DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 16-19, 21, 24, 26, 27, 30-33 and 39, 42-48, 50 and 51 are pending.
Claims 19, 21, 24, 26, 27, 30, 31, 43 and 44 have been withdrawn.
Claims 16-18, 32, 33, 39, 42 and 45-48, 50 and 51 are currently under examination.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. §119 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of the first paragraph of 35 U.S.C. 112. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 62/324,228, 62,345,556 and 62/458,954 fails to provide adequate support or enablement in the manner provided by the first paragraph of 35 U.S.C. 112 for one or more claims of this application.
Particularly, the provisional patent applications do not have support for dendritic or B-cells of patient-derived cells. Thus, claim 51 is hereby assigned the priority date of October 18, 2018, the filing date of the present application.
There is not support in the specification as filed for the limitation “a second population of patient-derived cells comprising HLA-peptide complexes, and isolating the HLA-peptide complexes from dendritic or B-cells of the patient-derived cells
Paragraphs 11-16 of the specification recite
(a) providing a population of cells expressing a single HLA allele;
(a) providing a population of cells expressing a single HLA class I allele;
(a) providing a population of cells expressing a pair of HLA Class II genes,
consisting of one α and one β subunit;
wherein the cells are dendritic cells, macrophages or B-cells
Paragraph 100 of the specification recites
“the method comprises providing a population of cells that expresses either a single class I HLA allele, a single pair of class II HLA alleles, or a single class I HLA allele and a single pair of class II HLA allele” and “that other cell populations can be generated which are class I and/or class II deficient; and that “the population of cells are professional antigen presenting cells such as macrophages, B cells and dendritic cells”. Thus, the dendritic cells and B cells were from cell lines constructed with single pairs of MHC alleles are not dendritic cells and B cells from patient-derived cells. The other recitals of dendritic cells in the specification are for a dendritic cell vaccine.
Applicant argues that with respect to patient-derived cells, the specification discloses that "[i]n preferred embodiments the cells are tumor cells or cells from a tumor cell line. In particular embodiments, the cells are cells isolated from a patient." [00102]. This passage explicitly supports the use of patient-derived cells in the claimed methods. The specification further states that "[i]n particular embodiments, the cells are tumor cells." [0017]. Reading these paragraphs together, a person of ordinary skill in the art would understand that tumor cells are inherently patient-derived cells
Paragraph 100 recites that the method comprises providing a population of cells that expresses either a single class I HLA allele, a single pair of class II HLA alleles, or a single class I HLA allele and a single pair of class II HLA alleles. Paragraph 101 referring back to paragraph 100 states that in a preferred embodiment the cells are dendritic cells or B cells. Paragraph 102 states the in preferred embodiments the cells are tumor cells and in particular embodiments the cells are isolated from a patient. There is no direct or indirect inference that the B cells or dendritic cells are from the cancer patient. Paragraph 12 recites (a) providing a population of cells expressing a single HLA class I allele while paragraph 17 recites that the cells are tumor cells. Applicant appears to be pick various disclosures in the specification to argue that the isolated peptides are from the patient’s B cell or dendritic cells. There is no inherent disclosure that peptides to be identified for a prediction algorithm are from a cancer patient’s B cells or dendritic cells.
35 USC § 112 rejections withdrawn
The rejections of claims 16-18, 32, 33, 39, 42 and 45-48 and 50 under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement are withdrawn in view of Applicant’s amendment to claim 16.
35 USC § 103(a) rejections withdrawn
The rejection of claims 16-18, 32, 33, 39, 42 45, 47, 48 and 50 under 35 U.S.C. 103 as being unpatentable over Min et al US 2015/0278441 published October 1, 2015) and Yelensky et al (2017/0199961, published 13 July 2017, effective filing date 4 April 2016, cited previously) in view of Fikes et al (US 2004/0018971, published 29 January 2004) and Bergeron et al (US 2009/0028888, published 29 January 2009) are withdrawn in view of Applicant’s amendments to claim 16.
The rejections of claims 16-18, 32, 33, 39, 41, 42 and 45-48 and 50 under 35 U.S.C. 103 as being unpatentable over Min et al US 2015/0278441 published October 1, 2015, cited previously) and Yelensky et al (2017/0199961, published 13 July 2017, effective filing date 4 April 2016, cited previously) in view of Bergeron et al (US 2009/0028888, published 29 January 2009, cited previously) and Fikes et al (US 2004/0018971, published 29 January 2004, cited previously) in further view of Johnston (US 2015/0079119, published 19 March 2015) and Rammensee et al (US 2017/0022251, published 26 January 2017, effective filing date 25 June 2015) are withdrawn in view of Applicant’s amendments to claim 16.
35 USC § 101 rejections maintained
The rejections of claims 16-18, 32, 33, 39, 42 and 45-48, 50 and 51 as not being directed to patent eligible subject matter under 35 USC § 101 are maintained
The claims recite “judicial exceptions” as a limiting element or step without reciting additional elements/steps that integrate the judicial exceptions into the claimed inventions such that the judicial exceptions are practically applied, and are sufficient to ensure that the claims amount to significantly more than the judicial exceptions themselves. In the instant case, the “judicial exception” include the abstract idea, “generating a prediction algorithm for identifying subject specific HLA binding neoantigenic peptides”, “generating an HLA-allele specific binding neoantigenic peptide sequence database comprising a plurality of sequences of peptides” and “training a machine with the HLA-allele specific binding neoantigenic peptide sequence database” which are not eligible for patent protection without significantly more recited in the claims.
Step 2A, Prong 1: Judicial Exception
Applicant argues that claim 16 as amended does not merely recite abstract ideas. Applicant argues that the claim recites specific physical steps including: "(ii) isolating class I HLA-peptide complexes from the first class I deficient cultured cells expressing a first distinct single class I HLA allele and isolating class I HLA-peptide complexes from the second class I deficient cultured cells expressing a second distinct single class I HLA allele"; and (iii) "isolating the endogenous peptides from the HLA-peptide complexes isolated in (ii) and sequencing the endogenous peptides by mass spectrometry, wherein said sequencing is performed by LC-MS/MS." Applicant argues that these steps involve physical manipulation of biological materials (cells, HLA-peptide complexes, peptides) and the use of laboratory instrumentation (mass spectrometry), which are not abstract mental processes or mathematical concepts.
In response, Applicant appears to be admitting that the claims include judicial exceptions but argue that other factors such as whether the judicial exception is integrated into a practical application or adds a specific limitation beyond the judicial exception that is not "well-understood, routine, conventional" in the field . As discussed previously the “judicial exceptions” include the abstract ideas, “training a machine with an HLA-allele specific binding peptide sequence database comprising sequences of peptides bound to an HLA of a population of cells” and “generating the prediction algorithm” which are not eligible for patent protection without significantly more recited in the claims. The arguments pertaining to Step 2A and Step 2B are addressed below.
Step 2A, Prong 2: Practical Application
Applicant argues that claim 16 as amended recites specific technical steps that transform biological samples into a useful database and prediction algorithm. Applicant argues that the claim requires "obtaining multiple populations of cells" including "a first population of class I deficient cultured cells expressing a first distinct single class I HLA allele" and "a second population of class I deficient cultured cells expressing a second distinct single class I HLA allele" as recited in step (i), which represents a specific combination of cell sources. The requirement for the multiple populations of "class I deficient cultured cells" as recited in steps (i), (ii), and (iv) of claim 16 as amended is a specific technical limitation that ties the method to a particular laboratory implementation. As disclosed in the specification, "[s]uitable cell populations include, e.g., class I deficient cells lines in which a single HLA class I allele is expressed" and "[a]s an exemplary embodiment, the class I deficient B cell line is B721.221." Applicant argues that this specific cell type ensures that all peptides isolated from the HLA-peptide complexes can be unambiguously attributed to the single expressed HLA allele. The claim further requires "isolating class I HLA peptide complexes" from both cell populations and "sequencing the endogenous peptides by mass spectrometry" as recited in steps (ii) and (iii), which are specific laboratory techniques that impose meaningful limits on the claim scope. Applicant argues that as disclosed in the specification, "[s]uch a database is very useful for predicting suitable HLA-binding peptides, identifying factors which play a role in HLA peptide presentation and generating a more accurate prediction algorithm for identifying HLA-allele specific binding peptides." Applicant argues that the practical application is the generation of a prediction algorithm for identifying HLA-allele specific binding peptides, which has direct utility in developing personalized cancer immunotherapies.
Applicant arguments have been considered but are not persuasive. A practical application for the judicial exceptions of “generating a prediction algorithm for identifying subject specific HLA binding neoantigenic peptides”, “generating an HLA-allele specific binding neoantigenic peptide sequence database comprising a plurality of sequences of peptides” and “training a machine with the HLA-allele specific binding neoantigenic peptide sequence database” would be to make and use the peptides in a treatment for cancer. Applicant appears to be arguing that the judicial exception of generating a prediction algorithm for identifying HLA-allele specific binding peptides, which has direct utility in developing personalized cancer immunotherapies would also be the practical application for the judicial exception. The judicial exception cannot be the practical application of the judicial exception.
Step 2B: Significantly More
Applicant argues that the combination of steps recited in claim 16 as amended-obtaining class I deficient cultured cells, each expressing a distinct single HLA allele, isolating class I HLA-peptide complexes from both sources, sequencing endogenous peptides by mass spectrometry, generating a database comprising peptides from both sources, and training a machine using expression level, peptide sequences, and cleavability as variables-represents a specific, unconventional combination of steps. Applicant argues that the Examiner has not provided evidence that this specific combination of steps is well-understood, routine, and conventional. Applicant argues that as noted during the interview referenced in the Office Action, the standard for "well-understood, routine, and conventional" under Step 2B is not the same as the obviousness standard under 35 U.S.C. § 103. MPEP 2106.05(d) requires that any finding that additional elements are well-understood, routine, and conventional must be supported by one of the following: (1) a citation to an express statement in the specification; (2) a citation to court decisions; (3) a citation to a publication that describes the state of the art; or (4) a statement of official notice with appropriate factual support.
Applicant argues that the Examiner's citations to individual references do not establish that the claimed combination of steps is well-understood, routine, and conventional. Applicant argues that these references individually disclose various aspects of peptide-HLA binding analysis, but none discloses the specific combination of obtaining peptides from a first population of class I deficient cultured cells expressing a first distinct single class I HLA allele comprising HLA-peptide complexes in combination with a second population of class I deficient cultured cells expressing a second distinct single class I HLA allele comprising HLA-peptide complexes, and using this combined data to train a machine with the specific variables recited in claim 16 as amended. Applicant argues that none of the cited references, alone or in combination, discloses the specific combination of: (1) "obtaining multiple populations of cells, wherein the multiple populations of cells comprise a first population of class I deficient cultured cells expressing a first distinct single class I HLA allele comprising HLA-peptide complexes and a second population of class I deficient cultured cells expressing a second distinct single class I HLA allele comprising HLA-peptide complexes, wherein the peptides of the HLA-peptide complexes are endogenous peptides" as recited in step (i); (2) "isolating class I HLA-peptide complexes from the class I deficient cultured cells expressing a distinct single class I HLA allele and isolating class I HLA-peptide complexes from the second class I deficient cultured cells expressing a second distinct single class I HLA allele" as recited in step (ii); and (3) training a machine with variables comprising "expression level of endogenous source proteins," "sequences of the endogenous peptides," and "cleavability of the endogenous peptides" as recited in claim 16 as amended.
Applicant argues that the Examiner has not demonstrated that the specific combination of elements in claim 16 as amended-including the use of "a first population of class I deficient cultured cells expressing a distinct single class I HLA allele" in combination with "a second population of class I deficient cultured cells expressing a second distinct single class I HLA allele comprising HLA-peptide complexes," the isolation of "class I HLA-peptide complexes" from both sources, and training with variables comprising "expression level of endogenous source proteins," "sequences of the endogenous peptides," and "cleavability of the endogenous peptides"-is well-understood, routine, and conventional.
Applicant’s arguments have been considered but are not persuasive. While the standard for "well-understood, routine, and conventional" under Step 2B is not the same as the obviousness standard under 35 U.S.C. § 103, there is no requirement that all the active method steps and limitations are in a single reference. As far as Applicant’s argument that the specific combination of steps is not well-understood, routine, and conventional, obtaining and identifying peptides from MHC molecules from cell lines and then using the obtained information to generate algorithms to be able to predict the which peptides from a protein would likely bind a particular MHC molecule likelihood was very routine and conventional as of the effective filing date. Applicant is using a known cell line, transfected with known albeit less studied class I molecules, using a known detection and identification system, LC-MS/MS, entering the peptide sequence information into a known machine learning system to obtain a prediction algorithm for a particular MHC molecule. Thus Applicant is using known methods to generate more date to create a more accurate prediction algorithm for particular MHC molecules. Min discloses that the ability to scale with increasing amounts of data is critical for further refinement of the prediction ability of peptide binding to MHC molecules.
As previously discussed, the purpose for isolating the HLA-peptide complexes from the cultured cells expressing a distinct single class I HLA allele and isolating the HLA-peptide complexes from dendritic or B-cells of the patient-derived populations of cells are the same, to identify peptides which bind to particular HLA alleles to categorize which amino acids at which positions optimize binding of the peptide to the particular MHC allele. Determining which amino acids at which positions were relevant to binding to a specific HLA allele was well known in the art (Rovero et al Mol Immunol 31:549-554, 1994). One could not differentiate the amino acid structure of peptides from cultured cells expressing a single class I HLA allele and peptides from B cells of a patient. The advantage of using cell lines expressing a single class I HLA allele is that the peptides would be easier to isolate and identify than peptides from tumor cells or cell lines comprising more than one HLA allele. Furthermore, peptides could be identified that bound less common HLA alleles to determine which amino acids of a peptide are important for binding to the less common HLA alleles. Cell lines expressing a single class I HLA allele had been known for over 25 years from Applicant’s filing date (Shimizu et al, J Immunol 142:3320-3328, 1989).
Min disclose that as the instant methods are based on the analysis of sequences of known binders and non-binders, the predictive performance will continue to improve with accumulation of the experimentally verified binding/non binding peptides (paragraph 8). This ability to accommodate and scale with increasing amounts of data is critical for further refinement of the prediction ability of the method (Id). Thus, the ability to predict which peptides will bind to a particular MHC allele will increase with the accumulation of binding data. In this regard the immune epitope database (IEDB) contains more than 15,000 journal article and more than 704,000 experiments as of 2014 (abstract; Vita et al Nucleic Acid Research 43:D405-D412, 2014). Given that the IEDB would include the published and public amino acid sequences of peptides bound to particular HAL alleles the IEDB would necessarily include peptides from cultured cells expressing a single class I HLA allele and peptides from B cells of a patient. Given that the amino acid sequences listed in the present specification were published they were likely added to the IEDB. As previously discussed, both cell lines, which would include cell lines expressing a single allele HLA and tumor samples have been used previously used to identify which amino acids are important for peptides which bind to the specific MHC allele.
The art discloses that peptides bound to isolated B cell tumor cells from patients (paragraphs 40, 64, 70 of Johnston; paragraphs 6, 39, 93, 145-147 of Yelensky).
The art discloses that peptides binding to a particular MHC can be identified using single-allele cell lines (paragraphs 35, 36, 93 of Yelensky; paragraphs 315, 316 of Bergeron; paragraph 476 of Fikes).
Schirle et al (Eur J Immunol 30:2216-2225, 2000) disclose that sources of peptides bound to HLA molecules are tumor cell lines and solid tumors (page 2217, 1st column). Schirle disclose transgenic mice expressing a single HLA allele page 2216, 2nd column to page 2217, 1st paragraph, 1st paragraph).
Both Yelensky and Johnston disclose peptides derived from that the tumor cells including B-cell lymphomas while Yelensky and Schirle discloses that both primary tumor cells and cell lines may be used to isolate and identify peptides that bind a specific HLA allele.
Thus, the sources for the isolation and identification of peptides bound to a particular HLA allele were known in the art as well as art disclosing both types of sources of isolated and identified peptides listed in the claims.
In addition, MPEP 2106.07 (a)(Ill)(D) states
A statement that the examiner is taking official notice of the well-understood, routine, conventional nature of the additional element(s). This option should be used only when examiners are certain, based upon their personal knowledge, that the additional element(s) represents well-understood, routine, conventional activity engaged in by those in the relevant art, in that the additional elements are widely prevalent or in common use in the relevant field, comparable to the types of activity or elements that are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. 112(a).
As discussed above, both cell lines, which would include cell lines expressing a single allele HLA and tumor samples have been used previously used to identify which amino acids of the peptides are important for binding to the specific MHC allele. The amino acid sequences of the peptides for a particular HLA from the different sources would likely overlap, given that it was known that particular amino acids at particular positions of a peptide are corelated with the binding of that peptide to a particular HLA allele. Thus, the amino acid sequences and the function of the identified peptides between the different sources of the peptides would overlap.
In addition, Applicant recites that peptides isolated using the method set out in Example 1 with peptides isolated using conventional techniques and present in the Immune Epitope Database (IEDB). The IEDB would include peptides isolated from cell lines and primary tumor cells. The cell lines would include cell lines expressing a single allele HLA. As previously discussed, cell lines expressing a single class I HLA allele had been known for over 25 years prior to Applicant’s filing date (Shimizu et al, J Immunol 142:3320-3328, 1989, cited previously). Thus, the data used to train a machine in Applicant’s disclosure included peptides isolated from cell lines expressing a single allele HLA molecules as well as peptides from tumor samples and cell lines expressing a multiple allele HLA molecules.
NEW REJECTIONS: based on amendments
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.
Claim 31 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 written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. This is a new matter rejection.
There is no support in the specification as filed for the limitation “obtaining multiple populations of cells, wherein the populations of cells comprise cultured cells expressing a distinct single class I HLA allele comprising HLA-peptide complexes and patient-derived cells comprising HLA-peptide complexes”. There is no disclosure concerning obtaining multiple populations of cells that include both cultured cells and patient-derived cells.
In addition, there is not support in the specification as filed for the limitation “and excluding peptides from the patient derived cells predicted to have less than 150 nM affinity for another HLA allele in the population of patient-derived cells and greater than 1000 nM affinity for the HLA allele”.
The specification does disclose that “for multi-allelic data sets, the evaluation excluded any MS peptides that obviously belonged to an HLA- or HLA-B allele other than the one in question (eg if predicting for A0I:01 for a cell line with genotype A0l :01, A02:01, B35:01, B44:02, MS-observed peptides with NetMHCpan-2.8 scores worse than 1000 nM for A0l :01 and better than 150 nM for A02:01, B35:01, or B44:02 were excluded”. One specific example is not sufficient to support a claim with the broad limitation “excluding peptides from the patient derived cells predicted to have less than 150 nM affinity for another HLA allele in the population of patient-derived cells and greater than 1000 nM affinity for the HLA allele”.
Claim Rejections - 35 USC § 103
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 16-18, 32, 33, 39, 42, 45, 47, 48, 50 and 51 are rejected under 35 U.S.C. 103 as being unpatentable over Min et al US 2015/0278441 published October 1, 2015) and Yelensky et al (2017/0199961, published 13 July 2017, effective filing date 4 April 2016, cited previously) in view of Fikes et al (US 2004/0018971, published 29 January 2004, cited previously), Bade-Doeding et al (Immunogenetics 56:83-88, 2004), Vitiello et al (WO 2014/012051, published January 16, 2014, IDS) and Bergeron et al (US 2009/0028888, published 29 January 2009, cited previously) in further view of Johnston (US 2015/0079119, published 19 March 2015, cited previously).
Min teaches a system to predict peptide-histocompatability complex class (MHC) interaction uses high-order semi-Restricted Boltzmann Machines with deep learning extensions to efficiently predict peptide-MHC binding (paragraph 6). Min further disclose a method for peptide binding prediction includes receiving a peptide sequence descriptor and optional structural descriptor of MHC protein-peptide interaction; generating a model with one or an ensemble of high order neural networks; pre-training the model by high-order semi-Restricted Boltzmann machine (RBM) or high-order denoising autoencoder; and generating a prediction as a binary output or continuous output (paragraph 7). Min discloses that the input data is provided to a model layer which can be a linear model, a kernel SVM, or an ensemble of traditional feed-forward neural networks (paragraph 3). Min disclose that the methods allow integration of both qualitative (i.e., binding/non-binding/eluted) and quantitative (experimental measurements of binding affinity) peptide-MHC binding data to enlarge the set of reference peptides and enhance predictive ability of the method (paragraph 8). Min disclose that in order for the peptides to bind to a particular MHC, the sequences of the binding peptides should be approximately superimposable: contain similar amino-acids or strings of amino acids (k- mers) at approximately the same positions along the peptide chain (paragraph 30). Min further disclose that sequence of the descriptors corresponding to the peptide can be modeled as an attributed set of descriptors corresponding to different positions (or groups of positions) in the peptide and amino acids or strings of amino acids occupying these positions (paragraph 32). Min disclose that each position in a peptide is described by a feature vector, with features derived from the amino acid occupying this position/or from a set of amino acids (paragraph 34). Thus, Min teaches training a machine wherein the machine combines one or more linear models, support vector machines, decision trees and neural networks wherein the variables used to train the machine comprise amino acid frequency at specific positions of the peptide.
Yelensky teaches a presentation model that can comprise a statistical regression or a machine learning (e.g., deep learning) model trained on a set of reference data (also referred to as a training data set (paragraph 93). The training data set includes tissue-specific expression, expression of the TAP protein, ability of a peptide to bind the TAP protein, and stability of the peptide (paragraphs 329, 336-338, 360, Fig. 4). Yelensky discloses using databases to train a machine (paragraphs 158, 184, 483, 492)
One of ordinary skill in the art would have been motivated to apply Yelensky’s method for training a machine using variables such as the expression level of the source protein, ability of a peptide to bind the TAP protein and stability of the peptide to Min’s method for training a machine with an HLA-specific binding peptide sequence database of known binders and non-binders along with quantitative measurements of binding affinity because both Yelensky and Min concern training a machine with machine learning to predict binding of peptides to a specific class I HLA allele.
Bergeron discloses using cell lines that do not express endogenous HLA molecules transfected with an expression construct encoding a single HLA allele for obtaining the motif-bearing peptides correlated with the particular HLA molecule expressed on the cell (paragraphs 315-316). Bergeron discloses peptide anchor residues for binding to class I (paragraphs 233, 238).
Fikes disclose obtaining the motif-bearing peptides correlated with the particular HLA molecule expressed on the cell using cell lines that do not express any
endogenous HLA molecules transfected with an expression construct encoding a single HLA allele (paragraph 476). Fikes disclose peptide anchor residues for binding to an HLA molecule (paragraphs 48, 81, 85; Table I).
Bade-Doeding disclose identifying endogenous peptides from an HLA-deficient lymphoblastoid cell line LCL721.221 transfected with HLA-A*6602 (page 84, 1st column to page 87, 2nd column).
Vitiello disclose that peptides with mutant sequence from the cancer cells that activate the patient's (or HLA-matched individuals) CTL lines specific for the cancer cells can be evaluated for their MHC restriction by recognition of a single allele transfectant of an EBV-transformed cell line with no Class I MHC (paragraph 65).
One of ordinary skill in the art would have been motivated to apply Bergeron, Fikes, Bade-Doeding and Vitiello’s disclosure of using cell lines transfected with an expression construct encoding a single HLA allele to Min and Yelensky’s method for training a machine with machine learning to predict binding of peptides to a specific class I HLA allele because Bergeron, Fikes Bade-Doeding and Vitiello disclose that using cell lines that do not express any endogenous HLA molecules transfected with an expression construct encoding a single HLA allele is an alternative method of isolating and identifying peptides bound to a particular HLA class I allele. It would have been prima facie obvious to combine Min and Yelensky’s method for training a machine with machine learning to predict binding of peptides to a specific class I HLA allele with Bergeron, Fikes Bade-Doeding and Vitiello’s use of cell lines that do not express any endogenous HLA molecules transfected with an expression construct encoding a single HLA allele to identify peptides bound to a specific HLA class I allele to have a method for generating a prediction algorithm for identifying HLA-allele specific binding peptides comprising training a machine with an HLA-allele specific binding peptide sequence database comprising sequences of peptides bound to an HLA of a population of cells, wherein each cell in the population of cells expresses a single class I HLA allele and wherein variables used to train the machine comprise the expression level of source proteins of the peptides within the population of cells.
Neither Min, Yelensky, Fikes, Bade-Doeding, Vitiello nor Bergeron disclose that the sequencing is performed by LC-MS/MS.
Johnston disclose using LC-MS/MS to identify peptide bound to HLA alleles (paragraphs 54,233, 234, 239)
One of ordinary skill in the art would have been motivated to apply Johnston’s method of detecting peptides bound to HLA alleles using LC-MS/MS to Min, Yelensky, Fikes, Bade-Doeding, Vitiello and Bergeron’s method for generating a prediction algorithm for identifying HLA-allele specific binding peptides comprising training a machine with an HLA-allele specific binding peptide sequence database because Min, Yelensky, Fikes, Bergeron, Bade-Doeding, Vitiello and Johnston teach isolating and identifying peptides bound to class I HLA alleles. It would have been prima facie obvious to combine Min, Yelensky, Fikes and Bergeron’s method for generating a prediction algorithm for identifying HLA-allele specific binding peptides comprising training a machine with an HLA-allele specific binding peptide sequence database with Johnston and Rammensee’s method of detecting peptides bound to HLA alleles using LC-MS/MS to have a method for generating a prediction algorithm for identifying HLA-allele specific binding peptides comprising training a machine with an HLA-allele specific binding peptide sequence database comprising sequences of peptides bound to an HLA of a population of cells using LC-MS/MS, wherein each cell in the population of cells expresses a single class I HLA allele and wherein variables used to train the machine comprise the expression level of source proteins of the peptides within the population of cells.
Claims 16-18, 32, 33, 39, 42 and 45-48, 50 and 51 are rejected under 35 U.S.C. 103 as being unpatentable over Min et al US 2015/0278441 published October 1, 2015, cited previously) and Yelensky et al (2017/0199961, published 13 July 2017, effective filing date 4 April 2016, cited previously) in view of Bergeron et al (US 2009/0028888, published 29 January 2009, cited previously), Bade-Doeding et al (Immunogenetics 56:83-88, 2004, cited previously), Vitiello et al (WO 2014/012051, published January 16, 2014, IDS, cited previously) and Fikes et al (US 2004/0018971, published 29 January 2004, cited previously) in further view of Johnston (US 2015/0079119, published 19 March 2015, cited previously) and Rammensee et al (US 2017/0022251, published 26 January 2017, effective filing date 25 June 2015, cited previously).
Neither Min, Yelensky, Fikes, Bade-Doeding, Vitiello, Bergeron nor Johnston disclose identify peptide bound to HLA alleles including HLA A*03:01.
Rammensee disclose using LC-MS/MS to identify peptide bound to HLA alleles including HLA A*03:01 (144-148, 359-364; Table 5B).
One of ordinary skill in the art would have been motivated to apply Rammensee’s method of detecting peptides bound to HLA alleles using LC-MS/MS to Min, Yelensky, Johnston, Fikes, Bade-Doeding, Vitiello and Bergeron’s method for generating a prediction algorithm for identifying HLA-allele specific binding peptides comprising training a machine with an HLA-allele specific binding peptide sequence database because Min, Yelensky, Fikes, Bade-Doeding, Vitiello and Bergeron, Johnston and Rammensee teach isolating and identifying peptides bound to class I HLA alleles. It would have been prima facie obvious to combine Min, Yelensky, Johnston, Fikes, Bade-Doeding, Vitiello and Bergeron’s method for generating a prediction algorithm for identifying HLA-allele specific binding peptides comprising training a machine with an HLA-allele specific binding peptide sequence database with Rammensee’s method of detecting peptides bound to HLA alleles using LC-MS/MS to have a method for generating a prediction algorithm for identifying HLA-allele specific binding peptides comprising training a machine with an HLA-allele specific binding peptide sequence database comprising sequences of peptides bound to an HLA of a population of cells using LC-MS/MS, wherein each cell in the population of cells expresses a single class I HLA allele and wherein variables used to train the machine comprise the expression level of source proteins of the peptides within the population of cells.
Relevant arguments to new rejections
Applicant argues that identifying endogenous peptides bound to HLAs in multiple class I deficient cells that express distinct single class I HLA alleles is a distinguishing feature because it ensures that all peptides isolated from the HLA-peptide complexes can be unambiguously attributed to the single distinct expressed HLA allele, eliminating the need for deconvolution or prediction of which HLA allele a peptide was bound to, and thereby providing higher quality training data for the machine learning algorithm. Applicant argues that identifying endogenous peptides bound to HLAs in multiple class I
deficient cells that express distinct single class I HLA alleles is also a distinguishing feature because although overexpressing antigens to identify peptide-MHC complexes is a common strategy employed to boost signal detection, it introduces several biological and technical artifacts that can skew the immunopeptidome and lead to inaccurate profiling of natural antigen presentation, identifying endogenous peptides bound to HLAs in multiple class I deficient cells that express distinct single class I HLA alleles allows for the identification endogenous antigenic peptides that have are expressed at low levels and avoids the inaccurate profiling of natural antigen presentation that arises when an antigen is overexpressed.
Applicant further argues that the inaccurate profiling of natural antigen presentation that arises when an exogenous antigen is overexpressed can be a result of protease saturation and cleavage artifacts, MHC binding competition and displacement, ectopic intracellular localization, and mass spectrometry overload. Applicant argues that artificially high protein concentrations saturate the cell's natural proteasome or endosomal/lysosomal pathways that can lead to improper antigen processing, the generation of unnatural or overly long peptides, and the presentation of cryptic epitopes that are never naturally processed.
In addition, Applicant argues that Bergeron does not teach or suggest identifying endogenous peptides bound to HLAs in class I deficient cells that express a distinct single class I HLA allele, the Office contends that "the cell lines encoding a single HLA allele would necessarily include endogenous peptides presented in the context of the single HLA allele. Applicant argues that the majority of the peptides presented in the context of the particular HLA allele would likely be endogenous. Applicant argues that the fact that the cell lines encoding a single HLA allele can be used to identify HLA allele specific peptides from a particular protein does not detract from their use in identifying the anchor residues and other relevant amino acids for use in prediction algorithms to identify which peptides would likely bind to a particular HLA allele." Applicant argues that even if "the majority of the peptides presented in the context of the particular HLA allele would likely be endogenous", Bergeron does not teach or suggest identifying any endogenous peptides, isolating any endogenous peptides, nor sequencing any of these endogenous peptides by LC-MS/MS. Applicants also argues that it is irrelevant whether cell lines encoding a single HLA allele can be used to identify the anchor residues and other relevant amino acids for use in prediction algorithms to identify which peptides would likely bind to a particular HLA allele. Applicant argues that several biological and technical artifacts that can skew the immunopeptidome and lead to inaccurate profiling of natural antigen presentation arise when using class I deficient cells that express distinct single class I HLA alleles and that overexpress an exogenous antigen, compared to using the same cells in which an exogenous antigen is not overexpressed. Applicant argues that Identifying endogenous peptides bound to HLAs in multiple class I deficient cells that express distinct single class I HLA alleles allows for the identification of endogenous antigenic peptides that have are expressed at low levels and avoids the inaccurate profiling of natural antigen presentation that arises when an exogenous antigen is overexpressed. Applicant argues that that even if the cell line could be used to identify other peptides from endogenous proteins as alleged by the Office, Fikes does not teach or suggest identifying any endogenous peptides, isolating any endogenous peptides, nor sequencing any of these endogenous peptides by LC-MS/MS. Applicants also argue that it is irrelevant whether cell lines encoding a single HLA allele can be used to identify the anchor residues and other relevant amino acids for use in prediction algorithms to identify which peptides would likely bind to a particular HLA allele. Applicant argues that several biological and technical artifacts that can skew the immunopeptidome and lead to inaccurate profiling of natural antigen presentation arise when using class I deficient cells that express distinct single class I HLA alleles and that overexpress an exogenous antigen, compared to using the same cells in which an exogenous antigen is not overexpressed. Applicant argues that identifying endogenous peptides bound to HLAs in multiple class I deficient cells that express distinct single class I HLA alleles allows for the identification endogenous antigenic peptides that have are expressed at low levels and avoids the inaccurate profiling of natural antigen presentation that arises when an exogenous antigen is overexpressed.
Applicant’s argument has been considered but is not persuasive. The extended phrase given by Applicant concerning Fikes was “they may be infected with a pathogenic organism or transfected with nucleic acid encoding an antigen of interest to isolate peptides corresponding to the pathogen or antigen of interest that have been presented on the cell surface. (paragraph 476). Fikes does not disclose that they must be infected with a pathogenic organism. A prior art reference is relevant for all its teachings, not only its examples. Merck & Co. v. Biocraft Labs., Inc., 874 F.2d 804, 807 (Fed. Cir. 1989) (holding that both preferred and unpreferred embodiments must be considered). Fikes discloses a system for identifying peptides that bound to a specific HLA allele. In addition, the cell line comprising cells that express only a single type of HLA molecule would necessarily include endogenous peptides, which would likely be the majority of the peptides bound to the HLA allele. The fact that Fikes was primarily interested in determining whether their peptides of interest bound to a specific HLA allele does not negate the fact that the cell line could not be used to identify other peptides from endogenous proteins. Fikes disclosed using the cell line for his particular purpose. However, the advantages of using a cell line comprising cells that express only a single type of HLA molecule would be identical for the purposes of identifying the anchor residues and other relevant amino acids for use in prediction algorithms to identify which peptides would likely bind to a particular HLA allele.
In response to Applicant’s argument that Bergeron discloses using cell lines that do not express endogenous peptides from cells encoding a single HLA allele for obtaining the motif-bearing peptides correlated with the particular HLA molecule expressed on the cell, the cell lines encoding a single HLA allele would necessarily include endogenous peptides presented in the context of the single HLA allele. In fact, the majority of the peptides presented in the context of the particular HLA allele would likely be endogenous. As described above for Fikes, Begeron discloses that the particular cell lines can be used to isolate and identify MAGE peptides. However, as described above, the advantages of using a cell line comprising cells that express only a single type of HLA molecule would be identical for the purpose of identifying the anchor residues and other relevant amino acids for use in prediction algorithms to identify which peptides would likely bind to a particular HLA allele. The fact that the cell lines encoding a single HLA allele can be used to identify HLA allele specific peptides from a particular protein does not detract from their use in identifying the anchor residues and other relevant amino acids for use in prediction algorithms to identify which peptides would likely bind to a particular HLA allele.
In addition, Bade-Doeding disclose identifying endogenous peptides from an HLA-deficient lymphoblastoid cell line LCL721.221 transfected with HLA-A*6602 (page 84, 1st column to page 87, 2nd column). Vitiello disclose that peptides with mutant sequence from the cancer cells that activate the patient's (or HLA-matched individuals) CTL lines specific for the cancer cells can be evaluated for their MHC restriction by recognition of a single allele transfectant of an EBV-transformed cell line with no Class I MHC (paragraph 65). Thus, both Bade-Doeding and Vitiello disclose identifying endogenous peptides from MHC class I alleles. Furthermore, neither Fikes nor Begeron preclude the measurement of endogenous peptides
It appears that the focus of Applicant’s argument is that the repertoire of peptides isolated and identified from the cell lines would be reduced and would not sufficiently identify amino acids and their positioning to accurately predict which peptides would bind a particular MHC molecule with high affinity. However, Applicant has not presented any evidence that the peptide repertoire was reduced in cell lines transfected with an exogenous protein or any evidence that predicting whether a peptide would bind to a particular MHC molecule was affected by whether an exogenous protein was transfected into the cell line. Applicant has not supplied any evidence for the inaccurate
profiling of natural antigen presentation that arises when an exogenous antigen is overexpressed. Furthermore, the specification uses an HLA-deficient lymphoblastoid cell line LCL 721.221 to isolate and identify peptides. Thus, peptides are only being identified from one cell type which would also reduce the repertoire of peptides binding to a particular MHC molecule as opposed to using a variety of other cell lines or tumor cells.
In addition, the class I deficient B cell line, B721.221 not completely class I deficient. As Shimizu discloses the B cell line does not express endogenous HLA-A, HLA-B, or HLA-C class I Ag. However, the are other non-classical MHC class I molecules such as HLA-E, HLA-F and HLA-G. It is not clear whether these other MHC class I molecules would affect the peptide repertoire isolated from the B cell line B721.221 B cell line transfected with a particular MHC molecule.
In response to Applicant’s argument that there is inaccurate profiling of natural antigen presentation that arises when an exogenous antigen is overexpressed, Applicant has not supplied any references or any data which support their contention. Furthermore, it would depend on the expression level of the exogenous antigen. And as discussed previously, both Bade-Doeding and Vitiello disclose identifying endogenous peptides from MHC class I alleles.
In response to Applicant’s arguments that previous prediction algorithms did not take into account protein expression, and endogenous processes that generate and transport peptides prior to binding are not taken into account, as discussed above, both Min and Yelensky disclose such factors when generating their prediction algorithms. Yelensky disclose that their training data set includes tissue-specific expression, expression of the TAP protein, ability of a peptide to bind the TAP protein, and stability of the peptide.
In response to Applicant’s argument that data from cell lines expressing single HLAs does not require deconvolution to determine which HLA the MS-observed peptide was bound, which has advantages over data from patient derived cells expressing multiple HLAs in which deconvolution is typically needed to predict which of these multiple HLAs the MS-observed peptide was bound, the use of cell lines expressing single HLAs for determining which peptides bound to a particular HLA molecule was well known prior to Applicant’s filing date. These same advantages would be applicable for transfecting exogenous antigens into cell lines expressing single HLA. It would be easier to isolate and identify peptides which bound to a particular HLA molecule. Identification of the peptides may be easier given that the structure of the exogenous antigen was known, but with mass spectrometry identification of unknown peptides bound to a particular HLA molecule in cell lines expressing single HLA is relatively straightforward. As previously discussed above, cell lines expressing a single class I HLA allele had been known for over 25 years from Applicant’s filing date (Shimizu et al, J Immunol 142:3320-3328, 1989, cited previously)
In addition, Applicant argues that the working Examples highlight the claimed method and unexpected results of the claims as amended. Applicant points to Example 1 of the application, describing methods of generating peptide sequence databases, each database comprising peptide sequences that all unambiguously bind to a specific HLA molecule. Applicant states that [00432] of Example 1 explains that the Applicant immunoaffinity-purified and sequenced HLA-associated peptides from class I deficient cells that were stably transduced to express a single class I HLA allele. Importantly, the inventors' use of cells expressing a single class I HLA allele allows peptides that do not closely match known motifs to be confidently reported as binders to that class I HLA molecule. Paragraph [00454] of the present application further explains the significance
of this difference: Examples 2-4 provide a comparison of peptides isolated using the method set out in Example 1 with peptides isolated using conventional techniques and present in the Immune Epitope Database (IEDB). In particular, paragraph [00442], explains that "the LC-MS/MS data captures new peptide-binding motifs not reflected in the IEDB". Furthermore, paragraph [00443] explains that highly expressed proteins are more likely to be processed and presented by the HLA class I pathway. Thus, Examples 2-4 identify peptide sequence and source protein expression as variables that determine the likelihood of a peptide to bind to a specific HLA class I molecule. These
variables are recited in independent claim 16.
Applicant argues that based on these findings, Applicant developed prediction algorithms for predicting whether peptides bind to class I HLA alleles. Example 5 describes the development of two new prediction algorithms, MSintrinsic and MSintrinsicEC. MSintrinsicEC was generated according to the method of claim 1. Paragraph [00520] explains that both MSintrinsic and MSintrinsicEC outperformed the
prior prediction algorithms NetMHC-4.0 and NetMHCpan-2.8, with an average positive predictive value (PPV) improvement of 20 and 30 percentage points for 'MSintrinsic' and 'MSintrinsicEC', respectively, in an internal 5-fold cross validation with 999n decoys.
Additionally, the algorithms were evaluated on an independent source of HLA class I LCMS/ MS data consisting of 7 cell lines expressing multiple HLA alleles. In these lines, the average PPV of 'MSintrinsic' is 49% better than either NetMHC-4.0 or NetMHCpan-2.8, and the average PPV of 'MSintrinsicEC' 97% better (see FIG. 5E of the published application).
Applicant’s arguments have been considered but are not persuasive. As an initial matter, as disclosed in the art, isolating and identifying HLA-associated peptides from class I deficient cells that were stably transduced to express a single class I HLA allele was well known in the art. As discussed previously, Bergeron, Fikes, Bade-Doeding and Vitiello disclose using cell lines transfected with an expression construct encoding a single HLA allele to identify peptides that bind a particular HLA allele. Yelensky disclosure of using pan-HLA antibodies and deconvolution from multiallelic cells does not detract from the wide spread use of using cell lines transfected with an expression construct encoding a single HLA allele to identify peptides that bind a particular HLA allele. Furthermore, as disclosed in the art, the variables used to train the machine comprise the expression level of source proteins, the sequence of the peptides, and cleavability of the peptides within the population of cells, were known in the art. That highly expressed proteins are more likely to be processed and presented by the HLA class I pathway was already known in the art. The IEDB database was constructed using data from peptides isolated from specific HLA molecules, more than likely with some of the data obtained from the use of cells expressing a single class I HLA allele. However, a database is only as good as the data used to construct the algorithm used to identify likely peptides that would bind a particular class I HLA allele. The more data obtained, the better the algorithm would be at identifying peptides likely to bind a particular class I HLA allele. With more data on which peptides bound to a particular MHC allele, a better pattern would develop as to which amino acids at which position would likely bind the particular MHC allele. It is also likely that the type of cell would influence the population of peptides bound to the particular MHLA allele. It is noted that the claims do not list specific peptide-binding motifs that are not reflected in the IEDB and would encompass peptides isolated and identified using single-HLA allele cell lines that are listed in the IEDB. If Applicant’s invention is the identification of peptides bound to unique HLA alleles that have not been previously examined the claims should be amended to reflect the new peptide-binding motifs. As previously discussed above, cell lines expressing a single class I HLA allele had been known for over 25 years prior to Applicant’s filing date (Shimizu et al, J Immunol 142:3320-3328, 1989, cited previously).
Furthermore, an algorithm will never be as accurate as actual experiments to identify peptides bound to a particular class I HLA allele, whether it was using isolated HLA alleles from multi-allelic cells or from cells expressing a single class I HLA allele. Furthermore, obtaining more binding data on uncommon alleles would necessarily improve algorithms predicting the binding of peptides to that particular allele. Thus, more data on the peptides bound to a particular class I HLA allele would be expected to increase the predictability of a particular algorithm for identifying peptides likely to bind a particular HLA allele. And actual experimental results to identify peptides bound to a particular class I HLA allele will be more accurate at identifying peptides bound to a particular class I HLA allele than a prediction algorithm.
Summary
Claims 16-18, 32, 33, 39, 42 and 45-48, 50 and 51 stand rejected
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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