Method for optimizing selection of an effective vaccine agent

Abstract

The present invention relates to a method for ranking of immunization agents composed of or encoding multiple potential human leukocyte antigen (HLA) ligands in order to provide a patient with the optimal treatment in terms of immunization. The method entails a step of matching the patient's HLA profile with the potential HLA ligands and based on this and optionally the expression pattern of the HLA ligands in the patient then ranking the immunization agents according to their probabilities of including a preselected number of true HLA ligands.

Description

[0001] METHOD FOR OPTIMIZING SELECTION OF AN EFFECTIVE VACCINE AGENT

[0002] FIELD OF THE INVENTION

[0003] The present invention relates to the field of cancer immunotherapy. In particular, the present invention relates to improved means and methods for optimizing precision anti-cancer vaccination which target expression products of genomic sequences, which are not or only to a very limited degree expressed in normal tissues, but which are found in individual patients' cancer tissue. Also, the invention relates to a method for treatment of cancer as well as a computer system.

[0004] BACKGROUND OF THE INVENTION

[0005] Treatment of malignant neoplasms in patients has traditionally focussed on eradication / re- moval of the malignant tissue via surgery, radiotherapy, and / or chemotherapy using cytotoxic or cytostatic drugs in dosage regimens that aim at preferential killing of malignant cells over killing of non-malignant cells.

[0006] In addition to the use of cytotoxic drugs, more recent approaches have focussed on targeting of specific biologic markers in the cancer cells in order to reduce systemic adverse effects exerted by classical chemotherapy. Monoclonal antibody therapy targeting cancer associated antigens has proven quite effective in prolonging life expectancy in a number of malignancies. While being successful drugs, monoclonal antibodies can due to their nature only be developed to target expression products that are known and appear in a plurality of patients, meaning that the vast majority of cancer specific antigens cannot be addressed by this type of therapy, because a large number of cancer specific antigens only appear intracellularly or in tumours from one single patient, cf. below.

[0007] As early as in the late 1950’ies the theory of immunosurveillance was formulated and suggested that lymphocytes recognize and eliminate autologous cells, such as cancer cells that exhibit altered antigenic determinants, and it is today generally accepted that the immune system inhibits carcinogenesis to a high degree. Nevertheless, immunosurveillance is not 100% effective and it is a continuing task to develop cancer therapies where the immune system's ability to eradicate cancer cells is sought improved / stimulated. One approach has been to induce immunity against cancer-associated antigens, but even though this approach has potential, it suffers the same drawback as antibody therapy that only a limited number of antigens can be addressed.

[0008] Many, if not all, tumours express mutations. These mutations may create new targetable antigens (neoantigens), which are potentially useful in specific ? cell immunotherapy if it is possible to identify the neoantigens and their antigenic determinants (neoepitopes) within a clinically relevant timeframe. With current technology it is possible to fully sequence the genome of cells and to analyse for existence of altered or new expression products within a few days, hence, within this timeframe it is possible to design personalized vaccines based on neoantigens and their neoepitopes.

[0009] Multiple bioinformatic pipelines exist for predicting / identifying neoepitopes from patient derived sequencing data (cf. Hundal, J. et al. 2016; Hundal, J. et al. Cancer Immunol. Res. 2020 8(3) : 409-420. DOI: 10.1158 / 2326-60662020; Bjerregaard, A. M. et al. 2017; Bais, P. et al. 2017; Rubinsteyn, A. et al. 2017; Schenck, R. O. et al. 2019, Diao, K. et al. Int. J. Mol. Sci. 2022 23(19) : 11624. DOI: 10.3390 / ijms2319116242022). Each pipeline takes a different set of features into account when selecting or ranking neoepitopes, underscoring that the neoepitope selection problem is still unsolved.

[0010] WO 2022 / 023521 discloses methods for selection of epitopes to include in individualized cancer vaccines; focus is put on identification and utilisation of neoepitopes encoded by somatic variants of expressed genes in cancer cells. The methods disclosed in WO 2022 / 023521 hence rely on an identification of short peptides present in expression products that differ from the normal expression products in the patient, and as such the method in WO 2022 / 023521 will always require an individual evaluation of the potential usefulness of such short peptides.

[0011] WO 2023 / 111306 discloses a cancer therapeutic approach, which relies on immunization against expression products of genomic sequences (typically endogenous viral elements ("EVEs"), such as endogenous retroviral sequences ("ERVs")), where the expression products appear in a small or negligible percentage of samples of normal tissue.

[0012] To date, it does not appear that anyone has provided a cancer therapeutic approach that combines targeting of multiple expression products from EVEs, where selection of the targeted antigens is optimized vis-a-vis the individual patient, i.e. the art has not provided any approaches where the advantages of the specific targeting of multiple neoepitopes (e.g., precision-based) are attained when targeting EVE expression products. OBJECT OF THE INVENTION

[0013] It is an object of embodiments of the invention to provide an optimized method for treatment of cancer patients by immunotherapy, and in particular to provide a method of evaluating the suitability for the individual patient of each of an array of immunization agents, that are tailored to include multiple potential HLA ligands.

[0014] SUMMARY OF THE INVENTION

[0015] It has been found by the present inventors that it is possible to optimize treatment of cancer patients by rationally selecting an immunization agent among immunization agents that are composed so as to include or express multiple potential HLA ligands.

[0016] So, in a first aspect the present invention relates to a method for selecting at least one immunization agent, which is suitable for use in active specific immunotherapy of a malignant neoplasm in a patient, wherein the at least one immunization agent is part of a set of non-identical immunization agents each comprising a) amino acid sequences, which can be expressed from genomic DNA in the cells of the malignant neoplasm, but are only expressed at a predetermined low level in cells of normal tissue in the patient or b) nucleic acids encoding said amino acid sequences, the method comprising

[0017] 1) identifying for each immunization agent potential human leukocyte antigen (HLA) ligands that match the patient's HLA profile, where the potential HLA ligands are constituted by amino acid sequences comprised in said immunization agent or are amino acid sequences encoded by said nucleic acids of the immunization agent, where each such potential HLA ligand exhibits a probability Pl that it constitutes a true HLA ligand in the patient,

[0018] 2) determining for each immunization agent a quantitative indication of the probability P2 that at least X potential HLA ligands comprised therein or encoded thereby are true HLA ligands, where X is a preselected integer >2 and <Y, where Y is the maximum number of potential HLA ligands comprised in or encoded by the immunization agent, and

[0019] 3) selecting among the immunization agents a) the one(s) exhibiting the highest probability in step 2 that at least X potential HLA ligands are true ligands or b) the one(s) that exhibit(s) in step 2 a minimum predetermined probability that at least X potential HLA ligands are true HLA ligands. In a second aspect, the invention relates to a method for treatment of a human patient suffering from a malignant neoplasm, the method comprising providing the human leukocyte antigen (HLA) profile of the patient, selecting from a set of non-identical immunization agents at least one immunization agent according to any embodiment of the method according to the first aspect of the invention, and subsequently immunizing the patient one or more times with the immunization agent(s) so selected.

[0020] LEGENDS TO THE FIGURE

[0021] Fig. 1 : Schematic depiction of the method for selection of expression plasmid(s) according to the invention.

[0022] Fig. 2: IFNy ELISPOT of splenocytes.

[0023] IFNy ELISpot on splenocytes from immunized BALB / c mice upon re-stimulation with immunization-relevant peptide pools, for the A20 tumour model (A) and the CT26 tumour model (B), and with plasmids designed for other tumours for another strain of mice (C57BL / 6) (C). Mice were immunized with plasmids designed to contain between 20-24 mERV hotspots, which were split into two pools of 10-12 corresponding mERV hotspot peptides each for the re-stimulation assay (i.e. Peptide pool 1 and Peptide pool 2). Data not shown: Mean SFUs for unstimulated cells (DMSO) : 3.3 SFUs per 105cells (A), 0 SFUs per 105cells (B), and 2 SFUs per 105cells (C). The assay was run with biological replicates (n=4 mice per group in part A, n=6 mice per group in part B, n = 5-6 mice in part C) and the bar represents the group mean. SFUs: spot forming units.

[0024] Fig. 3 : Graph showing result of A20 tumour study.

[0025] Groups of n = 13 BALB / c mice were immunized prophylactically with 25 pg of Personalized (A20) DNA plasmid followed by electroporation before s.c. inoculation with A20 tumour cells on day 0. The mice were housed with and tumour growth compared to untreated tumour bearing controls. Group mean tumour growth curves (in mm3) ± standard error of the mean (SEM).

[0026] DETAILED DISCLOSURE OF THE INVENTION

[0027] Definitions

[0028] An "endogenous retroelement" ("ERE") is a genetic element. EREs constitute nearly 50% of the human genome. These elements are present in almost all organisms and believed to be remnants of transposable elements that integrated in germline cells millions of years ago. Most ERE sequences contain mutated or truncated open reading frames and have lost their capacity to transpose in the genome. EREs comprise short and long interspersed retrotransposable elements (SINE and LINE), and these are collectively known as non-LTR elements. The remaining endogenous retroelements comprise LTR-bound elements comprising two major groups occupying comparable fractions of the genome: endogenous retroviruses (ERVs) and mammalian apparent LTR retrotransposons (MaLRs) (Kassiotis & Stoye, 2016).

[0029] An "endogenous viral element" ("EVE") is an ERE, which is member of a subset of genes, which is a result of an in silico filtering process based on the presence of viral motifs in the gene. As such, this group is mostly composed of ERVs, but can also contain members of the other different subcategories.

[0030] A "novel or unannotated open reading frame" (abbreviated a "nuORF") is a genomic sequence, which is not conventionally the source of a translated product, but where immunopeptidomic analyses have revealed the existence of MHC binding peptides derived from malignant tissue (Ouspenskaia T et al. 2021, Nature Biotechnology, doi.org / 10.1038 / s41587-021-01021-3).

[0031] A "malignant neoplasm" (also termed a cancer or malignant tumour) denotes a group of cells in a multicellular organism, which exhibit uncontrolled growth, invasive growth, and, normally, the ability to metastasize.

[0032] A "cancer specific" antigen, is an antigen, which does not appear as an expression product in an individual's non-malignant somatic cells, but which appears as an expression product in cancer cells in the individual. This is in contrast to "cancer-associated" antigens, which also appear - albeit at low abundance - in normal somatic cells but are found in higher levels in at least some malignant tumour cells. In general, the peptides identified according to the present invention are considered to be cancer specific.

[0033] The term "adjuvant" has its usual meaning in the art of vaccine technology, i.e. a substance or a composition of matter which is 1) not in itself capable of mounting a specific immune response against the immunogen of the vaccine, but which is 2) nevertheless capable of enhancing the immune response against the immunogen. Or, in other words, vaccination with the adjuvant alone does not provide an immune response against the immunogen, vaccination with the immunogen may or may not give rise to an immune response against the immunogen, but the combined vaccination with immunogen and adjuvant induces an immune response against the immunogen which is stronger than that induced by the immunogen alone.

[0034] An MHC molecule (major histocompatibility molecule) is a tissue antigen expressed by nucleated cells in vertebrates, which binds to peptide antigens and displays ("presents") the antigens to T-cells carrying T-cell receptors. MHC class I is expressed by all nucleated cells and primarily present proteolytically degraded protein fragments derived from proteins present in the cell. MHC class II is expressed by professional antigen presenting cells that typically take up extracellular protein, degrade it with lysosomal proteases, and present protein fragments on the surface. In humans, the MHC molecules are encoded at the human leukocyte antigens (HLA) loci, which in the present invention are the preferred MHC molecules to evaluate binding to.

[0035] A "T-cell epitope" is an MHC binding peptide, which is recognized as foreign (non-self) by a T- cell in a vertebrate due to specific binding between a T-cell receptor and the cell carrying the MHC-peptide complex on its surface. Hence, a peptide, which constitutes a T-cell epitope in one individual will not necessarily be a T-cell epitope in a different individual of the same species. First of all, two individuals having differing MHC molecules that bind different sets of peptides, do not necessarily present the same peptides complexed to MHC, and further, if a peptide is autologous in one of the individuals it may not be able to bind any T-cell receptor.

[0036] A "neoepitope" is an antigenic determinant (typically an MHC Class I or II restricted epitope), which does not exist as an expression product from normal somatic cells in an individual due to the lack of a gene encoding the neoepitope, but which exists as an expression product in mutated cells (such as cancer cells) in the same individual. As a consequence, a neoepitope is from an immunological viewpoint truly non-self in spite of its autologous origin and it can therefore be characterized as a tumour specific antigen in the individual, where it constitutes an expression product. Being non-self, a neoepitope has the potential of being able to elicit a specific adaptive immune response in the individual, where the elicited immune response is specific for antigens and cells that harbour the neoepitope. Neoepitopes are on the other hand specific for an individual as the chances that the same neoepitope will be an expression product in other individuals is minimal. Several features thus contrast a neoepitope from, e.g., epitopes of tumour specific antigens: the latter will typically be found in a plurality of cancers of the same type (as they can be expression products from activated oncogenes) and / or they will be present - albeit in minor amounts - in non-malignant cells because of over-expression of the relevant gene(s) in cancer cells.

[0037] A "neopeptide" is a peptide (i.e. a polyamino acid of up to about 50 amino acid residues), which includes within its sequence a neoepitope as defined herein. A neopeptide is typically "native", i.e. the entire amino acid sequence of the neopeptide constitutes a fragment of an expression product that can be isolated from the individual, but a neopeptide can also be "artificial", meaning that it is constituted by the sequence of a neoepitope and 1 or 2 appended amino acid sequences of which at least one is not naturally associated with the neoepitope. In the latter case the appended amino acid sequences may simply act as carriers of the neoepitope, or may even improve the immunogenicity of the neoepitope (e.g. by facilitating processing of the neopeptide by antigen-presenting cells, improving biologic halflife of the neopeptide, or modifying solubility).

[0038] The term "amino acid sequence" is the order in which amino acid residues, connected by peptide bonds, lie in the chain in peptides and proteins. Sequences are conventionally listed in the N to C terminal direction.

[0039] "An immunogenic carrier" is a molecule or moiety to which an immunogen or a hapten can be coupled in order to enhance or enable the elicitation of an immune response against the immunogen / hapten. Immunogenic carriers are in classical cases relatively large molecules (such as tetanus toxoid, KLH, diphtheria toxoid etc.) which can be fused or conjugated to an immunogen / hapten, which is not sufficiently immunogenic in its own right - typically, the immunogenic carrier is capable of eliciting a strong T-helper lymphocyte response against the combined substance constituted by the immunogen and the immunogenic carrier, and this in turn provides for improved responses against the immunogen by B-lymphocytes and cytotoxic lymphocytes. More recently, the large carrier molecules have to a certain extent been substituted by so-called promiscuous T-helper epitopes, i.e. shorter peptides that are recognized by a large fraction of HLA haplotypes in a population, and which elicit T-helper lymphocyte responses.

[0040] A "T-helper lymphocyte response" is an immune response elicited on the basis of a peptide, which is able to bind to an MHC class II molecule (e.g. an HLA class II molecule) in an antigen-presenting cell and which stimulates T-helper lymphocytes in an animal species as a consequence of T-cell receptor recognition of the complex between the peptide and the MHC Class II molecule presenting the peptide.

[0041] An "immunogen" is a substance of matter which is capable of inducing an adaptive immune response in a host, whose immune system is confronted with the immunogen. As such, immunogens are a subset of the larger genus "antigens", which are substances that can be recognized specifically by the immune system (e.g. when bound by antibodies or, alternatively, when fragments of the antigens bound to MHC molecules are being recognized by T-cell receptors) but which are not necessarily capable of inducing immunity - an immunogen is, however, always capable of eliciting immunity, meaning that a host that has an established memory immunity against the immunogen will mount a specific immune response against the immunogen.

[0042] An "adaptive immune response" is an immune response in response to confrontation with an antigen or immunogen, where the immune response is specific for antigenic determinants of the antigen / immunogen - examples of adaptive immune responses are induction of antigen specific antibody production or antigen specific induction / activation of T helper lymphocytes or cytotoxic lymphocytes.

[0043] A "protective, adaptive immune response" is an antigen-specific immune response induced in a subject as a reaction to immunization (artificial or natural) with an antigen, where the immune response is capable of protecting the subject against subsequent challenges with the antigen or a pathology-related agent that includes the antigen. Typically, prophylactic vaccination aims at establishing a protective adaptive immune response against one or several pathogens. In the present context the immune responses induced by the peptides identified are typically therapeutic immune responses against a cancer in a patient.

[0044] "Stimulation of the immune system" means that a substance or composition of matter exhibits a general, non-specific immunostimulatory effect. A number of adjuvants and putative adjuvants (such as certain cytokines) share the ability to stimulate the immune system. The result of using an immunostimulating agent is an increased "alertness" of the immune system, meaning that simultaneous or subsequent immunization with an immunogen induces a significantly more effective immune response compared to isolated use of the immunogen.

[0045] The term "polypeptide" is in the present context intended to mean both short peptides of from 2 to 50 amino acid residues, oligopeptides of from 50 to 100 amino acid residues, and polypeptides of more than 100 amino acid residues. Furthermore, the term is also intended to include proteins, i.e. functional biomolecules comprising at least one polypeptide; when comprising at least two polypeptides, these may form complexes, be covalently linked, or may be non-covalently linked. The polypeptide(s) in a protein can be glycosylated and / or lipidated and / or comprise prosthetic groups. An "HLA ligand" is a peptide defined by an amino acid sequence, which has a length and amino acid distribution that will allow to bind at least one HLA molecule.

[0046] A "hotspot sequence" denotes an amino acid sequence which comprises HLA ligand amino acid sequences with a high density, i.e. an above-normal number of HLA ligand amino acid sequences per base pair. A "potential HLA ligand" (also termed a "predicted" HLA ligand) is a peptide defined by an amino acid sequence, which has a length and amino acid distribution that will allow it to bind at least one HLA molecule in a patient.

[0047] A "true HLA ligand" is a peptide which in a given patient binds to an HLA molecule and is presented by antigen presenting cells.

[0048] Specific embodiments of the invention

[0049] 1staspect of the invention and embodiments thereof

[0050] The first aspect of the present invention relates to a method for selecting at least one immunization agent, which is suitable for use in active specific immunotherapy of a malignant neoplasm in a patient, wherein the at least one immunization agent is part of a set of nonidentical immunization agents each comprising a) amino acid sequences, which can be expressed from genomic DNA in the cells of the malignant neoplasm, but are only expressed at a predetermined low level in cells of normal tissue in the patient, or b) nucleic acids encoding said amino acid sequences, the method comprising 1) identifying for each immunization agent potential HLA ligands that match the patient's HLA profile, where the potential HLA ligands are constituted by amino acid sequences comprised in said immunization agent or are amino acid sequences encoded by said nucleic acids of the immunization agent, where each such potential HLA ligand exhibits a probability Pl that it constitutes a true HLA ligand in the patient, 2) determining for each immunization agent a quantitative indication of the probability P2 that at least X potential HLA ligands comprised therein or encoded thereby are true HLA ligands, where X is a preselected integer >2 and <Y, where Y is the maximum number of potential HLA ligands comprised in or encoded by the immunization agent, and 3) selecting among the immunization agents a) the one(s) exhibiting the highest probability in step 2 that at least X potential HLA ligands are true ligands or b) the one(s) that exhibit(s) in step 2 a minimum predetermined probability that at least X potential HLA ligands are true HLA ligands.

[0051] Hence, the invention involves a situation, where a patient suffering from a malignant neoplasm can be treated - either exclusively or as part of a combination treatment - with a vaccine capable of actively inducing specific immunity against antigens of the malignant neoplasm. The set of immunization agents are prepared in advance and each immunization agent in the set is designed to include a series of potential HLA ligands by including such potential HLA ligands that bind a broad variety of HLA molecules in distant subpopulations, e.g., ethnic populations or disease subpopulations. By exercising the method of the first aspect, the individual immunization agents of the set are ranked in order to identify those that have the highest chance of inducing a beneficial immune response in the patient by aligning the patient's HLA profile with the ability of the potential HLA ligands of the immunization agent or, if the immunization agent is a nucleic acid based vaccine, the potential HLA ligands encoded by the immunization agent, to be presented by the HLA molecules of the patient. In addition to merely matching the HLA ligands with the HLA profile, it is further of value to take into consideration the expression level in the patient of genetic material encoding the potential HLA ligands.

[0052] As noted, the at least one immunization agent can be at least one expression vector, such as at least one plasmid vector or a viral vector, or a composition comprising said at least one expression vector. The use of expression vectors is preferred, since it allows for design of very compact coding sequences that include a high density of coding regions for the potential HLA ligands. The composition of the potential HLA ligands encoded by the expression vector is preferably such that the at least one expression vector 1) encodes a polypeptide comprising a plurality of potential HLA ligands that can be expressed from DNA in the cells of the malignant neoplasm, but only expressed at a predetermined low level in cells of normal tissue in the patient, or 2) encodes multiple peptides that together comprise said plurality of potential HLA ligands. This choice of potential HLA ligands ensures that the immunization with the vector will exhibit a low probability of inducing undesired adverse events by inducing immune responses against normal cells in the patient.

[0053] It is also a possibility that the at least one immunization agent is a polypeptide or a set of peptides, or a composition comprising the polypeptide or set of peptides. Considerations concerning the choice of potential HLA ligands comprised in such an immunization agent are identical to those indicated above for the choice of potential HLA ligands encoded by an expression vector.

[0054] As indicated above, when evaluating the immunization agents of the set, each potential HLA ligand is preferably a verified expression product from cells of the malignant neoplasm. Thus, in this embodiment of the first aspect of the invention, even a potential HLA ligand exhibiting a high probability Pl, thereby contributing to a high probability P2, would be disregarded if the HLA ligand's source expression product(s) is / are not expressed in the patient's malignant cells. Likewise, if an immunization agent turns out to not include or express an HLA ligand found as an expression product in the patient, such an immunization agent is ranked low among the immunization agents.

[0055] In order to assign the probability P2, it must be >0. This means that the number X in the at least X potential HLA ligands used for the evaluation in steps 2 and 3 is so selected that it will allow a meaningful ranking of the individual immunization agents. For instance, if X is set at too low a value, all immunization agents would exhibit such high probabilities of including X true HLA ligands that they cannot be ranked. Likewise, if X is selected as a too high value, all immunization agents would exhibit a zero possibility of including X true HLA ligands. Hence, the number X is determined from experience of ranking the immunization agents (which in turn depends on the number and composition of potential HLA ligands in or encoded by the immunization agents). However, typically X is set at such a value that step 2 determines the probabilities P2 that at least 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 or 19 or 20 or 21 or 22 or 23 or 24 or 25 or 26 or 27 or 28 or 29 or 30 potential HLA ligands are true ligands.

[0056] As indicated in Example 1, it is convenient that the quantitative indication in step 2 is determined for each immunization agent by determining - via a simulation or exact calculation - the probabilities for the presence of each of at least 1 to Y true HLA ligands being comprised in or encoded by the immunization agent. As also indicated in the Example, the quantitative indication for each immunization agent can conveniently be expressed as an area under curve (AUC) of the probabilities determined, but it is also equally possible that the quantitative indication for each immunization agent is expressed as the probability that at least Z potential HLA ligands are true HLA ligands, where Z is a predetermined integer >1, where Z preferably is predetermined to be 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 or even higher. Again, as indicated for X above, this value of Z has to be selected to provide a meaningful evaluation of the immunization agents.

[0057] Preferably, the amino acid sequences, which are or can be expressed from genomic DNA in the cells of the malignant neoplasm, but only expressed at a predetermined low level in cells of normal tissue in the patient, are amino acid sequences encoded by normally non-coding DNA, such as endogenous viral elements (EVEs; in particular human endogenous retroviruses (ERVs)), but the amino acid sequences can also be those of tumour-associated antigens (TAAs), tumour-specific antigens (TSAs), or neoantigens.

[0058] However, it is preferred that the amino acid sequences, which are or can be expressed from genomic DNA in the cells of the malignant neoplasm, but only expressed at a predetermined low level in cells of normal tissue in the patient, are EVEs and in particular are those EVEs that comprise or are comprised of DNA sequences of human endogenous retroviruses (hERVs). 2ndaspect of the invention and embodiments thereof

[0059] This aspect relates to a method for treatment of a human patient suffering from a malignant neoplasm, the method comprising providing the human leukocyte antigen (HLA) profile of the patient, selecting from a set of non-identical immunization agents at least one immunization agent according to any embodiment of the method according to the first aspect of the invention, and subsequently immunizing the patient one or more times with the immunization agent(s) so selected. As mentioned above, the treatment may be the sole treatment of the patient, but often it will be part of a combination treatment wherein the patient further is treated with at least one additional cancer therapy, such as one or more of radiotherapy, therapy with cytostatic drugs, therapy with cytotoxic drugs, therapy with immune checkpoint inhibitors, therapy with immune-modulatory effects, chimeric antigen receptor (CAR)-T therapy, adoptive T-cell therapy, depletion therapy, targeted therapy, therapy with tyrosine kinase inhibitors, and cancer immunotherapy targeting a surface expressed determinant.

[0060] EXAMPLE 1

[0061] Selecting ERV-based plasmid constructs for vaccination of a model patient

[0062] Generally, a schematic depiction of the method is set forth in Fig. 1.

[0063] Step 1

[0064] The patient is HLA-typed following state-of-the-art procedures: traditional serology can be employed but PCR-based or Next generation sequencing (NGS) methods are preferred.

[0065] Useful PCR methods include use of any one of 1) sequence-specific oligonucleotides (SSO), i.e. an approach using fluorescently labelled oligonucleotide probes that hybridize to specific HLA sequences to allow resulting fluorescence signals to be detected and analyzed to determine the HLA type; 2) sequence-specific primers (SSP), i.e. an approach using sequence-specific primers that amplify target HLA genes through PCR. The presence or absence of amplification products indicates the HLA type, and 3) sequence-based typing (SBT), i.e. an approach involving PCR amplification of HLA genes followed by direct sequencing of the amplicons. Next-Generation Sequencing (NGS) enables the simultaneous sequencing of millions of DNA fragments, allowing for comprehensive and efficient HLA typing. NGS-based HLA typing can be performed using targeted gene panels or whole-genome sequencing.

[0066] In the present example, the patient is found to exhibit the following HLA Class 1 types: HLA- A*03:01, HLA-A*ll:01, HLA-B*35:37, HLA-B*52:01, HLA-C*12:02, and HLA-C*04:01, as well as the HLA Class 2 types: HLA-DRBl*01:01 and HLA-DRBl*04:04.

[0067] In addition to HLA-typing of the patient, the following additional steps can be taken:

[0068] A tumour biopsy can be obtained from the patient and mRNA therein sequenced. Subsequently mRNA reads are mapped to a human reference genome including ERV sequences, for instance using the STAR program (cf. Alexander Dobin et al. (2013), Bioinformatics 29(1) : 15-21, doi: 10.1093 / bioinformatics / bts635). Then, mRNA expression values (transcripts per million, TPM) are determined using the RSEM program (cf. Bo Li and Colin N Dewey (2011), BMC Bioinformatics 12, Article number: 323; Brian J Haas et al. (2013), Nature Protocols 8, 1494-1512) and ERVs with TPM values higher than a selected threshold value (e.g. TPM>1) are counted as expressed. Additionally, germline and somatic variants can be identified from RNA data (or optionally, DNA data). ERV oligomers that match the construct sequence 100% AND are present in expressed ERVs are counted as expressed oligomers.

[0069] Each plasmid construct in the set to select from contains a number (e.g. >1) of "hotspot sequences" that are variable in length so as to obtain a (comparably high) number of predicted HLA ligands per base pair. These hotspot sequences are extracted from one or more ERV DNA sequences. In the present example, 15 plasmids have been designed, each including 5-25 hotspot sequences. Below are some of the hotspot sequences encoded on an example plasmid comprising 20 hotspot sequences:

[0070] Hotspot sequence 1 (Contains ligands derived from ERV transcript ID Hsap38.chrl2.79864930.79865697.+) :

[0071] KLSLFTDDKI VYLQNPIVSA PNLLKLISNF SKFSGYKINV QKSQASLYTK (SEQ ID NO: 1), in which the predicted ligands are present over the entire sequence: FTDDKIVYL (SEQ ID NO: 2), IVSAPNLLK (SEQ ID NO: 3), up to and including KSQASLYTK (SEQ ID NO: 4).

[0072] Hotspot sequence 2 (Contains ligands derived from ERV transcript ID Hsap38.chrl2.79864930.79865697.+) :

[0073] LEAFHLKTAT RQGSPLSSLL FNIVLEVLAR AIRQEKAIKR IQIGREEVKL SLFTDDKIVY L (SEQ ID

[0074] NO: 5), in which the predicted ligands are present over the entire sequence: FTDDKIVYL (SEQ ID NO: 2).

[0075] Hotspot sequence 3 (Contains ligands derived from ERV transcript ID

[0076] Hsap38.chrl2.43978860.43979192.-) :

[0077] HTLNDYQKLL GNINWLRPSL NITTDKLQNL FSIPKGNTTL DSL (SEQ ID NO: 6), in which the predicted ligands are present over the entire sequence: YQKLLGNI (SEQ ID NO: 7), KLQNLFSIPK (SEQ ID NO: 8), up to and including SIPKGNTTL (SEQ ID NO: 9).

[0078] Hotspot sequence 20 (Contains ligands derived from ERV transcript IDs

[0079] Hsap38.chrl.167319501.167319884.-, Hsap38.chr3.185014621.185015133.-,

[0080] Hsap38.chr3.185108708.185109298.+, and Hsap38.chrY.13257234.13257596.+) : NAPGYTSQAL (SEQ ID NO: 10), in which the predicted ligands are present over the entire sequence: NAPGYTSQAL (SEQ ID NO: 10).

[0081] In this example, only the ERV transcripts corresponding to the above-indicated hotspot sequences 1, 2, and 20 are found not to be expressed in the model patient.

[0082] Step 3

[0083] Each ligand included in a construct may bind to multiple HLA alleles. Further, due to high redundancy in ERVs, each ligand may be present in more than one of the ERV hotspots included in the plasmid. Hence, first a list of potential ligand-HLA pairs is derived from the hotspot sequences in the construct and the patient's HLA profile.

[0084] Next, a probability for the ligand to be a true ligand is obtained for the entire list of potential ligands, for instance as shown in the following table for some of the potential ligands:

[0085] In the above table, the ligands in the last 2 rows are not expressed in the patient's RIMA data.

[0086] Optionally, the ligand list can be limited to only ERVs expressed in the patient, i.e. in this example only ligands #1-5. It is also possible to combine the ERV expression value with the probability score to provide a combined HLA ligand and expression probability as described in: WO 2022 / 023521 A2. Here, a ligand probability p(L) was defined as the probability of a peptide being presented on the surface of a cell (i.e. the peptide is a true ligand) by taking into consideration the probability of the peptide being a true MHC ligand and the expression of the source protein harbouring the peptide, e.g. by measuring the mRNA of said source protein. If the expression is incorporated into the ligand probability, the "Expressed" column in the table above is not relevant.

[0087] The present example construct contains 7 unique peptide:HLA pairs, of which 5 are expressed in the tumour biopsy. Step 4

[0088] The selection of one or more optimum plasmid constructs in the set relies on a ranking of the plasmid constructs based on either a simulation or an exact calculation:

[0089] Ranking by simulation For each ligand encoded by a plasmid construct, a random number between 0 and 1 is sampled. If this number is less than the predicted probability that a potential ligand encoded by the plasmid is a true ligand in the patient, the potential ligand is marked as a "hit" and the number of hits is counted : In the above table, the number of "hits" is 3 for the 5 assigned random numbers.

[0090] The process of sampling random numbers and marking peptides as hits is repeated a large number of times (e.g. 10,000 times) to gather statistics on the pool of ligands predicted for a patient; statistics are then calculated as the fraction of rounds that produces n or more hits, where n is an integer >0 and < the number of potential ligands evaluated. For instance (and in a simplified version), for a 10 round experiment with the above 5 ligands, the number of calculated hits could be [0,1, 1,1, 2, 2, 2, 2, 3, 3], thus producing the following hit distribution : The values in the "fraction column" can be interpreted as the individual probabilities that the plasmid construct includes at least n true HLA ligands.

[0091] Such a table can be summarized using an area under curve (AUC) calculation (in this case providing AUC=1.7) or by indicating the value of n, where a specific threshold fraction is exceeded (if the threshold is 0.75, the value n would be 1.5 in the above simplified case with only 5 potential ligands), or by indicating the fraction at a given value of n (if the value n = ">2", the fraction is 0.6 in the simplified example above).

[0092] Ultimately, the plasmid constructs are then ranked according to the same principle (AUC, threshold value-linked n-value or fraction at specific n), and the plasmid construct(s) exhibiting the highest AUC-value(s), n-value(s) or fraction(s) is / are selected.

[0093] Ranking by exact calculation

[0094] This type of ranking can be carried out as follows, where a table summarizes exact probabilities of n or more ligands:

[0095] Initially, an nxn table of probabilities is filled in where n is the number of potential ligands + 1. Each field is the probability of seeing exactly j "true ligands" by testing 0 to i ligands. The table is first filled in with 1.0 for [i=0, j = 0] (that is: 0 true ligands in 0 trials is 100% certain) and with 0.0 for i>j (i true ligands in j trials is impossible when i>j) :

[0096] Then, column j = 0 (no ligands are true ligands) can be filled out as the probability of a ligand not being a true ligand multiplied by the probability that no other ligand is true either, e.g. :

[0097] D[0, 1] = 1.0 x (1 - 0.394) = 0.606, D[0, 2] = 0.606 x (1 - 0.539) = 0.279, D[0, 3] = 0.279 x (1 - 0.448) = 0.154 D[0, 4] = 0.154 x (1 - 0.469) = 0.082

[0098] D[0, 5] = 0.082 x (1 - 0.468) = 0.044

[0099] Remaining fields can be filled out as the sum of:

[0100] 1) The probability that there already is j true ligands in i - 1 trials, so the i'th trial must fail, and 2) there is j - 1 true ligands in i - 1 trials, so the i'th trial must succeed:

[0101] D[l,l] = D[0,l] x(l-p[l])+D[0,0] xp[l] = 0.0x(l-0.394) + 1.0x0.394 = 0.394,

[0102] D[2,l] = D[l,l]x(l-p[2])+D[l,0]xp[2] = 0.394x(l-0.539)+0.606x0.539 = 0.508,

[0103] D[2,2] = D[l,2]x(l-p[2])+D[l,l]xp[2] = 0.0x(l-0.539)+0.394x0.539 = 0.212,

[0104] D[3,l] = D[2,l]x(l-p[3])+D[2,0]xp[3] = 0.508x(l-0.448)+0.279x0.448 = 0.406,

[0105] D[3,2] = D[2,2]x(l-p[3])+D[2,l]xp[3] = 0.212x(l-0.448)+0.508x0.448 = 0.345

[0106] D[3,3] = D[2,3]x(l-p[3])+D[2,2]xp[3] = 0.0x(l-0.448)+0.212x0.448 = 0.095

[0107] D[4,l] = D[3,l]x(l-p[4])+D[3,0]xp[3] = 0.406x(l-0.469)+0.154x0.469 = 0.288

[0108] D[4,2] = D[3,2]x(l-p[4])+D[3,l]xp[3] = 0.345x(l-0.469)+0.406x0.469 = 0.373

[0109] D[4,3] = D[3,3]x(l-p[4])+D[3,2]xp[3] = 0.095x(l-0.469)+0.345x0.469 = 0.212

[0110] D[4,4] = D[3,4]x(l-p[4])+D[3,3]xp[3] = 0.0x(l-0.469)+0.095x0.469 = 0.045

[0111] D[5,l] = D[4,l]x(l-p[5])+D[4,0]xp[5] = 0.288x(l-0.468)+0.082x0.468 = 0.192

[0112] D[5,2] = D[4,2]x(l-p[5])+D[4,l]xp[5] = 0.373x(l-0.468)+0.288x0.468 = 0.333

[0113] D[5,3] = D[4,3]x(l-p[5])+D[4,2]xp[5] = 0.212x(l-0.468)+0.373x0.468 = 0.288

[0114] D[5,4] = D[4,4]x(l-p[5])+D[4,3]xp[5] = 0.045x(l-0.468)+0.212x0.468 = 0.123

[0115] D[5,5] = D[4,5]x(l-p[5])+D[4,4]xp[5] = 0.0x(l-0.468)+0.288x0.045 = 0.021

[0116] Finally, the last row in the table is used to calculate the probability that n or more potential ligands are true ligands by summing from k to n, in the present case:

[0117] Probability that >1 potential ligands are true ligands: 0.192 + 0.333 + 0.288 + 0.123 + 0.021 = 0.956,

[0118] Probability that >2 potential ligands are true ligands:

[0119] 0.333 + 0.288 + 0.123 + 0.021 = 0.765,

[0120] Probability that >3 potential ligands are true ligands:

[0121] 0.288 + 0.123 + 0.021 = 0.431, Probability that >4 potential ligands are true ligands:

[0122] 0.123 + 0.021 = 0.144, and

[0123] Probability that 5 potential ligands are true ligands:

[0124] 0.021.

[0125] Again, a corresponding set of calculations are made for each plasmid construct to select the one(s) that exhibit(s) the highest calculated probability that at least n (a pre-selected threshold value) encoded potential ligands are true ligands in the patient. EXAMPLE 2

[0126] Immunization / challenge experiment

[0127] Plasmid DNA vaccine design

[0128] Three different design approaches have been taken to develop murine endogenous retrovirus (mERV) based vaccine designs: a "personalized approach" where the vaccine is tailored to target a specific tumour, a "precision approach" where it targets a tumour subpopulation, and finally, a "shared approach" that targets a full tumour population. In this test case four murine tumour cell lines, A20, CT26, C1498 and B16, have been chosen to represent a full tumour population.

[0129] Two tumour subpopulations were chosen based on tumour mouse strain origin: A20 and CT26 derived from BALB / c, and C1498 and B16 derived from C57BL / 6. In total seven vaccines have been designed; four personal (one for each tumour cell line), two precision (one for each mouse strain), and one shared vaccine covering all four murine tumour cell lines.

[0130] The vaccine designs have been developed based on tumour murine ERV (mERV) expression and MHC types of the chosen mouse strain(s). MHC types are readily available online for mouse strains BALB / c (H2-K*d, H2-D*d, H2-L*d and H2-IA*d) and C57BL / 6 (H2-K*b, H2- D*b and H2-IA*b), whereas tumour mERV expression has been estimated from in-house RNA sequencing data. RNA-sequencing data of the tumours from the four murine tumour cell lines A20, CT26, C1498 and B16 have been mapped to reference genome GRCm38 with gene annotations from Ensembl release 102 including murine endogenous retrovirus (mERV) annotations from the gEVE database version 1.1 using STAR version 2.7.10b. The RNA sequencing mappings were used to quantify mERV expression by transcripts per million (TPM) using RSEM version 1.3.1 for each of the murine tumours A20, CT26, C1498 and B16.

[0131] A personal vaccine design for each murine tumour has been developed by extracting all mERV sequences expressed by the tumour. Ligands of the relevant mouse strain MHC type (BALB / c for A20 and CT26, and C57BL / 6 for C1498 and B16) in the mERV sequences were predicted using EvaxMHC4, an in-house developed MHC ligand prediction tool. mERV subsequences dense in MHC ligands, henceforth referred to as "hotspots", were extracted and ranked by their ranking score (in the form of an area under curve, AUC, cf. the description in Example 1 under the description of ranking by simulation). An iterative optimization process was run to select a collection of hotspots that together had the highest possible score. A vaccine plasmid product was created by constructing DNA insert containing the collection of hotspots found by the optimization process and inserting it into the DNA plasmid backbone.

[0132] In the development of the precision and shared vaccine designs, the same procedure was followed with slight modifications. For the precision vaccine designs the sequences of mERVs expressed in the tumour subpopulations were extracted (BALB / c derived tumour population A20 and CT26, and the C57BL / 6 derived tumour population C1498 and B16) and each tumour subpopulation used its corresponding mouse strain MHC type for MHC ligand prediction. For the shared vaccine design sequences of expressed mERVs in any of the tumours A20, CT26, C1498 and B16, and both the BALB / C and C57BL / 6 MHC type were used for MHC ligand prediction.

[0133] A score for each developed vaccine design in each murine tumour have been calculated and can be seen below in the following table:

[0134] The ranking score for each vaccine design of each murine tumour, "-"marks cases where no score could be calculated due to the vaccine design containing no MHC ligands present in the murine tumour.

[0135] The score is an AUC determined from the predicted probabilities for each MHC ligand in the collection of hotspots, cf. Example 1.

[0136] As expected, the highest score for each murine tumour is its personalized design and the second highest score is the precision design that includes the given murine tumour. Interestingly, the personalized designs also show a reasonable score for the murine tumour derived from the same mouse strain. This can be explained by the two designs being optimized for the same MHC type coupled with a slight overlap in mERV expression. Finally, the shared design shows a reasonable score for all of the murine tumours it was developed to target. In vivo study design

[0137] BALB / c mice (8-12 weeks old) were prophylactically immunized every week in left and right tibialis anterior muscles (i.m.) with 25 pg of plasmid DNA vaccine (50 pl in each leg) followed by electroporation (E.P.), starting two weeks before tumour cell inoculation for a total of five immunizations ( / .e. immunizations at day -14, day -7, day 1, day 7 and day 14). On the day of tumour cell inoculation (defined as study day 0), in vitro expanded CT26 or A20 cells were harvested from culture flasks by trypsinization (CT26) or collection with pipette (A20) and washed in serum free medium. CT26 cells inoculated per mouse: 2xl05cells per 100 pl medium, A20 cells inoculated per mouse: 2xl05cells per 100 pl medium. Tumour cells were inoculated subcutaneously (s.c.) in the right flank of the mice. Once established, the tumour diameters were measured three times per week with a digital caliper. The tumour volumes were calculated using the following formula: tumour volume = ^ * (dt* d2)3 / 2, where dl and d2 are orthogonal diameters of the tumour. The mice were euthanized through cervical dislocation when the majority of tumours in the control groups reached the maximum allowed size of 15 mm diameter in either direction or upon reaching humane endpoints.

[0138] Upon euthanization, spleens were isolated from 7 mice from each group. The spleens were collected in cold RPMI supplemented with 10% FCS, followed by processing to single cells suspensions via GentleMACS processing (Miltenyi Biotec, C-tubes #130-096-334 and Dissociater #130-093-235) and passage through a 70 mm filter (Corning, CLS431751). Splenocytes were cryopreserved in FCS with 10% DMSO (Merck, #D8418). -linked

[0139] PVDF membrane plates (Merck Millipore, #MAIP4510) first activated with 35% v / v ethanol were coated overnight with 5 pg / ml anti-IFNg capture antibody (BD, #51-2525KZ, 1 :200). 5x l05splenocytes were plated per well and stimulated with 5 mg / ml synthetic peptides (purchased from Pepscan, Lelystad, Netherlands) or unstimulated (DMSO) in a total volume of 200 pl R10 medium. Cells were incubated overnight at 37°C and 5% CO2. To detect IFNy secreting cell spots, anti-IFNy detection antibody (BD, #51-1818KA, 1 :250), streptavidin- HRP enzyme (BD, #557630) and AEC chromogen substrate (BD, #551951) were applied sequentially following the manufacturer's protocol. ELISpot plates were imaged and IFNy spots were counted using an ELISpot reader (Cellular Technology, Ltd). Results

[0140] The highest magnitude immune response was raised upon vaccination with DNA plasmids encoding mERV epitope hotspots most specific to the given tumour cell line.

[0141] Splenocytes from BALB / c mice immunized with personalized, precision and shared mERV hotspot DNA plasmids displayed varying magnitude of response to immunization-relevant peptides, as apparent from IFNy ELISpot (Fig. 2). In both the A20 and CT26 tumour setting (tumours originating in BALB / c mice, hence matching the BALB / c haplotype), the personalized mERV hotspot design vaccines with the highest scores correspondingly gave rise to the highest level of immune response compared to the precision design and the shared design vaccines when restimulated with their cognate peptides (Fig 2A and 2B).

[0142] When BALB / c mice were immunized with DNA plasmids designed to contain mERV hotspots relevant to tumour cell lines originating in another mouse strain of a different haplotype (C57BL / 6 mice), the subsequent peptide re-stimulation and IFNy ELISpot revealed that no immune response to the peptides had been elicited (Fig. 2C). This observation corresponds well with the low score for these.

[0143] Vaccination with DNA plasmids encoding mERV epitope hotspots can impede tumour growth

[0144] Prophylactic immunization of BALB / c mice with Personalized (A20) DNA plasmid led to a lower average tumour volume compared to untreated tumour bearing control mice (Fig. 3). This impact on tumour growth and lower end tumour volume corroborates the concept of mERV hotspot selection and formulation into an anti-cancer vaccine.

Claims

CLAIMS1. A method for selecting at least one immunization agent, which is suitable for use in active specific immunotherapy of a malignant neoplasm in a patient, wherein the at least one immunization agent is part of a set of non-identical immunization agents each comprising a) amino acid sequences, which can be expressed from genomic DNA in the cells of the malignant neoplasm, but are only expressed at a predetermined low level in cells of normal tissue in the patient or b) nucleic acids encoding said amino acid sequences, the method comprising1) identifying for each immunization agent potential human leukocyte antigen (HLA) ligands that match the patient's HLA profile, where the potential HLA ligands are constituted by amino acid sequences comprised in said immunization agent or are amino acid sequences encoded by said nucleic acids of the immunization agent, where each such potential HLA ligand exhibits a probability Pl that it constitutes a true HLA ligand in the patient,2) determining for each immunization agent a quantitative indication of the probability P2 that at least X potential HLA ligands comprised therein or encoded thereby are true HLA ligands, where X is a preselected integer >2 and <Y, where Y is the maximum number of potential HLA ligands comprised in or encoded by the immunization agent, and3) selecting among the immunization agents a) the one(s) exhibiting the highest probability in step 2 that at least X potential HLA ligands are true ligands or b) the one(s) that exhibit(s) in step 2 a minimum predetermined probability that at least X potential HLA ligands are true HLA ligands.

2. The method according to claim 1, wherein the at least one immunization agent is at least one expression vector, such as at least one plasmid vector or a viral vector, or a composition comprising said at least one expression vector.

3. The method according to claim 2, wherein the at least one expression vector- encodes a polypeptide comprising a plurality of potential HLA ligands that can be expressed from DNA in the cells of the malignant neoplasm, but only expressed at a predetermined low level in cells of normal tissue in the patient, or- encodes multiple peptides that together comprise said plurality of potential HLA ligands.

4. The method according to claim 1, wherein the at least one immunization agent is a polypeptide or a set of peptides, or a composition comprising the polypeptide or set of peptides.

5. The method according to any one of the preceding claims, wherein each potential HLA ligand further is a verified expression product from cells of the malignant neoplasm.

6. The method according to any one of the preceding claims, wherein the probability P2 must be >0 and the at least X potential HLA ligands are at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, or at least 30, potential HLA ligands.

7. The method according to any one of the preceding claims, wherein the quantitative indication in step 2 is determined for each immunization agent by determining - via a simulation or exact calculation - the probabilities for the presence of each of at least 1 to Y true HLA ligands being comprised in or encoded by the immunization agent.

8. The method according to claim 7, where the quantitative indication for each immunization agent is expressed as an area under curve (AUC) of the probabilities determined.

9. The method according to claim 7, where the quantitative indication for each immunization agent is expressed as the probability that at least Z potential HLA ligands are true HLA ligands, where Z is a predetermined integer >1, where Z preferably is predetermined to be 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 or 30.

10. The method according to any one of the preceding claims, wherein the amino acid sequences, which are expressed from genomic DNA in the cells of the malignant neoplasm, but only expressed at a predetermined low level in cells of normal tissue in the patient, are amino acid sequences encoded by normally non-encoding DNA, preferably as endogenous viral elements (EVEs) such as endogenous retroviruses (ERVs), or amino acid sequences of tumour-associated antigens (TAAs), tumour-specific antigens (TSAs), or neoantigens.

11. The method according to any one of the preceding claims, wherein the amino acid sequences, which are expressed from genomic DNA in the cells of the malignant neoplasm, but only expressed at a predetermined low level in cells of normal tissue in the patient, are EVEs that comprise or are comprised of DNA sequences of human endogenous retroviruses (hERVs).

12. A method for treatment of a human patient suffering from a malignant neoplasm, the method comprising providing the human leukocyte antigen (HLA) profile of the patient, selecting from a set of non-identical immunization agents at least one immunization agent according to the method of any one of the preceding claims, and subsequently immunizing the patient one or more times with the immunization agent(s) so selected.

13. The method according to claim 12, wherein the patient further is treated with at least one additional cancer therapy, such as one or more of radiotherapy, therapy with cytostatic drugs, therapy with cytotoxic drugs, therapy with immune checkpoint inhibitors, therapy with immune-modulatory effects, chimeric antigen receptor (CAR)-T therapy, adoptive T-cell therapy, depletion therapy, targeted therapy, therapy with tyrosine kinase inhibitors, and cancer immunotherapy targeting a surface expressed determinant.

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