKER whose telephone number is (571)270-1109. The examiner can normally be reached 9:30AM - 6 PM EST M-F.
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/JENNIFER WECKER/Primary Examiner, Art Unit 1797
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