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 .
Herein, “the previous Office action” refers to the Non-Final Rejection filed 8/15/2025.
Amendments Received
Amendments to the claims were received on 10/28/2025, and have been entered.
Priority
As detailed on the Filing Receipt filed 11/17/2020, the instant application claims priority to as early as 11/8/2019. At this point in prosecution, all claims are accorded the earliest claimed priority date.
Information Disclosure Statement
The Information Disclosure Statement filed on 12/27/2025 is in compliance with the provisions of 37 CFR 1.97 and has been considered in full. A signed copy of the IDS is included with this Office Action.
Claim Status
Claims 2-3, 5-6, 8, 15, 23, 25, 27, 33-37 and 39 are canceled.
Claims 1, 4, 7, 9-14, 16-22, 24, 26, 28-32 and 38 are pending.
Claim 30 stands withdrawn pursuant to 37 CFR 1.142(b) as being directed to a nonelected invention, there being no currently allowable generic or linking claim. Election without traverse was made in the reply filed 6/22/2023.
Claims 1, 4, 7, 9-14, 16-22, 24, 26, 28-29, 31-32 and 38 are under examination.
Withdrawn Objections/Rejections
In light of Applicant’s cancelation of claims 23, 25 and 27, the rejection of these claims under 35 USC § 103, as being unpatentable over Newman, in view of Love and Rabadan, has been withdrawn.
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, 4, 7, 9-14, 16-22, 24, 26, 28-29, 31-32 and 38 are rejected under 35 USC § 103 as being unpatentable over Newman et al (WO 2019/018684; published 1/24/2019; previously cited), in view of Love et al (Genome Biology 15: article 550, pp. 1-21; published 2014; on IDS filed 3/12/2021; previously cited) and Rabadan et al (US 2020/0109455; effective filing date 10/1/2018; previously cited). This rejection is maintained from the previous Office action, and has been revised to address the amended claims (filed 10/28/2025).
Claim 1 recites a “method for deconvolving… RNA-seq data obtained from cells derived from a malignant tumor within a subject comprising”:
a) “obtaining input… comprising bulk or spatial RNA-seq data, single-cell RNA seq data, and cell type annotations”, wherein:
1. “the cells from which the bulk or spatial RNA-seq data were obtained comprise malignant tumor cells”;
b) “generating a UMI (unique molecular identifier) count matrix based on the bulk or spatial RNA-seq data, single cell RNA-seq data, and cell type annotations”, wherein:
1. “each column of the UMI count matrix corresponds to a cell within [a] malignant tumor or a spatial location within the malignant tumor”, and
2. “each row of the UMI count matrix corresponds to a gene in the malignant tumor”;
c) “selecting a subset of the most variably expressed genes from the UMI count matrix based on a union of genes whose expression is enriched in each cell type in the UMI count matrix, or a union of genes that vary above a predetermined level across all the cells in the UMI count matrix, or both”;
d) “estimating the mean and dispersion… of the selected genes… by fitting a negative binomial distribution for each gene in a cell type”;
e) “computing the cross cell type specificity of each [selected] gene… based on the estimated mean and dispersion parameters”;
f) “estimating cross-sample gene variability… based on a ratio of the average gene expression level and the variance of the selected genes”;
g) “estimating, using a regression-based approach, gene-wise scaling factors using both the bulk or spatial… and single cell RNA-seq data”; and
h) “generating a regression model based on the cross-cell type specificity of each selected gene, the cross-sample gene variability from the bulk or spatial RNA-seq data or from the single cell samples, and the gene-wise scaling factors”;
i) “estimating, using the regression model, cell type proportions in the bulk or spatial RNA-sequencing data, wherein the regression model assigns a weight to the selected genes with high cell type specificity and low cross-sample variability”, the cell type proportions including:
1. “the percentage of [] at least one cell type below [a] predetermined percentage threshold”;
j) “identifying the proportion of immune cells among the malignant tumor cells in the subject”;
k) “identifying the subject as a candidate for immunotherapy based on the proportion of immune cells among the malignant tumor cells in the subject”;
l) “administering an immunotherapy to the subject identified as a candidate”.
With respect to claim 1, Newman discloses “a method for enumerating cell composition from tissue gene expression profiles (GEPs) with techniques for cross-platform data normalization” (pg. 42, para. 0143) comprising:
a) “processing a biological sample” (pg. 2, para. 0006)” to obtain feature measurements, wherein “said biological sample… is derived from a solid tissue sample… compris[ing] a tumor sample” (pg. 2, para. 0007), and/or “obtaining feature measurements from… an RNA-seq database” (pg. 37, para. 0123), said measurements comprising:
I) “transcriptomes of… bulk RNA admixtures” (pg. 1, para. 0003),
II) “single-cell gene expression measurements… generated by single cell RNA sequencing (scRNA-seq)” (pg. 2, para. 0007), and wherein
III) “Only previously annotated cell phenotypes were considered” (pg. 22, para. 0052; see also mention of ‘labels’ at pg. 18, para. 0044), i.e., requiring use of cell type annotations;
b) “generat[ing] a feature profile… compris[ing] a plurality of gene expression profiles (GEPs) … each… corresponding to a distinct cell type” (pg. 2, paras. 0006- 7), wherein to “obtain expression profiles of individual cells… Sequencing reads may be processed… by… conversion into a digital count matrix… [including] number of unique molecular identifiers (UMIs) detected per cell” (pg. 43, para. 0147);
c) defining a gene signature matrix wherein “features may be selected for inclusion in the signature matrix based on fold change in the value of the feature for a distinct component, e.g., cell subset, as compared to other distinct components, e.g., cell subsets” (pg. 39, para. 0132), i.e., based on enriched expression in each cell type;
e) calculating cell-type specificity measurements, e.g., calculating pairwise Z scores for differential gene expression and converting to two-sided p-values for each cell type (pg. 77, para. 0251);
f) calculating gene variability measurements, e.g., “feature variance” (pg. 39, para. 0131) and “standard deviation… of each gene… across the… cell types” (pg. 56, para. 0183);
g) performance of various gene-wise scaling operations, e.g., “batch correction” (pg. 3, para. 0007; pg. 25, para. 0059), “Z-score normaliz[ation]” (pg. 24, para. 0055), and “regressing out the dependence of gene expression on the number of UMIs” (pg. 44, para. 0147);
h) “optimizing a regression between said feature profile and a reference matrix” (pg. 2, para. 0007), said regression comprising cell type-specific gene expression coefficients, using non-negative least squares regression (pg. 50, para. 0171; see definition of ‘NNLS’ at pg. 48, para. 0159);
i) using the regression to “estimat[e] the relative proportions of cell subsets… that contribute to the feature profile” (pg. 34, paras. 0109-10), wherein:
1. “[i]n some embodiments, the physical sample… includes at least one distinct component represented in a feature signature at a concentration of 10% or less” (pg. 40, para. 0136) and “may include cell subsets represented by the signature matrix that are present in low amounts, such as 10% or less” (pg. 41, para. 0138), i.e., a predetermined threshold;
j) “cell subsets may include tumor-infiltrating leukocytes (TILs)… in mixture with cancer cells in the biological sample” (pg. 36, para. 0116).”, i.e., estimating the proportion of leukocytes among the tumor cells; and
k) “predicting a clinical outcome of a disease therapy… comprising quantifying the abundance of a distinct cell type… present in [the] sample… comparing the quantified abundance… and a predetermined association of said distinct cell type with clinical outcomes of said therapy, and… predicting said clinical outcomes based at least on said comparison” (pg. 11, para. 0031), wherein “The disclosed methods and systems may… reveal[] associations between tumor infiltrating leukocytes (TILs) and immunotherapy response” (pg. 41, para. 0140).
Additionally, Newman discloses that “DEGs”, differentially-expressed genes, “were assessed in each purified cell type by ranking genes by fold change” (pg. 15, para. 0040) and “filter[ing] based on their geometric coefficient of variation… for each cell type, each geometric c.v. was ranked in ascending order… until the maximum c.v. ('cvmax') was reached, defined as the maximum of either 1 or the 75th percentile of all c.v. values… the point farthest from (0, cvmax) in Euclidean space… was used as an adaptive cell type-specific noise threshold” (pg. 75, para. 0244). As standard deviation (σ) is merely the square root of variance (σ2), calculating coefficients of variation is not patentably distinct from calculating variance-to-mean ratios. In this way Newman discloses selection of genes (features) for the regression model, based on a ratio of the average gene expression level and the variance of the selected genes, that exhibit high cell-type specificity.
Newman further discloses application of CIBERSORTx to perform cross-platform deconvolution including a tailored batch correction strategy (pg. 25, para. 0059), and states: “regardless of their source, CIBERSORTx signature matrices showed strong generalizability, whether applied across platforms, datasets, or tissues” (pg. 66, para. 0220). Cross-dataset accuracy requires selection of genes (features) for the regression model that exhibit low cross-sample variability.
Although Newman discusses calculation of z-scores and immunotherapy applications (see items (g) and (k) as indexed above), Newman does not specifically disclose estimating mean and dispersion parameters of selected genes by fitting a negative binomial distribution for each gene in a cell type; identifying the subject as a candidate for immunotherapy based on the proportion; or administering an immunotherapy.
Love discusses “analysis of count data… per gene in RNA-seq… using… fold changes” (pg. 1, Abstract), and teaches that “For each gene, we fit a generalized linear model (GLM) as follows. We model read counts… as following a negative binomial distribution… with mean… and dispersion [parameters]” (pg. 2, l. column). Love further teaches that “our methodology has high sensitivity and precision” (pg. 2, l. column), and “use of linear models… provides the flexibility to… analyze more complex designs, as is often useful in genomic studies” (pg. 2, r. column).
Love does not disclose identifying the subject as a candidate for immunotherapy based on the proportion; or administering an immunotherapy.
Rabadan discusses “techniques to identify patients (i.e., responder) who can respond to anti-PD-1 immunotherapy with an improved survival rate… [and] identify responsive and non-responsive tumors by assessing… microenvironments… and/or transcriptomic signatures” (pg. 4, para. 0055), i.e., RNA-seq data, and teaches that “Levels of T-cell infiltration in the tumor microenvironment can… be associated with the likelihood of response [to anti-PD-1 therapy]” (pg. 4, para. 0058).
Rabadan further teaches that “[these] techniques… can inform therapeutic options” (pg. 4, para. 0055) and teaches “administration of PD-1 inhibitors” (pg. 5, para. 0070).
With respect to claim 4, Newman discloses, “datasets may comprise… transcripts per million (TPM), [or] reads per kilobase of transcript per million (RPKM)” (pg. 45, para. 0151).
With respect to claim 7, Newman discloses performance of “gene set enrichment analysis” (pg. 18, para. 0043), wherein “features enriched in distinct components”, i.e., cell types, that are not represented in the signature matrix are not included” (pg. 39, para. 0131). Newman further discloses “a comprehensive toolkit… CIBERSORTx” (pg. 72, para. 0237) which implements the disclosed invention. In other words, a tool which accepts a single cell UMI count matrix and cell type annotations as input.
With respect to claim 9, Newman discloses that “features may be selected for inclusion in the signature matrix based on fold change in the value of the feature for a distinct component, e.g., cell subset, as compared to other distinct components” (pg. 39, para. 0132). Newman exemplifies a use-case wherein “DEGs were assessed in each purified cell type by ranking genes by fold change” (pg. 15, para. 0040).
With respect to claim 10, Newman discloses “evaluat[ing] impact of key signature matrix parameters on deconvolution performance… [including] the number of signature genes per cell type”, therein exemplifying “ranges of… 150-300” (pg. 80, para. 0261), e.g., about 200.
With respect to claim 11, Newman exemplifies “the reference matrix may include at least one feature (e.g., gene)… including at least 100 features that are associated with (e.g., expressed by) two or more, e.g., 5… of the distinct components (e.g., cell subsets), and in some cases, by 20 or fewer” (pg. 39, para. 0130), e.g., 5 or fewer.
With respect to claim 12, Newman discloses “cell type-specific DEGs” (pg. 16, para. 0042). Newman presents techniques for performance evaluation of their disclosed methods, comprising “systematically varying… the total number of cell types with DEGs (1, 2, 3, …, 8)” (pg. 21, para. 0051), and exemplifies use of “cells without DEGs” as negative controls versus said “cell types with DEGs” (Figs. 15B-C). Newman thereby discloses selection of genes presenting in a fixed number of cell types.
With respect to claim 13, Newman discloses “the reference matrix may include… 1,000 or fewer… features (e.g., genes)” (pg. 39, para. 0130).
With respect to claims 14 and 18, Newman discloses that “features”, i.e., genes, “with low values and/or variance may be filtered from the signature matrix… For example, features with values and/or variance that is in the lower 90%... as compared to other candidate features may be filtered out… [or] features with values and/or variance that is higher than 90%... as compared to other candidate features may be included” (pg. 39, para. 0131). Newman thus implicitly discloses calculating variance of each gene. Newman does not explicitly disclose variance-stabilizing transformation (VST).
Love teaches performance of “variance-stabilizing transformation (VST) for overdispersed counts” (pg. 8, l. column). Love discusses “the problem of heteroskedasticity… if the data are given… on the original count scale, the result will be dominated by highly expressed, highly variable genes” (pg. 7, r. column), and further teaches that “VST is… effective at stabilizing variance” (pg. 8, l. column).
With respect to claims 16-17 and 19, Newman discloses that “Clusters may be identified” (pp. 43-44, para. 0147). Newman further discloses that “Median centering was applied to each cell population”, i.e., cluster, “separately” (pg. 16, para. 0041), and “cell fractions were normalized to one before comparison” (pg. 81, para. 0262; Fig. 2e). Additionally, Newman discloses that “features with low values and/or variance may be filtered from the signature matrix” (pg. 39, para. 0131).
With respect to claim 20, Newman discloses that “genes were filtered based on their geometric coefficient of variation (geometric c.v.)… for each cell type, each geometric c.v. was ranked in ascending order… [and] The geometric c.v. corresponding to [a] threshold was used as an adaptive cell type-specific noise threshold” (pg. 75, para. 0244). Newman thereby discloses filtering of genes according to variance, and selection of genes with variance above a threshold. Newman further exemplifies selecting “a set of multiple (e.g., about 2,000)… genes” (pg. 58, para. 0188).
With respect to claims 21-22, Newman discloses, “data may be downloaded and analyzed with batch correction in non-log linear space, but without any additional preprocessing” (pg. 45, para. 0151), e.g., sample normalization. Newman additionally exemplifies calculating “expression of canonical marker genes in unadjusted”, i.e., raw, “RNA-Seq data” (pg. 17, para. 0043; Fig. 7F).
With respect to claim 24, Love teaches modeling read counts “[f]or each gene” (pg. 2, l. column).
With respect to claim 26, Love teaches “the GLM fit returns coefficients indicating… log2 fold change between treatment and control” (pg. 2, r. column), i.e., cell type-specificity weights.
With respect to claim 28, as noted above, Newman discloses analysis of “transcriptomes of… bulk RNA admixtures” (pg. 1, para. 0003).
With respect to claim 29, Newman discloses that “Simulated GEP mixtures… may be created using reference profiles” (pg. 55, para. 0183), wherein “synthetic cell type GEPs… may be combined according to their corresponding mixing coefficients” (pg. 56, para. 0183) and “for simulating mixing coefficients… the mean proportions of CDB T cells and HCT116”, i.e., given cell types, “may be set to their target abundances… this process may be repeated for each imputed cell type GEP” (pg. 57, para. 0186).
With respect to claim 31, Newman discloses performance of various gene-wise scaling operations, such as “Z-score normaliz[ation]” (pg. 24, para. 0055) or “log2 adjust[ment] and media-center[ing] for each gene” (pg. 22, para. 0052).
With respect to claim 32, Newman discloses “processing [a] biological sample to obtain a feature profile” (pg. 2, para. 0006), and “optimizing a regression between said feature profile and a reference matrix” (pg. 2, para. 0007). Newman thereby teaches performance of regression at the sample scale.
With respect to claim 38, Newman discloses that “The present method may provide for a sensitive method of estimating the fractional representation of a distinct component in a physical sample… where the distinct component is present at a low fraction” (pg. 40, para. 0136) and “a biological sample may include cell subsets represented by the signature matrix that are present in low amounts, such as… 5% or less, 2% or less, [etc]” (pg. 41, para. 0138).
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 estimation of mean and dispersion parameters, fitting data to a negative binomial distribution model, and variance-stabilizing transformation, as taught by Love, to enhance the deconvolution method of Newman, because Love teaches that modeling gene expression data on a negative binomial distribution using estimated mean and dispersion parameters is statistically sensitive and precise (pg. 2, l. column) and that VST solves the analytical problem of heteroskedasticity in gene expression data (pg. 7, r. column). Said practitioner would have had a reasonable expectation of success because Newman and Love both disclose methods of cell type deconvolution using RNA-seq count data.
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 candidates based on the proportion, and administration of immunotherapy, as taught by Rabadan, to enhance the deconvolution method of Newman because Newman teaches estimation of proportion of infiltrating leukocytes in a tumor sample while Rabadan teaches that patients responsive to immunotherapy (i.e., candidates) may be identified by assessing level of infiltrate in a tumor sample (pg. 4, paras. 0055 and 0058). Thus, administering an immunotherapy is an enabled clinical application of steps disclosed by Newman. Said practitioner would have had a reasonable expectation of success because Newman and Rabadan both discuss transcriptomic analysis and assessment of tumor infiltrate.
In this way the disclosure of Newman, in view of Love and Rabadan, makes obvious the limitations of instant claims 1, 4, 7, 9-14, 16-22, 24, 26, 28-29, 31-32 and 38.
Response to Arguments - Claim Rejections Under 35 USC § 103
In the remarks filed 10/28/2025, Applicant traverses the rejections under 35 USC § 103 and presents supporting arguments.
Applicant alleges unexpected discovery that definition of cross-sample gene variability by means of the ratio of the average gene expression level and variance of the selected genes, as claimed, avoids underweighting genes that have low expression and overweighting genes that are unstable (pg. 8, para. 4).
Evidence regarding unexpected results must be supported by an appropriate affidavit or declaration (see MPEP 716.01(c) § I). Applicant has not presented such an affidavit or declaration, and the disclosure does not include any objective evidence of unexpected results. The cited portion of the specification (at pg. 33, para. 1) does not present objective evidence of, e.g., a greater than expected result or presence of an unexpected property. Thus, the allegation of unexpected results is viewed as conclusory and found unpersuasive.
Applicant alleges that Newman fails to teach or suggest the claimed steps d-f) and i), while Love and Rabadan are similarly silent and so fail to remedy the deficiency of Newman (pg. 8, para. 5). The combined teachings of Newman, Love and Rabadan are considered to teach or suggest all limitations of the claims, including the specified steps, as detailed in the rejections. Thus, the argument is found unpersuasive. Applicant alleges that the cited references provide no motivation for a person of ordinary skill in the art to produce the method as claimed (pg. 8, para. 5 – pg. 9, para. 1).
The test for obviousness of claims under § 103 considers factors beyond express teachings or suggestions within the prior art references themselves, and sufficient motivation to combine prior art teachings may flow from a variety of sources including the background knowledge of one of ordinary skill in the art. See discussions in In re Keller, 642 F.2d 413, 425 (CCPA 1981); KSR International Co. v. Teleflex Inc., 550 U.S. 398, 418 (2007); Zup v. Nash Mfg., 896 F.3d 1365, 1371 (Fed. Cir. 2018).
The cited references are considered to teach or suggest elements that, in combination, make obvious the claimed invention. Motivation to combine the indexed reference elements is explained in each rejection. Thus, the argument of insufficient motive is found unpersuasive.
For the above reasons, the arguments are found unpersuasive and the rejection is maintained.
Conclusion
At this point in prosecution, no claim is allowed.
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Theodore C. Striegel whose telephone number is (571)272-1860. The examiner can normally be reached Mon-Fri 9am-5pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia M. Wise can be reached at (571)272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/T.C.S./Examiner, Art Unit 1685
/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 February 23, 2026