DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant’s Response
Applicant’s response, filed 06/15/2026, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Claims Status
Claims 2 and 5 are canceled.
Claims 21 and 22 are new.
Claims 1, 3, 4, and 6-22 are pending.
Claims 1, 3, 4, and 6-22 are examined.
Withdrawn Objections/Rejections
The objections to claims 5 and 9 are withdrawn in view of the amendments submitted.
The rejection of claim 12 under 35 USC 112(a) is withdrawn in view of the amendments submitted.
The rejection of claims 3 and 4 under 35 USC 112(b) is withdrawn in view of the amendments submitted.
The rejection of claims 1, 2, 17, 18, and 20 under 35 USC 102(a)(2) over Singer et al.
The rejection of claims 1-3, 17, and 20 on the ground of nonstatutory double patenting over US Patent No. 12, 249, 401 is withdrawn in view of the amendments submitted.
The rejection of claims 1 and 20 on the ground of nonstatutory double patenting over US Patent No. 10,167,514 is withdrawn in view of the amendments submitted.
The rejection of claims 1 and 18-20 on the ground of nonstatutory double patenting over US Patent No. 11,802,314 is withdrawn in view of the amendments submitted.
The rejection of claims 1 and 17-19 on the ground of nonstatutory double patenting over US Patent No. 12,031,183 is withdrawn in view of the amendments submitted.
The provisional rejection of claims 1 and 20 on the ground of nonstatutory double patenting over copending Application No. 19/064,276 is withdrawn in view of the amendments submitted
The provisional rejection of claim 1 on the ground of nonstatutory double patenting over copending Application No. 18/484,726 is withdrawn in view of the amendments submitted
The provisional rejection of claims 1 and 18-20 on the ground of nonstatutory double patenting over copending Application No. 19/057,774 is withdrawn in view of the amendments submitted.
The provisional rejection of claims 1 and 17-19 on the ground of nonstatutory double patenting over copending Application No. 19/057,757 is withdrawn in view of the amendments submitted.
Claim Objections
Claim 16 is objected to because of the following informalities:
“training the NMF model training further comprises” should read “training the NMF model
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 21 and 22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. This is a new rejection.
With respect to claim 21, the claim recites the limitation of “wherein the optimized gene expression profile comprises a signature matrix derived from single-cell or bulk-sorted transcriptome”. The claim is indefinite because there is no antecedent basis for “the optimized gene expression profile”. Claim 1, from which claim 21 depends, recites “a collection of optimized gene expression profiles” and “purifying a gene expression profile”. Thus, it is unclear if claim 21 is limiting the collection of optimized gene expression profiles or the gene expression profile that is purified.
Claim 22 is rejected due to its dependency on claim 21 without remedying the indefiniteness.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-4, 6-16, and 18-22 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to an abstract idea of mental steps, mathematic concepts, or a natural law without significantly more. This is a new rejection.
Claim 17 is eligible under 35 U.S.C. 101 because the judicial exceptions are integrated into a practical application through administration of particular treatments.
The MPEP at MPEP 2106.03 sets forth steps for identifying eligible subject matter:
(1) Are the claims directed to a process, machine, manufacture or composition of
matter?
(2A)(1) Are the claims directed to a judicially recognized exception, i.e. a law of nature,
a natural phenomenon, or an abstract idea?
(2A)(2) If the claims are directed to a judicial exception under Prong One, then is the
judicial exception integrated into a practical application?
(2B) If the claims are directed to a judicial exception and do not integrate the judicial
exception, do the claims provide an inventive concept?
With respect to step (1): Yes, the claims recite a method.
With respect to step (2A)(1): The claims are directed to abstract ideas of mathematical concepts and mental processes, and laws of nature.
“Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection” (MPEP 2106.04). Abstract ideas include mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations), certain methods of organizing human activity, and mental processes (procedures for observing, evaluating, analyzing/judging and organizing information (MPEP 2106.04(a)(2)). Laws of nature or natural phenomena include naturally occurring principles/relations that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature (MPEP 2106(b)).
Mathematical concepts recited in claim 1:
characterizing, using a machine learning framework, a tumor ecosystem for the tumor based on the gene expression data, wherein the tumor ecosystem is comprised of spatially and temporally-linked cell states
purifying a gene expression profile of the cell type of interest within the tumor with a digital cytometry by enumerating a frequency of each cell type in the collection of optimized gene expression profiles
Mental processes recited in claim 1:
identifying at least one cell state in the tumor based on the gene expression profiles
identifying the tumor ecosystem based on the at least one cell state
identifying an efficacious treatment for the tumor based on clinical treatment data, wherein the clinical treatment data identifies at least one treatment shown to be efficacious for a tumor exhibiting the tumor ecosystem
Dependent claims 3, 4, 6, 8, 9, 11-16, 21, and 22 recite additional steps that either are directed to abstract ideas or further limit the judicial exceptions in independent claim 1, and as such, are further directed to abstract ideas. Hence, the claims explicitly recite numerous elements that individually and in combination constitute abstract ideas. The relevant recitations are:
Claim 3: “using a trained non-negative matrix factorization (NMF) model to identify the tumor ecosystem”
Claim 4: “purifying gene expression profiles of cell types within the plurality of samples based on the cellular expression data; identifying cell states of the cell types, wherein identifying cell states of the cell types comprises clustering cell type-specific gene expression profiles; and classifying the plurality of samples into the tumor ecosystem subtypes, wherein classifying the plurality of samples into the tumor ecosystem subtypes comprises identifying cell states that co-occur in the sample”
Claim 6: “wherein the digital cytometry algorithm is CIBERSORTx”
Claim 8: “wherein the at least one cancer or tumor is selected from the group consisting of: lymphomas and carcinomas”
Claim 9: “wherein the at least one cancer or tumor is selected from the group consisting of: diffuse large B cell lymphoma, small cell lung cancer, breast cancer, colorectal cancer, and head and neck squamous cell carcinoma”
Claim 11: “wherein the NMF model is employed via Kullback-Leibler divergence minimization”
Claim 12: “wherein the identifying cell states comprises calculating a cophenetic coefficient for a range of cluster numbers as part of clustering”
Claim 13: “wherein the clustering further comprises filtering to remove low quality cell states”
Claim 14: “wherein the filter removes cell states with fewer than 10 genes”
Claim 15: “wherein the filter removes cell states with low levels of expression”
Claim 16: “wherein training the NMF model training further comprises updating the NMF model by iteratively updating the model until convergence”
Claim 21: “wherein the optimized gene expression profile comprises a signature matrix derived from single-cell or bulk-sorted transcriptome”
Claim 22: “wherein the signature matrix is an m x c signature matrix consisting of m marker genes by c distinct cell types”
The abstract ideas in the claims are evaluated under Broadest Reasonable Interpretation (BRI) and determined herein to each cover mental processes and mathematic concepts because the claims recite no more than using laws of nature of the association between gene expression levels and response to cancer therapies to characterize the expression levels of genes in cells of a tumor sample in order to identify an efficacious treatment.
With respect to step (2A)(2): The claims must therefore be examined further to determine whether they integrate that abstract idea into a practical application (MPEP 2106.04(d)). The claimed additional elements are analyzed alone or in combination to determine if the judicial exception is integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exception, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d).III).
Claim 1 recites the following additional elements that are not abstract ideas:
obtaining the gene expression data from a tumor obtain from an individual, wherein the gene expression data is a bulk tumor tissue transcriptome
receiving a collection of optimized gene expression profiles that discriminates a cell type of interest
treating the individual with the efficacious treatment for the tumor
The steps of obtaining gene expression data and receiving optimized gene expression profiles are directed to data gathering steps because they gather the data on which the judicial exceptions are performed. Data gathering does not impose any meaningful limitation on the abstract idea, or how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application (MPEP 2106.05(g)). The step of treating the individual does not integrate the judicial exceptions into a practical application because the limitation of an “efficacious treatment” is not specific enough to qualify as a particular treatment (see MPEP 2106.04(d)(2)).
Dependent claims 4, 7, 10, and 18-20 are directed to further data gathering steps or limiting the data gathered.
None of these dependent claims recite additional elements, alone or in combination, which would integrate a judicial exception into a practical application.
Lastly, the claims have been evaluated with respect to step (2B): Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims lack a specific inventive concept. Under said analysis, Applicant is reminded that the judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements described above do not rise to the level of significantly more than the judicial exception. As set forth in the MPEP at 2106.05(d).I, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represent well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to claim 1: The additional elements of obtaining gene expression data from a tumor obtained from an individual, wherein the gene expression data is a bulk tumor tissue transcriptome, receiving a collection of optimized gene expression profiles that discriminates a cell type of interest, and treating the individual with the efficacious treatment for the tumor do not rise to the level of significantly more than the judicial exception. With respect to obtaining gene expression data, the prior art to Lambrechts et al. (“Phenotype molding of stromal cells in the lung tumor microenvironment”, IDS reference) discloses conventional bulk RNA sequencing (page 1277, column 2, paragraph 2) and teaches that the tumor microenvironment is increasingly recognized as a cancer therapy target (page 1277, column 1, paragraph 2). Furthermore, the prior art Roberts (US 2020/0360408 A1, effectively filed August 2017) discloses conventional treatments for cancer (paragraph [0004]). With respect to receiving a collection of profiles, as exemplified in the MPEP at 2106.05(d).II with reference to buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014), a computer receiving data over a network is a routine and convention activity. As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claim 4: The additional element of obtaining cellular expression data from a plurality of samples from one or more tissue types does not rise to the level of significantly more than the judicial exception. The prior art to Lambrechts et al. discloses conventional bulk RNA sequencing (page 1277, column 2, paragraph 2). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claim 7: The additional element of the one or more tissue types includes at least one cancer or tumor does not rise to the level of significantly more than the judicial exception. The prior art Roberts discloses standard diagnostic methods such as tumor visualization, cytology, biopsy, blood assay for circulating tumor markers, etc. (paragraph [0122]). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claim 10: The additional element of the cellular expression data being obtained from single cell RNA sequencing does not rise to the level of significantly more than the judicial exception. The prior art Zhu et al. (“Advances in single-cell RNA sequencing and its applications in cancer research”, Oncotarget, published 2017) discloses that a plethora of single cell RNA-seq technologies have been developed (page 53763, column 2).
With respect to claim 18: The additional elements of obtaining a tumor sample or a cancer sample from an individual, wherein the gene expression data is obtained from the tumor sample or the cancer sample do not rise to the level of significantly more than the judicial exception. The prior art Roberts discloses standard diagnostic methods such as tumor visualization, cytology, biopsy, blood assay for circulating tumor markers, etc. (paragraph [0122]). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claim 19: The additional element of the tumor sample or the cancer sample being obtained from a biopsy does not rise to the level of significantly more than the judicial exception. The prior art Roberts discloses standard cancer diagnostic methods such as biopsy (paragraph [0122]). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claim 20: The additional element of the gene expression data being obtained from RNA sequencing, single cell RNA sequencing, or a microarray does not rise to the level of significantly more than the judicial exception. The prior art to Lambrechts et al. discloses conventional bulk RNA sequencing (page 1277, column 2, paragraph 2). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. Individually, the limitations of the claims and the claims as a whole have been found lacking.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 6, and 17-22 are rejected under 35 U.S.C. 103 as being unpatentable over Singer et al. (US 20210102168 A1, effectively filed 10/03/2019, cited in prior Office Action) in view of Newman et al. (“Determining cell type abundance and expression from bulk tissues with digital cytometry”, IDS reference). This is a new ground of rejection.
Regarding claim 1, Singer et al. teaches a method for treating an individual for a tumor, comprising:
obtaining gene expression data from a tumor obtained from an individual (paragraphs [0075]; [0089]; [0154]), wherein the gene expression data is a bulk tumor tissue transcriptome (paragraphs [0089], [0365]);
characterizing, using a machine learning framework (paragraph [0078]), a tumor ecosystem for the tumor based on the gene expression data, wherein the tumor ecosystem is comprised of spatially and temporally-linked cell states (paragraph [0075]); wherein characterizing the tumor ecosystem comprises:
receiving a collection of optimized gene expression profiles that discriminates a cell type of interest (paragraphs [0032], [0078], [0079], [0080]; Figures 24A, B);
purifying a gene expression profile of the cell type of interest within the tumor with cytometry by enumerating a frequency of each cell type in the collection of optimized gene expression profiles (paragraphs [0078], [0165]);
identifying at least one cell state in the tumor based on the gene expression profiles (paragraphs [0365]); [0089]); and
identifying the tumor ecosystem based on the at least one cell state (paragraphs [0365]); [0089]);
identifying an efficacious treatment for the tumor based on clinical treatment data, wherein the clinical treatment data identifies at least one treatment shown to be efficacious for a tumor exhibiting the tumor ecosystem (paragraphs [0075];[0093]; [0094]; etc.)
treating the individual with the efficacious treatment for the tumor (paragraphs [0093]; [0094]; etc.).
Singer et al. does not teach the claim elements of purifying a gene expression profile of the cell type of interest within the tumor with a digital cytometry algorithm.
However, Newman et al. teaches determining cell type abundance and expression from bulk tissues with digital cytometry (Abstract). Newman et al. teaches using CIBERSORTx, a digital cytometry algorithm, to purify gene expression profiles of cells in a tumor (Abstract)
Therefore, 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 incorporated the digital cytometry algorithm to the method of Singer et al. because Singer et al. is directed to identifying interacting cells and cell types and genes in a tumor microenvironment (Abstract), and Newman et al. is directed to identifying cell-type-specific gene expression profiles using a digital cytometry algorithm (Abstract). Newman et al. teaches that using CIBERSORTx allows for the inferring of cell-type-specific gene expression profiles without cell separation or prior knowledge (page 773, column 2, paragraph 2) and teaches that CIBERSORTx outperforms previous methods to facilitate rapid assessment of cell type gene expression profiles (page 781, column 1, Section Discussion, paragraph 3). Thus, one of ordinary skill in the art would have a reasonable expectation of success of using CIBERSORTx to deconvolute cell-type-specific gene expression profiles in order to identify tumor microenvironments and would be motivated to do so in order to rapidly assess the gene expression profiles and identify the tumor microenvironment without physical cell separation.
Regarding claim 6, the claim is directed to the digital cytometry algorithm being CIBERSORTx. Singer et al. teaches the method of claim 1 in view of Newman et al.
Singer et al. does not teach the claim element of the digital cytometry algorithm being CIBERSORTx.
However, Newman et al. teaches the digital cytometry algorithm being CIBERSORTx (Abstract).
Regarding claim 17, the claim is directed to the at least one treatment being selected from chemotherapeutics, immunotherapeutic, radiation, and combinations thereof. Singer et al. teaches the method of claim 1 in view of Newman et al. Singer et al. teaches the treatment being immunotherapeutic (paragraph [0175]), radiation or chemotherapy (paragraph [0126]), and teaches that the standards of care for cancer comprise radiation, chemotherapy and immunotherapy (paragraph [0193]).
Regarding claim 18, the claim is directed to obtaining a tumor sample or a cancer sample from an individual, wherein the gene expression data is obtained from the tumor sample or the cancer sample. Singer et al. teaches the method of claim 1 in view of Newman et al. Singer et al. also teaches the gene expression data being obtained from a tumor sample from an individual (paragraphs [0080];[0089])
Regarding claim 19, the claim is directed to the tumor sample or the cancer sample being obtained from a biopsy. Singer et al. teaches the method of claim 18 in view of Newman et al. Singer et al. teaches the removal of tumor tissue (paragraph [0152]).
Furthermore, Newman et al. teaches obtaining a tumor sample from a biopsy (Figure 1)
Regarding claim 20, the claim is directed to the gene expression data being obtained from RNA sequencing, single cell RNA sequencing, or a microarray. Singer et al. teaches the method of claim 1 in view of Newman et al. Singer et al. also teaches the gene expression data being obtained from single cell RNA sequencing (paragraphs [0086]; [0352]), and teaches detecting of biomarkers using a microarray (paragraph [0362]) and bulk sequencing (paragraph [0365]).
Regarding claim 21, the claim is directed to the optimized gene expression profile comprising a signature matrix derived from single-cell or bulk-sorted transcriptome. Singer et al. teaches the method of claim 1 in view of Newman et al. Singer et al. teaches optimized gene expression profiles comprising signature matrices and teaches these being derived from single-cell transcriptomics (paragraphs [0078], [0080]) or bulk transcriptomics [00365]).
Regarding claim 22, the claim is directed to the signature matrix being an m x c signature matrix consisting of m marker genes by c distinct cell types. Singer et al. teaches the method of claim 21 in view of Newman et al. Singer et al. also teaches a signature matrix being an m x c signature matrix consisting of m marker genes by c distinct cell types (Figure 24A, 24B, 26A).
Claims 3, 4, 7-10, and 12-16 are rejected under 35 U.S.C. 103 as being unpatentable over Singer et al. in view of Newman et al., as applied to claims 1, 6, and 17-22, and further in view of Yeh et al. (US 2022/0165363 A1, effectively filed 03/21/2019, cited in prior Office Action). This is a new ground of rejection.
Regarding claim 3, the claim is directed to identifying the tumor ecosystem step comprising using a trained non-negative matrix factorization (NMF) model to identify the tumor ecosystem. Singer et al. teaches the method of claim 1 in view of Newman et al. Singer et la. also teaches identifying the tumor ecosystem using an NMF model (paragraph [0078]).
Neither Singer et al. nor Newman et al. teach the claim element of the NMF model being trained.
However, Yeh et al. teaches de novo methods for deconvoluting multiple datasets for estimating compartment weights, specifically using gene expression data of a tumor (Abstract). Yeh et al. teaches studying signatures and subtypes of a sample using an NMF model (paragraph [0005]) and teaches the model being trained (paragraph [0007]).
Regarding claim 4, the claim is directed to training the NMF model, wherein training the NMF model comprises: obtaining cellular expression data from a plurality of samples from one or more tissue types; purifying gene expression profiles of cell types within the plurality of samples based on the cellular expression data; identifying cell states of the cell types, wherein identifying cell states of the cell types comprises clustering cell type-specific gene expression profiles; and classifying the plurality of samples into tumor ecosystem subtypes, wherein classifying the plurality of samples into the tumor ecosystem subtypes comprises identifying cell states that co-occur in the same sample. Singer et al. teaches the method of claim 3 in view of Newman et al. and further in view of Yeh et al. Singer et al. teaches obtaining gene expression data from a tumor obtained from an individual (paragraphs [0075]; [0089]; [0154]), characterizing a tumor ecosystem for the tumor based on the gene expression data, wherein the tumor ecosystem is comprised of spatially and temporally-linked cell states (paragraph [0075]). Singer et al. teaches clustering expression data to identify cell types to characterize a tumor by showing interactions occurring within the tumor (paragraph [0036]), and teaches identifying cell programs using an NMF model (paragraph [0078]).
Neither Singer et al. nor Newman et al. teach the claim elements of training the NMF model.
However, Yeh et al. teaches training an NMF model with RNA-seq expression profiles, deconvoluting the data, clustering the data (paragraph [0062]).
Regarding claim 7, the claim is directed to the one or more tissue types including at least one cancer or tumor. Singer et al. teaches the method of claim 4 in view of Newman et al. and further in view of Yeh et al. Singer et al. teaches tissue types including cancers or tumors (paragraphs [0369] and [0370]).
Regarding claim 8, the claim is directed to the at least one cancer or tumor being selected form the group consisting of: lymphomas and carcinomas. Singer et al. teaches the method of claim 7 in view of Newman et al. and further in view of Yeh et al. Singer et al. also teaches the tumor being a lymphoma (paragraph [0370]), or a carcinoma (paragraph [0371]).
Regarding claim 9, the claim is directed to the at least one cancer or tumor being selected from the group consisting of: diffuse large B cell lymphoma, small cell lung cancer, breast cancer, colorectal cancer, and head and neck squamous cell carcinoma. Singer et al. teaches the method of claim 7 in view of Newman et al. and further in view of Yeh et al. Singer et al. teaches the tumor being a head and neck squamous cell carcinoma, colorectal cancer, small cell lung cancer, breast cancer (paragraph [0371]). Singer et al. also teaches targeting diffuse large B-cell lymphomas (paragraph [0102]).
Regarding claim 10, the claim is directed to the cellular expression data being obtained from single cell RNA sequencing. Singer et al. teaches the method of claim 4 in view of Newman et al. and further in view of Yeh et al. Singer et al. also teaches obtaining cellular expression data from single cell RNA sequencing (paragraphs [0086]; [0089]).
Regarding claim 12, the claim is directed to identifying cell states comprising calculating a cophenetic coefficient for a range of cluster numbers as part of clustering. Singer et al. teaches the method of claim 4 in view of Newman et al. and further in view of Yeh et al.
Neither Singer et al. nor Newman et al. specifically teach the claim element of calculating a cophenetic coefficient for a range of cluster numbers as part of clustering.
However, Yeh et al. teaches evaluating the performance of an NMF model by cophenetic correlation coefficient at different K clusters (paragraph [0005]).
Regarding claim 13, the claim is directed to the clustering further comprising filtering to remove low quality cell states. Singer et al. teaches the method of claim 4 in view of Newman et al. and further in view of Yeh et al.
Singer et al. does not teach the claim element of filtering to remove low quality cell states.
However, Newman et al. teaches an adaptive noise filter to eliminate unreliably estimated genes for each cell type (page 776, column 1, paragraph 3).
Regarding claim 14, the claim is directed to the filter removing cell states with fewer than 10 genes. Singer et al. teaches the method of claim 13 in view of Newman et al. and further in view of Yeh et al. Singer et al. teaches the signature comprising ten or more genes (paragraph [0081]).
Singer et al. does not teach the claim element of removing cell states with fewer than 10 genes.
However, Newman et al. teaches quality control filtering and teaches the parameter of the minimum number of genes per cell type being 300 (page 783, column 1, paragraph 3).
Regarding claim 15, the claim is directed to the filter removing cell states with low levels of expression. Singer et al. teaches the method of claim 13 in view of Newman et al. and further in view of Yeh et al.
Neither Singer et al. nor Yeh et al. teach removing cell states with low levels of expression.
However, Newman et al. teaches quality control filtering wherein genes with low average expression and set to 0 as quality control (page 783, column 1, paragraph 3).
Regarding claim 16, the claim is directed to the NMF model training further comprising updating the NMF model by iteratively updating the model until convergence. Singer et al. teaches the method of claim 4 in view of Newman et al. and further in view of Yeh et al.
Neither Singer et al. nor Newman et al. teach the claim element of training the NMF model by iteratively updating the model until convergence.
However, Yeh et al. teaches training comprising updated the NMF model until convergence (paragraph [0062]).
Therefore, 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 incorporated the trained NMF algorithms and biopsies of Yeh et al. to the method of Singer et al. because both Singer et al. and Yeh et al. are directed to characterizing expression data in a tumor (see Abstract of both). Yeh et al. teaches using computational strategies, such as non-negative matrix factorization to deconvolve mixed signals present in bulk tumor samples to eliminate the need of relying on laser-capture microdissection or single-cell-based techniques (paragraph [0004]).Thus, one of ordinary skill in the art would have a reasonable expectation of success of using a trained NMF model to cluster gene expression data generated from a tumor biopsy sample by combining the prior art elements and would be motivated to do so in order to use a bulk tumor sample without having to perform singe-cell-based techniques which is expensive, resource intensive, and limited by the lack of comprehensive cell-sorting biomarkers (Yeh et al. paragraph [0003]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Singer et al. in view of Newman et al., further in view of Yeh et al., as applied to claims 3, 4, 7-10, and 12-16 above, and further in view of Li (US 2021/0310067 A1, effectively filed 01/24/2019, cited in prior Office Action). This is a new rejection.
Regarding claim 11, the claim is directed to the NMF model being employed via Kullback-Leibler divergence minimization. Singer et al. teaches the method of claim 4 in view of Newman et al., and further in view of Yeh et al.
Singer et al., Newman et al., and Yeh et al. do not teach the claim element of the NMF model being employed via Kullback-Leibler divergence minimization.
However, Li teaches methods for monitoring tissue health such as by extracting cfDNA to identify tissue-specific cfDNA copy number profiles and enable quantification of tissue fractions (Abstract). Li teaches the use of an NMF algorithm being applied to estimate tissue fractions in a sample and to ascertain a tissue cfDNA profile, the NMF algorithm being used with a Kullback-Leibler divergence as cost (paragraph [0082]).
Therefore, 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 incorporated the Kullback-Leibler divergence of Li to the method of Singer et al. because Li is directed to monitoring tissue health and disease and quantifying tissue fractions in cell-free DNA samples (Abstract). Li teaches analyzing cell-free DNA samples from tumor cells (paragraph [0027]) and Singer et al. is directed to characterizing the microenvironment of a tumor, including cell types and genes (Abstract). Thus, one of ordinary skill int eh art would have a reasonable expectation of success of monitoring the health of tissue of an individual and progression of cancer by quantifying cell types of cell-free DNA from a sample by combining the prior art elements.
Response to Arguments
Applicant has amended claim 1 to incorporate limitations from canceled claims 2 and 5. Applicant states that “A skilled artisan would also find that claim 1 is inventive and patentable over the combination of Singer and Yeh because the cited references do not teach or suggest a method of treating an individual of a tumor that includes the steps described in claim 1. […] Singer does not teach or suggest a method of treating an individual of a tumor that incorporates steps beyond a typical single cell RNA sequencing study, that is, it does not teach characterizing a tumor ecosystem for the tumor based on the gene expression data by receiving a collection of optimized gene expression profiles that discriminates a cell type of interest; purifying a gene expression profile of the cell type of interest within the tumor with a digital cytometry algorithm; identifying at least one cell state in the tumor based on the gene expression profiles; and identifying the tumor ecosystem based on the identified cell state prior to identifying a treatment shown to be efficacious for a tumor exhibiting the tumor ecosystem and treating the individual accordingly. Therefore, a skilled artisan would find that Singer is deficient in appreciating how to characterize a tumor ecosystem for a tumor based on a bulk tumor tissue gene expression data as described in claim 1, without single-cell sequencing or physical cell isolated”.
It is respectfully submitted that this is not persuasive. It is acknowledged that Singer et al. does not teach the claim element of using a digital cytometry algorithm , but Singer et al. does disclose bulk tumor tissue transcriptome data (paragraph [0365]), receiving and purifying a collection of optimized gene expression profiles that discriminate cell types of interest (paragraph [0032], [0078], [0079], [0080]; [0165]; Figures 24A, B). Singer et al. teaches using cytometry to purify the expression data (paragraphs [0078], [0165]) and identifying the tumor ecosystem based on the cell states (paragraphs [0365]); [0089]). Thus, Singer et al. discloses characterizing a tumor ecosystem based on bulk tumor tissue gene expression, and a new rejection is set forth to account for the claim amendments.
Furthermore, Applicant states that “To overcome the deficiencies of Singer, the Office Action combines Singer with Yeh to allege that it would be prima facie obvious to combine the trained NMF algorithms and biopsies of Yeh to the method of Singer as both references are directed to characterizing a tumor microenvironment. Additionally, the Office Action cites [paragraph] 0003 of Yeh to reject dependent claim 5 and allege that Yeh teaches the digital cytometry algorithm CIBERSORT to deconvolute cells in a tumor sample. Applicant respectfully disagrees. Yeh specifically references CIBERSORT in [paragraph] 0004 to teach away from the method of CIBERSORT or variants thereof as Yeh states, ‘they require knowledge of pure expression of compartments as the reference, which is difficult to obtain in practice’ […] Yeh’s disclosure is directed to overcoming the challenge of a priori knowledge of compartment-specific gene expression, stating “[i]t is thus an object of the presently disclosed subject matter to provide methods for de novo deconvoluting a dataset with multiple samples and/or estimating compartment weight for a single sample” […], and “[f]rom a clinical perspective, the ability of DECODER to identify sample compartments without the a priori assumption of an actual number of compartments, makes it a powerful tool for comparing tumor heterogeneity across samples and for the very challenging clinical conundrum of cancer of unknown primary” […]. In contrast, claim 1 recites the step of ‘receiving a collection of optimized gene expression profiles that discriminates a cell type of interest’ as the first step for characterizing a tumor ecosystem for the tumor based on a gene expression data. Furthermore, the Specification described using an a priori collection of optimized gene expression profiles that discriminates a cell type of interest, stating the digital cytometry algorithm ‘requires a collection of optimized expression profiles that discriminate each cell type of interest, commonly termed a ‘signature matrix’. Signature matrices can be derived from single-cell or bulk-sorted transcriptomes and should be designed to cover major lineages within a particular tissue type” […] Therefore, a skilled artisan would not incorporate the teachings of Yeh into the method of Singer to arrive at the method of claim 1 as using de novo deconvolution as described by Yeh in the method of Singer would not be suitable for the step of ‘purifying a gene expression profile of the cell type of interest within the tumor with a digital cytometry algorithm by enumerating a frequency of each cell type in the collection of optimized gene expression profiles’ in claim 1. […] Additionally, neither Li nor Lambrechts, alone and/or in combination, overcome the shortcomings of Singer and Yeh”.
Applicant's arguments are considered and a new rejection is set forth to teach the limitations of purifying gene expression profiles with digital cytometry and using CIBERSORTx.
Conclusion
No claims are allowed.
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/E.A.S./Examiner, Art Unit 1686
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
