Patent Application
20210379170
The present invention relates to a method for selecting cancer neoantigens for use in a personalized vaccine. This invention relates as well to a method for constructing a vector or collection of vectors carrying the neoantigens for a personalized vaccine. This invention further relates to vectors and collection of vectors comprising the personalized vaccine and the use of said vectors in cancer treatment.
Several tumor antigens have been identified and classified into different categories: cancer-germ-line, tissue differentiation antigens and neoantigens derived from mutated self-proteins (Anderson et al., 2012). Whether the immune responses against self-antigens have an impact on tumor growth is a matter of debate (reviewed in Anderson et al., 2012). In contrast, recent compelling evidences support the notion that neoantigens, generated in the tumor as a consequence of mutations in coding sequences of expressed genes, represent a promising target for vaccination against cancer (Fritsch et al., 2014).
Cancer neoantigens are antigens present exclusively on tumor cells and not on normal cells. Neoantigens are generated by DNA mutations in tumor cells and have been shown to play a significant role in recognition and killing of tumor cells by the T cell mediated immune response, mainly by CD8+ T cells (Yarchoan et al., 2017). The advent of massively parallel sequencing methods commonly referred to as next generation sequencing (NGS), which allows to determine the complete sequence of a cancer genome in a timely and inexpensive manner, unveiled the mutational spectra of human tumors (Kandoth et al., 2013). The most frequent type of mutation is a single nucleotide variant and the median number of single nucleotide variants found in tumors varies considerably according to their histology. Since very few mutations are generally shared among patients, the identification of mutations generating neoantigens requires a personalized approach.
Many mutations are indeed not seen by the immune system because either potential epitopes are not processed/presented by the tumor cells or because immune tolerance led to elimination of T cells reactive with the mutated sequence. Therefore, it is beneficial to select, among all potential neoantigens, those having the highest chance to be immunogenic, to define the optimal number to be encoded by a vaccine and finally a preferred vaccine layout for optimizing immunogenicity. Furthermore not only neoantigens generated by single nucleotide variant mutations but also neoantigens generated by insertions/deletion mutations that generate a frameshift peptide are important, the latter is expected to be particular immunogenic. Recently two different personalized vaccination approaches based either on RNA or on peptides have been evaluated in phase-I clinical studies. The data obtained shows that vaccination indeed can both expand pre-existing neoantigen-specific T cells and induce a broader repertoire of new T-cell specificity in cancer patients (Sahin et al., 2017). The main limitation of both approaches is the maximum number of neoantigens that are targeted by the vaccination. The upper limit for the peptide-based approach, based on their published data, is of twenty peptides and was not reached in all patients because in some cases peptides could not be synthesized. The described upper limit for the RNA-based approach is even lower, since they include only 10 mutations in each vaccine (Sahin et al., 2017).
The challenge for a cancer vaccine in curing cancer is to induce a diverse population of immune T cells capable of recognizing and eliminating as large a number of cancer cells as possible at once, to decrease the chance that cancer cells can “escape” the T cell response and are not being recognized by the immune response. Therefore, it is desirable that the vaccine encodes a large number of cancer specific antigens, i.e. neoantigens. This is particular relevant for a personalized genetic vaccine approach based on cancer specific neoantigens of an individual. In order to optimize the probability of success as many neoantigens as possible should be targeted by the vaccine. Moreover, experimental data support the notion that effective immunogenic neoantigens in patients cover a broad range of predicted affinities for the patient's MHC alleles (e.g. Gros et al., 2016). Most of the current prioritization methods instead apply an affinity threshold, for example the frequently used 500 nM limit, that may limit the selection of immunogenic neoantigens. There is therefore a need for a priorization method that avoids the limitations of current methods (e.g. exclusion due to low predicted affinity) and for a vaccination approach that allows for a personalized vaccine targeting a large and therefore broader and more complete set of neoantigens.
In a first aspect, the present invention provides a method for selecting cancer neoantigens for use in a personalized vaccine comprising the steps of:
In a second aspect, the present invention provides a method for constructing a personalized vector encoding a combination of neoantigens according to the first aspect of the invention for use as a vaccine, comprising the steps of:
In a third aspect, the present invention provides a vector encoding the list of neoantigens according to the first aspect of the invention or the combination of neoantigens according to the second aspect of the invention.
In a fourth aspect, the present invention provides a collection of vectors encoding each a different set of neoantigens according to the first aspect of the invention or the combination of neoantigens according to the second aspect of the invention, wherein the collection comprises 2 to 4, preferably 2, vectors and preferably wherein the vector inserts encoding the portion of the list are of about equal size in number of amino acids.
In a fifth aspect, the present invention provides a vector according to the third aspect of the invention or a collection of vectors according to the fourth aspect of the invention for use in cancer vaccination.
In the following, the content of the figures comprised in this specification is described. In this context please also refer to the detailed description of the invention above and/or below.
Before the present invention is described in detail below, it is to be understood that this invention is not limited to the particular methodology, protocols and reagents described herein as these may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention which will be limited only by the appended claims. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art.
Preferably, the terms used herein are defined as described in “A multilingual glossary of biotechnological terms: (IUPAC Recommendations)”, Leuenberger, H. G. W, Nagel, B. and Klbl, H. eds. (1995), Helvetica Chimica Acta, CH-4010 Basel, Switzerland).
Throughout this specification and the claims which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” and “comprising”, will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps. In the following passages, different aspects of the invention are defined in more detail. Each aspect so defined may be combined with any other aspect or aspects unless clearly indicated to the contrary. In particular, any feature indicated as being optional, preferred or advantageous may be combined with any other feature or features indicated as being optional, preferred or advantageous.
Several documents are cited throughout the text of this specification. Each of the documents cited herein (including all patents, patent applications, scientific publications, manufacturer's specifications, instructions etc.), whether supra or infra, is hereby incorporated by reference in its entirety. Nothing herein is to be construed as an admission that the invention is not entitled to antedate such disclosure by virtue of prior invention. Some of the documents cited herein are characterized as being “incorporated by reference”. In the event of a conflict between the definitions or teachings of such incorporated references and definitions or teachings recited in the present specification, the text of the present specification takes precedence.
In the following, the elements of the present invention will be described. These elements are listed with specific embodiments; however, it should be understood that they may be combined in any manner and in any number to create additional embodiments. The variously described examples and preferred embodiments should not be construed to limit the present invention to only the explicitly described embodiments. This description should be understood to support and encompass embodiments which combine the explicitly described embodiments with any number of the disclosed and/or preferred elements. Furthermore, any permutations and combinations of all described elements in this application should be considered disclosed by the description of the present application unless the context indicates otherwise.
In the following, some definitions of terms frequently used in this specification are provided. These terms will, in each instance of its use, in the remainder of the specification have the respectively defined meaning and preferred meanings.
As used in this specification and the appended claims, the singular forms “a”, “an”, and “the” include plural referents, unless the content clearly dictates otherwise.
The term “about” when used in connection with a numerical value is meant to encompass numerical values within a range having a lower limit that is 5% smaller than the indicated numerical value and having an upper limit that is 5% larger than the indicated numerical value.
In the context of the present specification, the term “major histocompatibility complex” (MHC) is used in its meaning known in the art of cell biology and immunology; it refers to a cell surface molecule that displays a specific fraction (peptide), also referred to as an epitope, of a protein. There a two major classes of MHC molecules: class I and class II. Within the MHC class I two groups can be distinguished based on their polymorphism: a) the classical (MHC-Ia) with corresponding polymorphic HLA-A, HLA-B, and HLA-C genes, and b) the non-classical (MHC-Ib) with corresponding less polymorphic HLA-E, HLA-F, HLA-G and HLA-H genes.
MHC class I heavy chain molecules occur as an alpha chain linked to a unit of the non-MHC molecule β2-microglobulin. The alpha chain comprises, in direction from the N-terminus to the C-terminus, a signal peptide, three extracellular domains (α1-3, with α1 being at the N terminus), a transmembrane region and a C-terminal cytoplasmic tail. The peptide being displayed or presented is held by the peptide-binding groove, in the central region of the α1/α2 domains.
The term “β2-microglobulin domain” refers to a non-MHC molecule that is part of the MHC class I heterodimer molecule. In other words, it constitutes the β chain of the MHC class I heterodimer.
Classical MHC-Ia molecules principle function is to present peptides as part of the adaptive immune response. MHC-Ia molecules are trimeric structures comprising a membrane-bound heavy chain with three extracellular domains (α1, α2 and α3) that associates non-covalently with β2-microglobulin (β2m) and a small peptide which is derived from self-proteins, viruses or bacteria. The α1 and α2 domains are highly polymorphic and form a platform that gives rise to the peptide-binding groove. Juxtaposed to the conserved α3 domain is a transmembrane domain followed by an intracellular cytoplasmic tail.
To initiate an immune response classical MHC-Ia molecules present specific peptides to be recognized by TCR (T cell receptor) present on CD8+ cytotoxic T lymphocytes (CTLs), while NK cell receptors present in natural killer cells (NK) recognize peptide motifs, rather than individual peptides. Under normal physiological conditions, MHC-Ia molecules exist as heterotrimeric complexes in charge of presenting peptides to CD8 and NK cells, however,
The term “human leukocyte antigen” (HLA) is used in its meaning known in the art of cell biology and biochemistry; it refers to gene loci encoding the human MHC class I proteins. The three major classical MHC-Ia genes are HLA-A, HLA-B and HLA-C, and all of these genes have a varying number of alleles. Closely related alleles are combined in subgroups of a certain allele. The full or partial sequence of all known HLA genes and their respective alleles are available to the person skilled in the art in specialist databases such as IMGT/HLA (http://www.ebi.ac.uk/ipd/imgt/hla/).
Humans have MHC class I molecules comprising the classical (MHC-Ia) HLA-A, HLA-B, and HLA-C, and the non-classical (MHC-Ib) HLA-E, HLA-F, HLA-G and HLA-H molecules. Both categories are similar in their mechanisms of peptide binding, presentation and induced T-cell responses. The most remarkable feature of the classical MHC-Ia is their high polymorphism, while the non-classical MHC-Ib are usually non-polymorphic and tend to show a more restricted pattern of expression than their MHC-Ia counterparts.
The HLA nomenclature is given by the particular name of gene locus (e.g. HLA-A) followed by the allele family serological antigen (e.g. HLA-A*02), and allele subtypes assigned in numbers and in the order in which DNA sequences have been determined (e.g. HLA-A*02:01). Alleles that differ only by synonymous nucleotide substitutions (also called silent or non-coding substitutions) within the coding sequence are distinguished by the use of the third set of digits (e.g. HLA-A*02:01:01). Alleles that only differ by sequence polymorphisms in the introns, or in the 5′ or 3′ untranslated regions that flank the exons and introns, are distinguished by the use of the fourth set of digits (e.g. HLA-A*02:01:01:02L).
MHC class I and class II binding affinity prediction; example of methods known in the art for the prediction of MHC class I or II epitopes and for the prediction of MHC class I and II binding affinity are Moutaftsi et al., 2006; Lundegaard et al., 2008; Hoof et al., 2009; Andreatta & Nielsen, 2016; Jurtz et al., 2017. Preferably the method described in Andreatta & Nielsen, 2016 is used and, in case this method does not cover one of the patients's MHC alleles, the alternative method decribed by Jurtz et al., 2017 is used.
Genes and epitopes related to human autoimmune reactions and the associated MHC alleles can be identified in the IEDB database (https://www.iedb.org) by applying the following query criteria: “Linear epitopes” for category Epitope, “Humans” for category Host and “Autoimmune disease” for category Disease.
The term “T cell enhancer element” refers to a polypeptide or polypeptide sequence that, when fused to an antigenic sequence or peptide, increases the induction of T cells against neo-antigens in the context of a genetic vaccination. Examples of T cell enhancers are an invariant chain sequence or fragment thereof; a tissue-type plasminogen activator leader sequence optionally including six additional downstream amino acid residues; a PEST sequence; a cyclin destruction box; an ubiquitination signal; a SUMOylation signal. Specific examples of T-cell enhancer elements are those of SEQ ID NOs 173 to 182.
The term ‘coding sequence’ refers to a nucleotide sequence that is transcribed and translated into a protein. Genes encoding proteins are a particular example for coding sequences.
The term ‘allele frequency’ refers to the relative frequency of a particular allele at a particular locus within a multitude of elements, such as a population or a population of cells. The allele frequency is expressed as a percentage or ratio. For example the allele frequency of a mutation in a coding sequence would be determined by the ratio of mutated versus non-mutated reads at the position of the mutation. A mutation allele frequency wherein at the location of the mutation 2 reads determined the mutated allele and 18 reads showed the non-mutated allele would define a mutation allele frequency of 10%. The mutation allele frequency for neoantigens generated from frameshift peptides is that of the insertion or deletion mutation causing the frameshift peptide, i.e. all mutated amino acids within the FSP would have the same mutation allele frequency, which is that of the frameshift causing insertion/deletion mutation.
The term ‘neoantigen’ refers to cancer-specific antigens that are not present in normal non-cancerous cells.
The term ‘cancer vaccine’ refers in the context of the present invention to a vaccine that is designed to induce an immune response against cancer cells.
The term ‘personalized vaccine’ refers to a vaccine that comprises antigenic sequences that are specific for a particular individual. Such a personalized vaccine is of particular interest for a cancer vaccine using neoantigens, since many neoantigens are specific for the particular cancer cells of an individual.
The term “mutation” in a coding sequence refers in the context of the present invention to a change in the nucleotide sequence of a coding sequence when comparing the nucleotide sequence of a cancerous cell to that of a non-cancerous cell. Changes in the nucleotide sequence that does not result in a change in the amino acid sequence of the encoded peptide, i.e. a ‘silent’ mutation, is not regarded as a mutation in the context of the present invention. Types of mutations that can result in the change of the amino acid sequence are without being limited to non-synonymous single nucleotide variants (SNV), wherein a single nucleotide of a coding triplet is changed resulting in a different amino acid in the translated sequence. A further example of a mutation resulting in a change in the amino acid sequence are insertion/deletion (indel) mutations, wherein one or more nucleotides are either inserted into the coding sequence or deleted from it. Of particular relevance are indel mutations that result in the shift of the reading frame which occurs if a number of nucleotides are inserted or deleted that are not dividable by three. Such a mutation causes a major change in the amino acid sequence downstream of the mutation which is referred to as a frameshift peptide (FSP).
The term ‘Shannon entropy’ refers to the entropy associated with the number of conformations of a molecule, e.g. a protein. Methods known in the art to calculate the Shannon entropy are Strait & Dewey, 1996 and Shannon 1996. For a polypeptide the Shannon entropy (SE) can be calculated as SE=(−Σpc(aai)·log(pc(aai)))/N wherein pc(aai) is the frequency of amino acid i in the polypeptide and the sum is calculated over all 20 different amino acids and N is the length of the polypeptide.
The term “expression cassette” is used in the context of the present invention to refer to a nucleic acid molecule which comprises at least one nucleic acid sequence that is to be expressed, e.g. a nucleic acid encoding a selection of neoantigens of the present invention or a part thereof, operably linked to transcription and translation control sequences. Preferably, an expression cassette includes cis-regulating elements for efficient expression of a given gene, such as promoter, initiation-site and/or polyadenylation-site. Preferably, an expression cassette contains all the additional elements required for the expression of the nucleic acid in the cell of a patient. A typical expression cassette thus contains a promoter operatively linked to the nucleic acid sequence to be expressed and signals required for efficient polyadenylation of the transcript, ribosome binding sites, and translation termination. Additional elements of the cassette may include, for example enhancers. An expression cassette preferably also contains a transcription termination region downstream of the structural gene to provide for efficient termination. The termination region may be obtained from the same gene as the promoter sequence or may be obtained from a different gene.
The “IC50” value refers to the half maximal inhibitory concentration of a substance and is thus a measure of the effectiveness of a substance in inhibiting a specific biological or biochemical function. The values are typically expressed as molar concentration. The IC50 of a molecule can be determined experimentally in functional antagonistic assays by constructing a dose-response curve and examining the inhibitory effect of the examined molecule at different concentrations. Alternatively, competition binding assays may be performed in order to determine the IC50 value. Typically, neoantigen fragments of the present invention exhibit an IC50 value of between 1500 nM-1 pM, more preferably 1000 nM to 10 pM, and even more preferably between 500 nM and 100 pM.
The term “massively parallel sequencing” refers to high-throughput sequencing methods for nucleic acids. Massively parallel sequencing methods are also referred to as next-generation sequencing (NGS) or second-generation sequencing. Many different massively parallel sequencing methods are known in the art that differ in setup and used chemistry. However, all these methods have in common that they perform a very large number of sequencing reactions in parallel to increase the speed of sequencing.
The term “Transcripts Per Kilobase Million” (TPM) refers to a gene-centered metric used in massively parallel sequencing of RNA samples that normalizes for sequencing depth and gene length. It is calculated by dividing the read counts by the length of each gene in kilobases, resulting in reads per kilobases (RPK). Divide the number of all RPK values in a sample by 1,000,000 resulting in a ‘per million scaling factor’. Divide the RPK values by the ‘per million scaling factor’ resulting in a TPM for each gene.
The overall expresion level of the gene harboring the mutation is expressed as TPM. Preferably, the “mutation-specific” expression values (corrTPM) is then determined from the number of mutated and non-mutated reads reads at the position of the mutation.
The corrected expression value corrTPM is calculated as corrTPM=TPM*(M+c)/(M+W+c). M is the number of reads spanning the location of the mutation generating the neoantigen and W is the number of reads without the mutation spanning the location of the mutation generating the neoantigens. The value c is a constant larger than 0, preferably 0.1. The value c is particular important if M and/or W is 0.
In the following different aspects of the invention are defined in more detail. Each aspect so defined may be combined with any other aspect or aspects unless clearly indicated to the contrary. In particular, any feature indicated as being preferred or advantageous may be combined with any other feature or features indicated as being preferred or advantageous. In a first aspect, the present invention provides a method for selecting cancer neoantigens for use in a personalized vaccine comprising the steps of:
Many cancer neoantigens are not ‘seen’ by the immune system because either potential epitopes are not processed/presented by the tumor cells or because immune tolerance led to elimination of T cells reactive with the mutated sequence. Therefore, it is beneficial to select, among all potential neoantigens, those having the highest chance to be immunogenic. Ideally a neoantigen would have to be present in a high number of cancer cells, being expressed in sufficient quantities and being presented efficiently to immune cells.
By selecting neoantigens comprising cancer specific mutations that have a certain mutation allele frequency, are abundantly expressed and are predicted to have a high binding affinity to MHC molecules, the chance of an immune response being induced is significantly increased. The present inventors have surprisingly found that these parameters can be most efficiently used to select suitable neoantigens elicits an increased immune response using a prioritizing method that the different parameters into account. Importantly, the method of the invention also considers neoantigens where allele frequency, expression level or predicted MHC binding affinity are not amongst the highest observed. For example a neoantigen with a high expression level and a high mutation allele frequency but a relatively low predicted MHC binding affinity can still be included in the list of selected neoantigens.
The method of the invention therefore does not use cut-off criteria commonly applied in selection processes but takes into account that neoantigens with a very high predicted suitability according to one parameter are not simply excluded from the list due to sub-optimal suitability in other parameters. This is in particular relevant for neoantigens with parameters only missing a certain cut-off criteria slightly.
Any mutation in a coding sequence (i.e. a genomic nucleic acid sequence being transcribed and translated) that is present only in cancer cells of an individual and not in healthy cells of the same individual are potentially of interest as immunogenic (i.e. capable of inducing an immune response) neoantigens. The mutation in the coding sequence must also result in changes in the translated amino acid sequence, i.e. a silent mutation only present on the nucleic acid level and without changing the amino acid sequence is therefore not suitable. Essential is that the mutation, regardless of the exact type of mutation (change of single nucleotides, insertion or deletions of single or multiple nucleotides, etc.), results in an altered amino acid sequences of the translated protein. Each amino acid present only in the altered amino acid sequence but not in the amino acid sequence resulting from the coding gene as present in the non-cancerous cells is considered to be a mutated amino acid in the context of this specification. For example mutations of the coding sequence such as insertion or deletion mutations resulting in frameshift peptides would result in a peptide wherein each amino acid that is encoded by a shifted reading frame is to be regarded as a mutated amino acid.
The mutation of the coding sequence can in principle be identified by any method of DNA sequencing of the sample obtained from an individual. A preferred method for obtaining the DNA sequence necessary to identify the mutation in the coding sequence of the individual is a massively parallel sequencing method.
The allele frequency of the mutation (i.e. the ratio of non-mutated vs mutated sequences at the position of the mutation) in the coding sequence is also an important factor for neoantigens being used in a vaccine. Neoantigens with a high allele frequency are present in a substantial number of cancer cells, resulting in neoantigens comprising these mutations being a promising target of a vaccine.
In a similar fashion it is of importance how abundantly a neoantigen is expressed within the cancer cells. The higher the expression of a neoantigen in cancer cells the more suitable is the neoantigen and the higher is the chance for a sufficient immune response against such cells. The present invention can be exercised with different ways of assessing the expression levels of neoantigens. The expression of the neoantigens can be assessed directly in the sample of cancerous cells. The expression can be measured by different methods that preferably represent the whole transcriptome, various such methods are known to the skilled person. Preferably, a method providing a fast, reliable and cost effective method to measure the transcriptome is used. One such preferred method is massively parallel sequencing.
Alternatively, if no direct measurement is available, which can e.g. be due to technical or economic reasons, expression databases can be used. The skilled person is aware of available expression databases containing gene expression data of different cancer types. A typical non-limiting example of such a database is TCGA (https://portal.gdc.cancer.gov/). The expression of genes comprising the mutation identified in step (a) of the method in the same type of tumor as the individual the vaccine is designed for can be searched in these databases and can be used to determine an expression value.
It is further of importance that the selected neoantigens are efficiently presented to immune cells by MHC molecules on the cancer cells. There are different methods known in the art to predict the binding affinity of peptides to MHC class I (and class II) molecules (Moutaftsi et al., 2006; Lundegaard et al., 2008; Hoof et al., 2009; Andreatta & Nielsen, 2016; Jurtz et al., 2017). Since the MHC molecules are a highly polymorphic group of proteins with significant differences between individuals it is important to determine the MHC binding affinity for the type of MHC molecules present on the individual's cells. The MHC molecules are encoded by the group of highly polymorphic HLA genes. The method therefore uses the DNA sequencing results utilized in step (a) to identify the mutations in coding sequences to identify the HLA alleles present in the individual. For each MHC molecule corresponding to the identified HLA alleles in the individual, the MHC binding affinity to the neoantigens is determined. Towards these ends the amino acid sequence of the neoantigen is determined by in silico translation of the coding sequence. The resulting neoantigen amino acid sequence is then divided into fragments consisting of 8 to 15, preferably 9 to 10, more preferably 9, contiguous amino acids, wherein the fragment must contain at least one of the mutated amino acids of the neoantigen. The size of the fragment is restricted by the size of peptides the MHC molecule can present. For each fragment the MHC binding affinity is predicted. The MHC binding affinity is usually measured as half maximal inhibitory concentration (IC50 in [nM]). Hence, the lower the IC50 value is the higher is the binding affinity of the peptide to the MHC molecule. The fragment with the highest MHC binding affinity determines the MHC binding affinity of the neoantigen the fragment is derived from.
The method of the present invention then uses the parameters determined in steps (b) to (d), i.e. mutation allele frequency, expression level and predicted MHC class I binding affinity of the neoantigen, to select the most suitable neoantigens by applying a prioritization method to these parameters. Therefore the parameters are sorted on a ranked list. The neoantigen with the highest mutation allele frequency is assigned the first rank, i.e. rank 1, in a first list of ranks. The neoantigen with the second highest mutation allele frequency is assigned the second rank in the first list of ranks etc. until all identified neoantigens are assigned a rank on the first list of ranks.
Similarly the expression level of each coding sequence is ranked from highest to lowest, with the neoantigen with the highest expression value being assigned rank 1, the neoantigen with the second highest levels is assigned rank 2 etc. until all identified neoantigens are assigned a rank on the second list of ranks.
The MHC class I binding affinity of the neoantigens are ranked from highest to lowest binding affinity with the neoantigen with the highest MHC class I binding affinity is assigned rank 1, the neoantigen with the second highest binding affinity is assigned rank 2 etc. until all neoantigens are assigned a rank on the third list of ranks.
If any of the neoantigens has an identical mutation allele frequency, expression level and/or MHC class I binding affinity as another neoantigens, both antigens are assigned the same rank on the relevant list of ranks.
The method then uses a prioritization method that takes into account all three rankings by calculating a rank sum of the three lists of ranks. For example a neoantigen that has rank 3 on the first list of ranks, rank 13 on the second list of ranks and rank 2 on the third list or ranks has a rank sum of 18 (3+13+2). After the rank sum has been calculated for each neoantigen the rank sums are ranked according to their rank sum with the lowest rank sum being assigned rank 1 etc. yielding a ranked list of neoantigens. Neoantigens with an identical rank sum are assigned the same rank on the ranked list of neoantigens.
The final number of neoantigens present in the list is dependent on the number of mutations detected in each patient. The number of neoantigens to be used in a vaccine is limited by the vehicle or vehicles used to deliver the vaccine. For example if a single viral vector is used as a delivery vehicle, as can be the case for a genetic vaccine, the maximum insert size of this vector would limit the number of neoantigens that can be used in each vector.
Therefore, the method of the present invention selects 25-250, 30-240, 30-150, 35-80, preferably 55-65, more preferably 60 neoantigens from the list of ranked neoantigens starting with the neoantigen that has the lowest rank (i.e. lowest rank number, rank 1). In case the neoantigens are selected to be present in one set (e.g. single vehicle of a monovalent vaccine) 25-80, 30-70, 35-70, 40-70, 55-65, preferably 60 neoantigens are selected. The neoantigens not included in the first set can however be encoded by additional viral vectors for a multi-valent vaccination based on co-administration of up to 4 viral vectors.
In a preferred embodiment of the first aspect of the present invention, steps (a) and (d)(I) are performed using massively parallel DNA sequencing of the samples.
In a preferred embodiment of the first aspect of the present invention, steps (a) and (d)(I) are performed using massively parallel DNA sequencing of the samples and the number of reads at the chromosomal position of the identified mutation is:
These criteria are applied to further select for neoantigens wherein the identified mutation is detected with a particular high technical reliability.
In a preferred embodiment of the first aspect of the present invention the method comprises a step (d′) in addition to or alternatively to step (d), wherein step (d′) comprises:
In this embodiment an alternative or additional selection parameter is added. The MHC class II binding affinity is predicted in slightly larger fragments due to the peptides presented by MHC class II molecules being larger in size than those of MHC class I peptides. The MHC class II binding affinity is also ranked from the highest to the lowest binding affinity, with the neoantigen with the highest MHC class II binding affinity being assigned rank 1 etc. until all neoantigens are assigned a rank in the fourth list of ranks.
In case the MHC class II binding affinity is used as an additional selection parameter the fourth list is included additionally in the rank sum calculation. In case the MHC class II binding affinity is used as an alternative to the MHC class I binding affinity of step (d) the rank sum in step (f) is calculated on the first, second and fourth list of ranks only.
In a preferred embodiment of the first aspect of the present invention the at least one mutation of step (a) is a single nucleotide variant (SNV) or an insertion/deletion mutation resulting in a frame-shift peptide (FSP).
In a preferred embodiment of the first aspect of the present invention wherein the mutation is a SNV and the neoantigen has the total size defined in step (a) and consists of the amino acid caused by the mutation, flanked on each side by a number of adjoining contiguous amino acids, wherein the number on each side does not differ by more than one unless the coding sequence does not comprise a sufficient number of amino acids on either side, wherein the neoantigen has the total size defined in step (a). Preferably the mutated amino acid resulting from a SNV is located within the ‘middle’ of the neoantigen (i.e. flanked by an equal number of amino acids). This provides an equal chance of the mutation being present at the end or start of an epitope. The neoantigen is therefore selected with approximately (i.e. differ by not more than one) the same number of surrounding amino acids resulting from the coding sequence on each side of the mutated amino acids.
In a preferred embodiment of the first aspect of the present invention wherein the mutation results in a FSP and each single amino acid change caused by the mutation results in a neoantigen that has the total size defined in step (a) and consists of:
(i) said single amino acid change caused by the mutation and 7 to 14, preferably 8, N-terminally adjoining contiguous amino acids, and
(ii) a number of contiguous amino acids adjoining the fragment of step (i) on either side, wherein the number of amino acids on either side differ by not more than one, unless the coding sequence does not comprise a sufficient number of amino acids on either side,
wherein the MHC class I binding affinity of step (d) and/or the MHC class II binding affinity of step (d′) is predicted for the fragment of step (i).
Each mutated amino acid of the FSP defines one distinct neoantigen. Each neoantigen consists of a mutated amino acid and a number of amino acids being one amino acid shorter than the size of the fragment used to determine MHC class I binding affinity (i.e. 7 to 14) which are located N-terminally of the mutated amino acid. The neoantigen further consists of a number of contiguous amino acids derived from the coding sequence that form with the sequence of the neoantigen fragment of step (i) a contiguous sequence in the coding sequence. The number of amino acids surrounding the neoantigen fragment of step (i) on either side differs by only one, wherein the total size of the neoantigen is as defined in step (a). The neoantigen fragment of step (i) is used to determine the MHC class I and/or class II binding affinity.
For example a mutated amino acid on relative position 20 of a translated coding sequence would define a neoantigen fragment including a contiguous amino acid sequence of 8 contiguous amino acids (i.e. fragment of step (i)) ranging from position 12 to 20. The complete neoantigen sequence of 25 amino acids according to step (ii) would consist of amino acids 4 to 28. The neoantigen fragment ranging from position 12 to 20 consisting of 9 amino acids would be used to determine the MHC binding affinity.
In a preferred embodiment of the first aspect of the present invention the mutation allele frequency of the neoantigen determined in step (b) in the sample of cancerous cells is at least 2%, preferably at least 5%, more preferably at least 10%.
In a preferred embodiment of the first aspect of the present invention step (g) further comprises removing neoantigens from genes linked to autoimmune disease, from the ranked list of neoantigens. The skilled person is aware of neoantigens associated with autoimmune diseases from public databases. One such example of a database is the IEDB database (www.iedb.org). Exclusion of a neoantigen candidate can be performed both at the gene level if the gene harboring the mutation belongs to one of those genes linked to autoimmune disease in the IEDB database or, in a less stringent manner, not only if the patient has a mutation in a gene known to be involved in autoimmunity but one of the patient's MHC alleles is also identical to the allele described in the IEDB database for the human autoimmune disease epitope in connection with the described autoimmune phenomenon.
In a preferred embodiment neoantigens associated with an autoimmune disease are not removed from the ranked list of neoantigens if the database specifies a certain MHC class I allele for this association and the corresponding HLA allele was not found in the individual in step (d)(I).
In a preferred embodiment of the first aspect of the present invention step (g) further comprises removing neoantigens with a Shannon entropy value for their amino acid sequence lower than 0.1 from said ranked list of neoantigens.
In a preferred embodiment of the first aspect of the present invention the expression level of said coding genes in step (c)(i) is determined by massively parallel transcriptome sequencing.
In a preferred embodiment of the first aspect of the present invention the expression level determined in step (c)(i) uses a corrected Transcripts Per Kilobase Million (corrTPM) value calculated according to the following formula
wherein M is the number of reads spanning the location of the mutation of step (a) that comprise the mutation and W is the number of reads spanning the location of the mutation of step (a) without the mutation and TPM is the Transcripts Per Kilobase Million value of the gene comprising the mutation and the c is a constant larger than 0, preferably c is 0.1.
In a preferred embodiment of the first aspect of the present invention the rank sum in step (f) is a weighted rank sum, wherein the number of neoantigens determined in step (a) is added to the rank value of each neoantigen:
This weighing of the MHC binding affinity penalizes a very low MHC class I and/or class II binding affinity by adding ranks.
In a preferred embodiment of the first aspect of the present invention the rank sum in step (f) is a weighted rank sum, wherein in case of step (c)(i) being performed by massively parallel transcriptome sequencing, the rank sum of step (f) is multiplied by a weighing factor (WF), wherein WF is
The weighing matrix penalizes certain neoantigens for which the sequencing results are either of poor quality (i.e. number of mapped reads is low) and/or if the expression value (i.e. TPM value) is below a certain threshold. This mode of weighing (i.e. prioritizing) certain parameters provides neoantigens with a better immunogenicity than using cutoff values for the single parameters, which would eliminate certain neoantigens due to a low suitability in one parameter even though other parameter qualifies the neoantigen as suitable.
In a preferred embodiment of the first aspect of the present invention step (g) comprises an alternative selection process, wherein the neoantigens are selected from the ranked list of neoantigens starting with the lowest rank until a set maximum size in total overall length in amino acids for all selected neoantigens is reached, wherein the maximum size is between 1200 and 1800, preferably 1500 amino acids for each vector. The process can be repeated in a multivalent vaccination approach, wherein the maximum size indicated above applies for each vehicle used in the multivalent approach. For example a multivalent approach based on 4 vectors could for example allow a total limit of 6000 amino acids. This embodiment takes the maximum size for neoantigens allowed by a certain delivery vehicle into account. Therefore, the number of neoantigens selected from the ranked list is not determined by the number of neoantigens but takes the size of neoantigens into account. A number of small neoantigens in the ranked list of antigens would allow to include more antigens within the list of selected antigens.
In a preferred embodiment of the first aspect of the present invention two or more neoantigens are merged into one new neoantigen if they comprise overlapping amino acid sequence segments. In some case neoantigens can contain overlapping amino acid sequences. This is particularly often the case for FSP derived neoantigens. In order to avoid redundant overlapping sequences the neoantigens are merged into a single new neoantigen that consists of the non-redundant portions of the merged neoantigens. A merged new neoantigen can have a size larger than defined in step (a) of the first aspect of the invention, depending on the number of neoantigens merged and the degree of overlap.
In a preferred embodiment of the first aspect of the present invention the personalized vaccine is a personalized genetic vaccine. The term ‘genetic vaccine’ is used synonymously to ‘DNA vaccine’ and refers to the use of genetic information as a vaccine and the cells of the vaccinated subject produce the antigen the vaccination is directed against.
In a preferred embodiment of the first aspect of the present invention the personalized vaccine is a personalized cancer vaccine.
In a second aspect, the present invention provides a method for constructing a personalized vector encoding a combination of neoantigens according to the first aspect of the invention for use as a vaccine, comprising the steps of:
(i) ordering the list of neoantigens in at least 10{circumflex over ( )}5-10{circumflex over ( )}8, preferably 10{circumflex over ( )}6 different combinations,
(ii) generating all possible pairs of neoantigen junction segments for each combination, wherein each junction segment comprises 15 adjoining contiguous amino acids on either side of the junction,
(iii) predicting the MHC class I and/or class II binding affinity for all epitopes in junction segments wherein only HLA alleles are tested that are present in the individual the vector is designed for, and
(iv) selecting the combination of neoantigens with the lowest number of junctional epitopes with an IC50 of ≤1500 nM and wherein if multiple combinations have the same lowest number of junctional epitopes the combination first encountered is selected.
The list of selected neoantigens according to the first aspect of the invention can be arranged into a single combined neoantigen. The junctions where the individual neoantigens are joined can result in novel epitopes that may lead to unwanted off target effects not related to epitopes being present on cancerous cells. Therefore, it is advantageous if the epitopes created by the junction of individual neoantigens have a low immunogenicity. Towards these ends the neoantigens are arranged in different orders resulting in different junction epitopes and the MHC class I and class II binding affinity of those junction epitopes is predicted. The combination with the lowest number of junctional epitopes with an IC50 value of ≤1500 nM is selected. The number of different combinations of selected neoantigens is limited primarily by computing power available. A compromise between computing resources used and accuracy needed is if 10{circumflex over ( )}5-10{circumflex over ( )}8, preferably 10{circumflex over ( )}6 different combinations of neoantigens are used wherein the MHC class I and/or class II binding affinity of the junctional epitopes of each neoantigen junction is predicted.
In an alternative second aspect, the present invention provides a method for constructing a personalized vector encoding a combination of neoantigens for use as a vaccine, comprising the steps of:
(i) ordering a list of neoantigens in at least 10{circumflex over ( )}5-10{circumflex over ( )}8, preferably 10{circumflex over ( )}6 different combinations,
(ii) generating all possible pairs of neoantigen junction segments for each combination, wherein each junction segment comprises 15 adjoining contiguous amino acids on either side of the junction,
(iii) predicting the MHC class I and/or class II binding affinity for all epitopes in junction segments wherein only HLA alleles are tested that are present in the individual the vector is designed for, and
(iv) selecting the combination of neoantigens with the lowest number of junctional epitopes with an IC50 of ≤1500 nM and wherein if multiple combinations have the same lowest number of junctional epitopes the combination first encountered is selected.
The list of neoantigens can be arranged into a single combined neoantigen. The junctions where the individual neoantigens are joined can result in novel epitopes that may lead to unwanted off target effects not related to epitopes being present on cancerous cells. Therefore, it is advantageous if the epitopes created by the junction of individual neoantigens have a low immunogenicity. Towards these ends the neoantigens are arranged in different orders resulting in different junction epitopes and the MHC class I and class II binding affinity of those junction epitopes is predicted. The combination with the lowest number of junctional epitopes with an IC50 value of ≤1500 nM is selected. The number of different combinations of selected neoantigens is limited primarily by computing power available. A compromise between computing resources used and accuracy needed is if 10{circumflex over ( )}5-10{circumflex over ( )}8, preferably 10{circumflex over ( )}6 different combinations of neoantigens are used wherein the MHC class I and/or class II binding affinity of the junctional epitopes of each neoantigen junction is predicted.
In a third aspect, the present invention provides a vector encoding the list of neoantigens according to the first aspect of the invention or the combination of neoantigens according to the second aspect of the invention.
It is preferred that the vector comprises one or more elements that enhance immunogenicity of the expression vector. Preferably such elements are expressed as a fusion to the neoantigens or neoantigens combination polypeptide or are encoded by another nucleic acid comprised in the vector, preferably in an expression cassette.
In a preferred embodiment of the third aspect of the invention the vector additionally comprises a T-cell enhancer element, preferably (SEQ ID NO: 173 to 182), more preferably SEQ ID NO: 175, that is fused to the N-terminus of the first neoantigen in the list.
The vector of the third aspect or the collection of vectors of the fourth aspect, wherein the vector in each case is independently selected from the group consisting of a plasmid; a cosmid; a liposomal particle, a viral vector or a virus like particle; preferably an alphavirus vector, a venezuelan equine encephalitis (VEE) virus vector, a sindbis (SIN) virus vector, a semliki forest virus (SFV) virus vector, a simian or human cytomegalovirus (CMV) vector, a Lymphocyte choriomeningitis virus (LCMV) vector, a retroviral vector, a lentiviral vector, an adenoviral vector, an adeno-associated virus vector a poxvirus vector, a vaccinia virus vector or a modified vaccinia ankara (MVA) vector. It is preferred that a collection of vectors, wherein each member of the collection comprises a polynucleotide encoding a different antigen or fragments thereof and, which is thus typically administered simultaneously uses the same vector type, e.g. an adenoviral derived vector.
The most preferred expression vectors are adenoviral vectors, in particular adenoviral vectors derived from human or non-human great apes. Preferred great apes from which the adenoviruses are derived are Chimpanzee (Pan), Gorilla (Gorilla) and orangutans (Pongo), preferably Bonobo (Pan paniscus) and common Chimpanzee (Pan troglodytes). Typically, naturally occurring non-human great ape adenoviruses are isolated from stool samples of the respective great ape. The most preferred vectors are non-replicating adenoviral vectors based on hAd5, hAd11, hAd26, hAd35, hAd49, ChAd3, ChAd4, ChAd5, ChAd6, ChAd7, ChAd8, ChAd9, ChAd10, ChAd11, ChAd16, ChAd17, ChAd19, ChAd20, ChAd22, ChAd24, ChAd26, ChAd30, ChAd31, ChAd37, ChAd38, ChAd44, ChAd55, ChAd63, ChAd73, ChAd82, ChAd83, ChAd146, ChAd147, PanAd1, PanAd2, and PanAd3 vectors or replication-competent Ad4 and Ad7 vectors. The human adenoviruses hAd4, hAd5, hAd7, hAd11, hAd26, hAd35 and hAd49 are well known in the art. Vectors based on naturally occurring ChAd3, ChAd4, ChAd5, ChAd6, ChAd7, ChAd8, ChAd9, ChAd10, ChAd11, ChAd16, ChAd17, ChAd19, ChAd20, ChAd22, ChAd24, ChAd26, ChAd30, ChAd31, ChAd37, ChAd38, ChAd44, ChAd63 and ChAd82 are described in detail in WO 2005/071093. Vectors based on naturally occurring PanAd1, PanAd2, PanAd3, ChAd55, ChAd73, ChAd83, ChAd146, and ChAd147 are described in detail in WO 2010/086189.
In a preferred embodiment of the third aspect of the present invention, the vector comprises two independent expression cassettes wherein each expression cassette encodes a portion of the list of neoantigens according to the first aspect of the invention or the combination of neoantigens according to the second aspect of the invention. Preferably, the portion of the list encoded by the expression cassettes are of about equal size in number of amino acids.
In a preferred embodiment of the third aspect of the present invention the vector comprises an expression cassette encoding the selected neoantigens of the ranked list of neoantigens according to the first aspect of the invention wherein the list of selected neoantigens is split into two parts of approximately equal length, wherein the two parts are separated by an internal ribosome entry site (IRES) element or a viral 2A region (Luke et al., 2008), for example the aphtovirus Foot and Mouth Disease Virus 2A region (SEQ ID NO: 184 APVKQTLNFDLLKLAGDVESNPGP) which mediates polyprotein processing by a translational effect known as ribosomal skip (Donnelly et al., J. Gen. Virology 2001). Optionally in each of the two parts a T-cell enhancer element, preferably (SEQ ID NO: 173 to 182), more preferably SEQ ID NO: 175, is fused to the N-terminus of the first neoantigen in the list.
In a fourth aspect, the present invention provides a collection of vectors encoding each a portion of the list of neoantigens according to the first aspect of the invention or the combination of neoantigens according to the second aspect of the invention, wherein the collection comprises 2 to 4, preferably 2, vectors and preferably wherein the vector inserts encoding the portion of the list are of about equal size in number of amino acids.
In a fifth aspect, the present invention provides a vector according to the third aspect of the invention or a collection of vectors according to the fourth aspect of the invention for use in cancer vaccination.
The vector of the third aspect of the invention or the collection of vectors according to the fourth aspect of the invention for use in cancer vaccination, wherein the cancer is selected from the group consisting of malignant neoplasms of lip, oral cavity, pharynx, a digestive organ, respiratory organ, intrathoracic organ, bone, articular cartilage, skin, mesothelial tissue, soft tissue, breast, female genital organs, male genital organs, urinary tract, brain and other parts of central nervous system, thyroid gland, endocrine glands, lymphoid tissue, and haematopoietic tissue.
In a preferred embodiment of the fifth aspect of the invention the vaccination regimen is a heterologous prime boost with two different viral vectors. Preferred combinations are Great Apes derived adenoviral vector for priming and a poxvirus vector, a vaccinia virus vector or a modified vaccinia ankara (MVA) vector for boosting. Preferably these are administered sequentially with an interval of at least 1 week, preferably of 6 weeks.
The present invention describes a method to score tumor mutations for their likelihood to give rise to immunogenic neoantigens. This approach analyzes the next generation DNA sequencing (NGS-DNA) data and, optionally, the next generation RNA sequencing (NGS-RNA) data of a tumor specimen and the NGS-DNA data of a normal sample obtained from the same patient as described below.
The personalized approach relies on NGS data obtained by analyzing samples collected from a cancer patient. For each patient, NGS-DNA exome data from tumor DNA are compared to those obtained from normal DNA in order to identify somatic mutations confidently present in the tumor and not in the normal sample that generate changes in the amino acid sequence of a protein.
Normal exome DNA is further analyzed to determine the patient HLA class I and class II alleles. NGS-RNA data from the tumor sample, if available, is analyzed to determine the expression of genes harbouring the mutations.
The examples below refer to the following aspect of the invention:
Example 1: Description of the prioritization method
Example 2: Application of the prioritization method to an existing literature NGS dataset
Example 3: Validation of the prioritization method
Validation of the prioritization method was performed by measuring its performance against a dataset (published studies) in which both NGS data and immunogenic neoantigens are described. In the example the prioritization method a and b are used. This example shows that by selecting the top 60 neoantigens a very high portion of known immunogenic neoantigens are included in the vaccine, both by using method a (with patient NGS-RNA) or method b (no patient NGS-RNA).
Example 4: optimization of neoantigen layout for synthetic genes encoding neoantigens to be delivered by a genetic vaccine vector.
Demonstration that splitting 62 selected neoantigens obtained from a mouse model into two syntetic genes (total 31+31=62 neoantigens) results in improved immunogenicity compared to the use of one synthetic gene encoding for 62 neoantigens.
Step 1: Identification of Mutations that can Generate a Neoantigen
Mutations defined as confidently present in the tumor ideally but not exclusively fulfil the following criteria:
Two types of somatic mutations are considered within the method of the present invention: single nucleotide variants (SNVs) generating a non-synonymous codon change with a resulting mutated amino acid in a protein and insertions/deletions (indels) that generate frameshift peptides (FSPs) by changing the reading frame of a protein-encoding mRNA.
For each mutation a neoantigen peptide sequence is generated in the following way:
A 25 amino acid long sequence is generated with the mutated amino located in the centre and flanked, on both sides, by preferably A=12 non-mutated amino acids (
Occasionally two (or even more) mutations, SNVs and/or indels, are present within a small distance (distance less than or equal to A amino acids) in the protein. In these cases the segment of the A non-mutated amino acids that is added N-terminal or C-terminal will be modified such that the additional mutation(s) is(are) present. (
For each neoantigen a MHC class I 9mer epitope prediction is then performed with the patient's HLA alleles identified from the NGS-DNA exome data. The IC50 value associated with the neoantigen is then chosen as the one with the lowest IC50 value across all predicted epitopes that comprise at least 1 mutated amino acids and across all of the patient's class I alleles.
For FSPs maximal N=12 non-mutated amino acids are added at the N-terminus of the FSP (
The resulting expanded FSP peptide sequence is then split into 9 amino acid long fragments and MHC class I 9mer epitope prediction is performed (with the patient's HLA alleles) on all fragments containing at least 1 mutated amino acid. The IC50 value associated with each fragment is then chosen as the lowest predicted IC50 value across all the alleles examined.
Each 9 amino acid fragment is then expanded into a 25 amino acid long neoantigen sequence by adding the 8 upstream and 8 downstream amino acids to the N-terminal and C-terminal end of the fragment, respectively (
The resulting neoantigen sequences with their associated IC50 are then added to the list of neoantigen sequences obtained from the SNVs.
An optional safety filter is then performed on the RSUM ranked list of neoantigens in order to remove those neoantigens that represent a potential risk of inducing autoimmunity. The filter examines if the gene encoding for the neoantigen is part of a black list of genes (for example retrieved from the IEDB database) containing known class I and class II MHC epitopes linked to autoimmune disease. If available, the list also contains the HLA allele of the epitope.
Neoantigens are removed if their originating mutation is from one of the genes in the black list and at the same time one of the HLA alleles of the patient corresponds to the HLA linked with the gene to autoimmunity disease.
For genes in the black list where no information on the epitope's HLA allele is available, the neoantigen is removed independently from the patient's HLA alleles.
The list of candidate neoantigens is then filtered to remove neoantigens that encode peptides with a low complexity amino acid sequence (presence of segments in the sequence where one or more amino acid(s) are repeated multiple times).
Once converted into a nucleotide sequences these segments are likely to represent regions with a high content in G or C nucleotides. These regions can therefore generate problems either during the initial construction/synthesis of the vaccine expression cassette and/or they could also negatively affect expression of the encoded polypeptides.
The identification of low complexity amino acid sequences is performed by estimating the Shannon entropy of the neoantigen sequence divided by its length in amino acids. The Shannon entropy is a metric commonly used in information theory and measures the average minimum number of bits needed to encode a string of symbols based on the alphabet size and the frequency of the symbols.
In the present method the metric has been applied to the string of amino acids present in neoantigen sequence. Neoantigens that have a Shannon entropy value lower than 0.10 are removed from the list.
Data required for performing the prioritization are
The prioritization strategy is based on an overall score obtained by the combination of three separate independent rank score values (RFREQ, REXPR, RIC50). The three rank score values are obtained by ordering the list of M neoantigens independently according to one of the following parameters (the result will therefore be three different ordered lists of neoantigens, each list thus providing a rank score).
Each neoantigens is associated with the observed tumor allele frequency of the mutation generating the neoantigen. The list of M neoantigens is ordered from the highest allele frequency to the lowest allele frequency. The neoantigen with the highest allele frequency has a rank score RFREQ equal to 1, the second highest a rank score RFREQ=2 and so on. If neoantigens with identical allele frequency are present they are given the same rank score RFREQ, i.e. the lowest rank score might be less than M (Table 1)
The expression level of each neoantigen is determined from the tumor NGS-RNA data by calculating the gene-centred Transcripts Per Kilobase Million (TPM) value (Li & Dewey, 2011) considering all mapped reads. The TPM value is then modified taking into account the number of mutated and wild type reads spanning the location of the mutation in the NGS-RNA transcriptome data (corrTPM):
A preferred value of 0.1 is added to both the numerator and enumerator in order to include also cases where no reads are present at the location of the mutation.
If no NGS-RNA sequencing data is available from the patient's tumor, the corrTPM is replaced, for each neoantigen, by the corresponding gene's median TPM value as present in an expression database from the same tumor type.
Neoantigens are then ranked according to the expression level as determined by the corrTPM value. Ordering is from highest expression (score REXP equal to 1) down to lowest expression. Neoantigens with the same corrTPM value are given the same rank score REXPR (Table 2).
For each SNV or FSP-derived neoantigen peptide, the likelihood of MHC class I binding is defined as the best predicted (lowest) IC50 value among all predicted 9mer epitopes that include the mutated amino acid(s) or include one mutated amino acid from the FSP. Prediction is performed only against the MHC class I alleles present in the patient determined by analysis of the normal DNA sample.
The list of neoantigens is then ordered from the lowest predicted IC50 value (RIC50 score equal to 1) to the highest predicted IC50 value. Neoantigens with the same IC50 value are given the same rank score RIC50 (Table 3).
The final prioritization (ranking) of the neoantigens is then done by calculating a weighted sum (RSUM) of the 3 individual rank scores and ranking the neoantigens from lowest to highest RSUM value (
RSUM=(RFREQ+REXPR+(k+RIC50))*WF Formula (I):
In formula (I) k is a constant value that is added to the RIC50 value in the case the predicted epitope has an IC50 value higher than 1000 nM (this penalizes neoantigens with a high RIC50 score value, i.e. with a high IC50 value).
The value for k is determined in the following way.
Occasionally NGS-RNA data, for technical reasons, does not provide coverage at the location of the mutation, neither for the non-mutated amino acids nor for the mutated amino acids in an otherwise expressed gene. WF is a down-weighting factor (down-weighting because the resulting RSUM value is increased and the neoantigen is ranked further down in the list) taking into account cases where no mutated reads were observed in the NGS-RNA transcriptome data.
This generates a RSUM ranked list of neoantigens.
Neoantigens that have the same RSUM score are further prioritized according to their RIC50 score (
The final list of M ranked neoantigens is then analyzed by a method that determines which and how many neoantigens can be included in the vaccine vector.
The method works with an iterative procedure. At each iteration a list of the N best ranked neoantigens necessary to reach the maximum insert size of L amino acids (preferably 1500 amino acids) is created. If the list of N neoantigens contains more than one partially overlapping neoantigens derived from the same FSP, a merging step is performed to avoid the inclusion of redundant stretch of the same amino acid sequence. (
The procedure stops when adding the next neoantigen to the already selected list of N neoantigens would exceed the maximum desired insert size L.
The precise value of N can therefore decrease due to the presence of merged FSP-derived neoantigens (length longer than a 25mer) or increase due to the presence of neoantigens containing mutations close to the N- or C-terminus of the protein (these neoantigens will be shorter than a 25mer).
Output is a list of N neoantigens with a total length less or equal to L=1500aa.
The ordered list is then split into two parts of approximately equal length (
The list of N selected neoantigen sequences is then re-ordered according to a method that minimizes the formation of predicted junctional epitopes that may be generated by the juxtaposition of two adjacent neoantigen peptides in an assembled polyneoantigen polypeptide. One million of scrambled layouts of the assembled polyneoantigen are generated each with a different neoantigen order. Each layout is then analyzed to determine the number of predicted junctional epitopes with an IC50<=1500 nM for one of the patient's HLA alleles. While looping over all one million layouts the layout with the minimal number of predicted junctional epitopes encountered up to that point is remembered. If later on a second layout with the same minimal number of predicted junctional epitopes is found the layout first encountered is kept.
The prioritization method described in Example 1 was applied to a NGS dataset from a pancreatic cancer sample (Pat_3942; Tran et al. 2015) for which one experimentally validated immunogenic reactivity has been reported. Tumor/normal exome and the tumor transcriptome NGS raw data were downloaded from the NCBI SRA database [SRA IDs:SRR2636946; SRR2636947; SRR4176783] and analyzed with a pipeline that characterizes the patient's mutanome.
The mutation detection pipeline utilized comprised 8 steps:
a) Quality control and optimization of reads:
The final list of 129 neoantigen encoding mutations confidently detected in patient Pat_3942 included 4 frameshift generating indels and 125 SNVs. The 125 SNVs generate 128 neoantigens, 3 out of which derived from mutations mapped on multiple alternative splicing isoforms. The 4 frameshift indels generate 4 FSPs with a total length of 307 amino acids and a total of 260 neoantigen sequences. The total length of all 388 neoantigens derived either from SNVs or frameshift indels was 3942 amino acids.
The maximal insert size (including expression control elements) that can be accommodated by genetic vaccines, for example adenoviral vectors, is limited thus imposing a maximal size of L amino acids to the encoded polyneoantigen. Typical values for L for adenoviral vectors are in the order of 1500 amino acids, smaller than the cumulative length of 3942 amino acids for all neoantigens. The prioritization strategy described in Example 1 was therefore applied in order to select an optimal subset of ranked neoantigens compatible with the 3942 amino acid limit
Table 4 reports all 60 selected neoantigens selected to reach a cumulative length of 1485 aa. The selection process included 6 neoantigen sequences derived from the FSP chr11:1758971_AC_-(2 nucleotide deletion), 2 neoantigen sequences from the FSP chr6:168310205_-_T (1 nucleotide insertion) and 1 neoantigen sequences from FSP chr163757295_GATAGCTGTAGTAGGCAGCATC_-(22 nucleotide deletion; SEQ ID NO:185). During selection several overlapping FSP-derived neoantigen sequences were merged in order to remove redundant sequence segments (Table 5). Details of the merged neoantigen sequences are shown in
All neoantigen sequences generated by the 129 confidently detected mutations in Pat_3942 are listed in Table 6 including the associated values of the three parameters (mutant allele frequency MFREQ, corrected expression value corrTPM, best predicted IC50 value for MHC class I 9mer epitopes MIC50), the resulting three independent rank scores (RFREQ, REXPR, RIC50), the weighting factor WF, the weighted RSUM value and the resulting RSUM rank.
Importantly, all three neoantigen sequences reported to induce T-cell reactivity in the patient (Tran et al., 2015) were selected within the top 60 neoantigens by the prioritization strategy.
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Example 3: Validation of the prioritization method In order to validate the prioritization method datasets with a total of 30 experimentally validated immunogenic neoantigens with CD8+ T-cell reactivity were analysed (Table 7). The datasets comprise biopsies from 13 cancer patients across 5 different tumor types for which NGS raw data (normal/tumor exome NGS-DNA and tumor NGS-RNA transcriptome) is available.
NGS data were downloaded from the NCBI SRA website and processed with the same NGS processing pipeline applied in Example 1. Mutations for 28 out of the 30 reported experimentally validated neoantigens were identified by applying the NGS processing pipeline disclosed in Example 2 (two mutations were not detected due to the very low number of mutated reads). For each patient sample the total list of all neoantigens identified was then ranked according to the method described in Step 3 in Example 1 assuming a target maximal polypeptide (polyneoantigen) size of 1500 amino acids.
Table 8 shows the predicted MHC class I IC50 values for the 28 neoantigens, for only 9mer epitope prediction or for predictions including epitopes from 8 up to 11 amino acids. In both cases several neoantigens are present where the best (lowest) IC50 values are well above (higher) than the 500 nM threshold value frequently applied in the art for the selection of neoantigen vaccine candidates and, consequently, would have been excluded from the personalized vaccine.
27 out of the 30 experimentally validated neoantigens (90%) are present in the top 60 neoantigens and therefore would have been included in the personalized vaccine vector. The priorization was then repeated assuming that no NGS-RNA expression data from the patient's tumor was available. The corrTPM expression value for each neoantigen was estimated as the median TPM value of the corresponding gene in the TCGA expression data for that particular tumor type [NCBI GEO accession:GSE62944].
Both results therefore confirmed that the prioritization method is able to select, in the presence but also in the absence of transcriptome data from the patient's tumor, a list of neoantigens that includes the most relevant neoantigens, i.e. those neoantigens with experimentally verified immunogenicity that should be included in a personalized vaccine vector.
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A polyneoantigen containing 60 neoantigens will result in an artificial protein with a total length of about 1500 amino acids that need to be encoded by an expression cassette inserted into a genetic vaccine vector. Expression of such a long artificial proteins can be suboptimal thus affecting the level of immunogenicity induced against the encoded neoantigens. Splitting the polyneoantigen into two pieces thus could help to obtain higher levels of induced immunogenicity.
A polyneoantigen composed of 62 neoantigens (Table 9) derived from the murine tumor cell line CT26 was therefore tested, using adenoviral vector GAd20, in different layouts (
Immunogenicity was determined in vivo by immunizing groups (n=6) of naïve BalbC mice intramuscularly once with a dose of 5×10{circumflex over ( )}8 viral particles (vp). T cell responses were measured 2 weeks post immunization on splenocytes by INFγ ELISpot for recognition of peptide pools containing the 25mer neoantigens.
GAd20-CT26-62, expressing the long polyneoantigen, demonstrated a sub-optimal induction of neoantigen specific T cell responses when compared to the co-administered two vector layout GAd-CT26-1-31/GAd-CT26-32-62 (
Dividing the long polyantigen into two approximately equally sized smaller polyneoantigens thus provides a vaccine vector composition (one dual cassette vector or two distinct vectors) with superior immunogenic properties.
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| Number | Date | Country | Kind |
|---|---|---|---|
| 18206599.5 | Nov 2018 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2019/081428 | 11/15/2019 | WO | 00 |
