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 .
Claims Status
Claims 1-20 are pending.
Claims 1-20 are examined.
Priority
The instant application is a national stage application of PCT/US20/59196, filed 11/05/2020, which claims priority to US provisional application No. 62/931047, filed 11/05/2019. Therefore, the Effective Filing Date (EFD) assigned to each of the claims 1-20 is the provisional filing date of application No. 62/931047, filed 11/05/2019.
Information Disclosure Statement
The Information Disclosure Statements filed 11/17/2025 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
The cited US Patent Application Publications are not considered because the cited publication numbers do not refer to any US published applications.
It is noted that certain references lack appropriate page numbers as is required under 37 CFR 1.97. The Examiner has annotated the references herein. Applicant is kindly reminded to provide proper citations in compliance with 37 CFR 1.97 in all future submission to the office.
Drawings
The drawings filed 05/05/2022 are accepted.
Claim Objections
Claims 5 and 9 are objected to because of the following informalities:
In claim 5, “the purifying step uses a digital cytometry algorithm for to purify the gene expression profiles” should read “the purifying step uses a digital cytometry algorithm
In claim 9, “-small cell lung cancer” should read “small cell lung cancer”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 12 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. The Specification describes “certain embodiments calculate a cophenetic coefficient for a range of cluster numbers, which can help determine the most stable number of cell states for each cell type” (paragraph [0075]) and “EcoTyper calculates the cophenetic coefficient for a range of cluster numbers, which helps determine the most stable number of cell state for each cell type” (paragraph [0106]). However, the Specification does not describe “the identifying cell states calculate a cophenetic coefficient for a range of cluster numbers as part of cluster” in such a manner that one of ordinary skill would be enabled to use the identified cell state to calculate the cophenetic coefficient, when the disclosure describes instead how to identifying cell states by calculating a cophenetic coefficient for a range of clusters. It is interpreted that this is erroneous and instead applicant intends the step to recite that the identifying cell states comprises calculating a cophenetic coefficient.
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 3 and 4 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.
With respect to claim 3, the claim recites the limitation of “using a trained negative matrix factorization (NMF) model. The claim is indefinite because the Specification describes “NMF” referring to both a “negative matrix factorization” and “nonnegative matrix factorization” model. Within the art, NMF refers to nonnegative matrix factorization and so it is unclear if this was an error and the claim is instead referring to using a nonnegative matrix factorization model, as it is unclear what a negative matrix factorization model would be.
With respect to claim 4, the claim recites the limitation of “purifying gene expression profiles of cell types within plurality of samples”. The claim is indefinite because it is unclear if the gene expression profiles that are purified are from the plurality of samples obtained in the first step of claim 4, or if instead this limitation is referring to a different plurality of samples.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 17, 18, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Singer et al. (US 20210102168 A1, effectively filed 10/03/2019).
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]);
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]);
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.).
Regarding claim 2, the claim is directed to characterizing a tumor ecosystem step comprises: purifying a gene expression profile of cell types within the tumor; identifying at least one cell state in the tumor based on the gene expression profiles; and identifying the tumor ecosystem based on the at least one cell state. Singer et al. teaches the method of claim 1. Singer et al. also teaches purifying a gene expression profile of cell types within the tumor, identifying cell states such as malignant and non-malignant, and identifying the tumor ecosystem based on the identified cell states (paragraphs [0365]); [0089]).
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. 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. Singer et al. also teaches the gene expression data being obtained from a tumor sample from an individual (paragraphs [0080];[0089])
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. 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]).
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 3-10, 12, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Singer et al., as applied to claims 1, 2, 17, 18, and 20 above, in view of Yeh et al. (US 2022/0165363 A1, effectively filed 03/21/2019).
Regarding claim 3, the claim is directed to identifying the tumor ecosystem step comprising using a trained negative matrix factorization (NMF) model to identify the tumor ecosystem. Singer et al. teaches the method of claim 2. Singer et la. also teaches identifying the tumor ecosystem using an NMF model (paragraph [0078]).
Singer et al. does not 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 the NMF model being trained by: 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 by clustering cell type-specific gene expression profiles; and classifying the plurality of samples into tumor ecosystem subtypes by identifying cell states that co-occur in the same sample. 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]).
Singer et la. does not 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 5, the claim is directed to the purifying step using a digital cytometry algorithm to purify the gene expression profiles. Singer et al. teaches the method of claim 4 in view of Yeh et al.
Singer et al. does not teach the claim element of the purifying step using a digital cytometry algorithm.
However, Yeh et al. teaches the digital cytometry algorithm CIBERSORT to deconvolute cells in a tumor sample (paragraph [0004]).
Regarding claim 6, the claim is directed to the digital cytometry algorithm being CIBERSORTx. Singer et al. teaches the method of claim 5 in view of Yeh et al.
Singer et al. does not teach the claim element of the digital cytometry algorithm being CIBERSORTx.
However, Yeh et al. teaches the digital cytometry algorithm CIBERSORT to deconvolute cells in a tumor sample (paragraph [0004]).
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 Yeh et al. Singer et al. teaches tissue types including cancers or tumors (paragraphs [0369] and [0370]).
Furthermore, Yeh et al. also teaches the tissue samples comprising a tumor or cancer sample (paragraph [0017]).
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 Yeh et al. Singer et al. also teaches the tumor being a lymphoma (paragraph [0370]), or a carcinoma (paragraph [0371]).
Furthermore, Yeh et al. teaches the cancer being a pancreatic ductal adenocarcinoma (paragraph [0057]).
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 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 Yeh et al. Singer et al. also teaches obtaining cellular expression data from single cell RNA sequencing (paragraphs [0086]; [0089]).
Furthermore, Yeh et al. teaches applying the model to single-cell sequencing data (paragraph [0102]).
Regarding claim 12, the claim is directed to identifying cell states 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 Yeh et al.
Singer et al. does not 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 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 Yeh et al.
Singer et al. does not teach the claim element of updating 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]).
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. Singer et al. teaches the removal of tumor tissue (paragraph [0152]).
Furthermore, Yeh et al. teaches a tumor or cancer tissue sample being collected from a subject using a biopsy (paragraph [0018])
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 a tumor microenvironment (see Abstract of both). 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.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Singer et al. in view of Yeh et al., as applied to claims 3-10, 12, 16, and 19 above, and further in view of Li (US 2021/0310067 A1, effectively filed 01/24/2019).
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 Yeh et al.
Neither Singer et al. nor Yeh et al. 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. in view of Yeh 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.
Claims 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Singer et al. in view of Yeh et al., as applied to claim 3-10, 12, 16, and 19 above, and further in view of Lambrechts et al. (“Phenotype molding of stromal cells in the lung tumor microenvironment”, IDS reference).
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 Yeh et al.
Neither Singer et al. nor Yeh et al. teaches filtering to remove low quality cell states.
However, Lambrechts et al. teaches phenotype molding of stromal cells in the lung tumor microenvironment (Abstract). Lambrechts et al. teaches quality filtering the data of transcripts within cells in which over 100 genes could be detected as expressed (page 1278, column 1, paragraph 1).
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 Yeh et al. and further in view of Lambrechts et al. Singer et al. teaches the signature comprising ten or more genes (paragraph [0081]).
Neither Singer et al. nor Yeh et al. teaches removing cell states with fewer than 10 genes.
However, Lambrechts et al. teaches removing cells with below 101 expressed genes (page 1290, column 1, Section “Single-cell gene expression quantification and determination of the major cell types”) and filtering the cell data (page 1291, column 1, Section “SCENIC analysis”).
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 Yeh et al. and further in view of Lambrechts et al.
Neither Singer et al. nor Yeh et al. teach removing cell states with low levels of expression.
However, Lambrechts et al. teaches filtering the gene data by removing genes with low levels of expression (page 1291, column 1, Section “SCENIC analysis”), and removing cells with too few unique molecules (page 1290, column 1, Section “Single-cell gene expression quantification and determination of the major cell types”).
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 filtering of Lambrechts et al. to the method of Singer et al. in view of Yeh et al. because both Lambrechts et al. and Singer et al. are directed to characterizing the state of a tumor microenvironment (see Abstract of both). Thus, one of ordinary skill in the art would have a reasonable expectation of success of filtering low quality data for effectively identifying the cell states in a tumor microenvironment to monitor the disease by combining the prior art elements.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-3, 17, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 4, 6, 9, and 11 of U.S. Patent No. 12,249,401. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of patent ‘401 encompass the instant claim limitations.
Instant Claims
Patent ‘401
Claims
Limitation
Claims
Limitation
1
A method for treating an individual for a tumor, comprising: obtaining gene expression data from a tumor obtained from an individual; 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; 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; and treating the individual with the efficacious treatment for the tumor.
1
A method comprising: (a) obtaining a biological sample comprising a plurality of biological macromolecules from a plurality of distinct cell types from a subject having cancer; (b) processing said biological sample to generate a feature profile of said plurality of biological macromolecules, wherein said feature profile comprises a plurality of features associated with said plurality of distinct cell types; (c) computer processing said feature profile, using a deconvolution module, to (1) quantify an abundance of at least one of said plurality of distinct cell types in said biological sample, wherein quantifying said abundance comprises applying a batch correction procedure to remove technical variation in said abundance, and (2) generate one or more differential gene expression profiles that are differential across subtypes of said at least one of said plurality of distinct cell types, wherein said batch correction procedure removes said technical variation between a reference matrix B of a plurality of feature signatures and said feature profile, wherein said batch correction procedure is applied in a single cell reference mode (S-mode) or a bulk reference mode (B-mode), (i) wherein said applying said batch correction procedure in said S-mode comprises removing technical differences between said reference matrix B derived from a set of single cell reference profiles and an input set of mixture samples M by: (1) obtaining a plurality of estimates of a plurality of cell frequencies F* within said input set of mixture samples M, given said reference matrix B and said set of single cell reference profiles R, and (2) refining said plurality of estimates of said plurality of cell frequencies F* by performing said batch correction procedure on said reference matrix B to obtain an adjusted reference matrix, and applying said adjusted reference matrix to said input set of mixture samples M, and (ii) wherein said applying said batch correction procedure in said B-mode comprises removing said technical differences between said reference matrix B derived from bulk reference profiles and an input set of mixture samples M by: (1) generating a plurality of mixture samples M* comprising a linear combination of a plurality of imputed cell type proportions in said input set of mixture samples M and corresponding profiles in said reference matrix B, and (2) performing said batch correction on said input set of mixture samples M to eliminate said batch effects between said input set of mixture samples M and said plurality of mixture samples M*; (d) predicting a clinical outcome of a cancer therapy on said subject for said cancer, based at least in part on said abundance and said one or more differential gene expression profiles of said at least one of said plurality of distinct cell types in said biological sample, wherein said at least one of said plurality of distinct cell types in said biological sample comprises a type of cancer cell; and (e) administering said cancer therapy to said subject based on said predicted clinical outcome of said cancer therapy, wherein said cancer therapy is selected from the group consisting of a chemotherapy, an immunotherapy, and an immunochemotherapy
11
wherein said biological sample comprises bulk tissue, a formalin-fixed, paraffin-embedded (FFPE) tissue, a frozen tissue, a blood sample, a sample derived from a solid tissue sample, or a tumor sample
2
wherein the characterizing a tumor ecosystem step comprises: purifying a gene expression profile of cell types within the tumor; identifying at least one cell state in the tumor based on the gene expression profiles; and identifying the tumor ecosystem based on the at least one cell state
1
A method comprising: (a) obtaining a biological sample comprising a plurality of biological macromolecules from a plurality of distinct cell types from a subject having cancer; (b) processing said biological sample to generate a feature profile of said plurality of biological macromolecules, wherein said feature profile comprises a plurality of features associated with said plurality of distinct cell types; (c) computer processing said feature profile, using a deconvolution module, to (1) quantify an abundance of at least one of said plurality of distinct cell types in said biological sample, wherein quantifying said abundance comprises applying a batch correction procedure to remove technical variation in said abundance, and (2) generate one or more differential gene expression profiles that are differential across subtypes of said at least one of said plurality of distinct cell types, wherein said batch correction procedure removes said technical variation between a reference matrix B of a plurality of feature signatures and said feature profile, wherein said batch correction procedure is applied in a single cell reference mode (S-mode) or a bulk reference mode (B-mode), (i) wherein said applying said batch correction procedure in said S-mode comprises removing technical differences between said reference matrix B derived from a set of single cell reference profiles and an input set of mixture samples M by: (1) obtaining a plurality of estimates of a plurality of cell frequencies F* within said input set of mixture samples M, given said reference matrix B and said set of single cell reference profiles R, and (2) refining said plurality of estimates of said plurality of cell frequencies F* by performing said batch correction procedure on said reference matrix B to obtain an adjusted reference matrix, and applying said adjusted reference matrix to said input set of mixture samples M, and (ii) wherein said applying said batch correction procedure in said B-mode comprises removing said technical differences between said reference matrix B derived from bulk reference profiles and an input set of mixture samples M by: (1) generating a plurality of mixture samples M* comprising a linear combination of a plurality of imputed cell type proportions in said input set of mixture samples M and corresponding profiles in said reference matrix B, and (2) performing said batch correction on said input set of mixture samples M to eliminate said batch effects between said input set of mixture samples M and said plurality of mixture samples M*; (d) predicting a clinical outcome of a cancer therapy on said subject for said cancer, based at least in part on said abundance and said one or more differential gene expression profiles of said at least one of said plurality of distinct cell types in said biological sample, wherein said at least one of said plurality of distinct cell types in said biological sample comprises a type of cancer cell; and (e) administering said cancer therapy to said subject based on said predicted clinical outcome of said cancer therapy, wherein said cancer therapy is selected from the group consisting of a chemotherapy, an immunotherapy, and an immunochemotherapy
3
the identifying the tumor ecosystem step comprises using a trained negative matrix factorization (NMF) model to identify the tumor ecosystem
6
wherein quantifying said abundance comprises optimizing a regression between said feature profile and said reference matrix B of feature signatures for a second plurality of distinct cell types, wherein said feature profile is modeled as a linear combination of said reference matrix B
9
wherein optimizing said regression comprises using a support vector regression (SVR) or a non-negative matrix factorization (NMF)
17
wherein the at least one treatment is selected from chemotherapeutics, immunotherapeutics, radiation, and combinations thereof
1
wherein said cancer therapy is selected from the group consisting of a chemotherapy, an immunotherapy, and an immunochemotherapy
20
wherein the gene expression data is obtained from RNA sequencing, single cell RNA sequencing, or a microarray
3
wherein said feature profile is generated from single-cell gene expression measurements of a plurality of cells of each of said plurality of distinct cell types
4
wherein said single-cell gene expression measurements are generated by single-cell RNA sequencing (scRNA-Seq)
Claims 1 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, and 6-8 of U.S. Patent No. 10,167,514. Although the claims at issue are not identical, they are not patentably distinct from each other because the claim elements are made obvious over the Specification of Patent ‘514.
Instant Claims
Patent ‘514
Claims
Limitation
Claims
Limitation
1
A method for treating an individual for a tumor, comprising: obtaining gene expression data from a tumor obtained from an individual; 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; 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; and treating the individual with the efficacious treatment for the tumor.
1
A method for identifying at least one of a plurality of cell populations in a sample, said method producing a significance value for said identification and comprising: a) providing a sample comprising said plurality of cell populations; b) processing said sample to generate a feature profile from said sample, wherein said feature profile comprises a gene expression profile, protein-protein interaction profile, protein phosphorylation profile, cellular electrical activity profile, chromatin modification profile, chromosome binding profile, enzymatic activity profile, metabolite profile, nuclear magnetic resonance (NMR) spectra, electromagnetic radiation absorbance or emission spectra, circular dichroism spectra, Raman spectra, mass spectra, or chromatograms, or a combination thereof; c) processing said feature profile, using a deconvolution module, to identify said at least one of said plurality of cell populations in said sample; and d) processing said feature profile, using a significance value module, to produce said significance value for said identification
6
wherein said sample is a tumor biopsy sample
20
wherein the gene expression data is obtained from RNA sequencing, single cell RNA sequencing, or a microarray
7
wherein said feature profile is a gene expression profile m
8
wherein said gene expression profile represents an RNA transcriptome of cells in said sample
Although the claims of Patent ‘514 are silent with regard to the steps of predicting and administering an efficacious treatment, the steps are made obvious column 6, line 15 of the Specification of Patent ‘514 which discloses recommending a future course of action for administering a therapy to the individual for the disease based on the predicted clinical outcome of the therapy. Thus, it would be obvious to one of ordinary skill to administer the treatment predicted to be efficacious to the individual.
Claims 1 and 18-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 9, 12, and 20 of U.S. Patent No. 11,802,314. Although the claims at issue are not identical, they are not patentably distinct from each other because the claim limitations are made obvious over the Specification of Patent ‘314.
Instant Claims
Patent ‘314
Claims
Limitation
Claims
Limitation
1
A method for treating an individual for a tumor, comprising: obtaining gene expression data from a tumor obtained from an individual; 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; 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; and treating the individual with the efficacious treatment for the tumor.
1
A method comprising: (a) obtaining a physical sample comprising a first plurality of distinct components; (b) generating a feature profile m from the physical sample, wherein the feature profile m comprises combinations of features associated with the first plurality of distinct components; (c) optimizing a regression between the feature profile m and a reference matrix B of feature signatures for a second plurality of distinct components in the physical sample, wherein the feature profile m is modeled as a linear combination of the reference matrix B, wherein the optimizing comprises solving for a set of regression coefficients f of the regression, wherein the solving minimizes a linear loss function and an L.sub.2-norm penalty function; and (d) estimating the fractional representation of one or more distinct components among the second plurality of distinct components present in the physical sample based at least in part on the set of regression coefficients f, wherein a non-negative regression coefficient of the set of regression coefficients f is indicative of a relative proportion of a corresponding distinct component among the second plurality of distinct components present in the physical sample
9
wherein the physical sample comprises a biological sample of a subject
18
further comprising 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
20
wherein the solid tissue sample comprises a biopsy sample
19
wherein the tumor sample or the cancer sample is obtained from a biopsy
20
wherein the gene expression data is obtained from RNA sequencing, single cell RNA sequencing, or a microarray
12
wherein the feature profile m comprises the gene expression profile of one or more genes, wherein the gene expression profile represents a ribonucleic acid (RNA) transcriptome of the cells in the biological sample
Although the claims of Patent ‘314 are silent with regard to the sample comprising a tumor and the steps of predicting and administering an efficacious treatment, the claim limitations are obvious over column 4, line 17 of the Specification of Patent ‘314, which discloses the tissue sample being a tumor sample, and column 6, line 20 of the Specification, which discloses recommending a course of action for administering a therapy based on the predicted clinical outcome of the therapy. Thus, it would be obvious to one of ordinary skill to sample a tumor and administer a treatment that was predicted to be efficacious.
Claims 1 and 17-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 6 of U.S. Patent No. 12,031,183. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of patent ‘183 encompass the instant claims.
Instant Claims
Patent ‘183
Claims
Limitation
Claims
Limitation
1
A method for treating an individual for a tumor, comprising: obtaining gene expression data from a tumor obtained from an individual; 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; 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; and treating the individual with the efficacious treatment for the tumor.
1
A method comprising: (a) providing a biological sample derived from a subject having cancer, the biological sample comprising a plurality of cell populations; (b) assaying said biological sample to generate a feature profile, wherein said feature profile comprises a gene expression profile, a genotype profile, a protein expression profile, a protein-protein interaction profile, a protein phosphorylation profile, a cellular electrical activity profile, a chromatin modification profile, a chromosome binding profile, an enzymatic activity profile, a metabolite profile, a nuclear magnetic resonance (NMR) spectrum, an electromagnetic radiation absorbance or emission spectrum, a circular dichroism spectrum, a Raman spectrum, a mass spectrum, a chromatogram, or a combination thereof; (c) computer processing said feature profile to determine (i) an estimation of relative proportions of at least one of said plurality of cell populations in said biological sample, and (ii) a significance value for said estimation; (d) predicting a clinical outcome of a cancer therapy for said cancer, based at least in part on said estimation of said relative proportions of said at least one of said plurality of cell populations in said biological sample; and (e) administering said cancer therapy to said subject based on said predicted clinical outcome of said cancer therapy, wherein said cancer therapy comprises a member selected from the group consisting of a chemotherapy, an immunotherapy, and an immunochemotherapy
6
wherein said biological sample comprises a tumor biopsy
17
wherein the at least one treatment is selected from chemotherapeutics, immunotherapeutics, radiation, and combinations thereof
1
wherein said cancer therapy comprises a member selected from the group consisting of a chemotherapy, an immunotherapy, and an immunochemotherapy
18
further comprising 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
6
wherein said biological sample comprises a tumor biopsy
19
the tumor sample or the cancer sample is obtained from a biopsy
Claims 1 and 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 71 of copending Application No. 19/064,276. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are made obvious over the Specification of Application ‘276.
Instant Claims
Application ‘276
Claims
Limitation
Claims
Limitation
1
A method for treating an individual for a tumor, comprising: obtaining gene expression data from a tumor obtained from an individual; 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; 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; and treating the individual with the efficacious treatment for the tumor.
71
A method comprising: (a) obtaining a biological sample from a subject, wherein the biological sample comprises a tumor sample comprising a first plurality of distinct cell subsets; (b) extracting ribonucleic acid (RNA) molecules from the biological sample; (c) assaying the RNA molecules to generate a feature profile m, wherein the assaying comprises RNA sequencing, wherein the RNA sequencing comprises (i) reverse transcribing the RNA molecules to produce complementary deoxyribonucleic acid (cDNA) molecules and (ii) amplifying the cDNA molecules, wherein the feature profile m comprises combinations of features associated with the first plurality of distinct cell subsets, wherein the feature profile m comprises a gene expression profile of cells in the biological sample, wherein the gene expression profile represents an RNA transcriptome of the cells in the biological sample; and (d) optimizing, by a computer processor, a support vector regression between the feature profile m and a reference matrix B of feature signatures for a second plurality of distinct cell subsets in the biological sample, wherein the feature profile m is modeled as a linear combination of the reference matrix B, wherein the reference matrix B comprises an LM22 signature matrix of relative gene expression values across 22 leukocyte subsets, wherein the LM22 signature matrix comprises ABCB4, ABCB9, ACAP1, ACHE, ACP5, ADAM28, ADAMDEC1, ADAMTS3, ADRB2, AIF1, AIM2, ALOX15, ALOX5, AMPD1, ANGPT4, ANKRD55, APOBEC3A, APOBEC3G, APOL3, APOL6, AQP9, ARHGAP22, ARRB1, ASGR1, ASGR2, ATHL1, ATP8B4, ATXN8OS, AZUl, BACH2, BANK1, BARX2, BCL11B, BCL2A1, BCL7A, BEND5, BFSP1, BHLHE41, BIRC3, BLK, BMP2K, BPI, BRAF, BRSK2, BST1, BTNL8, C11orf80, Clorf54, C3AR1, C5AR1, C5AR2, CA8, CAMP, CASP5, CCDC102B, CCL1, CCL13, CCL14, CCL17, CCL18, CCL19, CCL20, CCL22, CCL23, CCL4, CCL5, CCL7, CCL8, CCND2, CCR10, CCR2, CCR3, CCR5, CCR6, CCR7, CD160, CD180, CD19, CD1A, CD1B, CD1C, CD1D, CD1E, CD2, CD209, CD22, CD244, CD247, CD27, CD28, CD300A, CD33,CD37, CD38, CD3D, CD3E, CD3G, CD4, CD40, CD40LG, CD5, CD6, CD68, CD69, CD7, CD70, CD72, CD79A, CD79B, CD80, CD86, CD8A, CD8B, CD96, CDA, CDC25A, CDH12, CDHR1, CDK6, CEACAM3, CEACAM8, CEMP1, CFP, CHI3L1, CHI3L2, CHST15, CHST7, CLC, CLCA3P, CLEC1OA, CLEC2D, CLEC4A, CLEC7A, CLIC2, CMA1, COL8A2, COLQ, CPA3, CR2, CREB5, CRISP3, CRTAM, CRYBB1, CSF1, CSF2, CSF3R, CST7, CTLA4, CTSG, CTSW, CXCL10, CXCL11, CXCL13, CXCL3, CXCL5, CXCL9, CXCR1, CXCR2, CXCR5, CXCR6, CXorf57, CYP27A1, CYP27B1, DACH1, DAPK2, DCSTAMP, DEFA4, DENND5B, DEPDC5, DGKA, DHRS11, DHX58, DPEP2, DPP4, DSC1, DUSP2, EAF2, EBI3, EFNA5, EGR2, ELANE, EMR1, EMR2, EMR3, EPB41, EPHA1, EPN2, ETS1, ETV3, FAIM3, FAM124B, FAM174B, FAM198B, FAM212B, FAM65B, FASLG, FBXL8, FCER1A, FCER2, FCGR2B, FCGR3B, FCN1, FCRL2, FES, FFAR2, FLJ13197, FLT3LG, FLVCR2, FOSB, FOXP3, FPR1, FPR2, FPR3, FRK, FRMD4A, FRMD8, FZD2, FZD3, GAL3ST4, GALR1, GFI1, GGT5, GIPR, GNG7, GNLY, GPC4, GPR1, GPR171, GPR18, GPR183, GPR19, GPR25, GPR65, GPR97, GRAP2, GSTT1, GUSBP11, GYPE, GZMA, GZMB, GZMH, GZMK, GZMM, HAL, HCK, HDC, HESX1, HHEX, HIC1, HIST1H2AE, HIST1H2BG, HK3, HLA-DOB, HLA-DQA1, HMGB3P30, HNMT, HOXA1, HPGDS, HPSE, HRH1, HSPA6, HTR2B, ICA1, ICOS, IDO1,IFI44L, IFNA10, IFNG, IGHD, IGHE, IGHM, IGKC, IGLL3P, IGSF6, IL12B,IL12RB2, IL17A, IL18R1, IL18RAP, ILlA, ILlB, IL1RL1,IL21, IL26, IL2RA, IL2RB, IL3, IL4, IL4R, IL5, IL5RA, IL7, IL7R, IL9, IRF8, ITK, KCNA3, KCNG2, KIAA0226L, KIAA0754, KIR2DL1, KIR2DL4, KIR2DS4, KIR3DL2, KIRREL, KLRB1, KLRC3, KLRC4, KLRD1, KLRF1, KLRG1, KLRK1, KRT18P50, KYNU, LAG3, LAIR2, LAMP3, LAT, LCK, LEF1, LHCGR, LILRA2, LILRA3, LILRA4, LILRB2, LIME1, LINC00597, LINCO0921, LOC100130100, LOC126987, LRMP, LST1, LTA, LTB, LTC4S, LY86, L