DETAILED ACTION
The Applicant’s filing, received 04 May 2022, has been fully considered. The following rejections and/or objections constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-5, 7-13, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 are pending.
Claims 1-5, 7-13, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 are rejected.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claims 1-5, 7-13, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 are given the benefit of priority to U.S. Provisional Application No. 62/931,194, filed 05 November 2019.
Therefore, the effective filing date of the claimed invention is 05 November 2019.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 14 November 2022 and 31 August 2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings received 04 May 2022 are acceptable.
Claim Interpretation
Claim 1 recites the limitations “wherein the input composition comprises T cells selected from a biological sample from a subject” at step (a). This limitation is interpreted as describing the data that is being analyzed.
Claim 1 further recites the limitation “wherein: the therapeutic cell composition comprises T cells expressing a recombinant receptor and is to be produced from cells of the input composition at step (b).” This limitation is interpreted as describing an intended result of the method.
Claim 1 further recites “the process comprises a statistical learning model trained on training data comprising (i) the percentage, number, ratio, or proportion of T cells that have the first attributes from each of a plurality of input compositions comprising T cells and (ii) the percentage, number, ratio, or proportion of T cells that have the second attribute from each of a plurality of therapeutic cell compositions, wherein each of the plurality of therapeutic cell compositions comprises T cells expressing the recombinant receptor and has been produced from one of the plurality of input compositions.” This limitation is interpreted as reciting a product-by-process limitation, with the product being a trained statistical learning model, and further interpreted as not requiring the process of producing the product, i.e., not requiring the active steps of training the statistical learning model.
Claim 11 recites the limitation “wherein: the therapeutic cell composition comprises T cells expressing a recombinant receptor and is to be produced from cells of the input composition.” This limitation is interpreted as describing an intended result of the method.
Claim 11 further recites “the process comprises a statistical learning model trained on training data comprising (i) the percentage, number, ratio, or proportion of T cells that have the first attributes from each of a plurality of input compositions comprising T cells and (ii) the percentage, number, ratio, or proportion of T cells that have the second attribute from each of a plurality of therapeutic cell compositions, wherein each of the plurality of therapeutic cell compositions comprises T cells expressing the recombinant receptor and has been produced from one of the plurality of input compositions.” This limitation is interpreted as reciting a product-by-process limitation, with the product being a trained statistical learning model, and further interpreted as not requiring the process of producing the product, i.e., not requiring the active steps of training the statistical learning model.
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 1-5, 7-13, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 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.
Claim 1 is indefinite in view of the claim 2 limitation "the predicted second attribute" because it is not clear as to whether claim 1 requires there to be a prediction of an actual second attribute (e.g., a type of attribute), or else a prediction of a percentage, number, ratio, or proportion of T cells that have a second attribute (e.g., any attribute).
Claims 2-5, 7-10, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 are indefinite for depending from claim 1 and for failing to remedy the indefiniteness of claim 1.
Claim 2 recites the limitation "the predicted second attribute" in line two. There is insufficient antecedent basis for this limitation in the claim.
Claim 2 is further indefinite for reciting the limitation “whether the therapeutic cell composition is predicted to have a desired attribute” because claim 1 recites “a plurality of therapeutic cell compositions,” and therefore it is not clear as to which therapeutic cell composition claim 2 is referring to.
Claims 5, 7, 50, 51, 52, 53, 59,60, 61, 69, 70, 71, 72, 74, and 76 are indefinite for depending from claim 2 and for failing to remedy the indefiniteness of claim 2.
Claim 11 recites the limitation "the predicted second attribute" in step (d). There is insufficient antecedent basis for this limitation in the claim.
Claims 12 and 13 are indefinite for depending from claim 11 and for failing to remedy the indefiniteness of claim 11.
Claim 11 is further indefinite for reciting “whether the therapeutic cell composition is predicted to have a desired attribute” at step (d), because the claim recites “a plurality of therapeutic cell compositions” at step (c), and therefore it is not clear as to which therapeutic cell composition is being referred to at step (d).
Claims 12 and 13 are indefinite for depending from claim 11 and for failing to remedy the indefiniteness of claim 11.
Claim 53 is indefinite for reciting “the threshold percentage is at least or at least about 40%” because it is not clear as to whether the threshold percentage can be a value less than 40%.
Claim 61 is indefinite for reciting “the threshold percentage is at least or at least about 60%” because it is not clear as to whether the threshold percentage can be a value less than 60%.
Claim 70 is indefinite for reciting “the threshold percentage is at least or at least about 10%” because it is not clear as to whether the threshold percentage can be a value less than 10%.
Claim 72 is indefinite for reciting “the threshold percentage is at least or at least about 10%” because it is not clear as to whether the threshold percentage can be a value less than 10%.
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 1-5, 7-13, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-5, 7-10, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 are directed to a method (i.e., a process) of predicting attributes of a therapeutic cell composition; and claims 11-13 are directed to a method (i.e., a process) of manufacturing a therapeutic cell composition.
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
determining a percentage, number, ratio, or proportion of T cells in an input composition that have first attributes, wherein the first attributes comprise T cell phenotypes, and wherein the input composition comprises T cells selected from a biological sample from a subject at step (a) (i.e., mental processes and mathematical concepts);
applying the first attributes as input to a process configured to predict, based on the first attributes, a percentage, number, ratio, or proportion of T cells in a therapeutic cell composition that have a second attribute at step (b) (i.e., mental processes and mathematical concepts);
the therapeutic cell composition comprises T cells expressing a recombinant receptor and is to be produced from cells of the input composition (i.e., mental processes);
the second attribute is a T cell phenotype or a recombinant receptor-dependent activity (i.e., mental processes); and
the process comprises a statistical learning model trained on training data comprising (i) the percentage, number, ratio, or proportion of T cells that have the first attributes from each of a plurality of input compositions comprising T cells and (ii) the percentage, number, ratio, or proportion of T cells that have the second attribute from each of a plurality of therapeutic cell compositions, wherein each of the plurality of therapeutic cell compositions comprises T cells expressing the recombinant receptor and has been produced from one of the plurality of input compositions (i.e., mental processes and mathematical concepts).
Independent claim 11 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
determining a percentage, number, ratio, or proportion of T cells in the input composition having first attributes, wherein the first attributes comprise T cell phenotypes at step (b) (i.e., mental processes and mathematical concepts);
applying the first attributes as input to a process configured to predict, based on the first attributes, a percentage, number, ratio, or proportion of T cells in a therapeutic cell composition that have a second attribute at step (c) (i.e., mental processes and mathematical concepts);
the therapeutic cell composition comprises T cells expressing a recombinant receptor and is to be produced from cells of the input composition (i.e., mental processes);
the second attribute is a T cell phenotype or a recombinant receptor-dependent activity (i.e., mental processes); and
the process comprises a statistical learning model trained on training data comprising (i) the percentage, number, ratio, or proportion of T cells that have the first attributes from each of a plurality of input compositions comprising T cells and (ii) the percentage, number, ratio, or proportion of T cells that have the second attribute from each of a plurality of therapeutic cell compositions, wherein each of the plurality of therapeutic cell compositions comprises T cells expressing the recombinant receptor and has been produced from one of the plurality of input compositions (i.e., mental processes and mathematical concepts);
determining, based on the predicted second attribute, whether the therapeutic cell composition is predicted to have a desired attribute at step (d) (i.e., mental processes); and
(i) if the therapeutic cell composition is predicted to have the desired attribute, the therapeutic cell composition is manufactured from the input composition using a first manufacturing process (i.e., mental processes); or
(ii) if the therapeutic cell composition is predicted to not have the desired attribute, the therapeutic cell composition is manufactured from the input composition using a second manufacturing process, wherein the second manufacturing process comprises one or more steps that are altered compared to steps of the first manufacturing process (i.e., mental processes).
Dependent claims 2-5, 7-10, 12, 13, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 2 further recites:
determining, based on the predicted second attribute, whether the therapeutic cell composition is predicted to have a desired attribute (i.e., mental processes).
Dependent claim 3 further recites:
the statistical learning model is a lasso regression statistical learning model (i.e., mental processes and mathematical concepts).
Dependent claim 4 further recites:
the statistical learning model is a canonical correlation analysis (CCA) statistical learning model (i.e., mental processes and mathematical concepts).
Dependent claim 5 further recites:
if the therapeutic cell composition is predicted to have the desired attribute, the method comprises selecting a first manufacturing process to manufacture the therapeutic cell composition from the input composition (i.e., mental processes); or
if the therapeutic cell composition is predicted to not have the desired attribute, the method comprises selecting a second manufacturing process to manufacture the therapeutic cell composition from the input composition, wherein the second manufacturing process comprises one or more steps that are altered compared to steps of the first manufacturing process (i.e., mental processes).
Dependent claim 7 further recites:
if the therapeutic cell composition is predicted to have the desired attribute, the method comprises selecting a predetermined treatment regimen comprising the therapeutic cell composition for administration to the subject (i.e., mental processes); or
if the therapeutic cell composition is predicted to not have the desired attribute, the method comprises selecting an altered treatment regimen comprising the therapeutic cell composition for administration to the subject, wherein the altered treatment regimen is altered compared to the predetermined treatment regimen (i.e., mental processes).
Dependent claim 8 further recites:
the first attributes comprise T cell phenotypes that are positive or negative for CCR7, CD27, CD28, CD45RA, or an apoptotic marker (i.e., mental processes).
Dependent claim 9 further recites:
the second attribute is a T cell phenotype that is positive or negative for CCR7, CD27, CD28, CD45RA, or an apoptotic marker (i.e., mental processes).
Dependent claim 10 further recites:
the apoptotic marker is activated caspase 3 (3CAS) or annexin V (i.e., mental processes).
Dependent claim 12 further recites:
the statistical learning model is a lasso regression statistical learning model (i.e., mental processes and mathematical concepts).
Dependent claim 13 further recites:
the statistical learning model is a canonical correlation analysis (CCA) statistical learning model (i.e., mental processes and mathematical concepts).
Dependent claim 31 further recites:
the first attributes comprise one or more T cell phenotypes selected from 3CAS-/CCR7-/CD27-, 3CAS-/CCR7-/CD27+, 3CAS-/CCR7+, 3CAS-/CCR7+/CD27-, 3CAS-/CCR7+/CD27+, 3CAS-/CD27+, 3CAS-/CD28-/CD27-, 3CAS-/CD28-/CD27+, 3CAS-/CD28+, 3CAS-/CD28+/CD27-, 3CAS-/CD28+/CD27+, 3CAS-/CCR7-/CD45RA-, 3CAS-/CCR7-/CD45RA+, 3CAS-/CCR7+/CD45RA-, 3CAS-/CCR7+/CD45RA+, CAS+, and CAS+/CD3+ (i.e., mental processes).
Dependent claim 34 further recites:
the first attributes comprise one or more T cell phenotypes selected from CD4+/CCR7+/CD27+, CD4+/CCR7+/CD45RA+, CD4+/CD28+/CD27-, CD8+/CCR7+CD45RA-, and CD8+/CCR7+CD45RA+ (i.e., mental processes).
Dependent claim 35 further recites:
the first attributes comprise one or more T cell phenotypes selected from CD8+/CCR7+, CD4+/CCR7-/CD27-, CD8+/CCR7-/CD45RA+, and CD4+/CD28+ (i.e., mental processes).
Dependent claim 36 further recites:
the first attributes comprise a T cell phenotype that is CD4+/CCR7+/CD45RA+ (i.e., mental processes).
Dependent claim 37 further recites:
the second attribute is selected from 3CAS-/CCR7-/CD27-/CAR+, 3CAS-/CCR7-/CD27+/CAR+, 3CAS-/CCR7+/CAR+, 3CAS-/CCR7+/CD27-/CAR+, 3CAS-/CCR7+/CD27+/CAR+, 3CAS-/CD27+/CAR+, 3CAS-/CD28-/CD27-/CAR+, 3CAS-/CD28-/CD27+/CAR+, 3CAS-/CD28+/CAR+, 3CAS-/CD28+/CD27-/CAR+, 3CAS-/CD28+/CD27+/CAR+, 3CAS-/CCR7-/CD45RA-/CAR+, 3CAS-/CCR7-/CD45RA+/CAR+, 3CAS-/CCR7+/CD45RA-/CAR+, 3CAS-/CCR7+/CD45RA+/CAR+, CAS+ of CD3+/CAR+, CD3+, CYTO-/CAR+, EGFRt+, IFNG+, vector copy number (VCN), viability, CD3+/CAR+, CD3+/CD56+, CAR+, IFNG+/IL-2+/CAR+, IFNG+/IL-2+/IL17+/TNFA+/CAR+, IFNG+/IL-2+/TNFA/+CAR+, IFNG+ of CAR+, IFNG+/TNFA/+CAR+, IL13+ of CAR+, IL17+ of CAR+, IL2+ of CAR+, IL-2+/TNFA+/CAR+, TNFA+ of CAR+, cytolytic CD8+, GMCSF+, IFNG+, IL10+, IL13+, IL2+, IL5+, MIP1A+, MIP1B+, sCD137+, and TNFa+ (i.e., mental processes).
Dependent claim 40 further recites:
the second attribute is a T cell phenotype selected from CCR7-/CD27-/CD4+/CAR+, CD28+/CD27-/CD4+/CAR+, CD27+/CD4+/CAR+, CD28+/CD27+/CD4+/CAR+, CCR7+/CD4+/CAR+, CCR7+/CD27+ CD4+/CAR+, CCR7-/CD45RA+/CD4+/CAR+, and CCR7+/CD45RA+/CD4+/CAR+ (i.e., mental processes).
Dependent claim 41 further recites:
the second attribute is a T cell phenotype selected from CD28+/CD27-/CD8+/CAR+, CD27+/CD8+/CAR+, CD28+/CD27+/CD8+/CAR+, CCR7+/CD8+/CAR+, CCR7-/CD27-/CD8+/CAR+, CCR7-/CD45RA-/CD8+/CAR+, and CCR7+/CD45RA+/CD8+/CAR+ (i.e., mental processes).
Dependent claim 50 further recites:
the desired attribute is an attribute that is correlated with a positive clinical response to treatment with the therapeutic cell composition (i.e., mental processes).
Dependent claim 51 further recites:
the positive clinical response is a durable response or progression free survival (i.e., mental processes).
Dependent claim 52 further recites:
the desired attribute is a threshold percentage of naive-like T cells or central memory T cells (i.e., mental processes and mathematical concepts).
Dependent claim 53 further recites:
the threshold percentage is at least or at least about 40% of the cells in the therapeutic cell composition that are naive-like T cells or central memory T cells (i.e., mental processes and mathematical concepts).
Dependent claim 59 further recites:
the naive-like T cells or central memory T cells have a T cell phenotype selected from CD62L+/CCR7+, CD27+/CCR7+, CD62L+/CD45RA-, CCR7+/CD45RA-, CD62L+/CCR7+/CD45RA-, CD27+/CD28+/CD62L+/CD45RA-, CD27+/CD28+/CCR7+/CD45RA-, CD27+/CD28+/CD62L+/CCR7+, and CD27+/CD28+/CD62L+/CCR7+/CD45RA- (i.e., mental processes).
Dependent claim 60 further recites:
the desired attribute is a threshold percentage of CD27+/CCR7+ T cells in the therapeutic cell composition (i.e., mental processes and mathematical concepts).
Dependent claim 61 further recites:
the threshold percentage is at least or at least about 60% of the cells in the therapeutic cell composition that are CD27+/CCR7+ T cells (i.e., mental processes and mathematical concepts).
Dependent claim 69 further recites:
the desired attribute is a threshold percentage of IFNG+/IL-2+/CD4+/CAR+, IFNG+/IL-2+/IL-17+/TNFA+/CD4+/CAR+, IFNG+/IL-2+/TNFA+/CD4+/CAR+, IFNG+/TNFA+/CD4+/CAR+, IL-17+ of CD4+CAR+, IL-2+ of CD4+CAR+, or IL-2+/TNFA+/CD4+/CAR+ T cells in the therapeutic cell composition (i.e., mental processes and mathematical concepts).
Dependent claim 70 further recites:
the threshold percentage is at least or at least about 10% of the total number of CAR+/CD4+ T cells in the therapeutic cell composition (i.e., mental processes and mathematical concepts).
Dependent claim 71 further recites:
the desired attribute is a threshold percentage of IFNG+/IL-2+/CD8+/CAR+, IFNG+/IL-2+/IL-17+/TNFA+/CD8+/CAR+, IFNG+/IL-2+/TNFA+/CD8+/CAR+, IFNG+/TNFA+/CD8+/CAR+, IL-17+ of CD8+CAR+, IL-2+ of CD8+CAR+, or IL-2+/TNFA+/CD8+/CAR+ T cells in the therapeutic cell composition (i.e., mental processes and mathematical concepts).
Dependent claim 72 further recites:
the threshold percentage is at least or at least about 10% of the total number of CAR+/CD8+ T cells in the therapeutic cell composition (i.e., mental processes and mathematical concepts).
Dependent claim 74 further recites:
the first manufacturing process is an expanded process resulting in more than a 2-fold increase in cells in the therapeutic cell composition compared to the input composition (i.e., mental processes).
Dependent claim 76 further recites:
the second manufacturing process is a non-expanded or minimally expanded process resulting in less than a 2-fold increase in cells in the therapeutic cell composition compared to the input composition (i.e., mental processes).
Dependent claim 85 further recites:
the recombinant receptor is a chimeric antigen receptor (CAR) (i.e., mental processes).
Dependent claim 88 further recites:
the second attribute is a recombinant receptor-dependent activity that is recombinant receptor-dependent production of a cytokine (i.e., mental processes).
Dependent claim 89 further recites:
the second attribute is a recombinant receptor-dependent activity that is a recombinant receptor-dependent cytotoxic activity (i.e., mental processes).
Dependent claim 90 further recites:
the second attribute is viability (i.e., mental processes).
Dependent claim 91 further recites:
the second attribute is vector copy number (VCN) (i.e., mental processes).
Dependent claim 92 further recites:
the second attribute is positive recombinant receptor expression (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., the step of determining a percentage, number, ratio, or proportion of T cells in an input composition that have first attributes involves evaluations of data that can practically be performed by a human with pen and paper), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., applying the first attributes as input to a process that comprises a statistical learning model configured to predict, based on the first attributes, a percentage, number, ratio, or proportion of T cells in a therapeutic cell composition that have a second attribute, involves mathematical formulas and equations, and calculations) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-5, 7-13, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea 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 abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
Independent claim 1 and dependent claims 2-5, 7-10, 12, 13, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 11 include:
selecting T cells from a biological sample from a subject to produce an input composition comprising T cells at step (a); and
manufacturing the therapeutic cell composition at step (e).
The additional element of selecting T cells from a biological sample from a subject to produce an input composition comprising T cells is merely a pre-solution activity of gathering data for use in the claimed process – a nominal addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional element of manufacturing the therapeutic cell composition amounts to mere instructions to apply an exception (MPEP 2106.05(f)), because this type of recitation is equivalent to the words “apply it.” The claim limitation attempts to cover any solution to the identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, because the limitation of manufacturing the therapeutic cell composition could include any manufacturing process, and the therapeutic cell composition could include any of a plurality of cells, and any of a plurality of desired attributes, since there is not a restriction, limit, or indication as to what the constituents of the therapeutic cell composition actually are.
Thus, the additionally recited elements merely invoke a computer and/or computer related components as tools; and/or amount to insignificant extra-solution activity; and/or a field of use in which to apply a judicial exception; and as such, when all limitations in claims 1-5, 7-13, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-5, 7-13, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. 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 amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Independent claim 1 and dependent claims 2-5, 7-10, 12, 13, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 do not recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claim 11 are identified above, and carried over from Step 2A Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of selecting T cells from a biological sample from a subject to produce an input composition comprising T cells and manufacturing the therapeutic cell composition, are conventional. Evidence for the conventionality is shown by Magalhaes et al. (“Facing the future: challenges and opportunities in adoptive T cell therapy in cancer.” Expert Opinion on Biological Therapy, (30 April 2019), 19(8): pp. 811-827). Magalhaes et al. reviews the challenges and progress in tumor antigen target identification and selection, and cell product manufacturing for T cell adoptive cell therapy (Abstract). Magalhaes et al. discusses neoantigen prediction pipelines (page 814, col. 2, para. 3); machine-learning techniques for neoantigen prediction (page 815, col. 1); different targeting strategies (Figure 1); cell culturing systems and culturing conditions (Section 3.2.); and the manufacturing of the T cell products for adoptive cell therapy (page 822, col. 1).
Therefore, when taken alone, all additional elements in claims 1-5, 7-13, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-5, 7-13, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
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.
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.
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.
Claims 1-3, 5, 7-10, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 are rejected under 35 U.S.C. 103 as being unpatentable over Hause et al. (WO 2019/051335, as disclosed in the Information Disclosure Statement (IDS) received 14 November 2022) in view of Nobles et al. (WO 2019/210153, as disclosed in the Information Disclosure Statement (IDS) received 14 November 2022).
Hause et al. shows methods of identifying cellular attributes related to outcomes associated with cell therapy (Title) and methods for tracking certain cells associated with a cell therapy, such as from a starting cell composition or a sample prior to administration to a subject, including methods for assessing one or more parameters or attributes of such cells and methods of identifying cellular attributes associated with particular desired cells, and further shows that these methods can be used in connection with cell therapy, including adoptive transfer of engineered T cells or T cell precursors (Abstract).
Nobles et al. shows CAR T cell therapies with enhanced efficacy (Title) and methods for using parameters that can be measured, e.g., evaluated, to manufacture CAR T cell therapies with optimized properties (Abstract).
Regarding independent claims 1 and 11, Hause et al. shows a method of identifying a property or attribute of a cell including identifying the clonotype and/or a TCR sequence of all or a portion of a native TCR alpha and/or beta variable region or pair thereof of one or more T cell genetically engineered with a recombinant receptor in at least one test biological sample from a subject, wherein said clonotype is known to be, determined to be, or suspected of being present in a cell in a T cell composition, thereby identifying one or more originator T cell, wherein: the at least one test biological sample is obtained from the subject following administration of a cell therapy containing T cells expressing the recombinant receptor; and the T cell composition contains T cells that are or are derived from cells of a sample obtained from the subject prior to administering the cell therapy to the subject; and determining at least one or property or attribute of the one or more originator T cell (para. [0008]); the genetically engineered T cell in the test biological sample exhibits a predetermined phenotype, function, or parameter, wherein the predetermined phenotype, function, or attribute is a pharmacokinetic activity and the pharmacokinetic activity includes determining the number or relative number of recombinant-expressing T cells in the sample, and the predetermined phenotype, function, or attribute is a cell surface phenotype and the cell surface phenotype is a naïve phenotype or a long-lived memory phenotype (para. [0007]); the CAR induces a function of a T cell such as cytolytic activity or T-helper activity, such as secretion of cytokines or other factors (para. [0460]). Hause et al. further shows identifying at least one property or parameter of originator T cells that is present in a T cell composition from a majority of subjects, wherein the at least one property or parameter is identified as an attribute of a T cell composition that is predicted to increase likelihood or a desired property or outcome of a cell therapy following administration to a subject (page 227, at Nos. 13 & 14).
Further regarding independent claim 11, (step (d)), Hause et al. further shows products may be screened for a desired activity (para. [0709]); desired antibodies (para. [0443]); and further shows the utility of an approach in tracking phenotypic changes and attributes of a plurality of individual T cell clones at different stages of a cell engineering process, including patient material and drug product, e.g., to assess and/or identify attributes in starting material cells that may increase the likelihood of a desired property or outcome of a therapeutic cell product (para. [0728]).
Further regarding independent claim 11, (step (e)), Hause et al. further shows that the process of generating the cell therapy includes analyzing the cells or identifying cellular attributes of cells used in adoptive therapy, e.g., engineered T cells, and determining and identifying the phenotype, function, attribute, or property of cells at various stages of adoptive cell therapy, such as cells identified by clonotypic tracking of T cells, and further shows identifying features or attributes of T cells, such as T cells obtained from a subject and/or cells used in connection with manufacturing or formulating a drug product, that are predicted to or likely to result in one or more advantageous or desired features associated with cell therapy upon administration of the therapeutic T cell drug product containing one or more cells that express a recombinant receptor, e.g., a CAR (para. [0415]); and that a plurality of compositions are separately manufactured, produced, or generated, each containing a different population and/or sub-types of cells from the subject (para. [0643]).
Regarding independent claims 1 and 11, Hause et al. does not show the process comprises a statistical learning model trained on training data comprising (i) the percentage, number, ratio, or proportion of T cells that have the first attributes from each of a plurality of input compositions comprising T cells and (ii) the percentage, number, ratio, or proportion of T cells that have the second attribute from each of a plurality of therapeutic cell compositions, wherein each of the plurality of therapeutic cell compositions comprises T cells expressing the recombinant receptor and has been produced from one of the plurality of input compositions.
Regarding independent claims 1 and 11, Nobles et al. shows developing multivariate models as predictive tools (page 323, lines 17-32) for linking T cell biology and therapeutic efficacy (page 327, lines 30-34) that are generalizable to subjects not used initially to construct the model (page 324, lines 20-22).
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 modified the method shown by Hause et al. by incorporating methods for using a predictive statistical learning model to interrogate T cell sample data for biomarkers of success of a CAR T therapeutic composition, as shown by Nobles et al. (e.g., page 326, lines 1-9), and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Hause et al. with the methods of Nobles et al., because Nobles et al. shows that altering CAR T cells to favor a particular phenotype promoted long-term proliferation and function (page 326, lines 30-34) and further shows statistical methods for optimization of CAR-expressing cell therapy. This modification would have had a reasonable expectation of success given that both Hause et al. and Nobles et al. disclose methods for manufacturing CAR T cell therapies with optimized properties.
Regarding dependent claim 2, Hause et al. further shows methods used to identify features or attributes of T cells, such as T cells obtained from a subject and/or cells used in connection with manufacturing or formulating a drug product, that are predicted to or likely to result in one or more advantageous or desired features associated with cell therapy upon administration of the therapeutic T cell drug product (para. [0049]).
Regarding dependent claims 5 and 7, Hause et al. further shows that the process of generating the cell therapy includes analyzing the cells or identifying cellular attributes of cells used in adoptive therapy, e.g., engineered T cells, and determining and identifying the phenotype, function, attribute, or property of cells at various stages of adoptive cell therapy, such as cells identified by clonotypic tracking of T cells, and further shows identifying features or attributes of T cells, such as T cells obtained from a subject and/or cells used in connection with manufacturing or formulating a drug product, that are predicted to or likely to result in one or more advantageous or desired features associated with cell therapy upon administration of the therapeutic T cell drug product containing one or more cells that express a recombinant receptor, e.g., a CAR (para. [0415]); and that a plurality of compositions are separately manufactured, produced, or generated, each containing a different population and/or sub-types of cells from the subject (para. [0643]).
Regarding dependent claims 8 and 9, Hause et al. further shows at least one property or parameter is determined by single cell surface phenotyping of at least one T cell surface marker, in some cases selected from CD4, CD8, CD45RA, CD45RO, CD62L, cd69, cd25, CCR7, CD27, CD28, CD56, CD122, CD127, T-bet, IL-7Rα, CD95, IL-2Rβ, CXCR3, LFA-1, or KLRG1 (para. [0033]).
Regarding dependent claim 10, Hause et al. further shows caspase 3 (para. [0032]).
Regarding dependent claims 31, 34, 35, 36, 37, 40, 41, 59, 69, 71, Hause et al. further shows selecting cells from the sample for isolation based on surface expression of CD3 or based on surface expression of one or both of CD4 and CD8, optionally by positive or negative selection (para. [0020]); and exemplary phenotypes for assessment include expression of markers, e.g., cell surface markers, or other factors, e.g., cytokines or other factors, involved in function of immune cells, e.g., T cells, wherein such phenotypes include expression of markers that are associated with function, activation state, maturity, potential for differentiation, expansion, recirculation, localization, and/or persistence capacities, antigen-specificity, type of antigen receptor, presence in a particular organ or compartment, marker or cytokine secretion profile, and/or degree of differentiation in T cells, and wherein exemplary markers or factors for phenotypic determination include one or more of CD28, CD62L, CCR7, CD27, CD127, CD4, CD8, CD45RA, CD45RO, CD3, CD14, ROR1, granzyme B, granzyme H, CD20, CD11b, CD16, HLA-DR, ICOS, FOXP3, PMCH, CD80, CD86, CD40, CD70, GPR171, PD-L1, CD2, CD3d, IFNγ, KIRK1, CCL4, RUNX3, NKG7, IL-6, CD56, KLRG1, CD95, CD25, IL-2, IFN-γ, IL-4, IL-10, caspase 2, caspase 3, caspase 6, caspase 7, caspase 8, caspase 9, caspase 10, Bcl-2, Bax, Bad, Bid, CD196 (CCR6), CTLA-4 (CD152), PD-1 (CD279), TIGIT (VSIG9, VSTM3), LAG-3 (CD223), 2B4 (CD244), BTLA (CD272), TIM3 (HAVCR2), VISTA (PD1-H) and CD96. One or more of these phenotypes can be selected for phenotypic analysis of a single cell, together with clonotype determination (para. [0411]).
Regarding dependent claim 50, Hause et al. further shows single cell analysis of immune sequence to identify a TCR clonotype is coupled to single cell methods for analyzing phenotype and molecular signatures, such as gene expression, in order to identify and/or select features or attributes of T cell clones that are associated with a desired feature or property, such as greater persistence and/or high efficacy, when administered (para. [0057]).
Regarding dependent claim 51, Hause et al. further shows the parameter for assessing response can include durable response, e.g., response that persists after a period of time from initiation of therapy (para. [0096]).
Regarding dependent claim 85, Hause et al. further shows that the recombinant receptor is a chimeric antigen receptor (CAR) (para. [0068]).
Regarding dependent claim 88, Hause et al. further shows T cells are positive for intracellular expression of a cytokine (page 39, para. [0122]).
Regarding dependent claim 89, Hause et al. further shows a T cell subset that has a low cytotoxicity capacity (para. [0121]).
Regarding dependent claim 90, Hause et al. further shows cells are engineered to promote factors such as viability (para. [0594]).
Regarding dependent claim 91, Hause et al. further shows one or more conditions that includes the vector copy number (para. [0025]).
Regarding dependent claim 92, Hause et al. further shows CAR-expressing T cell therapy (para. [0071]).
Hause et al. further does not show the statistical learning model is a lasso regression statistical learning model (claim 3); the desired attribute is a threshold percentage of naive-like T cells or central memory T cells (claim 52); the threshold percentage is at least or at least about 40% of the cells in the therapeutic cell composition that are naive-like T cells or central memory T cells (claim 53); the desired attribute is a threshold percentage of CD27+/CCR7+ T cells in the therapeutic cell composition (claim 60); the threshold percentage is at least or at least about 60% of the cells in the therapeutic cell composition that are CD27+/CCR7+ T cells (claim 61); the threshold percentage is at least or at least about 10% of the total number of CAR+/CD4+ T cells in the therapeutic cell composition (claim 70); the threshold percentage is at least or at least about 10% of the total number of CAR+/CD8+ T cells in the therapeutic cell composition (claim 72); the first manufacturing process is an expanded process resulting in more than a 2-fold increase in cells in the therapeutic cell composition compared to the input composition (claim 74); or the second manufacturing process is a non-expanded or minimally expanded process resulting in less than a 2-fold increase in cells in the therapeutic cell composition compared to the input composition (claim 76).
Regarding dependent claim 3, Nobles et al. further shows prediction and validation of a clinical outcome from using data of features spanning population metrics, genomic features, and epigenetic features from patients in a classification model built using least absolute shrinkage and selection operator (LASSO) logistic regression statistical technique (page 29, lines 21-33; and FIGs. 13A-13F).
Regarding dependent claims 52 and 53, Nobles et al. further shows a population of cells comprising one or more cells comprising a CAR, wherein at least 50% of the population of cells have a central memory T cell phenotype (page 26, lines 31-33).
Regarding dependent claims 60 and 61, Nobles et al. further shows at least 50% of the population of cells express CD45RO and/or CCR7 (page 27, lines 1-3).
Regarding dependent claims 70 and 72, Nobles et al. further shows a population of cells comprising one or more cells comprising a CAR, wherein at least 50% of the population of cells have a T cell phenotype (page 26, lines 31-33).
Regarding dependent claims 74 and 76, Nobles et al. further shows the cells, e.g., a CD19 CAR, expanded for 5 days show at least a one-, two-, three-, or four-fold increase in cells doublings (page 239, lines 7-10).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Hause et al. in view of Nobles et al. as applied to claims 1-3, 5, 7-10, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 above, and further in view of Ono et al. (“Visualization of the T cell differentiation programme by Canonical Correspondence Analysis of transcriptomes.” BMC Genomics, (2014), 15:1028, pp. 1-15).
Hause et al. in view of Nobles et al. as applied to claims 1-3, 5, 7-10, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 above, does not show that the statistical learning model is a canonical correlation analysis (CCA) statistical learning model.
Regarding dependent claim 4, Ono et al. shows using Canonical Correspondence Analysis (CCA) to cross-analyze a transcriptomic dataset of interest (response data) and another transcriptomic dataset (explanatory data) that defines cellular differentiation programs, where the CCA measures and visualizes similarities (i.e., correlations) between elements across three different levels: genes, cells, and differentiation programs (page 2, col. 1, para. 3); and in particular, CCA was used to analyze transcriptomes of CD4+ T cells for T cell differentiation of functionally distinct T cell subsets (page 2, col. 2, para. 2).
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 modified the method shown by Hause et al. in view of Nobles et al. as applied to claims 1-3, 5, 7-10, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 above, by incorporating methods for multidimensional analysis of T cell subtypes using Canonical Correspondence Analysis (CCA), as shown by Ono et al., and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Hause et al. in view of Nobles et al. as applied to claims 1-3, 5, 7-10, 31, 34-37, 40, 41, 50-53, 59-61, 69-72, 74, 76, 85, and 88-92 above, with the methods of Ono et al., because Ono et al. shows methods for visualizing and classifying cross-level relationships of genes, cells, and differentiation using CCA, which is suitable for characterizing cells of interest in the context of cellular differentiation (Abstract), and identif