CANCER VACCINES FOR UTERINE CANCER

Abstract

The invention relates to the field of cancer, in particular uterine cancer. In particular, it relates to the field of immune system directed approaches for tumor reduction and control. Some aspects of the invention relate to vaccines, vaccinations and other means of stimulating an antigen specific immune response against a tumor in individuals. Such vaccines comprise neoantigens resulting from frameshift mutations that bring out-of-frame sequences of the ARID1A, KMT2B, KMT2D, PIK3R1, and PTEN genes in-frame. Such vaccines are also useful for ‘off the shelf’ use.

Claims

1. A vaccine for use in the treatment of uterine cancer, said vaccine comprising: (i) a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 530, an amino acid sequence having 90% identity to Sequence 530, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 530; and a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 531, an amino acid sequence having 90% identity to Sequence 531, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 531; preferably also comprising a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 532, an amino acid sequence having 90% identity to Sequence 532, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 532; (ii) at least two peptides, wherein each peptide, or a collection of tiled peptides, comprises a different amino acid sequence selected from Sequences 1-5, an amino acid sequence having 90% identity to Sequences 1-5, or a fragment thereof comprising at least 10 consecutive amino acids of Sequences 1-5; (iii) a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 102, an amino acid sequence having 90% identity to Sequence 102, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 102; and a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 103, an amino acid sequence having 90% identity to Sequence 103, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 103; (iv) a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 218, an amino acid sequence having 90% identity to Sequence 218, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 218; and a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 219, an amino acid sequence having 90% identity to Sequence 219, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 219; preferably also comprising a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 220, an amino acid sequence having 90% identity to Sequence 220, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 220; and/or (v) a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 473, an amino acid sequence having 90% identity to Sequence 473, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 473; and a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 474, an amino acid sequence having 90% identity to Sequence 474, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 474.

2. A collection of frameshift-mutation peptides comprising: (i) a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 530, an amino acid sequence having 90% identity to Sequence 530, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 530; and a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 531, an amino acid sequence having 90% identity to Sequence 531, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 531; preferably also comprising a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 532, an amino acid sequence having 90% identity to Sequence 532, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 532; (ii) at least two peptides, wherein each peptide, or a collection of tiled peptides, comprises a different amino acid sequence selected from Sequences 1-5, an amino acid sequence having 90% identity to Sequences 1-5, or a fragment thereof comprising at least 10 consecutive amino acids of Sequences 1-5; (iii) a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 102, an amino acid sequence having 90% identity to Sequence 102, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 102; and a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 103, an amino acid sequence having 90% identity to Sequence 103, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 103; (iv) a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 218, an amino acid sequence having 90% identity to Sequence 218, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 218; and a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 219, an amino acid sequence having 90% identity to Sequence 219, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 219; preferably also comprising a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 220, an amino acid sequence having 90% identity to Sequence 220, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 220; and/or (v) a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 473, an amino acid sequence having 90% identity to Sequence 473, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 473; and a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 474, an amino acid sequence having 90% identity to Sequence 474, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 474.

3. A peptide, or a collection of tiled peptides, comprising an amino acid sequence selected from the groups: (i) Sequences 530-560, an amino acid sequence having 90% identity to Sequences 530-560, or a fragment thereof comprising at least 10 consecutive amino acids of Sequences 530-560 (ii) Sequences 1-101, an amino acid sequence having 90% identity to Sequences 1-101, or a fragment thereof comprising at least 10 consecutive amino acids of Sequences 1-101; (iii) Sequences 102-217, an amino acid sequence having 90% identity to Sequences 102-217, or a fragment thereof comprising at least 10 consecutive amino acids of Sequences 102-217; (iv) Sequences 218-472, an amino acid sequence having 90% identity to Sequences 218-472, or a fragment thereof comprising at least 10 consecutive amino acids of Sequences 218-472; (v) Sequences 473-529, an amino acid sequence having 90% identity to Sequences 473-529, or a fragment thereof comprising at least 10 consecutive amino acids of Sequences 473-529.

4. The vaccine of claim 1, the collection of claim 2, or the peptide of claim 3, wherein said peptides are linked, preferably wherein said peptides are comprised within the same polypeptide.

5. One or more isolated nucleic acid molecules encoding the collection of peptides according to claim 2 or 4 or the peptide of claim 3 or 4, preferably wherein the nucleic acid is codon optimized.

6. One or more vectors comprising the nucleic acid molecules of claim 5, preferably wherein the vector is a viral vector.

7. A host cell comprising the isolated nucleic acid molecules according to claim 5 or the vectors according to claim 6.

8. A binding molecule or a collection of binding molecules that bind the peptide or collection of peptides according to any one of claims 2-4, where in the binding molecule is an antibody, a T-cell receptor, or an antigen binding fragment thereof.

9. A chimeric antigen receptor or collection of chimeric antigen receptors each comprising i) a T cell activation molecule; ii) a transmembrane region; and iii) an antigen recognition moiety; wherein said antigen recognition moieties bind the peptide or collection of peptides according to any one of claims 2-4.

10. A host cell or combination of host cells that express the binding molecule or collection of binding molecules according to claim 8 or the chimeric antigen receptor or collection of chimeric antigen receptors according to claim 9.

11. A vaccine or collection of vaccines comprising the peptide, collection of tiled peptides, or collection of peptides according to any one of claims 2-4, the nucleic acid molecules of claim 5, the vectors of claim 6, or the host cell of claim 7 or 10; and a pharmaceutically acceptable excipient and/or adjuvant, preferably an immune-effective amount of adjuvant.

12. The vaccine or collection of vaccines of claim 11 for use in the treatment of uterine cancer in an individual, preferably wherein the vaccine or collection of vaccines is used in a neo-adjuvant setting.

13. The vaccine or collection of vaccines for use according to claim 12, wherein said individual has uterine cancer and one or more cancer cells of the individual: (i) expresses a peptide having the amino acid sequence selected from Sequences 1-560, an amino acid sequence having 90% identity to any one of Sequences 1-560, or a fragment thereof comprising at least 10 consecutive amino acids of amino acid sequence selected from Sequences 1-560; (ii) or comprises a DNA or RNA sequence encoding an amino acid sequences of (i).

14. The vaccine or collection of vaccines of claim 11 for prophylactic use in the prevention of cancer in an individual, preferably wherein the cancer is uterine cancer.

15. The vaccine or collection of vaccines for use according to of any one of claims 12-14, wherein said individual is at risk for developing cancer.

16. A method of stimulating the proliferation of human T-cells, comprising contacting said T-cells with the peptide or collection of peptides according to any one of claims 2-4, the nucleic acid molecules of claim 5, the vectors of claim 6, the host cell of claim 7 or 10, or the vaccine of claim 11.

17. A method of treating an individual for uterine cancer or reducing the risk of developing said cancer, the method comprising administering to the individual in need thereof the vaccine or collection of vaccines of claim 11.

18. A storage facility for storing vaccines, said facility storing at least two different cancer vaccines of claim 11.

19. The storage facility for storing vaccines according to claim 18, wherein said facility stores a vaccine comprising: (i) a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 530, an amino acid sequence having 90% identity to Sequence 530, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 530; and a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 531, an amino acid sequence having 90% identity to Sequence 531, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 531; preferably also comprising a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 532, an amino acid sequence having 90% identity to Sequence 532, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 532; and one or more vaccines selected from: a vaccine comprising: (ii) at least two peptides, wherein each peptide, or a collection of tiled peptides, comprises a different amino acid sequence selected from Sequences 1-5, an amino acid sequence having 90% identity to Sequences 1-5, or a fragment thereof comprising at least 10 consecutive amino acids of Sequences 1-5; a vaccine comprising: (iii) a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 102, an amino acid sequence having 90% identity to Sequence 102, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 102; and a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 103, an amino acid sequence having 90% identity to Sequence 103, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 103; a vaccine comprising: (iv) a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 218, an amino acid sequence having 90% identity to Sequence 218, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 218; and a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 219, an amino acid sequence having 90% identity to Sequence 219, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 219; preferably also comprising a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 220, an amino acid sequence having 90% identity to Sequence 220, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 220; and/or a vaccine comprising: (v) a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 473, an amino acid sequence having 90% identity to Sequence 473, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 473; and a peptide, or a collection of tiled peptides, having the amino acid sequence selected from Sequence 474, an amino acid sequence having 90% identity to Sequence 474, or a fragment thereof comprising at least 10 consecutive amino acids of Sequence 474.

20. A method for providing a vaccine for immunizing a patient against a cancer in said patient comprising determining the sequence of ARID1A, KMT2B, KMT2D, PIK3R1, and/or PTEN in cancer cells of said cancer and when the determined sequence comprises a frameshift mutation that produces a neoantigen of Sequence 1-560 or a fragment thereof, providing a vaccine of claim 11 comprising said neoantigen or a fragment thereof.

21. The method of claim 20, wherein the vaccine is obtained from a storage facility of claim 18 or claim 19.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0224] FIG. 1 Frame shift initiated translation in the TCGA (n=10,186) cohort is of sufficient size for immune presentation. A. Peptide length distribution of frame shift mutation initiated translation up to the first encountered stop codon. Dark shades are unique peptide sequences derived from frameshift mutations, light shade indicates the total sum (unique peptides derived from frameshifts multiplied by number of patients containing that frameshift). B. Gene distribution of peptides with length 10 or longer and encountered in up to 10 patients.

[0225] FIG. 2 Neo open reading frame peptides (TCGA cohort) concerge on common peptide sequences. Graphical representation in an isoform of TP53, where amino acids are colored distinctly. A. somatic single nucleotide variants, B. positions of frame shift mutations on the −1 and the +1 frame. C. amino acid sequence of TP53. D. Peptide (10aa) library (n=1,000) selection. Peptides belonging to −1 or +1 frame are separated vertically E,F pNOPs for the different frames followed by all encountered frame shift mutations (rows), translated to a stop codon (lines) colored by amino acid.

[0226] FIG. 3 A recurrent peptide selection procedure can generate a ‘fixed’ library to cover up to 50% of the TOGA cohort. Graph depicts the number of unique patients from the TCGA cohort (10,186 patients) accommodated by a growing library of 10-mer peptides, picked in descending order of the number patients with that sequence in their NOPs. A peptide is only added if it adds a new patient from the TCGA cohort. The dark blue line shows that an increasing number of 10-mer peptides covers an increasing number of patients from the TCGA cohort (up to 50% if using 3000 unique 10-mer peptides). Light shaded blue line depicts the number of patients containing the peptide that was included (right Y-axis). The best peptide covers 89 additional patients from the TCGA cohort (left side of the blue line), the worst peptide includes only 1 additional patient (right side of the blue line).

[0227] FIG. 4 For some cancers up to 70% of patients contain a recurrent NOP. TCGA cohort ratio of patients separated by tumor type that could be ‘helped’ using optimally selected peptides for genes encountered most often within a cancer. Coloring represents the ratio, using 1, 2 . . . 10 genes, or using all encountered genes (lightest shade)

[0228] FIG. 5 Examples of NOPs. Selection of genes containing NOPs of 10 or more amino acids.

[0229] FIG. 6 Frame shift presence in mRNA from 58 COLE colorectal cancer cell lines.

[0230] a. Cumulative counting of RNAseq allele frequency (Samtools mpileup (XO:1/all)) at the genomic position of DNA detected frame shift mutations.

[0231] b. IGV examples of frame shift mutations in the BAM files of CCLE cell lines.

[0232] FIG. 7 Example of normal isoforms, using shifted frame.

[0233] Genome model of CDKN2A with the different isoforms are shown on the minus strand of the genome. Zoom of the middle exon depicts the 2 reading frames that are encountered in the different isoforms.

[0234] FIG. 8 Gene prevalence is Cancer type.

[0235] Percentage of frameshift mutations (resulting in peptides of 10 aa or longer), assessed by the type of cancer in the TCGA cohort. Genes where 50% or more of the frameshifts occur within a single tumor type are indicated in bold. Cancer type abbreviations are as follows:

[0236] LAML Acute Myeloid Leukemia

[0237] ACC Adrenocortical carcinoma

[0238] BLCA Bladder Urothelial Carcinoma

[0239] LGG Brain Lower Grade Glioma

[0240] BRCA Breast invasive carcinoma

[0241] CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma

[0242] CHOL Cholangiocarcinoma

[0243] LCML Chronic Myelogenous Leukemia

[0244] COAD Colon adenocarcinoma

[0245] CNTL Controls

[0246] ESCA Esophageal carcinoma

[0247] GBM Glioblastoma multiforme

[0248] HNSC Head and Neck squamous cell carcinoma

[0249] KICH Kidney Chromophobe

[0250] KIRC Kidney renal clear cell carcinoma

[0251] KIRP Kidney renal papillary cell carcinoma

[0252] LIHC Liver hepatocellular carcinoma

[0253] LUAD Lung adenocarcinoma

[0254] LUSC Lung squamous cell carcinoma

[0255] DLBC Lymphoid Neoplasm Diffuse Large B-cell Lymphoma

[0256] MESO Mesothelioma

[0257] MISC Miscellaneous

[0258] OV Ovarian serous cystadenocarcinoma

[0259] PAAD Pancreatic adenocarcinoma

[0260] PCPG Pheochromocytoma and Paraganglioma

[0261] PRAD Prostate adenocarcinoma

[0262] READ Rectum adenocarcinoma

[0263] SARC Sarcoma

[0264] SKCM Skin Cutaneous Melanoma

[0265] STAD Stomach adenocarcinoma

[0266] TGCT Testicular Germ Cell Tumors

[0267] THYM Thymoma

[0268] THCA Thyroid carcinoma

[0269] UCS Uterine Carcinosarcoma

[0270] UCEC Uterine Corpus Endometrial Carcinoma

[0271] UVM Uveal Melanoma

[0272] FIG. 9 NOPs in the MSK-IMPACT study

[0273] Frame shift analysis in the targeted sequencing panel of the MSK-IMPACT study, covering up to 410 genes in more 10,129 patients (with at least 1 somatic mutation). a. FS peptide length distribution, b. Gene count of patients containing NC/Ps of 10 or more amino acids. c. Ratio of patients separated by tumor type that possess a neo epitope using optimally selected peptides for genes encountered most often within a cancer. Coloring represents the ratio, using 1, 2 . . . 10 genes, or using all encountered genes (lightest shade) d. Examples of NOPs for 4 genes.

[0274] FIGS. 10-14 Out-of-frame peptide sequences based on frameshift mutations in uterine cancer patients, for FIG. 10 (ARID1A), FIG. 11 (PIK3R1), FIG. 12 (PTEN), FIG. 13 (KMT2B), and FIG. 14 (KMT2D).

EXAMPLES

[0275] We have analyzed 10,186 cancer genomes from 33 tumor types of the 40 TCGA (The Cancer Genome Atlas.sup.22) and focused on the 143,444 frame shift mutations represented in this cohort. Translation of these mutations after re-annotation to a RefSeq annotation, starting in the protein reading frame, can lead to 70,439 unique peptides that are 10 or more amino acids in length (a cut off we have set at a size sufficient to shape a distinct epitope in the context of MHC (FIG. 1a). The list of genes most commonly represented in the cohort and containing such frame shift mutations is headed nearly exclusively by tumor driver genes, such as NF1, RB, BRCA2 (FIG. 1b) whose whole or partial loss of function apparently contributes to tumorigenesis. Note that a priori frame shift mutations are expected to result in loss of gene function more than a random SNV, and more independent of the precise position. NOPs initiated from a frameshift mutation and of a significant size are prevalent in tumors, and are enriched in cancer driver genes. Alignment of the translated NOP products onto the protein sequence reveals that a wide array of different frame shift mutations translate in a common downstream stretch of neo open reading frame peptides (‘NOPs’), as dictated by the −1 and +1 alternative reading frames. While we initially screened for NOPs of ten or more amino acids, their open reading frame in the out-of-frame genome often extends far beyond that search window. As a result we see (FIG. 2) that hundreds of different frame shift mutations all at different sites in the gene nevertheless converge on only a handful of NOPs. Similar patterns are found in other common driver genes (FIG. 5). FIG. 2 illustrates that the precise location of a frame shift does not seem to matter much; the more or less straight slope of the series of mutations found in these 10,186 tumors indicates that it is not relevant for the biological effect (presumably reduction/loss of gene function) where the precise frame shift is, as long as translation stalls in the gene before the downstream remainder of the protein is expressed. As can also be seen in FIG. 2, all frame shift mutations alter the reading frame to one of the two alternative frames. Therefore, for potential immunogenicity the relevant information is the sequence of the alternative ORFs and more precisely, the encoded peptide sequence between 2 stop codons. We term these peptides ‘proto Neo Open Reading Frame peptides’ or pNOPs, and generated a full list of all thus defined out of frame protein encoding regions in the human genome, of 10 amino acids or longer. We refer to the total sum of all Neo-ORFs as the Neo-ORFeome. The Neo-ORFeome contains all the peptide potential that the human genome can generate after simple frame-shift induced mutations. The size of the Neo-ORFeome is 46.6 Mb. To investigate whether or not Nonsense Mediated Decay would wipe out frame shift mRNAs, we turned to a public repository containing read coverage for a large collection of cell lines (CCLE). We processed the data in a similar fashion as for the TCGA, identified the locations of frame shifts and subsequently found that, in line with the previous literature.sup.23-25, at least a large proportion of expressed genes also contained the frame shift mutation within the expressed mRNAs (FIG. 6). On the mRNA level, NOPs can be detected in RNAseq data. We next investigated how the number of patients relates to the number of NOPs. We sorted 10-mer peptides from NOPs by the number of new patients that contain the queried peptide. Assessed per tumor type, frame shift mutations in genes with very low to absent mRNA expression were removed to avoid overestimation. Of note NOP sequences are sometimes also encountered in the normal ORFeome, presumably as result of naturally occuring isoforms (e,g, FIG. 7). Also these peptides were excluded. We can create a library of possible ‘vaccines’ that is optimally geared towards covering the TCGA cohort, a cohort large enough that, also looking at the data presented here, it is representative of future patients (FIG. 10). Using this strategy 30% of all patients can be covered with a fixed collection of only 1,244 peptides of length 10 (FIG. 3). Since tumors will regularly have more than 1 frame shift mutation, one can use a ‘cocktail’ of different NOPs to optimally attack a tumor. Indeed, given a library of 1,244 peptides, 27% of the covered TCGA patients contain 2 or more ‘vaccine’ candidates. In conclusion, using a limited pool with optimal patient inclusion of vaccines, a large proportion of patients is covered. Strikingly, using only 6 genes (TP53, ARID1A, KMT2D, GATA3, APC, PTEN), already 10% of the complete TCGA cohort is covered. Separating this by the various tumor types, we find that for some cancers (like Pheochromocytoma and Paraganglioma (PCPG) or Thyroid carcinoma (THCA)) the hit rate is low, while for others up to 39% can be covered even with only 10 genes (Colon adenocarcinoma (COAD) using 60 peptides, Uterine Corpus Endometrial Carcinoma (UCEC) using 90 peptides), FIG. 4. At saturation (using all peptides encountered more than once) 50% of TCGA is covered and more than 70% can be achieved for specific cancer types (COAD, UCEC, Lung squamous cell carcinoma (LUSC) 72%, 73%, 73% respectively). As could be expected, these roughly follow the mutational load in the respective cancer types. In addition some frame shifted genes are highly enriched in specific tumor types (e.g. VHL, GATA3. FIG. 8). We conclude that at saturating peptide coverage, using only very limited set of genes, a large cohort of patients can be provided with off the shelf vaccines. To validate the presence of NOPs, we used the targeted sequencing data on 10,129 patients from the MSK-IMPACT cohort 26. For the 341-410 genes assessed in this cohort, we obtained strikingly similar results in terms of genes frequently affected by frame shifts and the NOPs that they create (FIG. 9). Even within this limited set of genes, 86% of the library peptides (in genes targeted by MSK-IMPACT) were encountered in the patient set. Since some cancers, like glioblastoma or pancreatic cancer, show survival expectancies after diagnosis measured in months rather than years (e.g. see 27), it is of importance to move as much of the work load and time line to the moment before diagnosis. Since the time of whole exome sequencing after biopsy is currently technically days, and since the scan of a resulting sequence against a public database describing these NOPs takes seconds, and the shipment of a peptide of choice days, a vaccination can be done theoretically within days and practically within a few weeks after biopsy. This makes it attractive to generate a stored and quality controlled peptide vaccine library based on the data presented here, possibly with replicates stored on several locations in the world. The synthesis in advance will—by economics of scale—reduce costs, allow for proper regulatory oversight, and can be quality certified, in addition to saving the patient time and thus provide chances. The present invention will likely not replace other therapies, but be an additional option in the treatment repertoire. The advantages of scale also apply to other means of vaccination against these common neoantigens, by RNA- or DNA-based approaches (e.g. 28), or recombinant bacteria (e.g. 29). The present invention also provides neoantigen directed application of the CAR-T therapy (For recent review see 30, and references therein), where the T-cells are directed not against a cell-type specific antigens (such as CD19 or CD20), but against a tumor specific neoantigen as provided herein. E.g. once one functional T-cell against any of the common p53 NOPs (FIG. 2) is identified, the recognition domains can be engineered into T-cells for any future patient with such a NOP, and the constructs could similarly be deposited in an off-the-shelf library. In the present invention, we have identified that various frame shift mutations can result in a source for common neo open reading frame peptides, suitable as pre-synthesized vaccines. This may be combined with immune response stimulating measures such as but not limited checkpoint inhibition to help instruct our own immune system to defeat cancer.

[0276] Methods:

[0277] TCGA frameshift mutations—Frame shift mutations were retrieved from Varscan and mutect files per tumor type via https://portal.gdc.cancer.gov/. Frame shift mutations contained within these files were extracted using custom perl scripts and used for the further processing steps using HG38 as reference genome build.

[0278] CCLE frameshift mutations—For the CCLE cell line cohort, somatic mutations were retrieved from

[0279] http ://www.broadinstitute.org/ccle/data/browseDate?conversationPropagation=begin (CCLE_hybrid_capture1650_hg19_NoCommonSNPs_NoNeutralVariants_CDS_201 2.02.20.maf). Frame shift mutations were extracted using custom perl scripts using hg19 as reference genome.

[0280] Refseq annotation—To have full control over the sequences used within our analyses, we downloaded the reference sequences from the NCBI website (2018 Feb. 27) and extracted mRNA and coding sequences from the gbff files using custom perl scripts. Subsequently, mRNA and every exon defined within the mRNA sequences were aligned to the genome (hg19 and hg38) using the BLAT suite. The best mapping locations from the psl files were subsequently used to place every mRNA on the genome, using the separate exons to perform fine placement of the exonic borders. Using this procedure we also keep track of the offsets to enable placement of the amino acid sequences onto the genome.

[0281] Mapping genome coordinate onto Refseq—To assess the effect of every mentioned frame shift mutation within the cohorts (CCLE or TCGA), we used the genome coordinates of the frameshifts to obtain the exact protein position on our reference sequence database, which were aligned to the genome builds. This step was performed using custom perl scripts taking into account the codon offsets and strand orientation, necessary for the translation step described below.

[0282] Translation of FS peptides—Using the reference sequence annotation and the positions on the genome where a frame shift mutation was identified, the frame shift mutations were used to translate peptides until a stop codon was encountered. The NOP sequences were recorded and used in downstream analyses as described in the text.

[0283] Verification of FS mRNA expression in the CCLE colorectal cancer cell lines—For a set of 59 colorectal cancer cell lines, the HG19 mapped bam files were downloaded from https://portal.gdc.cancer.gov/. Furthermore, the locations of FS mutations were retrieved from

[0284] CCLE_hybrid_capture1650_hg19_NoCommonSNPs_NoNeutralVariants_CDS_201 2.02.20.maf

[0285] (http://www.broadinstitute.org/ccle/data/browseData?conversationPropagation=beg in), by selection only frameshift entries. Entries were processed similarly to to the TCGA data, but this time based on a HG19 reference genome. To get a rough indication that a particular location in the genome indeed contains an indel in the RNAseq data, we first extracted the count at the location of a frameshift by making use of the pileup function in samtools. Next we used the special tag XO:1 to isolate reads that contain an indel in it. On those bam files we again used the pileup function to count the number of reads containing an indel (assuming that the indel would primarily be found at the frameshift instructed location). Comparison of those 2 values can then be interpreted as a percentage of indel at that particular location. To reduce spurious results, at least 10 reads needed to be detected at the FS location in the original bam file.

[0286] Defining peptide library—To define peptide libraries that are maximized on performance (covering as many patients with the least amount of peptides) we followed the following procedure. From the complete TCGA cohort, FS translated peptides of size 10 or more (up to the encountering of a stop codon) were cut to produce any possible 10-mer. Then in descending order of patients containing a 10-mer, a library was constructed. A new peptide was added only if an additional patient in the cohort was included. peptides were only considered if they were seen 2 or more times in the TCGA cohort, if they were not filtered for low expression (see Filtering for low expression section), and if the peptide was not encountered in the orfeome (see Filtering for peptide presence orfeome). In addition, since we expect frame shift mutations to occur randomly and be composed of a large array of events (insertions and deletions of any non triplet combination), frame shift mutations being encountered in more than 10 patients were omitted to avoid focusing on potential artefacts. Manual inspection indicated that these were cases with e.g. long stretches of Cs, where sequencing errors are common.

[0287] Filtering for low expression—Frameshift mutations within genes that are not expressed are not likely to result in the expression of a peptide. To take this into account we calculated the average expression of all genes per TCGA entity and arbitrarily defined a cutoff of 2 log2 units as a minimal expression. Any frameshift mutation where the average expression within that particular entity was below the cutoff was excluded from the library. This strategy was followed, since mRNA gene expression data was not available for every TCGA sample that was represented in the sequencing data set. Expression data (RNASEQ v2) was pooled and downloaded from the R2 platform (http://r2.amc.nl). In current sequencing of new tumors with the goal of neoantigen identification such mRNA expression studies are routine and allow routine verification of presence of mutant alleles in the mRNA pool.

[0288] Filtering for peptide presence orfeome—Since for a small percentage of genes, different isoforms can actually make use of the shifted reading frame, or by chance a 10-mer could be present in any other gene, we verified the absence of any picked peptide from peptides that can be defined in any entry of the reference sequence collection, once converted to a collection of tiled 10-mers.

[0289] Generation of cohort coverage by all peptides per gene To generate overviews of the proportion of patients harboring exhaustive FS peptides starting from the most mentioned gene, we first pooled all peptides of size 10 by gene and recorded the largest group of patients per tumor entity. Subsequently we picked peptides identified in the largest set of patients and kept on adding a new peptide in descending order, but only when at least 1 new patient was added. Once all patients containing a peptide in the first gene was covered, we progressed to the next gene and repeated the procedure until no patient with FS mutations leading to a peptide of size 10 was left.

[0290] proto-NOP (pNOP) and Neo-ORFeome proto—NOPs are those peptide products that result from the translation of the gene products when the reading frame is shifted by −1 or +1 base (so out of frame). Collectively, these pNOPs form the Neo-Orfeome. As such we generated a pNOP reference base of any peptide with length of 10 or more amino acids, from the RefSeq collection of sequences. Two notes: the minimal length of 10 amino acids is a choice; if one were to set the minimal window at 8 amino acids the total numbers go up a bit, e.g. the 30% patient covery of the library goes up. On a second note: we limited our definition to ORFs that can become in frame after a single insertion deletion on that location; this includes obviously also longer insertion or deletion stretches than +1 or −1. The definition has not taken account more complex events that get an out-of-frame ORF in frame, such as mutations creating or deleting splice sites, or a combination of two frame shifts at different sites that result in bypass of a natural stop codon; these events may and will occur, but counting those in will make the definition of the Neo-ORFeome less well defined. For the magnitude of the numbers these rare events do not matter much.

[0291] Visualizing nops—Visualization of the nops was performed using custom perl scripts, which were assembled such that they can accept all the necessary input data structures such as protein sequence, frameshifted protein sequences, somatic mutation data, library definitions, and the peptide products from frameshift translations.

[0292] Detection of frameshift resulting neopeptides in uterine cancer patients with cancer predisposition mutations—Somatic and germline mutation data were downloaded from the supplementary files attached to the manuscript posted here: https://www.biorxiv.org/content/biorxiv/early/2019/01/16/415133.full.pdf. Frameshift mutations were selected from the somatic mutation files and out-of-frame peptides were predicted using custom Perl and Python scripts, based on the human reference genome GRCh37. Out-of-frame peptides were selected based on their length (>=10 amino acids) and mapped against out of frame peptide sequences for each possible alternative transcript for genes present in the human genome, based on Ensembl annotation (ensembl.org).

REFERENCES

[0293] 1 Schumacher T. N., & Schreiber R. D. Neoantigens in cancer immunotherapy. Science. 348, 69-74 (2015). [0294] 2 Gubin M. M., Artyomov M. N., Mardis E. R., & Schreiber R. D. Tumor neoantigens: building a framework for personalized cancer immunotherapy. J Clin Incest. 125, 3413-21 (2015). [0295] 3 Ward J. P., Gubin M. M., & Schreiber R. D. The Role of Neoantigens in Naturally Occurring and Therapeutically Induced Immune Responses to Cancer. Adv Immunol. 130, 25-74 (2016). [0296] 4 DeWeerdt S. Calling cancer's bluff with neoantigen vaccines. Nature. 552, S76-S77 (2017). [0297] 5 Guo C., et al. Therapeutic cancer vaccines: past, present, and future. Adv Cancer Res. 119, 421-75 (2013). [0298] 6 Overwijk W. W., Wang E., Marincola F. M., Rammensee H. G., & Restifo N. P. Mining the mutanome: developing highly personalized Immunotherapies based on mutational analysis of tumors. J Immunother Cancer. 1, 11 (2013). [0299] 7 Yamada A., Sasada T., Noguchi M., & Itoh K. Next-generation peptide vaccines for advanced cancer. Cancer Sci. 104, 15-21 (2013). [0300] 8 Ott P. A., et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature. 547, 217-221 (2017). [0301] 9 Wirth T. C., & Kuhnel F. Neoantigen Targeting-Dawn of a New Era in Cancer Immunotherapy? Front Immunol. 8, 1848 (2017). [0302] 10 Yarchoan M., Hopkins A., & Jaffee E. M. Tumor Mutational Burden and Response Rate to PD-1 Inhibition. N Engl J Med. 377, 2500-2501 (2017). [0303] 11 Sahin U., et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature. 547, 222-226 (2017). [0304] 12 Linnebacher M., et al. Frameshift peptide-derived T-cell epitopes: a source of novel tumor-specific antigens. Int J Cancer. 93, 6-11 (2001). [0305] 13 Sonntag K., et al. Immune monitoring and TCR sequencing of CD4 T cells in a long term responsive patient with metastasized pancreatic ductal carcinoma treated with individualized, neoepitope derived multipeptide vaccines: a case report. J Transl Med. 16, 23 (2018). [0306] 14 MacArthur D. G., et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science. 335, 823-8 (2012). [0307] 15 Turajlic S., et al. Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis. Lancet Oncol. 18, 1009-1021 (2017). [0308] 16 Rammensee H., Bachmann J., Emmerich N. P., Bachor O. A., & Stevanovic S. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics. 50, 213-9 (1999). [0309] 17 Alvarez B., Barra C., Nielsen M., & Andreatta M. Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes. Proteomics. 18, e1700252 (2018). [0310] 18 Andreatta M., et al. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification. Immunogenetics. 67, 641-50 (2015). [0311] 19 Rizvi N. A., et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 348, 124-8 (2015). [0312] 20 Prickett T. D., et al. Durable Complete Response from Metastatic Melanoma after Transfer of Autologous T Cells Recognizing 10 Mutated Tumor Antigens. Cancer Immunol Res. 4, 669-78 (2016). [0313] 21 Liu R., et al. H7N9 T-cell epitopes that mimic human sequences are less immunogenic and may induce Treg-mediated tolerance. Hum Vaccin Immunother. 11, 2241-52 (2015). [0314] 22 Weinstein J. N., et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 45, 1113-20 (2013). [0315] 23 Lindeboom R. G., Supek F., & Lehner B. The rules and impact of nonsense-mediated mRNA decay in human cancers. Nat Genet. 48, 1112-8 (2016). [0316] 24 Longman D., Plasterk R. H., Johnstone I. L., & Caceres J. F. Mechanistic insights and identification of two novel factors in the C. elegans NMD pathway. Genes Dee. 21, 1075-85 (2007). [0317] 25 Nguyen L. S., Wilkinson M. F., & Gecz J. Nonsense-mediated mRNA decay: inter-individual variability and human disease. Neurosci Biobehav Ree. 46 Pt 2, 175-86 (2014). [0318] 26 Zehir A., et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 23, 703-713 (2017). [0319] 27 Fest J., et al. Underestimation of pancreatic cancer in the national cancer registry Eur J Cancer. 72, 186-191 (2017). [0320] 28 Boisguerin V., et al. Translation of genomics-guided RNA-based personalised cancer vaccines: towards the bedside. Br J Cancer. 111, 1469-75 (2014). [0321] 29 Keenan B. P., et al. A Listeria vaccine and depletion of T-regulatory cells activate immunity against early stage pancreatic intraepithelial neoplasms and prolong survival of mice. Gastroenterology. 146, 1784-94.e6 (2014). [0322] 30 Ramello M. C., Haura E. B., & Abate-Daga D. CAR-T cells and combination therapies: What's next in the immunotherapy revolution? Pharmacol Res. 129,194-203 (2018). [0323] 31 Giannakis, Marios, et al. “Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.” Cell Reports, vol. 17, no. 4, October 2016, p. 1206. [0324] 32 Linnebacher, M., et al. “Frameshift Peptide-Derived T-Cell Epitopes: A Source of Novel Tumor-Specific Antigens.” International Journal of Cancer. Journal International Du Cancer, vol. 93, no. 1, July 2001, pp. 6-11. [0325] 33 Maby, Pauline, et al. “Correlation between Density of CD8+ T-Cell Infiltrate in Microsatellite Unstable Colorectal Cancers and Frameshift Mutations: A Rationale for Personalized Immunotherapy.” Cancer Research, vol. 75, no. 17, September 2015, pp. 3446-55. [0326] 34 Saeterdal, I., et al. “A TGF betaRII Frameshift-Mutation-Derived CTL Epitope Recognised by HLA-A2-Restricted CD8+ T Cells.” Cancer Immunology, Immunotherapy: CII, vol. 50, no. 9, November 2001, pp. 469-76. [0327] 35 Turajlic, Samra, et al. “Insertion-and-Deletion-Derived Tumour-Specific Neoantigens and the Immunogenic Phenotype: A Pan-Cancer Analysis.” The Lancet Oncology, vol. 18, no. 8, August 2017, pp. 1009-21. [0328] 36 Williams, David S., et al. “Nonsense Mediated Decay Resistant Mutations Are a Source of Expressed Mutant Proteins in Colon Cancer Cell Lines with Microsatellite Instability.” PloS One, vol. 5, no. 12, December 2010, p. e16012.