DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
1. The Election filed July 17, 2026, in response to the Office Action of May 19, 2026, is acknowledged and has been entered. Applicants elected without traverse Group I, claims 39-46. Claims 39-58 are pending. Claims 47-58 have been withdrawn from further consideration by the examiner under 35 CFR 1.142(b) as being drawn to non-elected inventions. Claims 39-46 are currently under prosecution.
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.
2. Claims 39-46 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.
3. Claim 39 recites the limitation: “on the surface of tumor cells of the subject”. There is insufficient antecedent basis for this limitation in the claim. The subject was not previously identified to have tumor cells.
4. Claim 39 recites the limitation: “…the machine-learned presentation model comprising:
a plurality of parameters identified at least based on a training data set comprising:
a label obtained by mass spectrometry measuring presence of peptides presented by at least one MHC allele in a set of MHC alleles identified as present in each sample in a set of samples;
for each of the samples…”,
There is insufficient antecedent basis for the limitations of “each sample in a set of samples” and “each of the samples”. It is unclear what or which “each sample” or “the sample” is because no samples were previously recited in the claim and it is unclear where these samples came from and what they are. Clarification is required.
Given the origination or source of the samples is unclear, the scope of “peptides presented by at least one MHC allele in a set of MHC alleles identified as present in a set of MHC alleles identified as present in each sample in a set of samples” is unclear. What peptides and what MHC alleles?
5. Claim 39 recites the limitation: “…a plurality of parameters identified at least based on a training data set comprising:
a label obtained by mass spectrometry measuring presence of peptides presented by at least one MHC allele in a set of MHC alleles identified as present in each sample in a set of samples;
for each of the samples, training peptide sequences including information regarding a plurality of amino acids that make up the training peptide sequences and a set of positions of the amino acids in the training peptide sequences;…”
The claim is grammatically unclear regarding what the training peptide sequences are or where they originated from. Are they in the samples or isolated from the samples? Are they the “samples” themselves?
The claim is also unclear regarding what a “set of positions of the amino acids in a training peptide sequence” is. What amino acids and what position sets is the claim referencing and in what training peptide sequences?
6. Claim 39 recites the limitation:
“identifying, for the subject, the cassette sequence comprising a sequence of epitopes of a treatment subset of neoantigens selected from the set of neoantigens based on their presentation likelihoods,..”
There is insufficient antecedent basis for the limitation of “the cassette sequence” in the claim.
7. Claim 39 recites the limitation:
“identifying, for the subject, the cassette sequence comprising a sequence of epitopes of a treatment subset of neoantigens selected from the set of neoantigens based on their presentation likelihoods,..”
The claim is grammatically unclear in the phrase “a sequence of epitopes of a treatment subset of neoantigens”. Is the claim referring to an “amino acid sequence” of epitopes, or is the claim referring to epitopes aligned consecutively “in sequence” - one after another? The metes and bounds of the claims cannot be determined.
8. Claim 39 recites the limitation:
“selecting an ordering of the epitopes of the treatment subset in the cassette sequence according to presentation likelihoods of the one or more junction epitopes.”
The claim is grammatically unclear if the “ordering of the epitopes” is a 1) a hierarchy ranking of epitopes, forming a list of epitopes with ascending or descending presentation likelihood values; or 2) a design placing epitopes in a certain order/position within the cassette sequence. The metes and bounds of the claims cannot be determined.
Dependent claims are rejected for encompassing the rejected limitations of claim 39.
9. Claim 41 recites: “The method of claim 39, wherein a linker sequence is placed between a first epitope and a second epitope concatenated after the first epitope, and the one or more junction epitopes include a junction epitope overlapping with the linker sequence.”
Linker placed in what and where? There are no concatenated first and second epitopes recited in claim 39. No cassette sequence of first and second epitopes and one or more junction epitopes was manufactured or designed in claim 39. The metes and bounds of the claim cannot be determined.
10. Claim 42 recites: “The method of claim 39, wherein identifying the cassette sequence comprises: determining, for each ordered pair of epitopes, a set of junction epitopes that span the junction between the ordered pair of epitopes; and determining, for each ordered pair of epitopes, a distance metric indicating presentation of the set of junction epitopes for the ordered pair on the one or more MHC alleles of the subject.”
There is insufficient antecedent basis for the limitation of “each ordered pair of epitopes” in the claim because no pairs of ordered epitopes were previously mentioned. The claim is also unclear regarding the meaning of “ordered pair of epitopes”. Does that mean the epitopes are in consecutive/adjacent order, every other order, in an order on a list, or some other order?
11. Claim 43 recites: “The method of claim 39, further comprising manufacturing or having manufactured a tumor vaccine comprising the cassette sequence.”
The claim is unclear with regarding the step of “having manufactured a tumor vaccine comprising the cassette sequence” recited in past tense. The entire method of claim 39 is recited in present tense, occurring in the present, therefore it is unclear how the method could have already manufactured the cassette sequence in the past before it was even designed. The metes and bounds of the claim cannot be determined. Clarification is required.
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.
12. Claims 39-42 and 44-42 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a series of abstract steps manipulating neoantigen peptide sequence data obtained from a subject by:
- inputting the data into a computer processer, into a machine-learned presentation model to generate a presentation likelihood for the neoantigens, wherein the machine-learned presentation model comprises:
- mass spectrometry values of peptides presented by an MHC allele;
- training peptide amino acid sequences;
- identifying for the subject a cassette sequence comprising a sequence of epitopes of a treatment subset of neoantigens selected from the neoantigens obtained form the subject, based on their presentation likelihoods; wherein identifying the cassette comprises:
- inputting sequence data of one or more junction epitopes that span junctions between one or more pairs of the epitopes of the treatment subset into the machine-learned presentation model to determine presentation likelihood of the one or more junction epitopes; and
- selecting an ordering of epitopes of the treatment subset in the cassette sequence according to presentation likelihoods of the one or more junction epitopes.
All of the recited steps can be accomplished mentally or by routine computer analysis of peptide sequences, in silico MHC allele binding, and arranging a hierarchy of epitopes. The use of computers to determine MHC binding likelihood and rank/ordering peptide sequences most likely presented by MHC is routine and conventional, see for example US Patent Application Publication 2016/0058853, Sahin et al, and Abelin et al (Immunity, February 2017, 46:315-326) cited in the prior art rejection below. See also Toussaint et al (Expert Opinion Drug Discovery, 2009, 4(10):1047-1060) that teaches in silico prediction of peptide sequence binding to MHC molecules is a standard tool in immunology to select epitopes for inclusion in vaccines (abstract, entire paper; Figure 2). Toussaint et al teach several public databases for inputting sequences to identify likelihood of presentation by one or more MHC alleles and to optimize epitope selection for vaccine design (Table 1).
This judicial exception is not integrated into a practical application because the claim is directed to an abstract idea and the recited computer elements do not add a meaningful limitation to the abstract idea because the claimed method amounts to simply implementing the abstract idea on a computer. The mental or computer “ordering” or hierarchy grouping of selected epitopes is never practically applied, for example, by manufacturing a cassette comprising the epitopes.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite using generic, routine and conventional computer functions of inputting epitope sequence data and mass spectrometry data, performing routine in silico MHC-peptide binding analysis, and arranging a hierarchy of epitope sequences (see MPEP § 2106.05(d)).
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, 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.
13. Claim(s) 39-46 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication 2016/0058853, Sahin et al, published March 2016; in view of Abelin et al (Immunity, February 2017, 46:315-326).
Sahin et al teach a method of identifying and producing a cassette sequence for a neoantigen vaccine comprising:
(a) obtaining peptide sequences for a plurality of neoantigens from a cancer patient, wherein the neoantigens comprise somatic mutations distinct from wild-type peptide sequences and the mutations are not present in non-tumor normal cells of the patient, wherein the amino acid positions are identified in each peptide sequence, and wherein the peptide sequences are 7-20, 7-12, 8-11, or 1-10 amino acids in length;
(b) entering and comparing the peptide sequence data of the neoantigens into a publicly available immune epitope database (Immune epitope Database and Analysis Resource (IEDB)) to identify immunogenic peptides and predict MHC-binding capacity, preferably MHC Class I-binding capacity and binding to the patient’s MHC allele, and ranking the highest neoantigens for inclusion in the vaccine based on their presence in the patient’s tumor and immunogenicity ([60-64]; [96-97]; [331]; Example 1; [371-373]);
[0371] MHC binding: to determine the likelihood that an epitope containing the mutated peptide is binds to an MHC molecule, the iCAM pipeline runs a modified version of the MHC prediction software from the Immune Epitope Database (http://www.iedb.org/). The local installation includes modifications to optimize data flow through the algorithm. For the B16 and Black6 data, the prediction was run using all available black6 MHC class I alleles and all epitopes for the respective peptide lengths. Mutations are selected which fall in an epitope ranked in the 95th percentile of the prediction score distribution of the IEDB training data (http://mhcbindingpredictions.immuneepitope.org/dataset.html), considering all MHC alleles and all potential epitopes overlapping the mutation.
[0372] Mutation selection criteria: somatic mutations are selected by the following criteria: a) have unique sequence content, b) identified by all three programs, c) high mutation confidence, d) non-synonymous protein change, e) high transcript expression, f) and favorable MHC class I binding prediction.
[0373] The output of this process is a list of somatic mutations, prioritized based on likely immunogenicity.
(c) entering the neoantigen peptide sequence data into a machine learning algorithm that determines the quality of the mutation for vaccine inclusion based on false discovery rate (FDR), wherein a FDR ≤0.05 is preferred for inclusion in the vaccine (Example 9);
(d) identifying for the patient the subset of neoantigen peptide sequences having the highest MHC allele binding affinities with the highest probability of being presented and generating a tumor-specific immune response (i.e., “therapeutic epitopes” or treatment neoantigens), and ranking (“ordering”) their usability as epitopes for cancer vaccine based on MHC binding and immunogenicity ([10-66]; [158]; [162-187]; [256]; [371]; Figures 1, 2, 9, 16, and 18; Examples 1-5, 8, 9);
(e) identifying and manufacturing for the patient, a cassette sequence comprising a series of concatenated neoantigen epitopes (polyepitopic polypeptide or polyepitopic vaccine or polyepitope vaccine) that either:
(i) comprises linker(s) between the neoantigen epitope peptide sequences; wherein the cassette sequence linkers are selected to minimize MHC binding to prevent unwanted immune reactions to non-endogenous neoepitopes created by the linker epitopes/sequences, or the linker sequences code for amino acids that are preferentially not processed by cellular antigen processing machinery ([66-67]; Figures 5, 6 and 8; Examples 5 and 6); or
(ii) does not comprise linkers between the neoantigen epitope peptide sequences in order to minimize or prevent unwanted immune reactions to non-endogenous neoepitopes created by the linker sequences that could compete with the intended neoantigen epitopes (epitopes are lined up head-to-tail) ([66]; Figure 8; Example 6; [390]; Example 7); wherein the metric distance determined between these neoantigen epitope peptide sequence is zero (Figure 8);
wherein the polyepitopic vaccine or cassette sequence can comprise a predetermined number of neoantigens having the highest MHC allele binding affinities with the highest probability of being presented and generating a tumor-specific immune response such as 2 or more, up to 60 ([57]).
Sahin et al teach their experiments determined that the sequence of the linker between neoantigen epitopes in the vaccine is critically important for the creation of bad MHC binding epitopes. The length of the linker sequence impacts the number of bad MHC binding epitopes. They found that sequences that are G-rich hinder the creation of MHC binding signals. The size of the linker influences the number of junction peptides, and Sahin et al determined optimal linker sequences that avoid proteasomal processing that may create junction peptides/epitopes. Sahin et al teach the linker should allow the mutation-containing peptides to be efficiently translated and processed by the proteasome (Example 6; [393-398]). Sahin et al teach constructing and experimentally testing a polyepitopic vaccine with linkers that had a favorably low number of predicted junctional neoepitopes and teach choosing linkers that are preferentially not processed by the cellular antigen processing machinery ([384]; [394]; Examples 5 and 6). Sahin et al teach:
[0067] Furthermore, the linker should have no or only little immunogenic sequence elements. Linkers preferably should not create non-endogenous neo-epitopes like those generated from the junction suture between adjacent neo-epitopes, which might generate unwanted immune reactions. Therefore, the polyepitopic vaccine should preferably contain linker sequences which are able to reduce the number of unwanted MHC binding junction epitopes.
[0390] Linker: the linker sequence was designed to connect multiple mutation-containing peptides. The linker should enable creation and presentation of the mutation epitope while hinder creation of deleterious epitopes, such as those created at the junction suture between adjacent peptides or between linker sequence and endogenous peptides. These “junction” epitopes may not only compete with the intended epitopes to be presented on the cell surface, decreasing vaccine efficacy, but could generate an unwanted auto-immune reaction. Thus, we designed the linker sequence to a) avoid creating “junction” peptides that bind to MHC molecules, b) avoid proteasomal processing to create “junction” peptides, c) be efficiently translated and processed by the proteasome.
[0391] To avoid creation of “junction” peptides that bind MHC molecules, we compared different linker sequences. Glycine, for example, inhibits strong binding in MHC binding groove positions [Abastado J P. et al., J Immunol. 1993 Oct. 1; 151(7): 3569-75]. We examined multiple linker sequences and multiple linker lengths and calculated the number of “junction” peptides that bind MHC molecules. We used software tools from the Immune Epitope Database (IEDB, http://www.immuneepitope.org/) to calculate the likelihood that a given peptide sequence contains a ligand that will bind MHC Class I molecules.
[0392] In the B16 model, we identified 102 expressed, non-synonymous somatic mutations predicted to be presented on MHC Class I molecules. Using the 50 confirmed mutations, we computationally designed different vaccine constructs, including either the use of no linkers or the use of different linker sequences, and computed the number of deleterious “junction” peptides using the IEDB algorithm (FIG. 8).
Sahin further teaches obtaining synthetically generated negative control peptide VSV-NP to use in testing and comparison of immune responses to vaccine neoantigen epitopes (Example 8; [423]; [439]; [441]; Figures 3 and 10).
Sahin et al do not teach inputting their neoantigen peptide sequence into a machine-learned presentation model trained on mass spectrometry data of peptides presented by MHC alleles with amino acid positions of the peptides to rank or order peptide sequences for their vaccine. Sahin et al do not specify using their negative control peptide (not presented by an MHC allele) as a training peptide for the machine-learned presentation model. Sahin et al do not teach inputting their junction sequences into the machine-learned presentation model.
Abelin et al teach a machine-learned presentation model trained on mass spectrometry data of peptides presented by specific MHC class I alleles and peptide amino acid position sequence data for the purpose of identifying immunogenic epitopes (Methods; Figure 5). Abelin et al teach their machine-learned presentation model utilized synthetic negative control sequences for training (Figure 1; p. 317, col. 1, last paragraphs). Abelin et al teach their machine-learned presentation model provided a greater dataset of available endogenous immunogenic epitopes than utilizing the Immune Epitope Database and Analysis Resource (IEDB). MS-defined peptides clustered more closely to each other than to IEDB peptides themselves (Figure S2E) which suggests that MS recovers stronger binding motifs compared to a greater preponderance of weak binding peptide cluster in the IEBD binder sets (p. 317, col. 1-2; p. 318, col. 1; p. 323, col. 1-2). Abelin et al teach utilizing their method on patient-derived tumor samples or cell lines to personalize therapies against cancer. Abelin et al teach CD8+ T cells targeting mutated antigens in tumors have inspired cancer immunotherapy trials aimed at inducing personalized T cell responses targeting an individual’s tumor. More effective prediction of candidate antigens should contribute to the improvement of personalized cancer vaccines (p. 324, col. 1).
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was filed to utilize the machine learning method of Abelin et al to identify neoantigen peptide sequences for the vaccine of Sahin et al. One would have been motivated to because: (1) both Sahin et al and Abelin et al recognize the need to identify mutant immunogenic, strong MHC-binding peptides specific to patient tumors for improvement of personalized cancer vaccines; and (2) Abelin et al teach their machine learning method based on mass spectrometry MHC-peptide binding data, peptide sequence data, and negative control data provides an improved method of identifying such peptides over the IEBD program used by Sahin et al. One of ordinary skill in the art would have a reasonable expectation of success to use the machine learning method of Abelin et al to identify neoantigen peptide sequences for the vaccine of Sahin et al given Sahin et al demonstrate the success of the method for identifying more and better-quality peptide sequences binding to specific MHC alleles than the IEBD program.
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was filed to further utilize the machine-learned method of Abelin et al on the neoantigen peptide vaccine of Sahin et al to identify and minimize undesirable junction or linker immunogenic epitope or neoepitope sequences. One would have been motivated to and have a reasonable expectation of success to because: (1) Sahin et al recognize junction/linker sequences in the polyepitope vaccine can create bad MHC binding epitopes or non-endogenous neoepitopes; (2) Sahin et al teach the need to prevent unwanted immune reactions to non-endogenous neoepitopes created by these linker/junction epitopes/sequences that could compete with the intended neoantigen epitopes and teach removing such unwanted sequences from the vaccine; (3) Sahin et al teach the linker/junction sequence should allow the mutation-containing peptides to be efficiently translated and processed by the proteasome; (4) Sahin et al teach identifying undesirable immunogenic MHC- binding epitopes within the neoantigen peptide junction or linker sequences of the vaccine utilizing IEDB; and (5) Abelin et al teach their improved machine-learned method successfully identifies immunogenic MHC-binding epitopes within peptide sequences, better than IEBD as stated above. Given the cited art teaches the need for identifying and removing unwanted immunogenic MHC-binding junction/linker sequences in the polyepitope cancer vaccine, and the cited art identifies an improved machine-learned method for identifying immunogenic epitopes in peptides, one of skill in the art could have predictably and successfully utilized the machine-learned method of Abelin et al to identify the immunogenic junction/linker epitopes within the vaccine of Sahin et al and removed the unwanted neoepitope sequences comprising the undesirable immunogenic epitopes.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
14. Claims 39-46 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-9 of U.S. Patent No. 11,885,815. Although the claims at issue are not identical, they are not patentably distinct from each other because the US Patent claims a method utilizing the same data instantly claimed, inputted to the same computer and machine-learned presentation model claimed, to produce the same claimed therapeutic cassette comprising a set of ordered neoantigen and junction epitopes, thereby rendering obvious the instant claims. The US Patent claims:
1. A method of identifying a cassette sequence for a neoantigen vaccine, comprising:
obtaining, for a subject, data representing peptide sequences of each of a set of
neoantigens, wherein the peptide sequence of each neoantigen comprises at least one alteration that makes it distinct from a corresponding wild-type, parental peptide sequence identified from normal cells of the subject and includes information regarding a plurality of amino acids that make up the peptide sequence and a set of positions of the amino acids in the peptide sequence;
inputting the data representing peptide sequences of the neoantigens, using a computer
processor, into a machine-learned presentation model to generate a set of numerical presentation likelihoods for the set of neoantigens, each presentation likelihood in the set representing the likelihood that a corresponding neoantigen is presented by one or more MHC alleles on the surface of tumor cells of the subject, the machine-learned presentation model comprising:
a plurality of parameters identified at least based on a training data set comprising:
a label obtained by mass spectrometry measuring presence of peptides presented by at least one MHC allele in a set of MHC alleles identified as present in each sample in a set of samples;
for each of the samples, training peptide sequences including information
regarding a plurality of amino acids that make up the training peptide sequences and a set of positions of the amino acids in the training peptide sequences; and
identifying, for the subject, a treatment subset of neoantigens from the set of neoantigens,
the treatment subset of neoantigens comprising a predetermined number of neoantigens having presentation likelihoods above a predetermined threshold; and
identifying, for the subject, the cassette sequence comprising a sequence of concatenated
therapeutic epitopes that each include the peptide sequence of a corresponding neoantigen in the treatment subset of neoantigens, wherein identifying the cassette sequence comprises:
inputting sequences of one or more junction epitopes that span junctions between one or
more adjacent pairs of therapeutic epitopes into the machine-learned presentation model to determine presentation likelihoods of the one or more junction epitopes; and
selecting an ordering of the therapeutic epitopes in the cassette sequence according to
presentation likelihoods of the one or more junction epitopes.
2. The method of claim 1, wherein the one or more junction epitopes include a junction epitope overlapping with a sequence of a first therapeutic epitope and a sequence of a second therapeutic epitope concatenated after the first therapeutic epitope.
3. The method of claim 1, wherein a linker sequence is placed between a first therapeutic epitope and a second therapeutic epitope concatenated after the first therapeutic epitope, and the one or more junction epitopes include a junction epitope overlapping with the linker sequence.
4. The method of claim 1, wherein identifying the cassette sequence comprises:
determining, for an ordered pair of therapeutic epitopes, a set of junction epitopes that span the junction between the ordered pair of therapeutic epitopes; and
determining, for the ordered pair of therapeutic epitopes, a distance metric indicating presentation of the set of junction epitopes for the ordered pair on the one or more MHC alleles of the subject.
5. The method of claim 4, wherein determining the distance metric comprises combining the presentation likelihoods of junction epitopes in the set of junction epitopes for the ordered pair of therapeutic epitopes.
6. The method of claim 5, wherein combining the presentation likelihoods of junction epitopes comprises summating the presentation likelihoods of junction epitopes.
7. The method of claim 4, further comprising determining a presentation score based on the distance metric for each ordered pair of therapeutic epitopes.
8. The method of claim 7, wherein determining a presentation score based on the distance metric for each ordered pair of therapeutic epitopes comprises combining distance metrics for all ordered pairs of therapeutic epitopes in the cassette sequence.
9. The method of claim 1, further comprising manufacturing or having manufactured a tumor vaccine comprising the cassette sequence.
15. Conclusion: No claim is allowed.
16. Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAURA B GODDARD whose telephone number is (571)272-8788. The examiner can normally be reached Mon-Fri, 7am-3:30pm.
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/Laura B Goddard/Primary Examiner, Art Unit 1642
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