DETAILED ACTION
Applicant’s response, filed Dec 18 2025, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
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 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.
Election/Restrictions
Applicant’s provisional election of PS-ID No. 6 from Table 2 in the reply filed on Sep 6 2024 is acknowledged. Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election has been treated as an election without traverse (MPEP § 818.01(a)). Amendments claims 1 and 14, submitted Dec 18 2025, which recite “at least one peptide structure identification (PS-ID) selected from PS-ID No. 6 and any of PS-ID No. 1-5 and 7-35” recite the elected species PS-ID No. 6. PS-ID No. 1-5 and 7-35 are directed to unelected species. The claim requires only “at least one” PS-ID selected from the PS-ID No. 6 and any of PS-ID No. 1-5 and 7-35. Therefore the claims require only elected species PS-ID No. 6 and any information associated with PS-ID No. 6 recited in the dependent claims. For example, claims 11 and 25 require only glycan structure GL number 5401 of the composition Hex(5)HexNAc(4)Fuc(0)NeuAc(1), and claims 13 and 27 require only that “a bottommost N-acetylglucosamine of the glycan structure is attached to a linking site position in the peptide sequence”, based on Table 1 at p. 44 of the instant specification.
Claim Status
Claims 1, 4, 7-9, 11, 13-14, 17, 20-22, 25, and 27 are pending.
Claims 2-3, 5-6, 10, 12, 15-16, 18-19, 23-24, 26, and 28-30 are canceled.
Claims 1 and 14 are objected to.
Claims 1, 4, 7-9, 11, 13-14, 17, 20-22, 25, and 27 are rejected.
Priority
The instant Application claims domestic benefit to US provisional applications 63/500,852, filed May 8 2023, and 63/505,954, filed Jun 2, 2023.
This application is a CON of 18/451,015, filed Aug 16 2023, which claims priority to US provisional applications 63/500,852, filed May 8 2023, and 63/505,954, filed Jun 2, 2023.
Accordingly, each of claims 1, 4, 7-9, 11-14, 17, 20-22, and 25-27 are afforded the effective filing date of May 8 2023.
Claim Interpretation
The interpretation in the previous Office Action of claims 1 and 14 regarding the interpretation of PS-ID No. 6 is withdrawn in view of the amendments submitted herein.
Claim Objections
Unless otherwise noted, the outstanding objections to the claims are withdrawn in view of the amendments submitted herein.
The claims are objected to because of the following informalities.
Each new limitation of claims 1 and 14 should be indented to improve readability. The objection is newly stated based upon further consideration of the claims.
Claim 1, limitation 2, recites “PS-ID No. 6 and… PS-ID No. 1-5 and 7-35”. 37 CFR 1.831 sets forth that “(c) Where the description or claims of a patent application discuss a sequence that is set forth in the "Sequence Listing XML" in accordance with paragraph (a) of this section, reference must be made to the sequence by use of the sequence identifier, preceded by "SEQ ID NO:" or the like in the text of the description or claims” (see MPEP 2412.04). The instant Sequence Listing filed Jan 24 2024 includes the sequence identifier “SEQ ID NO:”. Claim 1 is objected to for including “PS-ID No.” instead of “SEQ ID NO:”. Claim 14 is similarly objected to. The objection is maintained from the previous Office Action.
Claims 1 and 14 recite “wherein the at least one supervised machine learning model is trained by a method that comprises…”. It is unclear whether the wherein clause is intended to require training the supervised machine learning model within the metes and bounds of the claimed invention, or if it is only further limiting the type of supervised machine learning model such that performing the training is not required within the metes and bounds of the invention. As set forth in MPEP 2111.04.I, “wherein” clauses raise the question as to the limiting effect of the language in a claim. The metes and bounds of the claims are therefore unclear. For compact examination, it is assumed that training the machine learning model is required. The rejection may be overcome by clarifying what steps are required to be performed, by, for example, either amending the claim to clearly state that the training is not required (e.g., “wherein the at least one supervised machine learning model was trained…”), or by actively reciting the training not in a “wherein” clause. Claims 4, 7-9, 11-13, 17, 20-22, and 25-27 are rejected based on their dependency from claims 1 and 14.
Claim 1, limitation 4, recites “wherein the first disease state includes one of CRC stage 1, CRC stage 2, and high-risk advanced adenoma disease state”, which should be amended to recite “or” instead of “and”, because the claim requires only one of the recited options. Claim 14 is similarly objected to. The objection is maintained from the previous Office Action.
Response to Applicant Arguments
At p. 9, 3-4, Applicant submits that the identifier “PS-ID No.” does not refer to a sequence that is set forth in the Sequence Listing and that PS-ID Nos. represent peptide structures described through the instant application and may comprise a sequence from the sequence listing, but are not intended to be represented in a sequence listing. Applicant submits that the use of “PS-ID No” is therefore proper.
It is respectfully submitted that this is not persuasive. As set forth in the previous Office Actions, the “PS-ID No.” has been interpreted to refer to the associated SEQ ID NO to define the scope of the claims (see the below 35 USC 112(b)) rejection). Therefore, it is maintained that “PS-ID No.” is not an appropriate designation to refer to the sequences disclosed in the sequence listing.
Claim Rejections- 35 USC § 112
The outstanding rejections to the claims are withdrawn in view of the amendments submitted herein.
35 USC § 112(b)
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.
Claims 1, 7-9, 11, 13-14, 20-22, 25, and 27 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. The instant rejection is newly stated and is necessitated by claim amendment.
Claim 1, limitation 2, recites “one peptide structure identification (PS-ID) selected from PS-ID No. 6 and any of PS-ID No. 1-5 and 7-35”. The metes and bounds of the term “PS-ID” are not clear for the following reasons. The term “PS-ID” had previously been interpreted to indicate the SEQ ID NO of the specific peptide. However, Applicant has stated in their remarks at p. 9, par. 3-4, that “the identifier "PS-ID No." does not refer to a sequence that is set forth in the "Sequence Listing XML" and are not included in the Sequence Listing filed in the present application on January 1, 2024.”, and that “The peptide structure represented by the PS-ID Nos. recited in the present claims are described throughout the instant application and may comprise a sequence or more from the sequence listing, however they are not intended to be represented in a sequence listing.”. Based on Applicant’s assertions, and the removal of reference to the Table 1 or Table 5 in the amendments to the claims, it is not clear what the PS-ID Nos. are intended to represent if not the sequence of the peptide as set forth in the sequence listing. It is acknowledged that Applicant appropriately removed reference to tables within the specification as suggested in the previous Office Action to overcome the 35 USC 112(b) rejection of record; however, Applicant has stated on the record that the interpretation of the claims as reciting a sequence as set forth in the sequence listing is not what is intended and has not explained the intended scope of the term “PS-ID No.” except in a general reference to disclosure in the specification. Applicant is reminded that during examination, claims must be given their broadest reasonable interpretation and that it is improper to import narrowing limitations found in the specification to the claims. See MPEP 2111.01. If Applicant intended for the PS-ID Nos. to indicate all of the structure as listed in Tables 1 or 5 in the specification, Applicant has not appropriately incorporated that information into the claim. For compact examination, the PS-ID Nos. will continue to be interpreted as requiring only the sequences listed in the sequence listing. The rejection may be overcome by amending the claims to clearly describe the scope of the peptides recited in the claims. Claim 14 is similarly rejected. Claims 7-9, 11, 13, 20-22, 25, and 27 are rejected based on their dependency from claims 1 and 14.
35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 4 and 17 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. The instant rejection is newly stated and is necessitated by claim amendment.
Claim 4 recites “wherein the at least one peptide sequence comprises a peptide sequence selected from SEQ ID NO: 1-35”. As stated above, the peptide sequences/PS-ID No. recited claim 1 are interpreted to require the sequence set forth in sequence listing, indicated by the accompanying SEQ ID NO. Therefore, claim 4 fails to further limit the subject matter of claim 1. Claim 17 is similarly rejected.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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.
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.
A. Claims 1, 4, 7-9, 14, 17, and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. (US 2022/0328129; previously cited) in view of Liu (Dissertation, Georgia State University, 2020, p. 1-150; previously cited). Instantly claimed elements which are considered to be equivalent to the prior art teachings are described in bold for all claims. Any newly recited portions are necessitated by claim amendment.
The prior art to Ma discloses methods such as multi-omic methods for assessing a disease such as cancer (abstract). Ma, indicated by the open circles, teaches the instant features, indicated by the closed circles, as follows.
Claim 1 discloses a method for diagnosing and treating a subject with respect to an advanced adenoma (AA) or colorectal cancer (CRC) disease state, the method comprising:
receiving peptide structure data corresponding to a biological sample obtained from the subject;
analyzing the peptide structure data using at least one supervised machine learning model to generate a disease indicator based on at least one peptide structure identification (PS-ID) selected from PS-ID No. 6 and any of PS-ID No. 1-5 and 7-35, wherein the at least one peptide structure comprises a peptide sequence having a glycan structure that is attached to a linking site, wherein the peptide structure data comprises site occupancy;
Ma teaches obtaining multi-omic data (claim 1), which includes peptides (i.e., peptide structure data) obtained from a biofluid sample collected from a subject suspected of having a disease (claim 1; Fig. 2A-2B; [0003]). Ma teaches that the multi-omic data may include glycomic data (i.e., glycan structure) [0237] of glycopeptides (i.e., the peptide structure data corresponding to the biological sample obtained from the subject comprises site occupancy) [0244; 0433]. Ma teaches that the disease may be colon cancer (claim 1; [0003; 0011; 0189]).
Ma teaches classifying proteomic data as indicative of colon cancer or as not indicative of colon cancer (Fig. 5, 10A, and 11A; [0011; 0181; 0187; 0439]). Ma teaches using machine learning analysis for early disease detection [0238; 0487-0518] in order to train a classifier [0487]. Ma teaches using supervised learning algorithms [0488; 0498].
PS-ID No. 6, VYIHPFHLVIHNESTCEQLAK, is a peptide from the protein angiotensinogen. Ma teaches that the proteomic data analyzed by the classifier [0361] may include protein measurements of the protein angiotensinogen [0364]. Ma does not teach Pep SEQ ID No. 6.
See below for teachings by Liu regarding PS-ID No. 6.
wherein the at least one supervised machine learning model is trained by a method that comprises: (i) inputting training data into the supervised machine learning model, wherein the training data comprises a peptide structure profile for at least one previous subject having a first disease state or a second disease state, and the peptide structure profile comprises relative abundance, concentration, and site occupancy data for at least one of PS ID No. 1-35, and (ii) inputting the data into a model, algorithm, system, or process that can use existing data to make a prediction; wherein the first disease state includes one of CRC stage 1, CRC stage 2, and high-risk advanced adenoma disease state; wherein the second disease state includes one of a colonoscopy negative control or non-advanced adenoma disease state;
Ma teaches that supervised learning algorithms can be algorithms that rely on the use of a set of labeled, paired training data examples to infer the relationship between an input data and output data [0488]. Ma teaches training samples may comprise a biological sample from a plurality of subjects, associated datasets obtained by assaying the biological sample (as described elsewhere herein (i.e., peptide structure profiles), and one or more known output values corresponding to the biological sample, such as a clinical diagnosis [0506]. Ma teaches that the training samples may be associated with the presence or absence of lung nodule-related state [0506]. As Ma teaches that their method may be used to detect colon cancer (Fig. 5, 10A, and 11A; [0011; 0181; 0187; 0439]) as described above, it would have been obvious to include training data from subjects with or without (i.e., non-advanced adenoma disease state) colon cancer, as instantly claimed. As Ma teaches determining the relative abundance of peptides to determine the outputs of their model [0612], it is considered that Ma fairly teaches training the model using relative abundance of the peptide data as instantly claimed. As Ma also teaches that proteins can be indicated as a concentration [0241], and that the biomarkers used for cancer diagnostics have associated concentrations in healthy samples ([0369; 0406; 0613; 0734]; Table 2), it is considered that Ma fairly teaches training the model using the concentrations of the peptide data as instantly claimed.
Ma teaches that their method includes analysis of proteomics assays which detect post-translational modifications, including glycosylation, of proteins or polypeptides [0433], but does not explicitly teach identifying site occupancy data as instantly claimed.
Ma does not teach that the plurality of subjects have a first disease state which includes one of CRC stage 1, CRC stage 2, and high-risk advanced adenoma disease state.
See below for teachings by Liu regarding the first disease state and site occupancy.
generating a diagnosis output based on the disease indicator; wherein the disease indicator indicates that the biological sample evidences the AA or CRC disease state; and subjecting the subject to a colonoscopy.
Ma teaches outputting a report for making a diagnosis from the classifier [0006; 0184; 0190]. Ma teaches using their methods to screen patients before colonoscopy [0183; 0220] in order to identify a person who likely has colon cancer and confirm that they should undergo further invasive testing [0196], such as colonoscopy (Fig. 41). Ma teaches recommending a colonoscopy and providing a treatment for the disease when the proteomic data is classified as indicative of colon cancer [0597]. Although Ma does not explicitly teach that a subject is subjected to a colonoscopy, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Ma to perform the actual colonoscopy on the subject when the proteomic data is classified as indicative of colon cancer. Ma motivates such a modification by teaching that their method provides improved decision making for an imaging or biopsy procedure (i.e., colonoscopy) [0183].
Claim 14 discloses method of screening a subject for an advanced adenoma or CRC disease state, the method comprising
analyzing the peptide structure data using at least one supervised machine learning model to generate a disease indicator based on at least one peptide structure identification (PS-ID) selected from PS-ID No. 6 and any of PS-ID No. 1-5 and 7-35 in Table or Table 5 wherein the at least one peptide structure comprises a peptide sequence having a glycan structure that is attached to a linking site, wherein the peptide structure data comprises site occupancy;
Ma teaches obtaining multi-omic data (claim 1), which includes peptides (i.e., peptide structure data) obtained from a biofluid sample collected from a subject suspected of having a disease (claim 1; Fig. 2A-2B; [0003]). Ma teaches that the multi-omic data may include glycomic data [0237] of glycopeptides (i.e., the peptide structure data corresponding to the biological sample obtained from the subject comprises site occupancy) [0244; 0433]. Ma teaches that the disease may be colon cancer (claim 1; [0003; 0011; 0189]).
Ma teaches classifying proteomic data as indicative of colon cancer or as not indicative of colon cancer (Fig. 5, 10A, and 11A; [0011; 0181; 0187; 0439]). Ma teaches using machine learning analysis for early disease detection [0238; 0487-0518] in order to train a classifier [0487]. Ma teaches using supervised learning algorithms [0488; 0498].
PS-ID No. 6, VYIHPFHLVIHNESTCEQLAK, is a peptide from the protein angiotensinogen. Ma teaches that the proteomic data analyzed by the classifier [0361] may include protein measurements of the protein angiotensinogen [0364]. Ma does not teach Pep SEQ ID No. 6.
See below for teachings by Liu regarding PS-ID No. 6.
wherein the at least one supervised machine learning model is trained by a method that comprises: (i) inputting training data into the supervised machine learning model, wherein the training data comprises a peptide structure profile for at least one previous subject having a first disease state or a second disease state, and the peptide structure profile comprises relative abundance, concentration, and site occupancy data for at least one of PS ID No. 1-35, and (ii) inputting the data into a model, algorithm, system, or process that can use existing data to make a prediction; wherein the first disease state includes one of CRC stage 1, CRC stage 2, and high-risk advanced adenoma disease state; wherein the second disease state includes one of a colonoscopy negative control or non-advanced adenoma disease state; and
Ma teaches that supervised learning algorithms can be algorithms that rely on the use of a set of labeled, paired training data examples to infer the relationship between an input data and output data [0488]. Ma teaches training samples may comprise a biological sample from a plurality of subjects, associated datasets obtained by assaying the biological sample (as described elsewhere herein (i.e., peptide structure profiles), and one or more known output values corresponding to the biological sample, such as a clinical diagnosis [0506]. Ma teaches that the training samples may be associated with the presence or absence of lung nodule-related state [0506]. As Ma teaches that their method may be used to detect colon cancer (Fig. 5, 10A, and 11A; [0011; 0181; 0187; 0439]) as described above, it would have been obvious to include training data from subjects with or without (i.e., non-advanced adenoma disease state) colon cancer, as instantly claimed. Ma does not teach that the plurality of subjects have a first disease state which includes one of CRC stage 1, CRC stage 2, and high-risk advanced adenoma disease state. As Ma teaches determining the relative abundance of peptides to determine the outputs of their model [0612], it is considered that Ma fairly teaches training the model using relative abundance of the peptide data as instantly claimed. As Ma also teaches that proteins can be indicated as a concentration [0241], and that the biomarkers used for cancer diagnostics have associated concentrations in healthy samples ([0369; 0406; 0613; 0734]; Table 2), it is considered that Ma fairly teaches training the model using the concentrations of the peptide data as instantly claimed.
Ma teaches that their method includes analysis of proteomics assays which detect post-translational modifications, including glycosylation, of proteins or polypeptides [0433], but does not explicitly teach identifying site occupancy data as instantly claimed.
Ma does not teach that the plurality of subjects have a first disease state which includes one of CRC stage 1, CRC stage 2, and high-risk advanced adenoma disease state.
See below for teachings by Liu regarding the first disease state and site occupancy.
wherein the disease indicator indicates that the biological sample evidences the AA or CRC disease state; outputting a recommendation to perform a colonoscopy; and subjecting the subject to a colonoscopy.
Ma teaches outputting a report for making a diagnosis from the classifier [0006; 0184; 0190]. Ma teaches using their methods to screen patients before colonoscopy [0183; 0220] in order to identify a person who likely has colon cancer and confirm that they should undergo further invasive testing [0196], such as colonoscopy (Fig. 41). Ma teaches recommending a colonoscopy and providing a treatment for the disease when the proteomic data is classified as indicative of colon cancer [0597]. Although Ma does not explicitly teach that a subject is subjected to a colonoscopy, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Ma to perform the actual colonoscopy on the subject when the proteomic data is classified as indicative of colon cancer. Ma motivates such a modification by teaching that their method provides improved decision making for an imaging or biopsy procedure (i.e., colonoscopy) [0183].
Regarding claims 1 and 14, Ma does not teach PS-ID No. 6, that PS-ID No. 6 is associated with the advanced adenoma or CRC disease state, or the first disease state.
However, the prior art to Liu discloses mass spectrometry techniques to analyze therapeutic glycoproteins and discover novel biomarkers of colon cancer (abstract). Liu teaches that angiotensinogen has increased glycan occupancy in colon cancer (Table 3.3, p. 69). Liu teaches identifying the peptide VYIHPFHLVIHJESTCEQLAK as an N-glycosylated peptide of protein P01019, which is angiotensinogen, in colon cancer (Table 3.9, p. 94). The sequence of Pep SEQ ID No. 6 is VYIHPFHLVIHNESTCEQLAK, which is 100% similar to VYIHPFHLVIHJESTCEQLAK where the J is interpreted as indicating the N-glycosylation site of asparagine via 18O labelling (abstract) in the data of Liu (i.e., the glycan binding site; site occupancy data). Liu teaches analyzing the glycoproteomes of patients with stage II, III, and IV colon cancer and controls samples of patients without colon cancer (p. 55-56, section 3.4.1).
Regarding claims 1 and 14, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, Ma and Liu because each reference discloses methods for using blood proteins to diagnose colon cancer. The motivation to use PS-ID No. 6 in the method of Ma to determine whether a patient has colon cancer would have been to examine angiotensinogen glycan occupancy, which increases in colon cancer, as taught by Liu (Table 3.3), especially as Ma already teaches methods for examining glycoproteins and angiotensinogen. The motivation to use samples from different CRC stages, especially those from early stages I and II, as training samples in the method of Ma would have been to meet the demand of early diagnostic methods for detecting CRC, as taught by Liu (p. 409, par. 1). Further, it would have been obvious of one of ordinary skill in the art to include the site occupancy data of PS-ID No. 6 as taught by Liu in the training method of Ma, because such Liu teaches that the data is indicative of colon cancer and one of ordinary skill in the art would have realized that such a modification would have produced the predictable result of detecting colon cancer.
Regarding claims 4 and 17, Ma in view of Liu teaches the methods of claims 1 and 14. Claims 4 and 17 further adds that the at least one peptide sequence comprises a peptide sequence selected from SEQ ID NO: 1-35, which is interpreted based on Applicant’s election and the above 35 USC 112(b) and (d) rejections as a glycopeptide with the sequence of SEQ ID NO: 6.
Ma teaches that the multi-omic data may include glycomic data [0237] of glycopeptides [0244]. Ma does not teach a glycopeptide with the sequence of SEQ ID NO 6.
However, Liu teaches identifying the peptide VYIHPFHLVIHJESTCEQLAK as an N-glycosylated peptide of protein P01019, which is angiotensinogen, in colon cancer (Table 3.9, p. 94) as set forth above.
Regarding claims 7 and 20, Ma in view of Liu teaches the method of claims 1 and 14. Claims 7 and 20 further adds that the peptide structure data comprises at least one of a raw abundance, an adjusted raw abundance, a peptide concentration, a glycopeptide concentration, or a normalized concentration.
Ma teaches that proteomic data may include information on the presence, absence, or amount of various proteins and peptides [0241] and glycopeptides [0244], and that proteins may be indicated as a concentration of quantity of proteins ([0241; 0336; 0349; 0357]; Table 2). Ma also teaches performing normalization of all intensity data for the proteomic data [0741].
Regarding claims 8 and 21, Ma in view of Liu teaches the methods of claims 1, 7, 14 and 20. Claims 8 and 21 further adds that the peptide structure data comprises normalized concentration data, wherein the normalized concentration data is a function of at least one of: peptide abundance data; corresponding internal standard abundance data; a spike-in concentration value; and a dilution factor.
This limitation is interpreted, under the BRI, as requiring at least of abundance data, corresponding internal standard abundance data, a spike-in concentration value, and a dilution factor when determining normalized concentration data. Ma teaches performing normalization of all intensity data for the proteomic data [0741] as described above, which is considered to read on peptide abundance data as instantly claimed.
Regarding claims 9 and 22, Ma in view of Liu teaches the method of claims 1 and 14. Claims 9 and 22 further adds creating a sample from the biological sample; preparing the sample using reduction, alkylation, and enzymatic digestion to form a prepared sample that includes a set of peptide structures; and generating the peptide structure data from the prepared sample using multiple reaction monitoring mass spectrometry (MRM-MS).
Ma teaches obtaining a sample and sample preparation for targeted mass spectrometry analysis [0631]. Ma teaches preparing the subset of proteins for mass spectrometric analysis using trypsin (i.e., enzymatic digestion), a buffer, an alkylating reagent, and a reductant [0674]. Ma teaches collecting data in multiple reaction monitoring (MRM) mode liquid chromatography-mass spectrometry [0858].
B. Claims 11, 13, 25, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Liu, as applied to claims 1 and 14 above, and in further view of Balog et al. (Molecular and Cellular Proteomics, 2012, 11(9):571-585; Supplemental Information, p. 1-151; previously cited). Instantly claimed elements which are considered to be equivalent to the prior art teachings are described in bold for all claims. Any newly recited portions are necessitated by claim amendment.
Regarding claims 11 and 25, Ma in view of Liu teaches the method of claims 1 and 14. Claims 11 and 25 further add that the glycan structure of the peptide sequence comprises a glycan structure GL number selected of… c) GL number 5401, having the composition Hex(5)HexNAc(4)Fuc(0)NeuAc(1)… Neither Ma nor Liu teach these limitations.
However, the prior art to Balog discloses comparing the N-glycan profiles from 13 colorectal cancer tumor tissues to corresponding control colon tissues (abstract). Balog SI teaches that identified spectra 85 and 86 correspond to Hex5HexNAc4NeuAc1, which has the structure
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, which is considered to teach the structure and composition of glycan number 5401 as instantly claimed.
Regarding claims 13 and 27, Ma in view of Liu teaches the method of claims 1, 11, 14, and 25. Claims 13 and 27 further adds that a bottommost N-acetylglucosamine of the glycan structure is attached to a linking site position in the peptide sequence, which is interpreted as the bottom-most square in
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. Balog SI discloses that that identified spectra 85 and 86 correspond to Hex5HexNAc4NeuAc1, which has the structure
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, showing that the right-most square is attached to the amino acid (p. 89-91), which is considered to read on the instant limitations because this square refers to HexNAc or N-acetylglucasamine (p. 1).
Regarding claims 11, 13, 25, and 27, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, Ma in view of Liu and Balog because each reference discloses methods for measuring glycoproteins to diagnose colon cancer. The motivation to examine the glycan structure taught by Balog in the method of Ma in view of Liu would have been to include N-glycan structures detected in colorectal cancer samples, as taught by Balog SI (p. 1).
Response to Applicant Arguments
At p. 11, par. 4 through p. 14, par. 1, Applicant submits that Ma and Liu fail to disclose all of the features of the present claim because the references do not teach training a supervised machine learning model using a peptide structure profile comprising relative abundance, concentration, and site occupancy data for at least one of PS ID No. 1-35. Applicant submits that Ma does not provide motivation for one of skill in the art to specifically use the relative abundance, concentration, and site occupancy data as recited in the present claims. Applicant submits that Ma does not identify glycosylation sites on glycopeptides, and that Liu does not remedy this because they simply disclose the attachment site and does not teach or motivate a person of ordinary skill in the art to arrive at the claimed invention because Liu does not teach the combination of the features of the peptide structure profile in a supervised learning method. Applicant submits that one of skill in the art would have no reasonable expectation of success in applying the teachings of Liu to Ma.
It is respectfully submitted that this is not persuasive. As set forth in the rejection above, Ma is considered to use the relative abundance and determined concentrations of the identified proteins and glycoproteins to train their diagnostic models. While Ma does not explicitly teach using the glycosylation site of the peptide in their model, such information is known regarding elected SEQ ID NO. 6, as taught by Liu. It therefore would be obvious to incorporate such information into the model of Ma as described in the above rejection. It is further noted that Liu specifically teaches that angiotensinogen has increased glycan occupancy in colon cancer (Table 3.3, p. 69), a finding that would motivate one of ordinary skill in the art to include this information in a supervised learning model to diagnose colon cancer based on glycopeptide data, as taught by Ma. Liu does not merely teach the glycosylation site of the specific peptide as claimed, as argued by Applicant, but also teaches that the glycosylation level is tied to the disease state, which would lead one of ordinary skill in the art to expect to successfully predict colon cancer using the presence of such a glycopeptide. Applicant has not provided arguments for why it would not be obvious to include any known glycopeptide associated with colon cancer to train a model to detect colon cancer based on the presence of such glycopeptides.
At p. 14, par. 2-4, Applicant submits that the claimed invention provides an improved method for classifying biological samples with a sensitivity as high as 80.9% and specificity as high as 90.35%, whereas Ma reports a sensitivity of 0.47 for proteins which improves with combined lipid data. Applicant submits that Ma therefore teaches away from using more specific data sets, such as those in the present claims, which include specific proteins and features.
It is respectfully submitted that this is not persuasive. As the instant claims do not recite a specific sensitivity or specificity, Applicant’s arguments comparing the sensitivity and specificity two methods are not commensurate with the scope of the claims. Applicant’s arguments that Ma teaches away from including specific proteins or peptides in their method are also not convincing because Ma teaches including the data of one or more specific proteins, including angiotensinogen, which SEQ ID NO. 6 is a peptide of. Applicant’s arguments are further not commensurate with the scope of the claims because the claims require generating a disease indicator based on at least one peptide structure identification, and does not actually limit the indicator to being based only on one peptide. Therefore the claims encompass embodiments where more than one peptide is used to generate the disease indicator, and nothing in the claims precludes the peptide data from being combined with other types of omics data to improve sensitivity and specificity, as taught by Ma. Further, Ma teaches determining the relative abundance and concentration of proteins in the sample, based on their peptide identifications, and Liu teaches determining the glycosylation sites of glycopeptides involved in colon cancer. It is considered that one of ordinary skill in the art would expect that including such features would improve the performance a detection model.
Regarding Applicant’s assertion of unexpected results as indicated by the heading of part C, it is noted that Applicant has not actually provided any arguments about the unexpected results of the invention in comparison with Ma, but only that Ma teaches away from the instant invention as described above. Therefore, Applicant has not provided the requisite arguments to demonstrate unexpected results (see MPEP 716.02).
Double Patenting – Non-statutory
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
A. Claims 1, 4, 7-9, 11, 13-14, 17, 20-22, 25, and 27 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-27 of copending Application No. 18/451,015 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other for the following reasons. Any newly stated portions are necessitated by claim amendment.
Instant application
Reference application (18/451,015)
1. A method for diagnosing and treating a subject with respect to an advanced adenoma (AA) or colorectal cancer (CRC) disease state, the method comprising:
receiving peptide structure data corresponding to a biological sample obtained from the subject;
analyzing the peptide structure data using at least one supervised machine learning model to generate a disease indicator based on at least one peptide structure identification (PS-ID) selected from PS-ID No. 6 and any of PS-ID No. 1-5 and 7-35, wherein the at least one PS-ID comprises a peptide sequence having a glycan structure that is attached to a linking site,
wherein the peptide structure data comprises site occupancy;
wherein the at least one supervised machine learning model is trained model is trained by a method that comprises: (i) inputting training data into the supervised machine learning model, wherein the training data comprises a peptide structure profile for at least one previous subject having a first disease state or a second disease state, and the peptide structure profile comprises relative abundance, concentration, and site occupancy data for at least one of PS ID No. 1-35, and (ii) inputting the data into a model, algorithm, system, or process that can use existing data to make a prediction;
wherein the first disease state includes one of CRC stage 1, CRC stage 2, and high-risk advanced adenoma disease state;
wherein the second disease state includes one of a colonoscopy negative control or non-advanced adenoma disease state;
generating a diagnosis output based on the disease indicator;
wherein the disease indicator indicates that the biological sample evidences the AA or CRC disease state; and
subjecting the subject to a colonoscopy.
1. A method for diagnosing a subject with respect to an advanced adenoma (AA) or colorectal cancer (CRC) disease state, the method comprising:
receiving peptide structure data corresponding to a biological sample obtained from the subject;
analyzing the peptide structure data using at least one supervised machine learning model to generate a disease indicator that indicates whether the biological sample evidences the advanced adenoma or CRC disease state based on at least one peptide structure selected from a group of peptide structures identified in Table 2 (it is noted that Tables 1, 2, and 5 include the same information regarding elected species SEQ ID NO 6);
wherein the group of peptide structures in Table 2 is associated with the advanced adenoma or CRC disease state; and
generating a diagnosis output based on the disease indicator.
4. The method of claim 1, wherein the at least one peptide structure comprises a glycopeptide structure defined by a peptide sequence and a glycan structure linked to the peptide sequence at a linking site of the peptide sequence, as identified in Table 2, with the peptide sequence being one of SEQ ID NOS: 3-9 (i.e., 6), 12, 14-16, 18, 25-28, and 31-35 as defined in Table 4C.
10. The method of claim 1, wherein the at least one peptide structure comprises a peptide sequence and a glycan structure, wherein the glycan structure is attached to a linking site position in the peptide sequence in accordance with Table 2 and Table 4C.
5. The method claim 1, further comprising:
training the at least one supervised machine learning model using training data, wherein the training data comprises a plurality of peptide structure profiles for a plurality of subjects, the plurality of subject having either a first disease state or a second disease state,
wherein the first disease state includes one of CRC stage 1, CRC stage 2, and high risk advanced adenoma disease state, wherein the second disease state includes one of colonoscopy negative control or non- advanced adenoma disease state.
2. The method of claim 1, wherein the disease indicator comprises a score, wherein generating the diagnosis output comprises:
determining that the score falls above a selected threshold; and
generating the diagnosis output based on the score falling above the selected threshold, wherein the diagnosis output includes a positive diagnosis for the advanced adenoma or CRC disease state.
15. The method of claim 14, wherein the subject is subjected to a colonoscopy when the recommendation to perform the colonoscopy is outputted.
4. The method of claim 1, wherein the at least one peptide sequence comprises a peptide sequence selected from SEQ ID NO: 1-35.
4. The method of claim 1, wherein the at least one peptide structure comprises a glycopeptide structure defined by a peptide sequence and a glycan structure linked to the peptide sequence at a linking site of the peptide sequence, as identified in Table 2, with the peptide sequence being one of SEQ ID NOS: 3-9 (i.e., 6), 12, 14-16, 18, 25-28, and 31-35 as defined in Table 4C.
7. The method of claim 1, wherein the peptide structure data comprises at least one of a raw abundance, an adjusted raw abundance, a peptide concentration, a glycopeptide concentration, or a normalized concentration.
7. The method of claim 1, wherein the peptide structure data comprises at least one of a raw abundance, an adjusted raw abundance, a peptide concentration, a glycopeptide concentration, or a normalized concentration.
8. The method of claim 7, wherein the peptide structure data comprises normalized concentration data, wherein the normalized concentration data is a function of at least one of peptide abundance data, corresponding internal standard abundance data, a spike-in concentration value, and a dilution factor.
8. The method of claim 7, wherein the peptide structure data comprises normalized concentration data, wherein the normalized concentration data is a function of at least one of peptide abundance data, corresponding internal standard abundance data, a spike-in concentration value, and a dilution factor.
11. The method of claim 1, wherein the glycan structure of the peptide sequence comprises a glycan structure GL number in selected from… c) GL number 5401, having the composition Hex(5)HexNAc(4)Fuc(0)NeuAc(1)…
11. The method of claim 10, wherein the glycan structure of the peptide sequence comprises a glycan structure GL number in accordance with Table 2 (i.e., 5401), wherein the glycan structure comprises a composition in accordance with the glycan structure GL number, Table 6A, and Table 6B (i.e., Hex(5)HexNAc(4)Fuc(1)NeuAc(1)).
13. The method of claim 12, wherein a rightmost N-acetylgalactosamine of the glycan structure is attached to a linking site position in the peptide sequence,
wherein a bottommost N-acetylglucosamine of the glycan structure is attached to a linking site position in the peptide sequence.
13. The method of claim 12, wherein a rightmost N-acetylgalactosamine of the glycan structure in Table 6B is attached to a linking site position in the peptide sequence in accordance with Table 2,
wherein a bottommost N-acetylglucosamine of the glycan structure in Table 6A is attached to a linking site position in the peptide sequence in accordance with Table 2.
14. A method of screening and treating a subject for an advanced adenoma or CRC disease state, the method comprising:
analyzing the peptide structure data using at least one supervised machine learning model to generate a disease indicator based on at least one peptide structure identification (PS-ID) selected from PS-ID No. 6 and any of PS-ID No. 1-5 and 7-35, wherein the at least one peptide structure comprises a peptide sequence having a glycan structure that is attached to a linking site,
wherein the peptide structure data comprises site occupancy;
wherein the at least one supervised machine learning model is trained by a method that comprises:
(i) inputting training data into the supervised machine learning model, wherein the training data comprises a peptide structure profile for at least one previous subject having a first disease state or a second disease state, and the peptide structure profile comprises relative abundance, concentration, and site occupancy data for at least one of PS ID No. 1-35, and
(ii) inputting the data into a model, algorithm, system, or process that can use existing data to make a prediction;
wherein the first disease state includes one of CRC stage 1, CRC stage 2, and high-risk advanced adenoma disease state;
wherein the second disease state includes one of a colonoscopy negative control or non-advanced adenoma disease state;
wherein the disease indicator indicates that the biological sample evidences the AA or CRC disease state;
outputting a recommendation to perform a colonoscopy; and
subjecting the subject to a colonoscopy.
14. A method of screening a subject for an advanced adenoma or CRC disease state, the method comprising analyzing
a peptide structure data using at least one supervised machine learning model to generate a disease indicator that indicates whether the biological sample evidences the advanced adenoma or CRC disease state based on at least one peptide structure selected from a group of peptide structures identified in Table 2 (i.e., PS-ID 6), wherein peptide structure data corresponds to a biological sample obtained from the subject;
24. The method of claim 14, wherein the at least one peptide structure comprises a peptide sequence and a glycan structure, wherein the glycan structure is attached to a linking site position in the peptide sequence in accordance with Table 2 and Table 4C.
18. The method of claim 14, further comprising:
training the at least one supervised machine learning model using training data, wherein the training data comprises a plurality of peptide structure profiles for a plurality of subjects, the plurality of subject having either a first disease state or a second disease state,
wherein the first disease state includes one of CRC stage 1, CRC stage 2, and high- risk advanced adenoma disease state,
wherein the second disease state includes one of colonoscopy negative control or non- advanced adenoma disease state.
14. outputting either a recommendation to perform a colonoscopy or to not perform the colonoscopy based on the disease indicator.
15. The method of claim 14, wherein the subject is subjected to a colonoscopy when the recommendation to perform the colonoscopy is outputted.
16. The method of claim 14, wherein the disease indicator comprises a score, wherein generating the diagnosis output comprises:
determining that the score falls above a selected threshold; and
generating the diagnosis output based on the score falling above the selected threshold, wherein the diagnosis output includes a positive diagnosis for the advanced adenoma or CRC disease state.
17. The method of claim 14, wherein the at least one peptide sequence comprises a peptide sequence selected from SEQ ID NO: 1-35.
17. The method of claim 14, wherein the at least one peptide structure comprises a glycopeptide structure defined by a peptide sequence and a glycan structure linked to the peptide sequence at a linking site of the peptide sequence, as identified in Table 2, with the peptide sequence being one of SEQ ID NOS: 3-9 (i.e., 6), 12, 14-16, 18, 25-28, and 31-35 as defined in Table 2 and Table 4C.
20. The method of claim 14, wherein the peptide structure data comprises at least one of a raw abundance, an adjusted raw abundance, a peptide concentration, a glycopeptide concentration, or a normalized concentration.
20. The method of claim 14, wherein the peptide structure data comprises at least one of a raw abundance, an adjusted raw abundance, a peptide concentration, a glycopeptide concentration, or a normalized concentration.
21. The method of any one of claims 20, wherein the peptide structure data comprises normalized concentration data, wherein the normalized concentration data is a function of at least one of peptide abundance data, corresponding internal standard abundance data, a spike- in concentration value, and a dilution factor.
21. The method of any one of claims 20, wherein the peptide structure data comprises normalized concentration data, wherein the normalized concentration data is a function of at least one of peptide abundance data, corresponding internal standard abundance data, a spike- in concentration value, and a dilution factor.
22. The method of claim 14, further comprising:
creating a sample from the biological sample;
preparing the sample using reduction, alkylation, and enzymatic digestion to form a prepared sample that includes a set of peptide structures; and
generating the peptide structure data from the prepared sample using multiple reaction monitoring mass spectrometry (MRM-MS).
22. The method of claim 14, further comprising:
creating a sample from the biological sample;
preparing the sample using reduction, alkylation, and enzymatic digestion to form a prepared sample that includes a set of peptide structures; and
generating the peptide structure data from the prepared sample using multiple reaction monitoring mass spectrometry (MRM-MS).
25. The method of claim 14, wherein the glycan structure of the peptide sequence comprises a glycan structure GL number in selected from… (c) GL number 5401, having the composition Hex(5)HexNAc(4)Fuc(0)NeuAc(1);…
25. The method of claim 24, wherein the glycan structure of the peptide sequence comprises a glycan structure GL number in accordance with Table 2 (i.e., 5401), wherein the glycan structure comprises a composition in accordance with the glycan structure GL number, Table 6A, and Table 6B (i.e., Hex(5)HexNAc(4)Fuc(0)NeuAc(1)).
27. The method of The method of wherein a rightmost N- acetylgalactosamine of the glycan structure is attached to a linking site position in the peptide sequence,
wherein a bottommost N-acetylglucosamine of the glycan structure is attached to a linking site position in the peptide sequence.
27. The method of The method of wherein a rightmost N- acetylgalactosamine of the glycan structure in Table 6B is attached to a linking site position in the peptide sequence in accordance with Table 2,
wherein a bottommost N-acetylglucosamine of the glycan structure in Table 6A is attached to a linking site position in the peptide sequence in accordance with Table 2.
B. Claims 1, 4, 7-9, 14, 17, and 20-22 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 31, 34, and 46 of copending Application No. 18/837,706 (reference application) in view of Ma et al. (US 2022/0328129; previously cited) and Liu (Dissertation, Georgia State University, 2020, p. 1-150; previously cited). Although the claims at issue are not identical, they are not patentably distinct from each other for the following reasons. Any newly recited portions are necessitated by claim amendment.
Instant application
Reference application (18/837,706)
1. A method for diagnosing and treating a subject with respect to an advanced adenoma (AA) or colorectal cancer (CRC) disease state, the method comprising:
receiving peptide structure data corresponding to a biological sample obtained from the subject;
analyzing the peptide structure data using at least one supervised machine learning model to generate a disease indicator based on at least one peptide structure identification (PS-ID) selected from PS-ID No. 6 and any of PS-ID No. 1-5 and 7-35, wherein the at least one peptide structure comprises a peptide sequence having a glycan structure that is attached to a linking site,
wherein the peptide structure data comprises site occupancy;
wherein the at least one supervised machine learning model is trained by a method that comprises:
(i) inputting training data into the supervised machine learning model, wherein the training data comprises a peptide structure profile for at least one previous subject having a first disease state or a second disease state, and the peptide structure profile comprises relative abundance, concentration, and site occupancy data for at least one of PS ID No. 1-35, and
(ii) inputting the data into a model, algorithm, system, or process that can use existing data to make a prediction;
wherein the first disease state includes one of CRC stage 1, CRC stage 2, and high-risk advanced adenoma disease state;
wherein the second disease state includes one of a colonoscopy negative control or non-advanced adenoma disease state;
generating a diagnosis output based on the disease indicator;
wherein the disease indicator indicates that the biological sample evidences the AA or CRC disease state; and
subjecting the subject to a colonoscopy.
46. A method for diagnosing a subject with respect to colorectal cancer (CRC) disease state that optionally includes one of adenoma, APL, and high-grade advanced pre-malignant lesion disease state, the method comprising:
receiving peptide structure data corresponding to a biological sample obtained from the subject;
analyzing the peptide structure data using at least one supervised machine learning model to generate a disease indicator that indicates whether the biological sample evidences the colorectal cancer (CRC) disease state that optionally includes one of adenoma, APL, and high-grade advanced pre-malignant lesion disease state based on at least one peptide structure selected from a group of peptide structures identified in Tables 1, 1B, 1C, 1D, and 13A;
It is noted that Seq ID 30 in Table 1 is VYIHPFHLVIHNESTCEQLAK, which is 100% similar to Seq ID NO. 6 in the instant application, and is indicated to have a glycol site with an associated glycan structure.
wherein the group of peptide structures in Tables 1, 1B, 1C, 1D, and 13A is associated with colorectal cancer (CRC) disease state that optionally includes one of adenoma, APL, and high-grade advanced pre-malignant lesion disease state; and
generating a diagnosis output based on the disease indicator.
34. The method of claim 31, wherein the subject is subjected to a colonoscopy when the recommendation to perform the colonoscopy is outputted.
The reference patent does not recite the instant limitations regarding training.
However, the prior art to Ma discloses methods such as multi-omic methods for assessing a disease such as cancer (abstract). Ma teaches that supervised learning algorithms can be algorithms that rely on the use of a set of labeled, paired training data examples to infer the relationship between an input data and output data [0488]. Ma teaches training samples may comprise a biological sample from a plurality of subjects, associated datasets obtained by assaying the biological sample (as described elsewhere herein (i.e., peptide structure profiles), and one or more known output values corresponding to the biological sample, such as a clinical diagnosis [0506]. Ma teaches that the training samples may be associated with the presence or absence of lung nodule-related state [0506]. As Ma teaches that their method may be used to detect colon cancer (Fig. 5, 10A, and 11A; [0011; 0181; 0187; 0439]) as described above, it would have been obvious to include training data from subjects with or without (i.e., non-advanced adenoma disease state) colon cancer, as instantly claimed. As Ma teaches determining the relative abundance of peptides to determine the outputs of their model [0612], it is considered that Ma fairly teaches training the model using relative abundance of the peptide data as instantly claimed. As Ma also teaches that proteins can be indicated as a concentration [0241], and that the biomarkers used for cancer diagnostics have associated concentrations in healthy samples ([0369; 0406; 0613; 0734]; Table 2), it is considered that Ma fairly teaches training the model using the concentrations of the peptide data as instantly claimed. Ma does not teach that the plurality of subjects have a first disease state includes one of CRC stage 1, CRC stage 2, and high-risk advanced adenoma disease state.
However, Liu teaches analyzing the glycoproteomes of patients with stage II, III, and IV colon cancer and controls samples of patients without colon cancer (p. 55-56, section 3.4.1).
Regarding instant claim 1, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the reference patent with Ma and Liu because each reference discloses methods for using blood proteins to diagnose colon cancer. The motivation to use samples from different CRC stages, especially those from early stages I and II, as training samples in the method of Ma would have been to meet the demand of early diagnostic methods for detecting CRC, as taught by Liu (p. 409 , par. 1).
Instant application
Reference application (18/451,015)
14. A method of screening a subject for an advanced adenoma or CRC disease state, the method comprising
analyzing the peptide structure data using at least one supervised machine learning model to generate a disease indicator based on at least one peptide structure identification (PS-ID) selected from PS-ID No. 6 and any of PS-ID No. 1-5 and 7-35, wherein the at least one peptide structure comprises a peptide sequence having a glycan structure that is attached to a linking site,
wherein the peptide structure data comprises site occupancy;
wherein the at least one supervised machine learning model is trained by a method that comprises:
(i) inputting training data into the supervised machine learning model, wherein the training data comprises a peptide structure profile for at least one previous subject having a first disease state or a second disease state, and the peptide structure profile comprises relative abundance, concentration, and site occupancy data for at least one of PS ID No. 1-35, and
(ii) inputting the data into a model, algorithm, system, or process that can use existing data to make a prediction;
wherein the first disease state includes one of CRC stage 1, CRC stage 2, and high-risk advanced adenoma disease state;
wherein the second disease state includes one of a colonoscopy negative control or non-advanced adenoma disease state;
wherein the disease indicator indicates that the biological sample evidences the AA or CRC disease state;
outputting a recommendation to perform a colonoscopy; and
subjecting the subject to a colonoscopy.
46. A method for diagnosing a subject with respect to colorectal cancer (CRC) disease state that optionally includes one of adenoma, APL, and high-grade advanced pre-malignant lesion disease state, the method comprising:
receiving peptide structure data corresponding to a biological sample obtained from the subject;
analyzing the peptide structure data using at least one supervised machine learning model to generate a disease indicator that indicates whether the biological sample evidences the colorectal cancer (CRC) disease state that optionally includes one of adenoma, APL, and high-grade advanced pre-malignant lesion disease state based on at least one peptide structure selected from a group of peptide structures identified in Tables 1, 1B, 1C, 1D, and 13A;
It is noted that Seq ID 30 in Table 1 is VYIHPFHLVIHNESTCEQLAK, which is 100% similar to Seq ID NO. 6 in the instant application, and is indicated to have a glycol site with an associated glycan structure.
wherein the group of peptide structures in Tables 1, 1B, 1C, 1D, and 13A is associated with colorectal cancer (CRC) disease state that optionally includes one of adenoma, APL, and high-grade advanced pre-malignant lesion disease state; and
generating a diagnosis output based on the disease indicator.
34. The method of claim 31, wherein the subject is subjected to a colonoscopy when the recommendation to perform the colonoscopy is outputted.
The reference patent does not recite the instant limitations regarding training.
However, the prior art to Ma discloses methods such as multi-omic methods for assessing a disease such as cancer (abstract). Ma teaches that supervised learning algorithms can be algorithms that rely on the use of a set of labeled, paired training data examples to infer the relationship between an input data and output data [0488]. Ma teaches training samples may comprise a biological sample from a plurality of subjects, associated datasets obtained by assaying the biological sample (as described elsewhere herein (i.e., peptide structure profiles), and one or more known output values corresponding to the biological sample, such as a clinical diagnosis [0506]. Ma teaches that the training samples may be associated with the presence or absence of lung nodule-related state [0506]. As Ma teaches that their method may be used to detect colon cancer (Fig. 5, 10A, and 11A; [0011; 0181; 0187; 0439]) as described above, it would have been obvious to include training data from subjects with or without (i.e., non-advanced adenoma disease state) colon cancer, as instantly claimed. . As Ma teaches determining the relative abundance of peptides to determine the outputs of their model [0612], it is considered that Ma fairly teaches training the model using relative abundance of the peptide data as instantly claimed. As Ma also teaches that proteins can be indicated as a concentration [0241], and that the biomarkers used for cancer diagnostics have associated concentrations in healthy samples ([0369; 0406; 0613; 0734]; Table 2), it is considered that Ma fairly teaches training the model using the concentrations of the peptide data as instantly claimed. Ma does not teach that the plurality of subjects have a first disease state includes one of CRC stage 1, CRC stage 2, and high-risk advanced adenoma disease state. Ma teaches outputting a report from the classifier [0006]. Ma teaches using their methods to screen patients before colonoscopy [0183; 0220] in order to identify a person who likely has colon cancer and confirm that they should undergo further invasive testing [0196], such as colonoscopy (Fig. 41). Ma teaches recommending a colonoscopy when the proteomic data is classified as indicative of colon cancer [0597].
However, Liu teaches analyzing the glycoproteomes of patients with stage II, III, and IV colon cancer and controls samples of patients without colon cancer (p. 55-56, section 3.4.1).
Regarding instant claim 14, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the reference patent with Ma and Liu because each reference discloses methods for using blood proteins to diagnose colon cancer. The motivation to use samples from different CRC stages, especially those from early stages I and II, as training samples in the method of Ma would have been to meet the demand of early diagnostic methods for detecting CRC, as taught by Liu (p. 409 , par. 1). Ma motivates outputting a recommendation to perform a colonoscopy in order to perform further treatment or screening when a subject is classified as having colon cancer [0597].
The reference patent does not recite the features of instant claims 4, 7-9, 17, and 20-22.
Regarding claims 4 and 17, Ma teaches that the multi-omic data may include glycomic data [0237] of glycopeptides [0244]. Ma does not teach a glycopeptide with the sequence of SEQ ID NO 6.
However, Liu teaches identifying the peptide VYIHPFHLVIHJESTCEQLAK as an N-glycosylated peptide of protein P01019, which is angiotensinogen, in colon cancer (Table 3.9, p. 94) as set forth above.
Regarding claims 7 and 20, Ma teaches that proteomic data may include information on the presence, absence, or amount of various proteins and peptides [0241] and glycopeptides [0244], and that proteins may be indicated as a concentration of quantity of proteins ([0241; 0336; 0349; 0357]; Table 2). Ma also teaches performing normalization of all intensity data for the proteomic data [0741].
Regarding claims 8 and 21, this limitation is interpreted, under the BRI, as requiring at least of abundance data, corresponding internal standard abundance data, a spike-in concentration value, and a dilution factor when determining normalized concentration data. Ma teaches performing normalization of all intensity data for the proteomic data [0741] as described above, which is considered to read on peptide abundance data as instantly claimed.
Regarding claims 9 and 22, Ma teaches obtaining a sample and sample preparation for targeted mass spectrometry analysis [0631]. Ma teaches preparing the subset of proteins for mass spectrometric analysis using trypsin (i.e., enzymatic digestion), a buffer, an alkylating reagent, and a reductant [0674]. Ma teaches collecting data in multiple reaction monitoring (MRM) mode liquid chromatography-mass spectrometry [0858].
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
C. Claims 11, 13, 25, and 27 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 31, 34, and 46 of copending Application No. 18/837,706 (reference application) in view of Ma and Liu as applied to claims 1 and 14 above, and in further view of Balog et al. (Molecular and Cellular Proteomics, 2012, 11(9):571-585; Supplemental Information, p. 1-151; previously cited). Although the claims at issue are not identical, they are not patentably distinct from each other for the following reasons. The rejection is newly recited based upon further consideration of the claims.
The reference patent does not recite the features of instant claims 11-13 and 25-27.
Regarding claims 11 and 25, the prior art to Balog discloses comparing the N-glycan profiles from 13 colorectal cancer tumor tissues to corresponding control colon tissues (abstract). Balog SI teaches that identified spectra 85 and 86 correspond to Hex5HexNAc4NeuAc1, which has the structure
PNG
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68
282
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Greyscale
, which is considered to teach the structure and composition of glycan number 5401 as instantly claimed.
Regarding claims 13 and 27, Balog SI discloses that that identified spectra 85 and 86 correspond to Hex5HexNAc4NeuAc1, which has the structure
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68
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, showing that the right-most square is attached to the amino acid (p. 89-91), which is considered to read on the instant limitations because this square refers to HexNAc or N-acetylglucasamine (p. 1).
Regarding claims 11-13 and 25-27, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, Ma in view of Liu and Balog because each reference discloses methods for measuring glycoproteins to diagnose colon cancer. The motivation to examine the glycan structure taught by Balog in the method of Ma in view of Liu would have been to include N-glycan structures detected in colorectal cancer samples, as taught by Balog SI (p. 1).
Regarding instant claim 11, 13, 25, and 27, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the reference patent in view of Ma and Liu with Balog because each reference discloses methods for using blood proteins to diagnose colon cancer. The motivation to examine the glycan structure taught by Balog in the method of the reference patent would have been to include N-glycan structures detected in colorectal cancer samples, as taught by Balog SI (p. 1).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Response to Applicant Arguments
At p. 14, Applicant requests that the statutory and non-statutory double patenting rejections be held in abeyance until all remaining rejections have been withdrawn.
It is respectfully submitted that this is not persuasive. In the interest of compact examination, the claims have been examined in regards to double patenting.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.N.S./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685