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 .
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 election without traverse of macrophages for species 2a, of cytokines and pathogen associated molecular patterns for species 2b, of LPS, polyl:C, IFNβ, P2CSK4, CpG, or TNF for species 2c, of RNA for species 2d, of all the recited genes for species 2e, and of high BMI for species 2f in the reply filed on Dec 16 2025 is acknowledged.
However, upon further consideration, the restriction requirement of 2a, 2d, 2f, and 2g are withdrawn.
Claim Status
Claims 1-3, 5, 13, 15, 21-23, 26-31, 37-41, and 78 are pending.
Claims 4, 6-12, 14, 16-20, 24-25, 32-36 are canceled.
Claims 3, 5, 13, 15, 26-31, and 37 are objected to.
Claims 1-3, 5, 13, 15, 21-23, 26-31, 37-41, and 78 are rejected.
Priority
The instant Application claims domestic benefit to US provisional application 63/332,584, filed Apr 19 2022. Accordingly, each of claims 1-3, 5, 13, 15, 21-23, 26-31, 37-39, 41, and 78 are afforded the effective filing date of the Apr 19 2022.
Information Disclosure Statement
The information disclosure statement (IDS) filed on Sep 5 2023 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
Drawings
The Drawings submitted Nov 30 2023 are accepted.
Specification
The amendments to the specification submitted Jun 22 2023 and Nov 30 2023 are accepted.
Claim Objections
The claims are objected to for the following informalities:
Claims 3, 5, 13, 15, 26, 28-29, 31, and 37 are objected to for not including a comma after “The method of claim X”, whereas the other dependent claims in the claim set do include a comma.
Claim 26 recites “wherein the wherein the”, which should be amended to remove one of the “wherein the” recitations.
In claims 26-31, the first “the” of the claims should be capitalized.
Claim Rejections - 35 USC § 112
35 U.S.C. 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 28-29, 31, 37, and 40 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.
Claim 28 recites “the method of claim 1 wherein scREAL-TIME analysis provides the response specificity score”. It is not clear what claim 28 is attempting to further limit because claim 1 does not recite scREAL-TIME analysis or “providing” the response specificity score. Claim 1 recites “iv. determining a response specificity score characterizing the responsiveness of the subject's innate immune system, said determination comprises use of theoretic information and machine learning methodologies”. Therefore, it is not clear if the scREAL-TIME analysis is intended to describe the theoretic information and machine learning methodologies used to determine the response specificity score or if the claim attempts to recite a different method for providing the response specificity score. Claim 29 is similarly unclear as to whether it is attempting to further limit both the theoretic information and machine learning methodologies, only one of the theoretic information and machine learning methodologies, or neither with the recitation of steps of scREAL-TIME. For compact examination, it is assumed that scREAL-TIME and the steps recited in claim 29 are intended to further limit the theoretic information and machine learning methodologies. The rejection may be overcome by clarifying the relationship between claims 1 and 28-29.
Claim 31 recites “the polarization state of macrophages in the sample are determined”. First, there is insufficient antecedent basis for this limitation in the claim as there is no previous recitation of macrophages in the sample or their polarization state. Second, is unclear whether the wherein clause is intended to require determining the polarization state of macrophages within the metes and bounds of the claimed invention, or if it is only further limiting the sample such that performing the determination 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. As the claims do not recite an active performance of the determination, the metes and bounds of the claims are unclear. For compact examination, it is assumed that the determination is not required to be performed. The rejection may be overcome by clarifying what steps are required to be performed.
Claim 37 recites “the macrophage polarity stimuli-related genes”. There is insufficient antecedent basis for this limitation in the claim as there is no previous recitation of macrophage polarity stimuli-related genes. For compact examination, it is assumed that the claim intends to further limit the one or more stimuli-related genes being examined with single cell mRNA expression.
Claim 40 recites “wherein the autoimmune diseases”. There is insufficient antecedent basis for this limitation in the claim as there is no previous recitation of autoimmune diseases. It is noted that claim 39 recites “wherein said condition or disease is… autoimmunity”. It is assumed that the claim should recite “where autoimmunity” or similar.
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 1-3, 5, 13, 15, 21-23, 26-31, 37-41, and 78 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to methods, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
The claims also recite a law of nature or a natural phenomenon. The MPEP at 2106.04(b) further explains that laws of nature and natural phenomena include naturally occurring principles/relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature.
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) as well as a law of nature or a natural phenomenon are as follows:
Independent claims 1 and 41: a. determining the responsiveness of the subject's innate immune system by a method comprising: determining a response specificity score characterizing the responsiveness of the subject's innate immune system, said determination comprises use of theoretic information and machine learning methodologies.
Independent claim 1: b. using the subject's response specificity score, querying an information repository comprising (i) innate immune system responsiveness of a plurality of healthy subjects and patients having the condition or disease, and (ii) a correlation between said innate immune system responsiveness and a therapeutic outcome of said patients to one or more therapeutic regimens, and identifying a correlation value for said subject.
Dependent claims 13, 15, 27-30, and 38 recite further steps that limit the judicial exceptions in independent claims 1 and 41 and, as such, also are directed to those abstract ideas. For example, claim 13 further limits determining the response specificity score to being based on a plurality of time points after exposing; claim 15 further limits the determining responses for a plurality of timepoints to comprising integral, fold change, peak amplitude, speed; claim 27 further limits determining said response specificity score to comprising a machine learning algorithm that uses t-distributed stochastic neighbor Embedding (t-SNE); claims 28-29 further limit the method to using scREAL-TIME analysis; claim 30 further limits determining said response specificity score to examining certain patterns or relationships; and claim 38 further limits the information repository to comprising one or more time points.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation because the method only requires a user to manually identify a correlation value for a subject to inform their treatment. Without further detail as to the methodology involved in “determining,” “using”, “querying”, and “identifying”, under the BRI, one may simply, for example, use pen and paper to determine a response specificity score of the patient based on the response of their blood derived monocytes or monocyte-derived cells to a combination of stimuli, and query a database to identify a correlation between the score and a therapeutic outcome. Some of these steps, such as the use of theoretic information and machine learning methodologies require mathematical techniques as the only supported embodiments, as is disclosed in the specification as published at: [0144; 0160], which describes mathematical modeling, and [0185; 210], which describes that the machine learning model may be random forest or weighted kNN classifiers, which are mathematical models.
The claims also recite a natural relationship between the subject’s own monocytes or monocyte-derived cells, their response to stimuli, and the correlation between that response and their therapeutic outcome.
Therefore, claims 1 and 41 and those claims dependent therefrom recite an abstract idea and a law of nature/natural phenomenon [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claims 1 and 41: i. obtaining a blood sample from said subject;
ii. exposing monocytes or monocyte-derived cells derived from said blood sample to one or a combination of stimuli selected from among cytokines, pathogen-associated molecular patterns, and damage-associated molecular patterns;
iii. determining responses using genome wide RNA profiling, single cell mRNA expression of one or more of stimuli-related genes, or protein expression, in response to the stimuli in the monocytes or monocyte-derived cells.
Independent claim 1: c. treating the subject with a therapeutic regimen when said correlation value indicates a positive therapeutic benefit for the subject; or avoiding treating the subject with a therapeutic regimen when said correlation value indicates an absence of or a negative therapeutic benefit for the subject.
Dependent claims 2-3, 5, 21-23, 26, 31, 37, 39-40, and 78 recite steps that further limit the recited additional elements in the claims. For example, claim 2 further limits the blood sample; claims 3 and 78 further limit the cells to macrophages; claim 5 further limits determining responses to a single time point or a plurality of time points; claims 21-23 further limit the stimuli; claims 26 and 37 further limit the evaluated genes; claim 31 further limits the method to determining the polarization state of macrophages; and claims 39-40 further limit the condition or disease.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “obtaining a blood sample”, “exposing” cells, “determining responses”, perform functions of collecting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Those steps directed to “treating the subject with a therapeutic regimen when said correlation value indicates a positive therapeutic benefit for the subject; or avoiding treating the subject with a therapeutic regimen when said correlation value indicates an absence of or a negative therapeutic benefit for the subject” recite a treatment step which is not particular and is instead merely instructions to “apply” the exception in a generic way. The treatment or prophylaxis limitation must be “particular,” i.e., specifically identified so that it does not encompass all applications of the judicial exception(s) (see MPEP 2106.04(d)(2)).
The specification as published discloses a need to develop methodologies that would provide quantitative measures for stimulus-specific responses of macrophages in various physiological or pathological conditions at [0011], but does not provide a clear explanation for how the additional elements provide these improvements. Therefore, the additional elements do not clearly improve the functioning of a computer, or comprise an improvement to any other technical field. Further, the additional elements do not clearly affect a particular treatment; they do not clearly require or set forth a particular machine; they do not clearly effect a transformation of matter; nor do they clearly provide a nonconventional or unconventional step (MPEP2106.04(d)).
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the prior art to Tedesco et al. (Frontiers in Pharmacology, 2018, 9(71):1-13; newly cited; see the abstract; entire document is relevant) discloses that as human peripheral-blood monocytes are used as an established in vitro system for generating macrophages, this is a data gathering element that is routine, well-understood and conventional in the art. Further, the specification as published discloses such acts as performed by methods well known in the art [0107]. The prior art review to Arango Duque et al. (Frontiers in Immunology, 2014, 5(491):1-12; newly cited; see the abstract and p. 3, col. 2, par. 2; p. 5, col. 2, par. 6; entire document is relevant) discloses that examining the response of RNA or gene expression to stimuli selected from among cytokines, pathogen-associated molecular patterns, and damage-associated molecular patterns, as well as treating patients, are data gathering elements that are routine, well-understood and conventional in the art. The courts have also found that determining the level of a biomarker by any means is a well-understood, routine, conventional activity in the life science arts when claimed in a merely generic manner (see MPEP 2106.05(d)(II)(i), Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017)). As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
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.
1A. Claims 1-3, 5, 13, 15, 23, 28, 30-31, and 38-39 are rejected under 35 U.S.C. 103 as being unpatentable over Adelaja et al. (Immunity, 2021, 54(5):916-930; newly cited) in view of Tedesco et al. (Frontiers in Pharmacology, 2018, 9(71):1-13; newly cited) and Davicioni et al. (US 20210317531; newly cited).
The prior art to Adelaja discloses an examination of how information about the stimulus is encoded in the dynamics of NFkB activity in macrophages (summary).
Claim 1 discloses a method for treating a subject having a condition or disease, comprising the steps of:
a. determining the responsiveness of the subject's innate immune system by a method comprising: i. obtaining a blood sample from said subject; ii. exposing monocytes or monocyte-derived cells derived from said blood sample to one or a combination of stimuli selected from among cytokines, pathogen-associated molecular patterns, and damage-associated molecular patterns; iii. determining responses using genome wide RNA profiling, single cell mRNA expression of one or more of stimuli-related genes, or protein expression, in response to the stimuli in the monocytes or monocyte-derived cells; and iv. determining a response specificity score characterizing the responsiveness of the subject's innate immune system, said determination comprises use of theoretic information and machine learning methodologies;
Adelaja teaches preparing bone marrow-derived macrophages obtained from mice (i.e., i. obtaining a sample from a subject), stimulating the macrophages with LPS, which is a pathogen-associated molecular stimulus, TNF, which is a cytokine stimulus, Pam3CSK4, poly(I:C), and CpG (i.e., ii.) (p. e2, par. 5; p. 917, col. 1, par. 4), measuring surface TNF receptor expression and single cell RNA-seq expression in stimulated cells (i.e., iii.) (p. e3, par. 2-4), and using information-theoretic and machine learning approaches to quantitatively determine stimulus-specific time-course trajectories of NFkB activities in single primary macrophage cells, including the specificity in gene expression response (i.e., iv) (p. 925, col. 2, par. 4; p. 926, col. 1, par. 2; p. 924, col. 1, par. 1-2).
See below for teachings by Tedesco regarding obtaining a blood sample.
b. using the subject's response specificity score, querying an information repository comprising (i) innate immune system responsiveness of a plurality of healthy subjects and patients having the condition or disease, and (ii) a correlation between said innate immune system responsiveness and a therapeutic outcome of said patients to one or more therapeutic regimens, and identifying a correlation value for said subject; and
c. treating the subject with a therapeutic regimen when said correlation value indicates a positive therapeutic benefit for the subject; or avoiding treating the subject with a therapeutic regimen when said correlation value indicates an absence of or a negative therapeutic benefit for the subject.
Adelaja does not teach obtaining a blood sample in step i. or steps b. and c.
However, the prior art to Tedesco discloses a comparison of cells from monocytic cell lines to human peripheral blood monocytes (abstract). Tedesco teaches that human peripheral-blood monocytes are used as an established in vitro system for generating macrophages (abstract; p. 2, col. 2, par. 2). Tedesco generating human monocyte-derived macrophages (p. 3, col. 1, par. 1 through col. 2, par. 2) and examining them for surface marker (i.e., protein) expression (p. 4, col. 1, par. 2).
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, Adelaja and Tedesco because each reference discloses methods for examining derived macrophages. Thus, it would have been obvious to one of ordinary skill in the art to replace the bone-marrow derived macrophages of Adelaja with the blood derived macrophages of Tedesco because one of ordinary skill in the art would have been able to carry out a substitution, and the results would be reasonably predictable.
Neither Adelaja nor Tedesco teach steps b. and c.
However, the prior art to Davicioni discloses a method for the diagnosis, prognosis and the determination of cancer progression of prostate cancer in a subject by using immune cell-specific gene expression in determining prognosis and identifying individuals in need of treatment for prostate cancer who will be responsive to radiation therapy (abstract). Davicioni teaches obtaining a biological sample comprising cancer cells from the patient; measuring levels of immune cell-specific gene expression in the biological sample; calculating an immune content score based on the level of expression of the immune cell-specific genes to determine whether or not the subject is likely to benefit from radiation therapy, wherein an immune content score greater than 0 indicates that the subject will benefit from radiation therapy and an immune content score less than or equal to 0 indicates that the subject will not benefit from radiation therapy (see Fig. 2F; i.e., querying an information repository); determining the prognosis of the patient based on the levels of the one or more immune cell types in the biological sample; and administering a treatment to the subject based on the prognosis [0010; 0036]. Davicioni teaches that the immune cells may be activated dendritic cells, resting dendritic cells, M0 macrophages, M1 macrophages, or M2 macrophages [0010]. Davicioni teaches using a machine learning algorithm to examine the expression level for a plurality of targets [0135-0138]. Davicioni teaches determining test scores indicative of prognosis and treating the patients accordingly [0139; 0183] based on clinical associations (i.e., correlations; patients having the condition or disease) [0187] for treatment response prediction [0188-0190]. Davicioni teaches calculating an immune content score based on the levels of immune cell-specific gene expression, wherein a higher immune content score for the patient compared to reference value ranges for a control subject (i.e., healthy subjects) indicates that the patient will have shorter biochemical recurrence free survival, shorter distant metastasis free survival, shorter prostate cancer-specific survival, or shorter overall survival than the control subject [0023].
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, Adelaja in view of Tedesco with Davicioni because Adelaja and Davicioni both teach methods for determining the response of immune cells to stimuli. The motivation would have been to combine the results of Adelaja with a method for determining an appropriate treatment for the subject based on those results, as taught by Davicioni (abstract).
Regarding claim 2, Adelaja in view of Tedesco and Davicioni teaches claim 1 as described above. Claim 2 further adds that the blood sample comprises peripheral blood mononuclear cells.
Adelaja teaches culturing monocytes from femurs to prepare macrophages (p. e2, par. 5), but does not teach peripheral blood mononuclear cells.
However, Tedesco teaches that human peripheral-blood monocytes are used as an established in vitro system for generating macrophages (abstract; p. 2, col. 2, par. 2).
Regarding claim 3, Adelaja in view of Tedesco and Davicioni teaches claim 1 as described above. Claim 3 further adds that the monocyte or monocyte-derived cells are macrophages or dendritic cells.
Adelaja teaches examining macrophages (summary).
Regarding claims 5 and 13, Adelaja in view of Tedesco and Davicioni teaches claim 1 as described above. Claim 5 further adds that determining responses is at a single time point after exposing, or at a plurality of time points after exposing. Claim 13 further adds that the determining the response specificity score is based on a plurality of time points after exposing.
Adelaja teaches examining the data from stimulation at multiple time points after exposure (p. 925, col. 1, par. 2; Figures 1C, 2, 6-7).
Regarding claim 15, Adelaja in view of Tedesco and Davicioni teaches claims 1 and 5 as described above. Claim 15 further adds that the determining responses for a plurality of timepoints comprises integral, fold change, peak amplitude, speed, or any combination thereof.
Adelaja teaches considering integrals, derivatives, peak activities, durations, and frequencies in their algorithm (p. 917, col. 2, par. 3), as well as peak amplitude/fold change (p. 927, col. 1, par. 2) and speed (p. e7, par. 4) (see also Figure 3).
Regarding claim 23, Adelaja in view of Tedesco and Davicioni teaches claim 1 as described above. Claim 23 further adds that a no stimulus control is included.
Adelaja teaches examining media only exposed macrophages as an untreated control for single cell RNA-seq expression (p. e3, par. 4).
Regarding claim 28, Adelaja in view of Tedesco and Davicioni teaches claim 1 as described above. Claim 28 further adds that scREAL-TIME analysis provides the response specificity score.
As described in the above 35 USC 112(b), it is not clear what “scREAL-TIME” is intended to further limit. Therefore, as Adelaja teaches the use of theoretic information and machine learning methodologies to provide a quantitative specificity determination as described above (p. 925, col. 2, par. 4; p. 926, col. 1, par. 2; p. 924, col. 1, par. 1-2), it is considered that Adelaja fairly teaches the limitations of the claim.
Regarding claim 30, Adelaja in view of Tedesco and Davicioni teaches claim 1 as described above. Claim 30 further adds that determining said response specificity score comprises a pre-established relationship between responses of said stimuli-related genes to said stimuli, a pattern of expression among signaling systems, a pattern of expression of gene regulatory systems, a temporal progression of any of the foregoing, or combinations thereof.
Adelaja at least teaches examining a temporal pattern of NFkB activity that drives gene expression (i.e., a pattern of expression of gene regulatory systems and its temporal progression) (abstract; Figures 1-7).
Regarding claim 31, Adelaja in view of Tedesco and Davicioni teaches claim 1 as described above. Claim 31 further adds that the polarization state of macrophages in the sample are determined.
Adelaja does not teach this.
However, Tedesco teaches polarizing resting macrophages to either M1 or M2 phenotypes and examining their gene expression (abstract).
Regarding claim 38, Adelaja in view of Tedesco and Davicioni teaches claim 1 as described above. Claim 38 further adds that said information repository comprises innate immune system responsiveness of said patients at one or more time points of said condition or disease.
Adelaja examining the data from stimulation at multiple time points after exposure (p. 925, col. 1, par. 2; Figures 1C, 2, 6-7), but does not teach including that information in an information repository.
However, Davicioni is considered to teach the information repository as described above (see at least FIG. 2F).
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, in the course of routine experimentation and with a reasonable expectation of success, the features of Adelaja in view of Tedesco with Davicioni to include an information repository that included multiple time points because each of those features are already taught in the cited art, and their combination would be obvious to one of ordinary skill in the art to predictably produce a database comprising time point response information.
Regarding claim 39, Adelaja in view of Tedesco and Davicioni teaches claim 1 as described above. Claim 39 further adds that said condition or disease is cancer, inflammatory disease, autoimmunity, high BMI, transplant rejection, tumor progression, tumor immunotherapy, sepsis, infection, or advanced age.
Adelaja teaches examining a mouse model of Sjogren’s syndrome (p. 917, col. 1, par. 2), which is an autoimmune disease (p. 916, col. 1, par. 1).
1B. Claims 21-22, 26, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Adelaja in view of Tedesco and Davicioni, as applied to claim 1 as above, and in further view of Cheng et al. (Cell Systems, 2017, 4:330-343; cited on the Sep 5 2023 IDS).
Regarding claims 21-22, Adelaja in view of Tedesco and Davicioni teaches claim 1 as described above. Claim 21 further adds that the stimuli comprise LPS, polyl:C, IFNβ, P3CSK4, CpG and TNF. Claim 22 further adds that the stimuli consist of LPS, poly I:C, IFNβ, P3CSK4, CpG and TNF.
Adelaja teaches stimulating the macrophages with LPS, TNF, Pam3CSK4, poly(I:C), and CpG (p. e2, par. 5), but does not teach IFNβ.
However, Cheng teaches stimulation of macrophages with IFNβ (p. e3, par. 7).
Regarding claims 26 and 37, Adelaja in view of Tedesco and Davicioni teaches claim 1 as described above. Claim 26 further limits the stimuli-related genes evaluated for each stimulus and claim 37 further limits the macrophage polarity stimuli-related genes evaluated for each stimulus, wherein the genes recited in each claim are: LPS: Cxcl11, Abtb2, Tmem200b, Arrdc4, and Hopx; polyl:c: Socs1, Myo1b, Rasgrp1, Fgl2, and Gm4951; IFNβ: Scos1, Plac8, Kdr, Il12rb1, and Tnfsf8; P3CSK4: Slamf8, Socs1, Nos2, Dups4, and Myo1b; CPG: Slamf8, Arrdc4, Glipr2, and Upp1; and TNF: Olr1, Htr2a, Myo1b, Rasgrp1, and Slamf8.
Adelaja teaches performing single cell RNA-seq expression (p. e3, par. 4), which would have inherently sequenced every expressed gene in the stimulated macrophages. It is therefore considered that Adelaja fairly teaches evaluated the lists of genes for each stimulus as instantly claimed because Adelaja teaches stimulating the macrophages with LPS, TNF, Pam3CSK4, poly(I:C), and CpG (p. e2, par. 5), except for IFNβ, which Adelaja does not teach examining.
However, Cheng teaches stimulation of macrophages with IFNβ (p. e3, par. 7).
Regarding claims 21-22, 26, and 37, 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, Adelaja in view of Tedesco and Davicioni with Cheng because each reference disclose methods for examining macrophage stimulation. The motivation to stimulate macrophages with IFNβ would have been to examine the response to IFNβ in macrophages because it is an important antiviral autocrine regulator that activates the IRF TF ISGF3, as taught by Cheng (p. 333, col. 1, par. 2). One of ordinary skill in the art would have been motivated by routine optimization, the limited choice of commonly used macrophage stimulants to select a list consisting only of macrophage stimulants taught by Adelaja and include IFNβ as taught by Cheng. Regarding claims 26 and 37, it would have been obvious to examine macrophages as in Adelaja as exposed to any stimulant, including IFNβ, as taught by Cheng, because each of the elements are known in the art, and the result would have been reasonably predictable to one of ordinary skill in the art.
1C. Claims 27 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Adelaja in view of Tedesco and Davicioni, as applied to claim 1 as above, and in further view of Rostom et al. (FEBS Letters, 591(15):2213-2225; newly cited).
Regarding claim 27, Adelaja in view of Tedesco and Davicioni teaches claim 1 as described above. Claim 27 further adds that determining said response specificity score comprises a machine learning algorithm that uses t-distributed stochastic neighbor Embedding (t-SNE).
Adelaja teaches performing PCA and UMAP on the sequencing data prior to machine learning (p. e6, par. 10), but does not teach t-SNE.
However, the prior art to Rostom discloses computation approaches for interpreting single-cell RNA sequencing data (abstract). Rostom teaches that t-SNE is another method besides PCA for dimensionality reduction of expression tables (Table 1; p. 2216, col. 1, par. 1).
Regarding claim 29, Adelaja in view of Tedesco and Davicioni teaches claims 1 and 28 as described above. Claim 29 further adds that scREAL-TIME comprises the steps of dimensionality reduction, k-means clustering, weighted random walks, spline fitting and recovering gene trajectories.
Adelaja teaches running PCA on single cell RNA-seq data (i.e., dimensionality reduction; supported by the specification as published at [0075]), performing UMAP on the PCA results, and performing machine learning with a random forest classifier (p. e6, par. 10), which is considered to read on recovering gene trajectories because the machine learning model of Adelaja is applied to single cell RNA-seq data. Adelaja does not teach k-means clustering, weighted random walks, and spline fitting.
However, Davicioni teaches k-mean clustering as part an unsupervised learning algorithm [0136].
However, Rostom teaches that t-SNE considers local distances between data points (cells) by combining dimensionality reduction with random walks on the nearest neighbour network with the goal of separating far-apart clusters (i.e., weighted; as supported by the instant specification as published at [0045], which discloses that random walks are weighted by distances of clusters), while also ensuring all data points can be seen by eye to allow for comparisons of cluster size (p. 2216, col. 1, par. 1 through col. 2, par. 1). Rostom teaches that splines are used to model expression dependence on pseudotime to allow nonlinear trends, where software packages allow for more than just expression levels to be modelled by the splines: with appropriate link functions, allelic expression balance or isoform usage can be modelled (i.e., spline fitting) (p. 2221, col. 1, par. 3).
Regarding claims 27 and 29, 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, Adelaja in view of Tedesco and Davicioni with Rostom because Adelaja and Rostom each disclose methods of single-cell RNA sequencing. The motivation to use the methods taught by Rostom would have been to use known methods developed for examining single-cell RNA sequencing data that requires high-dimensional data mining techniques, as taught by Rostom (abstract).
1D. Claim 40 is rejected under 35 U.S.C. 103 as being unpatentable over Adelaja in view of Tedesco and Davicioni, as applied to claims 1 and 39 as above, and in further view of Ma et al. (Frontiers in Immunology, 2019, 10(1140):1-24; newly cited).
Regarding claim 40, Adelaja in view of Tedesco and Davicioni teaches claims 1 and 39 as described above. Claim 40 further adds that the autoimmune diseases are rheumatoid arthritis, juvenile dermatomyositis, psoriasis, psoriatic arthritis, sarcoidosis, lupus, Crohn's disease, eczema, vasculitis, ulcerative colitis or multiple sclerosis.
Adelaja does not teach this.
However, the prior art to Ma discloses macrophages are involved in many autoimmune diseases (title), including Sjögren’s syndrome, rheumatoid arthritis (p. 1, par. 1), lupus (p. 2, col. 1, par. 3 through p. 6, col. 1, par. 4), Crohn’s disease, ulcerative colitis (p. 11, col. 2, par. 3), and multiple sclerosis (p. 8, col. 1, par. 3 through p. 9, col. 1, par. 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, Adelaja in view of Tedesco and Davicioni with Ma because Adelaja and Ma both disclose teachings about the involvement of macrophages in autoimmune disease. It would have been obvious to one of ordinary skill in the art to examine the stimuli response of macrophages derived from patients with the autoimmune diseases as taught by Ma because Adelaja in view of Tedesco and Davicioni teach a method for examining the stimuli response of macrophages to determine a treatment for a patient, and Ma teaches that macrophages are involved in many other autoimmune diseases. Therefore, the basic technique of examining the stimuli response of macrophages to determine a treatment for a patient in a patient with an autoimmune disease related to macrophages would have predictably resulted in the prediction of an appropriate treatment for that patient.
1E. Claims 41 and 78 are rejected under 35 U.S.C. 103 as being unpatentable over Adelaja et al. (Immunity, 2021, 54(5):916-930; newly cited) in view of Tedesco et al. (Frontiers in Pharmacology, 2018, 9(71):1-13; newly cited).
Claim 41 discloses a method of determining innate immune system responsiveness of a subject, comprising:
a. determining the responsiveness of the subject's innate immune system by a method comprising: i. obtaining a blood sample from said subject; ii. exposing monocytes or monocyte-derived cells derived from said blood sample to one or a combination of stimuli selected from among cytokines, pathogen-associated molecular patterns, and damage-associated molecular patterns; iii. determining responses using genome wide RNA profiling, single cell mRNA expression of one or more of stimuli-related genes, or protein expression, in response to the stimuli in the monocytes or monocyte-derived cells; and iv. determining a response specificity score characterizing the responsiveness of the subject's innate immune system, said determination comprises use of theoretic information theoretic and machine learning methodologies.
Adelaja teaches preparing bone marrow-derived macrophages obtained from mice (i.e., i. obtaining a sample from a subject), stimulating the macrophages with LPS, which is a pathogen-associated molecular stimulus, TNF, which is a cytokine stimulus, Pam3CSK4, poly(I:C), and CpG (i.e., ii.) (p. e2, par. 5; p. 917, col. 1, par. 4), measuring surface TNF receptor expression and single cell RNA-seq expression in stimulated cells (i.e., iii.) (p. e3, par. 2-4), and using information-theoretic and machine learning approaches to quantitatively determine stimulus-specific time-course trajectories of NFkB activities in single primary macrophage cells, including the specificity in gene expression response (i.e., iv) (p. 925, col. 2, par. 4; p. 926, col. 1, par. 2; p. 924, col. 1, par. 1-2).
Adelaja does not teach obtaining a blood sample in step i.
However, the prior art to Tedesco discloses a comparison of cells from monocytic cell lines to human peripheral blood monocytes (abstract). Tedesco teaches that human peripheral-blood monocytes are used as an established in vitro system for generating macrophages (abstract; p. 2, col. 2, par. 2). Tedesco generating human monocyte-derived macrophages (p. 3, col. 1, par. 1 through col. 2, par. 2) and examining them for surface marker (i.e., protein) expression (p. 4, col. 1, par. 2).
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, Adelaja and Tedesco because each reference discloses methods for examining derived macrophages. Thus, it would have been obvious to one of ordinary skill in the art to replace the bone-marrow derived macrophages of Adelaja with the blood derived macrophages of Tedesco because one of ordinary skill in the art would have been able to carry out a substitution, and the results would be reasonably predictable.
Regarding claim 78, Adelaja in view of Tedesco teaches claim 41 as described above. Claim 78 further adds that the monocytes or monocyte-derived cells are macrophages or dendritic cells.
Adelaja teaches examining macrophages (summary).
2A. Claims 1-3, 5, 13, 15, 21-23, 26, 28, 30-31, and 37-40 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng et al. (Cell Systems, 2017, 4:330-343; cited on the Sep 5 2023 IDS) in view of Tedesco et al. (Frontiers in Pharmacology, 2018, 9(71):1-13; newly cited), Jetka et al. (PLoS Comput. Biol., 2019, 15(7):e1007132, p. 1-23; cited on the Sep 5 2023 IDS), and Davicioni et al. (US 20210317531; newly cited).
The prior art to Cheng discloses a mechanistic modeling approach to elucidate transcription factor control logic from gene expression data in hundreds of genes in 85 datasets measuring the transcriptional responses of murine fibroblasts and macrophages to cytokines and pathogens (abstract).
Claim 1 discloses a method for treating a subject having a condition or disease, comprising the steps of:
a. determining the responsiveness of the subject's innate immune system by a method comprising: i. obtaining a blood sample from said subject; ii. exposing monocytes or monocyte-derived cells derived from said blood sample to one or a combination of stimuli selected from among cytokines, pathogen-associated molecular patterns, and damage-associated molecular patterns; iii. determining responses using genome wide RNA profiling, single cell mRNA expression of one or more of stimuli-related genes, or protein expression, in response to the stimuli in the monocytes or monocyte-derived cells; and iv. determining a response specificity score characterizing the responsiveness of the subject's innate immune system, said determination comprises use of theoretic information and machine learning methodologies;
Cheng teaches preparing primary bone marrow derived macrophages by culturing bone marrow cells from mice (i.e., obtaining a sample from a subject in step i.), stimulating the macrophages (i.e., monocyte-derived cells) (p. e2, par. 2) with cytokines and pathogens (i.e., step ii.) (abstract; p. e3, par. 7), performing experimental measurements of transcription factor activities via western blotting (i.e., protein) and macrophage transcriptome analyses (i.e., genome wide RNA profiling) (i.e., step iii.) (p. e4, par. 1 and 3), and examining gene regulatory network cluster fit scores in macrophages, the primary innate immune cells, using GRN models that combine thermodynamic expression for promoter activity and a kinetic expression for promoter-driven mRNA synthesis and first order mRNA degradation, are matured through parameter optimization to maximize fit scores, and evaluated for goodness of fit (i.e., determining a response specificity score characterizing the responsiveness of the subject's innate immune system using machine learning methodologies as in step iv.) (p. 337, col. 1, par. 1; p. e2, par. 4 through p. e3, par. 5).
See below for teachings by Tedesco regarding obtaining a blood sample and by Jetka regarding theoretic information.
b. using the subject's response specificity score, querying an information repository comprising (i) innate immune system responsiveness of a plurality of healthy subjects and patients having the condition or disease, and (ii) a correlation between said innate immune system responsiveness and a therapeutic outcome of said patients to one or more therapeutic regimens, and identifying a correlation value for said subject; and
c. treating the subject with a therapeutic regimen when said correlation value indicates a positive therapeutic benefit for the subject; or avoiding treating the subject with a therapeutic regimen when said correlation value indicates an absence of or a negative therapeutic benefit for the subject.
Cheng does not teach obtaining a blood sample in step i., theoretic information in step iv., or steps b. and c.
However, the prior art to Tedesco discloses a comparison of cells from monocytic cell lines to human peripheral blood monocytes (abstract). Tedesco teaches that human peripheral-blood monocytes are used as an established in vitro system for generating macrophages (abstract; p. 2, col. 2, par. 2). Tedesco generating human monocyte-derived macrophages (p. 3, col. 1, par. 1 through col. 2, par. 2) and examining them for surface marker (i.e., protein) expression (p. 4, col. 1, par. 2).
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, Cheng and Tedesco because each reference discloses methods for examining derived macrophages. Thus, it would have been obvious to one of ordinary skill in the art to replace the bone-marrow derived macrophages of Cheng with the blood derived macrophages of Tedesco because one of ordinary skill in the art would have been able to carry out a substitution, and the results would be reasonably predictable.
Neither Cheng nor Tedesco teach theoretic information in step iv. or steps b. and c.
However, the prior art to Jetka discloses exploring the information-theoretic approach through a novel algorithm, SLEMI — statistical learning (i.e., machine learning) based estimation of mutual information, to analyze signaling systems with high-dimensional outputs and a large number of input values (abstract). Jetka teaches that analyzing the NF-κB single—cell signaling responses to TNF-α reveals that NF-κB signaling dynamics improves discrimination of high concentrations of TNF-α with a relatively modest impact on discrimination of low concentrations (abstract; Results on p. 4-11; Methods on p. 12-20).
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, Cheng in view of Tedesco with Jetka because Cheng and Jetka both disclose methods for examining transcriptional responses of cells to stimuli. The motivation to use the statistical learning, information-theoretic approach of Jetka would have been to analyze signaling systems with high-dimensional outputs and a large number of input values in computationally efficient manner, as taught by Jetka (abstract). Therefore, the substitution of the computation method of Jetka for the computation method of Cheng in the overall method of Cheng in view of Tedesco is no more than the simple substitution of one known element for another, with the predictable result of identifying cell signaling responses.
Neither Cheng, Tedesco, nor Jetka teach steps b. and c.
However, the prior art to Davicioni discloses a method for the diagnosis, prognosis and the determination of cancer progression of prostate cancer in a subject by using immune cell-specific gene expression in determining prognosis and identifying individuals in need of treatment for prostate cancer who will be responsive to radiation therapy (abstract). Davicioni teaches obtaining a biological sample comprising cancer cells from the patient; measuring levels of immune cell-specific gene expression in the biological sample; calculating an immune content score based on the level of expression of the immune cell-specific genes to determine whether or not the subject is likely to benefit from radiation therapy, wherein an immune content score greater than 0 indicates that the subject will benefit from radiation therapy and an immune content score less than or equal to 0 indicates that the subject will not benefit from radiation therapy (see Fig. 2F; i.e., querying an information repository); determining the prognosis of the patient based on the levels of the one or more immune cell types in the biological sample; and administering a treatment to the subject based on the prognosis [0010; 0036]. Davicioni teaches that the immune cells may be activated dendritic cells, resting dendritic cells, M0 macrophages, M1 macrophages, or M2 macrophages [0010]. Davicioni teaches using a machine learning algorithm to examine the expression level for a plurality of targets [0135-0138]. Davicioni teaches determining test scores indicative of prognosis and treating the patients accordingly [0139; 0183] based on clinical associations (i.e., correlations; patients having the condition or disease) [0187] for treatment response prediction [0188-0190]. Davicioni teaches calculating an immune content score based on the levels of immune cell-specific gene expression, wherein a higher immune content score for the patient compared to reference value ranges for a control subject (i.e., healthy subjects) indicates that the patient will have shorter biochemical recurrence free survival, shorter distant metastasis free survival, shorter prostate cancer-specific survival, or shorter overall survival than the control subject [0023].
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, Cheng in view of Tedesco and Jetka with Davicioni because Cheng and Davicioni both teach methods for determining the response of immune cells to stimuli. The motivation would have been to combine the results of Cheng with a method for determining an appropriate treatment for the subject based on those results, as taught by Davicioni (abstract).
Regarding claim 2, Cheng in view of Tedesco, Jetka, and Davicioni teaches claim 1 as described above. Claim 2 further adds that the blood sample comprises peripheral blood mononuclear cells.
Cheng teaches culturing monocytes from femurs to prepare macrophages (p. e2, par. 3), but does not teach peripheral blood mononuclear cells.
However, Tedesco teaches that human peripheral-blood monocytes are used as an established in vitro system for generating macrophages (abstract; p. 2, col. 2, par. 2).
Regarding claim 3, Cheng in view of Tedesco, Jetka, and Davicioni teaches claim 1 as described above. Claim 3 further adds that the monocyte or monocyte-derived cells are macrophages or dendritic cells.
Cheng teaches examining macrophages (summary).
Regarding claims 5 and 13, Cheng in view of Tedesco, Jetka, and Davicioni teaches claim 1 as described above. Claim 5 further adds that determining responses is at a single time point after exposing, or at a plurality of time points after exposing. Claim 13 further adds that the determining the response specificity score is based on a plurality of time points after exposing.
Cheng teaches examining the data from stimulation at multiple time points after exposure (p. 333, col. 1, par. 4; p. 339, col. 1, par. 1; Figures 2-6).
Regarding claim 15, Cheng in view of Tedesco, Jetka, and Davicioni teaches claims 1 and 5 as described above. Claim 15 further adds that the determining responses for a plurality of timepoints comprises integral, fold change, peak amplitude, speed, or any combination thereof.
Cheng teaches that genes showing a greater or equal to 3-fold increase in macrophages at any time point upon LPS stimulation were selected (p. e4, par. 3), as well as examining fold changes in the GRN model (p. e3).
Regarding claims 21-22, Cheng in view of Tedesco, Jetka, and Davicioni teaches claim 1 as described above. Claim 21 further adds that the stimuli comprise LPS, polyl:C, IFNβ, P3CSK4, CpG and TNF. Claim 22 further adds that the stimuli consist of LPS, poly I:C, IFNβ, P3CSK4, CpG and TNF.
Cheng teaches stimulating the macrophages with LPS, TNF, Pam3CSK4, poly(I:C), and CpG, and IFNβ (p. e3, par. 7). One of ordinary skill in the art would have been motivated by routine optimization and the limited choice of commonly used macrophage stimulants to select a list consisting only of macrophage stimulants LPS, poly I:C, IFNβ, P3CSK4, CpG and TNF, as Cheng teaches examining each of these stimulants.
Regarding claim 23, Cheng in view of Tedesco, Jetka, and Davicioni teaches claim 1 as described above. Claim 23 further adds that a no stimulus control is included.
Cheng teaches comparing fold changes of genes to basal wild-type cells (i.e., no stimulus control) (p. e4, par. 5).
Regarding claims 26 and 37, Cheng in view of Tedesco, Jetka, and Davicioni teaches claim 1 as described above. Claim 26 further limits the stimuli-related genes evaluated for each stimulus and claim 37 further limits the macrophage polarity stimuli-related genes evaluated for each stimulus, wherein the genes recited in each claim are: LPS: Cxcl11, Abtb2, Tmem200b, Arrdc4, and Hopx; polyl:c: Socs1, Myo1b, Rasgrp1, Fgl2, and Gm4951; IFNβ: Scos1, Plac8, Kdr, Il12rb1, and Tnfsf8; P3CSK4: Slamf8, Socs1, Nos2, Dups4, and Myo1b; CPG: Slamf8, Arrdc4, Glipr2, and Upp1; and TNF: Olr1, Htr2a, Myo1b, Rasgrp1, and Slamf8.
Cheng teaches transcriptome analyses on the entire cell (p. e3, par. 4), which would have inherently sequenced every expressed gene in the stimulated macrophages. It is therefore considered that Cheng fairly teaches evaluated the lists of genes for each stimulus as instantly claimed because Cheng teaches stimulating the macrophages with LPS, TNF, Pam3CSK4, poly(I:C), and CpG, and IFNβ (p. e3, par. 7).
Regarding claim 28, Cheng in view of Tedesco, Jetka, and Davicioni teaches claim 1 as described above. Claim 28 further adds that scREAL-TIME analysis provides the response specificity score.
As described in the above 35 USC 112(b), it is not clear what “scREAL-TIME” is intended to further limit. Therefore, as Cheng (p. e2-e3) in view of Jetka (abstract; Results on p. 4-11; Methods on p. 12-20) teaches the use of theoretic information and machine learning methodologies to provide a quantitative specificity determination as described above, it is considered that Adelaja fairly teaches the limitations of the claim.
Regarding claim 30, Cheng in view of Tedesco, Jetka, and Davicioni teaches claim 1 as described above. Claim 30 further adds that determining said response specificity score comprises a pre-established relationship between responses of said stimuli-related genes to said stimuli, a pattern of expression among signaling systems, a pattern of expression of gene regulatory systems, a temporal progression of any of the foregoing, or combinations thereof.
Cheng teaches examining temporal profiles of transcription factor activities (i.e., a temporal progression of the pattern of expression of gene regulatory systems) (p. e3, par. 1).
Regarding claim 31, Cheng in view of Tedesco, Jetka, and Davicioni teaches claim 1 as described above. Claim 31 further adds that the polarization state of macrophages in the sample are determined.
Cheng does not teach this.
However, Tedesco teaches polarizing resting macrophages to either M1 or M2 phenotypes and examining their gene expression (abstract).
Regarding claim 38, Cheng in view of Tedesco, Jetka, and Davicioni teaches claim 1 as described above. Claim 38 further adds that said information repository comprises innate immune system responsiveness of said patients at one or more time points of said condition or disease.
Cheng examining the data from stimulation at multiple time points after exposure (p. 333, col. 1, par. 4; p. 339, col. 1, par. 1; Figures 2-6), but does not teach including that information in an information repository.
However, Davicioni is considered to teach the information repository as described above (see at least FIG. 2F).
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, in the course of routine experimentation and with a reasonable expectation of success, the features of Cheng in view of Tedesco and Jetka with Davicioni to include an information repository that included multiple time points because each of those features are already taught in the cited art, and their combination would be obvious to one of ordinary skill in the art to predictably produce a database comprising time point response information.
Regarding claims 39-40, Cheng in view of Tedesco, Jetka, and Davicioni teaches claim 1 as described above. Claim 39 further adds that said condition or disease is cancer, inflammatory disease, autoimmunity, high BMI, transplant rejection, tumor progression, tumor immunotherapy, sepsis, infection, or advanced age. Claim 40 further adds that the autoimmune diseases are rheumatoid arthritis, juvenile dermatomyositis, psoriasis, psoriatic arthritis, sarcoidosis, lupus, Crohn's disease, eczema, vasculitis, ulcerative colitis or multiple sclerosis.
Cheng does not teach this.
However, Davicioni teaches methods, systems, and kits for the diagnosis, prognosis and the determination of cancer progression (abstract). Because Cheng in view of Tedesco, Jetka, and Davicioni teaches cancer as instantly claimed, it is considered that Cheng in view of Tedesco, Jetka, and Davicioni also fairly teaches claim 40 because teaching autoimmune diseases is not required.
2B. Claims 27 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Tedesco, Jetka, and Davicioni, as applied to claim 1 as above, and in further view of Rostom et al. (FEBS Letters, 591(15):2213-2225; newly cited).
Regarding claim 27, Cheng in view of Tedesco, Jetka, and Davicioni teaches claim 1 as described above. Claim 27 further adds that determining said response specificity score comprises a machine learning algorithm that uses t-distributed stochastic neighbor Embedding (t-SNE).
Cheng does not teach this.
However, the prior art to Rostom discloses computation approaches for interpreting single-cell RNA sequencing data (abstract). Rostom teaches that t-SNE is another method besides PCA for dimensionality reduction of expression tables (Table 1; p. 2216, col. 1, par. 1).
Regarding claim 29, Cheng in view of Tedesco, Jetka, and Davicioni teaches claims 1 and 28 as described above. Claim 29 further adds that scREAL-TIME comprises the steps of dimensionality reduction, k-means clustering, weighted random walks, spline fitting and recovering gene trajectories.
Cheng does not teach this.
However, Davicioni teaches k-mean clustering as part an unsupervised learning algorithm [0136].
However, Rostom teaches that there are hundreds of dimensionality reduction methods available for gene expression data (p. 2214, col. 2, par. 5). Rostom teaches that t-SNE considers local distances between data points (cells) by combining dimensionality reduction with random walks on the nearest neighbour network with the goal of separating far-apart clusters (i.e., weighted; as supported by the instant specification as published at [0045], which discloses that random walks are weighted by distances of clusters), while also ensuring all data points can be seen by eye to allow for comparisons of cluster size (p. 2216, col. 1, par. 1 through col. 2, par. 1). Rostom teaches that splines are used to model expression dependence on pseudotime to allow nonlinear trends, where software packages allow for more than just expression levels to be modelled by the splines: with appropriate link functions, allelic expression balance or isoform usage can be modelled (i.e., spline fitting) (p. 2221, col. 1, par. 3).
Regarding claims 27 and 29, 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, Cheng in view of Tedesco, Jetka, and Davicioni with Rostom because Cheng and Rostom each disclose methods of RNA sequencing. The motivation to use the methods taught by Rostom would have been to use known methods developed for examining RNA sequencing data that requires high-dimensional data mining techniques, as taught by Rostom (abstract).
2C. Claims 41 and 78 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng et al. (Cell Systems, 2017, 4:330-343; cited on the Sep 5 2023 IDS) in view of Tedesco et al. (Frontiers in Pharmacology, 2018, 9(71):1-13; newly cited) and Jetka et al. (PLoS Comput. Biol., 2019, 15(7):e1007132, p. 1-23; cited on the Sep 5 2023 IDS).
Claim 41 discloses a method of determining innate immune system responsiveness of a subject, comprising:
a. determining the responsiveness of the subject's innate immune system by a method comprising: i. obtaining a blood sample from said subject; ii. exposing monocytes or monocyte-derived cells derived from said blood sample to one or a combination of stimuli selected from among cytokines, pathogen-associated molecular patterns, and damage-associated molecular patterns; iii. determining responses using genome wide RNA profiling, single cell mRNA expression of one or more of stimuli-related genes, or protein expression, in response to the stimuli in the monocytes or monocyte-derived cells; and iv. determining a response specificity score characterizing the responsiveness of the subject's innate immune system, said determination comprises use of theoretic information theoretic and machine learning methodologies.
Cheng teaches preparing primary bone marrow derived macrophages by culturing bone marrow cells from mice (i.e., obtaining a sample from a subject in step i.), stimulating the macrophages (i.e., monocyte-derived cells) (p. e2, par. 2) with cytokines and pathogens (i.e., step ii.) (abstract; p. e3, par. 7), performing experimental measurements of transcription factor activities via western blotting (i.e., protein) and macrophage transcriptome analyses (i.e., genome wide RNA profiling) (i.e., step iii.) (p. e4, par. 1 and 3), and examining gene regulatory network cluster fit scores in macrophages, the primary innate immune cells, using GRN models that combine thermodynamic expression for promoter activity and a kinetic expression for promoter-driven mRNA synthesis and first order mRNA degradation, are matured through parameter optimization to maximize fit scores, and evaluated for goodness of fit (i.e., determining a response specificity score characterizing the responsiveness of the subject's innate immune system using machine learning methodologies as in step iv.) (p. 337, col. 1, par. 1; p. e2, par. 4 through p. e3, par. 5).
Cheng does not teach obtaining a blood sample in step i. or theoretic information in step iv.
However, the prior art to Tedesco discloses a comparison of cells from monocytic cell lines to human peripheral blood monocytes (abstract). Tedesco teaches that human peripheral-blood monocytes are used as an established in vitro system for generating macrophages (abstract; p. 2, col. 2, par. 2). Tedesco generating human monocyte-derived macrophages (p. 3, col. 1, par. 1 through col. 2, par. 2) and examining them for surface marker (i.e., protein) expression (p. 4, col. 1, par. 2).
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, Cheng and Tedesco because each reference discloses methods for examining derived macrophages. Thus, it would have been obvious to one of ordinary skill in the art to replace the bone-marrow derived macrophages of Cheng with the blood derived macrophages of Tedesco because one of ordinary skill in the art would have been able to carry out a substitution, and the results would be reasonably predictable.
Neither Cheng nor Tedesco teach theoretic information in step iv.
However, the prior art to Jetka discloses exploring the information-theoretic approach through a novel algorithm, SLEMI — statistical learning (i.e., machine learning) based estimation of mutual information, to analyze signaling systems with high-dimensional outputs and a large number of input values (abstract). Jetka teaches that analyzing the NF-κB single—cell signaling responses to TNF-α reveals that NF-κB signaling dynamics improves discrimination of high concentrations of TNF-α with a relatively modest impact on discrimination of low concentrations (abstract; Results on p. 4-11; Methods on p. 12-20).
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, Cheng in view of Tedesco with Jetka because Cheng and Jetka both disclose methods for examining transcriptional responses of cells to stimuli. The motivation to use the statistical learning, information-theoretic approach of Jetka would have been to analyze signaling systems with high-dimensional outputs and a large number of input values in computationally efficient manner, as taught by Jetka (abstract). Therefore, the substitution of the computation method of Jetka for the computation method of Cheng in the overall method of Cheng in view of Tedesco is no more than the simple substitution of one known element for another, with the predictable result of identifying cell signaling responses.
Regarding claim 78, Cheng in view of Tedesco and Jetka teaches claim 41 as described above. Claim 78 further adds that the monocytes or monocyte-derived cells are macrophages or dendritic cells.
Cheng teaches examining macrophages (summary).
Conclusion
No claims are allowed.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JANNA NICOLE SCHULTZHAUS whose telephone number is (571)272-0812. The examiner can normally be reached on Monday - Friday 8-4.
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/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
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