Assignment of peptides to peptide groups for vaccine development

Abstract

To provide a system, method and storage medium for assigning peptides to peptide groups for vaccine development.SOLUTION: The system determines different peptides to be assigned to different groups of vaccines, determines a peptide characteristic of a peptide from the different peptides, assigns the peptide to a first group from the different groups based at least in part on the peptide characteristic, and generates information indicating that the peptide has been assigned to the first group. The first group has a first group characteristic based at least in part on a peptide characteristic of a peptide to be assigned to the first group. The first group characteristic is within a range of similarity to a second group characteristic of a second group from a different group.SELECTED DRAWING: Figure 2

Description

[Background technology]

[0001]

[0001] A variety of applications based on major histocompatibility complex (MHC) molecules and peptides bound by MHC molecules are available in the life sciences. For example, a better understanding of immune system functions, such as the interaction between T cells and antigen-presenting cells, can be achieved. This understanding can be used for diagnostics and disease identification, drug discovery, and vaccine development.

[0002]

[0002] Various embodiments according to the present disclosure are described with reference to the drawings. [Brief explanation of the drawings]

[0003] [Figure 1] FIG. 1 shows an example of a cancer vaccine containing multiple solutions corresponding to different peptide groups according to embodiments of the present disclosure. [Figure 2] FIG. 1 shows an example of the allocation of peptides to peptide groups for a cancer vaccine according to an embodiment of the present disclosure. [Figure 3] FIG. 1 illustrates an example computing environment for defining peptide groups for cancer vaccines according to embodiments of the present disclosure. [Figure 4] FIG. 1 shows an example of a flow for defining peptide groups for a cancer vaccine according to an embodiment of the present disclosure. [Figure 5] FIG. 1 illustrates an example flow for assigning peptides to peptide groups for a cancer vaccine according to an embodiment of the present disclosure. [Figure 6] FIG. 1 shows an example of peptide sorting according to an embodiment of the present disclosure. [Figure 7] FIG. 1 illustrates an example of a hierarchy definition according to an embodiment of the present disclosure. [Figure 8] FIG. 1 illustrates an example of hierarchical-based assignment of peptides to peptide groups according to an embodiment of the present disclosure. [Figure 9] FIG. 1 illustrates an example of a hierarchical-based shuffle according to an embodiment of the present disclosure. [Figure 10]FIG. 10 illustrates an example of tier redefinition according to an embodiment of the present disclosure. [Figure 11] FIG. 1 illustrates aspects of an exemplary environment for implementing aspects according to various embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0004]

[0014] Various embodiments are described below. For purposes of explanation, specific configurations and details are set forth in order to provide a thorough understanding of the embodiments. However, it will be apparent to those skilled in the art that the embodiments may be practiced without the specific details. Additionally, well-known features may be omitted or simplified so as not to obscure the described embodiments.

[0005]

[0015] Embodiments of the present disclosure relate, inter alia, to assigning peptides to peptide groups for vaccine development. In one example, multiple peptides are identified for a subject as candidate peptides for a personalized vaccine, and these peptides are predicted to induce a positive immunogenic response (e.g., a CD8+ immunogenic response and / or a CD4+ immunogenic response) in the subject based on their binding to the subject's major histocompatibility complex (MHC) molecules (e.g., MHC class I molecules and / or MHC class II molecules). Peptides can be assigned to peptide groups, and each group can be used in a vaccine composition, and each vaccine composition can be administered (e.g., injected into the same or different locations in the subject). In particular, the peptide groups identify a subset of candidate peptides for the vaccine composition. These identified peptides are formulated for administration, for example, with an adjuvant, an immunostimulant, or both. For example, in one embodiment, the identified peptides are combined in a solution (which may optionally contain an adjuvant, an immunostimulant, or both, such as polyinosinic-polycytidylic acid (polyICLC), and a solvent, such as dimethyl sulfoxide (DMSO), at a specific concentration) to form a vaccine composition. The peptides are assigned to different peptide groups so that the resulting peptide groups have similar immunogenic responses (e.g., responses that can be determined to have scores within a predetermined score range). For peptides assigned to a peptide group, the peptides are used in the manufacture of a corresponding vaccine composition. In this way, different vaccine compositions can be expected to induce immunogenic responses with similar efficacy. Optionally, peptide cosolubility is also determined, and cosolubic peptide groups are selected, thereby ensuring cosolubic peptides in the vaccine composition.

[0006]

[0016] To illustrate, consider an example of a cancer vaccine being developed for a human patient exhibiting a particular type of cancer. A biopsy can be performed on the human patient (e.g., its healthy or cancerous cells), and genome sequencing can be applied to the biopsy to determine the MHC class II alleles expressed in the human patient (e.g., for human leukocyte antigen (HLA)-DP, HLA-DQ, and HLA-DR). Using an artificial intelligence model, several (e.g., 60) neo-antigenic peptides can be identified, each of which has a likelihood of eliciting a positive immunogenic response (e.g., a CD4+ immunogenic response) by binding to an MHC class II molecule in the human patient's body. For a peptide, the corresponding likelihood represents a class II immunogenicity score. A peptide manufacturer can indicate peptides that can be manufactured based on solubility or other criteria (e.g., 40 of the 60 neo-antigenic peptides can be manufactured). The neo-antigenic peptides identified for manufacturing (e.g., 40 of them) can be used to develop a cancer vaccine for human patients. Of these neo-antigenic peptides, a subset (e.g., 18) of them can be selected as candidate peptides. Optionally, additional peptides, such as "universal" (binding to MHC molecules from various alleles) class II peptides, can be added to the peptides under consideration. Exemplary universal peptides include, but are not limited to, the "PADRE" peptide (Smahel et al., Gene Therapy, vol. 21, pp. 225-232 (2014)). For example, two PADRE peptides can be included in a total of 20 peptides. In the first vaccine strategy, the 20 peptides are assigned to four peptide groups, each containing five peptides from a different set of 20 peptides. The four peptide groups can be completely different (e.g., they do not contain any overlapping peptides), or some overlap can be tolerated (e.g., no more than one overlapping peptide). Furthermore, the average class II immunogenicity scores of the different peptide groups are within a similarity range (e.g., within a relative range of ±5, ±10, or ±20%).Using a different allocation of the 20 peptides to the four peptide groups, one or more additional vaccine plans can be defined from the same 20 peptides. Additionally or alternatively, one or more additional vaccine plans can be defined by replacing one or more of the 18 neoantigenic peptides with one or more of the remaining 22 neoantigenic peptides from the initial set of 40 candidates prior to allocation. Similar to the first vaccine plan, the different peptide groups within each additional vaccination plan have similar average Class II immunogenicity scores. The different vaccine plans can be identified by the peptide manufacturer. Once the peptide manufacturer confirms that the vaccine plan contains a peptide group containing a colytic peptide, the vaccine plan can be used to begin production of four cancer vaccine compositions. Each composition corresponds to a different peptide group of the vaccine plan and contains a peptide assigned to that peptide group.

[0007]

[0017] The embodiments of the present disclosure provide several advantages. For example, personalized vaccines can be developed for subjects. In embodiments in which a vaccine comprises multiple compositions, each composition can contain multiple colytic peptides. Furthermore, the immunogenic response of the compositions is similar, such that the compositions can be expected to induce immunogenic responses with similar efficacy when injected into different sites in a subject.

[0008]

[0018] For clarity of explanation, various embodiments are described herein using the example of peptides in a cancer vaccine for a human patient. However, the embodiments are not limited to such examples. For example, the embodiments equally apply to other types of peptide-based vaccines targeting specific immune responses and to other types of subjects (e.g., mammals or other animals). Generally, candidate peptides can be identified for a subject based on, for example, prediction of a target immunogenic response. In the case of cancer vaccines, these peptides include neoantigenic peptides. In the case of other types of vaccines, other types of peptides can be used, where the peptide type may depend on the target immune response. Peptides can be assigned to different peptide groups of a vaccine composition, and the immunogenic properties of different peptide groups are similar. The immunogenic properties of a peptide group can be defined based on the biological and / or chemical properties of individual peptides or the collection of peptides assigned to a peptide group. In the case of cancer vaccines, these properties can include, for example, a class I immunogenicity score, a class II immunogenicity score, an amino acid sequence length measure, or the distribution or frequency of cysteine ​​amino acids among the peptides in the peptide group.

[0009]

[0019] 1 shows an example of a cancer vaccine 110 that includes multiple solutions 112 corresponding to different peptide groups according to embodiments of the present disclosure. The cancer vaccine 110 can be personalized to a particular subject 120, with each of the solutions 112 containing a specific peptide that is predicted to provoke a positive immunogenic response by binding to the MHC molecules of the subject 120.

[0010]

[0020] In one example, the cancer vaccine 110 includes four solutions 112 (or some other total number of solutions 112). Each of the solutions 112 then contains five peptides (or some other total number of peptide types) that differ in amino acid sequence from one another, and the peptides are added to the solutions at specific concentrations. The peptides may differ between the solutions 112 by at least one peptide. It is also possible that the solutions 112 (i.e., different solutions) do not contain overlapping neo-antigen peptides. However, in some embodiments, a subset of the solutions 112 (e.g., one or two of them) may contain overlapping universal peptides. The universal peptide (also referred to in the literature as a "promiscuous peptide") may be, in one example, a peptide that can bind to at least a majority of commonly found HLA-DR alleles, and in some embodiments, also to HLA-DQ and HLA-DP alleles. See, e.g., Sinigaglia et al., Current Opinion in Immunology 6(1): February 1994, pp. 52-56. Thus, the universal peptide can be applicable to a population of subjects. Exemplary universal peptides include the "PADRE" peptide (Smahel et al., Gene Therapy 21, 225-232 (2014)), or a pan-DR binding peptide (see, e.g., U.S. Pat. No. 9,249,187), or diphtheria or tetanus toxoid (TT), e.g., TT 830-844 or TpD (Fraser et al., Vaccine 32(24), May 19, 2014, pp. 2896-2903), but are not limited thereto.

[0011]

[0021] The concentration of peptides in a composition (or the total number of peptides in the entire composition) can be the same or can vary within a given concentration range. For example, in some embodiments, a solution contains 0.3 milligrams per millimeter (mg / mL) of each peptide type for a total peptide concentration of 1.5 mg / mL. The solution may also contain corresponding concentrations of other components, such as 0.9% NaCl or 0.5 mg / mL polyICLC and 4% DMSO. Of course, other concentrations are possible for either the peptides and / or components, depending on the type of subject 120 and applicable regulatory requirements (e.g., concentrations of up to 20% DMSO can be used for human patients, although this concentration can be higher for other types of mammals, such as up to 70%).

[0012]

[0022] Four solutions 112 may be formulated, for example, for injection into subject 120. The injections may be in the same location (e.g., the left arm). Alternatively, each of the four solutions 112 may be injected into a different location on subject 120. For example, four quadrants are identified on subject 120 (shown in FIG. 1 as "Q1," "Q2," "Q3," and "Q4"). Each of the four solutions 112 may be associated with one of the quadrants and injected into a location within the quadrant (e.g., the left arm, right arm, right leg, or left leg). The associations may be predefined, as described further herein below.

[0013]

[0023] 1 shows cancer vaccine 110 using a liquid form, other cancer vaccine types are possible. For example, cancer vaccine 110 could similarly be developed to include multiple pills (e.g., four pills) or a spray, each containing a different set of peptide types (e.g., five peptides).

[0014]

[0024] 2 shows an example of assigning 202 peptides 210 to peptide groups 220 of a cancer vaccine according to an embodiment of the present disclosure. The peptide groups 220 can be used to define solutions, each representing a peptide pool. Once the peptides in a peptide pool are determined to be co-soluble, the peptide pool can be used as a vaccine shot.

[0015]

[0025] In one example, peptides are identified for a subject as candidate peptides predicted to elicit a target immunogenic response in the subject based on the subject's MHC molecules. The solubility of each peptide is tested. Peptides found to be soluble are identified as part of peptides 210. Peptides 210 are then subjected to peptide-group assignment 202 to distribute the peptides 210 among peptide groups 220 of a cancer vaccine. Different peptide groups 220 have similar immunogenic properties. If peptides within each peptide group are found to be co-soluble, these peptide groups 220 can be identified for the subject's vaccine plan and manufactured into a vaccine shot.

[0016]

[0026] Identification of peptides can rely on an artificial intelligence model trained to output identifiers for these peptides (e.g., the amino acid sequences that define each peptide) based on information about the subject's MHC alleles. To illustrate, consider an example of a human patient. The human patient may have six types of MHC class II molecules (e.g., MHC alleles), referred to as HLA class II molecules. Using genome sequencing (e.g., next-generation sequencing (NGS)) on a biopsy from the human patient, a set (e.g., a set of six or eight) of HLA class II molecules for the human patient is determined (e.g., each element in the set identifies one HLA allele). A database of neo-antigen peptides may be available for cancer vaccine development. Data identifying HLA class II molecules can be input into an artificial model. The model pairs neo-antigen peptides from the database with the subject's HLA class II molecules to generate candidate peptide-HLA class II molecule pairs. For each candidate pair, the artificial intelligence model generates a CD4+ immunogenic response prediction indicating the likelihood that the candidate pair will elicit a positive CD4+ immunogenic response. Candidate pairs with the highest likelihood (or likelihood above a predetermined threshold) are selected and ranked (eg, in descending order of their CD4+ immunogenic response prediction likelihood).

[0017]

[0027] From the ranked neoantigen peptides, the total number of neoantigen peptides is selected. For example, the top 60 neoantigen peptides (or any other number) are selected. These peptides are identified by a peptide manufacturer, for example, by providing the peptide manufacturer with the amino acid sequence of each neoantigen peptide. The term "neoantigen" refers to a cancer antigen.

[0018]

[0028] The peptide manufacturer, which may be different from the entity responsible for generating the peptides, but need not be different, can test the manufacturability and solubility of each neo-antigen peptide. For example, the peptide manufacturer produces a quantity of neo-antigen peptide given the identified amino acid sequence, if possible, and adds this amount to a solution at a specific concentration (e.g., 1.5 mg / mL). The solution contains other components at other concentrations, such as 0.9% sodium chloride (NaCl) and 4% DMSO, pH 6-8. Other concentrations can be used (e.g., 1-3 mg / mL for the neo-antigen peptide, 0.5-1.5% for NaCl, and 2-20% for DMSO). If the manufactured peptide is found to be soluble, the peptide manufacturer returns information indicating this. Otherwise, the peptide manufacturer, as appropriate, indicates that the neo-antigen peptide cannot be manufactured or is not soluble. Thus, from the initial set of 60 neo-antigen peptides, the peptide manufacturer identifies a subset of potential neo-antigen peptides (e.g., 40 or some other number).

[0019]

[0029] From the 40 (or some other number) neo-antigenic peptides, a smaller subset of neo-antigenic peptides (e.g., 18 of them) is selected and corresponds to peptides 210. For example, this subset corresponds to the top 18 neo-antigenic peptides. Several (e.g., one or two) PADRE peptides (and / or other types of universal peptides) are also identified and included in peptides 210, for a total of 20 peptides. In this example, peptides 210 form a set with 20 elements, although sets of different sizes are also possible. This set should be distributed into four peptide groups 220 (or some other number), each of which can be used in a different solution of a cancer vaccine. Thus, each peptide group 220 may contain five peptides (or some other number depending on the size of the set and the number of solutions to be developed).

[0020]

[0030] Next, peptide-group assignment 202 is performed using one or more assignment techniques to assign the 20 peptides to four peptide groups 220. Generally, these techniques ensure that the immunogenic responses of the peptide groups 220 are similar, but that the peptide groups 220 themselves are also different (e.g., each peptide group differs from the remaining peptide groups by having at least one peptide, two or more peptides, or all neo-antigenic peptides different between groups). Techniques can be divided into two categories. In the first category, random assignments of peptides 210 to peptide groups are generated, and then the overall immunogenic responses of the resulting peptide groups are estimated. If these immunogenic responses are similar, the peptide groups are set as peptide groups 220. Otherwise, another random assignment is performed. In the second category, the assignment itself ensures that the resulting peptide groups have similar immunogenic responses, without the need to estimate the immunogenic responses after assignment. Examples of such techniques include combinatorial optimization algorithms and tournament-style algorithms. These techniques are further described herein below.

[0021]

[0031] The above techniques may also follow different assignment rules. The assignment rules may be filtering rules. For example, the filtering rules may remove peptide groups if they contain peptides with two or more cysteines in their sequences. The assignment rules may also be overlap rules. For example, the overlap rule may specify that only two of the peptide groups 220 may contain a PADRE peptide. The overlap rule may also specify that no overlap of neo-antigen peptides is allowed, or, if one is allowed, the maximum number of overlaps of neo-antigen peptides allowed.

[0022]

[0032] Regardless of the assignment technique, applying the assignment rules, the peptide-group assignment 202 results in the definition of four peptide groups 220 (or a related number) per vaccine regimen. These peptide groups 220 are identified to the peptide manufacturer, for example, by identifying the vaccine regimen, the peptide group label, and the amino acid sequence of each peptide per peptide group.

[0023]

[0033] Here, the peptide manufacturer can test the cosolubility of peptides for each peptide group. For example, for each peptide group in a vaccine plan, the peptide manufacturer adds the identified amount of peptide to a solution (e.g., 0.3 mg / mL per peptide to a solution of 0.9% NaCl and 4% DMSO at pH 6-8, although other concentrations are possible as described herein above) and then performs a cosolubility test. If each peptide group in the vaccine plan contains a cosolubility peptide, the peptide manufacturer returns information indicating this. If not, the peptide manufacturer indicates that the vaccine plan is inappropriate and can identify specific peptide groups that should be blamed.

[0024]

[0034] 3 illustrates an example of a computing environment for defining peptide groups for cancer vaccines according to embodiments of the present disclosure. The computing environment includes a computer system 310 that hosts a peptide assignment tool 312. The computing environment also includes a user device 320 that is communicatively coupled to the computer system 310 via a data network (e.g., the Internet). The user device 320 can transmit peptide information 322 related to a subject, such as a human patient or another mammal type, to the computer system 310. The peptide assignment tool 312 then processes the peptide information 322 to define peptide groups 314. The computer system 310 transmits the definitions of the peptide groups 314 to the user device 320, where they are presented.

[0025]

[0035] In one example, computer system 310 may be any suitable system including one or more processors and one or more memories storing computer-readable instructions executable by the one or more processors to configure computer system 310 to host peptide assignment tool 312 and communicate with user device 320. For example, computer system 310 may be a server hosted in a data center or a cloud computing service.

[0026]

[0036] In comparison, user device 320 may be any suitable computing device that includes one or more processors and one or more memories that store computer-readable instructions executable by the one or more processors to receive input regarding peptide information 322, communicate with computer system 310, and configure user device 320 to present information regarding peptide groups 314. For example, user device 320 may be a smartphone, tablet, laptop, desktop computer, server, or cloud computing service hosted at a data center.

[0027]

[0037] 3 depicts the computer system 310 and the user device 320 as two separate computing components, embodiments of the present disclosure are not limited as such. For example, the computer system 310 and the user device 320 may be integrated as a single computing component. Furthermore, the configuration of the user device 320 need not be limited to receiving peptide information 322. Instead, the user device 320 may generate peptide information 322. For example, the user device 320 may be implemented as a genome sequencing system that generates MHC information about a subject and / or a system that hosts an artificial intelligence model that generates peptide information 322 based on the MHC information and transmits the peptide information 322 to the computer system 310 automatically or upon request.

[0028]

[0038] In one example, peptide information 322 is specific to a subject. For example, peptide information 322 identifies a set of candidate peptides (e.g., a sequence of 40 or several amino acids) that are found to be manufacturable and soluble and predicted to elicit an immunogenic response upon injection into a subject. In this example, peptide information 322 may be generated by performing a biopsy (e.g., of a subject's healthy or cancerous cells) and performing genomic sequencing (including next-generation sequencing (NGS)) on the biopsy to determine the subject's MHC alleles. Information regarding the MHC alleles may be input into an artificial intelligence model, which then outputs an identifier for the peptide and an immunogenic response prediction for each peptide, including, for example, a class I and class II immunogenic response likelihood. Communications can occur between user device 320 and a manufacturer's computing device to determine the manufacturability and solubility of the peptides. These communications can be automated (e.g., via a web interface or application programming interface (API)) or can involve a manual process (e.g., using electronic mail (email) messaging). The user device 320 can then rank the peptides that are manufacturable and soluble and transmit peptide information 322 to the computer system 310. The peptide information 322 can identify each peptide, its ranking (or its ranking relative to other peptides), its class I and class II immunogenic response likelihood, its amino acid sequence length, the number of cysteines therein (or an indication of whether it contains more than one cysteine), and / or other biological and / or chemical properties of the peptide.

[0029]

[0039] The information about peptide groups 314 may include one or more vaccine regimens. Each vaccine regimen may identify several peptide groups (e.g., four groups), each containing a subset of peptides. The subsets may be of the same length (e.g., each identifying five peptides) and may or may not identify overlapping peptides.

[0030]

[0040] 4-5 illustrate an example flow for developing a personalized cancer vaccine for a subject according to an embodiment of the present disclosure. Computer systems and / or user devices similar to computer system 310 and / or user device 320 of FIG. 3 may be used to perform the operations of the exemplary flow. For example, instructions for performing the operations may be stored as computer-readable instructions on one or more non-transitory computer-readable media of the computer system and / or user device. As stored, the instructions represent programmable modules containing code or data executable by one or more processors of the computer system and / or user device. Execution of such instructions configures the computer system and / or user device to perform specific operations illustrated in the corresponding figures and described herein. Each programmable module in combination with a respective processor represents a means for performing a respective operation. While the operations are shown in a particular order, it should be understood that a particular order is not required and one or more operations may be omitted, skipped, and / or reordered. Furthermore, for clarity of explanation, various examples are provided, describing the use of 20 peptides for assignment into four peptide groups, each identifying five peptides. However, the flow equally applies to defining a group of "P" peptides containing "Q" peptides from a total of "N" peptides, where "P", "Q", and "N" are positive integers strictly greater than 1.

[0031]

[0041] FIG. 4 shows an example flow for defining peptide groups for a cancer vaccine according to an embodiment of the present disclosure. The peptide groups correspond to vaccine compositions or solutions, whereby peptides identified in the peptide groups can be added to a solution along with other ingredients to create a cancer vaccine composition. Other uses of peptide groups are possible; for example, each peptide group can correspond to a pill composition for a cancer vaccine. Various numbers are listed along with the operations (e.g., 60 peptides, 40 peptides, the top 18 peptides, two PADRE peptides (and / or other types of universal peptides), etc.). These numbers are provided for illustrative purposes only; any set of numbers can be used.

[0032]

[0042] As shown, the flow may begin at operation 402, where information about candidate peptides is received. For example, this information may identify 60 (or some other total number) amino acid sequences predicted to elicit a class I and / or class II immunogenic response in a subject. For each neo-antigenic peptide, the information may include its class I and / or class II immunogenic response likelihood, relative ranking, amino acid sequence, number of cysteines in the amino acid sequence, length of the amino acid sequence, and / or other biological and / or chemical properties of the neo-antigenic peptide.

[0033]

[0043] In operation 404, some or all of the information may be sent to the peptide manufacturer. For example, this information may identify at least the amino acid sequence of each neo-antigen peptide. The information, along with a request for manufacturability and solubility analysis, may be sent to the peptide manufacturer's computing device via a web interface, API, or communication means (e.g., email message, file upload, etc.).

[0034]

[0044] In operation 406, information regarding the subset of candidate peptides is returned from the peptide manufacturer. This information may be received as a response from the peptide manufacturer's computing device and may identify at least a subset in which each neo-antigen within the subset was found to be manufacturable and soluble. For example, this subset may include 40 of the 60 neo-antigen peptides (or some other total number).

[0035]

[0045] In operation 408, a list of peptides is determined. The list includes some of the neo-antigen peptides from the subset, such as 18 of the 40 neo-antigen peptides (or some other total number). For example, the 40 neo-antigen peptides are ranked according to one or more of their biological and / or chemical properties, including their class I immunogenic response likelihood and / or class II immunogenic response likelihood, and the length of their amino acid sequence. The top 18 neo-antigen peptides are selected and identified in the list. Two PADRE peptides (and / or other types of universal peptides as well) are also identified in the list for a total of 20 peptides. Of course, a different number of peptides (whether neo-antigens or peptides) can be used. Furthermore, multiple lists can be determined, and one or more vaccine strategies can be derived from each list.

[0036]

[0046] In operation 410, peptides from the list are assigned to peptide groups, and one or more vaccine plans are generated, each including a set of peptide groups. For example, from a list of 20 peptides, a first vaccine plan is generated, each including four peptide groups that identify five of the 20 peptides. Similarly, additional vaccine plans are generated from the same list, but identifying different assignments of the 20 peptides to the four peptide groups. Similarly, one or more vaccine plans are generated from any additional lists. Different vaccine plans can be marked with a preferred order of use (e.g., the first vaccine plan is a preferred plan compared to the additional vaccine plans). The peptide manufacturer can test peptide co-solubility for each vaccine plan according to the preferred order. If a vaccine plan does not exhibit sufficient co-solubility, production proceeds to the next vaccine plan according to the preferred order.

[0037]

[0047] In one example, a peptide assignment tool, such as peptide assignment tool 612 in FIG. 6, performs peptide-peptide group assignment to generate a vaccine plan and define a preferred order. The assignment can be based on optimization parameters that bias the assignment so that peptide groups in the vaccine plan have similar group characteristics. The optimization parameters can be any or a combination of biological and / or physical properties of the peptides, including their Class I immunogenic response likelihood, Class II immunogenic response likelihood, and the length of their amino acid sequences. The group characteristics of a peptide group can include biological and / or chemical properties that collectively represent the individual biological and / or chemical properties of the peptides assigned to the peptide group. For example, the group characteristics can be the Class I immunogenic response likelihood, Class II immunogenic response likelihood, the length of their amino acid sequences, or a statistical measure (e.g., mean, median, etc.) of a combination of such individual peptide characteristics. The similarity of group characteristics can be defined as a statistical measure (e.g., difference or standard deviation) for a predetermined similarity range (e.g., ±10%).

[0038]

[0048] Additionally, allocation can be based on allocation rules that exclude peptide groups and / or set limits on peptide overlap. For example, a vaccine plan is eliminated if any of its peptide groups contains two or more peptides with cysteines. Universal peptides, such as PADRE peptides, may be allowed to overlap no more than two peptide groups per vaccine plan (or some other limit). Neo-antigenic peptides may not overlap at all. Alternatively, depending on the neo-antigenic peptide's Class I or Class II immunogenic likelihood (e.g., its Class I immunogenic response likelihood above a predetermined threshold likelihood and / or its Class II immunogenic response likelihood above the same or different predetermined threshold likelihood), neo-antigenic peptides may not overlap multiple times (e.g., up to two times to be assigned to three or more peptide groups of a vaccine plan, or up to four times for high-scoring neo-antigenic peptides to be assigned to each peptide group of a vaccine plan).

[0039]

[0049] The peptide assignment tool may use one or more assignment algorithms to assign the 20 peptides to four peptide groups according to the optimization parameters and assignment rules. In the first example, an iterative random search is used. In the iterations, random assignments are made. Group properties are determined for each peptide group from the individual peptide properties of the peptides randomly assigned to the peptide group. The group properties are compared, and if they are within each other's similarity range, the peptide assignment tool outputs the peptide group as a vaccine plan. Otherwise, these peptide groups are ignored, and the next iteration is performed. Multiple iterations can also be performed to define multiple vaccine plans.

[0040]

[0050] In a second example, the peptide assignment tool implements a combinatorial optimization algorithm. The algorithm may define a loss function as a similarity measure (e.g., difference or standard deviation) between group properties. To minimize the loss function, the combinatorial optimization algorithm explores different peptide-peptide group assignments as variables, subject to assignment rule constraints.

[0041]

[0051] In a third example, the peptide assignment tool implements a tournament-style algorithm, such as one that executes the flow of FIG. 5. Briefly, the algorithm can sort peptides in descending order based on one or more of their biological and / or chemical properties. Depending on the sorted order, a hierarchy can be defined and peptides can be associated therewith. Peptides in different hierarchies can then be assigned to peptide groups in a vaccine regimen. These assignments can follow assignment rules. The peptide-hierarchy associations can be shuffled and / or hierarchy definitions can be updated to generate additional vaccine regimens.

[0042]

[0052] Once multiple vaccine regimens are created, the peptide assignment tool can mark them in a preferred order of use. This marking can depend on several factors. For example, the more similar the group characteristics of the peptide groups within a vaccine regimen, the higher the vaccine regimen will be in the preferred order of use.

[0043]

[0053] In operation 412, information regarding one or more vaccine regimens is sent to the peptide manufacturer. For example, the information identifies at least the peptides for each vaccine regimen and their preferred order of use. This information, along with a request for a cosolubility analysis, may be sent to the peptide manufacturer's computing device via a web interface, API, or communication means (e.g., email message, file upload, etc.).

[0044]

[0054] In operation 414, confirmation is received from the peptide manufacturer regarding one or more of the vaccine regimens. For example, this confirmation may be received as a response from the peptide manufacturer's computing device indicating whether the vaccine regimen includes colytic peptides assigned to the peptide groups. If all peptide groups in the vaccine regimen contain colytic peptides, instructions may be sent to the peptide manufacturer to manufacture solutions (or pills) corresponding to such peptide groups.

[0045]

[0055] Figure 5 shows an example flow for allocating peptides to peptide groups for a cancer vaccine according to an embodiment of the present disclosure. This flow may represent a tournament-style allocation of peptides and may be implemented as a sub-operation of operation 410 in Figure 4. For purposes of explanation, the flow in Figure 5 is described in relation to a specific number of peptides and a specific number of peptide groups, and some of its operations are further illustrated in Figures 6-10.

[0046]

[0056] As shown, the flow may begin at operation 502, where optimization parameters are defined. In one example, the optimization parameters relate to reducing the likelihood of immunodominance. For example, immunodominance may occur when many neoantigens are simultaneously presented to T cells because the immune system is likely to respond to only a subset of them, rather than all of them. Thus, the optimization parameter may be sequence length. In particular, a peptide group (i.e., the sum of the peptide lengths in the group) is not too long. The length of a peptide group may be equal to the sum of the lengths of the peptides assigned to the group. The lengths of all peptide groups may be roughly similar (e.g., within ±10% of each other). The length of a peptide group may be defined as the sum of the amino acid sequence lengths of the peptides assigned to the group. This optimization parameter ensures that the number of peptides with a particular length (e.g., 9-10 mer peptides) is similar across peptide groups. Alternatively, peptides may be labeled as long or short (e.g., long means longer than 20 amino acids, short means shorter than 20 amino acids), or more granular length resolution may be used. In this case, the length of the peptide group can be defined as a certain distribution of long and short peptides (e.g., four long peptides and one short peptide). A similar distribution across peptide groups can be targeted in the allocation. For example, no more than two short peptides are allowed in a peptide group. Alternatively, one peptide group may contain short, top-ranked Class I peptides (e.g., those with the highest Class I immunogenicity likelihood), and the remaining three peptide groups may have comparable lengths.

[0047]

[0057] In another example, the optimization parameters relate to class I immunogenicity and / or class II immunogenicity. For example, the class I immunogenic likelihood of a peptide predicted for a subject by an artificial intelligence model represents the class I score of the peptide. Similarly, the class II immunogenic likelihood of a peptide predicted for a subject by an artificial intelligence model represents the class II score of the peptide. A statistical measure (e.g., median, mean, sum, etc.) for each peptide group can be derived from the class I score and / or class II score of the peptides assigned to that group. Peptide groups should have similar class I score and / or class II score (e.g., within a similarity range relative to each other).

[0048]

[0058] In yet another example, the optimization parameters relate to reducing the likelihood of immunodominance and immunogenic response. For example, the optimization parameters can be multidimensional, with each dimension containing one of the above parameters. A dimensionality reduction algorithm, such as principal component analysis (PCA), can be used to define a representative scalar for each peptide group for comparison with those of the remaining peptide groups. When using PCA, the class I score, class II score, and / or amino acid sequence length for each peptide are converted into a single scalar by taking the first principal component of the individual scores and projecting the neoantigen-specific score onto the first principal component. The assignment may require that the W first principal components be approximately equal between pools.

[0049]

[0059] Therefore, in operation, the optimization parameter M is

number

[0050]

[0060] In operation 504, one or more peptides are filtered out based on the filtering rules. For example, the filtering rules may remove any neo-antigenic peptides that meet any of the following criteria: (i) the peptide contains two or more cysteines in its sequence, (ii) the peptide cannot be synthesized by a peptide manufacturer, or (iii) the peptide is found to be insufficiently soluble.

[0051]

[0061] In operation 506, N peptides are selected. For example, N is equal to 20, including 18 neo-antigenic peptides and two copies of a PADRE peptide (or any other type of universal peptide). The two copies of the PADRE peptide correspond to an overlap rule that allows only two PADRE peptides to overlap. None of the 18 neo-antigenic peptides can be duplicates, reflecting an overlap rule that prohibits overlap of neo-antigenic peptides. Alternatively, if the overlap rule allows overlap of neo-antigenic peptides, some of the 18 neo-antigenic peptides can be copies of each other, with the number of copies limited by the overlap rule. Furthermore, such overlap rules can specify which neo-antigenic peptides can be duplicated (e.g., those with a Class I score or Class II score above a certain threshold, such as those with a Class I score or Class II score above a threshold score). i (those with:

[0052]

[0062] In operation 508, a value of M is assigned to the PADRE peptide (the same value may be assigned to two copies). This value may be a default value depending on how the peptides are to be ranked so that they can be sorted in descending order. For example, in the case of ranking by Class II score, the PADRE peptide is assigned an M value (e.g., a value of 0.8) that is superior to any of the 18 neo-antigenic peptides. In this operation and other operations in the flow, another type of PADRE peptide may additionally or alternatively be used. If so, a value of M may be assigned to such universal peptide, which may depend on the known immunogenic response and / or properties of the universal peptide, and is used in the remaining operations in the flow.

[0053]

[0063] In operation 510, the 20 peptides are sorted based on their individual M values ​​(e.g., M i For example, peptides are sorted based on their M i The results are ranked in descending order according to the following criteria and then sorted in descending order. An example of this sorting is shown in Figure 6.

[0054]

[0064] Referring to FIG. 6, an example of peptide sorting 602 according to an embodiment of the present disclosure is shown. A table 610 of 20 peptides is first identified (e.g., according to operation 506 of FIG. 5). Table 610 lists each peptide's identifier (ID), its Class I score (0 for PADRE peptide copies), its Class II score (default value of 0.8 for PADRE peptides), if it contains a cysteine ​​("0" indicates no cysteine, "1" otherwise), the peptide's label, and whether the peptide is long or short (e.g., "0" indicates short, such as less than 20 amino acids in length; "1" indicates long). In table 610, neoantigens with IDs "2" and "3" were removed according to the filtering rules.

[0055]

[0065] Sorting 602 is performed. In the example of Figure 6, this sorting 602 uses Class II scores, although sorting by Class I scores, sequence length, and / or first principal component (not shown in table 610) is also possible. The result of sorting 602 is an updated table 620, where peptides are identified in descending order of their Class II scores. Compared to table 610, the PADRE peptide copy remains top-ranked, but peptide with ID "42" has been sorted to third place, and so on.

[0056]

[0066] 5, in operation 512, the sorted peptides are associated with P tiers. Generally, the number P is a positive integer equal to the number of target peptides per peptide group, e.g., 5. In one example, the top four peptides are associated with tier "0," the next four peptides are associated with tier "1," and so on. An illustration of this tier association is shown in FIG. 7.

[0057]

[0067] Referring to Figure 7, an example of a tier definition according to an embodiment of the present disclosure is shown. In the example, table 620 of Figure 6 is updated by adding 702 peptide-tier associations to new columns in the table, resulting in updated table 710. The added tier column identifies the tier with which each peptide is associated. For example, each of the four ranked peptides is associated with tier "0," while each of the four worst-ranked peptides is associated with the final tier (e.g., tier "4").

[0058]

[0068] Referring back to FIG. 5, in operation 514, the peptides are assigned to peptide groups based on their association with a tier. For example, four peptide groups should be defined, each identifying five of the 20 peptides. Recall that each tier is associated with four peptides. Thus, one peptide from each tier can be assigned to each peptide group, resulting in four peptide groups, each with five peptides. In one example of this assignment, the top-ranked peptides in the first tier (e.g., the peptides with the highest M in this tier) are assigned to the first tier.i ) and the worst-ranked peptide in the final tier (e.g., the peptide with the smallest M i (having a rank of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 110, 119, 120, 121, 122, 123, 124, 125, 130, 131, 132, 133, 134, 135, 140, 1

[0059]

[0069] Referring to FIG. 8, an example of tier-based assignment 802 of peptides to peptide groups according to an embodiment of the present disclosure is shown. In the example, table 810 of FIG. 8 corresponds to table 710 of FIG. 7 and is used in tier-based assignment 802. The peptides have already been ranked according to their Class II scores (although different optimization parameters M are possible). Therefore, the rankings described below refer to Class II scores. Peptide ID "0" is ranked highest in tier "0" and is assigned to the first peptide group "A." Peptide ID "12" is ranked worst in the final tier "4" and is also assigned to the first peptide group "A." Next, peptide ID "1" is ranked second highest in tier "0" and is assigned to the second peptide group "B." Peptide ID "7" is ranked second worst in the final tier "4" and is also assigned to the second peptide group "B." Similarly, peptide ID "42" is ranked third highest in tier "0" and is assigned to the third peptide group "C". Peptide ID "4" is ranked third worst in the final tier "4" and is also assigned to the third peptide group "C". Furthermore, peptide ID "22" is ranked fourth highest in tier "0" and is assigned to the fourth peptide group "D". Peptide ID "8" is ranked fourth worst in the final tier "4" and is also assigned to the fourth peptide group "D". This tier-based assignment 802 is repeated in a similar manner for the remaining tiers.

[0060]

[0070] 5, in operation 516, a determination is made as to whether one or more assignment rules have been satisfied. If so, operation 518 follows operation 516. If not, operation 520 follows operation 516. The assignment rules may specify one or more of the following criteria for the assignment rules to be satisfied: (i) no more than one or two PADRE peptides are found in a peptide group, (ii) the neo-antigen peptides do not overlap (or a maximum number of neo-antigen peptide overlaps is satisfied), and / or (iii) the peptide groups do not contain two or more peptides containing cysteine.

[0061]

[0071] In operation 518, the group of peptides is labeled as belonging to a preferred vaccine regimen. The "preferred" label may be a relative term used to indicate that, if the peptides are found to be co-soluble when assigned, this vaccine regimen should be used instead of another vaccine regimen that does not have the "preferred" label for vaccine production. As indicated by the dashed arrow, operation 520 can follow operation 518, or optionally, once a preferred vaccine regimen has been identified, operation 524 follows operation 518.

[0062]

[0072] In operation 520, a determination is made as to whether a sufficient number of vaccine regimens have been defined. The minimum number is one, but many more vaccine regimens can be targeted. If not, operation 520 is followed by operation 522. Otherwise, operation 522 is followed by operation 524.

[0063]

[0073] In operation 522, the peptides are shuffled by tier or the tiers are redefined. If no vaccine regimens are defined from the 20 selected peptides (according to operation 506), or if additional vaccine regimens are to be defined from these peptides, the positions of all peptides can be randomly shuffled in a given tier, but the peptide-tier associations cannot be changed. An example of this intra-tier shuffling is shown in FIG. 9. If more than one vaccine regimen is to be defined from a different set of 20 peptides, the tiers can be redefined. An example of this tier redefinition is shown in FIG. 10. Furthermore, if the tier-based shuffling of FIG. 9 does not result in a sufficient number of vaccine regimens (e.g., for a target number), the tier redefinition of FIG. 10 can also be applied.

[0064]

[0074] Referring to Figure 9, an example of a hierarchical-based shuffle 902 according to an embodiment of the present disclosure is shown. In the example, table 910 of Figure 9 corresponds to table 820 of Figure 8 and is used as the starting point for hierarchical-based shuffle 902. The result of hierarchical-based shuffle 902 is table 920.

[0065]

[0075] In table 910, tier "0" is associated with peptides having IDs "0," "1," "42," and "22," which were sorted first in this sort order based on their Class II scores. Similarly, tier "4" is associated with peptides having IDs "8," "4," "7," and "12," which were also sorted first in this sort order based on their Class II scores. The tier-based shuffle 902 randomly updates the sort order in each tier, regardless of their Class II scores. As a result, in table 920, tier "0" is still associated with peptides having IDs "0," "1," "42," and "22." However, these peptides are now randomly sorted, and the updated sort order lists the peptides in descending order of IDs "0," "22," "1," and "42." Also in table 920, tier "4" is still associated with peptides having IDs "8," "4," "7," and "12." However, these peptides are now randomly sorted, and the updated sort order lists peptides in descending order of IDs 4, 7, 8, and 12. Similar random shuffling can be done at each tier.

[0066]

[0076] 10, an example of a hierarchy redefinition 1002 according to an embodiment of the present disclosure is shown. In the example, table 1010 in Figure 10 corresponds to table 920 in Figure 9, or a new table in which one or more peptides have been replaced by one or more peptides that were not selected in operation 506 (e.g., a neo-antigenic peptide has been replaced by another). Table 1010 is used as the starting point for the hierarchy redefinition, and the result is table 1020.

[0067]

[0077] In one example, two new super-tiers can be defined based on whether peptides are below or above the median Class II score (or some other statistical measure). The peptides in each super-tier are randomly shuffled, resulting in table 1020. For example, peptide with ID "0" remains in tier "0" after this reshuffling, but the association of peptide with ID "22" changes from tier "0" to tier "1."

[0068]

[0078] 5, there is a loop from operation 522 to operation 514. In this manner, new peptide-peptide group assignments may be identified after the hierarchical-based shuffling 902 of FIG. 9 and / or the redefinition of the hierarchy of FIG. 10.

[0069]

[0079] The vaccine regimens are output in operation 524. For example, each vaccine regimen identifies the peptide-peptide group assignment and whether it is a preferred regimen.

[0070]

[0080] In the above example, the neo-antigen peptide may be predicted to have a high Class I score and / or a high Class II score (e.g., by comparing these scores to corresponding threshold scores). If so, the neo-antigen peptide may allow for overlap of this neo-antigen in two or more of the peptide groups. Additionally or alternatively, the subject may have a localized tumor (e.g., one growing in one of the four quadrants described in Figure 1). Because this neo-antigen peptide is predicted to have a high immunogenic response, the vaccine plan can associate the peptide group to which this peptide is assigned with the quadrant (or location) into which the resulting vaccine shot should be injected. For example, if the tumor is growing in quadrant "Q1," the vaccine plan may indicate that a vaccine shot containing this high-efficacy peptide should be injected into the left upper arm.

[0071]

[0081] 11 illustrates aspects of an exemplary environment for practicing aspects according to various embodiments. This architecture may be used to implement some or all of the components of the computer systems described herein above (e.g., computer system 310 of FIG. 3). The computer architecture illustrated in FIG. 11 illustrates a server computer, workstation, desktop computer, laptop, tablet, network appliance, personal digital assistant ("PDA"), electronic reader, digital mobile phone, or other computing device that may be utilized to execute any aspects of the software components presented herein.

[0072]

[0082] Computer 1100 includes a baseboard 1102, or "motherboard," which is a printed circuit board to which numerous components or devices may be connected via a system bus or other electrical communication paths. In an exemplary embodiment, one or more central processing units ("CPUs") 1104 operate in conjunction with a chipset 1106. CPUs 1104 may be standard programmable processors that perform arithmetic and logical operations necessary for the operation of computer 1100.

[0073]

[0083] The CPU 1104 operates by transitioning from one discrete physical state to the next through the manipulation of switching elements that differentiate between these states. Switching elements may generally include electronic circuits that maintain one of two binary states, such as flip-flops, and electronic circuits that provide an output state based on a logical combination of the states of one or more other switching elements, such as logic gates. These basic switching elements may be combined to create more complex logic circuits, including registers, adders / subtractors, arithmetic logic units, floating-point units, and the like.

[0074]

[0084] Chipset 1106 provides an interface between CPU 1104 and the remaining components and devices on baseboard 1102. Chipset 1106 may provide an interface to random access memory (“RAM”) 1108, which is used as the main memory within computer 1100. Chipset 1106 may further provide an interface to a computer-readable storage medium, such as read-only memory (“ROM”) 1110 or non-volatile RAM (“NVRAM”), for storing the basic routines that start up computer 1100 and help transfer information between various components and devices. ROM 1110 or NVRAM may also store other software components necessary for operation of computer 1100 according to the embodiments described herein.

[0075]

[0085] The computer 1100 may operate in a networked environment using logical connections to remote computing devices and computer systems through a network, such as a local area network 1120. The chipset 1106 may include functionality for providing network connectivity through a NIC 1112, such as a Gigabit Ethernet adapter. The NIC 1112 may connect the computer 1100 to other computing devices through the network 1120. It should be understood that multiple NICs 1112 may be present in the computer 1100 to connect the computer to other types of networks and remote computer systems.

[0076]

[0086] The computer 1100 may be connected to a mass storage device 1118, which provides non-volatile storage for the computer. The mass storage device 1118 may store system programs, application programs, other program modules, and data, as described in more detail herein. The mass storage device 1118 may be connected to the computer 1100 through a storage controller 1114, which is connected to the chipset 1106. The mass storage device 1118 may be comprised of one or more physical storage devices. The storage controller 1114 may interface with the physical storage devices through a Serial Attached SCSI ("SAS") interface, a Serial Advanced Technology Attachment ("SATA") interface, a Fibre Channel ("FC") interface, or any other type of interface for physically connecting and transferring data between the computer and the physical storage devices.

[0077]

[0087] The computer 1100 may store data on the mass storage device 1118 by transforming the physical state of the physical storage device to reflect the stored information. The specific transformation of the physical state may depend on various factors in different implementations of the present specification. Examples of such factors may include, but are not limited to, the technology used to implement the physical storage device, whether the mass storage device 1118 is characterized as a primary or secondary storage device, etc.

[0078]

[0088] For example, computer 1100 may store information in mass storage device 1118 by issuing instructions via storage controller 1114 to change the magnetic properties of a particular location in a magnetic disk drive, the reflective or refractive properties of a particular location in an optical storage device, or the electrical properties of a particular capacitor, transistor, or other discrete component in a solid-state storage device. Other transformations of physical media are possible without departing from the scope and spirit of this specification, and the foregoing examples are provided solely to facilitate this description. Computer 1100 may further read information from mass storage device 1118 by detecting the physical state or properties of one or more particular locations in the physical storage device.

[0079]

[0089] In addition to the mass storage device 1118 described above, the computer 1100 may access other computer-readable storage media to store and retrieve information such as program modules, data structures, or other data. It should be appreciated by those skilled in the art that computer-readable storage media can be any available media that provide non-transitory data storage and that can be accessed by the computer 1100.

[0080]

[0090] By way of example and not limitation, computer-readable storage media may include volatile and non-volatile, removable and non-removable media implemented in any manner or technology, including, but not limited to, RAM, ROM, erasable programmable ROM ("EPROM"), electrically erasable programmable ROM ("EEPROM"), flash memory or other solid-state memory technology, compact disc ROM ("CD-ROM"), digital versatile disc ("DVD"), high-definition DVD ("HD-DVD"), BLU-RAY or other optical storage devices, magnetic cassettes, magnetic tape, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to non-temporarily store desired information.

[0081]

[0091] The mass storage device 1118 may store an operating system 1130 utilized to control the operation of the computer 1100. According to one embodiment, the operating system includes the LINUX operating system. According to another embodiment, the operating system includes the WINDOWS® SERVER operating system manufactured by MICROSOFT Corporation. According to further embodiments, the operating system may include the UNIX® or SOLARIS® operating systems. It should be understood that other operating systems may also be utilized. The mass storage device 1118 may store other system or application programs and data utilized by the computer 1100. The mass storage device 1118 may also store other programs and data not specifically identified herein.

[0082]

[0092] In one embodiment, the mass storage device 1118 or other computer-readable storage medium is encoded with computer-executable instructions that, when loaded into the computer 1100, transform the computer from a general-purpose computing system into a special-purpose computer capable of implementing the embodiments described herein. These computer-executable instructions transform the computer 1100 by specifying how the CPU 1104 transitions between states, as described above. According to one embodiment, the computer 1100 accesses a computer-readable storage medium that stores computer-executable instructions that, when executed by the computer 1100, perform the various routines described above. The computer 1100 may also include a computer-readable storage medium for performing any of the other computer-implemented operations described herein.

[0083]

[0093] Computer 1100 may also include one or more input / output controllers 1116 for receiving and processing input from several input devices, such as a keyboard, mouse, touchpad, touchscreen, electronic stylus, or other types of input devices. Similarly, input / output controller 1116 may provide output to a display, such as a computer monitor, flat panel display, digital projector, printer, plotter, or other type of output device. It will be understood that computer 1100 may not include all of the components shown in FIG. 11 , may include other components not explicitly shown in FIG. 11 , or may utilize an entirely different architecture than that shown in FIG. 11 . It will also be understood that many computers, such as computer 1100, may be utilized in combination to embody aspects of the various techniques disclosed herein.

[0084]

[0094] Also provided are vaccines, e.g., vaccines comprising a plurality of different vaccine compositions, each comprising a different set of peptides from a plurality of peptides predicted to elicit an immunogenic response in a subject, each vaccine composition having properties that are within a similarity range of the properties of the remaining vaccine compositions (e.g., as described above), the properties including at least one of a Class I immunogenicity score, a Class II immunogenicity score, or an amino acid sequence length. Each vaccine may be in a different container or otherwise separated from one another, e.g., in different ampoules or vials.

[0085]

[0095] Also provided is a vaccine comprising a plurality of different vaccine compositions identified as described herein. For example, each vaccine composition can comprise a different set of peptides identified from a plurality of different groups of peptides, the plurality of different groups being defined by at least: determining peptide characteristics of peptides from the different peptides to be assigned to the plurality of different groups, where the different peptides are predicted to generate an immunogenic response in a subject; determining that the peptide should be assigned to a first group from the plurality of different groups based at least in part on the peptide characteristics, where the first group has a first group characteristic based at least in part on the peptide characteristic of the first peptide to be assigned to the first group, and the first group characteristic is within a range of similarity to a second group characteristic of a second group from the plurality of different groups; and generating information indicating that the peptide has been assigned to the first group.

[0086]

[0096] Also provided are methods of inducing an immune response in a subject (e.g., a human, other mammal, or other animal, e.g., a bird) using the vaccines described herein. In some embodiments, the method can include administering to the subject one or more vaccine compositions of peptides from a plurality of vaccine compositions (e.g., as described above), thereby inducing an immune response to one or more peptides in the one or more vaccine compositions.

[0087]

[0097] In some embodiments, the vaccine is a cancer vaccine, and the plurality of vaccine compositions are generated from information assigning neo-antigenic peptides predicted to generate an immunogenic response in the subject to a plurality of groups of peptides, wherein a first group of peptides in the plurality of groups has a first group characteristic that is within a range of similarity to a second group characteristic of a second group of peptides in the plurality of groups. In some embodiments, two or more vaccine compositions from the plurality are administered to the subject. In some embodiments, the subject is a single subject whose neo-antigenic peptides are predicted to predict an immunogenic response and are assigned to a plurality of groups.

[0088]

[0098] Vaccines can be formulated using an amount of each peptide sufficient to generate an immune response. In some embodiments, vaccines are formulated to contain a final concentration of each peptide ranging from 0.2 to 200 μg / ml, e.g., 5 to 50 μg / ml. It will be understood that other concentrations may be used, and that each peptide can be assayed separately or together to determine the optimal concentration for inducing an immune response.

[0089]

[0099] Peptides can be modified, for example, to change their in vivo stability.For example, including one or more D-amino acids in peptides typically improves stability, especially when D-amino acid residues are substituted at one or both ends of the peptide sequence, or when peptides can be, for example, PEGylated.Stability can be assayed in various ways, such as measuring the half-life of protein during incubation with peptidase or human plasma or serum.Some such protein stability assays have been described (for example, Verhoef et al., Eur.J.Drug Metab.Pharmacokin.11:291-302(1986)).

[0090]

[0100] In some embodiments, vaccines or vaccine compositions can be prepared as injections, either as liquid solutions or suspensions. Injections can be subcutaneous, intramuscular, intravenous, intraperitoneal, intrathecal, intradermal, intraepidermal, or via "gene gun." Other types of administration include electroporation, implantation, suppository, oral ingestion, rectal application, inhalation, aerosolization, or nasal spray or drops. Solid forms suitable for dissolving or suspending in a liquid vehicle before injection can also be prepared. Preparations can also be emulsified or encapsulated in liposomes to enhance adjuvant effect.

[0091]

[0101] Liquid formulations may contain, for example, oils, polymers, vitamins, carbohydrates, amino acids, salts, buffers, albumin, surfactants, or bulking agents. Exemplary carbohydrates include sugars or sugar alcohols, such as monosaccharides, disaccharides, or polysaccharides, or water-soluble glucans. Sugars or glucans may include, for example, fructose, dextrose, lactose, glucose, mannose, sorbose, xylose, maltose, sucrose, dextran, pullulan, dextrin, α- and β-cyclodextrin, soluble starch, hydroxyethyl starch, and carboxymethylcellulose, or mixtures thereof. "Sugar alcohol" is defined as a C4-C8 hydrocarbon having an -OH group and includes galactitol, inositol, mannitol, xylitol, sorbitol, glycerol, and arabitol. These sugars or sugar alcohols may be used individually or in combination. There is no set limit to the amount of sugar or sugar alcohol used, as long as it is soluble in the aqueous formulation. In some embodiments, the sugar or sugar alcohol concentration is 1.0% (w / v) to 7.0% (w / v), e.g., 2.0 to 6.0% (w / v). Exemplary amino acids include levorotatory (L) forms of carnitine, arginine, and betaine. However, other amino acids may be added. Exemplary polymers include polyvinylpyrrolidone (PVP) with an average molecular weight of 2,000 to 3,000, or polyethylene glycol (PEG) with an average molecular weight of 3,000 to 5,000. In some embodiments, a buffer can be used in the composition to minimize pH changes in the solution before lyophilization or after reconstitution. Any physiological buffer may be used, but in some cases, the buffer may be selected from citrate buffer, phosphate buffer, succinate buffer, and glutamate buffer, or mixtures thereof.

[0092]

[0102] The terms "polypeptide," "peptide," and "protein" are used interchangeably herein to refer to a polymer of amino acid residues. This term encompasses amino acid polymers in which one or more amino acid residues are artificial chemical mimetics of a corresponding naturally occurring amino acid, as well as naturally occurring and non-naturally occurring amino acid polymers.

[0093]

[0103] The term "amino acid" refers to naturally occurring and synthetic amino acids, as well as amino acid analogs and amino acid mimetics that function similarly to naturally occurring amino acids. Naturally occurring amino acids are those encoded by the genetic code, as well as amino acids that are later modified, such as hydroxyproline, γ-carboxyglutamate, and O-phosphoserine. Amino acid analogs refer to compounds that have the same basic chemical structure as naturally occurring amino acids, i.e., an α-carbon bonded to a hydrogen, a carboxyl group, an amino group, and an R group, such as homoserine, norleucine, methionine sulfoxide, and methionine methylsulfonium. Such analogs have modified R groups (e.g., norleucine) or modified peptide backbones, but retain the same basic chemical structure as naturally occurring amino acids. Amino acid mimetics refer to chemical compounds that have a structure that differs from the general chemical structure of an amino acid but function similarly to a naturally occurring amino acid.

[0094]

[0104] Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. It will, however, be apparent that various modifications and changes may be made thereto without departing from the broader spirit and scope of the present disclosure as set forth in the appended claims.

[0095]

[0105] Example embodiments of the present disclosure may be described in light of the following clauses.

[0096]

[0106] Clause 1. A system comprising one or more processors and one or more memories storing computer readable instructions that, when executed by the one or more processors, configure the system to: determine, for a subject, different peptides to be assigned to different groups of a cancer vaccine for the subject, each group comprising two or more different peptides; determine peptide characteristics of peptides from the different peptides, the peptide characteristics comprising at least one of a class I immunogenicity score, a class II immunogenicity score, or an amino acid sequence length; define a first group of the different groups by assigning a first peptide from the different peptides to the first group; the first peptide is assigned to the first group based at least in part on the peptide characteristics; the first group has a first group characteristic comprising at least one measure of the class I immunogenicity score, the class II immunogenicity score, or the amino acid sequence length of the first peptide; and generate information indicating that the first peptide is assigned to the first group.

[0097]

[0107] Clause 2. The system of Clause 1, wherein the one or more memories store further computer-readable instructions that, when executed by the one or more processors, configure the system to: determine a sorted order of the distinct peptides based at least in part on individual peptide characteristics; associate a first subset of the distinct peptides with a first tier and associate a second subset of the distinct peptides with a second tier based at least in part on the sorted order; the peptides are first peptides associated with the first tier; and assign the first peptide associated with the first tier and the second peptide associated with the second tier to a first group.

[0098]

[0108] Clause 3. The system of clause 2, wherein the sorted order indicates that the first peptide has the top-ranked peptide property and the second peptide has the worst-ranked peptide property.

[0099]

[0109] Clause 4. The system of any one of clauses 1 to 3, wherein the one or more memories store further computer-readable instructions that, when executed by the one or more processors, configure the system to execute a combinatorial optimization algorithm configured to: (i) determine potential assignments of different peptides to different groups; (ii) calculate, for each of the different groups, a group property based at least in part on peptide properties of the peptides potentially assigned to the group; and (iii) reduce differences between the group properties of the different groups.

[0100]

[0110] Clause 5. A method comprising: determining peptide characteristics of peptides from different peptide groups to be assigned to different groups of a vaccine; determining that the peptide should be assigned to a first group from the different groups based at least in part on the peptide characteristics, wherein the first group has a first group characteristic based at least in part on the peptide characteristics of the first peptide to be assigned to the first group, and the first group characteristic is within a range of similarity to a second group characteristic of a second group from the different group; and generating information indicating that the peptide has been assigned to the first group.

[0101]

[0111] Clause 6. The method of clause 5, further comprising determining that different peptides are associated with the subject, wherein the different groups are assigned the same number of peptides, and the different groups are defined for a cancer vaccine of the subject.

[0102]

[0112] Clause 7. The method of any one of clauses 5 to 6, further comprising determining that the peptide is also assigned to a second group and removing the second group from the candidate set of groups for the vaccine.

[0103]

[0113] Clause 8. The method of any one of clauses 5 to 7, further comprising determining that two or more peptides having a particular amino acid are assigned to a second group, and removing the second group from the candidate set of vaccine groups.

[0104]

[0114] Clause 9. The method of any one of clauses 5 to 8, further comprising defining distinct groups by assigning different peptides to the distinct groups, wherein only a subset of the distinct groups are assigned PADRE peptides, and no more than one PADRE peptide is assigned per subset group.

[0105]

[0115] Clause 10. The method of any one of clauses 5 to 8, further comprising defining distinct groups by assigning distinct peptides to distinct groups, wherein the distinct peptides comprise neo-antigenic peptides, and the neo-antigenic peptides are assigned to only one of the distinct groups.

[0106]

[0116] Clause 11. The method of any one of clauses 5 to 10, further comprising: determining that the peptide is a neo-antigenic peptide having a peptide characteristic score greater than a threshold score, wherein the peptide characteristic score comprises at least one of a Class I immunogenic response score or a Class II immunogenic response score; and defining different groups by assigning different peptides to different groups, wherein the neo-antigenic peptides are assigned to two or more groups based at least in part on peptide characteristic scores greater than the threshold score.

[0107]

[0117] Clause 12. The method of any one of clauses 5 to 11, further comprising determining that different peptides are associated with subjects having tumors in a region, determining that the peptides are neo-antigenic peptides having peptide characteristic scores greater than a threshold score, and associating a first group with the region based at least in part on the neo-antigenic peptides assigned to the first group.

[0108]

[0118] Clause 13. One or more non-transitory computer-readable storage media storing instructions that, when executed on the system, cause the system to perform operations including: determining peptide characteristics of peptides from different peptides to be assigned to different groups of the vaccine; determining, based at least in part on the peptide characteristics, that the peptide should be assigned to a first group from the different groups, wherein the first group has a first group characteristic based at least in part on the peptide characteristics of the first peptide to be assigned to the first group, and the first group characteristic is within a range of similarity to a second group characteristic of a second group from the different group; and generating information indicating that the peptide has been assigned to the first group.

[0109]

[0119] Clause 14. One or more non-transitory computer-readable storage media according to clause 13, further storing additional instructions that, when executed on the system, cause the system to perform operations including defining different groups by assigning different peptides to different groups, wherein the different groups are assigned the same number of peptides.

[0110]

[0120] Clause 15. The one or more non-transitory computer-readable storage media of any one of clauses 13 to 14, further storing additional instructions that, when executed on the system, cause the system to perform operations including: determining a sorted order of the distinct peptides based at least in part on individual peptide characteristics; associating a first subset of the distinct peptides with a first tier and a second subset of the distinct peptides with a second tier based at least in part on the sorted order, wherein the peptides are first peptides associated with the first tier; and assigning the first peptides associated with the first tier and the second peptides associated with the second tier to a first group.

[0111]

[0121] Clause 16. The one or more non-transitory computer-readable storage media of Clause 15, wherein the second tier is associated with second peptides having a second sorted order, and the one or more non-transitory computer-readable storage media store further instructions that, when executed on the system, cause the system to perform operations including determining an updated order of the second subset by shuffling the second sorted order, defining an updated first group based at least in part on the updated order, associating the first group with a first vaccine regimen, and associating the updated first group with a second vaccine regimen.

[0112]

[0122] Clause 17. The one or more non-transitory computer-readable storage media of clause 16, further storing additional instructions that, when executed on the system, cause the system to perform operations including: determining that two or more peptides having a particular amino acid are not assigned to each group associated with the first vaccine regimen; and generating information indicating that the first vaccine regimen is preferred over the second vaccine regimen.

[0113]

[0123] Clause 18. The one or more non-transitory computer-readable storage media of Clause 15, wherein the first tier and the second tier are sorted in a second sorted order, and the one or more non-transitory computer-readable storage media store further instructions that, when executed on the system, cause the system to perform operations including: determining an updated order of the first tier and the second tier by shuffling the second sorted order; and defining an updated first group based at least in part on the updated order.

[0114]

[0124] Clause 19. The one or more non-transitory computer-readable storage media of any one of clauses 13 to 18, further storing additional instructions that, when executed on the system, cause the system to perform operations including: determining a total number of peptides to assign to different groups; generating a peptide set by associating the peptide with the peptide set and separating a second peptide from the different peptides from the peptide set, where the size of the peptide set is equal to the total number; defining different groups by assigning a subset of the peptide set to different groups; generating an updated peptide set by separating the peptide with the peptide set and associating a second peptide with the peptide set, where the size of the updated peptide set is equal to the total number; and defining additional groups by assigning a subset of the updated peptide set to the additional group.

[0115]

[0125] Clause 20. The one or more non-transitory computer-readable storage media of any one of clauses 13 to 19, further storing additional instructions that, when executed on the system, cause the system to perform operations including: (i) determining potential assignments of different peptides to different groups; (ii) calculating, for each of the different groups, a group property based at least in part on peptide properties of peptides potentially assigned to the group; and (iii) executing a combinatorial optimization algorithm configured to reduce differences between the group properties of the different groups.

[0116]

[0126] Clause 21. A vaccine comprising a plurality of different vaccine compositions, each vaccine composition corresponding to a different group of peptides from a plurality of different groups of peptides, the plurality of different groups being defined by: determining peptide characteristics of peptides from the different peptides to be assigned to the plurality of different groups, wherein the different peptides are predicted to generate an immunogenic response in the subject; determining that the peptide should be assigned to a first group from the plurality of different groups based at least in part on the peptide characteristics, wherein the first group has a first group characteristic based at least in part on the peptide characteristic of the first peptide to be assigned to the first group, the first group characteristic being within a range of similarity to a second group characteristic of a second group from the plurality of different groups; and generating information indicating that the peptide has been assigned to the first group.

[0117]

[0127] Clause 22. The vaccine peptide of clause 21, wherein the first group characteristic is within a range of similarity with the second group characteristic based at least in part on a comparison of the Class I immunogenicity score, Class II immunogenicity score, or amino acid sequence length of peptides included in the first group and the second group.

[0118]

[0128] Clause 23. A method of inducing an immune response in a subject, comprising administering to the subject one or more vaccine compositions of peptides from a plurality of vaccine compositions, thereby inducing an immune response to one or more peptides in the one or more vaccine compositions, wherein the plurality of vaccine compositions are generated from information assigning neo-antigenic peptides predicted to generate an immunogenic response in the subject to a plurality of groups of peptides, wherein a first group of peptides in the plurality of groups has a first group characteristic that is within a range of similarity to a second group characteristic of a second group of peptides in the plurality of groups.

[0119]

[0129] Clause 24. The method of clause 23, wherein two or more groups of vaccine compositions from the plurality are administered to the subject.

[0120]

[0130] Clause 25. The method of any one of clauses 23 to 24, wherein the subject is a single subject in which the neo-antigenic peptide is predicted to predict an immunogenic response and is assigned to multiple groups.

[0121]

[0131] Clause 26. A vaccine comprising a plurality of different vaccine compositions, each vaccine composition comprising a different set of peptides from a plurality of peptides predicted to elicit an immunogenic response in a subject, each vaccine composition having a property that is within a range of similarity of the properties of the remaining vaccine compositions, the property comprising at least one of a class I immunogenicity score, a class II immunogenicity score, or an amino acid sequence length.

[0122]

[0132] Other variations are within the spirit and scope of the present disclosure. Accordingly, while the disclosed technology is susceptible to various modifications and alternative constructions, specific illustrated embodiments thereof are shown in the drawings and have been described above in detail. It should be understood, however, that there is no intention to limit the invention to the particular form or forms disclosed, but on the contrary, the intention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined by the appended claims.

[0123]

[0133] Use of the terms "a," "an," and "the" and similar referents in the context of describing the disclosed embodiments (particularly in the context of the claims below) should be construed to encompass both the singular and the plural unless otherwise indicated herein or clearly contradicted by context. The terms "comprising," "having," "including," and "containing" should be construed as open-ended terms (i.e., meaning "including, but not limited to") unless otherwise indicated. The term "connected" should be construed as partially or wholly contained in, attached to, or joined together, even if there is intervening material. Recitation of ranges of values ​​herein is merely intended to serve as a shorthand method of individually referring to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated herein as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or clearly contradicted by context. The use of any and all examples or exemplary language (e.g., "etc.") provided herein is intended merely to better clarify embodiments of the invention and does not limit the scope of the invention unless specifically claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.

[0124]

[0134] Preferred embodiments of the present disclosure are described herein, including the best mode known to the inventors for carrying out the invention. Variations of these preferred embodiments may become apparent to those skilled in the art upon reading the foregoing description. The inventors expect those skilled in the art to employ such variations as appropriate, and the inventors intend the invention to be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, the invention includes any combination of the above-described elements in all possible variations thereof unless otherwise indicated herein or clearly contradicted by context.

[0125]

[0135] All references cited in this specification, including publications, patent applications, and patents, are herein incorporated by reference to the same extent as if each reference was individually and specifically indicated to be incorporated by reference and was set forth in its entirety herein.

Claims

1. 1. A system comprising: one or more processors; When executed by the one or more processors, the system determining, for a subject, different peptides to be assigned to different groups of a cancer vaccine for said subject, each group comprising two or more different peptides; determining peptide characteristics of peptides from the different peptides, the peptide characteristics comprising at least one of a class I immunogenicity score, a class II immunogenicity score, or an amino acid sequence length; defining said first group of said distinct groups by assigning a first peptide from said distinct groups to a first group; the first peptide is assigned to the first group based at least in part on the peptide characteristics; the first group has a first group characteristic comprising at least one measure of a class I immunogenicity score, a class II immunogenicity score, or an amino acid sequence length of the first peptide; and the first group characteristic is within a similarity range to a second group characteristic of a second group from the different group; and generating information indicating that said first peptide is assigned to said first group; one or more memories storing computer readable instructions for configuring the Including, the system.

2. The one or more memories, when executed by the one or more processors, configure the system to: determining a sorted order of the different peptides based at least in part on individual peptide properties; associate a first subset of the distinct peptides with a first tier and associate a second subset of the distinct peptides with a second tier based at least in part on the sorted order, the peptides being first peptides associated with the first tier; and assigning the first peptide associated with the first tier and the second peptide associated with the second tier to the first group; 10. The system of claim 1, further comprising: storing further computer readable instructions for configuring:

3. determining peptide characteristics of peptides from different peptides to be assigned to different groups of the vaccine; determining that the peptide should be assigned to a first group from the different groups based at least in part on the peptide characteristics, the first group having a first group characteristic based at least in part on a peptide characteristic of the first peptide to be assigned to the first group, the first group characteristic being within a range of similarity to a second group characteristic of a second group from the different groups; generating information indicating that the peptide is assigned to the first group; A method comprising:

4. determining that the different peptides are associated with the subject, wherein the different groups are assigned an equal number of peptides, and the different groups are defined for a cancer vaccine for the subject. The method of claim 3 further comprising:

5. determining that the peptide is also assigned to the second group; removing the second group from the candidate set of vaccine groups; The method of claim 3 further comprising:

6. determining that two or more peptides having a particular amino acid are assigned to said second group; removing the second group from the candidate set of vaccine groups; The method of claim 3 further comprising:

7. defining the distinct groups by assigning the distinct peptides to the distinct groups, wherein the distinct peptides include neo-antigenic peptides, and the neo-antigenic peptides are assigned to only one of the distinct groups. The method of claim 3 further comprising:

8. determining that the distinct peptides are associated with subjects having tumors in a region; determining that the peptide is a neo-antigenic peptide having a peptide property score greater than a threshold score; associating the first group with the region based at least in part on the neo-antigenic peptides assigned to the first group; The method of claim 3 further comprising:

9. When executed on a system, said system determining peptide characteristics of peptides from different peptides to be assigned to different groups of the vaccine; determining that the peptide should be assigned to a first group from the different groups based at least in part on the peptide characteristics, the first group having a first group characteristic based at least in part on a peptide characteristic of the first peptide to be assigned to the first group, the first group characteristic being within a range of similarity to a second group characteristic of a second group from the different groups; generating information indicating that the peptide is assigned to the first group; One or more non-transitory computer-readable storage media storing instructions for performing operations including:

10. When executed on the system, the system determining a sorted order of the different peptides based at least in part on individual peptide properties; associating a first subset of the distinct peptides with a first tier and a second subset of the distinct peptides with a second tier based at least in part on the sorted order, wherein the peptides are first peptides associated with the first tier; assigning the first peptide associated with the first tier and the second peptide associated with the second tier to the first group; 10. The one or more non-transitory computer-readable storage media of claim 9, further storing additional instructions that cause the execution of operations including:

11. When executed on the system, the system determining the total number of peptides assigned to the different groups; generating the peptide set by associating the peptide with a peptide set and separating a second peptide from the different peptides from the peptide set, wherein the size of the peptide set is equal to the total number; defining the distinct groups by assigning subsets of the set of peptides to the distinct groups; generating an updated peptide set by separating the peptide from the peptide set and associating the second peptide with the peptide set, wherein the size of the updated peptide set is equal to the total number; defining additional groups by assigning a subset of the updated peptide set to the additional groups; 10. The one or more non-transitory computer-readable storage media of claim 9, further storing additional instructions that cause the execution of operations including:

12. When executed on the system, the system (i) determining potential assignments of the different peptides to the different groups; (ii) calculating, for each group of the different groups, a group property based at least in part on peptide properties of peptides potentially assigned to the group; and (iii) executing a combinatorial optimization algorithm configured to reduce differences between group properties of the different groups.

10. The one or more non-transitory computer-readable storage media of claim 9, further storing additional instructions that cause the execution of operations including:

13. 1. A vaccine comprising a plurality of different vaccine compositions, each vaccine composition corresponding to a different group of peptides from a plurality of different groups of peptides, said plurality of different groups comprising at least: determining peptide characteristics of peptides from the plurality of distinct peptides to be assigned to distinct groups, wherein the distinct peptides are predicted to generate an immunogenic response in the subject; determining that the peptide should be assigned to a first group from the plurality of different groups based at least in part on the peptide characteristics, the first group having a first group characteristic based at least in part on a peptide characteristic of the first peptide to be assigned to the first group, the first group characteristic being within a range of similarity to a second group characteristic of a second group from the plurality of different groups; generating information indicating that the peptide is assigned to the first group; As defined by, vaccine.

14. 1. A method of inducing an immune response in a subject, comprising:

1. A method comprising administering to the subject one or more vaccine compositions of peptides from a plurality of vaccine compositions, thereby inducing an immune response to one or more peptides in the one or more vaccine compositions, wherein the plurality of vaccine compositions is generated from information assigning neo-antigenic peptides predicted to generate an immunogenic response in the subject to a plurality of groups of peptides, wherein a first group of peptides in the plurality of groups has a first group characteristic that is within a range of similarity to a second group characteristic of a second group of peptides in the plurality of groups.

15. 1. A vaccine comprising a plurality of different vaccine compositions, each vaccine composition comprising a different set of peptides from a plurality of peptides predicted to elicit an immunogenic response in a subject, each vaccine composition having a property that is within a range of similarity of the properties of the remaining vaccine compositions, said property comprising at least one of a class I immunogenicity score, a class II immunogenicity score, or an amino acid sequence length.

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