DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
As detailed on the Filing Receipt filed 6/2/2023 the instant application claims priority to as early as 4/8/2022. At this point in prosecution, all claims are accorded the earliest claimed priority date.
Information Disclosure Statement
The Information Disclosure Statements filed on 4/7/2023 and 3/12/2026 are in compliance with the provisions of 37 CFR 1.97 and have been considered in full. Signed copies of the IDS are included with this Office Action.
Claim Status
Claims 1-23 are pending, and under examination.
Claim Interpretation
Claim 21 is directed to a system comprising processors and memory, i.e., conventional computer hardware components. The specification indicates that “any [] methods described herein can be performed by computer executable instructions (e.g., causing a computing system to perform the method)… Such methods can be performed at least in part by a computing system (e.g., one or more computing devices)” (pg. 33, para. 6). In view of the specification, the claimed system is interpreted as encompassing a computer.
Objections to the Specification
Applicant is reminded of the proper content of, language and format for an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
The abstract of the disclosure is objected to because of the following informalities:
The abstract uses phrases which can be implied (e.g., “Provided herein are”);
The abstract refers to a purported merit of the invention (“with high accuracy”);
Computer-implemented methods of identifying treatment-response signatures in subjects with prostate cancer are known in the art, and the abstract does not appear to disclose the technical aspects of the invention that are new in the art.
A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
Additionally, 37 CFR 1.57(e) states that an incorporation by reference by hyperlink or other form of browser executable code is not permitted. The disclosure is objected to because it contains embedded hyperlinks and/or other forms of browser-executable code in at least the following locations of the as-filed specification:
pgs. 49-50, Table 1
pg. 66, Table 7
Applicant is required to delete the embedded hyperlinks and/or other forms of browser-
executable code. References to websites should be limited to the top-level domain name
without any prefix, such as http://, or other browser-executable code. See MPEP 608.01 § VII.
Claim Objections
Claim 12 is objected to because of the following informalities:
With respect to claim 12, the recited term “rater” (line 6) should be amended to “rate”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 USC § 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 3 and 20 are rejected under 35 USC § 112(d) as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends.
With respect to claim 3, this dependent claim does not appear to further limit the method of claim 1. The unique limitations of claim 3 pertain to the subject(s), or cell culture(s), from whence the received gene expression set is derived, and alternatively require that:
the subject(s) or cell culture(s) have not received the treatment;
the subject(s) or cell culture(s) have received the treatment; or
some of the subject(s) or cell culture(s) have received the treatment, and some of the subject(s) or cell culture(s) have not received the treatment.
These alternatives appear to encompass every claimed embodiment of the method of claim 1, with respect to possible treatment statuses of possible sources for the received gene expression dataset. In this way, claim 3 does not appear to further limit the subject matter of claim 1.
With respect to claim 20, this dependent claim does not appear to further limit the method of claim 1. The unique limitations of claim 20 pertain to the sample that has not received the treatment, the sample that is sensitive to the treatment, and the sample that has resistance to the treatment, and alternatively require that:
the samples are from the same subject;
the samples are from different subjects; or
any combination thereof (e.g., some of the samples are from the same subject and some of the samples are from different subjects).
These alternatives appear to encompass every claimed embodiment of the method of claim 1, with respect to possible sources for the recited samples. In this way, claim 20 does not appear to further limit the subject matter of claim 1.
Applicant may cancel the claims, amend the claims to place the claims in proper dependent form, rewrite the claims in independent form, or present a sufficient showing that the dependent claims comply with the statutory requirements.
The following is a quotation of 35 USC § 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 11-13 are rejected under 35 USC § 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.
With respect to claim 11 and dependents thereof, there is uncertainty regarding scope of the following recited limitations:
“wherein the pairwise comparisons are performed using linear regression” (lines 1-2); and
“wherein each transcriptional regulatory program vector is an independent variable in the regression analysis and each molecular pathway activity vector is a dependent variable in the regression” (lines 3-5).
These are presented as alternative limitations, joined by the term “and/or” (line 2). Use of “and/or” indicates that the claim encompasses embodiments where only one of these limitations is satisfied, as well as embodiments where both are satisfied. However, the second limitation appears to implicitly require that the user performs a regression analysis. It is unclear how the latter limitation alone could be satisfied, and how to interpret the recited reference to “the regression analysis” in the absence of the former limitation.
Thus, the claim is indefinite. For the purposes of applying prior art, the recited term “and/or” is interpreted as “and”. Hence, the claim is interpreted as requiring all recited limitations.
With respect to claim 12, there is uncertainty regarding antecedent basis of the referenced step of “incorporating each of the one or more statistically significant relationships into the network as an edge of the network” (lines 1-2). Claim 8, on which claim 12 depends, recites no such step. Therefore, the limiting effect of claim 12 is uncertain and the dependent claim is indefinite.
For purposes of applying prior art, claim 12 is interpreted as dependent on claim 9 (which does recite such a step) instead of claim 8.
For the above reasons, the claims are indefinite.
Claim Rejections - 35 USC § 101
35 USC § 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 1-23 are rejected under 35 USC § 101 because the claimed invention is directed to judicial exceptions without significantly more (i.e., non-statutory subject matter).
"Claims directed to nothing more than abstract ideas, natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 § I).
Abstract ideas include mathematical concepts (including formulas, equations and calculations), and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
Natural phenomena and laws of nature and include principles, relations, and products that are naturally occurring or do not have markedly different characteristics compared to what occurs in nature (MPEP 2106.04(b)).
The claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea and a natural phenomenon.
Step 1: The Four Categories of Statutory Subject Matter (MPEP 2106.03)
Claims 1-21 are directed to a method, which falls under the ‘process’ category of statutory subject matter.
Claim 22 is directed to a system comprising computer hardware, which falls under the ‘machine’ category of statutory subject matter.
Claim 23 is directed to computer-readable media. The claimed subject matter encompasses transitory embodiments (e.g., propagating signals) which do not fall under any category of statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007); Mentor Graphics Corp. v. EVE-USA, Inc., 851 F.3d 1275, 1294 (Fed. Cir. 2017).
The Examiner suggests amendment to, e.g., “One or more non-transitory computer readable media”. Direction of the claim to non-transitory embodiments would cause the claim to fall under a category of statutory subject matter and overcome this portion of the rejection.
However, this amendment alone would likely not overcome rejection for recitation of
judicial exceptions without significantly more. In the interest of compact prosecution, the recited subject matter of claim 12 has been interpreted according to the Examiner’s suggestion for further analysis below regarding recitation of judicial exceptions without significantly more.
Step 2A, Prong One: Whether the Claims Set Forth or Describe a Judicial Exception (MPEP 2106.04 § II.A.1)
‘Mathematical concepts’ are relationships between variables and numbers, numerical formulas or equations, or acts of calculation, which need not be expressed in mathematical symbols (MPEP 2106.04(a)(2) § I). The claims recite elements which encompass mathematical concepts, at least under their broadest reasonable interpretation, including:
performing a transcriptional regulatory program analysis, and a molecular pathway enrichment analysis, using the gene expression dataset (claim 4), e.g., evaluating algorithms (see, e.g., description of algorithmic analysis of transcriptional regulatory program activity at pg. 17, para. 3 of the specification; see, e.g., illustration of t-SNE clustering of molecular pathway data in Fig. 10C);
determining an activity vector for each molecular pathway and transcriptional regulatory program (claim 9), i.e., evaluating mathematical expressions (see mathematical definition of activity vectors at pg. 23, paras. 2-3 of the specification);
identifying one or more statistically significant relationships between each transcriptional regulatory program and each molecular pathway (claim 9), i.e., calculating significance metrics, wherein:
identifying comprises performing pairwise comparisons between each molecular pathway activity vector and each transcriptional regulatory network activity vector (claim 10),
the pairwise comparisons are performed using linear regression, [and] each transcriptional regulatory program activity vector is an independent variable in the regression analysis and each molecular pathway activity vector is a dependent variable in the regression analysis (claim 11),
a slope of the regression indicates a directionality of the relationship between the molecular pathway and the transcriptional regulatory program, wherein a positive slope of the regression indicates a positive relationship between the molecular pathway and the transcriptional regulatory program, and a negative slope of the regression indicates a negative relationship between the molecular pathway and the transcriptional regulatory program (claim 13);
performing multiple hypothesis testing to identify each transcriptional regulatory program that has a relationship with each molecular pathway (claim 12), i.e., evaluating a function (see description of algorithmic performance of multiple hypothesis testing at pg. 23, para. 4 – pg. 24, para. 1 of the specification);
calculating a false discovery rate (claim 12);
determining a weight of each edge incorporated into the network (claim 14), i.e., calculating regression coefficients, wherein:
determining comprises a bootstrap analysis, wherein the weight of each edge is the number of times the edge is identified and has the same directionality in the network and across a number of bootstrapped networks (claim 15);
datapoints of the gene expression dataset are normalized before [enrichment analysis] (claim 16);
identifying and clustering collinear transcriptional regulatory programs using the at least one non-collinear latent variable (claim 17), i.e., algorithmically clustering data;
calculating degree of closeness between each transcriptional regulatory program or cluster of collinear regulatory programs and each non-collinear latent variable (claim 17);
calculating degree of closeness between each transcriptional regulatory program or cluster of collinear regulatory programs and each molecular pathway (claim 17);
determining the robustness of the relationship between each transcriptional regulatory program or cluster of collinear regulatory programs and each molecular pathway using the calculated edge weights for each relationship (claim 17);
performing a partial least square regression analysis (claim 18); and
calculating the rank product of [three prior rankings] using the geometric mean (claim 19).
The recited acts of calculation (e.g., algorithmic analyses) constitute mathematical concepts.
‘Mental processes’ are processes that can be performed in the human mind at least with use of a physical aid, e.g., a slide rule or pen and paper (MPEP 2106.04(a)(2) § III). The claims recite elements that encompass processes that are practicably performable in the human mind, at least under their broadest reasonable interpretation, including:
identifying one or more transcriptional regulatory programs and one or more molecular pathways that are enriched in the gene expression dataset (claims 1 and 22-23), i.e., identifying information based on analytical results;
determining one or more relationships between the one or more transcriptional regulatory programs and the one or more molecular pathways enriched in the gene expression dataset, wherein the determining generates at least one network for the gene expression dataset (claims 1 and 22-23), i.e., assessing data dependence and organizing information;
identifying one or more molecular pathways and/or one or more transcriptional regulatory programs in the network that comprise one or more genes that are relatively upregulated or downregulated (claims 1-2 and 22-23), i.e., assessing data dependence;
generat[ing] a treatment-response signature (claims 1 and 22-23);
comparing the expression of the genes of [the gene expression dataset] and [a reference dataset], wherein the comparing generates a set of molecular pathways enriched in the gene expression dataset (claim 5), i.e., comparing values;
the reference gene expression set comprises genes ranked by differential expression between at least two samples (claim 6), i.e., data ordered according to calculated values;
removing transcriptional regulatory programs and molecular pathways from the network that are enriched with a p-value greater than 0.001 or a false discovery rate-corrected p-value greater than 0.05 (claim 8), i.e., removing data from a dataset based on calculated statistics;
incorporating each of the one or more statistically significant relationships into the network as an edge of the network (claim 9), i.e., adding data to a data structure;
incorporating each relationship into the network as an edge when the false discovery rate for the relationship is less than 0.05 (claim 12);
identifying molecular pathways and/or transcriptional regulatory programs that are significantly negatively enriched between the gene expression dataset from the sample that has not received the treatment and the gene expression dataset from the sample that is sensitive to the treatment (claim 17);
identifying molecular pathways and/or transcriptional regulatory programs that are significantly positively enriched between the gene expression dataset from the sample that is sensitive to the treatment and the gene expression dataset from the sample that is resistant to the treatment (claim 17);
comparing the expression of the genes in the molecular pathways and transcriptional programs identified… with expression of the genes in the molecular pathways and transcriptional programs in the network (claim 17), i.e., comparing values;
performing gene set enrichment analyses comparing the molecular pathways and transcriptional programs identified in [a first step] to the molecular pathways and transcriptional programs identified [in a second step] (claim 17);
selecting molecular pathways and transcriptional regulatory networks that are significantly enriched in [two identified sets] (claim 17);
identifying at least one non-collinear latent variable using the activity vector for each selected molecular pathway and the activity vector for each selected transcriptional regulatory program (claim 17);
ranking each transcriptional regulatory program or cluster of collinear regulatory programs by degree of closeness (claim 17);
ranking each transcriptional regulatory program or cluster of collinear regulatory programs by robustness of the relationship (claim 17); and
combining [data] rankings… and selecting the transcriptional regulatory program having the closest relationship to each molecular pathway (claim 17).
These recited steps of evaluating, manipulating and organizing information are practicably performable in the human mind, at least under their broadest reasonable interpretation, and thus constitute mental processes.
Mathematical concepts and mental processes constitute enumerated groupings of abstract ideas (MPEP 2106.04(a)(2) §§ I and III). Hence, the claims recite elements that, individually and in combination, constitute an abstract idea.
The claims further recite the following claim elements, which require that analyzed data embodies particular natural phenomena and/or laws of nature:
the received gene expression dataset is from (a) one or more subjects having a condition or disease; or (b) one or more cell cultures representative of the condition or disease (claims 1 and 22-23);
the identified one or more molecular pathways and/or transcriptional regulatory programs comprise genes that are:
relatively upregulated or downregulated in a gene expression dataset from a sample that has not received the treatment as compared to a sample that is sensitive to the treatment (claims 1 and 22-23),
relatively upregulated or downregulated in a gene expression dataset from a sample that has not received the treatment as compared to a sample that is resistant to the treatment (claims 1 and 22-23),
relatively upregulated or downregulated in a gene expression dataset from a sample that that is sensitive to the treatment as compared to a sample that exhibits resistance to the treatment (claims 1 and 22-23),
relatively upregulated in a gene expression dataset from a sample that has not received the treatment as compared to a sample that is sensitive to the treatment, relatively downregulated in a gene expression dataset from a sample that is sensitive to the treatment as compared to a sample that has not received the treatment and/or to a sample that exhibits resistance to the treatment, and relatively upregulated in a gene expression dataset from a sample that exhibits resistance to the treatment as compared to a sample that is sensitive to the treatment (claim 2),
relatively downregulated in a gene expression dataset from a sample that has not received the treatment as compared to a sample that is sensitive to the treatment, relatively upregulated in a gene expression dataset from a sample that is sensitive to the treatment as compared to a sample that has not received the treatment and/or to a sample that exhibits resistance to the treatment, and relatively downregulated in a gene expression dataset from a sample that exhibits resistance to the treatment as compared to a sample that is sensitive to the treatment (claim 2),
the one or more subjects having the condition or disease have not received the treatment (claim 3),
the one or more subjects having the condition or disease have received the treatment (claim 3),
the one or more cell cultures representative of the condition or disease have not received the treatment (claim 3),
the one or more cell cultures representative of the condition or disease have received the treatment (claim 3),
some of the one or more cell cultures representative of the condition or disease have received the treatment, and some of the one or more cell cultures representative of the condition or disease have not received the treatment (claim 3),
some of the one or more subjects having the condition or disease have received the treatment, and some of the one or more subjects having the condition or disease have not received the treatment (claim 3);
the reference gene expression set comprises genes ranked by differential expression between at least two samples (claim 6);
the at least two samples comprise at least one sample that has resistance to the treatment and at least one sample that has sensitivity to the treatment (claim 7);
a slope of the regression indicates a directionality of the relationship between the molecular pathway and the transcriptional regulatory program, wherein a positive slope of the regression indicates a positive relationship between the molecular pathway and the transcriptional regulatory program, and a negative slope of the regression indicates a negative relationship between the molecular pathway and the transcriptional regulatory program (claim 13);
the treatment-response signature comprises the selected molecular pathways and transcriptional regulatory networks (claim 17); and
the sample that has not received the treatment, the sample that is sensitive to the treatment, and the sample that has resistance to the treatment are samples from the same subject, different subjects, or any combination thereof (claim 20).
The above elements specify that analyzed data represents naturally occurring user attributes, i.e., natural phenomena, having naturally occurring relationships with user creatinine levels and health risk, i.e., laws of nature, that the claimed invention allows a user of the claimed method, system and/or computer-readable media to observe.
The claims must therefore be examined further to determine whether they integrate these judicial exceptions into a practical application (MPEP 2106.04(d)).
Step 2A, Prong Two: Whether the Claims Contain Additional Elements that Integrate the Judicial Exception(s) into a Practical Application (MPEP 2106.04 § II.A.2)
The claims recite additional elements that gather data necessary for performance of claimed method steps, including:
receiving a gene expression dataset (claims 1 and 22-23);
measuring expression of genes of the molecular pathways, and/or the transcriptional regulatory programs, in the gene expression dataset (claim 5); and
measuring expression of genes of the molecular pathways, and/or the transcriptional regulatory programs, in a reference dataset (claim 5).
Necessary data gathering is considered to be insignificant pre-solution activity, and as such insufficient to integrate an abstract idea into a practical application (MPEP 2106.05(g)).
The claims further recite additional elements that require performance of claimed functions on a computer and/or constitute computer hardware for performing claimed functions, including:
the method is a computer implemented method (claim 21);
a system comprising processor(s) and memory coupled to the processor(s), wherein the memory comprises computer-executable instructions causing the processor(s) to perform a process comprising [claimed method steps] (claim 22); and
one or more computer-readable media having encoded thereon computer-executable instructions that, when executed, cause a computing system to perform a method comprising [clamed method steps] (claim 23).
The claims do not describe any specific computational steps by which a computer performs or carries out functions drawn to the judicial exceptions, nor do they provide any details of how specific structures of a computer are used to implement these functions. The claims state nothing more than that a generic computer performs functions drawn to the judicial exceptions, and are therefore mere instructions to apply the judicial exceptions using a computer. As such, the claims do not integrate the judicial exceptions into a practical application (see MPEP 2106.04(d) § I and 2106.05(f)).
No further additional elements are recited.
When the claims are considered as a whole: they do not improve the functioning of a computer, other technology, or technical field (MPEP 2106.04(d)(1) and 2106.05(a)); they do not apply the judicial exceptions to effect a particular treatment or prophylaxis for a disease or medical condition (MPEP 2106.04(d)(2)); they do not implement the judicial exceptions with, or in conjunction with, a particular machine (MPEP 2106.05(b)); they do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)); and they do not apply or use the judicial exceptions in some other meaningful way beyond linking the use of the judicial exceptions to a particular technological environment and/or field of use (e.g., computerized analysis of treatment response; MPEP 2106.05(e) and 2106.05(h)).
Hence, the recited judicial exceptions are not integrated into a practical application. See MPEP 2106.04(d) § I.
Because the claims recite an abstract idea and a natural phenomenon, and do not integrate those judicial exceptions into a practical application, the claims are directed to those judicial exceptions. Claims that are directed to judicial exceptions must be examined further to determine whether the additional elements besides the judicial exceptions render the claims significantly more than the judicial exceptions. Additional elements besides the judicial exceptions may constitute inventive concepts that are sufficient to render the claims significantly more (MPEP 2106.05).
Step 2B: Whether the Claims Contain Additional Elements that Amount to an Inventive Concept (MPEP 2106.05)
As noted above, several recited additional elements amount to insignificant extra-solution activity. Mere addition of insignificant extra-solution activity does not amount to an inventive concept that would render the claims significantly more than the recited judicial exceptions, particularly when the activities are well-understood or conventional (MPEP 2106.05(g)). The conventionality of recited additional elements that amount to insignificant extra-solution activity must be further considered.
Recited additional elements amounting to insignificant extra-solution activity encompass the following processes, which are indicated as activity that may be performed using publicly-available resources, and/or well-understood, routine and conventional activity, the instant specification (see MPEP 2106.07(a) § III):
receiving a gene expression dataset (pg. 12, para. 3: “Expression data can further include gene or gene expression data from a variety of sources, such as… publicly accessible databases… such as the Cancer Genome Atlas or the Genomics Data Commons database, portal.gdc.cancer.gov”); and
measuring gene expression (pg. 11, para. 4: “gene expression can be detected and quantitated using RNA sequencing… RNA-seq is most frequently used for analyzing differential gene expression between samples”, and see subsequent description of “traditional RNA-seq analyses”).
Additionally, recited additional elements amounting to insignificant extra-solution activity encompass the following computer-implemented functions, which the courts have held as coextensive with a general-purpose computer and/or well-understood, routine and conventional:
Receiving, storing, and processing data (In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1316 (Fed. Cir. 2011); EON Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 622 (Fed. Cir. 2015)).
Hence, the encompassed extra-solution activity is considered well-understood, routine and conventional. Well-understood, routine and conventional activity is insufficient to constitute an inventive concept that would render the claims significantly more than judicial exceptions (MPEP 2106.05(d)).
Mere instructions to implement judicial exceptions using a computer are, when considered individually, similarly insufficient to constitute an inventive concept that would render the claims significantly more than said judicial exceptions (see MPEP 2106.05(f)).
When the claims are considered as a whole, they do not integrate the judicial exceptions into a practical application; they do not confine the use of the judicial exceptions to a particular technology; they do not solve a problem rooted in or arising from the use of a
particular technology; they do not improve a technology by allowing the technology to
perform a function that it previously was not capable of performing; and they do not
provide any limitations beyond generally linking the use of the judicial exceptions to a particular technological environment and/or field of use (e.g., computerized analysis of treatment response). See MPEP 2106.05(h).
Hence, the claims do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. See MPEP 2106.05.
Conclusion: Claims are Directed to Non-statutory Subject Matter
For these reasons, the claims, when the limitations are considered individually and as a whole, are directed to judicial exceptions and lack an inventive concept. Hence, the claimed invention does not constitute significantly more than the judicial exceptions, so the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 USC §§ 102 and 103 is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 USC § 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 USC § 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 USC § 102(b)(2)(C) for any potential 35 USC § 102(a)(2) prior art against the later invention.
Claims 1-8, 12, 16 and 20-23 are rejected under 35 USC § 103 as being unpatentable over Mazzu (WO 2020/214718; effectively filed 4/16/2019; on IDS filed 3/12/2026), in view of King (Oncotarget 8(67): 111084-111095; published 11/20/2017).
Claim 1 is directed to a method of identifying treatment-response signatures, comprising:
receiving a gene expression dataset from
one or more subjects having a condition or disease, or
one or more cell cultures representative of the condition or disease;
identifying one or more transcriptional regulatory programs and one or more molecular pathways that are enriched in the gene expression dataset;
determining one or more relationships between the one or more transcriptional regulatory programs and the one or more molecular pathways enriched in the gene expression dataset, wherein
the determining generates at least one network for the gene expression dataset;
identifying one or more molecular pathways and/or one or more transcriptional regulatory programs in the network that comprise one or more genes that are: relatively upregulated or downregulated in
a gene expression dataset from a sample that has not received the treatment as compared to a sample that is sensitive to the treatment,
a gene expression dataset from a sample that has not received the treatment as compared to a sample that is resistant to the treatment, or
a gene expression dataset from a sample that is sensitive to the treatment as compared to a sample that exhibits resistance to the treatment;
wherein the identifying generates a treatment-response signature.
With respect to claim 1, Mazzu discloses methods with utility for determining whether a patient will benefit from particular cancer treatments, based on detecting elevated expression levels of specific signature genes (Abstract), and discusses supportive analyses of publicly-available prostate cancer cohort data (para. 00123), i.e., gene expression datasets from subjects having prostate cancer, as well as total RNA sequencing data from PC-3, LNCaP and C4-2 cell lines (para. 00126), i.e., cell cultures representative of prostate cancer. Mazzu describes investigation of functional pathway and transcriptional regulatory mechanisms associated with RRM2 expression in cancer cells via various gene ontology (GO) and gene set enrichment analyses (GSEA) of these datasets, in further detail (e.g., paras. 0013, 00200; Figs. 13A-E and 16A-C).
For example, Mazzu describes analysis of global transcriptomic changes induced by repression of the gene RRM2, and presents findings that inhibition of RRM2 in turn represses genes enriched in certain regulatory processes, such as cell proliferation, and oncogenic pathways, such as the MYC pathway (para. 00182). In this way, Mazzu describes identifying one or more transcriptional regulatory programs and one or more molecular pathways that are enriched in a gene expression data set; and determining one or more relationships between the enriched regulatory program(s) and molecular pathway(s).
The presented analytical findings also include the relative upregulation and downregulation of particular genes as compared between samples of various types. For example, Mazzu describes identification of genes that are relatively upregulated in single-cell RNA-seq data from samples of patients who did not receive enzalutamide treatment (‘naïve’) as compared to samples from ‘resistant’ patients whose cancer showed progression during enzalutamide therapy, and identification of an 11-gene signature, with utility in predicting enzalutamide resistance, therefrom (paras. 006-7 and 00145; Figs. 6A and 23).
Mazzu does not disclose generating a network for the gene expression dataset, and identifying pathways and/or regulatory programs in the network, as claimed.
King discusses profiling of pathways and genes enriched in gene expression datasets from paired enzalutamide-sensitive and enzalutamide-resistant prostate cancer cell lines via the Pathway Representation and Analysis by Direct Reference on Graphical Models (PARADIGM) tool (pg. 111084, Abstract). King describes construction of differentially regulated cellular networks via the PARADIGM tool, identification of sub-networks that contribute to enzalutamide resistance, and identification of particular nodes (i.e., genes) that may be targeted therapeutically (pg. 111085, l. column; pg. 111087, Fig. 2; pg. 111089, Fig. 4).
King teaches that the PARADIGM approach provides additional biological context that traditional differential expression analyses lack, thus enabling improved patient stratification and understanding of emergent resistance mechanisms, and can be applied to prioritize targets most relevant to overcoming enzalutamide resistance (pg. 111091, l. column).
With respect to claim 2, Mazzu describes identification of genes that are relatively upregulated and downregulated between numerous combinations of cell lines, such as between LNCaP-EV and LNCaP-RRM2 cells (respectively sensitive and resistant, see Fig. 6E), and between ENZ-naïve and ENZ-resistant subjects (paras. 00135, 00145; Figs. 1B, 6A). Mazzu discloses identification of genes that are differentially regulated between three sample groups (e.g., Figs. 1G and 2B), including genes that are upregulated in one group and downregulated in another (e.g., Fig. 21B).
Mazzu discloses identifying genes that are relatively upregulated and downregulated between sample groups that have not received enzalutamide, are sensitive to enzalutamide, and exhibit resistance to enzalutamide. Mazzu also discloses comparison of genes between three groups. In this way, Mazzu is considered to make obvious the claimed alternative analytical combinations (e.g., identifying genes that are relatively upregulated in naïve samples as compared to sensitive samples, downregulated in sensitive samples as compared to resistant samples, and upregulated in resistant samples as compared to sensitive samples).
With respect to claim 3, Mazzu describes analysis of gene expression data from various subject cohorts and cell lines, including identification of genes differentially regulated between ENZ-naïve and ENZ-resistant subjects (para. 00145; Fig. 6A).
With respect to claim 4, King discusses various algorithms available for analyses of pathways and transcription factor networks (pg. 111089, l. column).
With respect to claim 5, Mazzu discloses measuring levels of gene expression via methods well-known in the art (para. 0020). Mazzu also discloses a kit for comparing the levels (i.e., identifying relative increase or decrease) of various genes in a test sample with those in a reference sample (para. 00115).
With respect to claim 6, Mazzu discloses performance of Gene Set Enrichment Analysis (GSEA) comprising calculating enrichment scores (para. 0014 and 00136), and depicts ordered graphical representations of the differential expression of sets of particular genes between different datasets, e.g., enzalutamide naïve vs. resistant (Fig. 6A). In this way, Mazzu is considered to disclose generation of a reference gene expression set comprising genes ranked by differential expression between at least two samples.
With respect to claim 7, Mazzu discloses analysis of enzalutamide resistance among grouped samples, and indicates that certain percentages of each group were determined to be resistant (para. 00145). One of ordinary skill in the art would understand non-resistant samples to be sensitive.
With respect to claim 8, King teaches that genes with a q-value (i.e., a false-discovery rate-corrected p-value) < 0.05 were deemed significant (pg. 111091, r. column). In other words, those with a q-value greater than 0.05 were considered insignificant. One of ordinary skill in the art would find it obvious to remove insignificant data from the analysis.
With respect to claim 16, Mazzu discloses normalization of transcript levels prior to analysis (para. 00163).
With respect to claim 20, Mazzu discloses analysis of enzalutamide-naïve and enzalutamide resistant samples from different subjects in a cohort (para. 00145; Fig. 6A).
With respect to claim 21, Mazzu discloses performance of discussed analytical techniques using a variety of web-based and computational resources, such as cBioPortal and R software (para. 00127). Mazzu thereby discloses computer implementation of their methods.
Claim 22 recites a treatment-response signature identification system comprising one or more processors and memory coupled thereto, wherein the memory comprises computer-executable instructions causing the one or more processors to perform a process comprising functional limitations of substantive similarity to the process limitations of claim 1. The teachings of Mazzu, in view of King, are considered to apply to the substantively similar limitations of the claim in the same manner as detailed above regarding the process limitations of claim 1.
With respect to the unique limitations of claim 22, Mazzu discloses performance of discussed analytical techniques using a variety of web-based and computational resources, such as cBioPortal and R software (para. 00127). Mazzu thereby discloses computer implementation of their methods. Processors and memory are conventional computer hardware components, and would be constituents of a general-purpose computer system applied to this task.
Claim 23 recites one or more computer-readable media having encoded thereon computer-executable instructions that, when executed, cause a computing system to perform a treatment-response signature identification method, comprising functional limitations of substantive similarity to the process limitations of claim 1. The teachings of Mazzu, in view of King, are considered to apply to the substantively similar limitations of the claim in the same manner as detailed above regarding the process limitations of claim 1.
With respect to the unique limitations of claim 23, Mazzu discloses performance of data analysis using software (paras. 00115 and 00127). One of ordinary skill in the art would find it obvious to encode executable software on computer-readable media.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have combined network analysis, as taught by King, with the analytical methods of Mazzu, because King teaches that their network-based approach provides additional biological context that traditional differential expression analyses lack, thus enabling improved patient stratification and understanding of emergent resistance mechanisms, and can be applied to prioritize targets most relevant to overcoming treatment resistance (pg. 111091, l. column). Said practitioner would have had a reasonable expectation of success because Mazzu and King both concern functional analyses of gene set enrichment in prostate cancer based on gene expression datasets.
In this way the disclosure of Mazzu, in view of King, makes obvious the limitations of claims 1-8, 12, 16 and 20-23. Thus, the claimed invention is prima facie obvious.
Claims 9-15 are rejected under 35 USC § 103 as being unpatentable over Mazzu, in view of King, as applied to claim 1 above, and further in view of Liu (npj Systems Biology and Applications 5: 40, 10 pages; published 11/11/2019).
With respect to claim 9, Mazzu discloses identifying one or more statistically significant relationships between transcriptional regulatory programs and molecular pathways (e.g., Fig. 16C).
Mazzu does not disclose determining activity vectors for each molecular pathway and transcriptional regulatory program; or incorporating each of the one or more statistically significant relationships into the network as an edge of a network.
King describes construction of differentially regulated cellular networks via the PARADIGM tool, identification of sub-networks that contribute to enzalutamide resistance, and identification of particular nodes (i.e., genes) that may be targeted therapeutically (pg. 111085, l. column; pg. 111087, Fig. 2; pg. 111089, Fig. 4). King indicates that determination of differentially regulated genes involves assessment of statistical significance, and exemplifies a q-value threshold of 0.05 for inclusion in the network (pg. 111085, r. column; pg. 111087, Fig. 2A caption).
King characterizes the network as representing pathway activity of constituent nodes, including transcriptional regulatory activity (pg. 111086, l. column; pg. 111089, r. column and Fig. 4 caption), but does not teach determining activity vectors for each molecular pathway and transcriptional regulatory program as claimed. Neither does King teach incorporating each of the one or more statistically significant relationships into the network as an edge of the network.
Liu discusses application of CARNIVAL, a causal network contextualization tool which derives network architectures from gene expression data, to identify upstream processes that drive gene expression changes, e.g., in disease (pg. 1, Abstract). Liu teaches that CARNIVAL can implement the PROGENy and DoRothEA algorithms to respectively predict continuous pathway activities (i.e., activity vectors for each molecular pathway) and continuous TF activities (i.e., activity vectors for each transcriptional regulatory program) from input gene expression levels, then generate a causal signaling network therefrom according to a linear objective function (pg. 7, r. column – pg. 8, l. column). Liu indicates that edges of the network represent reactions, both activatory (i.e., upregulation) and inhibitory (i.e., downregulation), between nodes of the network (pg. 5, Fig. 4 caption).
Liu teaches that summarization of gene expression values as ‘activities’ avoids noise from discretization of individual differentially-expressed genes, and increases computational efficiency of the pipeline by reducing the number of inputs to the objective function (pg. 7, r. column).
With respect to claim 10, Mazzu discloses identifying relationships with p < 0.05 between expression of genes in the RRM2 pathway and each of depicted transcription factors (Fig. 16C).
With respect to claim 11, Mazzu discloses performance of statistical comparisons via linear regression (para. 00131). Mazzu does not disclose embodiments wherein each transcriptional regulatory program activity vector is an independent variable in the regression analysis and each molecular pathway activity vector is a dependent variable in the regression analysis.
Liu teaches construction of a causal network where transcriptional factor activities are utilized to infer downstream nodes (i.e., genes) and pathway activities (pg. 1, r. column; pg. 5, Fig. 4). In other words, each transcriptional regulatory program activity vector is an independent variable and each molecular pathway activity vector is a dependent variable.
With respect to claim 12, King teaches performance of multiple comparisons correction based on the false discovery rate, wherein genes with a q-value (i.e., a false-discovery rate-corrected p-value) < 0.05 were deemed significant (pg. 111091, r. column). One of ordinary skill in the art would find it obvious to incorporate significant data into the network.
Additionally, Liu characterizes the function of CARNIVAL as generating hypotheses on potential disease-linked upstream alterations (i.e., disease-specific transcriptional regulatory program features) that propagate through signaling networks (pg. 1, Abstract). Liu further describes performing gene set enrichment analyses by executing 10,000 permutations of each statistical test, adjusting p-values by the determined false discovery rate, and analyzing relationships at a significance level of 0.01 (pg. 5, Fig. 5 caption; pg. 8, r. column).
With respect to claim 13, linear regression produces a regression equation with slope indicating directionality of the relationship between the considered variables, wherein a positive slope indicates a positive relationship and a negative slope indicates a negative relationship. The application of linear regression to assess pairwise relationships of molecular pathways and transcriptional regulatory programs will necessarily produce a regression equation that satisfies the limitations of the claim. Therefore, the limitations of the claim are considered obvious in light of the combined teachings of Mazzu and Liu as applied to claims 9 and 11 above.
With respect to claim 14, Liu teaches that performance was improved with the additional introduction of pathway weights inferred via PROGENy (pg. 6, r. column).
With respect to claim 15, Liu teaches implementation of a node penalty which is sign-adjusted through PROGENy weights, wherein the anticipated direction corresponding to a pathway score is penalized less in the expected direction while more in the counterpart (pg. 8, l. column). Liu also teaches calculation of pathway scores with 10,000 gene-wise permutations (pg. 7, r. column; pg. 8, r. column).
Liu does not teach performance of bootstrapping as claimed. However, permutation and bootstrapping are both conventional resampling techniques which are routinely employed in the field of bioinformatics to validate statistical analyses, and would be well-known to one of ordinary skill in the art. In light of conventional knowledge in the field of the invention, the claimed performance of bootstrapping is considered an obvious variant that does not patentably distinguish the claimed invention from the combined teachings of Mazzu, King and Liu.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented determination and analysis of activity vectors for pathways and regulatory programs, as taught by Liu, with the analytical methods of Mazzu, in view of King, because Liu teaches that summarization of gene expression values as ‘activities’ avoids noise from discretization of individual differentially-expressed genes, and increases computational efficiency of the pipeline by reducing the number of inputs to the objective function (pg. 7, r. column). Said practitioner would have had a reasonable expectation of success because Mazzu, King and Liu all concern functional analyses of gene set enrichment in prostate cancer based on gene expression datasets. Additionally, King and Liu both particularly concern network-based analyses.
In this way the disclosure of Mazzu, in view of King and Liu, makes obvious the limitations of claims 9-15. Thus, the claimed invention is prima facie obvious.
Claims 17-19 are rejected under 35 USC § 103 as being unpatentable over Mazzu, in view of King and Liu, as applied to claims 1 and 9 above, and further in view of Vittrant (OncoImmunology 9: e1851950, 11 pages; published 12/1/2020) and Uzunangelov (‘Prediction of cancer phenotypes through the integration of multi-omic data and prior information’ [dissertation], UC Santa Cruz; published June 2019).
With respect to claim 17, Mazzu discloses identification of molecular pathways, transcriptional regulatory programs and constituent genes that are relatively downregulated (i.e., negatively enriched) and upregulated (i.e., positively enriched) between numerous combinations of sample types, such as between LNCaP-EV and LNCaP-RRM2 cells (respectively sensitive and resistant, see Fig. 6E), and between ENZ-naïve and ENZ-resistant subjects (e.g., paras. 0013 and 00145; Figs. 1B and 6A). Mazzu further discloses clustering and ranking of RNA-seq data based on gene expression (paras. 0005-6 and 00145; Figs. 5A-B and 6A).
Mazzu does not disclose identifying at least one non-collinear latent variable using activity vectors for each pathway and transcriptional regulatory program; identifying and clustering collinear transcriptional regulatory programs using the at least one non-collinear latent variable; calculating degrees of closeness; ranking by degrees of closeness; determining robustness of relationships using edge weights; ranking by robustness; combining the rankings; and selecting the transcriptional regulatory program having the closest relationship to each pathway as claimed.
King discusses profiling of pathways and genes enriched in gene expression datasets from paired enzalutamide-sensitive and enzalutamide-resistant prostate cancer cell lines (pg. 111084, Abstract), and clustering of samples based on gene expression (pg. 111090, Fig. 5 caption). King does not teach identifying at least one non-collinear latent variable using activity vectors for each pathway and transcriptional regulatory program; identifying and clustering collinear transcriptional regulatory programs using the at least one non-collinear latent variable; calculating degrees of closeness; ranking by degrees of closeness; determining robustness of relationships using edge weights; ranking by robustness; combining the rankings; and selecting the transcriptional regulatory program having the closest relationship to each pathway as claimed.
Liu teaches generation of a causal signaling network, based on pathway activities and TF activities, according to a linear objective function (pg. 7, r. column – pg. 8, l. column). Liu further details aspects of the method that are believed to meet a number of outstanding claim limitations, as follows:
Liu teaches processing differential gene expression t-values with the DoRothEA and VIPER algorithms to perform analytic Rank-based Enrichment Analysis (thereby ranking genes), scoring degree of dysregulation of each TF based on the gene rankings, and selecting TF based on the scores (pg. 2, l. column; pg. 7, r. column). In other words, ranking each transcriptional regulatory program based on a calculated degree metric and selecting transcriptional regulatory programs based on rankings.
Liu also describes calculation of pathway weights with 10,000 gene-wise permutations of the PROGENy algorithm, and analyzing Jaccard similarity between reshuffled networks to assess robustness (pg. 3, r. column – pg. 4, l. column). (pg. 6, r. column; pg. 7, r. column; pg. 8, r. column). Calculating pathway weights via gene-wise permutations and assessing robustness of reshuffled networks is considered equivalent to determining the robustness of each relationship using edge weights as claimed.
Liu describes construction of the signaling network via an objective function that parameterizes the pathway weights and TF activities as mismatch and node penalties, calculation of quantitative network topology measures for each node, and selection of highly relevant signaling pathways and regulators therefrom based on the network topology measures (pg. 4, l. column; pg. 7, l. column). Liu does not teach identifying at least one non-collinear latent variable using the activity vectors; identifying and clustering collinear transcriptional regulatory programs using the at least one non-collinear latent variable; combining rankings; and selecting the transcriptional regulatory program having the closest relationship to each pathway as claimed.
Vittrant discusses identification of immune genes associated with prostate cancer progression via multi-omics analysis (pg. 1, Abstract), and teaches identification of gene-related features (i.e., latent variables) associated with post-treatment cancer recurrence (BCR) via predictive modeling (pg. 9, r. column). Vittrant also discusses analysis of feature associations with various pathways (e.g., antigen processing) and regulatory programs (e.g., LILR genes), and further describes merging all identified recurrence-associated features (pg. 2, r. column). This is considered equivalent to identifying at least one non-collinear latent variable using activity vectors; and clustering collinear transcriptional regulatory programs based on the latent variables.
Vittrant teaches that the merged gene-related features near-perfectly predicted the clinical state of interest (pg. 2, r. column; pg. 3, Fig. 2). Vittrant does not teach combining rankings as claimed.
Uzunangelov presents algorithms for multimodal genomic data analysis with respect to cancer pathology including AKLIMATE, a Random Forest-based method that calculates a kernel similarity matrix to capture non-linear feature relationships among gene expression data (pg. 76, para. 2). Uzunangelov further defines the kernel similarity matrix as the geometric mean of the Hadamard product of three component similarity matrices, which are calculated for each feature set (pg. 124, Fig. 4.15 caption). Construction of the described kernel similarity matrix is considered to read on combining three calculated similarity-based measures as claimed.
Uzunangelov teaches that the described kernel similarity matrix re-weights the features by importance, a key property when dealing with noisy or partially relevant feature set definitions (pg. 76, para. 2), and presents findings that AKLIMATE achieves improved performance over competing methods on a variety of cancer-related prediction tasks (e.g., pg. 98, Fig. 4.5). Additionally, Uzunangelov also describes an algorithm (hVIPER) that implements activity network-based analysis of gene dysregulation for identification of novel therapeutic targets (TFs) in treatment-refractory castration-resistant prostate cancer and achieves improved performance over a prior method (pg. 30, para. 3 – pg. 34, para. 1 and Figs. 2.8-2.9; pg. 43, para. 1).
With respect to claim 18, Vittrant teaches identification of gene-related features (i.e., latent variables) via sparse partial least square-discriminant analysis and regression (pg. 9, r. column).
With respect to claim 19, Uzunangelov describes construction of a sample matrix that is the geometric mean of three component similarity matrices calculated for each feature set (pg. 124, Fig. 4.15 caption), which is considered mathematically equivalent to calculating the rank product using the geometric mean as claimed.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented identification of latent features via partial least squares regression analysis, and clustering based thereon, as taught by Vittrant, with the analytical methods of Mazzu, in view of King and Liu, because Vittrant teaches that identifying and combining features using this method allows for near-perfect prediction of an associated clinical state of interest (pg. 2, r. column; pg. 3, Fig. 2). Said practitioner would have had a reasonable expectation of success because Mazzu, King, Liu and Vittrant all concern functional analyses of gene set enrichment in cancer based on gene expression datasets. Additionally, King, Liu and Vittrant all particularly concern network-based analyses.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented calculation of the rank product of three component similarity matrices using the geometric mean, as taught by Uzunangelov, with the analytical methods of Mazzu, in view of King and Liu, because Uzunangelov teaches that the implementing algorithm (AKLIMATE) re-weights input features, thus handling noisy and partially relevant feature set definitions, and achieves improved performance over competing methods on a variety of cancer-related prediction tasks (pg. 76, para. 2; pg. 98, Fig. 4.5). Said practitioner would have had a reasonable expectation of success because Mazzu, King, Liu, Vittrant and Uzunangelov all concern functional analyses of gene set enrichment in cancer based on gene expression datasets. Additionally, King, Liu, Vittrant and Uzunangelov all discuss network-based analyses.
In this way the disclosure of Mazzu, in view of King, Liu, Vittrant and Uzunangelov, makes obvious the limitations of claims 17-19. Thus, the claimed invention is prima facie obvious.
Conclusion
At this point in prosecution, no claim is allowed.
The following prior art, made of record and not relied upon, is considered pertinent to applicant's disclosure:
Davies (Nature Cell Biology 23(9): 1023-1034; published 9/6/2021) discusses analysis of transcriptional modulation of gene networks in cancer as an adaptive response to treatment with androgen receptor-pathway inhibitors (pg. 1023, Abstract and l. column);
Modlin (US 2019/0259471; effectively filed 2/22/2018) discloses methods for evaluating the response to a prostate cancer therapy (Abstract) by measuring and analyzing gene expression levels (e.g., para. 0009);
Smith (Scientific Reports 10: 21750, 13 pages; published 2020) discusses modeling of therapy response in prostate cancers to drug treatments based on measured gene expression (Abstract);
Zhang (npj Precision Oncology 1: 25, 15 pages; published 8/8/2017) reviews identification of molecular mechanisms in cancer, and applications to treatment, via network-based analyses of genomic data;
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/T.C.S./Examiner, Art Unit 1685
/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 June 26, 2026
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