DETAILED ACTION
Applicant’s response, 13 Feb. 2016 has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 5-6, 19-21, and 26 are cancelled.
Claims 34-35 are newly added.
Claims 1-4, 7-18, 22-25, and 27-35 are pending.
Claims 11-17 and 24 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention and species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 12 May 2023.
Claims 1-4, 7-10, 18, 22-23, 25, and 27-35 are rejected.
Priority
The effective filing date of the claimed invention is 31 Dec. 2018.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02 Dec. 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the list of cited references were considered by the examiner.
Claim Interpretation
Claim 1 recites “a deconvoluted RNA expression data model comprising at least one cluster”. Claim 4 recites “…the deconvoluted RNA expression model comprises a first dimension reflecting a number of samples and a second dimension reflecting a number of genes in the initial normalized bulk RNA expression data”. In the response filed 18 Sept. 2023, Applicant remarks that the deconvolution process involves identifying clusters of gene expression data, these clusters are then used to generate a train model that can be applied to subsequent RNA expression data (see para. 69 of the specification), and in other words, the model includes information about the clusters, and thus the meaning of the term model is clear and not indefinite (Applicant’s remarks at pg. 16, para. 2-3). Therefore, in light of Applicant’s remarks at pg. 16, para. 2-3, under the broadest reasonable interpretation of the claim, the limitations of “a deconvoluted RNA expression data model comprising at least one cluster” and “…the deconvoluted RNA expression model comprises a first dimension reflecting a number of samples and a second dimension reflecting a number of genes….” is interpreted to mean a model/algorithm that utilizes or is based on at least one cluster and data with a first dimension.
Claim Rejections - 35 USC § 112(a)
The rejection of claims 18, 22-23, 25, 27, and 31 under 35 U.S.C. 112(a) as failing to comply with the written description requirement in the Office action mailed 13 Nov. 2025 has been withdrawn in view of claim amendments received 13 Feb. 2026.
Claim Rejections - 35 USC § 112(b)
The rejection of claims 1-4, 7-10, 22-23, 25, and 27-33 under 35 U.S.C. 112(b) in the Office action mailed 13 Nov. 2025 has been withdrawn in view of claim amendments received 13 Feb. 2026.
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 1-4, 7-10, 18, 22-23, 25, and 27-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the judicial exception of an abstract idea and law of nature without significantly more. Any newly recited portion herein is necessitated by claim amendment.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claims 1 and 18 being representative) is directed to a method for deconvoluting RNA expression data. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claims 1 and 18 recite the following steps which fall under the mathematical concepts and/or mental processes groupings of abstract ideas:
performing unsupervised clustering on the initial normalized bulk RNA expression data, wherein each of the plurality of nonmetastatic tissue samples and the plurality metastatic cancer tissue samples is assigned to at least one of a plurality of clusters;
generating, based on the unsupervised clustering, a deconvoluted RNA expression data model comprising at least one cluster identified as corresponding to [a] biological indication of one or more pathologies;
validating the deconvoluted RNA expression data model by: (i) applying the deconvoluted RNA expression data model to RNA expression data of known in silico mixtures to generate a set of deconvoluted RNA matrices, and (ii) comparing the set of deconvoluted RNA matrices to corresponding known mixture compositions of the known in silico mixtures using clustering;
normalizing the bulk RNA expression data, resulting in normalized bulk RNA expression data of the sample of tumor tissue;
deconvoluting the normalized bulk RNA expression data of the sample of tumor tissue by applying the validated deconvoluted RNA expression data model to remove background tissue expression from the normalized bulk RNA expression data, including generating a deconvoluted RNA matrix corresponding to the normalized bulk RNA expression data of the sample of tumor tissue;
identifying from the deconvoluted RNA matrix corresponding to the normalized bulk RNA expression data of the sample of the tumor tissue, one or more overexpressed or underexpressed genes relative to tumor and normal tissue, to reduce false expression calls;
classifying, based on the deconvoluted RNA matrix corresponding to the normalized bulk RNA expression data of the sample of tumor tissue and the one or more overexpressed and underexpressed genes, the sample of tumor tissue as the biological indication of the one or more pathologies, wherein the biological indication is a cancer type, and wherein the cancer type is a primary or metastatic cancer (claim 1 only); and
determining a predicted biological indication of the sample of tumor tissue based at least in part on the deconvoluted RNA matrix corresponding to the normalized bulk RNA expression data of the sample of tumor tissue and the one or more overexpressed or underexpressed genes, wherein the predicted biological indication of the sample of tumor tissue is a cancer type, and wherein the cancer type is a primary or metastatic cancer (claim 18 only).
The identified claim limitations falls into the group of abstract ideas of mental processes, for the following reasons. The steps of performing unsupervised clustering on bulk RNA expression data of a plurality of samples and assigning each sample to a cluster can be performed mentally by calculating distances between expression values for each sample, and identifying clusters of samples based on the calculated distances. Furthermore, generating a deconvoluted RNA expression data model comprising at least one cluster can be performed mentally by generating a linear regression model comprising a matrix with gene expressions for each sample for a particular cluster corresponding to the biological indication (e.g. X = SA, where X is an observed mixed expression matrix and S and A are matrices for tissue specific expression matrix and a proportions/cluster matrix, respectively). Validating the deconvoluted RNA matrix by applying the model to RNA expression data and comparing the matrix to RNA expressions of known in silico mixtures using clustering can be performed mentally for the same reasons discussed above regarding the unsupervised clustering (e.g. distances between points of the deconvoluted matrix and known matrix are calculated), and furthermore, because using the deconvoluted RNA expression data model involves performing mathematical operations (e.g. a matrix decomposition) to determine the proportions of particular cell types in the in silico mixtures and corresponding expression levels organized in a matrix, (e.g. the matrices, S and A in the above example), which can be practically performed in the mind aided with pen and paper. A step of deconvoluting the additional normalized bulk RNA expression data, or RNA expression information, using the deconvoluted RNA expression data model, including generating a matrix, involves performing mathematical operations (e.g. a matrix decomposition), as discussed above regarding the in silico mixtures in the validation step, which can be practically performed in the mind aided with pen and paper. Identifying, from the deconvoluted RNA matrix to identify over or under-expressed genes amounts to a mere analysis of data involving the analysis of expression levels for cell types in the matrix to identify expression levels higher or lower than expected compared to a normal sample. Last, step of classifying the sample of tumor tissue as the biological indication, based on the deconvolution in claim 18, can be performed mentally by determining the tumor tissue includes cell types belonging to the cluster corresponding to the biological indication and determining certain over- and under- expressed genes correspond to the biological indication, which amounts to a mere analysis of data that can be performed mentally. That is, other than reciting the above limitations are performed by one or more processors, nothing in the claims precludes the steps from being practically performed in the mind. See MPEP 2106.04(a)(2) III.
Furthermore, the above steps of performing unsupervised clustering, generating a deconvoluted RNA expression data model, validating the model by (i) applying the model to expression data and (ii) comparing the deconvoluted matrices using clustering, deconvoluting the additional RNA expression data/information, further recite a mathematical concept. A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. See MPEP 2106.04(a)(2) I. C. The steps of performing unsupervised clustering and validating using clustering amount to a textual equivalent to performing mathematical operations (e.g. calculating distances between points and clustering points according to the distances), and thus recites a mathematical calculation. Similarly, deconvoluting the additional expression data or in-silico RNA expression data using a deconvolution model determines expression values for particular tissue/cell types uses mathematical methods (e.g. linear regression, as described in Applicant’s specification at para. [0087]) to determine a number (deconvoluted expression) and thus recites a mathematical calculation. Last, generating a deconvoluted RNA expression model comprising a cluster represents a mathematical relationship between expression values and various cell/tissue types (e.g. the clusters). Therefore, these limitations recite a mathematical concept. See MPEP 2106.04(a)(2) I.
In addition, the claims recite the law of nature of a nature correlation between RNA expression of cells and primary or metastatic cancer, similar to the natural correlation between the presence of myeloperoxidase in a bodily sample (such as blood or plasma) and cardiovascular disease risk in Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1361, 123 USPQ2d 1081, 1087 (Fed. Cir. 2017).
Dependent claims 2-4, 7-9, 22-23, 25, 29, and 32-35 further recite an abstract idea and/or further limit the abstract idea of claims 1 and 18 above. Dependent claims 2-3 further recites the mental process and mathematical concept of performing the clustering on RNA expression data with a grade of membership clustering operation iteratively until the at least one cluster corresponding to the biological indication is identified. Dependent claim 4 further recites the mental process and mathematical concept of the deconvoluted RNA expression data model comprising a first dimension reflecting a number of samples and a second dimension reflecting a number of genes in the RNA expression data. Dependent claims 7-8 further limit the RNA data for which clustering is performed to include RNA expression data from normal tissue samples or primary cancer tissue samples and from metastatic samples, which is part of the mental process and mathematical concept of performing clustering of claim 1. Dependent claims 7-9 further limit the biological indication to correspond to primary cancer, metastatic cancer, or one of the recited cancers as the biological indication, respectively, which is part of the mathematical concept and mental process of generating the deconvoluted RNA expression data model of claim 1. Dependent claim 22 further recites the mental process of generating enriched gene expressions and the mental process and mathematical concept of classifying the enriched gene expressions in a biological indication data model. Dependent claim 23 further recites the mental process of receiving a percent assignment to each cluster of a plurality of clusters and the mental process and mathematical concept of scaling the normalized bulk RNA expression information for one or more genes based on membership associations. Dependent claim 25 further limits the abstract idea of determining the indication to be performed after deconvolution and further recites the mental process and mathematical concept of performing the deconvolution using an unsupervised machine learning model. Dependent claim 29 further limits the mental process and mathematical concept of performing unsupervised clustering to use expression data from a primary cancer tissue sample or a non-cancerous tissue sample. Dependent claims 32-33 further recite the mental process of determining (i) consensus molecular subtypes associated with the sample of tumor tissue based on the deconvoluted RNA matrix, and then matching the sample of tumor tissue to therapies or clinical trials. Dependent claims 34-45 further recite the mental process of generating a report comprising the cancer type and one or more of (i) a therapy recommendation targeting the one or more overexpressed or underexpressed genes, or (ii) a clinical trial match, which encompasses mentally analyzing the overexpressed or under expressed genes to determine a therapy recommendation and the cancer type and organizing the information in a report via pen and paper. Therefore, claims 1-4, 7-10, 18, 22-23, 25, and 27-35 recite an abstract idea and law of nature. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
Claims 2-4, 7-9, 22-23, 25, 29, and 32-35 further recite an abstract idea and/or are part of the abstract idea, as discussed above, but do not recite any elements in addition to the judicial exception.
The additional elements of claims 1 and 18 include:
a computer (claims 1 and 18);
one or more processors (claim 18);
receiving initial normalized bulk RNA expression data of (i) a plurality of nonmetastatic tissue samples, and (ii) a plurality of mixed purity metastatic cancer tissue samples (i.e. receiving data) (claims 1 and 18);
preparing, using a transcription capture-based approach, an RNA sequencing (RNA-seq) library from a sample of tumor tissue of a patient (claims 1 and 18);
generating bulk RNA expression data from the RNA-seq library (claims 1 and 18);
The additional elements of claims 10, 27-28, and 30-31 includes:
wherein the sample of tumor tissue is obtained from liver tissue, breast tissue, pancreatic tissue, colon tissue, bone marrow, lymph node tissue, skin, kidney tissue, lung tissue, bladder tissue, bone, prostate tissue, ovarian tissue, muscle tissue, intestinal tissue, nerve tissue, testicular tissue, thyroid tissue, brain tissue, fluid samples, or any combination thereof (claim 10);
receiving the sample of tumor tissue collected by a tumor biopsy method, wherein the tumor biopsy method is a surgical biopsy, skin biopsy, punch biopsy, prostate biopsy, bone biopsy, bone marrow biopsy, needle biopsy, CT-guided biopsy, ultrasound-guided biopsy, fine needle aspiration, aspiration biopsy, blood collection, or a tumor sample collection method (claims 27-28).
wherein the sample of tumor tissue of the patient is a formalin-fixed paraffin-embedded (FFPE) sample (claim 30); and
wherein the sample of tumor tissue of the patient is a formalin-fixed paraffin-embedded (FFPE) sample (claim 31).
The additional elements of a computer, processors, and receiving data in claims 1 and 18 are generic computer components and/or functions. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). Therefore the above additional elements do not integrate the recited judicial exception into a practical application.
The additional elements of claims 1, 10, 18, and 27-28 of receiving a tissue sample from a tumor biopsy, using a capture-based method to preparing an RNA-seq library, and sequencing the sample of tumor to generate the RNA expression information only serves to collect information for use by the abstract idea (e.g. for deconvolution and classifying a biological indication), which amounts to insignificant extra-solution activity and is not sufficient to integrate the recited judicial exception. See MPEP 2106.05(g).
Therefore, the additionally recited elements merely invokes computers as a tool and/or amount to insignificant extra-solution activity and, as such, the claims as a whole do no integrate the abstract idea into practical application. Thus, claims 1-4, 7-10, 18, 22-23, 25, and 27-35 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
The claims do not include any additional steps appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception for the following reasons.
Claims 2-4, 7-9, 22-23, 25, 29, and 32-35 further recite a judicial exception and/or are part of the judicial exception, as discussed above, but do not recite any elements in addition to the judicial exception.
The additional elements of claims 1 and 18 include:
a computer (claims 1 and 18);
one or more processors (claim 18);
receiving initial normalized bulk RNA expression data of (i) a plurality of nonmetastatic tissue samples, and (ii) a plurality of mixed purity metastatic cancer tissue samples (i.e. receiving data) (claims 1 and 18);
preparing, using a transcription capture-based approach, an RNA sequencing (RNA-seq) library from a sample of tumor tissue of a patient (claims 1 and 18);
generating bulk RNA expression data from the RNA-seq library (claims 1 and 18);
The additional elements of claims 10, 27-28, and 30-31 includes:
wherein the sample of tumor tissue is obtained from liver tissue, breast tissue, pancreatic tissue, colon tissue, bone marrow, lymph node tissue, skin, kidney tissue, lung tissue, bladder tissue, bone, prostate tissue, ovarian tissue, muscle tissue, intestinal tissue, nerve tissue, testicular tissue, thyroid tissue, brain tissue, fluid samples, or any combination thereof (claim 10);
receiving the sample of tumor tissue collected by a tumor biopsy method, wherein the tumor biopsy method is a surgical biopsy, skin biopsy, punch biopsy, prostate biopsy, bone biopsy, bone marrow biopsy, needle biopsy, CT-guided biopsy, ultrasound-guided biopsy, fine needle aspiration, aspiration biopsy, blood collection, or a tumor sample collection method (claims 27-28).
wherein the sample of tumor tissue of the patient is a formalin-fixed paraffin-embedded (FFPE) sample (claim 30); and
wherein the sample of tumor tissue of the patient is a formalin-fixed paraffin-embedded (FFPE) sample (claim 31).
The additional elements of a computer and receiving data in claims 1 and 18 are conventional computer components and/or functions. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Furthermore, the additional element of receiving a tissue sample collect by a tumor sample collection method, using a capture-based method for RNA-seq library prep, and sequencing a tumor sample to generate RNA expression information are well-understood, routine, and conventional. This position is supported by Cummings et al. (The Role of Next-Generation Sequencing in Enabling Personalized Oncology Therapy, 2016, Clin. Transl. Sci., 9, pg. 283-292; previously cited). Cummings overviews the role of next-generation sequencing in oncology (Abstract), and discloses that RNA-sequencing of tumors is performed for quantifying pathway activation of tumors or identifying patients sensitive to particular therapies (Table 2; pg. 289, col. 2, para. 2-3), and cites numerous studies that have performed RNA sequencing of a tumor (pg. 284, col. 2, para. 3 to col. 1, para. 2; pg. 289, col. 1, para. 2-3). Cummings further discloses many cancer genomics studies employ whole-exome sequencing in which library constructions if followed by an enrichment step that targets only exons of protein-coding genes, or an investigator can use targeted sequencing where the enrichment step typically uses a pool of probes to target a number of loci (pg. 254, col. 1, para. 3). Cummings further discloses that tumor tissue is the gold standard for molecular diagnostic assays and involves collection of fresh biopsies of patients, including a surgical biopsy (pg. 285, col. 1, para. 3 to col. 2, para. 1; Figure 2). Cummings further discloses next-generation sequencing technologies utilize computing infrastructure for data analysis (pg. 283, col. 2, para. 1), demonstrating the combination of sequencing the tumor sample and computers are also well-understood, routine, and conventional.
Furthermore, the sample of tumor tissue being a formalin-fixed-embedded (FFPE) sample is well-understood, routine, and conventional. This position is supported by Gaffney et al. (Factors that drive the increasing use of FFPE tissue in basic and translational cancer research, Aug. 2018, 93(5), pg. 373-386; previously cited.). Gaffney overviews the use of FFPE tissue in cancer research (Abstract), and discloses fresh human tissue obtained by biopsy is typically processed to FFPE blocks, and when researchers pursue projects that require samples, the material is often available in FFPE (pg. 374, col. 1, para. 1 to col. 2, para. 1). Gaffney discloses that RNA can be extracted from FFPE tissue and cites various studies that sequence mRNA from FFPE samples (pg. 379, col. 1, para. 2), demonstrating the combination of the above sequencing additional elements with a FFPE sample are conventional.
Therefore, 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]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea and law of nature without significantly more. For additional guidance, applicant is directed generally to applicant is directed generally to the MPEP § 2106.
Response to Arguments
Applicant's arguments filed 13 Feb. 2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant remarks the claims recite any abstract idea into a practical application because they improve a non-abstract technology of transcriptome analysis for metastatic tissue samples, and that the specification at para. [0004]-[0005] explains that tumor purity can influence sequencing results, genomic interpretation, and associations with clinical outcomes and that expression from normal adjacent cells can wash out relevant expression signals, and thus there is a need for improved transcriptome deconvolution techniques (Applicant’s remarks at pg. 12, para. 2-3). Applicant remarks to address the challenge of mixed purity samples, claim 1 receives expression data, and performs unsupervised clustering on the initial bulk RNA expression data, such that the method learns to distinguish tumor expression from background tissue expression, addressing the technical problem identified in para. [0004] (Applicant’s remarks a pg. 13, para. 1-2). Applicant then remarks: (1) the step of “validating” enables the model to identify RNA expressions from known mixtures; (2) the step of “deconvoluting” articulates the technical function of the deconvolution; (3) the step of “identifying” articulates the technical improvement of reduced false expression calls; and (4) the step of “classifying” results in more accurate cancer type determinations (Applicant’s remarks at p g. 13, para. 3 to pg. 14, para. 3).
This argument is not persuasive. The judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. Furthermore, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. See MPEP 2106.05(a).
Applicant states there is a need for “improved transcriptome deconvolution techniques”, and points to the various steps of validating, deconvoluting, identifying, and classifying as provided the improvement. As discussed in the above rejection, these steps are part of the abstract idea, and thus cannot provide the improvement alone. Applicant has not provided an explanation regarding how any additional elements in the claim provide the improvement, either alone or in combination with the judicial exception. Instead, Applicant describes an allegedly improved analysis of bulk RNA expression data provided by the abstract idea alone (i.e. an improved abstract idea). The claims do not appear to improve any particular assay for generating RNA expression data from a mixed purity sample or any other technology.
Applicant remarks the claims include additional elements that amount to significantly more than the judicial exception under Step 2B, by reciting “validating…by: (i) applying the deconvoluted RNA expression data model…” and “(ii) comparing the set of deconvoluted RNA matrices”, which is not a routine and conventional approach (Applicant’s remarks at pg. 14, para. 4 to pg. 15, para. 1). Applicant further points to the step of “identifying” as demonstrating the claimed method achieves beyond what is routine processing, and states that the ordered combination provides significantly more than any abstract idea and reflects the technical improvement (Applicant’s remarks at pg. 15, para. 2 to pg. 16, para. 3).
This argument is not persuasive. First, the claims do not reflect an improvement to technology for the reasons discussed above. Second, under Step 2B, Examiners carry over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h) and furthermore, evaluate whether any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. See MPEP 2106.05 I.
In the instant case the above limitations Applicant argues are unconventional are part of the abstract idea, rather than additional elements analyzed under step 2B. Whether the abstract idea is unconventional is not considered under step 2B, and simply because an abstract idea is novel does not guarantee eligibility. "Likewise, Einstein could not patent his celebrated law that E=mc2; nor could Newton have patented the law of gravity." Id. Nor can one patent "a novel and useful mathematical formula," Parker v. Flook, 437 U.S. 584, 585, 198 USPQ 193, 195 (1978).
Furthermore, the additional elements of “preparing… an RNA sequencing (RNA-seq)…” and “generating bulk RNA expression data…” are well-understood, routine and conventional for the reasons discussed above in the outstanding rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4, 7-10, 18, 22-23, 25, and 27-33 are rejected under 35 U.S.C. 103 as being unpatentable over Newman (2018) in view of Dimitrakopoulou (2018) and Puram (2018). Any newly recited portion is necessitated by claim amendment.
Cited references:
Newman et al., US 2020/0176080 A1, effectively filed 19 July 2018, based on priority to PCT/US18/42949 (previously cited);
Dimitrakopoulou et al., Deblender: a semi-/unsupervised multi-operational computational method for complete deconvolution of expression data form heterogenous samples, 07 Nov. 2018, BMC Bioinformatics, 19(408), pg. 1-17 (previously cited).
Puram et al., WO 2018/191553 A1; Pub. Date: 12 Oct. 2018 (previously cited).
Regarding claims 1 and 18, Newman discloses a computer-implemented method for analyzing a mixed biological sample (Abstract; [0025]-[0026]), which comprises the following steps:
Newman discloses receiving bulk RNA expression data of tissue samples (FIG. 1; [0160], e.g. gene expression from bulk tissues; [0165]; [0222], e.g. bulk tissue RNA profiles used and mathematically separated), wherein bulk RNA expression data is normalized ( [0149], e.g. RNA-seq data in TPM; [0151], e.g. TPM expression normalization); and [0216], e.g. bulk RNA-seq reference profiles).
Newman discloses performing a mathematical separation on normalized bulk RNA expression data of tissue samples to identify cell-specific expression profiles (e.g. corresponding to clusters in view of Dimitrakopoulou, discussed below) in a reference matrix (FIG. 1; FIG. 3A and F-G, see group-level cell expression profiles, [0160], e.g. gene expression from bulk tissues; [0165]; [0222], e.g. bulk tissue RNA profiles used and mathematically separated; [0149], e.g. RNA-seq data in TPM; [0151], e.g. TPM expression normalization); and [0216], e.g. bulk RNA-seq reference profiles).
Newman further discloses the tissue samples include both primary cancer samples (i.e. nonmetastatic tissue samples) and metastatic cancer samples ([0154]; [0219] and FIG. 2C, e.g. signature matrix developed and validated from 18 primary tumors and 5 lymph node metastases from patients; FIG. 2C ), wherein the metastatic cancer tissue samples are of mixed cell types including malignant cells and nonmalignant cells (samples with mixed purity) (FIG. 1; FIG. 2A-C, e.g. training sample has various cell types). Newman further discloses each sample includes at least one of a plurality of cell types (corresponding to clusters) ([0217] and FIG. 2A and 3A and F-G, e.g. each sample is classified into cell subsets/clusters based on in silico purified bulk expression data).
Newman discloses generating a deconvolution gene expression model ([0226]), wherein the deconvolution model comprises a variable for, and takes as input, a signature matrix generated from the cell-specific expression profiles (FIG. 1; FIG. 3A and F-G; FIG. 12A), wherein the signature matrix comprises a cell-type/cluster corresponding to a biological indication of a pathology (FIG. 2A; FIG. 2C, e.g. cluster for "malignant" which corresponds to cancer).
Newman discloses testing the model’s capability for high resolution cell purification (i.e. validating the deconvolution model ([0226]-[0228]; FIG. 2) by performing the following steps:
Newman discloses (i) applying the model to a series of synthetic mixtures each containing differentially expressed genes in one or more cell types (i.e. RNA expression data of known in silico mixtures) ([0227]), thereby generating deconvoluted RNA matrices (FIG. 12; [0226], e.g. cell-type specific expression matrices; FIG. 4).
Newman discloses (ii) assessing the performance of the model to the synthetic mixtures by comparing the cell-type specific matrices to the known mixtures ([0228]; FIG. 15).
Newman discloses bulk RNA sequencing data is obtained by performing library preparation using an RNA exome kit (i.e. capturing the exome) from a tumor tissue of a patient ([0148]; FIG. 1)
Newman discloses generating bulk RNA expression data from the RNA-seq library ( [0148]; FIG. 1).
Newman discloses normalizing the bulk RNA expression data ([0177], e.g. bulk expression data normalized to transcripts per million).
Newman discloses deconvoluting the additional bulk normalized RNA expression data using the deconvoluted RNA expression data model (FIG. 1,e.g. CIBERSORTx deconvoluted expression profile into cell type proportions and group-level expression profiles; FIG. 12A-C, e.g. model used to get unknown fraction and cell type expression profiles), including generating a deconvoluted RNA matrix of the normalized RNA bulk expression data ([0152]; FIG. 12A). Newman discloses this deconvolution model identifies at least one distinct cell type of a plurality of cell types and a gene expression profile of each cell type, and performs “digital purification” of a biological sample to identify a gene expression feature profile of a distinct cell type ([0005]; [0008]-[0009]; [0023]; [0241]-[0243]). In other words, the deconvolution model of Newman removes background tissue expression from the bulk RNA expression data to produce expression profiles for a distinct cell type(s).
Newman discloses identifying differentially expressed genes (i.e. overexpressed or underexpressed genes) based on the deconvoluted RNA expression data (FIG. 1, see (3)-(4); FIG. 3-5; [0041]), wherein the differentially expressed genes are between conditions of interest and cell types, including between malignant and non-malignant cells ([0051]; [0160]; FIG. 15)
Newman discloses classifying the sample as a disease comprising a cancer (i.e. the biological indication) based on the RNA expression matrix indicating cell types and features of the expression matrix ([0027]-[0029]; [0091], e.g. the disease is cancer), wherein the features include gene expression of genes differentially expressed in distinct cell subsets ([0073]; [0130]; [0132], e.g. features 2-fold higher in a distinct component)
Regarding claims 1 and 18, and additionally dependent claims 7-8, Newman does not disclose the following:
First, regarding claims 1 and 18¸ Newman does not disclose performing unsupervised clustering on the normalized bulk RNA expression data of the tissue sample[s] to assign cluster(s). Although, Newman discloses an alternative method to obtain the cell-type specific expression profiles in the reference matrix, which includes performing unsupervised clustering on single-cell sequencing data or sequencing data of bulk sorted cells of the tissue samples to obtain clusters (Figure 1). Newman then explains that these cell-type specific transcriptome profiles derived from single cells or bulk sorted populations may be difficult to obtain, and thus mathematical separation of bulk tissue RNA profiles into cell type-specific transcriptomes can overcome these problems ([0222]). Newman further discloses these mathematically separated gene expression profiles for each cell type, derived from bulk tissue samples, were derived using “a common approach”, but does not specify that the approach comprises unsupervised clustering.
However, Dimitrakopoulou discloses an approach for deriving gene expression profiles of specific cell types comprising unsupervised clustering as follows:
Dimitrakopoulou discloses a method of deconvoluting expression data of a heterogenous tissue sample into cell-specific expression signatures (Abstract), which comprises performing unsupervised clustering on mixed expression data to ultimately determine clusters and corresponding cell type specific expression profiles (Figure 1). Dimitrakopoulou discloses that using unsupervised clustering alleviates the need for marker genes to be known a priori (pg. 2, col. 2, para. 2), and furthermore that the method can be used to seed other in silico reference-based techniques that may provide a more accurate deconvolution (pg. 2, col. 2, para. 4 to pg. 4, col. 2, para. 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 have modified the method of Newman to have performed unsupervised clustering on the bulk normalized RNA expression data to assign each tissue samples to one or more clusters corresponding to cell types according to Dimitrakopoulou, as discussed above, thus obtaining cell-type specific expression profiles directly from the bulk sequencing data. One of ordinary skill in the art would have been motivated to combine the methods of Newman and Dimitrakopoulou in order to identify cell type specific expression profiles from mixed tissue without the need for marker genes to be known a priori and to seed other in silico reference-based techniques, as performed in Newman, to provide a more accurate deconvolution, as shown by Dimitrakopoulou (pg. 2, col. 2, para. 2, 4 and pg. 4, col. 2, para. 1), and furthermore, to allow for the generation of cell-specific expression profiles when cell-type specific transcriptome profiles from single cells or bulk sorted populations are difficult to obtain, as shown by Newman ([0222]). This modification would have had a reasonable expectation of success because Dimitrakopoulou discloses this method may be used to seed other in silico reference-based techniques, as performed in Newman, and thus the cell-specific expression profiles of Dimitrakopoulou are applicable to the reference signature matrix of the deconvolution method of Newman.
Further regarding claims 1 and 18, while Newman does disclose assessing the performance of the model to the synthetic mixtures by comparing the cell-type specific matrices to the known mixtures ([0228]; FIG. 15), Newman does not explicitly disclose the comparing is performed using clustering.
However, Newman does disclose an embodiment in which T-SNE (i.e. clustering) is used to visualize and explore the distribution of in silico purified samples (e.g. deconvoluted RNA matrices) and compare the results to FACS-purified NSCLC cell types (i.e. known cell types) ([0233]; FIG. 5E-F). Newman discloses the T-SNE comparison with the known cell types allows for the validation of the in-silico findings and provides strong evidence of successful in-silico purification ([0232]-[0233]).
Therefore, it would have been prima facie obvious, to one of ordinary skill in the art, before the effective date of the claimed invention to have modified the comparison of the deconvoluted RNA matrices of in-silico mixtures with the known mixtures of Newman to have been performed by performing T-SNE (i.e. clustering) as shown by Newman in the embodiment using FACS-purified NSCLC cell types ([0233]; FIG. 5E-F). One of ordinary skill in the art would have been motivated to combine the various teachings of Newman to provide strong evidence of successful in-silico purification and facilitate the validation of the in-silico findings, as shown by Newman ([0232]-[0233]). This modification would have had a reasonable expectation of success given Newman discloses using T-SNE to compare predicted expression matrices (e.g. the in-silico purified expression data) to known expression data from FACS-purified cell types, which is applicable to the known and predicted data using in-silico mixtures.
Further regarding claims 1, 7-8, and 18, Newman in view of Dimitrakopoulou does not disclose the biological indication is a cancer type that is a primary or metastatic cancer as recited in claim 1, that the at least one cluster corresponds to primary cancer as the biological indication, as recited in claim 7, or that that at least one cluster corresponds to metastatic cancer as the biological indication, as recited in claim 8.
However, regarding claims 1, 7-8, and 18. Puram discloses a method for analyzing RNA-seq expression data to identify metastatic cancers (Abstract), which comprises analyzing RNA-seq expression data from primary tumors and matched metastases (FIG. 1), performing clustering on the RNA-seq expression data to identify at least one cluster associated with primary tumors and metastatic tumors and subsequent deconvolution to identify malignant cells based on the clusters ([0016]; [0028]; [0050]; claims 16-20; FIG. 5A), and using the deconvoluted expression data for the at least one cluster corresponding to primary and metastatic tumors to predict whether a tumor is metastatic (i.e. identify a primary or metastatic tumor) (claims 16-20; [0030]; [0037]; FIG. 7; FIG. 14, e.g. a high p-EMT score indicates a metastatic tumor). Puram further discloses the above method can be used to predict whether a tumor is metastatic and direct subsequent treatment decisions ([0050]).
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 have modified the method made obvious by Newman in view of Dimitrakopoulou to have identified a cluster associated with primary tumors and metastatic tumors as a biological indication and predicted whether a tumor is metastatic, as shown by Puram ([0028]; [0030]; [0037]; FIG. 5A; FIG. 7; FIG. 14). One of ordinary skill in the art would have been motivated to combine the methods of Newman and Puram to predict whether a tumor is metastatic or not (i.e. primary) to direct subsequent treatment decisions, as shown by Puram ([0050]), given Newman discloses diagnosing a cancer in a subject ([0027]; FIG. 1). This modification would have a reasonable expectation of success because both Newman and Puram analyzing RNA-seq expression data of tumors using clustering and deconvolution to identify cell types, such that the method of Puram is applicable to the method of Newman.
Regarding claim 4, Newman discloses the deconvolution model comprises a first dimension representing a number of samples and a second dimension reflecting a number of genes in the RNA expression data (FIG. 12A-B).
Regarding claim 9, Newman discloses the biological indication can be cancer of the head or neck ([0038]; FIG. 2C-D).
Regarding claim 10, Newman discloses the tumor tissue sample is obtained from lymph node tissue ([01011]).
Regarding claim 19, Newman discloses the biological indication of the one or more pathologies or the tumor tissue is a cancer type ([0027]; [0091], e.g. the cancer is diagnosed based on cancer type of reference individual).
Regarding claim 20, Newman discloses the tumor tissue originates from an organ ([0104]).
Regarding claim 22, Newman discloses generating enriched gene expression profiles using the deconvolution model ([0144]; FIG> 1., e.g. "expression purification" or enrichment; FIG. 12A, e.g. "cell type GEPs"; FIG. 12C). Newman further discloses analyzing differential expression between the purified (i.e. enriched) gene expression profiles ([0162]-[0163]). Newman further discloses classifying the purified gene expressions in a biological indication model (FIG. 21C-D, e.g. enriched CD8 T cell expression profile used in survival model; FIG. 21E, e.g. purified expression in response model; [0057]).
Regarding claim 23, Newman discloses generating the enriched expression profiles comprises receiving fractions (i.e. percent assignments) of each cell subset (i.e. cluster) in the RNA expression information of the tumor and scaling the RNA expression information for one or more genes based on the fractional abundances of each cell subset (i.e. membership associations) of each cluster (FIG. 1; FIG. 12A, e.g. "unknown cell type GEPs" determined by scaling Input mixtures by Cibersortx fractions; [0007] and claim 28, e.g. fractional abundance of cell types determined in biological sample).
Regarding claim 25, Newman discloses the step of determining the biological indication is performed after performing the deconvolution ([0027], e.g. deconvolution performed in step (b) and diagnosis determined in step (c)). Newman further discloses the deconvolution is performed using unsupervised approaches (i.e. unsupervised machine learning) ([0165]; FIG. 12(c)).
Regarding claim 26, Newman discloses receiving the RNA expression information of the tumor tissue comprises sequencing the sample of tumor to generate the bulk RNA expression data ([0009]; [0011]; Figure 3; Claims 1 and 5)
Regarding claim 27, Newman discloses obtaining a tissue sample from a tumor biopsy (i.e. receiving a tissue sample collected by a tumor sample collection method) ([0100]).
Regarding claim 28, Newman discloses obtaining a tissue sample from a tumor biopsy (i.e. receiving a tissue sample collected by a tumor sample collection method) ([0100]).
Regarding claim 29, Newman discloses the nonmetastatic tissue samples are primary cancer samples ([0154]; [0219] and FIG. 2C, e.g. signature matrix developed and validated from 18 primary tumors and 5 lymph node metastases from patients; FIG. 2C).
Regarding claims 30-31, Newman discloses the tumor tissue of the patient is a FFPE sample (Claim 130; [0007]).
Regarding claim 32, Newman discloses determining features of the expression data of the tumor sample, wherein the features include biomarkers ([0073]; [0075]; FIG. 1).
Regarding claim 33, Newman discloses exploring the cell type specific expression signatures of the tumor sample to identify relevance to anti-cancer therapies ([0004]; [0143]; [0239]).
Therefore, the invention is prima facie obvious.
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Newman (2017) in view of Dimitrakopoulou (2018) and Puram (2018), as applied to claim 1 above, and further in view of Dey (2016). This rejection is previously cited.
Cited references: Dey et al., Clustering RNA-seq expression data using grade of membership, 2016, bioRxiv preprint, pg. 1-34 (previously cited).
Regarding claims 2-3, Newman in view of Dimitrakopoulou and Puram disclose the method of claim 1, as applied above.
Further regarding claims 2-3, Newman in view of Dimitrakopoulou and Puram, as applied to claim 1 above, does not disclose the following:
Regarding claim 2, Newman in view of Dimitrakopoulou and Puram, as applied to claim 1 above, does not disclose performing the clustering operation on the initial RNA expression data is performed using a grade of membership clustering operation.
However, Dey overviews using grade of membership models for clustering RNA-seq expression data (Abstract), and discloses that grade of membership clustering models allow each sample to have a partial membership in multiple clusters (pg. 2, para. 2). Dey further discloses that traditional clustering methods attempt to partition samples into distinct groups with similar expression patterns, but the structure of typical expression data is too complex to be fully captured by this distinct partitioning, and that grade of membership clustering models can capture “continuous” variation among cells as well as more as the “discrete” variation captured by cluster models (pg. 2, para. 2 to pg. 3, para. 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 have modified the method of Newman in view of Dimitrakopoulou and Puram, as applied to claim 1 above, to have performed the clustering on the initial bulk RNA expression data with a grade of membership clustering operation, as shown by Dey (pg. 2, para. 2 to pg. 3, para. 1). One of ordinary skill in the art would have been motivated to combine the methods of Newman and Dey to capture “continuous” variation among cells in addition to the more “discrete” variation captured by traditional cluster models, thus capturing more complex expression data, as shown by Dey (pg. 2, para. 2 to pg. 3, para. 1). This modification would have had a reasonable expectation of success because Newman in view of Dimitrakopoulou performs clustering on RNA expression data derived from RNA-seq expression data, as applied to claim 1, such that the method of clustering RNA-seq expression of Dey is applicable to the method of Newman in view of Dimitrakopoulou in view of Puram.
Regarding claim 3¸ Notably, Newman discloses using the deconvolution method to identify cell types of interest ([0143]; [0146]), which would require a cluster corresponding to the cell type of interest is identified, and Dimitrakopoulou discloses iterating the value k in the clustering to obtain a best fit of the data (Figure 1; pg. 14, col. 2, para. 3). However, Newman in view of Dimitrakopoulou and Puram, does not disclose performing the clustering on the RNA expression data iteratively until the at least one cluster corresponding to the biological indication is identified.
However, Dey further discloses that the number of clusters K is set by the analyst, and it is helpful to explore multiple values of K (pg. 3, para. 2-3). Dey further discloses performing the clustering for multiple values of K (i.e. the clustering is performed iteratively), and that increasing K highlights finer structure in the data, with tissues that cluster together in smaller K are subdivide into distinct subgroups for larger K (pg. 4, para. 3). Dey further discloses initially only analyzing three clusters to a particular tissue type, and then repeating the clustering with larger K to uncover additional substructure within the tissue missed by the initial clustering (i.e. iteratively performing the clustering until a tissue of interest is identified) (pg. 5, para. 2; Fig. 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 have modified the method of Newman in view of Dimitrakopoulou and Puram, to have iteratively performed the clustering until the at least one clustering corresponding to the biological indication is identified, as shown by Dey (pg. 3, para. 2-3; pg. 4, para. 3; pg. 5, para. 2; Fig. 1). One of ordinary skill in the art would have been motivated to further combine the methods of Newman in view of Dimitrakopoulou and Dey to explore multiple values of K and uncover additional substructure within a tissue, as shown by Dey (pg. 3, para. 2-3; pg. 5, para. 1; Fig. 1), given Newman discloses the deconvolution method allows for individual cell types of interest (e.g. clusters corresponding to the biological indication) to be digitally purified ([0143]; [0146]), which would require that a cluster corresponding to the cell type of interest is identified. This modification would have had a reasonable expectation of success because both Dey and Newman perform clustering on RNA-seq expression data, such that the method of performing iterative clustering of Dey is applicable to the clustering of Newman.
Therefore, the invention is prima facie obvious.
Claims 34-35 are rejected under 35 U.S.C. 103 as being unpatentable over Newman (2017) in view of Dimitrakopoulou (2018) and Puram (2018), as applied to claims 1 and 18 above, and further in view of Rhodes (2015). This rejection is newly recited and necessitated by claim amendment.
Cited reference: Rhodes et al. US 2015/0080239 A1 (newly cited).
Regarding claims 34-35, Newman in view of Dimitrakopoulou and Puram disclose the methods of claims 1 and 18 as applied above.
Newman in view of Dimitrakopoulou and Puram make obvious that the biological indication is a cancer type and predicting whether a tumor is metastatic, as applied to claims 1 and 18 above.
Newman further discloses generating a visual display (i.e. a report) indicative of feature profiles from biological samples and abundances of distinct cell types, which would include the metastatic cancer cell type made obvious in claims 1 and 18 as applied above (i.e. generating a report comprising the cancer type) ([0206]).
Further regarding claims 34-35, While, Newman discloses exploring the cell type specific expression signatures of the tumor sample to identify relevance to anti-cancer therapies ([0004]; [0143]; [0239]), Newman in view of Dimitrakopoulou and Puram do not disclose the report comprises one or more of (i) a therapy recommendation targeting the one or more overexpressed or underexpressed genes, or (ii) a clinical trial match based on the cancer type and the one or more overexpressed or underexpressed genes.
However, Rhodes discloses a method for detecting a plurality of genes in a sample from a subject with cancer and generating an associated report (Abstract), which comprises assessing a sample of a subject for expression analysis ([0028]; [0159]), and generating a report comprising recommendations for an FDA approved drug or a clinical trial for the subject being analyzed ([0141]; [0144] and FIG. 2 and 4, e.g. report takes into account gene expression levels and gene variants). Rhodes discloses the recommendations are determined by using assays for biomarkers that are treated with a potential activator, inhibitor, or modulator ([0046]), wherein biomarkers include overexpressed or underexpressed genes ([0028])
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 have modified the method of Newman in view of Dimitrakopoulou and Puram to have further included a therapy recommendation or clinical trial recommendation based on the overexpressed or underexpressed genes, as shown by Rhodes, discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Newman in view of Dimitrakopoulou and Puram with Rhodes to provide actional treatment recommendations to the subject, as shown by Rhodes (Abstract; [0003]). This modification would have a reasonable expectation of success given Newman identifies cancer in a subject based on gene expression, and thus the generated report is applicable to the subject of Newman.
Therefore, the invention is prima facie obvious.
Response to Arguments
Applicant's arguments filed 13 Feb. 2026 regarding 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant remarks that the cited combination does not disclose “deconvoluting the normalized bulk RNA expression data…by applying the validated…model to remove background tissue expression from the normalized bulk RNA expression data…” because Newman provides a framework for cell type enumeration and cell type-specific gene expression purification”, while the instant claims recite deconvolution “to remove background tissue expression from the normalized bulk RNA expression data”, which is a fundamentally different objective, and address the problem of background expression that washes out the relevant tumor signal (Applicant’s remarks at pg. 16, para. 4 to pg. 17, para. 3).
This argument is not persuasive. As understood by one of ordinary skill in the art, and described by Newman, deconvolution models allow for “digital purification” of a biological sample and resolve the expression profile for a distinct cell type from bulk RNA expression data of a mixed sample (Abstract; [0023]). Purifying the expression profile for a given cell type, or cell types, from RNA expression data of a mixed sample removes “background tissue expression from the normalized bulk RNA expression data” to obtain the purified expression profile of a distinct cell type (expression data from other cells are “background tissue”).
Applicant remarks that Dimitrakopoulou and Puram do not cure the deficiencies of Newman (Applicant’s remarks at pg. 17, para. 3 to pg. 18, para. 2).
This argument is not persuasive because Dimitrakopoulou and Puram are not relied upon to disclose the above feature taught by Newman.
Applicant remarks there is not motivation to combine the references to arrive at the deconvolution step because the cited references address different technical problems, stating that Newman does cell type enumeration and purification of cell type-specific expression profiles, while Dimitrakopoulou focuses on estimating mixture proportions and cell type-specific expression profiles, and Puram focuses on identifying gene signatures, and further remarks that none of these references recognize or address the specific technical problem of background tissue expression in metastatic cancers causing tumor signal to be washed out (Applicant’s remarks at pg. 18, para. 3 to 5).
This argument is not persuasive. First, Dimitrakopoulou and Puram are not relied upon to teach the deconvolution removing background tissue expression, as discussed above with respect to Newman.
Furthermore, Applicant states that Newman and Dimitrakopoulou address different technical problems while simultaneously stating both Newman and Dimitrakopoulou focus on “cell type-specific expression profiles”. Notably, this is also what the instant invention does, as discussed in the specification at para. [0005]. Furthermore, Puram does not simply identify gene signatures, but also performs clustering on the RNA-seq expression data to identify at least one cluster associated with primary tumors and metastatic tumors and subsequent deconvolution, as performed in the instant claims, Newman, and Dimitrakopoulou, in order to identify malignant cells ([0016]; [0028]; [0050]; claims 16-20; FIG. 5A), and then uses the deconvoluted expression data for the at least one cluster corresponding to primary and metastatic tumors to predict whether a tumor is metastatic (i.e. identify a primary or metastatic tumor) (claims 16-20; [0030]; [0037]; FIG. 7; FIG. 14, e.g. a high p-EMT score indicates a metastatic tumor). Each of the cited references use deconvolution to identify expression profiles of particular cell types. As explained above with respect to Newman, performing deconvolution to resolve the expression profile of a single cell type from a bulk tissue mixture necessarily involves removing background tissue expression from the normalized bulk RNA expression data to achieve the “purified” expression profile of a given cell type. This directly addresses the washing out issue discussed by Applicant. In fact, this is the purpose of deconvolution methods on bulk RNA (also explained by Newman above).
Puram applies deconvolution, as performed in the instant claims and in Newman, to distinguish between primary tumors and metastatic tumors, and why it would have been obvious to use the deconvolution method of Newman in view of Dimitrakopoulou to identify a metastatic cancer is provided in the above rejection.
Applicant remarks, regarding claims 2-3, that Dey alone or in combination with Newman, DImitrakopoulou, and Puram do not cure the deficiencies discussed above for claim 1, and thus the rejection should be withdrawn (Applicant’s remarks at pg. 19, para. 2-3).
This argument is not persuasive for the same reasons discussed above for claim 1.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685