NZ617217B2 - Individualized vaccines for cancer - Google Patents
Individualized vaccines for cancer Download PDFInfo
- Publication number
- NZ617217B2 NZ617217B2 NZ617217A NZ61721712A NZ617217B2 NZ 617217 B2 NZ617217 B2 NZ 617217B2 NZ 617217 A NZ617217 A NZ 617217A NZ 61721712 A NZ61721712 A NZ 61721712A NZ 617217 B2 NZ617217 B2 NZ 617217B2
- Authority
- NZ
- New Zealand
- Prior art keywords
- cells
- cancer
- tumor
- rna
- mutations
- Prior art date
Links
- 201000011510 cancer Diseases 0.000 title claims abstract description 234
- 229960005486 vaccines Drugs 0.000 title claims abstract description 120
- 230000035772 mutation Effects 0.000 claims abstract description 393
- 206010069754 Acquired gene mutation Diseases 0.000 claims abstract description 93
- 238000009566 cancer vaccine Methods 0.000 claims abstract description 17
- 238000002360 preparation method Methods 0.000 claims abstract description 13
- 239000003814 drug Substances 0.000 claims abstract description 10
- 210000004027 cells Anatomy 0.000 claims description 402
- 206010028980 Neoplasm Diseases 0.000 claims description 275
- 108091007172 antigens Proteins 0.000 claims description 222
- 102000038129 antigens Human genes 0.000 claims description 222
- 239000000427 antigen Substances 0.000 claims description 221
- 208000005443 Circulating Neoplastic Cells Diseases 0.000 claims description 89
- 229920003013 deoxyribonucleic acid Polymers 0.000 claims description 85
- 150000001413 amino acids Chemical class 0.000 claims description 70
- 150000007523 nucleic acids Chemical class 0.000 claims description 66
- 108020004707 nucleic acids Proteins 0.000 claims description 62
- 238000007481 next generation sequencing Methods 0.000 claims description 55
- 229920001184 polypeptide Polymers 0.000 claims description 53
- 230000004044 response Effects 0.000 claims description 30
- 238000002255 vaccination Methods 0.000 claims description 27
- 229920000160 (ribonucleotides)n+m Polymers 0.000 claims description 14
- 230000001225 therapeutic Effects 0.000 claims description 14
- 210000003283 T-Lymphocytes, Helper-Inducer Anatomy 0.000 claims description 10
- 210000002443 helper T lymphocyte Anatomy 0.000 claims description 9
- 229920002477 rna polymer Polymers 0.000 description 232
- 230000020382 suppression by virus of host antigen processing and presentation of peptide antigen via MHC class I Effects 0.000 description 126
- 239000000523 sample Substances 0.000 description 124
- 210000001744 T-Lymphocytes Anatomy 0.000 description 94
- 102000004196 processed proteins & peptides Human genes 0.000 description 86
- 108090000765 processed proteins & peptides Proteins 0.000 description 86
- 235000001014 amino acid Nutrition 0.000 description 76
- 102000004169 proteins and genes Human genes 0.000 description 70
- 108090000623 proteins and genes Proteins 0.000 description 70
- 210000001519 tissues Anatomy 0.000 description 69
- 235000018102 proteins Nutrition 0.000 description 68
- 230000014509 gene expression Effects 0.000 description 59
- DHMQDGOQFOQNFH-UHFFFAOYSA-N glycine Chemical compound NCC(O)=O DHMQDGOQFOQNFH-UHFFFAOYSA-N 0.000 description 51
- 230000028993 immune response Effects 0.000 description 48
- 230000027455 binding Effects 0.000 description 46
- 201000010099 disease Diseases 0.000 description 46
- 241000282414 Homo sapiens Species 0.000 description 44
- 239000002773 nucleotide Substances 0.000 description 42
- 125000003729 nucleotide group Chemical group 0.000 description 42
- 238000003752 polymerase chain reaction Methods 0.000 description 42
- 238000000338 in vitro Methods 0.000 description 40
- 125000003275 alpha amino acid group Chemical group 0.000 description 39
- 238000000034 method Methods 0.000 description 39
- 210000000612 Antigen-Presenting Cells Anatomy 0.000 description 37
- 230000002068 genetic Effects 0.000 description 37
- 239000000203 mixture Substances 0.000 description 36
- 210000004369 Blood Anatomy 0.000 description 34
- 239000008280 blood Substances 0.000 description 34
- 206010027476 Metastasis Diseases 0.000 description 33
- 201000001441 melanoma Diseases 0.000 description 32
- 230000035897 transcription Effects 0.000 description 32
- 206010025650 Malignant melanoma Diseases 0.000 description 31
- 210000004881 tumor cells Anatomy 0.000 description 31
- 108091008153 T cell receptors Proteins 0.000 description 30
- 102000016266 T-Cell Antigen Receptors Human genes 0.000 description 30
- 239000000463 material Substances 0.000 description 30
- 238000004166 bioassay Methods 0.000 description 28
- 238000006243 chemical reaction Methods 0.000 description 28
- 230000001939 inductive effect Effects 0.000 description 28
- 239000004471 Glycine Substances 0.000 description 26
- 108020004999 Messenger RNA Proteins 0.000 description 25
- 230000003053 immunization Effects 0.000 description 25
- 229920002106 messenger RNA Polymers 0.000 description 25
- 210000004443 Dendritic Cells Anatomy 0.000 description 24
- 238000004458 analytical method Methods 0.000 description 24
- 102100013077 CD4 Human genes 0.000 description 23
- 101700022938 CD4 Proteins 0.000 description 23
- 229920002676 Complementary DNA Polymers 0.000 description 23
- 239000002299 complementary DNA Substances 0.000 description 23
- 210000001151 cytotoxic T lymphocyte Anatomy 0.000 description 23
- 210000000056 organs Anatomy 0.000 description 23
- 229920001405 Coding region Polymers 0.000 description 21
- 108020004412 RNA 3' Polyadenylation Signals Proteins 0.000 description 21
- 230000002163 immunogen Effects 0.000 description 20
- 238000003559 rna-seq method Methods 0.000 description 20
- 230000000240 adjuvant Effects 0.000 description 19
- 239000002671 adjuvant Substances 0.000 description 19
- 239000002585 base Substances 0.000 description 19
- 210000000805 Cytoplasm Anatomy 0.000 description 18
- 230000000259 anti-tumor Effects 0.000 description 18
- 238000002649 immunization Methods 0.000 description 18
- 238000001514 detection method Methods 0.000 description 17
- 239000011324 bead Substances 0.000 description 16
- 230000000392 somatic Effects 0.000 description 16
- 101700011961 DPOM Proteins 0.000 description 15
- 101710029649 MDV043 Proteins 0.000 description 15
- 101700061424 POLB Proteins 0.000 description 15
- 101700054624 RF1 Proteins 0.000 description 15
- 230000004083 survival Effects 0.000 description 15
- 102000004127 Cytokines Human genes 0.000 description 14
- 108090000695 Cytokines Proteins 0.000 description 14
- 101700086956 IFNG Proteins 0.000 description 14
- 102100016020 IFNG Human genes 0.000 description 14
- 210000004940 Nucleus Anatomy 0.000 description 14
- 210000000400 T-Lymphocytes, Cytotoxic Anatomy 0.000 description 14
- 102000004965 antibodies Human genes 0.000 description 14
- 108090001123 antibodies Proteins 0.000 description 14
- 230000000875 corresponding Effects 0.000 description 14
- 210000004602 germ cell Anatomy 0.000 description 14
- 230000001965 increased Effects 0.000 description 14
- 229920001850 Nucleic acid sequence Polymers 0.000 description 13
- 238000001574 biopsy Methods 0.000 description 13
- 230000002708 enhancing Effects 0.000 description 13
- 238000001914 filtration Methods 0.000 description 13
- 230000001900 immune effect Effects 0.000 description 13
- 230000036647 reaction Effects 0.000 description 13
- 239000011780 sodium chloride Substances 0.000 description 13
- 239000000126 substance Substances 0.000 description 13
- 101710038109 KIF18B Proteins 0.000 description 12
- 230000012010 growth Effects 0.000 description 12
- 238000007637 random forest analysis Methods 0.000 description 12
- 230000001105 regulatory Effects 0.000 description 12
- -1 serine amino acids Chemical class 0.000 description 12
- 239000000243 solution Substances 0.000 description 12
- 238000010200 validation analysis Methods 0.000 description 12
- LKKMLIBUAXYLOY-UHFFFAOYSA-N 3-Amino-1-methyl-5H-pyrido[4,3-b]indole Chemical compound N1C2=CC=CC=C2C2=C1C=C(N)N=C2C LKKMLIBUAXYLOY-UHFFFAOYSA-N 0.000 description 11
- 206010061289 Metastatic neoplasm Diseases 0.000 description 11
- 101710009757 UROD Proteins 0.000 description 11
- 230000003321 amplification Effects 0.000 description 11
- 239000000969 carrier Substances 0.000 description 11
- 230000000694 effects Effects 0.000 description 11
- 238000009169 immunotherapy Methods 0.000 description 11
- 230000003211 malignant Effects 0.000 description 11
- 238000003199 nucleic acid amplification method Methods 0.000 description 11
- 239000008194 pharmaceutical composition Substances 0.000 description 11
- MTCFGRXMJLQNBG-UWTATZPHSA-N D-serine Chemical compound OC[C@@H](N)C(O)=O MTCFGRXMJLQNBG-UWTATZPHSA-N 0.000 description 10
- 210000004698 Lymphocytes Anatomy 0.000 description 10
- 230000004913 activation Effects 0.000 description 10
- 239000003795 chemical substances by application Substances 0.000 description 10
- 238000002474 experimental method Methods 0.000 description 10
- 230000002401 inhibitory effect Effects 0.000 description 10
- 238000002347 injection Methods 0.000 description 10
- 239000007924 injection Substances 0.000 description 10
- 230000001394 metastastic Effects 0.000 description 10
- 230000004048 modification Effects 0.000 description 10
- 238000006011 modification reaction Methods 0.000 description 10
- 150000003839 salts Chemical class 0.000 description 10
- 238000005070 sampling Methods 0.000 description 10
- 230000004936 stimulating Effects 0.000 description 10
- 238000006467 substitution reaction Methods 0.000 description 10
- 230000004614 tumor growth Effects 0.000 description 10
- 210000003719 B-Lymphocytes Anatomy 0.000 description 9
- 210000000987 Immune System Anatomy 0.000 description 9
- 241001465754 Metazoa Species 0.000 description 9
- 230000002159 abnormal effect Effects 0.000 description 9
- 230000036755 cellular response Effects 0.000 description 9
- 238000003114 enzyme-linked immunosorbent spot assay Methods 0.000 description 9
- 239000002502 liposome Substances 0.000 description 9
- 238000003786 synthesis reaction Methods 0.000 description 9
- 208000009956 Adenocarcinoma Diseases 0.000 description 8
- 108010088652 Histocompatibility Antigens Class I Proteins 0.000 description 8
- 102000008949 Histocompatibility Antigens Class I Human genes 0.000 description 8
- 210000000265 Leukocytes Anatomy 0.000 description 8
- 210000000952 Spleen Anatomy 0.000 description 8
- 238000007792 addition Methods 0.000 description 8
- 238000010367 cloning Methods 0.000 description 8
- 150000001875 compounds Chemical class 0.000 description 8
- 238000002856 computational phylogenetic analysis Methods 0.000 description 8
- 238000011161 development Methods 0.000 description 8
- 230000018109 developmental process Effects 0.000 description 8
- 230000004927 fusion Effects 0.000 description 8
- 238000004519 manufacturing process Methods 0.000 description 8
- 238000011160 research Methods 0.000 description 8
- 210000000170 Cell Membrane Anatomy 0.000 description 7
- 102000004594 DNA Polymerase I Human genes 0.000 description 7
- 108010017826 DNA Polymerase I Proteins 0.000 description 7
- 229920000665 Exon Polymers 0.000 description 7
- 210000004185 Liver Anatomy 0.000 description 7
- 210000004072 Lung Anatomy 0.000 description 7
- 206010058467 Lung neoplasm malignant Diseases 0.000 description 7
- 206010033128 Ovarian cancer Diseases 0.000 description 7
- 102000004245 Proteasome Endopeptidase Complex Human genes 0.000 description 7
- 108090000708 Proteasome Endopeptidase Complex Proteins 0.000 description 7
- 208000006265 Renal Cell Carcinoma Diseases 0.000 description 7
- 230000024932 T cell mediated immunity Effects 0.000 description 7
- 230000030741 antigen processing and presentation Effects 0.000 description 7
- 230000001413 cellular Effects 0.000 description 7
- 239000000839 emulsion Substances 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 7
- 238000007480 sanger sequencing Methods 0.000 description 7
- NINIDFKCEFEMDL-UHFFFAOYSA-N sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 description 7
- 229910052717 sulfur Inorganic materials 0.000 description 7
- 239000011593 sulfur Substances 0.000 description 7
- 238000002560 therapeutic procedure Methods 0.000 description 7
- 210000001185 Bone Marrow Anatomy 0.000 description 6
- 210000001266 CD8-Positive T-Lymphocytes Anatomy 0.000 description 6
- 210000000349 Chromosomes Anatomy 0.000 description 6
- 102100019126 HBB Human genes 0.000 description 6
- 230000036499 Half live Effects 0.000 description 6
- 108091005902 Hemoglobin subunit beta Proteins 0.000 description 6
- 241000282412 Homo Species 0.000 description 6
- 102000011931 Nucleoproteins Human genes 0.000 description 6
- 108010061100 Nucleoproteins Proteins 0.000 description 6
- 229920000795 Polyadenylation Polymers 0.000 description 6
- 101710026335 TP53 Proteins 0.000 description 6
- 229920000401 Three prime untranslated region Polymers 0.000 description 6
- 108020003635 Untranslated Regions Proteins 0.000 description 6
- 229920000146 Untranslated region Polymers 0.000 description 6
- 230000015572 biosynthetic process Effects 0.000 description 6
- 238000004422 calculation algorithm Methods 0.000 description 6
- 230000001461 cytolytic Effects 0.000 description 6
- 230000004069 differentiation Effects 0.000 description 6
- 230000036039 immunity Effects 0.000 description 6
- 238000011081 inoculation Methods 0.000 description 6
- 238000003780 insertion Methods 0.000 description 6
- 230000003834 intracellular Effects 0.000 description 6
- 101710030587 ligN Proteins 0.000 description 6
- 101700077585 ligd Proteins 0.000 description 6
- 238000010606 normalization Methods 0.000 description 6
- 229910052760 oxygen Inorganic materials 0.000 description 6
- MYMOFIZGZYHOMD-UHFFFAOYSA-N oxygen Chemical compound O=O MYMOFIZGZYHOMD-UHFFFAOYSA-N 0.000 description 6
- 239000001301 oxygen Substances 0.000 description 6
- 230000022983 regulation of cell cycle Effects 0.000 description 6
- 230000004043 responsiveness Effects 0.000 description 6
- 238000010186 staining Methods 0.000 description 6
- 238000001890 transfection Methods 0.000 description 6
- 229920002287 Amplicon Polymers 0.000 description 5
- 206010059512 Apoptosis Diseases 0.000 description 5
- 108020004705 Codon Proteins 0.000 description 5
- 210000001156 Gastric Mucosa Anatomy 0.000 description 5
- 108010027412 Histocompatibility Antigens Class II Proteins 0.000 description 5
- 102000018713 Histocompatibility Antigens Class II Human genes 0.000 description 5
- 210000001165 Lymph Nodes Anatomy 0.000 description 5
- 210000002540 Macrophages Anatomy 0.000 description 5
- 229920000272 Oligonucleotide Polymers 0.000 description 5
- 210000004681 Ovum Anatomy 0.000 description 5
- 206010060862 Prostate cancer Diseases 0.000 description 5
- 210000002784 Stomach Anatomy 0.000 description 5
- 238000000692 Student's t-test Methods 0.000 description 5
- 241000700605 Viruses Species 0.000 description 5
- 230000006907 apoptotic process Effects 0.000 description 5
- 201000009030 carcinoma Diseases 0.000 description 5
- 230000020411 cell activation Effects 0.000 description 5
- 238000007405 data analysis Methods 0.000 description 5
- 238000009826 distribution Methods 0.000 description 5
- 238000000605 extraction Methods 0.000 description 5
- 238000003205 genotyping method Methods 0.000 description 5
- 125000002887 hydroxy group Chemical group [H]O* 0.000 description 5
- 238000010348 incorporation Methods 0.000 description 5
- 230000002147 killing Effects 0.000 description 5
- 201000005202 lung cancer Diseases 0.000 description 5
- 230000001404 mediated Effects 0.000 description 5
- 238000005457 optimization Methods 0.000 description 5
- 230000035755 proliferation Effects 0.000 description 5
- 230000001681 protective Effects 0.000 description 5
- 230000000638 stimulation Effects 0.000 description 5
- 230000002194 synthesizing Effects 0.000 description 5
- 101700014681 ACTN4 Proteins 0.000 description 4
- 101700028175 CASP9 Proteins 0.000 description 4
- 102100006400 CSF2 Human genes 0.000 description 4
- 102100007097 DCT Human genes 0.000 description 4
- 238000001712 DNA sequencing Methods 0.000 description 4
- 102000008422 EC 2.7.1.78 Human genes 0.000 description 4
- 108010021757 EC 2.7.1.78 Proteins 0.000 description 4
- 102000004190 Enzymes Human genes 0.000 description 4
- 108090000790 Enzymes Proteins 0.000 description 4
- 102000018651 Epithelial Cell Adhesion Molecule Human genes 0.000 description 4
- 108010066687 Epithelial Cell Adhesion Molecule Proteins 0.000 description 4
- 210000000981 Epithelium Anatomy 0.000 description 4
- 210000004907 Glands Anatomy 0.000 description 4
- 108010017213 Granulocyte-Macrophage Colony-Stimulating Factor Proteins 0.000 description 4
- 102100007847 KIF18B Human genes 0.000 description 4
- 206010024324 Leukaemias Diseases 0.000 description 4
- 210000004379 Membranes Anatomy 0.000 description 4
- 210000003819 Peripheral blood mononuclear cell Anatomy 0.000 description 4
- 101700062375 TRRAP Proteins 0.000 description 4
- 210000001550 Testis Anatomy 0.000 description 4
- 230000001594 aberrant Effects 0.000 description 4
- 230000004075 alteration Effects 0.000 description 4
- 230000002939 deleterious Effects 0.000 description 4
- 108010051081 dopachrome isomerase Proteins 0.000 description 4
- 230000000534 elicitor Effects 0.000 description 4
- 238000010828 elution Methods 0.000 description 4
- 210000002865 immune cell Anatomy 0.000 description 4
- 150000002500 ions Chemical class 0.000 description 4
- 239000003550 marker Substances 0.000 description 4
- 239000012528 membrane Substances 0.000 description 4
- 125000000956 methoxy group Chemical group [H]C([H])([H])O* 0.000 description 4
- 239000003921 oil Substances 0.000 description 4
- 231100000590 oncogenic Toxicity 0.000 description 4
- 230000002246 oncogenic Effects 0.000 description 4
- 239000011886 peripheral blood Substances 0.000 description 4
- 230000003389 potentiating Effects 0.000 description 4
- 230000003405 preventing Effects 0.000 description 4
- 230000026938 proteasomal ubiquitin-dependent protein catabolic process Effects 0.000 description 4
- 108020001580 protein domains Proteins 0.000 description 4
- 230000004853 protein function Effects 0.000 description 4
- 238000000746 purification Methods 0.000 description 4
- 239000002096 quantum dot Substances 0.000 description 4
- 102000005962 receptors Human genes 0.000 description 4
- 108020003175 receptors Proteins 0.000 description 4
- 125000002652 ribonucleotide group Chemical group 0.000 description 4
- 201000000849 skin cancer Diseases 0.000 description 4
- 230000002195 synergetic Effects 0.000 description 4
- 102100017481 ACTN4 Human genes 0.000 description 3
- 108060006202 ATM Proteins 0.000 description 3
- 210000001015 Abdomen Anatomy 0.000 description 3
- 238000003339 Best practice Methods 0.000 description 3
- 210000001124 Body Fluids Anatomy 0.000 description 3
- 210000004556 Brain Anatomy 0.000 description 3
- 206010006187 Breast cancer Diseases 0.000 description 3
- 208000005623 Carcinogenesis Diseases 0.000 description 3
- 206010073251 Clear cell renal cell carcinoma Diseases 0.000 description 3
- 206010009944 Colon cancer Diseases 0.000 description 3
- 101710023287 DDB1 Proteins 0.000 description 3
- 101710007779 EEF2 Proteins 0.000 description 3
- 101700054888 FAT1 Proteins 0.000 description 3
- 101700009028 FZD7 Proteins 0.000 description 3
- 102100005615 HLA-DQA1 Human genes 0.000 description 3
- 108010066345 MHC binding peptide Proteins 0.000 description 3
- 241000124008 Mammalia Species 0.000 description 3
- 208000002154 Non-Small-Cell Lung Carcinoma Diseases 0.000 description 3
- 206010058823 Ovarian mass Diseases 0.000 description 3
- 101710018349 PDGFRA Proteins 0.000 description 3
- 101700070413 PLOD2 Proteins 0.000 description 3
- 101700009419 PTEN Proteins 0.000 description 3
- 102100005499 PTPRC Human genes 0.000 description 3
- 101700059076 PTPRC Proteins 0.000 description 3
- 102000007079 Peptide Fragments Human genes 0.000 description 3
- 108010033276 Peptide Fragments Proteins 0.000 description 3
- 108010061844 Poly(ADP-ribose) Polymerases Proteins 0.000 description 3
- 102000012338 Poly(ADP-ribose) Polymerases Human genes 0.000 description 3
- 229920000789 Polyinosinic:polycytidylic acid Polymers 0.000 description 3
- 238000010357 RNA editing Methods 0.000 description 3
- 230000026279 RNA modification Effects 0.000 description 3
- 206010038389 Renal cancer Diseases 0.000 description 3
- 101710014396 SLC35B2 Proteins 0.000 description 3
- 208000000587 Small Cell Lung Carcinoma Diseases 0.000 description 3
- 108010090804 Streptavidin Proteins 0.000 description 3
- 102100019730 TP53 Human genes 0.000 description 3
- 102000000852 Tumor Necrosis Factor-alpha Human genes 0.000 description 3
- 108010001801 Tumor Necrosis Factor-alpha Proteins 0.000 description 3
- 210000004291 Uterus Anatomy 0.000 description 3
- 101700058470 XPOT Proteins 0.000 description 3
- QTBSBXVTEAMEQO-UHFFFAOYSA-N acetic acid Chemical compound CC(O)=O QTBSBXVTEAMEQO-UHFFFAOYSA-N 0.000 description 3
- 239000002253 acid Substances 0.000 description 3
- 150000007513 acids Chemical class 0.000 description 3
- 230000000890 antigenic Effects 0.000 description 3
- 239000010839 body fluid Substances 0.000 description 3
- UIIMBOGNXHQVGW-UHFFFAOYSA-M buffer Substances [Na+].OC([O-])=O UIIMBOGNXHQVGW-UHFFFAOYSA-M 0.000 description 3
- 238000010804 cDNA synthesis Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 125000002091 cationic group Chemical group 0.000 description 3
- 150000001768 cations Chemical class 0.000 description 3
- 239000003153 chemical reaction reagent Substances 0.000 description 3
- KRKNYBCHXYNGOX-UHFFFAOYSA-N citric acid Chemical compound OC(=O)CC(O)(C(O)=O)CC(O)=O KRKNYBCHXYNGOX-UHFFFAOYSA-N 0.000 description 3
- 238000003776 cleavage reaction Methods 0.000 description 3
- 230000009089 cytolysis Effects 0.000 description 3
- 230000001809 detectable Effects 0.000 description 3
- 229940079593 drugs Drugs 0.000 description 3
- 238000003379 elimination reaction Methods 0.000 description 3
- 239000012149 elution buffer Substances 0.000 description 3
- 210000002919 epithelial cells Anatomy 0.000 description 3
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 3
- 238000009472 formulation Methods 0.000 description 3
- 230000000762 glandular Effects 0.000 description 3
- 125000003630 glycyl group Chemical group [H]N([H])C([H])([H])C(*)=O 0.000 description 3
- 238000009396 hybridization Methods 0.000 description 3
- 201000010982 kidney cancer Diseases 0.000 description 3
- 238000001001 laser micro-dissection Methods 0.000 description 3
- 239000003446 ligand Substances 0.000 description 3
- 230000002934 lysing Effects 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 229920001894 non-coding RNA Polymers 0.000 description 3
- 230000002611 ovarian Effects 0.000 description 3
- 230000037361 pathway Effects 0.000 description 3
- 230000002265 prevention Effects 0.000 description 3
- 108091007521 restriction endonucleases Proteins 0.000 description 3
- 230000003248 secreting Effects 0.000 description 3
- BUGBHKTXTAQXES-UHFFFAOYSA-N selenium Chemical compound [Se] BUGBHKTXTAQXES-UHFFFAOYSA-N 0.000 description 3
- 229910052711 selenium Inorganic materials 0.000 description 3
- 239000011669 selenium Substances 0.000 description 3
- 241000894007 species Species 0.000 description 3
- 230000000087 stabilizing Effects 0.000 description 3
- 239000001226 triphosphate Substances 0.000 description 3
- 238000002604 ultrasonography Methods 0.000 description 3
- 239000011534 wash buffer Substances 0.000 description 3
- 238000005406 washing Methods 0.000 description 3
- 108060000552 ASCC2 Proteins 0.000 description 2
- 101700037792 AURKB Proteins 0.000 description 2
- 102000010825 Actinin Human genes 0.000 description 2
- 108010063503 Actinin Proteins 0.000 description 2
- 210000000601 Blood Cells Anatomy 0.000 description 2
- 102100009007 CASP9 Human genes 0.000 description 2
- 101710010269 CAVIN1 Proteins 0.000 description 2
- 101710040446 CD40 Proteins 0.000 description 2
- 102100013137 CD40 Human genes 0.000 description 2
- 102100019451 CD80 Human genes 0.000 description 2
- 101700080477 CD80 Proteins 0.000 description 2
- 241000283707 Capra Species 0.000 description 2
- 102000019034 Chemokines Human genes 0.000 description 2
- 108010012236 Chemokines Proteins 0.000 description 2
- 210000001072 Colon Anatomy 0.000 description 2
- UHDGCWIWMRVCDJ-PSQAKQOGSA-N Cytidine Natural products O=C1N=C(N)C=CN1[C@@H]1[C@@H](O)[C@@H](O)[C@H](CO)O1 UHDGCWIWMRVCDJ-PSQAKQOGSA-N 0.000 description 2
- UHDGCWIWMRVCDJ-XVFCMESISA-N Cytidine Chemical compound O=C1N=C(N)C=CN1[C@H]1[C@H](O)[C@H](O)[C@@H](CO)O1 UHDGCWIWMRVCDJ-XVFCMESISA-N 0.000 description 2
- 102000012698 DDB1 Human genes 0.000 description 2
- 230000004568 DNA-binding Effects 0.000 description 2
- 108090000626 DNA-directed RNA polymerases Proteins 0.000 description 2
- 102000004163 DNA-directed RNA polymerases Human genes 0.000 description 2
- 101710009540 DNAJB12 Proteins 0.000 description 2
- 102000016911 Deoxyribonucleases Human genes 0.000 description 2
- 108010053770 Deoxyribonucleases Proteins 0.000 description 2
- 108010092799 EC 2.7.7.49 Proteins 0.000 description 2
- 210000003743 Erythrocytes Anatomy 0.000 description 2
- 210000001723 Extracellular Space Anatomy 0.000 description 2
- 101710010648 FANCD2 Proteins 0.000 description 2
- 101710030892 FLT1 Proteins 0.000 description 2
- NYHBQMYGNKIUIF-UUOKFMHZSA-N Guanosine Chemical group C1=NC=2C(=O)NC(N)=NC=2N1[C@@H]1O[C@H](CO)[C@@H](O)[C@H]1O NYHBQMYGNKIUIF-UUOKFMHZSA-N 0.000 description 2
- 101710029866 HLA-DPA1 Proteins 0.000 description 2
- 101710031487 HLA-DQA1 Proteins 0.000 description 2
- 101710031278 HLA-DRB3 Proteins 0.000 description 2
- 208000006572 Human Influenza Diseases 0.000 description 2
- 102100008696 IMPACT Human genes 0.000 description 2
- 108050004449 IMPACT Proteins 0.000 description 2
- 101700073649 ITSN2 Proteins 0.000 description 2
- 102000018358 Immunoglobulins Human genes 0.000 description 2
- 108060003951 Immunoglobulins Proteins 0.000 description 2
- 206010022000 Influenza Diseases 0.000 description 2
- 102000013462 Interleukin-12 Human genes 0.000 description 2
- 108010065805 Interleukin-12 Proteins 0.000 description 2
- 229940117681 Interleukin-12 Drugs 0.000 description 2
- 102000011782 Keratins Human genes 0.000 description 2
- 108010076876 Keratins Proteins 0.000 description 2
- 210000000822 Killer Cells, Natural Anatomy 0.000 description 2
- 102100005410 LINE-1 retrotransposable element ORF2 protein Human genes 0.000 description 2
- 229920001320 Leader sequence (mRNA) Polymers 0.000 description 2
- 206010025323 Lymphomas Diseases 0.000 description 2
- 108060004667 MDM1 Proteins 0.000 description 2
- 101710042539 MTHFD1L Proteins 0.000 description 2
- 102000018697 Membrane Proteins Human genes 0.000 description 2
- 108010052285 Membrane Proteins Proteins 0.000 description 2
- 108090000143 Mouse Proteins Proteins 0.000 description 2
- 241000699670 Mus sp. Species 0.000 description 2
- 101700048023 OBSL1 Proteins 0.000 description 2
- 108060005708 ORC2 Proteins 0.000 description 2
- 229940092253 Ovalbumin Drugs 0.000 description 2
- 108010058846 Ovalbumin Proteins 0.000 description 2
- 210000001672 Ovary Anatomy 0.000 description 2
- 210000003101 Oviducts Anatomy 0.000 description 2
- 102100013574 POU2F1 Human genes 0.000 description 2
- 101710006192 POU2F1 Proteins 0.000 description 2
- 101710004009 PPP1R7 Proteins 0.000 description 2
- 229920002224 Peptide nucleic acid Polymers 0.000 description 2
- 102000004503 Perforin Human genes 0.000 description 2
- 108010056995 Perforin Proteins 0.000 description 2
- 102000017343 Phosphatidylinositol kinase Human genes 0.000 description 2
- 108050005377 Phosphatidylinositol kinase Proteins 0.000 description 2
- 210000002826 Placenta Anatomy 0.000 description 2
- 229920000776 Poly(Adenosine diphosphate-ribose) polymerase Polymers 0.000 description 2
- 210000000664 Rectum Anatomy 0.000 description 2
- 101710011339 SLC45A2 Proteins 0.000 description 2
- 101700077212 SNX15 Proteins 0.000 description 2
- 210000003491 Skin Anatomy 0.000 description 2
- YYGNTYWPHWGJRM-RUSDCZJESA-N Squalene Natural products C(=C\CC/C(=C\CC/C=C(\CC/C=C(\CC/C=C(\C)/C)/C)/C)/C)(\CC/C=C(\C)/C)/C YYGNTYWPHWGJRM-RUSDCZJESA-N 0.000 description 2
- 241000282887 Suidae Species 0.000 description 2
- 101710026354 TP63 Proteins 0.000 description 2
- DRTQHJPVMGBUCF-UCVXFZOQSA-N Uridine Natural products O[C@H]1[C@H](O)[C@H](CO)O[C@@H]1N1C(=O)NC(=O)C=C1 DRTQHJPVMGBUCF-UCVXFZOQSA-N 0.000 description 2
- DRTQHJPVMGBUCF-XVFCMESISA-N Uridine Chemical compound O[C@@H]1[C@H](O)[C@@H](CO)O[C@H]1N1C(=O)NC(=O)C=C1 DRTQHJPVMGBUCF-XVFCMESISA-N 0.000 description 2
- 229940045145 Uridine Drugs 0.000 description 2
- 210000003932 Urinary Bladder Anatomy 0.000 description 2
- 241000251539 Vertebrata <Metazoa> Species 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 2
- OIRDTQYFTABQOQ-KQYNXXCUSA-N adenosine group Chemical group [C@@H]1([C@H](O)[C@H](O)[C@@H](CO)O1)N1C=NC=2C(N)=NC=NC12 OIRDTQYFTABQOQ-KQYNXXCUSA-N 0.000 description 2
- 229940037003 alum Drugs 0.000 description 2
- 230000001668 ameliorated Effects 0.000 description 2
- 238000003766 bioinformatics method Methods 0.000 description 2
- OYPRJOBELJOOCE-UHFFFAOYSA-N calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 description 2
- 239000011575 calcium Substances 0.000 description 2
- 229910052791 calcium Inorganic materials 0.000 description 2
- 231100000504 carcinogenesis Toxicity 0.000 description 2
- 230000015556 catabolic process Effects 0.000 description 2
- 230000021164 cell adhesion Effects 0.000 description 2
- 238000002512 chemotherapy Methods 0.000 description 2
- 201000011231 colorectal cancer Diseases 0.000 description 2
- 238000010192 crystallographic characterization Methods 0.000 description 2
- 230000001186 cumulative Effects 0.000 description 2
- 230000001086 cytosolic Effects 0.000 description 2
- 230000034994 death Effects 0.000 description 2
- 231100000517 death Toxicity 0.000 description 2
- 230000004059 degradation Effects 0.000 description 2
- 238000006731 degradation reaction Methods 0.000 description 2
- 230000004041 dendritic cell maturation Effects 0.000 description 2
- 238000004520 electroporation Methods 0.000 description 2
- 230000029578 entry into host Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000000684 flow cytometry Methods 0.000 description 2
- 238000002866 fluorescence resonance energy transfer Methods 0.000 description 2
- 210000002980 germ line cell Anatomy 0.000 description 2
- 230000028996 humoral immune response Effects 0.000 description 2
- 230000001976 improved Effects 0.000 description 2
- 238000000126 in silico method Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000001990 intravenous administration Methods 0.000 description 2
- 238000002372 labelling Methods 0.000 description 2
- 230000000670 limiting Effects 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 229910052744 lithium Inorganic materials 0.000 description 2
- 201000007270 liver cancer Diseases 0.000 description 2
- 238000001325 log-rank test Methods 0.000 description 2
- 201000005296 lung carcinoma Diseases 0.000 description 2
- 201000005244 lung non-small cell carcinoma Diseases 0.000 description 2
- 230000036210 malignancy Effects 0.000 description 2
- 230000035800 maturation Effects 0.000 description 2
- 239000000693 micelle Substances 0.000 description 2
- 238000010369 molecular cloning Methods 0.000 description 2
- 231100000350 mutagenesis Toxicity 0.000 description 2
- 239000002105 nanoparticle Substances 0.000 description 2
- 230000001613 neoplastic Effects 0.000 description 2
- 101700050775 oct-1 Proteins 0.000 description 2
- 201000010279 papillary renal cell carcinoma Diseases 0.000 description 2
- 239000012188 paraffin wax Substances 0.000 description 2
- 230000036961 partial Effects 0.000 description 2
- 230000001575 pathological Effects 0.000 description 2
- 239000000825 pharmaceutical preparation Substances 0.000 description 2
- NBIIXXVUZAFLBC-UHFFFAOYSA-N phosphoric acid Chemical compound OP(O)(O)=O NBIIXXVUZAFLBC-UHFFFAOYSA-N 0.000 description 2
- 229940115272 poly I:C Drugs 0.000 description 2
- OZAIFHULBGXAKX-UHFFFAOYSA-N precursor Substances N#CC(C)(C)N=NC(C)(C)C#N OZAIFHULBGXAKX-UHFFFAOYSA-N 0.000 description 2
- 230000002335 preservative Effects 0.000 description 2
- 239000003755 preservative agent Substances 0.000 description 2
- 230000003449 preventive Effects 0.000 description 2
- 230000001737 promoting Effects 0.000 description 2
- 230000002633 protecting Effects 0.000 description 2
- 238000003908 quality control method Methods 0.000 description 2
- 230000002829 reduced Effects 0.000 description 2
- 238000004007 reversed phase HPLC Methods 0.000 description 2
- 230000002441 reversible Effects 0.000 description 2
- 150000007949 saponins Chemical class 0.000 description 2
- 235000017709 saponins Nutrition 0.000 description 2
- 238000000926 separation method Methods 0.000 description 2
- 238000007841 sequencing by ligation Methods 0.000 description 2
- 230000019491 signal transduction Effects 0.000 description 2
- FAPWRFPIFSIZLT-UHFFFAOYSA-M sodium chloride Chemical compound [Na+].[Cl-] FAPWRFPIFSIZLT-UHFFFAOYSA-M 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 238000001356 surgical procedure Methods 0.000 description 2
- 239000000725 suspension Substances 0.000 description 2
- 230000002459 sustained Effects 0.000 description 2
- 239000012096 transfection reagent Substances 0.000 description 2
- 230000001052 transient Effects 0.000 description 2
- 230000032258 transport Effects 0.000 description 2
- 230000004222 uncontrolled growth Effects 0.000 description 2
- 230000003827 upregulation Effects 0.000 description 2
- 238000011144 upstream manufacturing Methods 0.000 description 2
- 239000003981 vehicle Substances 0.000 description 2
- 108040005185 1-phosphatidylinositol-3-kinase activity proteins Proteins 0.000 description 1
- 102000010400 1-phosphatidylinositol-3-kinase activity proteins Human genes 0.000 description 1
- 108020005345 3' Untranslated Regions Proteins 0.000 description 1
- 108020003589 5' Untranslated Regions Proteins 0.000 description 1
- ZAYHVCMSTBRABG-JXOAFFINSA-N 5-Methylcytidine Chemical compound O=C1N=C(N)C(C)=CN1[C@H]1[C@H](O)[C@H](O)[C@@H](CO)O1 ZAYHVCMSTBRABG-JXOAFFINSA-N 0.000 description 1
- YXHLJMWYDTXDHS-IRFLANFNSA-N 7-Aminoactinomycin D Chemical compound C[C@H]1OC(=O)[C@H](C(C)C)N(C)C(=O)CN(C)C(=O)[C@@H]2CCCN2C(=O)[C@@H](C(C)C)NC(=O)[C@H]1NC(=O)C1=C(N)C(=O)C(C)=C2OC(C(C)=C(N)C=C3C(=O)N[C@@H]4C(=O)N[C@@H](C(N5CCC[C@H]5C(=O)N(C)CC(=O)N(C)[C@@H](C(C)C)C(=O)O[C@@H]4C)=O)C(C)C)=C3N=C21 YXHLJMWYDTXDHS-IRFLANFNSA-N 0.000 description 1
- OGHAROSJZRTIOK-KQYNXXCUSA-O 7-Methylguanosine Chemical compound C1=2N=C(N)NC(=O)C=2[N+](C)=CN1[C@@H]1O[C@H](CO)[C@@H](O)[C@H]1O OGHAROSJZRTIOK-KQYNXXCUSA-O 0.000 description 1
- OIRDTQYFTABQOQ-GAWUUDPSSA-N 9-β-D-XYLOFURANOSYL-ADENINE Chemical compound C1=NC=2C(N)=NC=NC=2N1[C@@H]1O[C@H](CO)[C@H](O)[C@H]1O OIRDTQYFTABQOQ-GAWUUDPSSA-N 0.000 description 1
- 101700014023 AFP Proteins 0.000 description 1
- 102100008450 AFP Human genes 0.000 description 1
- 102100011141 ALK Human genes 0.000 description 1
- 102100006761 ARMC1 Human genes 0.000 description 1
- 101700054504 ARMC1 Proteins 0.000 description 1
- 102100015009 ASCC2 Human genes 0.000 description 1
- 102100016051 ASF1B Human genes 0.000 description 1
- 101700073689 ASF1B Proteins 0.000 description 1
- 101700058112 ASH1L Proteins 0.000 description 1
- 102100003251 ASH1L Human genes 0.000 description 1
- 102100000648 ATM Human genes 0.000 description 1
- 102100005361 ATP11A Human genes 0.000 description 1
- 101710040565 ATP11A Proteins 0.000 description 1
- 101700069996 ATRN Proteins 0.000 description 1
- 102100002851 ATRN Human genes 0.000 description 1
- 230000035533 AUC Effects 0.000 description 1
- 210000000683 Abdominal Cavity Anatomy 0.000 description 1
- 240000005020 Acaciella glauca Species 0.000 description 1
- 108010085238 Actins Proteins 0.000 description 1
- 102000007469 Actins Human genes 0.000 description 1
- 208000010507 Adenocarcinoma of Lung Diseases 0.000 description 1
- OIRDTQYFTABQOQ-SXVXDFOESA-N Adenosine Natural products Nc1ncnc2c1ncn2[C@@H]3O[C@@H](CO)[C@H](O)[C@@H]3O OIRDTQYFTABQOQ-SXVXDFOESA-N 0.000 description 1
- ZKHQWZAMYRWXGA-KQYNXXCUSA-N Adenosine triphosphate Chemical compound C1=NC=2C(N)=NC=NC=2N1[C@@H]1O[C@H](COP(O)(=O)OP(O)(=O)OP(O)(O)=O)[C@@H](O)[C@H]1O ZKHQWZAMYRWXGA-KQYNXXCUSA-N 0.000 description 1
- 210000004100 Adrenal Glands Anatomy 0.000 description 1
- 108010005474 Anaplastic Lymphoma Kinase Proteins 0.000 description 1
- 229960001230 Asparagine Drugs 0.000 description 1
- 229960005261 Aspartic Acid Drugs 0.000 description 1
- 102100011514 B2M Human genes 0.000 description 1
- 101700004551 BRAF Proteins 0.000 description 1
- 102100007283 BRCA2 Human genes 0.000 description 1
- 108020000946 Bacterial DNA Proteins 0.000 description 1
- 206010004146 Basal cell carcinoma Diseases 0.000 description 1
- 210000002469 Basement Membrane Anatomy 0.000 description 1
- 229960000686 Benzalkonium Chloride Drugs 0.000 description 1
- 210000000988 Bone and Bones Anatomy 0.000 description 1
- 241000588832 Bordetella pertussis Species 0.000 description 1
- 229940052491 Bordetella pertussis Drugs 0.000 description 1
- KGBXLFKZBHKPEV-UHFFFAOYSA-N Boric acid Chemical compound OB(O)O KGBXLFKZBHKPEV-UHFFFAOYSA-N 0.000 description 1
- 241000283690 Bos taurus Species 0.000 description 1
- 210000000481 Breast Anatomy 0.000 description 1
- 206010055113 Breast cancer metastatic Diseases 0.000 description 1
- 210000003123 Bronchioles Anatomy 0.000 description 1
- 208000003170 Bronchiolo-Alveolar Adenocarcinoma Diseases 0.000 description 1
- 239000002126 C01EB10 - Adenosine Substances 0.000 description 1
- 102100015174 CAVIN1 Human genes 0.000 description 1
- 210000004366 CD4-Positive T-Lymphocytes Anatomy 0.000 description 1
- 108010029697 CD40 Ligand Proteins 0.000 description 1
- 102100003729 CD40LG Human genes 0.000 description 1
- 101700078950 CD44 Proteins 0.000 description 1
- 102100003735 CD44 Human genes 0.000 description 1
- 101700080416 CD69 Proteins 0.000 description 1
- 102100005832 CD69 Human genes 0.000 description 1
- 102000033243 CDKN2A Human genes 0.000 description 1
- 101710022338 CDKN2A Proteins 0.000 description 1
- 210000003996 CFU-GM Anatomy 0.000 description 1
- 101710038583 CRYBG1 Proteins 0.000 description 1
- 102100009119 CRYBG1 Human genes 0.000 description 1
- 241000282472 Canis lupus familiaris Species 0.000 description 1
- 102000004040 Capsid Proteins Human genes 0.000 description 1
- 108090000565 Capsid Proteins Proteins 0.000 description 1
- 230000036714 Caverage Effects 0.000 description 1
- 241000700198 Cavia Species 0.000 description 1
- 210000003855 Cell Nucleus Anatomy 0.000 description 1
- 210000004671 Cell-Free System Anatomy 0.000 description 1
- 206010008342 Cervix carcinoma Diseases 0.000 description 1
- OSASVXMJTNOKOY-UHFFFAOYSA-N Chlorobutanol Chemical compound CC(C)(O)C(Cl)(Cl)Cl OSASVXMJTNOKOY-UHFFFAOYSA-N 0.000 description 1
- 229960004926 Chlorobutanol Drugs 0.000 description 1
- 208000009060 Clear Cell Adenocarcinoma Diseases 0.000 description 1
- 241000699800 Cricetinae Species 0.000 description 1
- 108010009392 Cyclin-Dependent Kinase Inhibitor p16 Proteins 0.000 description 1
- 102100013390 DAG1 Human genes 0.000 description 1
- 102100000605 DDX23 Human genes 0.000 description 1
- 101700017334 DDX23 Proteins 0.000 description 1
- 102100001225 DEF8 Human genes 0.000 description 1
- 108060002188 DEF8 Proteins 0.000 description 1
- 230000004544 DNA amplification Effects 0.000 description 1
- 230000033616 DNA repair Effects 0.000 description 1
- 102100006751 DNAJB12 Human genes 0.000 description 1
- 101710028159 DNTT Proteins 0.000 description 1
- 102100002445 DNTT Human genes 0.000 description 1
- 101700009894 DPF2 Proteins 0.000 description 1
- 102100000461 DPF2 Human genes 0.000 description 1
- 210000000188 Diaphragm Anatomy 0.000 description 1
- 230000036947 Dissociation constant Effects 0.000 description 1
- 108020004461 Double-Stranded RNA Proteins 0.000 description 1
- 241000255581 Drosophila <fruit fly, genus> Species 0.000 description 1
- 102100014127 EEF2 Human genes 0.000 description 1
- 101700025368 ERBB2 Proteins 0.000 description 1
- 102100016662 ERBB2 Human genes 0.000 description 1
- 210000003981 Ectoderm Anatomy 0.000 description 1
- 210000001671 Embryonic Stem Cells Anatomy 0.000 description 1
- 210000001900 Endoderm Anatomy 0.000 description 1
- 206010014733 Endometrial cancer Diseases 0.000 description 1
- 241000283086 Equidae Species 0.000 description 1
- 241000588724 Escherichia coli Species 0.000 description 1
- 210000002744 Extracellular Matrix Anatomy 0.000 description 1
- 102100008327 FAT1 Human genes 0.000 description 1
- 102100014838 FCGRT Human genes 0.000 description 1
- 101710003435 FCGRT Proteins 0.000 description 1
- 102100006565 FLT1 Human genes 0.000 description 1
- 102100001838 FNDC3B Human genes 0.000 description 1
- 101710009383 FNDC3B Proteins 0.000 description 1
- 102100006280 FZD7 Human genes 0.000 description 1
- 210000003608 Feces Anatomy 0.000 description 1
- 241000282326 Felis catus Species 0.000 description 1
- 108010003471 Fetal Proteins Proteins 0.000 description 1
- 102000004641 Fetal Proteins Human genes 0.000 description 1
- 101710037135 GAPC2 Proteins 0.000 description 1
- 101710037116 GAPC3 Proteins 0.000 description 1
- 101710025049 GAPDG Proteins 0.000 description 1
- 101710008404 GAPDH Proteins 0.000 description 1
- 102100006425 GAPDH Human genes 0.000 description 1
- 102000033185 GNAS Human genes 0.000 description 1
- 101700086896 GNAS Proteins 0.000 description 1
- 102100014501 GOLGB1 Human genes 0.000 description 1
- 101710006312 GOLGB1 Proteins 0.000 description 1
- 101710010461 Gapdh1 Proteins 0.000 description 1
- 206010017758 Gastric cancer Diseases 0.000 description 1
- 206010018338 Glioma Diseases 0.000 description 1
- 102000003886 Glycoproteins Human genes 0.000 description 1
- 108090000288 Glycoproteins Proteins 0.000 description 1
- 241001585408 Gracilimonas Species 0.000 description 1
- NYHBQMYGNKIUIF-PXMDKTAGSA-N Guanosine Natural products C1=2NC(N)=NC(=O)C=2N=CN1[C@H]1O[C@@H](CO)[C@@H](O)[C@H]1O NYHBQMYGNKIUIF-PXMDKTAGSA-N 0.000 description 1
- 229940029575 Guanosine Drugs 0.000 description 1
- RQFCJASXJCIDSX-UUOKFMHZSA-N Guanosine monophosphate Chemical compound C1=2NC(N)=NC(=O)C=2N=CN1[C@@H]1O[C@H](COP(O)(O)=O)[C@@H](O)[C@H]1O RQFCJASXJCIDSX-UUOKFMHZSA-N 0.000 description 1
- XKMLYUALXHKNFT-UUOKFMHZSA-N Guanosine-5'-triphosphate Chemical compound C1=2NC(N)=NC(=O)C=2N=CN1[C@@H]1O[C@H](COP(O)(=O)OP(O)(=O)OP(O)(O)=O)[C@@H](O)[C@H]1O XKMLYUALXHKNFT-UUOKFMHZSA-N 0.000 description 1
- 101700000918 HBA2 Proteins 0.000 description 1
- 102200118182 HBB S90R Human genes 0.000 description 1
- 102100020458 HLA-A Human genes 0.000 description 1
- 108010075704 HLA-A Antigens Proteins 0.000 description 1
- 102100020459 HLA-B Human genes 0.000 description 1
- 108010058607 HLA-B Antigens Proteins 0.000 description 1
- 102100020457 HLA-C Human genes 0.000 description 1
- 108010052199 HLA-C Antigens Proteins 0.000 description 1
- 102100006341 HLA-DPA1 Human genes 0.000 description 1
- 108010093061 HLA-DPA1 antigen Proteins 0.000 description 1
- 102100015626 HLA-DPB1 Human genes 0.000 description 1
- 108010045483 HLA-DPB1 antigen Proteins 0.000 description 1
- 108010086786 HLA-DQA1 antigen Proteins 0.000 description 1
- 102100005617 HLA-DQB1 Human genes 0.000 description 1
- 108010065026 HLA-DQB1 antigen Proteins 0.000 description 1
- 108010067802 HLA-DR alpha-Chains Proteins 0.000 description 1
- 102100012464 HLA-DRA Human genes 0.000 description 1
- 102100020485 HLA-DRB1 Human genes 0.000 description 1
- 108010039343 HLA-DRB1 Chains Proteins 0.000 description 1
- 241000691979 Halcyon Species 0.000 description 1
- 210000000474 Heel Anatomy 0.000 description 1
- 208000002250 Hematologic Neoplasms Diseases 0.000 description 1
- 241000238631 Hexapoda Species 0.000 description 1
- 102100004115 ICAM1 Human genes 0.000 description 1
- 101700051176 ICAM1 Proteins 0.000 description 1
- 101700070228 IFN Proteins 0.000 description 1
- 101700066403 IFNA1 Proteins 0.000 description 1
- 101700023446 IFNT Proteins 0.000 description 1
- 229940072221 IMMUNOGLOBULINS Drugs 0.000 description 1
- 108060004739 INTS11 Proteins 0.000 description 1
- 102100016286 INTS11 Human genes 0.000 description 1
- 102100002872 ITSN2 Human genes 0.000 description 1
- 102000009490 IgG Receptors Human genes 0.000 description 1
- 108010073807 IgG Receptors Proteins 0.000 description 1
- UGQMRVRMYYASKQ-KMPDEGCQSA-N Inosine Natural products O[C@H]1[C@H](O)[C@@H](CO)O[C@@H]1N1C(N=CNC2=O)=C2N=C1 UGQMRVRMYYASKQ-KMPDEGCQSA-N 0.000 description 1
- UGQMRVRMYYASKQ-KQYNXXCUSA-N Inosine Chemical compound O[C@@H]1[C@H](O)[C@@H](CO)O[C@H]1N1C2=NC=NC(O)=C2N=C1 UGQMRVRMYYASKQ-KQYNXXCUSA-N 0.000 description 1
- 108010002352 Interleukin-1 Proteins 0.000 description 1
- 210000000936 Intestines Anatomy 0.000 description 1
- 241000581650 Ivesia Species 0.000 description 1
- 102100019508 KIAA2013 Human genes 0.000 description 1
- 101710031727 KIAA2013 Proteins 0.000 description 1
- 102100000310 KLHL22 Human genes 0.000 description 1
- 101710042649 KLHL22 Proteins 0.000 description 1
- 101710033922 KRAS Proteins 0.000 description 1
- 108010003046 KSR-1 protein kinase Proteins 0.000 description 1
- 102100019632 KSR1 Human genes 0.000 description 1
- 101700047952 KSR1 Proteins 0.000 description 1
- 210000002510 Keratinocytes Anatomy 0.000 description 1
- 210000003734 Kidney Anatomy 0.000 description 1
- 108010063296 Kinesin Proteins 0.000 description 1
- 102000010638 Kinesin Human genes 0.000 description 1
- DCXYFEDJOCDNAF-REOHCLBHSA-N L-asparagine Chemical compound OC(=O)[C@@H](N)CC(N)=O DCXYFEDJOCDNAF-REOHCLBHSA-N 0.000 description 1
- CKLJMWTZIZZHCS-REOHCLBHSA-N L-aspartic acid Chemical compound OC(=O)[C@@H](N)CC(O)=O CKLJMWTZIZZHCS-REOHCLBHSA-N 0.000 description 1
- 102000003960 Ligases Human genes 0.000 description 1
- 108090000364 Ligases Proteins 0.000 description 1
- 239000005089 Luciferase Substances 0.000 description 1
- 108060001084 Luciferase family Proteins 0.000 description 1
- 206010050017 Lung cancer metastatic Diseases 0.000 description 1
- 210000002751 Lymph Anatomy 0.000 description 1
- 210000001365 Lymphatic Vessels Anatomy 0.000 description 1
- 102000008072 Lymphokines Human genes 0.000 description 1
- 108010074338 Lymphokines Proteins 0.000 description 1
- 230000037364 MAPK/ERK pathway Effects 0.000 description 1
- 101710025050 MK0970 Proteins 0.000 description 1
- 102100014842 MKRN1 Human genes 0.000 description 1
- 101700056668 MTA1 Proteins 0.000 description 1
- 102100013434 MTA1 Human genes 0.000 description 1
- 101700062207 MTA3 Proteins 0.000 description 1
- 102100017561 MTHFD1L Human genes 0.000 description 1
- 210000002752 Melanocytes Anatomy 0.000 description 1
- 235000016247 Mentha requienii Nutrition 0.000 description 1
- 235000002899 Mentha suaveolens Nutrition 0.000 description 1
- 210000003716 Mesoderm Anatomy 0.000 description 1
- LXCFILQKKLGQFO-UHFFFAOYSA-N Methylparaben Chemical compound COC(=O)C1=CC=C(O)C=C1 LXCFILQKKLGQFO-UHFFFAOYSA-N 0.000 description 1
- 102000028664 Microtubules Human genes 0.000 description 1
- 108091022031 Microtubules Proteins 0.000 description 1
- 210000004688 Microtubules Anatomy 0.000 description 1
- 229920000460 Mitochondrial DNA Polymers 0.000 description 1
- 108020005196 Mitochondrial DNA Proteins 0.000 description 1
- 210000001616 Monocytes Anatomy 0.000 description 1
- 241000204031 Mycoplasma Species 0.000 description 1
- 101710033916 NRAS Proteins 0.000 description 1
- 102100001256 NUMA1 Human genes 0.000 description 1
- 101700023323 NUMA1 Proteins 0.000 description 1
- 206010029260 Neuroblastoma Diseases 0.000 description 1
- 210000000440 Neutrophils Anatomy 0.000 description 1
- 210000001331 Nose Anatomy 0.000 description 1
- 102000007999 Nuclear Proteins Human genes 0.000 description 1
- 108010089610 Nuclear Proteins Proteins 0.000 description 1
- 102100009434 OBSL1 Human genes 0.000 description 1
- 102100006869 ORC2 Human genes 0.000 description 1
- 210000003463 Organelles Anatomy 0.000 description 1
- 241000283973 Oryctolagus cuniculus Species 0.000 description 1
- 206010025310 Other lymphomas Diseases 0.000 description 1
- 206010061328 Ovarian epithelial cancer Diseases 0.000 description 1
- 241000283898 Ovis Species 0.000 description 1
- 102100009578 PBK Human genes 0.000 description 1
- 102100000519 PCDHGA11 Human genes 0.000 description 1
- 101710038370 PCDHGA11 Proteins 0.000 description 1
- 102100004940 PDGFRA Human genes 0.000 description 1
- 101710008052 PI4K2B Proteins 0.000 description 1
- 102100019857 PI4K2B Human genes 0.000 description 1
- 102100012891 PLOD2 Human genes 0.000 description 1
- 101700004802 PML Proteins 0.000 description 1
- 101710039569 POLM Proteins 0.000 description 1
- 102100013780 PPP1R7 Human genes 0.000 description 1
- 208000008443 Pancreatic Carcinoma Diseases 0.000 description 1
- 241001296119 Panteles Species 0.000 description 1
- 210000004197 Pelvis Anatomy 0.000 description 1
- 210000004303 Peritoneum Anatomy 0.000 description 1
- 108010081690 Pertussis Toxin Proteins 0.000 description 1
- 206010057249 Phagocytosis Diseases 0.000 description 1
- 210000003800 Pharynx Anatomy 0.000 description 1
- 108091000081 Phosphotransferases Proteins 0.000 description 1
- 210000004011 Plasma Cells Anatomy 0.000 description 1
- 241000288906 Primates Species 0.000 description 1
- 206010036790 Productive cough Diseases 0.000 description 1
- 102000001253 Protein Kinases Human genes 0.000 description 1
- 102000016971 Proto-Oncogene Proteins c-kit Human genes 0.000 description 1
- 108010014608 Proto-Oncogene Proteins c-kit Proteins 0.000 description 1
- PTJWIQPHWPFNBW-GBNDHIKLSA-N Pseudouridine Chemical compound O[C@@H]1[C@H](O)[C@@H](CO)O[C@H]1C1=CNC(=O)NC1=O PTJWIQPHWPFNBW-GBNDHIKLSA-N 0.000 description 1
- 210000003456 Pulmonary Alveoli Anatomy 0.000 description 1
- XPPKVPWEQAFLFU-UHFFFAOYSA-J Pyrophosphate Chemical compound [O-]P([O-])(=O)OP([O-])([O-])=O XPPKVPWEQAFLFU-UHFFFAOYSA-J 0.000 description 1
- 101710037934 QRSL1 Proteins 0.000 description 1
- 101700062114 RAD9B Proteins 0.000 description 1
- 102100000885 RANBP2 Human genes 0.000 description 1
- 101710003868 RANBP2 Proteins 0.000 description 1
- 108020004532 RAS Proteins 0.000 description 1
- 101710037972 RASSF7 Proteins 0.000 description 1
- 210000003324 RBC Anatomy 0.000 description 1
- 108020005161 RNA Caps Proteins 0.000 description 1
- 108010065868 RNA polymerase SP6 Proteins 0.000 description 1
- 229920001186 RNA-Seq Polymers 0.000 description 1
- 102100014460 RPL13A Human genes 0.000 description 1
- 101710036098 RPL13A Proteins 0.000 description 1
- 108050008067 Rad9 Proteins 0.000 description 1
- 241000700159 Rattus Species 0.000 description 1
- 108020004511 Recombinant DNA Proteins 0.000 description 1
- 206010038038 Rectal cancer Diseases 0.000 description 1
- 241000220010 Rhode Species 0.000 description 1
- 229920001914 Ribonucleotide Polymers 0.000 description 1
- 210000003705 Ribosomes Anatomy 0.000 description 1
- 239000006146 Roswell Park Memorial Institute medium Substances 0.000 description 1
- 101710023376 S100A13 Proteins 0.000 description 1
- 102100003385 S100A13 Human genes 0.000 description 1
- 102100011062 SBNO1 Human genes 0.000 description 1
- 101700067889 SBNO1 Proteins 0.000 description 1
- 101710023762 SEMA3B Proteins 0.000 description 1
- 102100011127 SEMA3B Human genes 0.000 description 1
- 102100014620 SNX15 Human genes 0.000 description 1
- 102100015896 SNX5 Human genes 0.000 description 1
- 101700081834 SNX5 Proteins 0.000 description 1
- 240000004808 Saccharomyces cerevisiae Species 0.000 description 1
- YGSDEFSMJLZEOE-UHFFFAOYSA-N Salicylic acid Chemical compound OC(=O)C1=CC=CC=C1O YGSDEFSMJLZEOE-UHFFFAOYSA-N 0.000 description 1
- 108050000099 Sema domain Proteins 0.000 description 1
- 102000009203 Sema domain Human genes 0.000 description 1
- 108050003978 Semaphorin Proteins 0.000 description 1
- 102000014105 Semaphorin Human genes 0.000 description 1
- 208000004548 Serous Cystadenocarcinoma Diseases 0.000 description 1
- 241000580858 Simian-Human immunodeficiency virus Species 0.000 description 1
- 108020004682 Single-Stranded DNA Proteins 0.000 description 1
- 206010041067 Small cell lung cancer Diseases 0.000 description 1
- 210000003802 Sputum Anatomy 0.000 description 1
- 229940031439 Squalene Drugs 0.000 description 1
- 206010041823 Squamous cell carcinoma Diseases 0.000 description 1
- KDYFGRWQOYBRFD-UHFFFAOYSA-N Succinic acid Natural products OC(=O)CCC(O)=O KDYFGRWQOYBRFD-UHFFFAOYSA-N 0.000 description 1
- 102100005445 THUMPD3 Human genes 0.000 description 1
- 101710027274 THUMPD3 Proteins 0.000 description 1
- 101710029353 TM9SF3 Proteins 0.000 description 1
- 102100016417 TM9SF3 Human genes 0.000 description 1
- 102100009534 TNF Human genes 0.000 description 1
- 101710040537 TNF Proteins 0.000 description 1
- 102100008755 TNPO3 Human genes 0.000 description 1
- 101700015807 TNPO3 Proteins 0.000 description 1
- 102200108120 TP53 A39V Human genes 0.000 description 1
- 108060008443 TPPP Proteins 0.000 description 1
- 102100010732 TRRAP Human genes 0.000 description 1
- 101710025594 TUBB3 Proteins 0.000 description 1
- 102100008054 TUBB3 Human genes 0.000 description 1
- 206010043276 Teratoma Diseases 0.000 description 1
- 229940033663 Thimerosal Drugs 0.000 description 1
- RTKIYNMVFMVABJ-UHFFFAOYSA-L Thiomersal Chemical compound [Na+].CC[Hg]SC1=CC=CC=C1C([O-])=O RTKIYNMVFMVABJ-UHFFFAOYSA-L 0.000 description 1
- 210000001541 Thymus Gland Anatomy 0.000 description 1
- 229960001295 Tocopherol Drugs 0.000 description 1
- 229920001949 Transfer RNA Polymers 0.000 description 1
- 108020004566 Transfer RNA Proteins 0.000 description 1
- 206010052779 Transplant rejections Diseases 0.000 description 1
- 108010078814 Tumor Suppressor Protein p53 Proteins 0.000 description 1
- 108020004417 Untranslated RNA Proteins 0.000 description 1
- 210000002700 Urine Anatomy 0.000 description 1
- 241000700618 Vaccinia virus Species 0.000 description 1
- 108020000999 Viral RNA Proteins 0.000 description 1
- 229930003427 Vitamin E Natural products 0.000 description 1
- 229940046009 Vitamin E Drugs 0.000 description 1
- 101700005284 WDR82 Proteins 0.000 description 1
- 102100008080 WDR82 Human genes 0.000 description 1
- 102100005599 WWP2 Human genes 0.000 description 1
- 101700012942 WWP2 Proteins 0.000 description 1
- 102100018234 XPOT Human genes 0.000 description 1
- NBIIXXVUZAFLBC-UHFFFAOYSA-K [O-]P([O-])([O-])=O Chemical compound [O-]P([O-])([O-])=O NBIIXXVUZAFLBC-UHFFFAOYSA-K 0.000 description 1
- 239000012190 activator Substances 0.000 description 1
- 230000002730 additional Effects 0.000 description 1
- 229960005305 adenosine Drugs 0.000 description 1
- GFFGJBXGBJISGV-UHFFFAOYSA-N adenyl group Chemical group N1=CN=C2N=CNC2=C1N GFFGJBXGBJISGV-UHFFFAOYSA-N 0.000 description 1
- 201000005188 adrenal gland cancer Diseases 0.000 description 1
- 229910052783 alkali metal Inorganic materials 0.000 description 1
- 229910052784 alkaline earth metal Inorganic materials 0.000 description 1
- 230000000735 allogeneic Effects 0.000 description 1
- 229910000147 aluminium phosphate Inorganic materials 0.000 description 1
- 125000000539 amino acid group Chemical group 0.000 description 1
- 230000033115 angiogenesis Effects 0.000 description 1
- 238000010171 animal model Methods 0.000 description 1
- 230000000798 anti-retroviral Effects 0.000 description 1
- 230000007961 antigen processing and presentation of exogenous peptide antigen via MHC class I, TAP-dependent Effects 0.000 description 1
- 230000005164 antigen processing and presentation of exogenous peptide antigen via MHC class I, TAP-independent Effects 0.000 description 1
- 235000009582 asparagine Nutrition 0.000 description 1
- 235000003704 aspartic acid Nutrition 0.000 description 1
- 230000001363 autoimmune Effects 0.000 description 1
- 230000001580 bacterial Effects 0.000 description 1
- 108010028263 bacteriophage T3 RNA polymerase Proteins 0.000 description 1
- 108010003152 bacteriophage T7 RNA polymerase Proteins 0.000 description 1
- 108010081355 beta 2-Microglobulin Proteins 0.000 description 1
- 235000006682 bigleaf mint Nutrition 0.000 description 1
- 230000003851 biochemical process Effects 0.000 description 1
- 239000000560 biocompatible material Substances 0.000 description 1
- 230000031018 biological processes and functions Effects 0.000 description 1
- 239000012472 biological sample Substances 0.000 description 1
- 201000000053 blastoma Diseases 0.000 description 1
- 210000002798 bone marrow cell Anatomy 0.000 description 1
- 238000010322 bone marrow transplantation Methods 0.000 description 1
- 239000004327 boric acid Substances 0.000 description 1
- KQNZDYYTLMIZCT-KQPMLPITSA-N brefeldin A Chemical compound O[C@@H]1\C=C\C(=O)O[C@@H](C)CCC\C=C\[C@@H]2C[C@H](O)C[C@H]21 KQNZDYYTLMIZCT-KQPMLPITSA-N 0.000 description 1
- 159000000007 calcium salts Chemical class 0.000 description 1
- 230000036952 cancer formation Effects 0.000 description 1
- 239000002775 capsule Substances 0.000 description 1
- 101710025091 cbbGC Proteins 0.000 description 1
- 230000030833 cell death Effects 0.000 description 1
- 230000032823 cell division Effects 0.000 description 1
- 230000010261 cell growth Effects 0.000 description 1
- 230000012292 cell migration Effects 0.000 description 1
- 230000004663 cell proliferation Effects 0.000 description 1
- 239000006285 cell suspension Substances 0.000 description 1
- 201000010881 cervical cancer Diseases 0.000 description 1
- 230000002759 chromosomal Effects 0.000 description 1
- 238000005352 clarification Methods 0.000 description 1
- 238000007374 clinical diagnostic method Methods 0.000 description 1
- 239000011248 coating agent Substances 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
- 201000003963 colon carcinoma Diseases 0.000 description 1
- 238000002648 combination therapy Methods 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
- 230000000295 complement Effects 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000000139 costimulatory Effects 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 230000001351 cycling Effects 0.000 description 1
- 230000016396 cytokine production Effects 0.000 description 1
- 230000003013 cytotoxicity Effects 0.000 description 1
- 231100000135 cytotoxicity Toxicity 0.000 description 1
- 238000006481 deamination reaction Methods 0.000 description 1
- 230000003247 decreasing Effects 0.000 description 1
- 230000001419 dependent Effects 0.000 description 1
- 230000000779 depleting Effects 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000029087 digestion Effects 0.000 description 1
- 239000003085 diluting agent Substances 0.000 description 1
- 235000011180 diphosphates Nutrition 0.000 description 1
- 201000008325 diseases of cellular proliferation Diseases 0.000 description 1
- 239000002552 dosage form Substances 0.000 description 1
- 229940000406 drug candidates Drugs 0.000 description 1
- 239000003937 drug carrier Substances 0.000 description 1
- 238000009509 drug development Methods 0.000 description 1
- 210000003162 effector T lymphocyte Anatomy 0.000 description 1
- 235000013601 eggs Nutrition 0.000 description 1
- 238000001493 electron microscopy Methods 0.000 description 1
- 238000001962 electrophoresis Methods 0.000 description 1
- 201000008184 embryoma Diseases 0.000 description 1
- 230000012202 endocytosis Effects 0.000 description 1
- 201000003908 endometrial adenocarcinoma Diseases 0.000 description 1
- 230000003511 endothelial Effects 0.000 description 1
- 239000002158 endotoxin Substances 0.000 description 1
- 239000003623 enhancer Substances 0.000 description 1
- 230000001973 epigenetic Effects 0.000 description 1
- 201000004101 esophageal cancer Diseases 0.000 description 1
- 210000003527 eukaryotic cell Anatomy 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000002349 favourable Effects 0.000 description 1
- 230000001605 fetal Effects 0.000 description 1
- 239000000945 filler Substances 0.000 description 1
- 238000011049 filling Methods 0.000 description 1
- MHMNJMPURVTYEJ-UHFFFAOYSA-N fluorescein-5-isothiocyanate Chemical compound O1C(=O)C2=CC(N=C=S)=CC=C2C21C1=CC=C(O)C=C1OC1=CC(O)=CC=C21 MHMNJMPURVTYEJ-UHFFFAOYSA-N 0.000 description 1
- 238000001917 fluorescence detection Methods 0.000 description 1
- 238000001943 fluorescence-activated cell sorting Methods 0.000 description 1
- 230000004907 flux Effects 0.000 description 1
- WSFSSNUMVMOOMR-UHFFFAOYSA-N formaldehyde Chemical compound O=C WSFSSNUMVMOOMR-UHFFFAOYSA-N 0.000 description 1
- 238000005755 formation reaction Methods 0.000 description 1
- 239000012537 formulation buffer Substances 0.000 description 1
- 125000002485 formyl group Chemical group [H]C(*)=O 0.000 description 1
- 238000007672 fourth generation sequencing Methods 0.000 description 1
- 231100000221 frame shift mutation induction Toxicity 0.000 description 1
- 210000004475 gamma-delta T lymphocyte Anatomy 0.000 description 1
- 101710025070 gapdh-2 Proteins 0.000 description 1
- 230000002496 gastric Effects 0.000 description 1
- 238000001502 gel electrophoresis Methods 0.000 description 1
- 238000001476 gene delivery Methods 0.000 description 1
- 238000001415 gene therapy Methods 0.000 description 1
- 238000010353 genetic engineering Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 102000018146 globin family Human genes 0.000 description 1
- 108060003196 globin family Proteins 0.000 description 1
- 239000001963 growth media Substances 0.000 description 1
- 235000013928 guanylic acid Nutrition 0.000 description 1
- 230000003394 haemopoietic Effects 0.000 description 1
- 101700058970 hbaA Proteins 0.000 description 1
- 201000010536 head and neck cancer Diseases 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 201000005787 hematologic cancer Diseases 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000001024 immunotherapeutic Effects 0.000 description 1
- 239000000411 inducer Substances 0.000 description 1
- 230000006882 induction of apoptosis Effects 0.000 description 1
- 238000001764 infiltration Methods 0.000 description 1
- 238000001802 infusion Methods 0.000 description 1
- 239000003112 inhibitor Substances 0.000 description 1
- 230000000977 initiatory Effects 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 229960003786 inosine Drugs 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 201000002313 intestinal cancer Diseases 0.000 description 1
- 238000010253 intravenous injection Methods 0.000 description 1
- 238000009114 investigational therapy Methods 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 230000003902 lesions Effects 0.000 description 1
- 231100000518 lethal Toxicity 0.000 description 1
- 230000001665 lethal Effects 0.000 description 1
- 150000002632 lipids Chemical class 0.000 description 1
- 239000010807 litter Substances 0.000 description 1
- 238000011068 load Methods 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 239000007937 lozenge Substances 0.000 description 1
- 201000005249 lung adenocarcinoma Diseases 0.000 description 1
- 201000009546 lung large cell carcinoma Diseases 0.000 description 1
- 201000010453 lymph node cancer Diseases 0.000 description 1
- 239000012139 lysis buffer Substances 0.000 description 1
- 239000006249 magnetic particle Substances 0.000 description 1
- 210000004962 mammalian cells Anatomy 0.000 description 1
- 108010031099 mannose receptor Proteins 0.000 description 1
- 239000002609 media Substances 0.000 description 1
- 230000002503 metabolic Effects 0.000 description 1
- 230000000813 microbial Effects 0.000 description 1
- 244000005700 microbiome Species 0.000 description 1
- 238000000386 microscopy Methods 0.000 description 1
- 239000011707 mineral Substances 0.000 description 1
- 235000006679 mint Nutrition 0.000 description 1
- 238000007479 molecular analysis Methods 0.000 description 1
- 229940035032 monophosphoryl lipid A Drugs 0.000 description 1
- 230000037023 motor activity Effects 0.000 description 1
- 201000010879 mucinous adenocarcinoma Diseases 0.000 description 1
- 230000036438 mutation frequency Effects 0.000 description 1
- 239000011807 nanoball Substances 0.000 description 1
- 239000002086 nanomaterial Substances 0.000 description 1
- 230000001264 neutralization Effects 0.000 description 1
- 239000002547 new drug Substances 0.000 description 1
- 230000003000 nontoxic Effects 0.000 description 1
- 231100000252 nontoxic Toxicity 0.000 description 1
- 239000000346 nonvolatile oil Substances 0.000 description 1
- 238000010899 nucleation Methods 0.000 description 1
- 239000002777 nucleoside Substances 0.000 description 1
- 150000003833 nucleoside derivatives Chemical class 0.000 description 1
- 201000001539 ovarian carcinoma Diseases 0.000 description 1
- 201000006588 ovary adenocarcinoma Diseases 0.000 description 1
- 238000007911 parenteral administration Methods 0.000 description 1
- 230000001717 pathogenic Effects 0.000 description 1
- 244000052769 pathogens Species 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 230000002093 peripheral Effects 0.000 description 1
- 230000008782 phagocytosis Effects 0.000 description 1
- 239000002831 pharmacologic agent Substances 0.000 description 1
- 238000002205 phenol-chloroform extraction Methods 0.000 description 1
- 239000010452 phosphate Substances 0.000 description 1
- 230000004962 physiological condition Effects 0.000 description 1
- 210000004694 pigment cell Anatomy 0.000 description 1
- 230000001402 polyadenylating Effects 0.000 description 1
- 159000000001 potassium salts Chemical class 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 230000000770 pro-inflamatory Effects 0.000 description 1
- 210000001236 prokaryotic cell Anatomy 0.000 description 1
- 230000000069 prophylaxis Effects 0.000 description 1
- 201000001514 prostate carcinoma Diseases 0.000 description 1
- 230000004224 protection Effects 0.000 description 1
- 238000007388 punch biopsy Methods 0.000 description 1
- 239000001397 quillaja saponaria molina bark Substances 0.000 description 1
- 108010062219 ran-binding protein 2 Proteins 0.000 description 1
- 239000000376 reactant Substances 0.000 description 1
- 108091007921 receptor tyrosine kinases Proteins 0.000 description 1
- 102000027656 receptor tyrosine kinases Human genes 0.000 description 1
- 230000010837 receptor-mediated endocytosis Effects 0.000 description 1
- 201000001275 rectum cancer Diseases 0.000 description 1
- 235000003499 redwood Nutrition 0.000 description 1
- 230000031539 regulation of cell division Effects 0.000 description 1
- 230000022532 regulation of transcription, DNA-dependent Effects 0.000 description 1
- 230000003252 repetitive Effects 0.000 description 1
- 230000001850 reproductive Effects 0.000 description 1
- 238000002271 resection Methods 0.000 description 1
- 238000010839 reverse transcription Methods 0.000 description 1
- 238000003757 reverse transcription PCR Methods 0.000 description 1
- 239000003161 ribonuclease inhibitor Substances 0.000 description 1
- 239000002336 ribonucleotide Substances 0.000 description 1
- 229920002973 ribosomal RNA Polymers 0.000 description 1
- 102220005430 rs33911106 Human genes 0.000 description 1
- 230000028327 secretion Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 201000010208 seminoma Diseases 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 125000003607 serino group Chemical group [H]OC(=O)C([H])(N([H])*)C([H])([H])O[H] 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 description 1
- 238000011125 single therapy Methods 0.000 description 1
- 159000000000 sodium salts Chemical class 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
- 238000002798 spectrophotometry method Methods 0.000 description 1
- 239000003381 stabilizer Substances 0.000 description 1
- 239000007858 starting material Substances 0.000 description 1
- 210000000130 stem cell Anatomy 0.000 description 1
- 201000011549 stomach cancer Diseases 0.000 description 1
- 230000004960 subcellular localization Effects 0.000 description 1
- 235000011044 succinic acid Nutrition 0.000 description 1
- 150000003444 succinic acids Chemical class 0.000 description 1
- 230000001502 supplementation Effects 0.000 description 1
- 230000002522 swelling Effects 0.000 description 1
- 239000006188 syrup Substances 0.000 description 1
- 235000020357 syrup Nutrition 0.000 description 1
- 239000003826 tablet Substances 0.000 description 1
- 238000002626 targeted therapy Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- RYYWUUFWQRZTIU-UHFFFAOYSA-K thiophosphate Chemical group [O-]P([O-])([O-])=S RYYWUUFWQRZTIU-UHFFFAOYSA-K 0.000 description 1
- 238000007671 third-generation sequencing Methods 0.000 description 1
- 230000002992 thymic Effects 0.000 description 1
- 201000002510 thyroid cancer Diseases 0.000 description 1
- 239000011732 tocopherol Substances 0.000 description 1
- 235000010384 tocopherol Nutrition 0.000 description 1
- 229930003799 tocopherols Natural products 0.000 description 1
- 230000002463 transducing Effects 0.000 description 1
- 230000001131 transforming Effects 0.000 description 1
- 238000009966 trimming Methods 0.000 description 1
- 235000011178 triphosphate Nutrition 0.000 description 1
- UNXRWKVEANCORM-UHFFFAOYSA-I triphosphate(5-) Chemical compound [O-]P([O-])(=O)OP([O-])(=O)OP([O-])([O-])=O UNXRWKVEANCORM-UHFFFAOYSA-I 0.000 description 1
- 230000001573 trophoblastic Effects 0.000 description 1
- 239000000107 tumor biomarker Substances 0.000 description 1
- 238000004450 types of analysis Methods 0.000 description 1
- 241001430294 unidentified retrovirus Species 0.000 description 1
- 230000003612 virological Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 235000019165 vitamin E Nutrition 0.000 description 1
- 239000011709 vitamin E Substances 0.000 description 1
- 150000003712 vitamin E derivatives Chemical class 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
- GVJHHUAWPYXKBD-IEOSBIPESA-N α-tocopherol Chemical compound OC1=C(C)C(C)=C2O[C@@](CCC[C@H](C)CCC[C@H](C)CCCC(C)C)(C)CCC2=C1C GVJHHUAWPYXKBD-IEOSBIPESA-N 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
- A61K39/00—Medicinal preparations containing antigens or antibodies
- A61K2039/80—Vaccine for a specifically defined cancer
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
- A61K39/00—Medicinal preparations containing antigens or antibodies
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
- A61K39/00—Medicinal preparations containing antigens or antibodies
- A61K39/0005—Vertebrate antigens
- A61K39/0011—Cancer antigens
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
- A61K9/00—Medicinal preparations characterised by special physical form
- A61K9/0012—Galenical forms characterised by the site of application
- A61K9/0019—Injectable compositions; Intramuscular, intravenous, arterial, subcutaneous administration; Compositions to be administered through the skin in an invasive manner
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
- A61P35/00—Antineoplastic agents
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
- A61P37/00—Drugs for immunological or allergic disorders
- A61P37/02—Immunomodulators
- A61P37/04—Immunostimulants
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6804—Nucleic acid analysis using immunogens
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6813—Hybridisation assays
- C12Q1/6827—Hybridisation assays for detection of mutation or polymorphism
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6844—Nucleic acid amplification reactions
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6844—Nucleic acid amplification reactions
- C12Q1/686—Polymerase chain reaction [PCR]
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6869—Methods for sequencing
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6869—Methods for sequencing
- C12Q1/6874—Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6881—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for tissue or cell typing, e.g. human leukocyte antigen [HLA] probes
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
- C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/156—Polymorphic or mutational markers
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
Disclosed is a method for providing an individualised cancer vaccine comprising the steps: (a) identifying cancer specific somatic mutations in a tumour specimen of a cancer patient to provide a cancer mutation signature of the patient; and (b) providing a vaccine featuring the cancer mutation signature obtained in step (a). Also disclosed is the use of an individualised cancer vaccine in the preparation of a medicament for treating cancer in a patient, wherein the individualised cancer vaccine is provided by the method referred above. gnature obtained in step (a). Also disclosed is the use of an individualised cancer vaccine in the preparation of a medicament for treating cancer in a patient, wherein the individualised cancer vaccine is provided by the method referred above.
Description
INDIVIDUALIZED VACCINES FOR CANCER
TECHNICAL FIELD OF THE INVENTION
The present invention relates to the provision of vaccines which are specific for a patient's
tumor and are potentially useful for immunotherapy of the primary tumor as well as tumor
metastases.
BACKGROUND OF THE INVENTION
Cancer is a primary cause of mortality, accounting for 1 in 4 of all deaths. The treatment of
cancer has traditionally been based on the law of averages - what works best for the largest
number of patients. However, owing to the molecular heterogeneity in cancer, often less than
% of treated individuals profit from the approved therapies. Individualized medicine based
on tailored treatment of patients is regarded as a potential solution to low efficacies and high
costs for innovation in drug development.
Antigen specific immunotherapy aims to enhance or induce specific immune responses in
patients and has been successfully used to control cancer diseases. T cells play a central role
in cell-mediated immunity in humans and animals. The recognition and binding of a particular
antigen is mediated by the T cell receptors (TCRs) expressed on the surface of T cells. The T
cell receptor (TCR) of a T cell is able to interact with immunogenic peptides (epitopes) bound
to major histocompatibility complex (MHC) molecules and presented on the surface of target
cells. Specific binding of the TCR triggers a signal cascade inside the T cell leading to
proliferation and differentiation into a maturated effector T cell.
The identification of a growing number of pathogen- and tumor-associated antigens (TAA)
led to a broad collection of suitable targets for immunotherapy. Cells presenting immunogenic
peptides (epitopes) derived from these antigens can be specifically targeted by either active or
passive immunization strategies. Active immunization may tend to induce and expand antigen
specific T cells in the patient, which are able to specifically recognize and kill diseased cells.
Different antigen formats can be used for tumor vaccination including whole cancer cells,
proteins, peptides or immunizing vectors such as RNA, DNA or viral vectors that can be
applied either directly in vivo or in vitro by pulsing of DCs following transfer into the patient.
Cancers may arise from the accumulation of genomic mutations and epigenetic changes, of
which a fraction may have a causative role. In addition to tumor associated antigens, human
cancers carry on average 100-120 non-synonymous mutations, of which many are targetable
by vaccines. More than 95% of mutations in a tumor are unique and patient specific (Weide et
al. 2008: J. Immunother. 31, 180-188). The number of protein changing somatic mutations,
which may result in tumor specific T cell epitopes, is in the range of 30 to 400. It has been
predicted in silico that there are 40 to 60 HLA class I restricted epitopes per patient derived
from tumor specific somatic mutations (Azuma et al. 1993: Nature 366, 76-79). Moreover, de
novo immunogenic HLA class II restricted epitopes likely also result from tumor-associated
mutations, however their number is still unknown.
Notably, some non-synonymous mutations are causally involved in neoplastic transformation,
crucial for maintaining the oncogenic phenotype (driver mutations) and may represent a
potential “Achilles’ heel” of cancer cells. As such non-synonymous mutations are not subject
to central immune tolerance, they can be ideal candidates for individual cancer vaccine
development. Mutations found in the primary tumor may also be present in metastases.
However, several studies demonstrated that metastatic tumors of a patient acquire additional
genetic mutations during individual tumor evolution which are often clinically relevant
(Suzuki et al. 2007: Mol. Oncol. 1 (2), 172- 180; Campbell et al. 2010: Nature 467 (7319),
1109-1113). Furthermore, also the molecular characteristics of many metastases deviate
significantly from those of primary tumors.
The technical problem underlying the present invention is to provide a highly effective
individualized cancer vaccine.
The present invention is based on the identification of patient specific cancer mutations and
targeting a patient's individual cancer mutation “signature”. Specifically, the present invention
which involves a genome, preferably exome, or transcriptome sequencing based
individualized immunotherapy approach aims at immunotherapeutically targeting multiple
individual mutations in cancer. Sequencing using Next Generation Sequencing (NGS) allows
a fast and cost effective identification of patient specific cancer mutations.
The identification of non-synonymous point mutations resulting in amino acid changes that
will be presented the patient's major histocompatibility complex (MHC) molecules provides
novel epitopes (neo-epitopes) which are specific for the patient's cancer but are not found in
normal cells of the patient. Collecting a set of mutations from cancer cells such as circulating
tumor cells (CTC) allows the provision of a vaccine which induces an immune response
potentially targeting the primary tumor even if containing genetically distinct subpopulations
as well as tumor metastases. For vaccination, such neo-epitopes identified according to the
present application are provided in a patient in the form of a polypeptide comprising said neo-
epitopes and following appropriate processing and presentation by MHC molecules the neo-
epitopes are displayed to the patient's immune system for stimulation of appropriate T cells.
Preferably, such polypeptide is provided in the patient by administering RNA encoding the
polypeptide. A strategy wherein in vitro transcribed RNA (IVT-RNA) is directly injected into
a patient by different immunization routes has been successfully tested in various animal
models. RNA may be translated in transfected cells and the expressed protein following
processing presented on the MHC molecules on the surface of the cells to elicit an immune
response.
The advantages of using RNA as a kind of reversible gene therapy include transient
expression and a non-transforming character. RNA does not need to enter the nucleus in order
to be expressed and moreover cannot integrate into the host genome, thereby eliminating the
risk of oncogenesis. Transfection rates attainable with RNA are relatively high. Furthermore,
the amounts of protein achieved correspond to those in physiological expression.
The rationale for the immunotherapeutic targeting of multiple individual mutations is that (i)
these mutations are exclusively expressed, (ii) mutated epitopes can be expected to be ideal
for T cell immunotherapy since T cells recognizing them have not undergone thymic
selection, (iii) tumor immune escape can be reduced e.g. by targeting “driver mutations” that
are highly relevant for the tumor phenotype, and (iv) a multiepitopic immune response has a
higher likelihood to result in improved clinical benefit.
DESCRIPTION OF INVENTION
SUMMARY OF THE INVENTION
The present invention relates to efficient methods for providing individualized recombinant
cancer vaccines inducing an efficient and specific immune response in a cancer patient and
potentially targeting the primary tumor as well as tumor metastases. The cancer vaccines
provided according to the invention when administered to a patent provide a collection of
MHC presented epitopes specific for the patient's tumor suitable for stimulating, priming
and/or expanding T cells directed against cells expressing antigens from which the MHC
presented epitopes are derived. Thus, the vaccines described herein are preferably capable of
inducing or promoting a cellular response, preferably cytotoxic T cell activity, against a
cancer disease characterized by presentation of one or more cancer expressed antigens with
class I MHC. Since a vaccine provided according to the present invention will target cancer
specific mutations it will be specific for the patient's tumor.
In one aspect, the present invention relates to a method for providing an individualized cancer
vaccine comprising the steps:
(a) identifying cancer specific somatic mutations in a tumor specimen of a cancer patient to
provide a cancer mutation signature of the patient; and
(b) providing a vaccine featuring the cancer mutation signature obtained in step (a).
In one embodiment, the method of the invention comprises the following steps:
i) providing a tumor specimen from a cancer patient and a non-tumorigenous
specimen which preferably is derived from the cancer patient;
ii) identifying sequence differences between the genome, exome and/or
transcriptome of the tumor specimen and the genome, exome and/or
transcriptome of the non-tumorigenous specimen;
iii) designing a polypeptide comprising epitopes incorporating the sequence
differences determined in step (ii);
iv) providing the polypeptide designed in step (iii) or a nucleic acid, preferably
RNA, encoding said polypeptide; and
v) providing a vaccine comprising the polypeptide or nucleic acid provided in
step (iv).
According to the invention a tumor specimen relates to any sample such as a bodily sample
derived from a patient containing or being expected of containing tumor or cancer cells. The
bodily sample may be any tissue sample such as blood, a tissue sample obtained from the
primary tumor or from tumor metastases or any other sample containing tumor or cancer cells.
Preferably, a bodily sample is blood and cancer specific somatic mutations or sequence
differences are determined in one or more circulating tumor cells (CTCs) contained in the
blood. In another embodiment, a tumor specimen relates to one or more isolated tumor or
cancer cells such as circulating tumor cells (CTCs) or a sample containing one or more
isolated tumor or cancer cells such as circulating tumor cells (CTCs).
A non-tumorigenous specimen relates to any sample such as a bodily sample derived from a
patient or another individual which preferably is of the same species as the patient, preferably
a healthy individual not containing or not being expected of containing tumor or cancer cells.
The bodily sample may be any tissue sample such as blood or a sample from a non-
tumorigenous tissue.
According to the invention, the term "cancer mutation signature" may refer to all cancer
mutations present in one or more cancer cells of a patient or it may refer to only a portion of
the cancer mutations present in one or more cancer cells of a patient. Accordingly, the present
invention may involve the identification of all cancer specific mutations present in one or
more cancer cells of a patient or it may involve the identification of only a portion of the
cancer specific mutations present in one or more cancer cells of a patient. Generally, the
method of the invention provides for the identification of a number of mutations which
provides a sufficient number of neo-epitopes to be included into a vaccine. A "cancer
mutation" relates to a sequence difference between the nucleic acid contained in a cancer cell
and the nucleic acid contained in a normal cell.
Preferably, the mutations identified in the methods according to the present invention are non-
synonymous mutations, preferably non-synonymous mutations of proteins expressed in a
tumor or cancer cell.
In one embodiment, cancer specific somatic mutations or sequence differences are determined
in the genome, preferably the entire genome, of a tumor specimen. Thus, the method of the
invention may comprise identifying the cancer mutation signature of the genome, preferably
the entire genome of one or more cancer cells. In one embodiment, the step of identifying
cancer specific somatic mutations in a tumor specimen of a cancer patient comprises
identifying the genome-wide cancer mutation profile.
In one embodiment, cancer specific somatic mutations or sequence differences are determined
in the exome, preferably the entire exome, of a tumor specimen. The exome is part of the
genome of an organism formed by exons, which are coding portions of expressed genes. The
exome provides the genetic blueprint used in the synthesis of proteins and other functional
gene products. It is the most functionally relevant part of the genome and, therefore, it is most
likely to contribute to the phenotype of an organism. The exome of the human genome is
estimated to comprise 1.5% of the total genome (Ng, PC et al., PLoS Gen., 4(8): 1-15, 2008).
Thus, the method of the invention may comprise identifying the cancer mutation signature of
the exome, preferably the entire exome of one or more cancer cells. In one embodiment, the
step of identifying cancer specific somatic mutations in a tumor specimen of a cancer patient
comprises identifying the exome-wide cancer mutation profile.
In one embodiment, cancer specific somatic mutations or sequence differences are determined
in the transcriptome, preferably the entire transcriptome, of a tumor specimen. The
transcriptome is the set of all RNA molecules, including mRNA, rRNA, tRNA, and other
non-coding RNA produced in one cell or a population of cells. In context of the present
invention the transcriptome means the set of all RNA molecules produced in one cell, a
population of cells, preferably a population of cancer cells, or all cells of a given individual at
a certain time point. Thus, the method of the invention may comprise identifying the cancer
mutation signature of the transcriptome, preferably the entire transcriptome of one or more
cancer cells. In one embodiment, the step of identifying cancer specific somatic mutations in a
tumor specimen of a cancer patient comprises identifying the transcriptome-wide cancer
mutation profile.
In one embodiment, the step of identifying cancer specific somatic mutations or identifying
sequence differences comprises single cell sequencing of one or more, preferably 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or even more cancer cells. Thus, the method
of the invention may comprise identifying a cancer mutation signature of said one or more
cancer cells. In one embodiment, the cancer cells are circulating tumor cells. The cancer cells
such as the circulating tumor cells may be isolated prior to single cell sequencing.
In one embodiment, the step of identifying cancer specific somatic mutations or identifying
sequence differences involves using next generation sequencing (NGS).
In one embodiment, the step of identifying cancer specific somatic mutations or identifying
sequence differences comprises sequencing genomic DNA and/or RNA of the tumor
specimen.
To reveal cancer specific somatic mutations or sequence differences the sequence information
obtained from the tumor specimen is preferably compared with a reference such as sequence
information obtained from sequencing nucleic acid such as DNA or RNA of normal non-
cancerous cells such as germline cells which may either be obtained from the patient or a
different individual. In one embodiment, normal genomic germline DNA is obtained from
peripheral blood mononuclear cells (PBMCs)
A vaccine provided according to the methods of the present invention relates to a vaccine
which when administered to a patent preferably provides a collection of MHC presented
epitopes, such as 2 or more, 5 or more, 10 or more, 15 or more, 20 or more, 25 or more, 30 or
more and preferably up to 60, up to 55, up to 50, up to 45, up to 40, up to 35 or up to 30 MHC
presented epitopes, incorporating sequence changes based on the identified mutations or
sequence differences. Such MHC presented epitopes incorporating sequence changes based
on the identified mutations or sequence differences are also termed "neo-epitopes" herein.
Presentation of these epitopes by cells of a patient, in particular antigen presenting cells,
preferably results in T cells targeting the epitopes when bound to MHC and thus, the patient's
tumor, preferably the primary tumor as well as tumor metastases, expressing antigens from
which the MHC presented epitopes are derived and presenting the same epitopes on the
surface of the tumor cells.
For providing a vaccine, the method of the invention may comprise the arbitrary inclusion of
a sufficient number of neo-epitopes (preferably in the form of an encoding nucleic acid) into a
vaccine or it may comprise the further step of determining the usability of the identified
mutations in epitopes for cancer vaccination. Thus further steps can involve one or more of
the following: (i) assessing whether the sequence changes are located in known or predicted
MHC presented epitopes, (ii) in vitro and/or in silico testing whether the sequence changes are
located in MHC presented epitopes, e.g. testing whether the sequence changes are part of
peptide sequences which are processed into and/or presented as MHC presented epitopes, and
(iii) in vitro testing whether the envisaged mutated epitopes, in particular when present in
their natural sequence context, e.g. when flanked by amino acid sequences also flanking said
epitopes in the naturally occurring protein, and when expressed in antigen presenting cells are
able to stimulate T cells of the patient having the desired specificity. Such flanking sequences
each may comprise 3 or more, 5 or more, 10 or more, 15 or more, 20 or more and preferably
up to 50, up to 45, up to 40, up to 35 or up to 30 amino acids and may flank the epitope
sequence N-terminally and/or C-terminally.
Mutations or sequence differences determined according to the invention may be ranked for
their usability as epitopes for cancer vaccination. Thus, in one aspect, the method of the
invention comprises a manual or computer-based analytical process in which the identified
mutations are analyzed and selected for their usability in the respective vaccine to be
provided. In a preferred embodiment, said analytical process is a computational algorithm-
based process. Preferably, said analytical process comprises one or more, preferably all of the
following steps:
- identifying expressed, protein modifying mutations, e.g. by analyzing
transcripts;
- identifying mutations which are potentially immunogenic, i.e. by comparing
the data obtained with available datasets of confirmed immunogenic epitopes,
e.g. those contained in public immune epitope databases such as i.e. the
IMMUNE EPITOPE DATABASE AND ANALYSIS RESOURCE at
http://www.immunoepitope.org
The step of identifying mutations which are potentially immunogenic may comprise
determining and/or ranking epitopes according to a prediction of their MHC-binding capacity,
preferably MHC class-I binding capacity.
In another embodiment of the invention, the epitopes can be selected and/or ranked by using
further parameters such as protein impact, associated gene expression, sequence uniqueness,
predicted presentation likelihood, and association with oncogenes.
Multiple CTC analyses also allow selection and prioritization of mutations. For example, a
mutation which is found in a larger portion of CTC may be prioritized higher than a mutation
found in a lower portion of CTC.
The collection of mutation based neo-epitopes identified according to the invention and
provided by a vaccine of the invention is preferably present in the form of a polypeptide
comprising said neo-epitopes (polyepitopic polypeptide) or a nucleic acid, in particular RNA,
encoding said polypeptide. Furthermore, the neo-epitopes may be present in the polypeptide
in the form of a vaccine sequence, i.e. present in their natural sequence context, e.g. flanked
by amino acid sequences also flanking said epitopes in the naturally occurring protein. Such
flanking sequences each may comprise 5 or more, 10 or more, 15 or more, 20 or more and
preferably up to 50, up to 45, up to 40, up to 35 or up to 30 amino acids and may flank the
epitope sequence N-terminally and/or C-terminally. Thus, a vaccine sequence may comprise
or more, 25 or more, 30 or more, 35 or more, 40 or more and preferably up to 50, up to 45,
up to 40, up to 35 or up to 30 amino acids. In one embodiment, the neo-epitopes and/or
vaccine sequences are lined up in the polypeptide head-to-tail.
In one embodiment, the neo-epitopes and/or vaccine sequences are spaced by linkers, in
particular neutral linkers. The term "linker" according to the invention relates to a peptide
added between two peptide domains such as epitopes or vaccine sequences to connect said
peptide domains. There is no particular limitation regarding the linker sequence. However, it
is preferred that the linker sequence reduces steric hindrance between the two peptide
domains, is well translated, and supports or allows processing of the epitopes. Furthermore,
the linker should have no or only little immunogenic sequence elements. Linkers preferably
should not create non-endogenous neo-epitopes like those generated from the junction suture
between adjacent neo-epitopes, which might generate unwanted immune reactions. Therefore,
the polyepitopic vaccine should preferably contain linker sequences which are able to reduce
the number of unwanted MHC binding junction epitopes. Hoyt et al. (EMBO J. 25(8), 1720-9,
2006) and Zhang et al. (J. Biol. Chem., 279(10), 8635-41, 2004) have shown that glycine-rich
sequences impair proteasomal processing and thus the use of glycine rich linker sequences act
to minimize the number of linker-contained peptides that can be processed by the proteasome.
Furthermore, glycine was observed to inhibit a strong binding in MHC binding groove
positions (Abastado et al., J. Immunol. 151(7), 3569-75, 1993). Schlessinger et al. (Proteins,
61(1), 115-26, 2005) had found that amino acids glycine and serine included in an amino acid
sequence result in a more flexible protein that is more efficiently translated and processed by
the proteasome, enabling better access to the encoded neo-epitopes. The linker each may
comprise 3 or more, 6 or more, 9 or more, 10 or more, 15 or more, 20 or more and preferably
up to 50, up to 45, up to 40, up to 35 or up to 30 amino acids. Preferably the linker is enriched
in glycine and/or serine amino acids. Preferably, at least 50%, at least 60%, at least 70%, at
least 80%, at least 90%, or at least 95% of the amino acids of the linker are glycine and/or
serine. In one preferred embodiment, a linker is substantially composed of the amino acids
glycine and serine. In one embodiment, the linker comprises the amino acid sequence
(GGS) (GSS) (GGG) (SSG) (GSG) wherein a, b, c, d and e is independently a number
a b c d e
selected from 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 and
wherein a + b + c + d + e are different from 0 and preferably are 2 or more, 3 or more, 4 or
more or 5 or more. In one embodiment, the linker comprises a sequence as described herein
including the linker sequences described in the examples such as the sequence
GGSGGGGSG.
In another embodiment of the present invention the collection of mutation based neo-epitopes
identified according to the invention and provided by a vaccine of the invention is preferably
present in the form of a collection of polypeptides comprising said neo-epitopes on different
polypeptides, wherein said polypeptides each comprise one or more neo-epitopes, which can
also be overlapping, or a collection of nucleic acids, in particular RNAs, encoding said
polypeptides.
In one particularly preferred embodiment, a polyepitopic polypeptide according to the present
invention is administered to a patient in the form of a nucleic acid, preferably RNA such as in
vitro transcribed or synthetic RNA, which may be expressed in cells of a patient such as
antigen presenting cells to produce the polypeptide. The present invention also envisions the
administration of one or more multiepitopic polypeptides which for the purpose of the present
invention are comprised by the term "polyepitopic polypeptide", preferably in the form of a
nucleic acid, preferably RNA such as in vitro transcribed or synthetic RNA, which may be
expressed in cells of a patient such as antigen presenting cells to produce the one or more
polypeptides. In the case of an administration of more than one multiepitopic polypeptide the
neo-epitopes provided by the different multiepitopic polypeptides may be different or partially
overlapping. Once present in cells of a patient such as antigen presenting cells the polypeptide
according to the invention is processed to produce the neo-epitopes identified according to the
invention. Administration of a vaccine provided according to the invention may provide MHC
class II-presented epitopes that are capable of eliciting a CD4+ helper T cell response against
cells expressing antigens from which the MHC presented epitopes are derived. Alternatively
or additionally, administration of a vaccine provided according to the invention may provide
MHC class I-presented epitopes that are capable of eliciting a CD8+ T cell response against
cells expressing antigens from which the MHC presented epitopes are derived. Furthermore,
administration of a vaccine provided according to the invention may provide one or more neo-
epitopes (including known neo-epitopes and neo-epitopes identified according to the
invention) as well as one or more epitopes not containing cancer specific somatic mutations
but being expressed by cancer cells and preferably inducing an immune response against
cancer cells, preferably a cancer specific immune response. In one embodiment,
administration of a vaccine provided according to the invention provides neo-epitopes that are
MHC class II-presented epitopes and/or are capable of eliciting a CD4+ helper T cell response
against cells expressing antigens from which the MHC presented epitopes are derived as well
as epitopes not containing cancer-specific somatic mutations that are MHC class I-presented
epitopes and/or are capable of eliciting a CD8+ T cell response against cells expressing
antigens from which the MHC presented epitopes are derived. In one embodiment, the
epitopes not containing cancer-specific somatic mutations are derived from a tumor antigen.
In one embodiment, the neo-epitopes and epitopes not containing cancer-specific somatic
mutations have a synergistic effect in the treatment of cancer. Preferably, a vaccine provided
according to the invention is useful for polyepitopic stimulation of cytotoxic and/or helper T
cell responses.
In a further aspect, the present invention provides a vaccine which is obtainable by the
method according to the invention. Accordingly, the present invention relates to a vaccine
comprising a recombinant polypeptide comprising mutation based neo-epitopes, said neo-
epitopes resulting from cancer specific somatic mutations in a tumor specimen of a cancer
patient, or a nucleic acid encoding said polypeptide. Such recombinant polypeptide may also
include epitopes not including cancer specific somatic mutations as discussed above.
Preferred embodiments of such vaccine are as described above in the context of the method of
the invention.
A vaccine provided according to the invention may comprise a pharmaceutically acceptable
carrier and may optionally comprise one or more adjuvants, stabilizers etc. The vaccine may
in the form of a therapeutic or prophylactic vaccine.
Another aspect relates to a method for inducing an immune response in a patient, comprising
administering to the patient a vaccine provided according to the invention.
Another aspect relates to a method of treating a cancer patient comprising the steps:
(a) providing an individualized cancer vaccine by the method according to the invention; and
(b) administering said vaccine to the patient.
Another aspect relates to a method of treating a cancer patient comprising administering the
vaccine according to the invention to the patient.
In further aspects, the invention provides the vaccines described herein for use in the methods
of treatment described herein, in particular for use in treating or preventing cancer.
The treatments of cancer described herein can be combined with surgical resection and/or
radiation and/or traditional chemotherapy.
Another aspect of the invention relates to a method for determining a false discovery rate
based on next generation sequencing data, said method including:
taking a first sample of genetic material from an animal or human;
taking a second sample of genetic material from an animal or human;
taking a first sample of genetic material from tumor cells;
taking a second sample of genetic material from said tumor cells;
determining a common coverage tumor comparison by counting all bases of the
reference genome which is included in both the tumor and at least one of said first sample of
genetic material from an animal or human and said second sample of genetic material from an
animal or human;
determining a common coverage same vs. same comparison by counting all bases of
the reference genome which are covered by both said first sample of genetic material from an
animal or human and said second sample of genetic material from an animal or human;
dividing said common coverage tumor comparison by said common coverage same vs.
same comparison to form a normalization;
determining a false discovery rate by dividing 1) the number of single nucleotide
variations with a quality score greater than Q in a comparison of said first sample of genetic
material from an animal or human and said second sample of genetic material from an animal
or human, by 2) the number of single nucleotide variations with a quality score greater than Q
in a comparison of said first sample of genetic material from said tumor cells and said second
sample of genetic material from said tumor cells and 3) multiplying the result by said
normalization.
In one embodiment, said genetic material is a DNA.
In one embodiment, Q is determined by:
establishing a set of quality properties S=(s ,…,s ) wherein S is preferable to
T=(t ,…,t ), denoted by S>T, when s > t for all i=1,…,n;
1 n i i
defining an intermediate false discovery rate by dividing 1) the number of single
nucleotide variations with a quality score S>T in a comparison of said first DNA sample from
an animal or human and said second DNA sample from an animal or human, by 2) the
number of single nucleotide variations with a quality score S>T in a comparison of said first
DNA sample from said tumor cells and said second DNA sample from said tumor cells and 3)
multiplying the result by said normalization,
determining the value range for each property for m mutations with n quality
properties each;
sampling up to p values out of said value range;
creating each possible combination of sampled quality values which results in p data
points;
using a random sample of said data points as a predictor for random forest training;
using the corresponding intermediate false discovery rate value as a response for said
random forest training,
wherein the resulting regression score of said random forest training is Q.
In one embodiment, said second DNA sample from an animal or human is allogenic to said
first DNA sample from an animal or human. In one embodiment, said second DNA sample
from an animal or human is autologous to said first DNA sample from an animal or human. In
one embodiment, said second DNA sample from an animal or human is xenogenic to said first
DNA sample from an animal or human.
In one embodiment, said genetic material is a RNA.
In one embodiment, Q is determined by:
establishing a set of quality properties S=(s ,…,s ) wherein S is preferable to
T=(t ,…,t ), denoted by S>T, when s > t for all i=1,…,n;
1 n i i
defining an intermediate false discovery rate by dividing 1) the number of single
nucleotide variations with a quality score S>T in a comparison of said first RNA sample from
an animal or human and said second RNA sample from an animal or human, by 2) the number
of single nucleotide variations with a quality score S>T in a comparison of said first RNA
sample from said tumor cells and said second RNA sample from said tumor cells and 3)
multiplying the result by said normalization,
determining the value range for each property for m mutations with n quality
properties each;
sampling up to p values out of said value range;
creating each possible combination of sampled quality values which results in p data
points;
using a random sample of said data points as a predictor for random forest training;
using the corresponding intermediate false discovery rate value as a response for said
random forest training,
wherein the resulting regression score of said random forest training is Q.
In one embodiment, said second RNA sample from an animal or human is allogenic to said
first RNA sample from an animal or human. In one embodiment, said second RNA sample
from an animal or human is autologous to said first RNA sample from an animal or human. In
one embodiment, said second RNA sample from an animal or human is xenogenic to said first
RNA sample from an animal or human.
In one embodiment, said false discovery rate is used to make a vaccine formulation. In one
embodiment, said vaccine is deliverable intravenously. In one embodiment, said vaccine is
deliverable dermally. In one embodiment, said vaccine is deliverable muscularly. In one
embodiment, said vaccine is deliverable subcutaneously. In one embodiment, said vaccine is
tailored for a specific patient.
In one embodiment, one of said first sample of genetic material from an animal or human and
said second sample of genetic material from an animal or human is from said specific patient.
In one embodiment, said step of determining a common coverage tumor comparison by
counting all bases of the reference genome which is included in both the tumor and at least
one of said first sample of genetic material from an animal or human and said second sample
of genetic material from an animal or human uses an automated system to count all bases.
In one embodiment, said step of determining a common coverage same vs. same comparison
by counting all bases of the reference genome which are covered by both said first sample of
genetic material from an animal or human and said second sample of genetic material from an
animal or human uses said automated system.
In one embodiment, said step of dividing said common coverage tumor comparison by said
common coverage same vs. same comparison to form a normalization uses said automated
system.
In one embodiment, said step of determining a false discovery rate by dividing 1) the number
of single nucleotide variations with a quality score greater than Q in a comparison of said first
sample of genetic material from an animal or human and said second sample of genetic
material from an animal or human, by 2) the number of single nucleotide variations with a
quality score greater than Q in a comparison of said first sample of genetic material from said
tumor cells and said second sample of genetic material from said tumor cells and 3)
multiplying the result by said normalization uses said automated system.
Another aspect of the invention relates to a method for determining an estimated receiver
operating curve (ROC), said method including:
receiving a dataset of mutations, each mutation associated with a false discovery rate
(FDR); and
for each mutation:
determining a true positive rate (TPR) by subtracting said FDR from one; and
determining a false positive rate (FPR) by setting said FPR equal to said FDR;
forming an estimated ROC by plotting, for each mutation, a point at the cumulative
TPR and FPR values up to said mutation, divided by the sum of all TPR and FPR values.
Other features and advantages of the instant invention will be apparent from the following
detailed description and claims.
DETAILED DESCRIPTION OF THE INVENTION
Although the present invention is described in detail below, it is to be understood that this
invention is not limited to the particular methodologies, protocols and reagents described
herein as these may vary. It is also to be understood that the terminology used herein is for the
purpose of describing particular embodiments only, and is not intended to limit the scope of
the present invention which will be limited only by the appended claims. Unless defined
otherwise, all technical and scientific terms used herein have the same meanings as commonly
understood by one of ordinary skill in the art.
In the following, the elements of the present invention will be described. These elements are
listed with specific embodiments, however, it should be understood that they may be
combined in any manner and in any number to create additional embodiments. The variously
described examples and preferred embodiments should not be construed to limit the present
invention to only the explicitly described embodiments. This description should be
understood to support and encompass embodiments which combine the explicitly described
embodiments with any number of the disclosed and/or preferred elements. Furthermore, any
permutations and combinations of all described elements in this application should be
considered disclosed by the description of the present application unless the context indicates
otherwise. For example, if in a preferred embodiment RNA comprises a poly(A)-tail
consisting of 120 nucleotides and in another preferred embodiment the RNA molecule
comprises a 5’-cap analog, then in a preferred embodiment, the RNA comprises the poly(A)-
tail consisting of 120 nucleotides and the 5’-cap analog.
Preferably, the terms used herein are defined as described in "A multilingual glossary of
biotechnological terms: (IUPAC Recommendations)", H.G.W. Leuenberger, B. Nagel, and H.
Kölbl, Eds., (1995) Helvetica Chimica Acta, CH-4010 Basel, Switzerland.
The practice of the present invention will employ, unless otherwise indicated, conventional
methods of biochemistry, cell biology, immunology, and recombinant DNA techniques which
are explained in the literature in the field (cf., e.g., Molecular Cloning: A Laboratory Manual,
2 Edition, J. Sambrook et al. eds., Cold Spring Harbor Laboratory Press, Cold Spring
Harbor 1989).
Throughout this specification and the claims which follow, unless the context requires
otherwise, the word "comprise", and variations such as "comprises" and "comprising", will be
understood to imply the inclusion of a stated member, integer or step or group of members,
integers or steps but not the exclusion of any other member, integer or step or group of
members, integers or steps although in some embodiments such other member, integer or step
or group of members, integers or steps may be excluded, i.e. the subject-matter consists in the
inclusion of a stated member, integer or step or group of members, integers or steps. The
terms "a" and "an" and "the" and similar reference used in the context of describing the
invention (especially in the context of the claims) are to be construed to cover both the
singular and the plural, unless otherwise indicated herein or clearly contradicted by context.
Recitation of ranges of values herein is merely intended to serve as a shorthand method of
referring individually to each separate value falling within the range. Unless otherwise
indicated herein, each individual value is incorporated into the specification as if it were
individually recited herein.
All methods described herein can be performed in any suitable order unless otherwise
indicated herein or otherwise clearly contradicted by context. The use of any and all
examples, or exemplary language (e.g., "such as"), provided herein is intended merely to
better illustrate the invention and does not pose a limitation on the scope of the invention
otherwise claimed. No language in the specification should be construed as indicating any
non-claimed element essential to the practice of the invention.
Several documents are cited throughout the text of this specification. Each of the documents
cited herein (including all patents, patent applications, scientific publications, manufacturer's
specifications, instructions, etc.), whether supra or infra, are hereby incorporated by reference
in their entirety. Nothing herein is to be construed as an admission that the invention is not
entitled to antedate such disclosure by virtue of prior invention.
The vaccine provided according to the invention is a recombinant vaccine.
The term "recombinant" in the context of the present invention means "made through genetic
engineering". Preferably, a "recombinant entity" such as a recombinant polypeptide in the
context of the present invention is not occurring naturally, and preferably is a result of a
combination of entities such as amino acid or nucleic acid sequences which are not combined
in nature. For example, a recombinant polypeptide in the context of the present invention may
contain several amino acid sequences such as neo-epitopes or vaccine sequences derived from
different proteins or different portions of the same protein fused together, e.g., by peptide
bonds or appropriate linkers.
The term "naturally occurring" as used herein refers to the fact that an object can be found in
nature. For example, a peptide or nucleic acid that is present in an organism (including
viruses) and can be isolated from a source in nature and which has not been intentionally
modified by man in the laboratory is naturally occurring.
According to the invention, the term "vaccine" relates to a pharmaceutical preparation
(pharmaceutical composition) or product that upon administration induces an immune
response, in particular a cellular immune response, which recognizes and attacks a pathogen
or a diseased cell such as a cancer cell. A vaccine may be used for the prevention or treatment
of a disease. The term "individualized cancer vaccine" concerns a particular cancer patient
and means that a cancer vaccine is adapted to the needs or special circumstances of an
individual cancer patient.
The term "immune response" refers to an integrated bodily response to an antigen and
preferably refers to a cellular immune response or a cellular as well as a humoral immune
response. The immune response may be protective/preventive/prophylactic and/or therapeutic.
"Inducing an immune response" may mean that there was no immune response against a
particular antigen before induction, but it may also mean that there was a certain level of
immune response against a particular antigen before induction and after induction said
immune response is enhanced. Thus, "inducing an immune response" also includes
"enhancing an immune response". Preferably, after inducing an immune response in a subject,
said subject is protected from developing a disease such as a cancer disease or the disease
condition is ameliorated by inducing an immune response. For example, an immune response
against a tumor expressed antigen may be induced in a patient having a cancer disease or in a
subject being at risk of developing a cancer disease. Inducing an immune response in this case
may mean that the disease condition of the subject is ameliorated, that the subject does not
develop metastases, or that the subject being at risk of developing a cancer disease does not
develop a cancer disease.
A "cellular immune response", a "cellular response", a "cellular response against an antigen"
or a similar term is meant to include a cellular response directed to cells characterized by
presentation of an antigen with class I or class II MHC. The cellular response relates to cells
called T cells or T-lymphocytes which act as either "helpers" or "killers". The helper T cells
(also termed CD4 T cells) play a central role by regulating the immune response and the
killer cells (also termed cytotoxic T cells, cytolytic T cells, CD8 T cells or CTLs) kill
diseased cells such as cancer cells, preventing the production of more diseased cells. In
preferred embodiments, the present invention involves the stimulation of an anti-tumor CTL
response against tumor cells expressing one or more tumor expressed antigens and preferably
presenting such tumor expressed antigens with class I MHC.
An "antigen" according to the invention covers any substance that will elicit an immune
response. In particular, an "antigen" relates to any substance, preferably a peptide or protein,
that reacts specifically with antibodies or T-lymphocytes (T cells). According to the present
invention, the term "antigen" comprises any molecule which comprises at least one epitope.
Preferably, an antigen in the context of the present invention is a molecule which, optionally
after processing, induces an immune reaction, which is preferably specific for the antigen
(including cells expressing the antigen). According to the present invention, any suitable
antigen may be used, which is a candidate for an immune reaction, wherein the immune
reaction is preferably a cellular immune reaction. In the context of the embodiments of the
present invention, the antigen is preferably presented by a cell, preferably by an antigen
presenting cell which includes a diseased cell, in particular a cancer cell, in the context of
MHC molecules, which results in an immune reaction against the antigen. An antigen is
preferably a product which corresponds to or is derived from a naturally occurring antigen.
Such naturally occurring antigens include tumor antigens.
In a preferred embodiment, the antigen is a tumor antigen, i.e., a part of a tumor cell such as a
protein or peptide expressed in a tumor cell which may be derived from the cytoplasm, the
cell surface or the cell nucleus, in particular those which primarily occur intracellularly or as
surface antigens of tumor cells. For example, tumor antigens include the carcinoembryonal
antigen, α1-fetoprotein, isoferritin, and fetal sulphoglycoprotein, α2-H-ferroprotein and γ-
fetoprotein. According to the present invention, a tumor antigen preferably comprises any
antigen which is expressed in and optionally characteristic with respect to type and/or
expression level for tumors or cancers as well as for tumor or cancer cells. In one
embodiment, the term "tumor antigen" or "tumor-associated antigen" relates to proteins that
are under normal conditions specifically expressed in a limited number of tissues and/or
organs or in specific developmental stages, for example, the tumor antigen may be under
normal conditions specifically expressed in stomach tissue, preferably in the gastric mucosa,
in reproductive organs, e.g., in testis, in trophoblastic tissue, e.g., in placenta, or in germ line
cells, and are expressed or aberrantly expressed in one or more tumor or cancer tissues. In this
context, "a limited number" preferably means not more than 3, more preferably not more than
2. The tumor antigens in the context of the present invention include, for example,
differentiation antigens, preferably cell type specific differentiation antigens, i.e., proteins that
are under normal conditions specifically expressed in a certain cell type at a certain
differentiation stage, cancer/testis antigens, i.e., proteins that are under normal conditions
specifically expressed in testis and sometimes in placenta, and germ line specific antigens.
Preferably, the tumor antigen or the aberrant expression of the tumor antigen identifies cancer
cells. In the context of the present invention, the tumor antigen that is expressed by a cancer
cell in a subject, e.g., a patient suffering from a cancer disease, is preferably a self-protein in
said subject. In preferred embodiments, the tumor antigen in the context of the present
invention is expressed under normal conditions specifically in a tissue or organ that is non-
essential, i.e., tissues or organs which when damaged by the immune system do not lead to
death of the subject, or in organs or structures of the body which are not or only hardly
accessible by the immune system.
According to the invention, the terms "tumor antigen", "tumor expressed antigen", "cancer
antigen" and "cancer expressed antigen" are equivalents and are used interchangeably herein.
The term "immunogenicity" relates to the relative effectivity of an antigen to induce an
immune reaction.
An "antigen peptide" according to the invention preferably relates to a portion or fragment of
an antigen which is capable of stimulating an immune response, preferably a cellular response
against the antigen or cells characterized by expression of the antigen and preferably by
presentation of the antigen such as diseased cells, in particular cancer cells. Preferably, an
antigen peptide is capable of stimulating a cellular response against a cell characterized by
presentation of an antigen with class I MHC and preferably is capable of stimulating an
antigen-responsive cytotoxic T-lymphocyte (CTL). Preferably, the antigen peptides according
to the invention are MHC class I and/or class II presented peptides or can be processed to
produce MHC class I and/or class II presented peptides. Preferably, the antigen peptides
comprise an amino acid sequence substantially corresponding to the amino acid sequence of a
fragment of an antigen. Preferably, said fragment of an antigen is an MHC class I and/or class
II presented peptide. Preferably, an antigen peptide according to the invention comprises an
amino acid sequence substantially corresponding to the amino acid sequence of such fragment
and is processed to produce such fragment, i.e., an MHC class I and/or class II presented
peptide derived from an antigen.
If a peptide is to be presented directly, i.e., without processing, in particular without cleavage,
it has a length which is suitable for binding to an MHC molecule, in particular a class I MHC
molecule, and preferably is 7-20 amino acids in length, more preferably 7-12 amino acids in
length, more preferably 8-11 amino acids in length, in particular 9 or 10 amino acids in
length.
If a peptide is part of a larger entity comprising additional sequences, e.g. of a vaccine
sequence or polypeptide, and is to be presented following processing, in particular following
cleavage, the peptide produced by processing has a length which is suitable for binding to an
MHC molecule, in particular a class I MHC molecule, and preferably is 7-20 amino acids in
length, more preferably 7-12 amino acids in length, more preferably 8-11 amino acids in
length, in particular 9 or 10 amino acids in length. Preferably, the sequence of the peptide
which is to be presented following processing is derived from the amino acid sequence of an
antigen, i.e., its sequence substantially corresponds and is preferably completely identical to a
fragment of an antigen. Thus, an antigen peptide or vaccine sequence according to the
invention in one embodiment comprises a sequence of 7-20 amino acids in length, more
preferably 7-12 amino acids in length, more preferably 8-11 amino acids in length, in
particular 9 or 10 amino acids in length which substantially corresponds and is preferably
completely identical to a fragment of an antigen and following processing of the antigen
peptide or vaccine sequence makes up the presented peptide. According to the invention, such
peptide produced by processing comprises the identified sequence change.
According to the invention, an antigen peptide or epitope may be present in a vaccine as a part
of a larger entity such as a vaccine sequence and/or a polypeptide comprising more than one
antigen peptide or epitope. The presented antigen peptide or epitope is produced following
suitable processing.
Peptides having amino acid sequences substantially corresponding to a sequence of a peptide
which is presented by the class I MHC may differ at one or more residues that are not
essential for TCR recognition of the peptide as presented by the class I MHC, or for peptide
binding to MHC. Such substantially corresponding peptides are also capable of stimulating an
antigen-responsive CTL and may be considered immunologically equivalent. Peptides having
amino acid sequences differing from a presented peptide at residues that do not affect TCR
recognition but improve the stability of binding to MHC may improve the immunogenicity of
the antigen peptide, and may be referred to herein as "optimized peptide". Using existing
knowledge about which of these residues may be more likely to affect binding either to the
MHC or to the TCR, a rational approach to the design of substantially corresponding peptides
may be employed. Resulting peptides that are functional are contemplated as antigen peptides.
An antigen peptide when presented by MHC should be recognizable by a T cell receptor.
Preferably, the antigen peptide if recognized by a T cell receptor is able to induce in the
presence of appropriate co-stimulatory signals, clonal expansion of the T cell carrying the T
cell receptor specifically recognizing the antigen peptide. Preferably, antigen peptides, in
particular if presented in the context of MHC molecules, are capable of stimulating an
immune response, preferably a cellular response against the antigen from which they are
derived or cells characterized by expression of the antigen and preferably characterized by
presentation of the antigen. Preferably, an antigen peptide is capable of stimulating a cellular
response against a cell characterized by presentation of the antigen with class I MHC and
preferably is capable of stimulating an antigen-responsive CTL. Such cell preferably is a
target cell.
"Antigen processing" or "processing" refers to the degradation of a polypeptide or antigen
into procession products, which are fragments of said polypeptide or antigen (e.g., the
degradation of a polypeptide into peptides) and the association of one or more of these
fragments (e.g., via binding) with MHC molecules for presentation by cells, preferably
antigen presenting cells, to specific T cells.
"Antigen presenting cells" (APC) are cells which present peptide fragments of protein
antigens in association with MHC molecules on their cell surface. Some APCs may activate
antigen specific T cells.
Professional antigen-presenting cells are very efficient at internalizing antigen, either by
phagocytosis or by receptor-mediated endocytosis, and then displaying a fragment of the
antigen, bound to a class II MHC molecule, on their membrane. The T cell recognizes and
interacts with the antigen-class II MHC molecule complex on the membrane of the antigen-
presenting cell. An additional co-stimulatory signal is then produced by the antigen-
presenting cell, leading to activation of the T cell. The expression of co-stimulatory molecules
is a defining feature of professional antigen-presenting cells.
The main types of professional antigen-presenting cells are dendritic cells, which have the
broadest range of antigen presentation, and are probably the most important antigen-
presenting cells, macrophages, B-cells, and certain activated epithelial cells.
Dendritic cells (DCs) are leukocyte populations that present antigens captured in peripheral
tissues to T cells via both MHC class II and I antigen presentation pathways. It is well known
that dendritic cells are potent inducers of immune responses and the activation of these cells is
a critical step for the induction of antitumoral immunity.
Dendritic cells are conveniently categorized as "immature" and "mature" cells, which can be
used as a simple way to discriminate between two well characterized phenotypes. However,
this nomenclature should not be construed to exclude all possible intermediate stages of
differentiation.
Immature dendritic cells are characterized as antigen presenting cells with a high capacity for
antigen uptake and processing, which correlates with the high expression of Fcγ receptor and
mannose receptor. The mature phenotype is typically characterized by a lower expression of
these markers, but a high expression of cell surface molecules responsible for T cell activation
such as class I and class II MHC, adhesion molecules (e. g. CD54 and CD11) and
costimulatory molecules (e. g., CD40, CD80, CD86 and 4-1 BB).
Dendritic cell maturation is referred to as the status of dendritic cell activation at which such
antigen-presenting dendritic cells lead to T cell priming, while presentation by immature
dendritic cells results in tolerance. Dendritic cell maturation is chiefly caused by biomolecules
with microbial features detected by innate receptors (bacterial DNA, viral RNA, endotoxin,
etc.), pro-inflammatory cytokines (TNF, IL-1, IFNs), ligation of CD40 on the dendritic cell
surface by CD40L, and substances released from cells undergoing stressful cell death. The
dendritic cells can be derived by culturing bone marrow cells in vitro with cytokines, such as
granulocyte-macrophage colony-stimulating factor (GM-CSF) and tumor necrosis factor
alpha.
Non-professional antigen-presenting cells do not constitutively express the MHC class II
proteins required for interaction with naive T cells; these are expressed only upon stimulation
of the non-professional antigen-presenting cells by certain cytokines such as IFNγ.
"Antigen presenting cells" can be loaded with MHC class I presented peptides by transducing
the cells with nucleic acid, preferably RNA, encoding a peptide or polypeptide comprising the
peptide to be presented, e.g. a nucleic acid encoding the antigen.
In some embodiments, a pharmaceutical composition of the invention comprising a gene
delivery vehicle that targets a dendritic or other antigen presenting cell may be administered
to a patient, resulting in transfection that occurs in vivo. In vivo transfection of dendritic cells,
for example, may generally be performed using any methods known in the art, such as those
described in WO 97/24447, or the gene gun approach described by Mahvi et al., Immunology
and cell Biology 75: 456-460, 1997.
According to the invention, the term "antigen presenting cell" also includes target cells.
"Target cell" shall mean a cell which is a target for an immune response such as a cellular
immune response. Target cells include cells that present an antigen or an antigen epitope, i.e.
a peptide fragment derived from an antigen, and include any undesirable cell such as a cancer
cell. In preferred embodiments, the target cell is a cell expressing an antigen as described
herein and preferably presenting said antigen with class I MHC.
The term "epitope" refers to an antigenic determinant in a molecule such as an antigen, i.e., to
a part in or fragment of the molecule that is recognized by the immune system, for example,
that is recognized by a T cell, in particular when presented in the context of MHC molecules.
An epitope of a protein such as a tumor antigen preferably comprises a continuous or
discontinuous portion of said protein and is preferably between 5 and 100, preferably between
and 50, more preferably between 8 and 30, most preferably between 10 and 25 amino acids
in length, for example, the epitope may be preferably 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
, 21, 22, 23, 24, or 25 amino acids in length. It is particularly preferred that the epitope in
the context of the present invention is a T cell epitope.
According to the invention an epitope may bind to MHC molecules such as MHC molecules
on the surface of a cell and thus, may be a "MHC binding peptide" or "antigen peptide". The
term "MHC binding peptide" relates to a peptide which binds to an MHC class I and/or an
MHC class II molecule. In the case of class I MHC/peptide complexes, the binding peptides
are typically 8-10 amino acids long although longer or shorter peptides may be effective. In
the case of class II MHC/peptide complexes, the binding peptides are typically 10-25 amino
acids long and are in particular 13-18 amino acids long, whereas longer and shorter peptides
may be effective.
The terms "epitope", "antigen peptide", "antigen epitope", "immunogenic peptide" and "MHC
binding peptide" are used interchangeably herein and preferably relate to an incomplete
representation of an antigen which is preferably capable of eliciting an immune response
against the antigen or a cell expressing or comprising and preferably presenting the antigen.
Preferably, the terms relate to an immunogenic portion of an antigen. Preferably, it is a
portion of an antigen that is recognized (i.e., specifically bound) by a T cell receptor, in
particular if presented in the context of MHC molecules. Preferred such immunogenic
portions bind to an MHC class I or class II molecule. As used herein, an immunogenic portion
is said to "bind to" an MHC class I or class II molecule if such binding is detectable using any
assay known in the art.
As used herein the term "neo-epitope" refers to an epitope that is not present in a reference
such as a normal non-cancerous or germline cell but is found in cancer cells. This includes, in
particular, situations wherein in a normal non-cancerous or germline cell a corresponding
epitope is found, however, due to one or more mutations in a cancer cell the sequence of the
epitope is changed so as to result in the neo-epitope.
The term "portion" refers to a fraction. With respect to a particular structure such as an amino
acid sequence or protein the term "portion" thereof may designate a continuous or a
discontinuous fraction of said structure. Preferably, a portion of an amino acid sequence
comprises at least 1%, at least 5%, at least 10%, at least 20%, at least 30%, preferably at least
40%, preferably at least 50%, more preferably at least 60%, more preferably at least 70%,
even more preferably at least 80%, and most preferably at least 90% of the amino acids of
said amino acid sequence. Preferably, if the portion is a discontinuous fraction said
discontinuous fraction is composed of 2, 3, 4, 5, 6, 7, 8, or more parts of a structure, each part
being a continuous element of the structure. For example, a discontinuous fraction of an
amino acid sequence may be composed of 2, 3, 4, 5, 6, 7, 8, or more, preferably not more than
4 parts of said amino acid sequence, wherein each part preferably comprises at least 5
continuous amino acids, at least 10 continuous amino acids, preferably at least 20 continuous
amino acids, preferably at least 30 continuous amino acids of the amino acid sequence.
The terms "part" and "fragment" are used interchangeably herein and refer to a continuous
element. For example, a part of a structure such as an amino acid sequence or protein refers to
a continuous element of said structure. A portion, a part or a fragment of a structure
preferably comprises one or more functional properties of said structure. For example, a
portion, a part or a fragment of an epitope, peptide or protein is preferably immunologically
equivalent to the epitope, peptide or protein it is derived from. In the context of the present
invention, a "part" of a structure such as an amino acid sequence preferably comprises,
preferably consists of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at
least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 92%, at least 94%, at
least 96%, at least 98%, at least 99% of the entire structure or amino acid sequence.
The term "immunoreactive cell" in the context of the present invention relates to a cell which
exerts effector functions during an immune reaction. An "immunoreactive cell" preferably is
capable of binding an antigen or a cell characterized by presentation of an antigen or an
antigen peptide derived from an antigen and mediating an immune response. For example,
such cells secrete cytokines and/or chemokines, secrete antibodies, recognize cancerous cells,
and optionally eliminate such cells. For example, immunoreactive cells comprise T cells
(cytotoxic T cells, helper T cells, tumor infiltrating T cells), B cells, natural killer cells,
neutrophils, macrophages, and dendritic cells. Preferably, in the context of the present
invention, "immunoreactive cells" are T cells, preferably CD4 and/or CD8 T cells.
Preferably, an "immunoreactive cell" recognizes an antigen or an antigen peptide derived
from an antigen with some degree of specificity, in particular if presented in the context of
MHC molecules such as on the surface of antigen presenting cells or diseased cells such as
cancer cells. Preferably, said recognition enables the cell that recognizes an antigen or an
antigen peptide derived from said antigen to be responsive or reactive. If the cell is a helper T
cell (CD4 T cell) bearing receptors that recognize an antigen or an antigen peptide derived
from an antigen in the context of MHC class II molecules such responsiveness or reactivity
may involve the release of cytokines and/or the activation of CD8 lymphocytes (CTLs)
and/or B-cells. If the cell is a CTL such responsiveness or reactivity may involve the
elimination of cells presented in the context of MHC class I molecules, i.e., cells
characterized by presentation of an antigen with class I MHC, for example, via apoptosis or
perforin-mediated cell lysis. According to the invention, CTL responsiveness may include
sustained calcium flux, cell division, production of cytokines such as IFN-γ and TNF-α, up-
regulation of activation markers such as CD44 and CD69, and specific cytolytic killing of
antigen expressing target cells. CTL responsiveness may also be determined using an artificial
reporter that accurately indicates CTL responsiveness. Such CTL that recognizes an antigen
or an antigen peptide derived from an antigen and are responsive or reactive are also termed
"antigen-responsive CTL" herein. If the cell is a B cell such responsiveness may involve the
release of immunoglobulins.
The terms "T cell" and "T lymphocyte" are used interchangeably herein and include T helper
cells (CD4+ T cells) and cytotoxic T cells (CTLs, CD8+ T cells) which comprise cytolytic T
cells.
T cells belong to a group of white blood cells known as lymphocytes, and play a central role
in cell-mediated immunity. They can be distinguished from other lymphocyte types, such as B
cells and natural killer cells by the presence of a special receptor on their cell surface called T
cell receptor (TCR). The thymus is the principal organ responsible for the maturation of T
cells. Several different subsets of T cells have been discovered, each with a distinct function.
T helper cells assist other white blood cells in immunologic processes, including maturation
of B cells into plasma cells and activation of cytotoxic T cells and macrophages, among other
functions. These cells are also known as CD4+ T cells because they express the CD4 protein
on their surface. Helper T cells become activated when they are presented with peptide
antigens by MHC class II molecules that are expressed on the surface of antigen presenting
cells (APCs). Once activated, they divide rapidly and secrete small proteins called cytokines
that regulate or assist in the active immune response.
Cytotoxic T cells destroy virally infected cells and tumor cells, and are also implicated in
transplant rejection. These cells are also known as CD8+ T cells since they express the CD8
glycoprotein at their surface. These cells recognize their targets by binding to antigen
associated with MHC class I, which is present on the surface of nearly every cell of the body.
A majority of T cells have a T cell receptor (TCR) existing as a complex of several proteins.
The actual T cell receptor is composed of two separate peptide chains, which are produced
from the independent T cell receptor alpha and beta (TCRα and TCRβ) genes and are called
α- and β-TCR chains. γδ T cells (gamma delta T cells) represent a small subset of T cells that
possess a distinct T cell receptor (TCR) on their surface. However, in γδ T cells, the TCR is
made up of one γ-chain and one δ-chain. This group of T cells is much less common (2% of
total T cells) than the αβ T cells.
The first signal in activation of T cells is provided by binding of the T cell receptor to a short
peptide presented by the major histocompatibility complex (MHC) on another cell. This
ensures that only a T cell with a TCR specific to that peptide is activated. The partner cell is
usually a professional antigen presenting cell (APC), usually a dendritic cell in the case of
naïve responses, although B cells and macrophages can be important APCs. The peptides
presented to CD8+ T cells by MHC class I molecules are typically 8-10 amino acids in length;
the peptides presented to CD4+ T cells by MHC class II molecules are typically longer, as the
ends of the binding cleft of the MHC class II molecule are open.
According to the present invention, a T cell receptor is capable of binding to a predetermined
target if it has a significant affinity for said predetermined target and binds to said
predetermined target in standard assays. "Affinity" or "binding affinity" is often measured by
equilibrium dissociation constant (K ). A T cell receptor is not (substantially) capable of
binding to a target if it has no significant affinity for said target and does not bind
significantly to said target in standard assays.
A T cell receptor is preferably capable of binding specifically to a predetermined target. A T
cell receptor is specific for a predetermined target if it is capable of binding to said
predetermined target while it is not (substantially) capable of binding to other targets, i.e. has
no significant affinity for other targets and does not significantly bind to other targets in
standard assays.
Cytotoxic T lymphocytes may be generated in vivo by incorporation of an antigen or an
antigen peptide into antigen-presenting cells in vivo. The antigen or antigen peptide may be
represented as protein, as DNA (e.g. within a vector) or as RNA. The antigen may be
processed to produce a peptide partner for the MHC molecule, while a fragment thereof may
be presented without the need for further processing. The latter is the case in particular, if
these can bind to MHC molecules. In general, administration to a patient by intradermal
injection is possible. However, injection may also be carried out intranodally into a lymph
node (Maloy et al. (2001), Proc Natl Acad Sci USA 98:3299-303). The resulting cells present
the complex of interest and are recognized by autologous cytotoxic T lymphocytes which then
propagate.
Specific activation of CD4+ or CD8+ T cells may be detected in a variety of ways. Methods
for detecting specific T cell activation include detecting the proliferation of T cells, the
production of cytokines (e.g., lymphokines), or the generation of cytolytic activity. For CD4+
T cells, a preferred method for detecting specific T cell activation is the detection of the
proliferation of T cells. For CD8+ T cells, a preferred method for detecting specific T cell
activation is the detection of the generation of cytolytic activity.
The term "major histocompatibility complex" and the abbreviation "MHC" include MHC
class I and MHC class II molecules and relate to a complex of genes which occurs in all
vertebrates. MHC proteins or molecules are important for signaling between lymphocytes and
antigen presenting cells or diseased cells in immune reactions, wherein the MHC proteins or
molecules bind peptides and present them for recognition by T cell receptors. The proteins
encoded by the MHC are expressed on the surface of cells, and display both self antigens
(peptide fragments from the cell itself) and non-self antigens (e.g., fragments of invading
microorganisms) to a T cell.
The MHC region is divided into three subgroups, class I, class II, and class III. MHC class I
proteins contain an α-chain and β2-microglobulin (not part of the MHC encoded by
chromosome 15). They present antigen fragments to cytotoxic T cells. On most immune
system cells, specifically on antigen-presenting cells, MHC class II proteins contain α- and β-
chains and they present antigen fragments to T-helper cells. MHC class III region encodes for
other immune components, such as complement components and some that encode cytokines.
In humans, genes in the MHC region that encode antigen-presenting proteins on the cell
surface are referred to as human leukocyte antigen (HLA) genes. However the abbreviation
MHC is often used to refer to HLA gene products. HLA genes include the nine so-called
classical MHC genes: HLA-A, HLA-B, HLA-C, HLA-DPA1, HLA-DPB1, HLA-DQA1,
HLA-DQB1, HLA-DRA, and HLA-DRB1.
In one preferred embodiment of all aspects of the invention an MHC molecule is an HLA
molecule.
By "cell characterized by presentation of an antigen" or "cell presenting an antigen" or similar
expressions is meant a cell such as a diseased cell, e.g. a cancer cell, or an antigen presenting
cell presenting the antigen it expresses or a fragment derived from said antigen, e.g. by
processing of the antigen, in the context of MHC molecules, in particular MHC Class I
molecules. Similarly, the terms "disease characterized by presentation of an antigen" denotes
a disease involving cells characterized by presentation of an antigen, in particular with class I
MHC. Presentation of an antigen by a cell may be effected by transfecting the cell with a
nucleic acid such as RNA encoding the antigen.
By "fragment of an antigen which is presented" or similar expressions is meant that the
fragment can be presented by MHC class I or class II, preferably MHC class I, e.g. when
added directly to antigen presenting cells. In one embodiment, the fragment is a fragment
which is naturally presented by cells expressing an antigen.
The term "immunologically equivalent" means that the immunologically equivalent molecule
such as the immunologically equivalent amino acid sequence exhibits the same or essentially
the same immunological properties and/or exerts the same or essentially the same
immunological effects, e.g., with respect to the type of the immunological effect such as
induction of a humoral and/or cellular immune response, the strength and/or duration of the
induced immune reaction, or the specificity of the induced immune reaction. In the context of
the present invention, the term "immunologically equivalent" is preferably used with respect
to the immunological effects or properties of a peptide used for immunization. For example,
an amino acid sequence is immunologically equivalent to a reference amino acid sequence if
said amino acid sequence when exposed to the immune system of a subject induces an
immune reaction having a specificity of reacting with the reference amino acid sequence.
The term "immune effector functions" in the context of the present invention includes any
functions mediated by components of the immune system that result, for example, in the
killing of tumor cells, or in the inhibition of tumor growth and/or inhibition of tumor
development, including inhibition of tumor dissemination and metastasis. Preferably, the
immune effector functions in the context of the present invention are T cell mediated effector
functions. Such functions comprise in the case of a helper T cell (CD4 T cell) the recognition
of an antigen or an antigen peptide derived from an antigen in the context of MHC class II
molecules by T cell receptors, the release of cytokines and/or the activation of CD8
lymphocytes (CTLs) and/or B-cells, and in the case of CTL the recognition of an antigen or
an antigen peptide derived from an antigen in the context of MHC class I molecules by T cell
receptors, the elimination of cells presented in the context of MHC class I molecules, i.e.,
cells characterized by presentation of an antigen with class I MHC, for example, via apoptosis
or perforin-mediated cell lysis, production of cytokines such as IFN-γ and TNF-α, and
specific cytolytic killing of antigen expressing target cells.
The term "genome" relates to the total amount of genetic information in the chromosomes of
an organism or a cell. The term "exome" refers to the coding regions of a genome. The term
"transcriptome" relates to the set of all RNA molecules.
A "nucleic acid" is according to the invention preferably deoxyribonucleic acid (DNA) or
ribonucleic acid (RNA), more preferably RNA, most preferably in vitro transcribed RNA
(IVT RNA) or synthetic RNA. Nucleic acids include according to the invention genomic
DNA, cDNA, mRNA, recombinantly produced and chemically synthesized molecules.
According to the invention, a nucleic acid may be present as a single-stranded or double-
stranded and linear or covalently circularly closed molecule. A nucleic acid can, according to
the invention, be isolated. The term "isolated nucleic acid" means, according to the invention,
that the nucleic acid (i) was amplified in vitro, for example via polymerase chain reaction
(PCR), (ii) was produced recombinantly by cloning, (iii) was purified, for example, by
cleavage and separation by gel electrophoresis, or (iv) was synthesized, for example, by
chemical synthesis. A nucleic can be employed for introduction into, i.e. transfection of, cells,
in particular, in the form of RNA which can be prepared by in vitro transcription from a DNA
template. The RNA can moreover be modified before application by stabilizing sequences,
capping, and polyadenylation.
The term "genetic material" refers to isolated nucleic acid, either DNA or RNA, a section of a
double helix, a section of a chromosome, or an organism's or cell's entire genome, in
particular its exome or transcriptome.
The term "mutation" refers to a change of or difference in the nucleic acid sequence
(nucleotide substitution, addition or deletion) compared to a reference. A "somatic mutation"
can occur in any of the cells of the body except the germ cells (sperm and egg) and therefore
are not passed on to children. These alterations can (but do not always) cause cancer or other
diseases. Preferably a mutation is a non-synonymous mutation. The term "non-synonymous
mutation" refers to a mutation, preferably a nucleotide substitution, which does result in an
amino acid change such as an amino acid substitution in the translation product.
According to the invention, the term "mutation" includes point mutations, Indels, fusions,
chromothripsis and RNA edits.
According to the invention, the term “Indel” describes a special mutation class, defined as a
mutation resulting in a colocalized insertion and deletion and a net gain or loss in nucleotides.
In coding regions of the genome, unless the length of an indel is a multiple of 3, they produce
a frameshift mutation. Indels can be contrasted with a point mutation; where an Indel inserts
and deletes nucleotides from a sequence, a point mutation is a form of substitution that
replaces one of the nucleotides.
Fusions can generate hybrid genes formed from two previously separate genes. It can occur as
the result of a translocation, interstitial deletion, or chromosomal inversion. Often, fusion
genes are oncogenes. Oncogenic fusion genes may lead to a gene product with a new or
different function from the two fusion partners. Alternatively, a proto-oncogene is fused to a
strong promoter, and thereby the oncogenic function is set to function by an upregulation
caused by the strong promoter of the upstream fusion partner. Oncogenic fusion transcripts
may also be caused by trans-splicing or read-through events.
According to the invention, the term “chromothripsis“ refers to a genetic phenomenon by
which specific regions of the genome are shattered and then stitched together via a single
devastating event.
According to the invention, the term “RNA edit“ or “RNA editing” refers to molecular
processes in which the information content in an RNA molecule is altered through a chemical
change in the base makeup. RNA editing includes nucleoside modifications such as cytidine
(C) to uridine (U) and adenosine (A) to inosine (I) deaminations, as well as non-templated
nucleotide additions and insertions. RNA editing in mRNAs effectively alters the amino acid
sequence of the encoded protein so that it differs from that predicted by the genomic DNA
sequence.
The term "cancer mutation signature" refers to a set of mutations which are present in cancer
cells when compared to non-cancerous reference cells.
According to the invention, a "reference" may be used to correlate and compare the results
obtained in the methods of the invention from a tumor specimen. Typically the "reference"
may be obtained on the basis of one or more normal specimens, in particular specimens which
are not affected by a cancer disease, either obtained from a patient or one or more different
individuals, preferably healthy individuals, in particular individuals of the same species. A
"reference" can be determined empirically by testing a sufficiently large number of normal
specimens.
Any suitable sequencing method can be used according to the invention, Next Generation
Sequencing (NGS) technologies being preferred. Third Generation Sequencing methods
might substitute for the NGS technology in the future to speed up the sequencing step of the
method. For clarification purposes: the terms “Next Generation Sequencing” or “NGS” in the
context of the present invention mean all novel high throughput sequencing technologies
which, in contrast to the “conventional” sequencing methodology known as Sanger chemistry,
read nucleic acid templates randomly in parallel along the entire genome by breaking the
entire genome into small pieces. Such NGS technologies (also known as massively parallel
sequencing technologies) are able to deliver nucleic acid sequence information of a whole
genome, exome, transcriptome (all transcribed sequences of a genome) or methylome (all
methylated sequences of a genome) in very short time periods, e.g. within 1-2 weeks,
preferably within 1-7 days or most preferably within less than 24 hours and allow, in
principle, single cell sequencing approaches. Multiple NGS platforms which are
commercially available or which are mentioned in the literature can be used in the context of
the present invention e.g. those described in detail in Zhang et al. 2011: The impact of next-
generation sequencing on genomics. J. Genet Genomics 38 (3), 95-109; or in Voelkerding et
al. 2009: Next generation sequencing: From basic research to diagnostics. Clinical chemistry
55, 641-658. Non-limiting examples of such NGS technologies/platforms are
1) The sequencing-by-synthesis technology known as pyrosequencing implemented e.g.
in the GS-FLX 454 Genome Sequencer of Roche-associated company 454 Life
Sciences (Branford, Connecticut), first described in Ronaghi et al. 1998: A sequencing
method based on real-time pyrophosphate". Science 281 (5375), 363-365. This
technology uses an emulsion PCR in which single-stranded DNA binding beads are
encapsulated by vigorous vortexing into aqueous micelles containing PCR reactants
surrounded by oil for emulsion PCR amplification. During the pyrosequencing
process, light emitted from phosphate molecules during nucleotide incorporation is
recorded as the polymerase synthesizes the DNA strand.
2) The sequencing-by-synthesis approaches developed by Solexa (now part of Illumina
Inc., San Diego, California) which is based on reversible dye-terminators and
implemented e.g. in the Illumina/Solexa Genome Analyzer and in the Illumina
HiSeq 2000 Genome Analyzer . In this technology, all four nucleotides are added
simultaneously into oligo-primed cluster fragments in flow-cell channels along with
DNA polymerase. Bridge amplification extends cluster strands with all four
fluorescently labeled nucleotides for sequencing.
3) Sequencing-by-ligation approaches, e.g. implemented in the SOLid platform of
Applied Biosystems (now Life Technologies Corporation, Carlsbad, California). In
this technology, a pool of all possible oligonucleotides of a fixed length are labeled
according to the sequenced position. Oligonucleotides are annealed and ligated; the
preferential ligation by DNA ligase for matching sequences results in a signal
informative of the nucleotide at that position. Before sequencing, the DNA is
amplified by emulsion PCR. The resulting bead, each containing only copies of the
same DNA molecule, are deposited on a glass slide. As a second example, he
Polonator G.007 platform of Dover Systems (Salem, New Hampshire) also employs
a sequencing-by-ligation approach by using a randomly arrayed, bead-based, emulsion
PCR to amplify DNA fragments for parallel sequencing.
4) Single-molecule sequencing technologies such as e.g. implemented in the PacBio RS
system of Pacific Biosciences (Menlo Park, California) or in the HeliScope platform
of Helicos Biosciences (Cambridge, Massachusetts). The distinct characteristic of this
technology is its ability to sequence single DNA or RNA molecules without
amplification, defined as Single-Molecule Real Time (SMRT) DNA sequencing. For
example, HeliScope uses a highly sensitive fluorescence detection system to directly
detect each nucleotide as it is synthesized. A similar approach based on fluorescence
resonance energy transfer (FRET) has been developed from Visigen Biotechnology
(Houston, Texas). Other fluorescence-based single-molecule techniques are from U.S.
TM TM
Genomics (GeneEngine ) and Genovoxx (AnyGene ).
) Nano-technologies for single-molecule sequencing in which various nanostructures are
used which are e.g. arranged on a chip to monitor the movement of a polymerase
molecule on a single strand during replication. Non-limiting examples for approaches
based on nano-technologies are the GridON platform of Oxford Nanopore
Technologies (Oxford, UK), the hybridization-assisted nano-pore sequencing
(HANS ) platforms developed by Nabsys (Providence, Rhode Island), and the
proprietary ligase-based DNA sequencing platform with DNA nanoball (DNB)
technology called combinatorial probe–anchor ligation (cPAL ).
6) Electron microscopy based technologies for single-molecule sequencing, e.g. those
developed by LightSpeed Genomics (Sunnyvale, California) and Halcyon Molecular
(Redwood City, California)
7) Ion semiconductor sequencing which is based on the detection of hydrogen ions that
are released during the polymerisation of DNA. For example, Ion Torrent Systems
(San Francisco, California) uses a high-density array of micro-machined wells to
perform this biochemical process in a massively parallel way. Each well holds a
different DNA template. Beneath the wells is an ion-sensitive layer and beneath that a
proprietary Ion sensor.
Preferably, DNA and RNA preparations serve as starting material for NGS. Such nucleic
acids can be easily obtained from samples such as biological material, e.g. from fresh, flash-
frozen or formalin-fixed paraffin embedded tumor tissues (FFPE) or from freshly isolated
cells or from CTCs which are present in the peripheral blood of patients. Normal non-mutated
genomic DNA or RNA can be extracted from normal, somatic tissue, however germline cells
are preferred in the context of the present invention. Germline DNA or RNA is extracted from
peripheral blood mononuclear cells (PBMCs) in patients with non-hematological
malignancies. Although nucleic acids extracted from FFPE tissues or freshly isolated single
cells are highly fragmented, they are suitable for NGS applications.
Several targeted NGS methods for exome sequencing are described in the literature (for
review see e.g. Teer and Mullikin 2010: Human Mol Genet 19 (2), R145-51), all of which can
be used in conjunction with the present invention. Many of these methods (described e.g. as
genome capture, genome partitioning, genome enrichment etc.) use hybridization techniques
and include array-based (e.g. Hodges et al. 2007: Nat. Genet. 39, 1522-1527) and liquid-
based (e.g. Choi et al. 2009: Proc. Natl. Acad. Sci USA 106, 19096-19101) hybridization
approaches. Commercial kits for DNA sample preparation and subsequent exome capture are
also available: for example, Illumina Inc. (San Diego, California) offers the TruSeq DNA
Sample Preparation Kit and the Exome Enrichment Kit TruSeq Exome Enrichment Kit.
In order to reduce the number of false positive findings in detecting cancer specific somatic
mutations or sequence differences when comparing e.g. the sequence of a tumor sample to the
sequence of a reference sample such as the sequence of a germ line sample it is preferred to
determine the sequence in replicates of one or both of these sample types. Thus, it is preferred
that the sequence of a reference sample such as the sequence of a germ line sample is
determined twice, three times or more. Alternatively or additionally, the sequence of a tumor
sample is determined twice, three times or more. It may also be possible to determine the
sequence of a reference sample such as the sequence of a germ line sample and/or the
sequence of a tumor sample more than once by determining at least once the sequence in
genomic DNA and determining at least once the sequence in RNA of said reference sample
and/or of said tumor sample. For example, by determining the variations between replicates of
a reference sample such as a germ line sample the expected rate of false positive (FDR)
somatic mutations as a statistical quantity can be estimated. Technical repeats of a sample
should generate identical results and any detected mutation in this "same vs. same
comparison" is a false positive. In particular, to determine the false discovery rate for somatic
mutation detection in a tumor sample relative to a reference sample, a technical repeat of the
reference sample can be used as a reference to estimate the number of false positives.
Furthermore, various quality related metrics (e.g. coverage or SNP quality) may be combined
into a single quality score using a machine learning approach. For a given somatic variation
all other variations with an exceeding quality score may be counted, which enables a ranking
of all variations in a dataset.
According to the invention, a high-throughput genome-wide single cell genotyping method
can be applied.
In one embodiment of the high-throughput genome-wide single cell genotyping the Fluidigm
platform may be used. Such approach may comprise the following steps:
1. Sample tumor tissue/cells and healthy tissue from a given patient.
2. The genetic material is extracted from cancerous and healthy cells and then its exome
(DNA) is sequenced using standard next generation sequencing (NGS) protocols. The
coverage of the NGS is such that heterozygote alleles with at least 5% frequency can be
detected. The transcriptome (RNA) is also extracted from the cancer cells, converted into
cDNA and sequenced to determine which genes are expressed by the cancer cells.
3. Non-synonymous expressed single nucleotide variations (SNVs) are identified as
described herein. Sites that are SNPs in the healthy tissue are filtered out.
4. N=96 mutations from (3) are selected spanning different frequencies. SNP genotyping
assays based on florescence detection are designed and synthesized for these mutations
(examples of such assays include: TaqMan based SNP assays by Life Technologies or
SNPtype assays by Fluidigm). Assays will include specific target amplification (STA) primers
to amplify amplicons containing the given SNV (this is standard in TaqMan and SNPtype
assays).
. Individual cells will be isolated from the tumor and from healthy tissue either by laser
microdissection (LMD) or by disaggregation into single-cell suspensions followed by sorting
as previously described (Dalerba P. et al. (2011) Nature Biotechnology 29: 1120-1127). Cells
can either be chosen without pre-selection (i.e., unbiased), or alternatively, cancerous cells
can be enriched. Enriching methods include: specific staining, sorting by cell size, histological
inspection during LMD, and so on.
6. Individual cells will be isolated in PCR tubes containing a master mix with the STA
primers and the amplicons containing the SNVs will be amplified. Alternatively the genome
of the single cell will be amplified via whole genome amplification (WGA) as previously
described (Frumkin D. et al. (2008) Cancer Research 68: 5924). Cell lysis will be achieved
either via the 95°C heating step or via a dedicated lysis buffer.
7. STA amplified samples are diluted and loaded onto the Fluidigm genotyping array.
8. Samples from healthy tissue will be used as positive controls to determine homozygote
allele clusters (no mutation). Since NGS data indicates that homozygote mutations are
extremely rare, typically only two clusters are expected: XX and XY, with X=healthy.
9. The number of arrays that can be executed is not limited, allowing, in practice up to
~1000 single cells to be assayed (~10 arrays). If performed in 384 plates sample prep can be
reduced to a few days.
. SNVs for each cell are then determined.
In another embodiment of the high-throughput genome-wide single cell genotyping the NGS
platform may be used. Such approach may comprise the following steps:
1. Steps 1 through 6 above are identical, except that N (number of SNVs assayed) can be
much larger than 96. In case of WGA, several cycles of STA will be performed after. STA
primers will contain two universal tag sequences on each primer.
2. After the STA, barcode primers will be PCR amplified into the amplicons. Barcode
primers contain unique barcode sequences and the above universal tag sequences. Each cell
will thus contain a unique barcode.
3. Amplicons from all cells will be mixed and sequenced via NGS. The practical
limitation on the number of cells that can be multiplexed is the number of plates that can be
prepared. Since samples can be prepared in 384 plates, a practical limit would be ~5000 cells.
4. Based on sequence data SNVs (or other structural anomalies) of the individual cells
are detected.
For prioritizing antigens, tumor phylogenetic reconstruction based on single cell genotyping
(“phylogenetic antigen prioritization”) may be used according to the invention. Besides
antigen prioritization based on criteria such as expression, the type of mutation (non-
synonymous versus other), MHC binding characteristics and so on, a further dimension for
prioritization designed to cope with intra and inter-tumor heterogeneity and biopsy bias can be
used as described for example below.
1. Identifying the most abundant antigens
The frequency of each SNV can be accurately estimated based on the single cell assay
described above in connection with the high-throughput genome-wide single cell genotyping
method and the most abundant SNVs present can be selected for providing individualized
vaccines for cancer (IVAC).
2. Identifying primary basal antigens based on rooted tree analysis
NGS data from tumors suggest that homozygote mutations (hits in both alleles) are rare
events. Therefore there is no need for haplotyping and a phylogenetic tree of the tumor
somatic mutations can be created from the singe cell SNV dataset. The germline sequence
will be used to root the tree. Using algorithms to reproduce ancestral sequences the sequences
of nodes near the root of the tree will be reproduced. These sequences contain the earliest
mutations predicted to exist in the primary tumor (defined here as the primary basal
mutations/antigens). Due to the low probability that two mutations will occur on the same
alleles in the same position on the genome, the mutations in the ancestral sequences are
predicted to be fixed in the tumor.
Prioritizing primary basal antigens is not equivalent to prioritizing the most frequent
mutations in the biopsy (although primary basal mutations are expected to be among the most
frequent in the biopsy). The reason is the following: say two SNVs appear to be present in all
cells derived from a biopsy (and thus have the same frequency – 100%), but one mutation is
basal and the other is not, then the basal mutation should be selected for IVAC. This is
because the basal mutation is likely to present in all regions of the tumor, whereas the latter
mutation may be a more recent mutation that by chance was fixed in the region where the
biopsy was taken. In addition, basal antigens are likely to exist in metastatic tumors derived
from the primary tumor. Therefore by prioritizing basal antigens for IVAC one may greatly
increase the chance that IVAC will be able to eradicate the entire tumor and not just a part of
the tumor.
If secondary tumors exist and these were also sampled, an evolutionary tree of the all tumors
can be estimated. This can improve the robustness of the tree and allow the detection of
mutations basal to all tumors.
3. Identifying antigens that maximally span the tumor(s)
Another approach to obtaining antigens that maximally cover all tumor sites is to take several
biopsies from the tumor. One strategy would be to select antigens identified by the NGS
analysis to be present in all biopsies. To improve the odds of identifying basal mutations, a
phylogenetic analysis based on single cell mutations from all biopsies can be performed.
In case of metastasis, biopsies from all tumors can be obtained and mutations identified via
NGS which are common to all tumors can be selected.
4. Using CTCs to prioritize antigens that inhibit metastasis
It is believed that metastatic tumors are derived from single cells. Therefore by genotyping
individual cells extracted from different tumors of a given patient in conjunction with
genotyping the patient’s circulating tumor cells (CTCs), one can reconstruct the evolutionary
history of the cancer. The expectation is to observe the metastatic tumor evolving from the
original tumor through a clade of CTCs derived from the primary tumor.
Below (unbiased method to identify, count and genetically probe CTCs) we describe an
extension of the above described high-throughput genome-wide single cell genotyping
method for an unbiased isolation and genomic analysis CTCs. Using the analysis described
above, one can then reconstruct a phylogenetic tree of the primer tumor, CTCs and secondary
tumors arising from metastasis (if they exist). Based on this tree one can identify mutations
(passenger or driver) that occurred at the time or closely after CTCs first detached from the
primary tumor. The expectation is that the genomes of CTCs arising from the primary tumor
are evolutionary more similar to the primary tumor genomes than to secondary tumor
genomes. Furthermore it is expected that the genomes of CTCs arising from the primary
tumor will contain unique mutations that are fixed in the secondary tumors, or that will likely
be fixed if secondary tumors will be formed in the future. These unique mutations can be
prioritized for IVAC to target (or prevent) metastasis.
The advantage of prioritizing CTC mutations versus primary basal mutations is that antigens
derived from CTCs can mobilize T cells specifically to target metastasis, and therefore will be
an independent arm from the T cells targeting the primary tumor (using different antiges). In
addition, if there are few (or no) secondary tumors, then the chance for immune escape from
CTC derived antigens is expected to be lower as the probably for tumor escape should scale
with the number of cancer cells carrying the given antigen.
. Identifying antigens co-occurring on the same cell (the “cocktail” IVAC)
It is believed that the tumor evolves to suppress mutations due to the selection pressure of the
immune system and therapy. Cancer vaccines targeting multiple antigens that co-occur on the
same cell and that are also frequent in the tumor have a greater chance of overriding tumor
escape mechanisms and therefore reduce the chance for relapse. Such “cocktail vaccines”
would be analogous to the antiretroviral combination therapy for HIV+ patients. Co-occurring
mutations can be identified by phylogenetic analysis or by inspecting the SNV alignment of
all cells.
Furthermore, according to the invention, an unbiased method to identify, count and
genetically probe CTCs can be used. Such approach may comprise the following steps:
1. Obtain biopsy of tumor(s) and determine atlas of somatic mutations.
2. Option 1: Select N≥96 mutations for further investigation based on previously
established prioritization schemes.
Option 2: Perform single cell assay (see above described high-throughput genome-
wide single cell genotyping method) followed phylogenetic analysis to select N≥96 primary
basal mutations and possibly more recent mutations to maximize diversity. The former
mutations are useful for identifying the CTCs (see below), and the latter for generating a
phylogenetic analysis (see section "Identifying antigens co-occurring on the same cell (the
“cocktail” IVAC")).
3. Obtain whole blood from the cancer patient
4. Lyse red blood cells
. Remove white blood cells by depleting CD45+ cells (e.g., via sorting, magnetic beads
conjugated to anti CD45 antibody, etc.) to enrich for CTCs.
6. Remove free DNA by DNAase digestion. The origin of free DNA can be DNA present
in the blood or DNA from dead cells.
7. Sort remaining cells into PCR tubes, perform the STA (based on selected mutations)
and screen on Fluidigm (above described high-throughput genome-wide single cell
genotyping method). CTCs should generally be positive for multiple SNVs.
8. Cells identified as cancerous (=CTCs) can be then be further analyzed
phylogenetically based on the panel of SNVs screened (see section "Identifying antigens co-
occurring on the same cell (the “cocktail” IVAC")).
It is also possible to combine this method with previous established methods for isolated
CTCs. For example, one can sort for EpCAM+ cells, or cells positive for cytokeratins (Rao
CG. et al. (2005) International journal of oncology 27: 49; Allard WJ. et al. (2004) Clinical
Cancer Research 10: 6897-6904). These putative CTCs can then be verified/profiled on the
Fluidigm/NGS to derive their mutations.
This method can be used to count CTCs. Since the method does not rely on one particular
marker, which may nor may not be expressed by the cancer cells, but rather on the mutation
profile of cancer somatic mutations unique to the patient, this is an unbiased method to detect
and enumerate CTCs.
According to the invention, an approach involving tumor phylogenetic reconstruction based
on single cell genotyping to enrich for driver mutations (“phylogenetic filtering”) may be
used.
In one embodiment of this approach, a pan-tumor phylogenetic analysis to recover driver
mutations is performed.
For example, driver mutations from n=1 tumors may be detected.
In the above section "Identifying primary basal antigens based on rooted tree analysis" we
describe a method to recover ancestral sequences and/or identify cells that have sequences
close to the root of the tree. The number of mutations in these sequences is expected to be
significantly less than the number of mutations in the bulk sample of the cancer since by
definition these are sequences close to the root of the tree. Therefore, by selecting sequences
close to the root of the tree many passenger mutations are expected to be “phylogenetically
filtered” out. This procedure has the potential to greatly enrich for driver mutations. Driver
mutations can then be used to identify/selects treatment for a patient or can be used as leads
for novel therapies.
In another example, driver mutations from n>1 tumors of a given type may be detected.
By reconstructing primary basal mutations from many tumors of a particular type one can
greatly increase the chance of detecting driver mutations. Since basal sequences near the root
of the tree filter out many passenger mutations, the signal to noise ratio in detecting driver
mutations is expected to greatly increase. This method therefore has the potential to detect (1)
less frequent driver mutation (2) frequent driver mutations from less samples.
In another embodiment of the approachinvolving tumor phylogenetic reconstruction based on
single cell genotyping to enrich for driver mutations (“phylogenetic filtering”), a phylogenetic
analysis to recover metastasis causing driver mutations is performed.
In the above section "Using CTCs to prioritize antigens that inhibit metastasis" we describe a
method to detect CTC-associated mutations. This method can also be used to enrich for driver
mutations leading to metastasis. For example, by mapping the combined phylogeny of the
primer tumor, secondary tumors and CTCs, CTCs derived from the primary tumor should
connect between the clades of the primary secondary tumors. Such a phylogenetic analysis
can help pinpoint the mutations unique at this transition between primer and secondary
tumors. A fraction of these mutations can be driver mutations. Furthermore, by comparing
unique CTC mutations from different instances of the same cancer (i.e., n>1 tumors), one can
further enrich for the unique driver mutations causing metastasis.
According to the invention, phylogenetic analysis to identify primary versus secondary
tumors may be used.
In case of metastasis, if all tumors are sampled, a rooted tree can be used to predict the
temporal order that tumors appeared: which tumor is the primary tumor (nodes closest to the
root of the tree) and which tumors are the most recent ones. This can be helpful in cases
where it is difficult to determine which tumor is the primary.
In the context of the present invention, the term "RNA" relates to a molecule which comprises
at least one ribonucleotide residue and preferably being entirely or substantially composed of
ribonucleotide residues. "Ribonucleotide" relates to a nucleotide with a hydroxyl group at the
2’-position of a β-D-ribofuranosyl group. The term "RNA" comprises double-stranded RNA,
single-stranded RNA, isolated RNA such as partially or completely purified RNA, essentially
pure RNA, synthetic RNA, and recombinantly generated RNA such as modified RNA which
differs from naturally occurring RNA by addition, deletion, substitution and/or alteration of
one or more nucleotides. Such alterations can include addition of non-nucleotide material,
such as to the end(s) of a RNA or internally, for example at one or more nucleotides of the
RNA. Nucleotides in RNA molecules can also comprise non-standard nucleotides, such as
non-naturally occurring nucleotides or chemically synthesized nucleotides or
deoxynucleotides. These altered RNAs can be referred to as analogs or analogs of naturally-
occurring RNA.
According to the present invention, the term "RNA" includes and preferably relates to
"mRNA". The term "mRNA" means "messenger-RNA" and relates to a "transcript" which is
generated by using a DNA template and encodes a peptide or polypeptide. Typically, an
mRNA comprises a 5’-UTR, a protein coding region, and a 3’-UTR. mRNA only possesses
limited half-life in cells and in vitro. In the context of the present invention, mRNA may be
generated by in vitro transcription from a DNA template. The in vitro transcription
methodology is known to the skilled person. For example, there is a variety of in vitro
transcription kits commercially available.
According to the invention, the stability and translation efficiency of RNA may be modified
as required. For example, RNA may be stabilized and its translation increased by one or more
modifications having a stabilizing effects and/or increasing translation efficiency of RNA.
Such modifications are described, for example, in incorporated herein
by reference. In order to increase expression of the RNA used according to the present
invention, it may be modified within the coding region, i.e. the sequence encoding the
expressed peptide or protein, preferably without altering the sequence of the expressed
peptide or protein, so as to increase the GC-content to increase mRNA stability and to
perform a codon optimization and, thus, enhance translation in cells.
The term "modification" in the context of the RNA used in the present invention includes any
modification of an RNA which is not naturally present in said RNA.
In one embodiment of the invention, the RNA used according to the invention does not have
uncapped 5'-triphosphates. Removal of such uncapped 5'-triphosphates can be achieved by
treating RNA with a phosphatase.
The RNA according to the invention may have modified ribonucleotides in order to increase
its stability and/or decrease cytotoxicity. For example, in one embodiment, in the RNA used
according to the invention 5-methylcytidine is substituted partially or completely, preferably
completely, for cytidine. Alternatively or additionally, in one embodiment, in the RNA used
according to the invention pseudouridine is substituted partially or completely, preferably
completely, for uridine.
In one embodiment, the term "modification" relates to providing an RNA with a 5’-cap or 5’-
cap analog. The term "5’-cap" refers to a cap structure found on the 5'-end of an mRNA
molecule and generally consists of a guanosine nucleotide connected to the mRNA via an
unusual 5' to 5' triphosphate linkage. In one embodiment, this guanosine is methylated at the
7-position. The term "conventional 5’-cap" refers to a naturally occurring RNA 5’-cap,
preferably to the 7-methylguanosine cap (m G). In the context of the present invention, the
term "5’-cap" includes a 5’-cap analog that resembles the RNA cap structure and is modified
to possess the ability to stabilize RNA and/or enhance translation of RNA if attached thereto,
preferably in vivo and/or in a cell.
Preferably, the 5’ end of the RNA includes a Cap structure having the following general
formula:
HN NH
O O O
N O P O P O P O
H N N NH
W X Y
OH OH
- - -
wherein R and R are independently hydroxy or methoxy and W , X and Y are
independently oxygen, sulfur, selenium, or BH . In a preferred embodiment, R and R are
3 1 2
- - -
hydroxy and W , X and Y are oxygen. In a further preferred embodiment, one of R and R ,
- - -
preferably R is hydroxy and the other is methoxy and W , X and Y are oxygen. In a further
- - - -
preferred embodiment, R and R are hydroxy and one of W , X and Y , preferably X is
sulfur, selenium, or BH , preferably sulfur, while the other are oxygen. In a further preferred
embodiment, one of R and R , preferably R is hydroxy and the other is methoxy and one of
1 2 2
- - - -
W , X and Y , preferably X is sulfur, selenium, or BH , preferably sulfur while the other are
oxygen.
In the above formula, the nucleotide on the right hand side is connected to the RNA chain
through its 3’ group.
- - -
Those Cap structures wherein at least one of W , X and Y is sulfur, i.e. which have a
phosphorothioate moiety, exist in different diastereoisomeric forms all of which are
encompassed herein. Furthermore, the present invention encompasses all tautomers and
stereoisomers of the above formula.
For example, the Cap structure having the above structure wherein R is methoxy R is
1 , 2
- - -
hydroxy, X is sulfur and W and Y are oxygen exists in two diastereoisomeric forms (Rp and
Sp). These can be resolved by reverse phase HPLC and are named D1 and D2 according to
their elution order from the reverse phase HPLC column. According to the invention, the D1
7,2’-O
isomer of m Gpp pG is particularly preferred.
Providing an RNA with a 5’-cap or 5’-cap analog may be achieved by in vitro transcription of
a DNA template in presence of said 5’-cap or 5’-cap analog, wherein said 5’-cap is co-
transcriptionally incorporated into the generated RNA strand, or the RNA may be generated,
for example, by in vitro transcription, and the 5’-cap may be attached to the RNA post-
transcriptionally using capping enzymes, for example, capping enzymes of vaccinia virus.
The RNA may comprise further modifications. For example, a further modification of the
RNA used in the present invention may be an extension or truncation of the naturally
occurring poly(A) tail or an alteration of the 5’- or 3’-untranslated regions (UTR) such as
introduction of a UTR which is not related to the coding region of said RNA, for example, the
exchange of the existing 3’-UTR with or the insertion of one or more, preferably two copies
of a 3’-UTR derived from a globin gene, such as alpha2-globin, alpha1-globin, beta-globin,
preferably beta-globin, more preferably human beta-globin.
RNA having an unmasked poly-A sequence is translated more efficiently than RNA having a
masked poly-A sequence. The term "poly(A) tail" or "poly-A sequence" relates to a sequence
of adenyl (A) residues which typically is located on the 3’-end of a RNA molecule and
"unmasked poly-A sequence" means that the poly-A sequence at the 3’ end of an RNA
molecule ends with an A of the poly-A sequence and is not followed by nucleotides other than
A located at the 3’ end, i.e. downstream, of the poly-A sequence. Furthermore, a long poly-A
sequence of about 120 base pairs results in an optimal transcript stability and translation
efficiency of RNA.
Therefore, in order to increase stability and/or expression of the RNA used according to the
present invention, it may be modified so as to be present in conjunction with a poly-A
sequence, preferably having a length of 10 to 500, more preferably 30 to 300, even more
preferably 65 to 200 and especially 100 to 150 adenosine residues. In an especially preferred
embodiment the poly-A sequence has a length of approximately 120 adenosine residues. To
further increase stability and/or expression of the RNA used according to the invention, the
poly-A sequence can be unmasked.
In addition, incorporation of a 3’-non translated region (UTR) into the 3’-non translated
region of an RNA molecule can result in an enhancement in translation efficiency. A
synergistic effect may be achieved by incorporating two or more of such 3’-non translated
regions. The 3’-non translated regions may be autologous or heterologous to the RNA into
which they are introduced. In one particular embodiment the 3’-non translated region is
derived from the human β-globin gene.
A combination of the above described modifications, i.e. incorporation of a poly-A sequence,
unmasking of a poly-A sequence and incorporation of one or more 3’-non translated regions,
has a synergistic influence on the stability of RNA and increase in translation efficiency.
The term "stability" of RNA relates to the "half-life" of RNA. "Half-life" relates to the period
of time which is needed to eliminate half of the activity, amount, or number of molecules. In
the context of the present invention, the half-life of an RNA is indicative for the stability of
said RNA. The half-life of RNA may influence the "duration of expression" of the RNA. It
can be expected that RNA having a long half-life will be expressed for an extended time
period.
Of course, if according to the present invention it is desired to decrease stability and/or
translation efficiency of RNA, it is possible to modify RNA so as to interfere with the
function of elements as described above increasing the stability and/or translation efficiency
of RNA.
The term "expression" is used according to the invention in its most general meaning and
comprises the production of RNA and/or peptides or polypeptides, e.g. by transcription and/or
translation. With respect to RNA, the term "expression" or "translation" relates in particular to
the production of peptides or polypeptides. It also comprises partial expression of nucleic
acids. Moreover, expression can be transient or stable.
According to the invention, the term expression also includes an "aberrant expression" or
"abnormal expression". "Aberrant expression" or "abnormal expression" means according to
the invention that expression is altered, preferably increased, compared to a reference, e.g. a
state in a subject not having a disease associated with aberrant or abnormal expression of a
certain protein, e.g., a tumor antigen. An increase in expression refers to an increase by at
least 10%, in particular at least 20%, at least 50% or at least 100%, or more. In one
embodiment, expression is only found in a diseased tissue, while expression in a healthy
tissue is repressed.
The term "specifically expressed" means that a protein is essentially only expressed in a
specific tissue or organ. For example, a tumor antigen specifically expressed in gastric
mucosa means that said protein is primarily expressed in gastric mucosa and is not expressed
in other tissues or is not expressed to a significant extent in other tissue or organ types. Thus,
a protein that is exclusively expressed in cells of the gastric mucosa and to a significantly
lesser extent in any other tissue, such as testis, is specifically expressed in cells of the gastric
mucosa. In some embodiments, a tumor antigen may also be specifically expressed under
normal conditions in more than one tissue type or organ, such as in 2 or 3 tissue types or
organs, but preferably in not more than 3 different tissue or organ types. In this case, the
tumor antigen is then specifically expressed in these organs. For example, if a tumor antigen
is expressed under normal conditions preferably to an approximately equal extent in lung and
stomach, said tumor antigen is specifically expressed in lung and stomach.
In the context of the present invention, the term "transcription" relates to a process, wherein
the genetic code in a DNA sequence is transcribed into RNA. Subsequently, the RNA may be
translated into protein. According to the present invention, the term "transcription" comprises
"in vitro transcription", wherein the term "in vitro transcription" relates to a process wherein
RNA, in particular mRNA, is in vitro synthesized in a cell-free system, preferably using
appropriate cell extracts. Preferably, cloning vectors are applied for the generation of
transcripts. These cloning vectors are generally designated as transcription vectors and are
according to the present invention encompassed by the term "vector". According to the
present invention, the RNA used in the present invention preferably is in vitro transcribed
RNA (IVT-RNA) and may be obtained by in vitro transcription of an appropriate DNA
template. The promoter for controlling transcription can be any promoter for any RNA
polymerase. Particular examples of RNA polymerases are the T7, T3, and SP6 RNA
polymerases. Preferably, the in vitro transcription according to the invention is controlled by a
T7 or SP6 promoter. A DNA template for in vitro transcription may be obtained by cloning of
a nucleic acid, in particular cDNA, and introducing it into an appropriate vector for in vitro
transcription. The cDNA may be obtained by reverse transcription of RNA.
The term "translation" according to the invention relates to the process in the ribosomes of a
cell by which a strand of messenger RNA directs the assembly of a sequence of amino acids
to make a peptide or polypeptide.
Expression control sequences or regulatory sequences, which according to the invention may
be linked functionally with a nucleic acid, can be homologous or heterologous with respect to
the nucleic acid. A coding sequence and a regulatory sequence are linked together
"functionally" if they are bound together covalently, so that the transcription or translation of
the coding sequence is under the control or under the influence of the regulatory sequence. If
the coding sequence is to be translated into a functional protein, with functional linkage of a
regulatory sequence with the coding sequence, induction of the regulatory sequence leads to a
transcription of the coding sequence, without causing a reading frame shift in the coding
sequence or inability of the coding sequence to be translated into the desired protein or
peptide.
The term "expression control sequence" or "regulatory sequence" comprises, according to the
invention, promoters, ribosome-binding sequences and other control elements, which control
the transcription of a nucleic acid or the translation of the derived RNA. In certain
embodiments of the invention, the regulatory sequences can be controlled. The precise
structure of regulatory sequences can vary depending on the species or depending on the cell
type, but generally comprises 5’-untranscribed and 5’- and 3’-untranslated sequences, which
are involved in the initiation of transcription or translation, such as TATA-box, capping-
sequence, CAAT-sequence and the like. In particular, 5’-untranscribed regulatory sequences
comprise a promoter region that includes a promoter sequence for transcriptional control of
the functionally bound gene. Regulatory sequences can also comprise enhancer sequences or
upstream activator sequences.
Preferably, according to the invention, the RNA to be expressed in a cell is introduced into
said cell. In one embodiment of the methods according to the invention, the RNA that is to be
introduced into a cell is obtained by in vitro transcription of an appropriate DNA template.
According to the invention, terms such as "RNA capable of expressing" and "RNA encoding"
are used interchangeably herein and with respect to a particular peptide or polypeptide mean
that the RNA, if present in the appropriate environment, preferably within a cell, can be
expressed to produce said peptide or polypeptide. Preferably, RNA according to the invention
is able to interact with the cellular translation machinery to provide the peptide or polypeptide
it is capable of expressing.
Terms such as "transferring", "introducing" or "transfecting" are used interchangeably herein
and relate to the introduction of nucleic acids, in particular exogenous or heterologous nucleic
acids, in particular RNA into a cell. According to the present invention, the cell can form part
of an organ, a tissue and/or an organism. According to the present invention, the
administration of a nucleic acid is either achieved as naked nucleic acid or in combination
with an administration reagent. Preferably, administration of nucleic acids is in the form of
naked nucleic acids. Preferably, the RNA is administered in combination with stabilizing
substances such as RNase inhibitors. The present invention also envisions the repeated
introduction of nucleic acids into cells to allow sustained expression for extended time
periods.
Cells can be transfected with any carriers with which RNA can be associated, e.g. by forming
complexes with the RNA or forming vesicles in which the RNA is enclosed or encapsulated,
resulting in increased stability of the RNA compared to naked RNA. Carriers useful according
to the invention include, for example, lipid-containing carriers such as cationic lipids,
liposomes, in particular cationic liposomes, and micelles, and nanoparticles. Cationic lipids
may form complexes with negatively charged nucleic acids. Any cationic lipid may be used
according to the invention.
Preferably, the introduction of RNA which encodes a peptide or polypeptide into a cell, in
particular into a cell present in vivo, results in expression of said peptide or polypeptide in the
cell. In particular embodiments, the targeting of the nucleic acids to particular cells is
preferred. In such embodiments, a carrier which is applied for the administration of the
nucleic acid to a cell (for example, a retrovirus or a liposome), exhibits a targeting molecule.
For example, a molecule such as an antibody which is specific for a surface membrane protein
on the target cell or a ligand for a receptor on the target cell may be incorporated into the
nucleic acid carrier or may be bound thereto. In case the nucleic acid is administered by
liposomes, proteins which bind to a surface membrane protein which is associated with
endocytosis may be incorporated into the liposome formulation in order to enable targeting
and/or uptake. Such proteins encompass capsid proteins of fragments thereof which are
specific for a particular cell type, antibodies against proteins which are internalized, proteins
which target an intracellular location etc.
According to the present invention, the term "peptide" refers to substances comprising two or
more, preferably 3 or more, preferably 4 or more, preferably 6 or more, preferably 8 or more,
preferably 10 or more, preferably 13 or more, preferably 16 more, preferably 21 or more and
up to preferably 8, 10, 20, 30, 40 or 50, in particular 100 amino acids joined covalently by
peptide bonds. The term "polypeptide" or "protein" refers to large peptides, preferably to
peptides with more than 100 amino acid residues, but in general the terms "peptide",
"polypeptide" and "protein" are synonyms and are used interchangeably herein.
According to the invention, the term "sequence change" with respect to peptides or proteins
relates to amino acid insertion variants, amino acid addition variants, amino acid deletion
variants and amino acid substitution variants, preferably amino acid substitution variants. All
these sequence changes according to the invention may potentially create new epitopes.
Amino acid insertion variants comprise insertions of single or two or more amino acids in a
particular amino acid sequence.
Amino acid addition variants comprise amino- and/or carboxy-terminal fusions of one or
more amino acids, such as 1, 2, 3, 4 or 5, or more amino acids.
Amino acid deletion variants are characterized by the removal of one or more amino acids
from the sequence, such as by removal of 1, 2, 3, 4 or 5, or more amino acids.
Amino acid substitution variants are characterized by at least one residue in the sequence
being removed and another residue being inserted in its place.
The term "derived" means according to the invention that a particular entity, in particular a
particular sequence, is present in the object from which it is derived, in particular an organism
or molecule. In the case of amino acid sequences, especially particular sequence regions,
"derived" in particular means that the relevant amino acid sequence is derived from an amino
acid sequence in which it is present.
The term "cell" or "host cell" preferably is an intact cell, i.e. a cell with an intact membrane
that has not released its normal intracellular components such as enzymes, organelles, or
genetic material. An intact cell preferably is a viable cell, i.e. a living cell capable of carrying
out its normal metabolic functions. Preferably said term relates according to the invention to
any cell which can be transformed or transfected with an exogenous nucleic acid. The term
"cell" includes according to the invention prokaryotic cells (e.g., E. coli) or eukaryotic cells
(e.g., dendritic cells, B cells, CHO cells, COS cells, K562 cells, HEK293 cells, HELA cells,
yeast cells, and insect cells). The exogenous nucleic acid may be found inside the cell (i)
freely dispersed as such, (ii) incorporated in a recombinant vector, or (iii) integrated into the
host cell genome or mitochondrial DNA. Mammalian cells are particularly preferred, such as
cells from humans, mice, hamsters, pigs, goats, and primates. The cells may be derived from a
large number of tissue types and include primary cells and cell lines. Specific examples
include keratinocytes, peripheral blood leukocytes, bone marrow stem cells, and embryonic
stem cells. In further embodiments, the cell is an antigen-presenting cell, in particular a
dendritic cell, a monocyte, or macrophage.
A cell which comprises a nucleic acid molecule preferably expresses the peptide or
polypeptide encoded by the nucleic acid.
The term "clonal expansion" refers to a process wherein a specific entity is multiplied. In the
context of the present invention, the term is preferably used in the context of an
immunological response in which lymphocytes are stimulated by an antigen, proliferate, and
the specific lymphocyte recognizing said antigen is amplified. Preferably, clonal expansion
leads to differentiation of the lymphocytes.
Terms such as "reducing" or "inhibiting" relate to the ability to cause an overall decrease,
preferably of 5% or greater, 10% or greater, 20% or greater, more preferably of 50% or
greater, and most preferably of 75% or greater, in the level. The term "inhibit" or similar
phrases includes a complete or essentially complete inhibition, i.e. a reduction to zero or
essentially to zero.
Terms such as "increasing", "enhancing", "promoting" or "prolonging" preferably relate to an
increase, enhancement, promotion or prolongation by about at least 10%, preferably at least
%, preferably at least 30%, preferably at least 40%, preferably at least 50%, preferably at
least 80%, preferably at least 100%, preferably at least 200% and in particular at least 300%.
These terms may also relate to an increase, enhancement, promotion or prolongation from
zero or a non-measurable or non-detectable level to a level of more than zero or a level which
is measurable or detectable.
The agents, compositions and methods described herein can be used to treat a subject with a
disease, e.g., a disease characterized by the presence of diseased cells expressing an antigen
and presenting an antigen peptide. Particularly preferred diseases are cancer diseases. The
agents, compositions and methods described herein may also be used for immunization or
vaccination to prevent a disease described herein.
According to the invention, the term "disease" refers to any pathological state, including
cancer diseases, in particular those forms of cancer diseases described herein.
The term "normal" refers to the healthy state or the conditions in a healthy subject or tissue,
i.e., non-pathological conditions, wherein "healthy" preferably means non-cancerous.
"Disease involving cells expressing an antigen" means according to the invention that
expression of the antigen in cells of a diseased tissue or organ is detected. Expression in cells
of a diseased tissue or organ may be increased compared to the state in a healthy tissue or
organ. An increase refers to an increase by at least 10%, in particular at least 20%, at least
50%, at least 100%, at least 200%, at least 500%, at least 1000%, at least 10000% or even
more. In one embodiment, expression is only found in a diseased tissue, while expression in a
healthy tissue is repressed. According to the invention, diseases involving or being associated
with cells expressing an antigen include cancer diseases.
Cancer (medical term: malignant neoplasm) is a class of diseases in which a group of cells
display uncontrolled growth (division beyond the normal limits), invasion (intrusion on and
destruction of adjacent tissues), and sometimes metastasis (spread to other locations in the
body via lymph or blood). These three malignant properties of cancers differentiate them from
benign tumors, which are self-limited, and do not invade or metastasize. Most cancers form a
tumor but some, like leukemia, do not.
Malignant tumor is essentially synonymous with cancer. Malignancy, malignant neoplasm,
and malignant tumor are essentially synonymous with cancer.
According to the invention, the term "tumor" or "tumor disease" refers to an abnormal growth
of cells (called neoplastic cells, tumorigenous cells or tumor cells) preferably forming a
swelling or lesion. By "tumor cell" is meant an abnormal cell that grows by a rapid,
uncontrolled cellular proliferation and continues to grow after the stimuli that initiated the
new growth cease. Tumors show partial or complete lack of structural organization and
functional coordination with the normal tissue, and usually form a distinct mass of tissue,
which may be either benign, pre-malignant or malignant.
A benign tumor is a tumor that lacks all three of the malignant properties of a cancer. Thus,
by definition, a benign tumor does not grow in an unlimited, aggressive manner, does not
invade surrounding tissues, and does not spread to non-adjacent tissues (metastasize).
Neoplasm is an abnormal mass of tissue as a result of neoplasia. Neoplasia (new growth in
Greek) is the abnormal proliferation of cells. The growth of the cells exceeds, and is
uncoordinated with that of the normal tissues around it. The growth persists in the same
excessive manner even after cessation of the stimuli. It usually causes a lump or tumor.
Neoplasms may be benign, pre-malignant or malignant.
"Growth of a tumor" or "tumor growth" according to the invention relates to the tendency of a
tumor to increase its size and/or to the tendency of tumor cells to proliferate.
For purposes of the present invention, the terms "cancer" and "cancer disease" are used
interchangeably with the terms "tumor" and "tumor disease".
Cancers are classified by the type of cell that resembles the tumor and, therefore, the tissue
presumed to be the origin of the tumor. These are the histology and the location, respectively.
The term "cancer" according to the invention comprises leukemias, seminomas, melanomas,
teratomas, lymphomas, neuroblastomas, gliomas, rectal cancer, endometrial cancer, kidney
cancer, adrenal cancer, thyroid cancer, blood cancer, skin cancer, cancer of the brain, cervical
cancer, intestinal cancer, liver cancer, colon cancer, stomach cancer, intestine cancer, head
and neck cancer, gastrointestinal cancer, lymph node cancer, esophagus cancer, colorectal
cancer, pancreas cancer, ear, nose and throat (ENT) cancer, breast cancer, prostate cancer,
cancer of the uterus, ovarian cancer and lung cancer and the metastases thereof. Examples
thereof are lung carcinomas, mamma carcinomas, prostate carcinomas, colon carcinomas,
renal cell carcinomas, cervical carcinomas, or metastases of the cancer types or tumors
described above. The term cancer according to the invention also comprises cancer metastases
and relapse of cancer.
The main types of lung cancer are small cell lung carcinoma (SCLC) and non-small cell lung
carcinoma (NSCLC). There are three main sub-types of the non-small cell lung carcinomas:
squamous cell lung carcinoma, adenocarcinoma, and large cell lung carcinoma.
Adenocarcinomas account for approximately 10% of lung cancers. This cancer usually is seen
peripherally in the lungs, as opposed to small cell lung cancer and squamous cell lung cancer,
which both tend to be more centrally located.
Skin cancer is a malignant growth on the skin. The most common skin cancers are basal cell
cancer, squamous cell cancer, and melanoma. Malignant melanoma is a serious type of skin
cancer. It is due to uncontrolled growth of pigment cells, called melanocytes.
According to the invention, a "carcinoma" is a malignant tumor derived from epithelial cells.
This group represents the most common cancers, including the common forms of breast,
prostate, lung and colon cancer.
"Bronchiolar carcinoma" is a carcinoma of the lung, thought to be derived from epithelium of
terminal bronchioles, in which the neoplastic tissue extends along the alveolar walls and
grows in small masses within the alveoli. Mucin may be demonstrated in some of the cells
and in the material in the alveoli, which also includes denuded cells.
"Adenocarcinoma" is a cancer that originates in glandular tissue. This tissue is also part of a
larger tissue category known as epithelial tissue. Epithelial tissue includes skin, glands and a
variety of other tissue that lines the cavities and organs of the body. Epithelium is derived
embryologically from ectoderm, endoderm and mesoderm. To be classified as
adenocarcinoma, the cells do not necessarily need to be part of a gland, as long as they have
secretory properties. This form of carcinoma can occur in some higher mammals, including
humans. Well differentiated adenocarcinomas tend to resemble the glandular tissue that they
are derived from, while poorly differentiated may not. By staining the cells from a biopsy, a
pathologist will determine whether the tumor is an adenocarcinoma or some other type of
cancer. Adenocarcinomas can arise in many tissues of the body due to the ubiquitous nature
of glands within the body. While each gland may not be secreting the same substance, as long
as there is an exocrine function to the cell, it is considered glandular and its malignant form is
therefore named adenocarcinoma. Malignant adenocarcinomas invade other tissues and often
metastasize given enough time to do so. Ovarian adenocarcinoma is the most common type of
ovarian carcinoma. It includes the serous and mucinous adenocarcinomas, the clear cell
adenocarcinoma and the endometrioid adenocarcinoma.
Renal cell carcinoma also known as renal cell cancer or renal cell adenocarcinoma is a kidney
cancer that originates in the lining of the proximal convoluted tubule, the very small tubes in
the kidney that filter the blood and remove waste products. Renal cell carcinoma is by far the
most common type of kidney cancer in adults and the most lethal of all the genitorurinary
tumors. Distinct subtypes of renal cell carcinoma are clear cell renal cell carcinoma and
papillary renal cell carcinoma. Clear cell renal cell carcinoma is the most common form of
renal cell carcinoma. When seen under a microscope, the cells that make up clear cell renal
cell carcinoma appear very pale or clear. Papillary renal cell carcinoma is the second most
common subtype. These cancers form little finger-like projections (called papillae) in some, if
not most, of the tumors.
Lymphoma and leukemia are malignancies derived from hematopoietic (blood-forming) cells.
Blastic tumor or blastoma is a tumor (usually malignant) which resembles an immature or
embryonic tissue. Many of these tumors are most common in children.
By "metastasis" is meant the spread of cancer cells from its original site to another part of the
body. The formation of metastasis is a very complex process and depends on detachment of
malignant cells from the primary tumor, invasion of the extracellular matrix, penetration of
the endothelial basement membranes to enter the body cavity and vessels, and then, after
being transported by the blood, infiltration of target organs. Finally, the growth of a new
tumor, i.e. a secondary tumor or metastatic tumor, at the target site depends on angiogenesis.
Tumor metastasis often occurs even after the removal of the primary tumor because tumor
cells or components may remain and develop metastatic potential. In one embodiment, the
term "metastasis" according to the invention relates to "distant metastasis" which relates to a
metastasis which is remote from the primary tumor and the regional lymph node system.
The cells of a secondary or metastatic tumor are like those in the original tumor. This means,
for example, that, if ovarian cancer metastasizes to the liver, the secondary tumor is made up
of abnormal ovarian cells, not of abnormal liver cells. The tumor in the liver is then called
metastatic ovarian cancer, not liver cancer.
In ovarian cancer, metastasis can occur in the following ways: by direct contact or extension,
it can invade nearby tissue or organs located near or around the ovary, such as the fallopian
tubes, uterus, bladder, rectum, etc.; by seeding or shedding into the abdominal cavity, which
is the most common way ovarian cancer spreads. Cancer cells break off the surface of the
ovarian mass and "drop" to other structures in the abdomen such as the liver, stomach, colon
or diaphragm; by breaking loose from the ovarian mass, invading the lymphatic vessels and
then traveling to other areas of the body or distant organs such as the lung or liver; by
breaking loose from the ovarian mass, invading the blood system and traveling to other areas
of the body or distant organs.
According to the invention, metastatic ovarian cancer includes cancer in the fallopian tubes,
cancer in organs of the abdomen such as cancer in the bowel, cancer in the uterus, cancer in
the bladder, cancer in the rectum, cancer in the liver, cancer in the stomach, cancer in the
colon, cancer in the diaphragm, cancer in the lungs, cancer in the lining of the abdomen or
pelvis (peritoneum), and cancer in the brain. Similarly, metastatic lung cancer refers to cancer
that has spread from the lungs to distant and/or several sites in the body and includes cancer in
the liver, cancer in the adrenal glands, cancer in the bones, and cancer in the brain.
The term "circulating tumor cells" or "CTCs" relates to cells that have detached from a
primary tumor or tumor metastases and circulate in the bloodstream. CTCs may constitute
seeds for subsequent growth of additional tumors (metastasis) in different tissues. Circulating
tumor cells are found in frequencies in the order of 1-10 CTC per mL of whole blood in
patients with metastatic disease. Research methods have been developed to isolate CTC.
Several research methods have been described in the art to isolate CTCs, e.g. techniques
which use of the fact that epithelial cells commonly express the cell adhesion protein
EpCAM, which is absent in normal blood cells. Immunomagnetic bead-based capture
involves treating blood specimens with antibody to EpCAM that has been conjugated with
magnetic particles, followed by separation of tagged cells in a magnetic field. Isolated cells
are then stained with antibody to another epithelial marker, cytokeratin, as well as a common
leukocyte marker CD45, so as to distinguish rare CTCs from contaminating white blood cells.
This robust and semi-automated approach identifies CTCs with an average yield of
approximately 1 CTC/mL and a purity of 0.1% (Allard et al., 2004: Clin Cancer Res 10,
6897-6904). A second method for isolating CTCs uses a microfluidic-based CTC capture
device which involves flowing whole blood through a chamber embedded with 80,000
microposts that have been rendered functional by coating with antibody to EpCAM. CTCs are
then stained with secondary antibodies against either cytokeratin or tissue specific markers,
such as PSA in prostate cancer or HER2 in breast cancer and are visualized by automated
scanning of microposts in multiple planes along three dimensional coordinates. CTC-chips are
able to identifying cytokerating-positive circulating tumor cells in patients with a median
yield of 50 cells/ml and purity ranging from 1–80% (Nagrath et al., 2007: Nature 450, 1235-
1239). Another possibility for isolating CTCs is using the CellSearch Circulating Tumor
Cell (CTC) Test from Veridex, LLC (Raritan, NJ) which captures, identifies, and counts
CTCs in a tube of blood. The CellSearch system is a U.S. Food and Drug Administration
(FDA) approved methodology for enumeration of CTC in whole blood which is based on a
combination of immunomagnetic labeling and automated digital microscopy. There are other
methods for isolating CTCs described in the literature all of which can be used in conjunction
with the present invention.
A relapse or recurrence occurs when a person is affected again by a condition that affected
them in the past. For example, if a patient has suffered from a tumor disease, has received a
successful treatment of said disease and again develops said disease said newly developed
disease may be considered as relapse or recurrence. However, according to the invention, a
relapse or recurrence of a tumor disease may but does not necessarily occur at the site of the
original tumor disease. Thus, for example, if a patient has suffered from ovarian tumor and
has received a successful treatment a relapse or recurrence may be the occurrence of an
ovarian tumor or the occurrence of a tumor at a site different to ovary. A relapse or recurrence
of a tumor also includes situations wherein a tumor occurs at a site different to the site of the
original tumor as well as at the site of the original tumor. Preferably, the original tumor for
which the patient has received a treatment is a primary tumor and the tumor at a site different
to the site of the original tumor is a secondary or metastatic tumor.
By "treat" is meant to administer a compound or composition as described herein to a subject
in order to prevent or eliminate a disease, including reducing the size of a tumor or the
number of tumors in a subject; arrest or slow a disease in a subject; inhibit or slow the
development of a new disease in a subject; decrease the frequency or severity of symptoms
and/or recurrences in a subject who currently has or who previously has had a disease; and/or
prolong, i.e. increase the lifespan of the subject. In particular, the term "treatment of a
disease" includes curing, shortening the duration, ameliorating, preventing, slowing down or
inhibiting progression or worsening, or preventing or delaying the onset of a disease or the
symptoms thereof.
By "being at risk" is meant a subject, i.e. a patient, that is identified as having a higher than
normal chance of developing a disease, in particular cancer, compared to the general
population. In addition, a subject who has had, or who currently has, a disease, in particular
cancer, is a subject who has an increased risk for developing a disease, as such a subject may
continue to develop a disease. Subjects who currently have, or who have had, a cancer also
have an increased risk for cancer metastases.
The term "immunotherapy" relates to a treatment involving activation of a specific immune
reaction. In the context of the present invention, terms such as "protect", "prevent",
"prophylactic", "preventive", or "protective" relate to the prevention or treatment or both of
the occurrence and/or the propagation of a disease in a subject and, in particular, to
minimizing the chance that a subject will develop a disease or to delaying the development of
a disease. For example, a person at risk for a tumor, as described above, would be a candidate
for therapy to prevent a tumor.
A prophylactic administration of an immunotherapy, for example, a prophylactic
administration of the composition of the invention, preferably protects the recipient from the
development of a disease. A therapeutic administration of an immunotherapy, for example, a
therapeutic administration of the composition of the invention, may lead to the inhibition of
the progress/growth of the disease. This comprises the deceleration of the progress/growth of
the disease, in particular a disruption of the progression of the disease, which preferably leads
to elimination of the disease.
Immunotherapy may be performed using any of a variety of techniques, in which agents
provided herein function to remove diseased cells from a patient. Such removal may take
place as a result of enhancing or inducing an immune response in a patient specific for an
antigen or a cell expressing an antigen.
Within certain embodiments, immunotherapy may be active immunotherapy, in which
treatment relies on the in vivo stimulation of the endogenous host immune system to react
against diseased cells with the administration of immune response-modifying agents (such as
polypeptides and nucleic acids as provided herein).
The agents and compositions provided herein may be used alone or in combination with
conventional therapeutic regimens such as surgery, irradiation, chemotherapy and/or bone
marrow transplantation (autologous, syngeneic, allogeneic or unrelated).
The term "immunization" or "vaccination" describes the process of treating a subject with the
purpose of inducing an immune response for therapeutic or prophylactic reasons.
The term "in vivo" relates to the situation in a subject.
The terms "subject", "individual", “organism” or "patient" are used interchangeably and relate
to vertebrates, preferably mammals. For example, mammals in the context of the present
invention are humans, non-human primates, domesticated animals such as dogs, cats, sheep,
cattle, goats, pigs, horses etc., laboratory animals such as mice, rats, rabbits, guinea pigs, etc.
as well as animals in captivity such as animals of zoos. The term "animal" as used herein also
includes humans. The term "subject" may also include a patient, i.e., an animal, preferably a
human having a disease, preferably a disease as described herein.
The term "autologous" is used to describe anything that is derived from the same subject. For
example, "autologous transplant" refers to a transplant of tissue or organs derived from the
same subject. Such procedures are advantageous because they overcome the immunological
barrier which otherwise results in rejection.
The term "heterologous" is used to describe something consisting of multiple different
elements. As an example, the transfer of one individual’s bone marrow into a different
individual constitutes a heterologous transplant. A heterologous gene is a gene derived from a
source other than the subject.
As part of the composition for an immunization or a vaccination, preferably one or more
agents as described herein are administered together with one or more adjuvants for inducing
an immune response or for increasing an immune response. The term "adjuvant" relates to
compounds which prolongs or enhances or accelerates an immune response. The composition
of the present invention preferably exerts its effect without addition of adjuvants. Still, the
composition of the present application may contain any known adjuvant. Adjuvants comprise
a heterogeneous group of compounds such as oil emulsions (e.g., Freund’s adjuvants),
mineral compounds (such as alum), bacterial products (such as Bordetella pertussis toxin),
liposomes, and immune-stimulating complexes. Examples for adjuvants are monophosphoryl-
lipid-A (MPL SmithKline Beecham). Saponins such as QS21 (SmithKline Beecham), DQS21
(SmithKline Beecham; WO 96/33739), QS7, QS17, QS18, and QS-L1 (So et al., 1997, Mol.
Cells 7: 178-186), incomplete Freund’s adjuvants, complete Freund’s adjuvants, vitamin E,
montanid, alum, CpG oligonucleotides (Krieg et al., 1995, Nature 374: 546-549), and various
water-in-oil emulsions which are prepared from biologically degradable oils such as squalene
and/or tocopherol.
Other substances which stimulate an immune response of the patient may also be
administered. It is possible, for example, to use cytokines in a vaccination, owing to their
regulatory properties on lymphocytes. Such cytokines comprise, for example, interleukin-12
(IL-12) which was shown to increase the protective actions of vaccines (cf. Science 268:1432-
1434, 1995), GM-CSF and IL-18.
There are a number of compounds which enhance an immune response and which therefore
may be used in a vaccination. Said compounds comprise co-stimulating molecules provided in
the form of proteins or nucleic acids such as B7-1 and B7-2 (CD80 and CD86, respectively).
According to the invention, a "tumor specimen" is a sample such as a bodily sample
containing tumor or cancer cells such as circulating tumor cells (CTC), in particular a tissue
sample, including body fluids, and/or a cellular sample. According to the invention, a "non-
tumorigenous specimen" is a sample such as a bodily sample not containing tumor or cancer
cells such as circulating tumor cells (CTC), in particular a tissue sample, including body
fluids, and/or a cellular sample.Such bodily samples may be obtained in the conventional
manner such as by tissue biopsy, including punch biopsy, and by taking blood, bronchial
aspirate, sputum, urine, feces or other body fluids. According to the invention, the term
"sample" also includes processed samples such as fractions or isolates of biological samples,
e.g. nucleic acid or cell isolates.
The therapeutically active agents, vaccines and compositions described herein may be
administered via any conventional route, including by injection or infusion. The
administration may be carried out, for example, orally, intravenously, intraperitoneally,
intramuscularly, subcutaneously or transdermally. In one embodiment, administration is
carried out intranodally such as by injection into a lymph node. Other forms of administration
envision the in vitro transfection of antigen presenting cells such as dendritic cells with
nucleic acids described herein followed by administration of the antigen presenting cells.
The agents described herein are administered in effective amounts. An "effective amount"
refers to the amount which achieves a desired reaction or a desired effect alone or together
with further doses. In the case of treatment of a particular disease or of a particular condition,
the desired reaction preferably relates to inhibition of the course of the disease. This
comprises slowing down the progress of the disease and, in particular, interrupting or
reversing the progress of the disease. The desired reaction in a treatment of a disease or of a
condition may also be delay of the onset or a prevention of the onset of said disease or said
condition.
An effective amount of an agent described herein will depend on the condition to be treated,
the severeness of the disease, the individual parameters of the patient, including age,
physiological condition, size and weight, the duration of treatment, the type of an
accompanying therapy (if present), the specific route of administration and similar factors.
Accordingly, the doses administered of the agents described herein may depend on various of
such parameters. In the case that a reaction in a patient is insufficient with an initial dose,
higher doses (or effectively higher doses achieved by a different, more localized route of
administration) may be used.
The pharmaceutical compositions of the invention are preferably sterile and contain an
effective amount of the therapeutically active substance to generate the desired reaction or the
desired effect.
The pharmaceutical compositions of the invention are generally administered in
pharmaceutically compatible amounts and in pharmaceutically compatible preparation. The
term "pharmaceutically compatible" refers to a nontoxic material which does not interact with
the action of the active component of the pharmaceutical composition. Preparations of this
kind may usually contain salts, buffer substances, preservatives, carriers, supplementing
immunity-enhancing substances such as adjuvants, e.g. CpG oligonucleotides, cytokines,
chemokines, saponin, GM-CSF and/or RNA and, where appropriate, other therapeutically
active compounds. When used in medicine, the salts should be pharmaceutically compatible.
However, salts which are not pharmaceutically compatible may used for preparing
pharmaceutically compatible salts and are included in the invention. Pharmacologically and
pharmaceutically compatible salts of this kind comprise in a non-limiting way those prepared
from the following acids: hydrochloric, hydrobromic, sulfuric, nitric, phosphoric, maleic,
acetic, salicylic, citric, formic, malonic, succinic acids, and the like. Pharmaceutically
compatible salts may also be prepared as alkali metal salts or alkaline earth metal salts, such
as sodium salts, potassium salts or calcium salts.
A pharmaceutical composition of the invention may comprise a pharmaceutically compatible
carrier. The term "carrier" refers to an organic or inorganic component, of a natural or
synthetic nature, in which the active component is combined in order to facilitate application.
According to the invention, the term "pharmaceutically compatible carrier" includes one or
more compatible solid or liquid fillers, diluents or encapsulating substances, which are
suitable for administration to a patient. The components of the pharmaceutical composition of
the invention are usually such that no interaction occurs which substantially impairs the
desired pharmaceutical efficacy.
The pharmaceutical compositions of the invention may contain suitable buffer substances
such as acetic acid in a salt, citric acid in a salt, boric acid in a salt and phosphoric acid in a
salt.
The pharmaceutical compositions may, where appropriate, also contain suitable preservatives
such as benzalkonium chloride, chlorobutanol, paraben and thimerosal.
The pharmaceutical compositions are usually provided in a uniform dosage form and may be
prepared in a manner known per se. Pharmaceutical compositions of the invention may be in
the form of capsules, tablets, lozenges, solutions, suspensions, syrups, elixirs or in the form of
an emulsion, for example.
Compositions suitable for parenteral administration usually comprise a sterile aqueous or
nonaqueous preparation of the active compound, which is preferably isotonic to the blood of
the recipient. Examples of compatible carriers and solvents are Ringer solution and isotonic
sodium chloride solution. In addition, usually sterile, fixed oils are used as solution or
suspension medium.
The present invention is described in detail by the figures and examples below, which are
used only for illustration purposes and are not meant to be limiting. Owing to the description
and the examples, further embodiments which are likewise included in the invention are
accessible to the skilled worker.
FIGURES
Figure 1:
Top: Process to discover and prioritize likely immunogenic somatic mutations in bulk tumor
samples. Bottom: Process as applied to the B16 and Black6 system.
Figure 2: Example Validated Mutation in Kif18b
A mutation identified in gene Kif18b by NGS exome-sequencing that was confirmed by
Sanger sequencing. In the wild type cells, the sequence is T/T. In the tumor cells, the
sequence is a mix of T/G.
Figure 3: Immunologic reactivity against mutated sequences
Mice (n=5) were immunized twice (d0, d7) with mutated peptide sequences (100 µg + 50 µg
PolyI:C; s.c.). At day 12 mice were sacrificed and the spleen cells harvested. IFNγ ELISpot
was performed using 5x10 spleen cells /well as effectors and 5x10 bone marrow dendritic
cells loaded with peptides (2 µg/ml for 2h at 37°C and 5% CO ) as target cells. The effector
spleen cells were tested against the mutated peptide, the wild type peptide and a control
peptide (vesiculostomatitis virus nucleoprotein, VSV-NP, aa 52 - 59). Shown is the mean
measured spot number from which the background spots against VSV-NP were subtracted for
every mouse (empty circles: mice immunized with wildtype peptide; filled boxes: mice
immunized with mutated peptides). Data are shown for each mouse and mean ± SEM is
depicted.
Figure 4: Survival benefit for mice vaccinated with newly identified mutated peptide
sequence
B16F10 cells (7,5 x 10 )were inoculated subcutaneously on d0. Mice were vaccinated with
peptide 30 (Jerini Peptide Technologies (Berlin); 100 µg peptide + 50 µg PolyI:C s.c.
(Invivogen)) on day -4, day +2, day +9. The control group received only Poly I:C (50 µg s.c.).
Tumor growth was monitored until day + 16 *, p < 0,05 in Log-rank (Mantel-Cox) test.
Figure 5:
(A) Examples of enhanced protein expression (left eGFP, right Luciferase) with RNA
optimized for stability and translational efficiency (B) Example of polyepitopic expansion of
antigen-specific CD8 and CD4 T cells with RNA optimized for effective antigen routing (s.
Reference Kreiter, Konrad, Sester et al, Cancer Immunol. Immunother. 56: 1577-1587, 2007).
T (C) Example of a preclinical proof of antitumoral efficacy in B16 melanoma model using an
RNA vaccine that codes for a single epitope (OVA-SIINFEKL). Survival data were obtained
for mice treated with vaccine alone or vaccine in combination with adjuvant. (D)
Individualized, poly-neo-epitopic vaccine design. The vaccine vehicle integrates functional
elements for increased expression and optimized immunogenicity. Up to 30 mutated epitopes
that are spaced by linkers can be integrated per molecule in their natural sequence context.
Figure 6: Construct design
(A) Schematic diagram of a RNA polyepitope construct. Cap : cap analogon; 5´UTR :
´untranslated region; L : linker; Seq. 1 : RNA sequence coding for peptide containing
mutated aa; 3´UTR : 3´untranslated seuquence; poly-A : poly-A tail. (B) Sequence of the
RNA constructs coding for 2 aa sequences including a mutated aa from B16F10. The start-
and stop-codon as well as the signal peptide and the MITD sequence are not part of the
schematic drawing which is symbolized by “….”.
Figure 7: Functionality of RNA poly epitope
(A-C) Data for IFNγ ELISpot using 5 x 10 spleen cells per well as effectors and 5 x 10
BMDC as target cells. The BMDC were loaded with peptide (2 µg/ml for 2h at 37°C and 5%
CO ) or transfected with RNA (20 µg) by electroporation. The control RNA was eGFP (left
panel) or a RNA construct coding for 2 unrelated peptides containing mutated aa separated by
a linker. Data are shown as mean ± SEM. (A) Data for mutation peptide 30, wild type peptide
and RNA coding for mutation 30 and 31 are shown. (B) Data for mutation peptide 12, wild
type peptide 12 and RNA coding for mutation 12 and 39 are shown. (C) Representative
ELISpot scan from a single mouse of the read-out shown in (B) is depicted.
Figure 8: Two embodiments of RNA poly-neo-epitopic vaccines showing junction
epitopes
The RNA vaccine can be constructed with (top) or without linkers (bottom) between
mutation-encoding peptides. Good epitopes include those that include the somatic mutation
(“*”) and bind to MHC molecules. Bad epitopes include epitopes that bind to MHC molecules
but contain either parts of two peptides (bottom) or parts of peptide and linker sequences
(top).
Figure 9: Discovery and characterization of the “T-cell druggable mutanome”
(A) Flow chart gives an overview of the experimental procedure starting from B16F10 and
C57BL/6 samples to ELISPOT readout. (B) The number of hits for each evaluation step and
the process for selection of mutations for DNA validation and immunogenicity testing is
shown. Mutations selected for validation and immunogenicity testing were those predicted to
be immunogenic and in genes expressed at RPKM > 10. (C) The T-cell druggable mutanome
was mapped to the genome of B16F10. Rings from outside to inside stand for following
subsets: (1) present in all triplicates, (2) have an FDR < 0.05, (3) are located in protein coding
regions , (4) cause nonsynonymous changes, (5) are locaized in expressed genes , and (6) are
in the validated set. Mouse chromosomes (outer circle), gene density (green), gene expression
(green(low)/yellow/red(high)), and somatic mutations (orange).
Figure 10: Immune responses elicited in vivo by vaccination of mice with mutation
representing long synthetic peptides
(A,B) IFN-γ ELISPOT analysis of T-cell effectors from mice vaccinated with mutation
coding peptides. Columns represent means (±SEM) of 5 mice per group. Asterisks indicate
statistically significant differences of reactivity against mutation and wild-type peptide
(student´s t-test; value p < 0.05). (A) Splenocytes of vaccinated mice were restimulated with
BMDCs transfected with the mutation coding peptide used for vaccination, the corresponding
wild-type peptide and an irrelevant control peptide (VSV-NP). (B) For analysis of T-cell
reactivity against endogenously processed mutations splenocytes of vaccinated mice were
restimulated with BMDCs transfected with control RNA (eGFP) or a RNA coding for the
indicted mutation. (C) Mutation 30 (gene Kif18B, protein Q6PFD6, mutation p.K739N).
Sanger sequencing trace and sequence of mutation (top). Protein domains and mutation
location (bottom).
Figure 11: Antitumoral effects of mutated peptide vaccines in mice with aggressively
growing B16F10 tumors
(A) C57BL/6 mice (n = 7) were inoculated with 7.5 x 10 B16F10 cells s.c. into the flank of
the mice. On day 3 and 10 after tumor inoculation the mice were vaccinated with 100 μg
MUT30 or MUT44 peptide + 50 μg poly(I:C) or with adjuvant alone. (B) C57BL/6 mice (n =
) received one immunization of 100 μg MUT30 peptide + 50 μg poly(I:C) on day -4. On day
0 7.5 x 10 B16F10 cells were inoculated s.c. into the flank of the mice. Booster
immunizations with MUT30 peptid (+ poly(I:C)) were done on days 2 and 9.
Kaplan-Meier survival Blot (left). Tumor growth kinetics (right).
Figure 12: Vaccination with mutation coding RNAs leads to CD4 and CD8 T-cell
responses
Intracellular cytokine staining analysis data for IFN-γ in CD4 and CD8 T-cell effectors from
mice vaccinated with mutation coding RNAs. RNAs were coding for 1 (Monoepitope, upper
row), 2 (Biepitope, middle row), or 16 (Polyepitope, lower row) different mutations. Dots
represent means of 3 mice per group. Asterisks indicate statistically significant differences of
reactivity against mutation and control peptide (VSV-NP) (student´s t-test; value p < 0.05).
FACS plots show effectors from the highest IFN-γ secreting animal for each mutation and
indicate phenotype of the T-cell response.
Figure 13: Vaccination with mutation coding Polyepitope RNA leads T-cell reponses
against several mutations
IFN-γ ELISPOT analysis of T-cell effectors from mice vaccinated with mutation coding
Polyepitope including 16 different mutations. Columns represent means (±SEM) of 3 mice
per group. Photograph shows triplicate wells of cells from one exemplary animal restimulated
with the indicated peptides.
Figure 14: Vaccination with 5 different model epitopes encoded by one RNA leads to
immune responses against all encoded epitopes
A) IFN-γ ELISPOT analysis of T-cell effectors from mice vaccinated with mutation coding
model Polyepitope including 5 different model epitopes (SIINFEKL, Trp2, VSV-NP, Inf-NP,
OVA class II). Splenocytes were restimulated with the indicated peptides. Spots represent
means of triplicate wells from 5 mice per group. B) Pentamer staining of blood lymphocytes
of one control mouse and one mouse immunized with the model Polyepitope. Inf-NP
Pentamer stained CD8 cells are specific for the Inf-NP peptide.
Figure 15: A CD4 T-cell inducing mutation can induce a potent anti-tumoral effect
B16F10 melanoma in synergy with a weak CD8 T-cell epitope
C57BL/6 mice (n = 8) were inoculated with 1 x 10 B16F10 cells s.c. into the flank of the
mice. On day 3, 10 and 17 after tumor inoculation the mice were vaccinated with 100 µg
MUT30, Trp2 or both peptides + 50 µg poly(I:C). A) Shown are the mean tumor growth
kinetics of each group. On day 28 the mean values between the single treatment groups and
the untreated animals and the combination group are statistically different (Mann-Whitney
test, p-value < 0.05). B) Kaplan-Meyer survival plot of the different groups. The survival
curves of MUT30 and MUT30 + Trp2 vaccinated mice are statistically different (Log-Rank
test, p-value = 0.0029).
Figure 16: Overview of process for finding somatic mutations in B16
Numbers for the individual steps are given as an example for one B16 sample, compared to
one black6 sample. “Exons“ refers to the exon coordinates defined by all protein coding
RefSeq transcripts.
Figure 17: Venn diagramm showing the numbers of somatic variations in protein coding
exons, found by the individual, two or all three software tools, respectively
The numbers were calculated after filtering and represent the consensus of all three samples.
Figure 18: A Examples of single nucleotide variations found: A somatic mutation found in all
three B16 samples (left), a non-somatic mutation found in all B16 and black6 samples
(middle) and a mutation found in only one black6 sample (right). B The calculated FDR
distribution for the dataset of which the validated mutations were selected; the distribution is
visualized as an average estimated ROC curve with the grey bars giving the 95% confidence
interval for the mean in both dimensions at uniformly sampled positions. The mean was
obtained from the distribution of estimated ROC curves of the FDRs for all possible 18
combinations (see text).
Figure 19: A Estimated ROC curves for the comparison of the three different software tools
(duplicates, 38x coverage). B Estimated ROC curves for the comparison of different average
sequencing depths (samtools, no replication). 38x denotes the coverage obtained by the
experiment, while other coverages were downsampled starting with this data. C Estimated
ROC curves visualizing the effect of experiment replication (38x coverage, samtools). D
Estimated ROC curves for different sequencing protocols (samtools, no replication). The
curves were calculated using the results of the 2x100 nt library.
Figure 20: A Ten validated mutations with the lowest FDRs, selected using the optimal set of
parameters out of a final set of 2396 variations. None of these mutations is present in dbSNP
(version 128; genome assembly mm9). B Relative amount of variations found in the same
dataset as A for a given FDR cutoff, plotted separately for all variants in the dataset and the
validated mutations. For visual clarity only values of 0 to 10% FDR are shown.
Figure 21: Antitumoral activity of a mutation-encoding polyepitope RNA vaccine
C57BL/6 mice (n = 10) were inoculated with 1 x 10 B16F10 cells s.c. into the flank of the
mice. On day 3, 6, 10, 17 and 21 after tumor inoculation the mice were vaccinated with a
polytope RNA formulated a liposomal RNA transfection reagent. The control group received
liposomes without RNA. The figure shows the Kaplan-Meyer survival plot of the different
groups. The survival curves statistically different (Log-Rank test, p-value = 0.0008).
EXAMPLES
The techniques and methods used herein are described herein or carried out in a manner
known per se and as described, for example, in Sambrook et al., Molecular Cloning: A
Laboratory Manual, 2 Edition (1989) Cold Spring Harbor Laboratory Press, Cold Spring
Harbor, N.Y. All methods including the use of kits and reagents are carried out according to
the manufacturers’ information unless specifically indicated.
Example 1: Mutation detection and prioritization
We first demonstrate sequence profiling of tumor and normal samples to identify somatic
mutations in an unbiased manner. We demonstrate this not only for bulk tumor samples but
also, for the first time, demonstrate the ability to identify mutations from individual
circulating tumor cells. Next, we prioritize the mutations for inclusion in a poly-neo-epitopic
vaccine based on the predicted immunogenicity of the mutation and demonstrate that the
identified mutations are indeed immunogenic.
Mutation detection
The rationale for using CTCs: the detection of circulating tumor cells (CTC) from the
peripheral blood of cancer patients is a recognized independent prognostic marker for the
clinical course of tumors (Pantel et al, Trends Mol Med 2010; 16(9):398-406). For many
years, the clinical significance of CTCs has been the subject of intense scientific and clinical
research in oncology. It has been shown that the detection of CTCs in the blood of patients
with metastatic breast, prostate and colorectal cancer has prognostic relevance, providing
additional information to conventional imaging techniques and other prognostic tumor
biomarkers. Sequential blood samples drawn from a patient before, during an early stage, and
after treatment with a therapeutic agent (systemic or targeted) provides information on
treatment response/failure. The molecular analysis of drug-resistant CTCs may provide a
further insight into resistance mechanisms (e.g. mutations in specific signaling pathways or
loss of target expression) in individual patients. An additional possibility from the profiling
and genetic characterization of CTCs is the identification of novel cancer targets for the
development of new targeted therapies. This new diagnostic strategy is referred to as "Liquid
Tumor Biopsy.” As this profiling could be quickly and repetitively done, requiring only
patient blood and no surgery, this would provide a “real time” view of the tumor state.
Mutations from tumor cells: We demonstrate our ability to identify mutations using B16
melanoma cells, exome capture to extract protein coding regions, next-generation sequencing
using our HiSeq 2000, followed by bioinformatics analysis using our “iCAM” software
pipeline (Figure 1). We identify 2448 non-synonymous mutations and selected 50 for
confirmation. We were able to confirm all 50 somatic mutations.
The following is an example of the protein impact of a discovered somatic mutation in B16
melanoma cells:
Mutations from individual circulating tumor cells (CTCs): Next, we were able to identify
tumor-specific somatic mutations from NGS profiling of RNA from single CTCs. Labeled
B16 melanoma cells were intravenously injected into mouse tails, mice were sacrificed, blood
was collected from hearts, cells sorted to retrieve labeled circulating B16 cells (CTCs), RNA
extracted, a SMART-based cDNA synthesis and unspecific amplification performed, followed
by the NGS RNA-Seq assay and subsequence data analysis (below).
We profiled eight individual CTCs and identified somatic mutations. Furthermore, in eight of
eight cells, previously identified somatic mutations were identified. In multiple cases, the data
showed heterogeneity at the individual cell level. For example, at position 144078227 on
chromosome 2 (assembly mm9), in gene Snx15, two cells showed the reference nucleotide
(C) while two cells showed the mutated nucleotide (T).
This demonstrates that we are able to profile individual CTCs to identify somatic mutations, a
fundamental path to a “real-time” iVAC (individualized vaccine), in which patients are
profiled repetitively and the results reflect the current patient status rather than the status at an
earlier time point. Furthermore, this demonstrates that we are able to identify heterogeneous
somatic mutations that are present in a subset of tumor cells, enabling evaluation of mutation
frequency, such as for identification of major mutations and rare mutations.
Methods
Samples: For the profiling experiment, samples included 5-10mm tail samples from C57BL/6
mice (“Black6”) and highly aggressive B16F10 murine melanoma cells (“B16”), which are
originally derived from Black6 mice.
Circulating tumor cells (CTCs) were created using fluorescent labeled B16 melanoma cells.
B16 cells were resuspended in PBS and an equal volume of freshly prepared CFSE-Solution
(5 µM in PBS) was added to the cells. The sample was gentle mixed by vortex followed by
incubation for 10 min at room temperature. To stop the labeling reaction, the equal amount of
PBS containing 20% FSC was added to the sample and mixed gently by vortex. Following 20
min incubation at room temperature, the cells were washed twice using PBS. Finally, the cells
were resuspended in PBS and injected intravenously (i.v.) in mice. After 3 minutes the mice
were sacrificed and blood collected.
Erythrocytes from the blood samples were lysed by adding 1,5 ml fresh prepared PharmLyse
Solution (Beckton Dickinson) per 100 µl blood. After one washing step, 7-AAD was added to
the sample and incubated for 5 min at room temperature. The incubation was followed by two
washing steps and the sample was resuspended in 500 µl PBS.
The CFSE labeled circulating B16 cells were sorted with an Aria I cells-sorter (BD). Single
cells were sorted on 96-well-v-bottem plated prepared with 50 µl/well RLT buffer (Quiagen).
After finishing the sorting the plates were stored at -80°C until the Nucleic acid extraction and
sample preparation started.
Nucleic acid extraction and sample preparation: nucleic acids from B16 cells (DNA and
RNA) and Black6 tail tissue (DNA) were extracted using Qiagen DNeasy Blood and Tissue
kit (DNA) and Qiagen RNeasy Micro kit (RNA).
For individual sorted CTCs, RNA was extracted and a SMART-based cDNA synthesis and
unspecific amplification performed. RNA from sorted CTC cells was extracted with the
RNeasy Micro Kit (Qiagen, Hilden, Germany) according to the instructions of the supplier. A
modified BD SMART protocol was used for cDNA synthesis: Mint Reverse Transcriptase
(Evrogen, Moscow, Russia) was combined with oligo(dT)-T-primer long for priming of the
first-strand synthesis reaction and TS-short (Eurogentec S.A., Seraing, Belgium) introducing
an oligo(riboG) sequence to allow for creation of an extended template by the terminal
transferase activity of the reverse transcriptase and for template switch [Chenchik, A., Y. et al.
1998. Generation and use of high quality cDNA from small amounts of total RNA by SMART
PCR. In Gene Cloning and Analysis by RT-PCR. P. L. J. Siebert, ed. BioTechniques Books,
MA, Natick. 305-319]. First strand cDNA synthesized according to the manufacturer’s
instructions was subjected to 35 cycles of amplification with 5 U PfuUltra Hotstart High-
Fidelity DNA Polymerase (Stratagene, La Jolla, CA) and 0.48 µM primer TS-PCR primer in
the presence of 200 µM dNTP (cycling conditions: 2 min at 95 °C for, 30 s at 94 °C, 30 s at
65 °C, 1 min at 72 °C for, final extension of 6 min at 72 °C). Successful amplification of the
CTC genes was controlled with specific primers to monitor actin and GAPDH.
Next-generation sequencing, DNA sequencing: Exome capture for DNA resequencing was
performed using the Agilent Sure-Select solution-based capture assay [Gnirke A et al:
Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted
sequencing. Nat Biotechnol 2009, 27:182-189], in this case designed to capture all mouse
protein coding regions.
Shortly, 3 ug purified genomic DNA was fragmented to 150-200 bp’s using a Covaris S2
ultrasound device. gDNA fragments were end repaired using T4 DNA polymerase, Klenow
DNA polymerase and 5’ phosphorylated using T4 polynucleotide kinase. Blunt ended gDNA
fragments were 3’ adenylated using Klenow fragment (3’ to 5’ exo minus). 3’ single T-
overhang Illumina paired end adapters were ligated to the gDNA fragments using a 10:1
molar ratio of adapter to genomic DNA insert using T4 DNA ligase. Adapter ligated gDNA
fragments were enriched pre capture and flow cell specific sequences were added using
Illumina PE PCR primers 1.0 and 2.0 and Herculase II polymerase (Agilent) using 4 PCR
cycles.
500 ng of adapter ligated, PCR enriched gDNA fragments were hybridized to Agilent’s
SureSelect biotinylated mouse whole exome RNA library baits for 24 hrs at 65 °C.
Hybridized gDNA/RNA bait complexes where removed using streptavidin coated magnetic
beads. gDNA/RNA bait complexes were washed and the RNA baits cleaved off during
elution in SureSelect elution buffer leaving the captured adapter ligated, PCR enriched gDNA
fragments. gDNA fragments were PCR amplified post capture using Herculase II DNA
polymerase (Agilent) and SureSelect GA PCR Primers for 10 cycles.
All cleanups were done using 1,8x volume of AMPure XP magnetic beads(Agencourt) All
quality controls were done using Invitrogen’s Qubit HS assay and fragment size was
determined using Agilent’s 2100 Bioanalyzer HS DNA assay.
Exome enriched gDNA libraries were clustered on the cBot using Truseq SR cluster kit v2.5
using 7 pM and 50 bps were sequenced on the Illumina HiSeq2000 using Truseq SBS kit-HS
50 bp.
Next-generation sequencing, RNA sequencing (RNA-Seq): Barcoded mRNA-seq cDNA
libraries were prepared from 5 ug of total RNA using a modified version of the Illumina
mRNA-seq protocol. mRNA was isolated using Seramag Oligo(dT) magnetic beads (Thermo
Scientific). Isolated mRNA was fragmented using divalent cations and heat resulting in
fragments ranging from 160-220 bp. Fragmented mRNA was converted to cDNA using
random primers and SuperScriptII (Invitrogen) followed by second strand synthesis using
DNA polymerase I and RNaseH. cDNA was end repaired using T4 DNA polymerase, Klenow
DNA polymerase and 5’ phosphorylated using T4 polynucleotide kinase. Blunt ended cDNA
fragments were 3’ adenylated using Klenow fragment (3’ to 5’ exo minus). 3’ single T-
overhang Illumina multiplex specific adapters were ligated using a 10:1 molar ratio of adapter
to cDNA insert using T4 DNA ligase.
cDNA libraries were purified and size selected at 200-220 bp using the E-Gel 2% SizeSelect
gel (Invitrogen). Enrichment, adding of Illumina six base index and flow cell specific
sequences was done by PCR using Phusion DNA polymerase (Finnzymes). All cleanups were
done using 1,8x volume of AgencourtAMPure XP magnetic beads. All quality controls were
done using Invitrogen’s Qubit HS assay and fragment size was determined using Agilent’s
2100 Bioanalyzer HS DNA assay.
Barcoded RNA-Seq libraries were clustered on the cBot using Truseq SR cluster kit v2.5
using 7 pM and 50 bps were sequenced on the Illumina HiSeq2000 using Truseq SBS kit-HS
50 bp.
CTCs: For the RNA-Seq profiling of CTCs, a modified version of this protocol was used in
which 500-700 ng SMART amplified cDNA was used, paired end adapters were ligated and
PCR enrichment was done using Illumina PE PCR primers 1.0 and 2.0.
NGS data analysis, gene expression: To determine expression values, the output sequence
reads from RNA samples from the Illumina HiSeq 2000 were preprocessed according to the
Illumina standard protocol. This includes filtering for low quality reads and demultiplexing.
For RNA-Seq transcriptome analysis, sequence reads were aligned to the reference genomic
sequence [Mouse Genome Sequencing Consortium. Initial sequencing and comparative
analysis of the mouse genome. Nature, 420, 520-562 (2002)] using bowtie (version 0.12.5)
[Langmead B. et al. Ultrafast and memory-efficient alignment of short DNA sequences to the
human genome. Genome Biol 10:R25] using parameters “-v2 –best” for genome alignments
and default parameters for transcript alignments. The alignment coordinates were compared to
the exon coordinates of the RefSeq transcripts [Pruitt KD. et al. NCBI Reference Sequence
(RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins.
Nucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4] and for each transcript the counts
of overlapping alignments were recorded. Sequence reads not alignable to the genomic
sequence were aligned to a database of all possible exon-exon junction sequences of the
RefSeq transcripts. The counts of reads aligning to the splice junctions were aggregated with
the respective transcript counts obtained in the previous step and normalized to RPKM
(number of reads which map per kilobase of exon model per million mapped reads
[Mortazavi, A. et al. (2008). Mapping and quantifying mammalian transcriptomes by rna-seq.
Nat Methods, 5(7):621-628]) for each transcript. Both gene expression and exon expression
values were calculated based on the normalized number of reads overlapping each gene or
exon, respectively.
Mutation discovery, bulk tumor: 50 nt, single end, reads from the Illumina HiSeq 2000 were
aligned using bwa (version 0.5.8c) [Li H. and Durbin R. (2009) Fast and accurate short read
alignment with Burrows-Wheeler Transform. Bioinformatics, 25:1754-60] using default
options to the reference mouse genome assembly mm9. Ambiguous reads – those reads
mapping to multiple locations of the genome - were removed, the remaining alignments were
sorted, indexed and converted to a binary and compressed format (BAM) and the read quality
scores converted from the Illumina standard phred+64 to standard Sanger quality scores using
shell scripts.
For each sequencing lane, mutations were identified using three software programs: including
samtools (version 0.1.8) [Li H. Improving SNP discovery by base alignment quality.
Bioinformatics. 2011 Apr 15;27(8):1157-8. Epub 2011 Feb 13], GATK (version 1.0.4418)
[McKenna A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing
next-generation DNA sequencing data. Genome Res. 2010 Sep;20(9):1297-303. Epub 2010
Jul 19], and SomaticSniper (http://genome.wustl.edu /software/somaticsniper). For samtools,
the author-recommend options and filter criteria were used, including first round filtering,
maximum coverage 200. For samtools second round filtering, the minimum indel qualtify
score was 50, the point mutation minimum quality was 30. For GATK mutation calling, we
followed the author-designed best practice guidelines presented on the GATK user manual
(http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit). The
variant score recalibration step was omitted and replaced by the hard-filtering option. For
SomaticSniper mutation calling, the default options were used and only predicted mutations
with a “somatic score” of 30 or more were considered further.
Mutation discovery, CTCs: As per the bulk tumor iCAM process, 50 nt, single end, reads
from the Illumina HiSeq 2000 were aligned using bwa (version 0.5.8c) [5]) using default
options to the reference mouse genome assembly mm9. As CTC NGS reads were derived
from the RNA-Seq assay, reads were also aligned to transcriptome sequences, including exon-
exon junctions, using bowtie (above). Using all alignments, the nucleotide sequences from the
reads were compared to both the reference genome and the bulk-tumor derived B16
mutations. Identified mutations were evaluated both using perl scripts and manually using the
software program samtools and the IGV (Integrated Genome Viewer) to image the results.
The output of “mutation discovery” is the identification of somatic mutations in tumor cells,
from sample to NGS data to a list of mutations. In the B16 samples, we identified 2448
somatic mutations using exome resequencing.
Mutation prioritization
Next, we demonstrate a possibility of a mutation prioritization pipeline for vaccine inclusion.
This method, called “individual cancer mutation detection pipeline” (iCAM) identifies and
prioritizes somatic mutations through a series of steps incorporating multiple cutting edge
algorithms and bioinformatics methods. The output of this process is a list of somatic
mutations, prioritized based on likely immunogenicity.
Somatic mutation identification: Mutations are identified using three different algorithms, for
both the B16 and Black6 samples (Mutation discovery, above). The first iCAM step is to
combine the output lists from each algorithm to generate a high-confidence list of somatic
mutations. GATK and samtools report variants in one sample relative to a reference genome.
To select high confidence mutations with few false-positives for a given sample (i.e., tumor or
normal), mutations are selected that are identified in all replicates. Then, variants are selected
which are present in the tumor sample but not present in the normal sample. SomaticSniper
automatically reports potential somatic variations from tumor and normal data pairs. We
further filtered results through the intersection of the results obtained from replicates. To
remove as many false positive calls as possible, we intersected the list of mutations derived
from the use of all three algorithms and all replicates. The final step for each somatic mutation
is to assign a confidence value (p-value) for each mutation based on coverage depth, SNP
quality, consensus quality and mapping quality.
Mutation impact: the impact of the filtered, consensus, somatic mutations is determined by a
script within the iCaM mutation pipeline. First, mutations that occur in genomic regions that
are not unique within the genome, such as occur for some protein paralogs and pseudogenes,
are excluded from analysis as sequence reads that align to multiple locations are removed.
Second, whether the mutation occurs in a transcript is determined. Third, whether the
mutation occurs in a protein-coding region is determined. Fourth, the transcript sequence is
translated with and without the mutation to determine if there is a change in amino acid
sequence.
Mutation expression: the iCAM pipeline selects somatic mutations that are found in genes and
exons that are expressed in tumor cells. Expression levels are determined through NGS RNA-
Seq of tumor cells (above). The number of reads that overlap a gene and an exon indicates
expression levels. These counts are normalized to RPKM (Reads Per Kilobase of exon model
per Million mapped reads, [Mortazavi A. et al. Mapping and quantifying mammalian
transcriptomes by RNA-Seq. Nat Methods. 2008 Jul;5(7):621-8. Epub 2008 May 30]) and
those expressed above 10 RPKM are selected.
MHC binding: to determine the likelihood that an epitope containing the mutated peptide is
binds to an MHC molecule, the iCAM pipeline runs a modified version of the MHC
prediction software from the Immune Epitope Database (http://www.iedb.org/). The local
installation includes modifications to optimize data flow through the algorithm. For the B16
and Black6 data, the prediction was run using all available black6 MHC class I alleles and all
epitopes for the respective peptide lengths. Mutations are selected which fall in an epitope
ranked in the 95th percentile of the prediction score distribution of the IEDB training data
(http://mhcbindingpredictions.immuneepitope.org/dataset.html), considering all MHC alleles
and all potential epitopes overlapping the mutation.
Mutation selection criteria: somatic mutations are selected by the following criteria: a) have
unique sequence content, b) identified by all three programs, c) high mutation confidence, d)
non-synonymous protein change, e) high transcript expression, f) and favorable MHC class I
binding prediction.
The output of this process is a list of somatic mutations, prioritized based on likely
immunogenicity. In B16 melanoma cells, there are 2448 somatic mutations. 1247 of these
mutations are found in gene transcripts. Of these, 734 cause non-synonymous protein
changes. Of these, 149 are in genes expressed in the tumor cells. Of these, 102 of these
expressed, non-synonymous mutations are predicted to be presented on MHC molecules.
These 102 likely immunogenic mutations are then passed to mutation confirmation (below).
Mutation confirmation
Somatic mutations from DNA exome-resequencing were confirmed by either of two methods,
resequencing of the mutated region and RNA-Seq analysis.
For the confirmation of the mutations by resequencing, a genomic region containing the
mutation was amplified by standard PCR from 50 ng of both the tumor DNA and the normal
control DNA. The size of the amplified products was in the range of 150 to 400 nt. The
specificity of the reaction was controlled by loading the PCR product on the Qiaxel device
(Qiagen). PCR products were purified using the minElute PCR purification kit (Qiagen).
Specific PCR products were sequenced using the standard Sanger sequencing method
(Eurofins), followed by electropherogram analysis.
Mutation confirmation was also accomplished through examination of tumor RNA. Tumor
gene and exon expression values were generated from RNA-Seq (NGS of RNA), which
generates nucleotide sequences that were mapped to transcripts and counted. We examined
sequence data itself to identify mutations in the tumor sample [Berger MF. et al. Integrative
analysis of the melanoma transcriptome. Genome Res. 2010 Apr;20(4):413-27. Epub 2010
Feb 23], providing an independent confirmation of the DNA-derived identified somatic
mutations.
Table 1: List of genes containing the 50 validated mutations
Genes containing the 50 identified and confirmed somatic mutations, with annotation
regarding gene symbol, gene name, and predicted localization and function.
ID Symbol Entrez Gene Name Location
NM_021895 ACTN4 actinin, alpha 4 Cytoplasm
NM_028840 ARMC1 armadillo repeat containing 1 unknown
NM_029291 ASCC2 activating signal cointegrator 1 complex subunit 2 unknown
NM_024184 ASF1B ASF1 anti-silencing function 1 homolog B (S. cerevisiae) Nucleus
NM_138679 ASH1L ash1 (absent, small, or homeotic)-like (Drosophila) Nucleus
NM_015804 ATP11A ATPase, class VI, type 11A Plasma Membrane
NM_009730 ATRN attractin Extracellular Space
NM_028020 CPSF3L cleavage and polyadenylation specific factor 3-like Nucleus
NM_010017 DAG1 dystroglycan 1 (dystrophin-associated glycoprotein 1) Plasma Membrane
NM_015735 DDB1 damage-specific DNA binding protein 1, 127kDa Nucleus
NM_001080981 DDX23 DEAD (Asp-Glu-Ala-Asp) box polypeptide 23 Nucleus
NM_054046 DEF8 differentially expressed in FDCP 8 homolog (mouse) unknown
NM_019965 DNAJB12 DnaJ (Hsp40) homolog, subfamily B, member 12 Cytoplasm
NM_011262 DPF2 D4, zinc and double PHD fingers family 2 Nucleus
NM_007907 EEF2 eukaryotic translation elongation factor 2 Cytoplasm
NM_001081286 FAT1 FAT tumor suppressor homolog 1 (Drosophila) Plasma Membrane
NM_173182 FNDC3B fibronectin type III domain containing 3B unknown
NM_008057 FZD7 frizzled homolog 7 (Drosophila) Plasma Membrane
NM_201617 GNAS GNAS complex locus Plasma Membrane
NM_030035 GOLGB1 golgin B1 Cytoplasm
NM_011365 ITSN2 intersectin 2 Cytoplasm
NM_029841 KIAA2013 KIAA2013 unknown
NM_197959 KIF18B kinesin family member 18B unknown
NM_145479 KLHL22 kelch-like 22 (Drosophila) unknown
NM_018810 MKRN1 makorin ring finger protein 1 unknown
NM_001170785 MTHFD1L methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1-like Cytoplasm
NM_133947 NUMA1 nuclear mitotic apparatus protein 1 Nucleus
NM_178884 OBSL1 obscurin-like 1 unknown
NM_008765 ORC2 origin recognition complex, subunit 2 Nucleus
NM_023209 PBK PDZ binding kinase Cytoplasm
NM_033594 PCDHGA11 protocadherin gamma subfamily A, 11 Plasma Membrane
NM_025951 PI4K2B phosphatidylinositol 4-kinase type 2 beta Cytoplasm
NM_011961 PLOD2 procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2 Cytoplasm
NM_023200 PPP1R7 protein phosphatase 1, regulatory (inhibitor) subunit 7 Nucleus
NM_008986 PTRF polymerase I and transcript release factor Nucleus
NM_011240 RANBP2 RAN binding protein 2 Nucleus
NM_009438 RPL13A ribosomal protein L13a Cytoplasm
NM_009113 S100A13 S100 calcium binding protein A13 Cytoplasm
NM_001081203 SBNO1 strawberry notch homolog 1 (Drosophila) unknown
sema domain, immunoglobulin domain (Ig), short basic domain, secreted,
NM_009153 SEMA3B (semaphorin) 3B Extracellular Space
NM_026912 SNX15 sorting nexin 15 Cytoplasm
NM_024225 SNX5 sorting nexin 5 Cytoplasm
NM_008188 THUMPD3 THUMP domain containing 3 unknown
NM_133352 TM9SF3 transmembrane 9 superfamily member 3 Cytoplasm
NM_177296 TNPO3 transportin 3 Cytoplasm
NM_011640 TP53 tumor protein p53 Nucleus
NM_023279 TUBB3 tubulin, beta 3 Cytoplasm
NM_029896 WDR82 WD repeat domain 82 unknown
NM_025830 WWP2 WW domain containing E3 ubiquitin protein ligase 2 Cytoplasm
NM_001081056 XPOT exportin, tRNA (nuclear export receptor for tRNAs) Nucleus
Example 2: IVAC selection algorithm enables the detection of immunogenic mutations
To investigate if specific T-cell responses could be induced against the confirmed mutations
from B16F10 melanoma cells, naïve C57BL/6 mice (n=5/peptide) were immunized twice (d0,
d7) subcutaneously with 100 µg peptide (+ 50 µg PolyI:C as adjuvant) comprising either the
mutated or the wild type aa sequence (see Table 2). All peptides had a length of 27 aa with the
mutated/wild type aa at the central position. At day 12 mice were sacrificed and the spleen
cells were harvested. As read-out method IFNγ ELISpot was performed using 5x10 spleen
cells/well as effectors and 5x10 bone marrow dendritic cells loaded with peptides (2 µg/ml)
as target cells. The effector spleen cells were tested against the mutated peptide, the wild type
peptide and a control peptide (vesiculostomatitis virus nucleoprotein, VSV-NP).
With 44 sequences tested we observed that 6 of them induced a T-cell immunity directed
against the mutated sequence only but not to the wild type peptide (Fig. 3).
The data prove that the identified and prioritized mutations can be utilized to induce tumor
specific T-cell immunity after being utilized as peptide vaccine in antigen naïve mice.
Table 2: Listing of mutated sequences that induced a T-cell reactivity specific for the
mutated versus the wild type peptide. The amino acid exchange is marked underlined.
Num RefSeq ID Sequence Peptide Sequence T-cell
ber Type reactivit
y (mice)
12 NM_00107750, Mutated TPPPEEAMPFEFNGPAQGDHSQPPLQV 5/5
NM_010309,
NM_201618,
NM_201617
TPPPEEAMPFEFNEPAQGDHSQPPLQV
Wild Type 0/5
RVTCNRAGEKHCFSSNEAARDFGGAIQ
16 NM_008188 Mutated 3/5
Wild Type RVTCNRAGEKHCFTSNEAARDFGGAIQ 0/5
NM_023279 Mutated FRRKAFLHWYTGEAMDEMEFTEAESNM 5/5
FRRKAFLHWYTGEGMDEMEFTEAESNM
Wild Type 1/5
PSKPSFQEFVDWENVSPELNSTDQPFL
NM_197959 Mutated 5/5
Wild Type PSKPSFQEFVDWEKVSPELNSTDQPFL 1/5
34 NM_145479 Mutated HLTQQLDTYILKNVVAFSRTDKYRQLP 3/5
Wild Type HLTQQLDTYILKNFVAFSRTDKYRQLP 0/5
CGTAFFINFIAIYHHASRAIPFGTMVA
36 NM_133352 Mutated 5/5
CGTAFFINFIAIYYHASRAIPFGTMVA
Wild Type 0/5
Example 3: Identified mutations can provide therapeutic anti-tumor immunity
In order to validate whether the identified mutations have the potential to confer anti-tumor
immunity after vaccination to naïve mice we investigated this question with the peptide for
mutation number 30 that was shown to induce a mutation selective T-cell reactivity. B16F10
cells (7,5 x 10 ) were inoculated subcutaneously on d0. Mice were vaccinated with peptide 30
(see table 1; 100 µg peptide + 50 µg PolyI:C s.c.) on day -4, day +2, and day +9. The control
group received only Poly I:C (50 µg s.c.). Tumor growth was monitored every other day. At
day +16 we observed that only 1out of 5 mice in the peptide vaccine group had developed a
tumor whereas in the control group 4 out of 5 mice showed tumor growth.
The data prove that a peptide sequence incorporating a B16F10 specific mutation can confer
anti tumor immunity that is efficiently able to destroy tumor cells (see Fig. 4). Since B16F10
is a highly aggressive tumor cell line the finding that the methodology applied to identify and
prioritize mutations finally led to the selection of a mutation that by itself already is potent as
a vaccine is an important proof of concept for the whole process.
Example 4: Data supporting polyepitopic antigen presentation
Validated mutations from protein-coding regions of a patient constitute the pool from which
candidates can be selected for assembly of the poly-neo-epitope vaccine template to be used
as precursor for GMP manufacturing of the RNA vaccine. Suitable vector cassettes as vaccine
backbone has been already described (Holtkamp, S. et al., Blood, 108: 4009-4017, 2006;
Kreiter, S. et al., Cancer Immunol. Immunother., 56: 1577-1587, 2007; Kreiter, S. et al.,
J.Immunol., 180: 309-318, 2008). The preferred vector cassettes are modified in their coding
and untranslated regions (UTR) and ensure maximized translation of the encoded protein for
extended periods (Holtkamp, S. et al., Blood, 108: 4009-4017, 2006; Kuhn, A. N. et al., Gene
Ther., 17: 961-971, 2010). Furthermore, the vector backbone contains antigen routing
modules for the simultaneous expansion of cytotoxic as well as helper T-cells (Kreiter, S. et
al., Cancer Immunol. Immunother., 56: 1577-1587, 2007; Kreiter, S. et al., J. Immunol., 180:
309-318, 2008; Kreiter, S. et al., Cancer Research, 70 (22), 9031-9040, 2010 (Figure 5).
Importantly, we have proven that such RNA vaccine can be used to present multiple MHC
class I and class II epitopes simultaneously.
The IVAC poly-neo-epitope RNA vaccine sequences are built from stretches of up to 30
amino acids that include the mutation in the center. These sequences are connected head-to-
tail via short linkers to form a poly-neo-epitope vaccine coding for up to 30 or more selected
mutations and their flanking regions. These patient-specific individually tailored inserts are
codon-optimized and cloned into the RNA backbone described above. Quality control of such
constructs includes in vitro transcription and expression in cells for validation of functional
transcription and translation. Analysis of translation will be performed with antibodies against
the c-terminal targeting domain.
Example 5: Scientific proof of concept for the RNA poly-neo epitope construct
The RNA poly-neo epitope concept is based on a long in vitro transcribed mRNA which
consists of sequentially arranged sequences coding for the mutated peptides connected by
linker sequences (see Fig. 6). The coding sequences are chosen from the non synonymous
mutations and are always built up of the codon for the mutated amino acid flanked by regions
of 30 to 75 base-pairs from the original sequence context. The linker sequence codes for
amino acids that are preferentially not processed by the cellular antigen processing machinery.
In vitro transcription constructs are based on the pST1-A120 vector containing a T7 promotor,
a tandem beta-globin 3’ UTR sequence and a 120-bp poly(A) tail, which have been shown to
increase the stability and translational efficiency of the RNA thereby enhancing the T-cell
stimulatory capacity of the encoded antigen (Holtkamp S. et al., Blood 2006; PMID:
16940422). In addition, an MHC class I signal peptide fragment and the transmembrane and
cytosolic domains including the stop-codon (MHC class I trafficking signal or MITD)
flanking a poly-linker sequence for cloning the epitopes were inserted (Kreiter S. et al., J.
Immunol., 180: 309-318, 2008). The latter have been shown to increase the antigen
presentation, thereby enhancing the expansion of antigen-specific CD8+ and CD4+ T cells
and improving effector functions.
For a first proof of concept, biepitopic vectors were used, i.e. encoding one polypeptide
containing two mutated epitopes. Codon optimized sequences coding for (i) a mutated epitope
of 20 to 50 amino acids, (ii) a glycine/serine-rich linker, (iii) a second mutated epitope of 20
to 50 amino acids, and (iv) an additional glycine/serine-rich linker – flanked by suitable
recognition sites for restriction endonucleases to be cloned into the pST1-based construct as
described above – were designed and synthesized by a commercial provider (Geneart,
Regensburg, Germany). After verification of the sequence, these were cloned into the pST1-
based vector backbone to obtain constructs as depicted in Figure 6.
The pST1-A120-based plasmids as described above were linearized with a class IIs restriction
endonuclease. The linearized plasmid DNAs were purified by phenol chloroform extraction
and ethanol precipitation. Linearized vector DNAs were quantified spectrophotometrically
and subjected to in vitro transcription essentially as described by Pokrovskaya and Gurevich
(1994, Anal. Biochem. 220: 420-423). A cap analog has been added to the transcription
reaction to obtain RNAs with the correspondingly modified 5'-cap structures. In the reactions,
GTP was present at 1.5 mM, while the cap-analog was present at 6.0 mM. All other NTPs
were present at 7.5 mM. At the end of the transcription reaction, linearized vector DNA was
digested with 0.1 U/µl TURBO DNase (Ambion, Austin/TX, USA) for 15 minutes at 37°C.
RNAs were purified from these reactions using the MEGAclear Kit (Ambion, Austin/TX,
USA) as per manufacturer’s protocol. RNA concentration and quality were assessed by
spectrophotometry and analysis on a 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA).
In order to proof that a sequence incorporating a mutated amino acid and being 5´- as well as
3´-flanked by the linker sequence can be processed, presented and recognized by antigen
specific T-cells we used T-cells from peptide vaccinated mice as effector cells. In an IFNγ
ELISpot we tested whether the T-cells induced by peptide vaccination as described above are
capable of recognizing the target cells (bone marrow dendritic cells, BMDC) either pulsed
with peptide (2 µg/ml for 2h at 37°C and 5% CO ) or transfected with RNA (20 µg produced
as described above) by electroporation. As exemplified in Fig. 7 for mutation 12 and 30 (see
table 2) we could observe that the RNA construct is able to give rise to the epitope recognized
by mutation specific T-cells.
With the data provided we could demonstrate that an RNA encoded poly-neo epitope
including glycine / serine rich linker can be translated and processed in antigen presenting
cells leading to presentation of the correct epitope that is recognized by the antigen specific T-
cells.
Example 6: Poly-neo-epitope vaccine design – The relevance of the linker
The poly-neo-epitope RNA construct contains a backbone construct into which multiple
somatic mutation-encoding peptides connected with a linker peptide sequence are placed. In
addition to codon optimization and increased RNA stability and translational efficiency due to
the backbone, one embodiment of the RNA poly-neo-epitope vaccine contains linkers
designed to increase MHC Class I and II presentation of antigenic peptides and decrease
presentation of deleterious epitopes.
Linker: the linker sequence was designed to connect multiple mutation-containing peptides.
The linker should enable creation and presentation of the mutation epitope while hinder
creation of deleterious epitopes, such as those created at the junction suture between adjacent
peptides or between linker sequence and endogenous peptides. These “junction” epitopes may
not only compete with the intended epitopes to be presented on the cell surface, decreasing
vaccine efficacy, but could generate an unwanted auto-immune reaction. Thus, we designed
the linker sequence to a) avoid creating “junction” peptides that bind to MHC molecules, b)
avoid proteasomal processing to create “junction” peptides, c) be efficiently translated and
processed by the proteasome.
To avoid creation of “junction” peptides that bind MHC molecules, we compared different
linker sequences. Glycine, for example, inhibits strong binding in MHC binding groove
positions [Abastado JP. et al., J Immunol. 1993 Oct 1;151(7): 3569-75]. We examined
multiple linker sequences and multiple linker lengths and calculated the number of “junction”
peptides that bind MHC molecules. We used software tools from the Immune Epitope
Database (IEDB, http://www.immuneepitope.org/) to calculate the likelihood that a given
peptide sequence contains a ligand that will bind MHC Class I molecules.
In the B16 model, we identified 102 expressed, non-synonymous somatic mutations predicted
to be presented on MHC Class I molecules. Using the 50 confirmed mutations, we
computationally designed different vaccine constructs, including either the use of no linkers
or the use of different linker sequences, and computed the number of deleterious “junction”
peptides using the IEDB algorithm (Figure 8).
Table 5 shows the results of several different linkers, different linker lengths, and the use of
no linker and five linkers. The number of MHC-binding junction peptides ranges from 2 to 91
for the 9 aa and 10 aa epitope predictions (top and middle). The size of the linker influences
the number of junction peptides (bottom). For this sequence, the fewest 9 aa epitopes are
predicted for the 7 aa linker sequence GGSGGGG.
The Linker 1 and Linker 2 used in the RNA poly-neo epitope vaccine constructs tested
experimentally (see below) also had a favorably low number of predicted junctional
neoepitopes. This holds true for predictions of 9-mers and 10-mers.
This demonstrates that the sequence of the linker is critically important for the creation of bad
MHC binding epitopes. Furthermore, the length of the linker sequence impacts the number of
bad MHC binding epitopes. We find that sequences that are G-rich hinder the creation of
MHC-binding ligands.
Table 3. Impact of Linker (10 aa epitopes). The predicted number of bad epitopes defined
as MHC Class I binding epitopes that contain junction sequences, for each peptide linker.
Here, 10 amino acid epitopes are considered. Glycine-rich linkers have the fewest junction
epitopes.
Linker # bad epitopes (10 aa)
none 14
TSLNALLNAH 54
SIINFEKL 65
SSSSSSSSSS 85
GGGGGGGGGG 6
GGSGGGGSGG (Linker 1) 8
GGSGGGSGGG (Linker 2) 9
Table 4. Impact of Linker Part (9 aa epitopes). The predicted number of bad epitopes,
defined as MHC Class I binding epitopes that contain junction sequences, for each peptide
linker. Here, 9 amino acid epitopes are considered. Glycine-rich linkers have the fewest
junction epitopes.
Linker # bad epitopes (9 aa)
none 17
TSLNALLNAH 83
SIINFEKL 64
SSSSSSSSSS 33
GGGGGGGGGG 2
GGSGGGGSGG (Linker 1) 4
GGSGGGSGGG (Linker 2) 3
Table 5: Impact of Linker Part. The predicted number of bad epitopes, defined as MHC
Class I binding epitopes that contain junction sequences, for each peptide linker. Here, 9
amino acid epitopes are considered. Top: the number of 9 aa junction epitopes for no linker
and 5 diverse linkers. Middle: the number of 10 aa junction epitopes for no linker and 5
diverse linkers. Lower: the number of 99 aa junction epitopes for similar linkers of different
lengths. Glycine-rich linkers have the fewest junction epitopes.
To avoid proteasomal processing that may create “junction” peptides, we explored usage of
different amino acids in the linker. Glycine rich sequences impair proteasomal processing
[Hoyt MA et al. (2006). EMBO J 25 (8): 1720–9; Zhang M. and Coffino P. (2004) J Biol
Chem 279 (10): 8635–41]. Thus glycine rich linker sequences act to minimize the number of
linker-containing peptides that can be processed by the proteasome.
The linker should allow the mutation-containing peptides to be efficiently translated and
processed by the proteasome. Amino acids glycine and serine are flexible [Schlessinger A and
Rost B., Proteins. 2005 Oct 1;61(1):115-26]; including them in a linker results in a more
flexible protein. We incorporate glycine and serine into the linker to increase protein
flexibility which should allow more efficient translation and processing by the proteasome, in
turn enabling better access to the encoded antigenic peptides.
Thus, the linker should be glycine rich to hinder the creation of MHC binding bad epitopes;
should hinder the ability of the proteasome to process linker peptides, which can be
accomplished through inclusion of glycine; and should be flexible to increase access to
mutation containing peptides, which can be accomplished through the combination of glycine
and serine amino acids. Therefore, in one embodiment of the vaccine construct of the
invention, the sequences GGSGGGGSGG and GGSGGGSGGS are preferably included as
linker sequences.
Example 7: RNA poly-neo epitope vaccine
The RNA poly-neo epitope vaccine constructs are based on the pST1-A120 vector containing
a T7 promotor, a tandem beta-globin 3’ UTR sequence and a 120-bp poly(A) tail, which have
been shown to increase the stability and translational efficiency of the RNA thereby
enhancing the T-cell stimulatory capacity of the encoded antigen ((Holtkamp S. et al., Blood
2006; PMID: 16940422). In addition, an MHC class I signal peptide fragment and the
transmembrane and cytosolic domains including the stop-codon (MHC class I trafficking
signal or MITD) flanking a poly-linker sequence for cloning the epitopes were inserted
(Kreiter S. et al., J. Immunol., 180: 309-318, 2008). The latter have been shown to increase
the antigen presentation, thereby enhancing the expansion of antigen-specific CD8+ and
CD4+ T cells and improving effector functions.
To provide RNA poly-neo epitope constructs for the 50 identified and validated mutations of
B16F10 3 RNA constructs were generated. The construct consists of codon optimized
sequences coding for (i) a mutated epitope of 25 amino acids, (ii) a glycine/serine-rich linker,
(iii) repetitions of mutated epitope sequence followed by a glycine/serine-rich linker. The
chain of mutated epitope containing sequences and linkers is flanked by suitable recognition
sites for restriction endonucleases to be cloned into the pST1-based construct as described
above. The vaccine constructs were designed and synthesized by GENEART. After
verification of the sequence, these were cloned into the pST1-based vector backbone to obtain
the RNA poly-neo epitope vaccine constructs.
Description of the Clinical Approach
The Clinical Application will cover following steps:
• Eligible patients must consent to DNA analysis by next generation sequencing.
• Tumor specimen obtained from routine diagnostic procedures (paraffin embedded
formalin fixed tissue) and peripheral blood cells will be obtained and used for
mutation analysis as described.
• Discovered mutations will be confirmed
• Based on Prioritization vaccine will be designed. For RNA vaccines a master plasmid
template will be generated by gene synthesis and cloning
• Plasmids will be used for clinical grade RNA production, quality control and release
of the RNA vaccine.
• The vaccine drug product will be sent to the respective trial center for clinical
application.
• The RNA vaccine can be used as a naked vaccine in formulation buffer or
encapsulated into nanoparticles or liposomes for direct injection into e.g. lymph nodes,
s.c., i.v., i.m.. Alternatively, the RNA vaccine can be used for in vitro transfection e.g
of dendritic cells for adoptive transfer.
The whole clinical process takes less than 6 weeks. The “lag phase” between patient informed
consent and availability of the drug will be carefully addressed by the clinical trial protocol,
including allowing the standard treatment regimen to be continued until the investigational
drug product is available.
Example 8: Identification of tumor mutations and exploiting them for tumor vaccination
We applied NGS exome resequencing for mutation discovery in the B16F10 murine
melanoma cell line and identified 962 non-synonymous somatic point mutations, 563 in
expressed genes. Potential driver mutations occur in classical tumor suppressor genes (Pten,
Trp53, Tp63, Pml) and genes involved in proto-oncogenic signaling pathways that control cell
proliferation (e.g. Mdm1, Pdgfra), cell adhesion and migration (e.g. Fdz7, Fat1) or apoptosis
(Casp9). Moreover, B16F10 harbors mutations in Aim1 and Trrap that were previously
described to be frequently altered in human melanoma.
The immunogenicity and specificity of 50 validated mutations were assayed using C57BL/6
mice immunized with long peptides encoding the mutated epitopes. One third (16/50) of them
were shown to be immunogenic. Of these, 60% elicited immune responses preferentially
directed against the mutated sequence as compared to the wild type sequence.
We tested the hypothesis in tumor transplant models. Immunization with peptides conferred in
vivo tumor control in protective and therapeutic settings, qualifying mutated epitopes
containing single amino acid substitutions as effective vaccines.
Animals
C57BL/6 mice (Jackson Laboratories) were kept in accordance with federal and state policies
on animal research at the University of Mainz.
Cells
B16F10 melanoma cell line was purchased in 2010 from the American Type Culture
Collection (Product: ATCC CRL-6475, Lot Number: 58078645). Early (3rd, 4th) passages of
cells were used for tumor experiments. Cells were routinely tested for Mycoplasma. Re-
authentification of cells has not been performed since receipt.
Next-generation sequencing
Nucleic acid extraction and sample preparation: DNA and RNA from bulk B16F10 cells and
DNA from C57BL/6 tail tissue were extracted in triplicate using Qiagen DNeasy Blood and
Tissue kit (for DNA) and Qiagen RNeasy Micro kit (for RNA).
DNA exome sequencing: Exome capture for DNA resequencing was performed in triplicate
using the Agilent Sure-Select mouse solution-based capture assay (Gnirke A et al., Nat
Biotechnol 2009;27:182-9), designed to capture all mouse protein coding regions. 3 μg
purified genomic DNA (gDNA) was fragmented to 150-200 bp using a Covaris S2 ultrasound
device. Fragments were end repaired and 5’ phosphorylated and 3’ adenylated according to
the maufacturer’s instructions. Illumina paired end adapters were ligated to the gDNA
fragments using a 10:1 molar ratio of adapter to gDNA. Enriched pre capture and flow cell
specific sequences were added using Illumina PE PCR primers 1.0 and 2.0 for 4 PCR cycles.
500 ng of adapter ligated, PCR enriched gDNA fragments were hybridized to Agilent’s
SureSelect biotinylated mouse whole exome RNA library baits for 24 hrs at 65 °C.
Hybridized gDNA/RNA bait complexes where removed using streptavidin coated magnetic
beads, washed and the RNA baits cleaved off during elution in SureSelect elution buffer.
These eluted gDNA fragments were PCR amplified post capture 10 cycles. Exome enriched
gDNA libraries were clustered on the cBot using Truseq SR cluster kit v2.5 using 7 pM and
50 bps were sequenced on the Illumina HiSeq2000 using Truseq SBS kit-HS 50 bp.
RNA gene expression “transcriptome” profiling (RNA-Seq): Barcoded mRNA-seq cDNA
libraries were prepared in triplicate, from 5 μg of total RNA (modified Illumina mRNA-seq
protocol). mRNA was isolated using Seramag Oligo(dT) magnetic beads (Thermo Scientific)
and fragmented using divalent cations and heat. Resulting fragments (160-220 bp) were
converted to cDNA using random primers and SuperScriptII (Invitrogen) followed by second
strand synthesis using DNA polymerase I and RNaseH. cDNA was end repaired, 5’
phosphorylated and 3’ adenylated according to the manufacturer’s instructions. 3’ single T-
overhang Illumina multiplex specific adapters were ligated with T4 DNA ligase using a 10:1
molar ratio of adapter to cDNA insert. cDNA libraries were purified and size selected at 200-
220 bp (E-Gel 2% SizeSelect gel, Invitrogen). Enrichment, adding of Illumina six base index
and flow cell specific sequences was done by PCR using Phusion DNA polymerase
(Finnzymes). All cleanups up to this step were done with 1,8x volume of AgencourtAMPure
XP magnetic beads. All quality controls were done using Invitrogen’s Qubit HS assay and
fragment size was determined using Agilent’s 2100 Bioanalyzer HS DNA assay. Barcoded
RNA-Seq libraries were clustered and sequenced as described above.
NGS data analysis, gene expression: The output sequence reads from RNA samples were
preprocessed according to the Illumina standard protocol, including filtering for low quality
reads. Sequence reads were aligned to the mm9 reference genomic sequence (Waterston RH
et al., Nature 2002;420:520-62) with bowtie (version 0.12.5) (Langmead B et al., Genome
Biol 2009;10:R25). For genome alignments, two mismatches were allowed and only the best
alignment (“-v2 –best”) was recorded; for transcriptome alignments the default parameters
were used. Reads not alignable to the genomic sequence were aligned to a database of all
possible exon-exon junction sequences of RefSeq transcripts (Pruitt KD et al., Nucleic Acids
Res 2007;35:D61-D65). Expression values were determined by intersecting read coordinates
with those of RefSeq transcripts, counting overlapping exon and junction reads, and
normalizing to RPKM expression units (Reads which map per Kilobase of exon model per
million mapped reads) (Mortazavi A et al., Nat Methods 2008;5:621-8).
NGS data analysis, somatic mutation discovery: Somatic mutations were identified as
described in Example 9. 50 nucleotide (nt), single-end reads were aligned to the mm9
reference mouse genome using bwa (default options, version 0.5.8c) (Li H and Durbin R,
Bioinformatics 2009;25:1754-60). Ambiguous reads mapping to multiple locations of the
genome were removed. Mutations were identified using three software programs: samtools
(version 0.1.8) (Li H, Bioinformatics 2011;27:1157-8), GATK (version 1.0.4418) (McKenna
A et al, Genome Res 2010;20:1297-303), and SomaticSniper
(http://genome.wustl.edu/software/somaticsniper) (Ding L et al., Hum Mol Genet
2010;19:R188-R196). Potential variations identified in all B16F10 triplicates were assigned a
“false discovery rate” (FDR) confidence value (cf. Example 9).
Mutation selection, validation, and function
Selection: Mutations had to fulfill following criteria to be selected: (i) present in all B16F10
and absent in all C57BL/6 triplicates, (ii) FDR ≤0.05, (iii) homogeneous in C57BL/6, (iv)
occur in a RefSeq transcript, and (v) cause non-synonymous changes to be scored as an
authentic mutation. Selection for validation and immunogenicity testing required that
mutations are expressed genes (median RPKM across replicates >10).
Validation: DNA-derived mutations were classified as validated if confirmed by either Sanger
sequencing or the B16F10 RNA-Seq reads. All selected variants were amplified from 50 ng of
DNA from B16F10 cells and C57BL/6 tail tissue using flanking primers, products visualized
(QIAxcel system, Qiagen) and purified (QIAquick PCR Purification Kit, Qiagen). The
amplicon of the expected size was excised from the gel, purified (QIAquick Gel Extraction
Kit, Qiagen) and subjected to Sanger sequencing (Eurofins MWG Operon, Ebersberg,
Germany) with the forward primer used for PCR amplification.
Functional impact: The programs SIFT (Kumar P et al., Nat Protoc 2009;4:1073-81) and
POLYPHEN-2 (Adzhubei IA et al., Nat Methods 2010;7:248-9), which predict the functional
significance of an amino acid on protein function based on the location of protein domains
and cross-species sequence conservation, were employed to assess the impact of selected
mutations. Ingenuity IPA tools were used to infer gene function.
Synthetic peptides and adjuvants
All peptides including ovalbumin class I (OVA ), class II (OVA class II ), influenza
258-265 330-338
nucleoprotein (Inf-NP ), vesiculo-stomatitis virus nucleoprotein (VSV-NP ) and
366-374 52-59
tyrosinase-related protein 2 (Trp2 ) were purchased from Jerini Peptide Technologies
180-188
(Berlin, Germany). Synthetic peptides were 27 amino acids long with the mutated (MUT) or
wild type (WT) amino acid on position 14. Polyinosinic:polycytidylic acid (poly(I:C),
InvivoGen) was used as subcutaneously injected adjuvant. MHC-Pentamer specific for the
Inf-NP peptide was purchased from ProImmune Ltd..
366-374
Immunization of mice
Age-matched female mice C57BL/6 mice were injected subcutaneously with 100 μg peptide
and 50 μg poly(I:C) formulated in PBS (200 μl total volume) into the lateral flank (5 mice per
group). Every group was immunized on day 0 and day 7 with two different mutation coding
peptides, one peptide per flank. Twelve days after the initial injection mice were sacrificed
and splenocytes were isolated for immunological testing.
Alternatively, age-matched female mice C57BL/6 mice were injected intravenously with
µg in vitro transcribed RNA formulated with 20 µl Lipofectamine™ RNAiMAX
(Invitrogen) in PBS in a total injection volume of 200 µl (3 mice per group). Every group was
immunized on day 0, 3, 7, 14 and 18. Twenty-three days after the initial injection mice were
sacrificed and splenocytes were isolated for immunological testing. DNA-sequences
representing one (Monoepitope), two (Biepitope), or 16 mutations (Polyepitope) were
constructed using 50 amino acids (aa) with the mutation on position 25 (Biepitope) or 27 aa
with the mutation on position 14 (Mono- and Polyepitope), were separated by a glycin/serine
linker of 9aa and cloned into the pST1-2BgUTR-A120 backbone (Holtkamp et al., Blood
2006;108:4009-17). In vitro transcription from this template and purification were previously
described (Kreiter et al., Cancer Immunol Immunother 2007;56:1577-87).
Enzyme-linked immunospot assay
Enzyme-linked immunospot (ELISPOT) assay (Kreiter S et al., Cancer Res 2010;70:9031-40)
and generation of syngeneic bone marrow derived dendritic cells (BMDCs) as stimulators
were previously described (Lutz MB et al., J Immunol Methods 1999;223:77-92). BMDCs
were either peptide pulsed (2 μg/ml), or transfected with in vitro transcribed (IVT) RNA
coding for the indicated mutation or for control RNA (eGFP-RNA). Sequences representing
two mutations, each comprising 50 amino acids with the mutation on position 25 and
separated by a glycin/serine linker of 9aa were cloned into the pST1-2BgUTR-A120
backbone (Holtkamp S et al., Blood 2006;108:4009-17). In vitro transcription from this
template and purification were previously described (Kreiter S et al., Cancer Immunol
Immunother 2007;56:1577-87). For the assay, 5 × 10 peptide or RNA engineered BMDCs
were coincubated with 5 × 10 freshly isolated splenocytes in a microtiter plate coated with
anti-IFN-γ antibody (10 μg/mL, clone AN18; Mabtech). After 18 hours at 37°C, cytokine
secretion was detected with an anti-IFN-γ antibody (clone R4-6A2; Mabtech). Spot numbers
were counted and analyzed with the ImmunoSpot® S5 Versa ELISPOT Analyzer, the
ImmunoCaptureTM Image Acquisition software and the ImmunoSpot® Analysis software
Version 5. Statistical analysis was done by student´s t-test and Mann-Whitney test (non-
parametric test). Responses were considered significant, when either the test gave a p-value <
0.05 and the mean spot numbers were >30 spots/5x10 effector cells. Reactivities were rated
by mean spot numbers (-: <30; +: >30; ++: >50; +++ >200 spots/well).
Intracellular cytokine assay
Aliquots of the splenocytes prepared for the ELISPOT assay were subjected to analysis of
cytokine production by intracellular flow cytometry. To this end 2 x 10 splenocytes per
sample were plated in culture medium (RPMI + 10% FCS) supplemented with the Golgi
inhibitor Brefeldin A (10µg/mL) in a 96-well plate. Cells from each animal were restimulated
for 5h at 37°C with 2 x 10 peptide pulsed BMDCs. After incubation the cells were washed
with PBS, resuspended in 50µl PBS and extracellularly stained with the following anti-mouse
antibodies for 20 min at 4°C: anti-CD4 FITC, anti-CD8 APC-Cy7 (BD Pharmingen). After
incubation the cells were washed with PBS and subsequently resuspended in 100µL
Cytofix/Cytoperm (BD Bioscience) solution for 20 min at 4°C for permeabilization of the
outer membrane. After permeabilization the cells were washed with Perm/Wash-Buffer (BD
Bioscience), resuspended in 50µL/sample in Perm/Wash-Buffer and intracellularly stained
with the following anti-mouse antibodies for 30 min at 4°C: anti-IFN- γ PE, anti-TNF-α PE-
Cy7, anti-IL2 APC (BD Pharmingen). After washing with Perm/Wash-Buffer the cells were
resuspended in PBS containing 1% paraformyldehyde for flow cytometry analysis. The
samples were analyzed using a BD FACSCanto™ II cytometer and FlowJo (Version 7.6.3).
B16 melanoma tumor model
For tumor vaccination experiments 7.5 × 10 B16F10 melanoma cells were inoculated s.c.
into the flanks of C57BL/6 mice. In the prophylactic setting, immunization with mutation-
specific peptide was performed 4 days before and on days 2 and 9 after tumor inoculation. For
the therapeutic experiment the peptide vaccine was administered on days 3 and 10 after tumor
injection. The tumor sizes were measured every three days and mice were sacrificed when
tumor diameter reached 15 mm.
Alternatively, for tumor vaccination experiments 1 × 10 B16F10 melanoma cells were
inoculated s.c. into the flanks of age-matched female C57BL/6 mice. Peptide vaccination was
performed on days 3, 10 and 17 after tumor inoculation with 100 µg peptide and 50 µg
poly(I:C) formulated in PBS (200 µl total volume) injected subcutaneously into the lateral
flank. RNA immunizations were performed using 20 µg in vitro transcribed mutation-
encoding RNA formulated with 20 µl Lipofectamine™ RNAiMAX (Invitrogen) in PBS in a
total injection volume of 200 µl. As control one group of animals was injected with
RNAiMAX (Invitrogen) in PBS. The animals were immunized on days 3, 6, 10, 17 and 21
after tumor inoculation. The tumor sizes were measured every three days using a caliper and
mice were sacrificed when tumor diameter reached 15 mm.
Identification of non-synonymous mutations in B16F10 mouse melanoma
Our objective was to identify potentially immunogenic somatic point mutations in B16F10
mouse melanoma by NGS and to test these for in vivo immunogenicity by peptide vaccination
of mice measuring elicited T-cell responses by ELISPOT assay (Figure 9A). We sequenced
the exomes of the C57BL/6 wild type background genome and of B16F10 cells, each with
triplicate extractions and captures. For each sample, more than 100 million single-end 50 nt
reads were generated. Of these 80%, align uniquely to the mouse mm9 genome and 49% align
on target, demonstrating successful target enrichment and resulting in over 20-fold coverage
for 70% of the target nucleotides in each of the triplicate samples. RNA-Seq of B16F10 cells,
also profiled in triplicate, generated a median of 30 million single-end 50 nt reads, of which
80% align to the mouse transcriptome.
DNA reads (exome-capture) from B16F10 and C57BL/6 were analyzed to identify somatic
mutations. Copy number variation analysis (Sathirapongsasuti JF et al., Bioinformatics
2011;27:2648-54) demonstrated DNA amplifications and deletions in B16F10, including the
homozygous deletion of tumor suppressor Cdkn2a (Cyclin-dependent kinase inhibitor 2A,
p16Ink4A). Focusing on point mutations to identify possible immunogenic mutations, we
identified 3570 somatic point mutations at FDR ≤ 0.05 (Figure 9B). The most frequent class
of mutations were C>T / G>A transitions, typically resulting from ultraviolet light (Pfeifer GP
et al., Mutat Res 2005;571:19-31). Of these somatic mutations, 1392 occur in transcripts, with
126 mutations in untranslated regions. Of the 1266 mutations in coding regions, 962 cause
non-synonymous protein changes and 563 of these occur in expressed genes (Figure 9B).
Assignment of identified mutations to carrier genes and validation
Noteworthy, many of the mutated genes (962 genes containing non-synonymous somatic
point mutations) have been previously associated with the cancer phenotypes. Mutations were
found in established tumor suppressor genes, including Pten, Trp53 (also called p53), and
Tp63. In Trp53, the best established tumor suppressor (Zilfou JT et al., Cold Spring Harb
Perspect Biol 2009;1:a001883), the asparagine to aspartic acid mutation at protein position
127 (p.N127D) is localized in the DNA binding domain and is predicted by SIFT to alter
function. Pten contained two mutations (p.A39V, p.T131P), both of which are predicted to
have deleterious impact on protein function. The p.T131P mutation is adjacent to a mutation
(p.R130M) shown to diminish phosphatase activity (Dey N et al., Cancer Res 2008;68:1862-
71). Moreover, mutations were found in genes associated with DNA repair pathways, such as
Brca2 (breast cancer 2, early onset), Atm (ataxia telangiectasia mutated), Ddb1 (damage-
specific DNA binding protein 1) and Rad9b (RAD9 homolog B). Furthermore, mutations
occur in other tumor associated genes, including Aim1 (tumor suppressor “Absent In
Melanoma 1”), Flt1 (oncogene Vegr1, fms-related tyrosine kinase 1), Pml (tumor suppressor
“promyelocytic leukemia”), Fat1 (“FAT tumor suppressor homolog 1”), Mdm1 (TP53
binding nuclear protein), Mta3 (metastasis associated 1 family, member 3), and Alk
(anaplastic lymphoma receptor tyrosine kinase). We found a mutation at p.S144F in Pdgfra
(platelet-derived growth factor receptor, alpha polypeptide), a cell-membrane-bound receptor
tyrosine kinase of the MAPK/ERK pathway, previously identified in tumors (Verhaak RG et
al., Cancer Cell 2010;17:98-110). A mutation occurs at p.L222V in Casp9 (caspase 9,
apoptosis-related cysteine peptidase). CASP9 proteolytically cleaves poly(ADP-ribose)
polymerase (PARP), regulates apoptosis, and has been linked to several cancers (Hajra KM et
al., Apoptosis 2004;9:691-704). The mutation we found may potentially impact PARP and
apoptosis signaling. Most interestingly, no mutations were found in Braf, c-Kit, Kras or Nras.
However, mutations were identified in Rassf7 (RAS-associated protein) (p.S90R), Ksr1
(kinase suppressor of ras 1) (p.L301V), and Atm (PI3K pathway) (p.K91T), all of which are
predicted to have significant impact on protein function. Trrap (transformation/transcription
domain-associated protein) was identified earlier this year in human melanoma specimens as
a novel potential melanoma target (Wei X et al., Nat Genet 2011;43:442-6). In B16F10, a
Trrap mutation occurs at p.K2783R and is predicted to disturb the overlapping
phosphatidylinositol kinase (PIK)-related kinase FAT domain.
From the 962 non-synonymous mutations identified using NGS, we selected 50 mutations,
including 41 with FDR < 0.05, for PCR-based validation and immunogenicity testing.
Selection criteria were location in an expressed gene (RPKM > 10) and predicted
immunogenicity. Noteworthy, we were able to validate all 50 mutations (Table 6, Figure 9B).
Table 6: Mutations selected for validation. From left: assigned ID, gene symbol, amino
acid substitution and position, gene name, predicted subcellular localization and type
(Ingenuity).
Figure 9C shows the locations of the B16F10 chromosomes, genes density, gene expression,
mutations, and filtered mutations (inner rings).
In vivo testing of immunogenicity testing with mutation-representing long peptides
To provide antigens for immunogenicity testing of these mutations, we employed long
peptides which have many advantages over other peptides for immunization (Melief CJ and
van der Burg SH, Nat Rev Cancer 2008;8:351-60). Long peptides are capable of inducing
antigen-specific CD8+ as well as CD4+ T-cells (Zwaveling S et al., Cancer Res
2002;62:6187-93; Bijker MS et al., J Immunol 2007;179:5033-40). Moreover, long peptides
require processing to be presented on MHC molecules. Such uptake is most efficiently done
by dendritic cells, which are optimal for priming a potent T-cell response. Fitting peptides, in
contrast, do not require trimming and are loaded exogenously on all cells expressing MHC
molecules, including non-activated B and T-cells, leading to induction of tolerance and
fratricide (Toes RE et al., J Immunol 1996;156:3911-8; Su MW et al., J Immunol
1993;151:658-67). For each of the 50 validated mutations, we designed peptides of 27 amino
acids length with the mutated or wild type amino acid positioned centrally. Thus, any
potential MHC class I and class II epitope of 8 to 14 amino acid length carrying the mutation
could be processed from this precursor peptide. As adjuvant for peptide vaccination we used
poly(I:C) which is known to promote cross presentation and increase vaccine efficacy (Datta
SK et al., J Immunol 2003;170:4102-10; Schulz O et al., Nature 2005;433:887-92). The 50
mutations were tested in vivo in mice for induction of T-cells. Impressively, 16 out of 50
mutation-coding peptides were found to elicit immune responses in immunized mice. The
induced T-cells displayed different reactivity patterns (Table 7).
Table 7: Summary of T-cell reactivities determined consecutive to vaccination with
mutation encoding peptide. Statistical analysis was done by student´s t-test and Mann-
Whitney test (non-parametric test). Responses were considered significant, when either test
gave a p-value < 0,05 and the mean spot numbers were >30 spots/5x10 effector cells.
Reactivities were rated by mean spot numbers -: <30; +: >30; ++: >50; +++ >200 spots/well.
Eleven peptides induced an immune response preferentially recognizing the mutated epitope.
This is exemplified for mice immunized with mutations 30 (MUT30, Kif18b) and 36
(MUT36, Plod2) (Figure 10A). ELISPOT testing revealed strong mutation-specific immune
responses without cross reactivity against the wild-type peptide or an unrelated control
peptide (VSV-NP). With five peptides, including mutations 05 (MUT05, Eef2) and 25
(MUT25, Plod2) (Figure 10A), immune responses with comparable recognition of both the
mutated as well as the wild-type peptide were obtained. The majority of mutated peptides
were not capable of inducing significant T-cell responses as exemplified by mutations 01
(MUT01, Fzd7), 02 (MUT02, Xpot), and 07 (MUT07, Trp53). Immune responses induced by
several of the discovered mutations were well in the range of immunogenecity (500
spots/5x10 cells) generated by immunizing mice as a positive control with a described MHC-
class I epitope from the murine melanoma tumor antigen tyrosinaserelated protein 2
(Trp2180-188, Figure 10A) (Bloom MB et al., Exp Med 1997;185:453-9; Schreurs MW et al.
Cancer Res 2000;60:6995-7001). For selected peptides that induce a strong mutation-specific
T-cell response, we confirmed immune recognition by an independent approach. Instead of
long peptides, in vitro transcribed RNA (IVT RNA) coding for the mutated peptide fragments
MUT17, MUT30 and MUT44 was used for the immunological read-out. BMDCs transfected
with mutation-coding RNA or irrelevant RNA served as antigen presenting cells (APCs) in an
ELISPOT assay, whereas spleen cells of immunized mice served as effector cell population.
BMDCs transfected with MUT17, MUT30 and MUT44 encoding mRNA were specifically
and strongly recognized by splenocytes of mice immunized with the respective long peptides
(Figure 10B). Significantly lower reactivity against control RNA-transfected BMDCs was
recorded, which is likely due to the unspecific activation of the BMDCs by the single stranded
RNA (student´s t-test; MUT17: p = 0.0024, MUT30: p = 0.0122, MUT44: p = 0.0075). These
data confirm that the induced mutation-specific T-cells in effect recognize endogenously
processed epitopes. Two mutations that induce a preferred recognition of mutated epitopes are
in genes Actn4 and Kif18b. The somatic mutation in ACTN4 (actinin, alpha 4) is at p.F835V
in the calcium binding “EF-hand” protein domain. While both SIFT and POLYPHEN predict
a significant impact of this mutation on protein function, the gene is not an established
oncogene. However, mutation-specific T-cells against ACTN4 have been recently associated
with a positive patient outcome (Echchakir H et al., Cancer Res 2001;61:4078-83). KIF18B
(kinesin family member 18B) is a kinesin with microtubule motor activity and ATP and
nucleotide binding that is involved in regulation of cell division (Lee YM et al., Gene
2010;466:16-25) (Figure 10C). The DNA sequence at the position encoding p.K739 is
homogeneous in the reference C57BL/6, whereas B16F10 DNA reads reveal a heterozygous
somatic mutation. Both nucleotides were detected in the B16F10 RNA-Seq reads and
validated by Sanger sequencing. KIF18B has not been previously associated with a cancer
phenotype. The mutation p.K739N is not localized in a known functional or conserved protein
domain (Figure 10C, bottom) and thus most likely is a passenger rather than a driver
mutation. These examples suggest a lack of correlation between the capability of inducing
mutation-recognizing immune response and a functional or immunological relevance.
In vivo assessment of antitumoral activity of vaccine candidates
To assess whether immune responses elicited in vivo translate in anti-tumoral effects in tumor
bearing mice, we chose MUT30 (mutation in Kif18b) and MUT44 as examples. These
mutations had been shown to induce a strong immune reaction preferentially against the
mutated peptide and to be endogenously processed (Figure 10A, B). The therapeutical
potential of vaccinating with mutated peptides was explored by immunizing mice with either
MUT30 or MUT44 and adjuvant 3 and 10 days after grafting with 7.5x10 B16F10. Growth
of tumors was inhibited by both peptide vaccinations as compared to the control group
(Figure 11A). As B16F10 is a very aggressively growing tumor, we also tested protective
immune responses. Mice were immunized with MUT30 peptide, inoculated s.c. with 7.5x10
B16F10 cells 4 days later and boosted with MUT30 2 and 9 days after tumor challenge.
Complete tumor protection and survival of 40% of the mice treated with MUT30 were
observed, whereas all mice in the control treated group died within 44 days (Figure 11B left).
In those mice, developing tumors despite immunization with MUT30, growth of tumors was
slower resulting in an elongation of the median survival by 6 days as compared to the control
group (Figure 11B right). These data imply that already vaccination against a single mutation
is able to confer anti-tumoral effects.
Immunization with mutation-coding RNAs
The 50 validated mutations from the B16F10 melanoma cell line were used to construct
different RNA vaccines. DNA-sequences representing one (Monoepitope), two (Biepitope),
or 16 different mutations (Polyepitope), were constructed using 50 amino acids (aa) with the
mutation on position 25 (Biepitope) or 27 aa with the mutation on position 14 (Mono- and
Polyepitope) and were separated by a glycine/serine linker of 9aa. These constructs were
cloned into the pST1-2BgUTR-A120 backbone for in vitro transcription of mRNA (Holtkamp
et al., Blood 2006;108:4009-17).
To test the in vivo ability to induce T-cell responses against the different RNA-vaccines
groups of three C57BL/6 mice were immunized by formulation of the RNA with RNAiMAX
lipofectamine and subsequent intravenous injection. After 5 immunizations the mice were
sacrificed and splenocytes were analyzed for mutation-specific T-cell responses using
intracellular cytokine staining and IFN-γ ELISPOT analysis after restimulation with the
corresponding mutation coding peptide or control peptide (VSV-NP).
Figure 12 shows one example for each vaccine design. In the upper row the mice were
vaccinated with the Monoepitope-RNA coding for MUT30 (mutation in Kif18b), which
induces MUT30-specific CD4 T-cells (see exemplary FACS-plot). In the middle row the
graph and FACS-plot show induction of MUT08-specific (mutation in Ddx23) CD4 T-cells
after immunization with the Biepitope coding for MUT33 and MUT08. In the lower row mice
were immunized with a Polyepitope encoding 16 different mutations including MUT08,
MUT33 and MUT27 (see Table 8). The graph and FACS-plot illustrate that MUT27 reactive
T-cells are of a CD8 phenotype.
Table 8. Overview of mutations and gene names encoded by Mono-, Bi- and Polyepitope
RNA-vaccines.
Construct Encoded mutation Gene annotation
Monoepitope MUT30 Kif18b
MUT33 Pbk
Biepitope
MUT08 Ddx23
MUT01 Fzd7
MUT02 Xpot
MUT03 Ranbp2
MUT04 Dnajb12
MUT05 Eef2
MUT06 Ptrf
MUT07 Trp53
MUT08 Ddx23
Polyepitope
MUT26 Orc2
MUT27 Obsl1
MUT28 Ppp1r7
MUT29 Mthfd1l
MUT30 Kif18b
MUT31 Ascc2
MUT32 Itsn2
MUT33 Pbk
The same Polyepitope was used to generate the data shown in Figure 13. The graph shows
ELISPOT data after restimulation of splenocytes with control (VSV-NP), MUT08, MUT27
and MUT33 peptides, proving that the Polyepitope vaccine can induce specific T-cell
responses against several different mutations.
Taken together the data show the possibility to induce mutation-specific T-cells using RNA-
encoded Mono-,Bi- and Polyepitopes. Furthermore, the data show induction of CD4 and
CD8 T cells and the induction of several different specificities from one construct.
Immunization with model epitopes
To further characterize the polyepitopic RNA-vaccine design a DNA-sequence was
constructed, which included five different known model epitopes including one MHC class II
epitope (ovalbumin class I (SIINFEKL), class II (OVA class II), influenza nucleoprotein (Inf-
NP), vesiculo-stomatitis virus nucleoprotein (VSV-NP) and tyrosinase-related protein 2
(Trp2)). The epitopes were separated with the same glycine/serine linker of 9aa used for the
mutation Polyepitope. This constructs was cloned into the pST1-2BgUTR-A120 backbone for
in vitro transcription of mRNA.
The in vitro transcribed RNA was used to vaccinate five C57BL/6 mice by intranodal
immunization (four immunizations with 20µg of RNA into the inguinal lymphnodes). Five
days after the last immunization blood samples and splenocytes were taken from the mice for
analysis. Figure 14A shows IFN-γ ELISPOT analysis of the splenocytes restimulated with the
indicated peptides. It can be clearly seen that all three MHC-class I epitope (SIINFEKL, Trp2
and VSV-NP) induce a very high number of antigen-specific CD8 T cells. Also the MHC-
class II epitope OVA class II induces a strong CD4 T-cell response. The fourth MHC class I
epitope was analyzed by staining of Inf-NP-specific CD8 T-cells with a fluorescence-labeled
pentameric MHC-peptide complex (Pentamer) (Figure 14B).
These data prove that the polyepitope design using the glycine/serine linker to separate
different immunogenic MHC-class I and -class II epitopes is able to induce specific T-cells
against every encoded epitope, regardless of its immunodominance.
Anti-tumoral response after therapy with a mutation-encoding polyepitopic RNA
vaccine
The same Polyepitope which was analyzed in Figure 13 for immunogenicity was used to
investigate the anti-tumoral activity of the mutation-encoding RNAs against the B16F10
tumor cells. In detail, groups of C57BL/6 mice (n=10) were subcutaneously inoculated with 1
x 10 B16F10 melanoma cells into the flank. On days 3, 6, 10, 17 and 21 the mice were
immunized with the polytopic RNA using a liposomal transfection reagent. The control group
was injected with liposomes alone.
Figure 21 shows the survival curves of the groups, revealing a strongly improved median
survival of 27 days with 1 of 10 mice surviving without tumor compared to 18,5 days median
survival in the control group.
Anti-tumoral response after therapy with a combination of mutated and normal peptide
Anti-tumoral activity of the validated mutations was evaluated by a therapeutic in vivo tumor
experiment by using the MUT30 as a peptide vaccine. In detail, groups of C57BL/6 mice
(n=8) were subcutaneously inoculated with 1 x 10 B16F10 melanoma cells into the flank. On
day 3, 10 and 17 the mice were immunized using polyI:C as adjuvant with MUT30,
tyrosinase-related protein 2 (Trp2 ) or a combination of both peptides. Trp2 is a known
180-188
CD8 epitope expressed by the B16F10 melanoma cells.
Figure 15 A shows the mean tumor growth of the groups. It can be clearly seen that until day
28 the tumor growth is almost completely inhibited in the group which was immunized with
the combination of the known CD8 T-cell epitope and the CD4 T-cell inducing MUT30.
The known Trp2 epitope alone is not sufficient to provide a good anti-tumoral effect in this
setting, but both single therapy groups (MUT30 and Trp2) still provide a tumor growth
inhibition in comparison to the untreated group in the beginning of the experiment up to day
. These data are strengthened by the survival curves shown in Figure 15 B. Clearly the
median survival is increased by the mice injected with the single peptides, with 1/8 mice
surviving in the group with Trp2 vaccination. In addition the group treated with both peptides
shows an even better median survival with 2/8 mice surviving.
Taken together both epitopes act in a synergistic manner to provide a strong anti-tumoral
effect.
Example 9: Framework for confidence-based somatic mutation detection and
application to B16-F10 melanoma cells
NGS is unbiased in that it enables a high throughput discovery of variations within an entire
genome or targeted regions, such as protein coding exons.
However, while revolutionary, the NGS platform is still prone to errors leading to erroneous
variation calls. Furthermore, the quality of results is dependent on experimental design
parameters and analysis methodologies. While variation calls typically include scores
designed to differentiate true variations from errors, the utility of these scores is not fully
understood, nor is their interpretation with regard to optimization of experiments. This is
particularly true when comparing tissue states, such comparing tumor and normal for somatic
mutations. As a consequence, researchers are forced to rely on personal experience to
determine experimental parameters and arbitrary filtering thresholds for selecting mutations.
Our study aims a) to establish a framework for comparing parameters and methods to identify
somatic mutations and b) to assign a confidence value to identified mutations. We sequence
triplicate samples from C57BL/6 mice and the B16-F10 melanoma cell line. Using these data,
we formulate the false discovery rate of detected somatic mutations, a measure that we then
use to evaluate existing mutation discovery software and lab protocols.
Various experimental and algorithmic factors contribute to the false positive rate for
variations found by NGS [Nothnagel, M. et al., Hum. Genet. 2011 Feb 23 [Epub ahead of
print]]. The error sources include PCR artifacts, biases in priming [Hansen, K.D., et al.,
Nucleic. Acids. Res. 38, e131 (2010); Taub, M.A. et al., Genome Med. 2, 87 (2010)] and
targeted enrichment [Bainbridge, M.N. et al., Genome Biol. 11, R62 (2010)], sequence effects
[Nakamura, K. et al., Acids Res.(2011) first published online May 16, 2011
doi:10.1093/nar/gkr344], base calling causing sequence errors [Kircher, M. et al., Genome
Biol. 10, R83 (2009). Epub 2009 Aug 14] and read alignment [Lassmann, T. et al.,
Bioinformatics 27, 130–131 (2011)], causing variation in coverage and sequencing errors
which influence the further downstream analysis, e.g. variant calling around indels [Li, H.,
Bioinformatics 27, 1157-1158 (2011)].
No general statistical model has been described to describe the impact of different error
sources on somatic mutation calls; only individual aspects are covered without removing all
bias. Recent computational methods to measure the expected amount of false positive
mutation calls include utilization of the transition/transversion ratio of a set of variations
[Zhang, Z., Gerstein, M., Nucleic Acids Res 31, 5338-5348 (2003); DePristo, M.A. et al.,
Nature Genetics 43, 491–498 (2011)], machine learning [DePristo, M.A. et al., Nature
Genetics 43, 491–498 (2011)] and inheritance errors when working with family genomes
[Ewen, K.R. et al., Am. J. Hum. Genet. 67, 727-736 (2000)] or pooled samples [Druley, T.E.
et al., Nature Methods 6, 263 - 265 (2009); Bansal, V., Bioinformatics 26, 318-324 (2010)].
For optimization purposes, Druley et al. [Druley, T.E. et al., Nature Methods 6, 263 - 265
(2009)] relied on short plasmid sequence fragments, which however might not be
representative for the sample. For a set of single nucleotide variations (SNVs) and selected
experiments, a comparison to SNVs identified by other techniques is feasible [Van Tassell,
C.P. et al., Nature Methods 5, 247 - 252 (2008)] but is difficult to evaluate in terms of novel
somatic mutations.
Using an exome sequencing project as an example, we propose the calculation of a false
discovery rate (FDR) based on NGS data alone. The method is not only applicable to the
selection and prioritization of diagnostic and therapeutic targets, but also supports algorithm
and method development by allowing us to define confidence-driven recommendations for
similar experiments.
To discover mutations, DNA from tail tissue of three C57BL/6 (black6) mice (litter mates)
and DNA from B16-F10 (B16) melanoma cells, in triplicate, were individually enriched for
protein coding exons (Agilent Sure Select Whole Mouse Exome), resulting in 6 samples.
RNA was extracted from B16 cells in triplicate. Single end 50 nt (1x50 nt) and paired end 100
nt (2x100 nt) reads were generated on an Illumina HiSeq 2000. Each sample was loaded into
an individual lane, resulting in an average of 104 million reads per lane. DNA reads were
aligned to the mouse reference genome using bwa [Li, H. Durbin, R., Bioinformatics 25,
1754-1760 (2009)] and RNA reads were aligned with bowtie [Langmead, B. et al., Genome
Biol. 10, R25 (2009)]. A mean coverage of 38 fold of 97% of the targeted regions was
achieved for the 1x50 nt libraries, while the 2x100 nt experiment yielded an average coverage
of 165 fold for 98% of the targeted regions.
Somatic variations were independently identified using the software packages SAMtools [Li,
H. et al., Bioinformatics 25, 2078-2079 (2009)], GATK [DePristo, M.A. et al., Nature
Genetics 43, 491–498 (2011)] and SomaticSNiPer [Ding, L. et al., Hum. Mol. Genet (2010)
first published online September 15, 2010] (Fig. 16) by comparing the single nucleotide
variations found in B16 samples to the corresponding loci in the black6 samples (B16 cells
were originally derived from a black6 mouse). The potential mutations were filtered
according to recommendations by the respective software authors (SAMtools and GATK) or
by selecting an appropriate lower threshold for the somatic score of SomaticSNiPer,
respectively.
To create a false discovery rate (FDR) for mutation discovery, we first intersected the
mutation sites and obtained 1,355 high quality somatic mutations as consensus among all
three programs (Fig. 17). However, the observed differences in the results of the applied
software tools are substantial. To avoid erroneous conclusions, we developed a method to
assign a FDR to each mutation using the replicates. Technical repeats of a sample should
generate identical results and any detected mutation in this “same vs. same comparison” is a
false positive. Thus, to determine the false discovery rate for somatic mutation detection in a
tumor sample relative to a normal sample (“tumor comparison”), we can use a technical
repeat of the normal sample as a reference to estimate the number of false positives.
Figure 18A shows examples of variations found in the black6/B16 data, including a somatic
mutation (left), non-somatic variation to the reference (middle), and possible false positive
(right). Each somatic mutation can be associated with a quality score Q. The number of false
positives in the tumor comparison indicates a number of false positives in the same vs. same
comparison. Thus, for a given mutation with quality score Q detected in the tumor
comparison, we estimate the false discovery rate by computing the ratio of same vs. same
mutations with a score of Q or better to the overall number of mutations found in the tumor
comparison with a score of Q or better.
A challenge arises in defining Q since most mutation detection frameworks compute multiple
quality scores. Here, we apply a random forest classifier [Breiman, L., Statist. Sci. 16, 199-
231 (2001)] to combine multiple scores into a single quality score Q. We refer to the methods
section for details regarding details of the quality score and FDR computation.
A potential bias in comparing methods is differential coverage; we thus normalize the false
discovery rate for the coverage:
We calculate the common coverage by counting all bases of the reference genome which are
covered by both the tumor and normal sample or by both “same vs. same” samples,
respectively.
By estimating the number of false positives and positives at each FDR (see Methods), we
generate receiver operating characteristic (ROC) curves and calculate the AUC (area under
the curve) for each mutation discovery method, thus enabling a comparison of strategies for
mutation discovery (Fig. 18B).
Furthermore, the selection of the reference data might influence the calculation of the FDRs.
Using the available black6/B16 data it is possible to create 18 triplets (combinations of black6
vs. black6 and black6 vs. b16). When comparing the resulting FDR distributions for the sets
of somatic mutations, the results are consistent (Fig. 18B).
Using this definition of a false discovery rate, we have established a generic framework for
evaluating the influence of numerous experimental and algorithmic parameters on the
resulting set of somatic mutations. Next, we apply this framework to study the influence of
software tools, coverage, paired end sequencing and the number of technical replicates on
somatic mutation identification.
First, the choice of the software tool has a clear impact on the identified somatic mutations
(Fig. 19A). On the tested data, SAMtools produces the highest enrichment of true positives in
a set of somatic mutations ranked by the FDR. However, we note that all tools offer many
parameters and quality scores for the individual mutations. Here, we have used the default
settings as specified by the algorithm developers; we expect that the parameters could be
optimized and emphasize that the FDR framework defined here is designed for running and
evaluating such an optimization.
For the described B16 sequencing experiment, we sequenced each sample in an individual
flowcell lane and achieved a target region mean base coverage of 38 fold for the individual
samples. However, this coverage might not be needed to obtain an equally good set of somatic
mutations, possibly reducing costs. Also, the impact of the depth of caverage on whole
genome SNV detection has been discussed recently [Ajay, S.S. et al., Genome Res. 21, 1498-
1505 (2011)]. In order to study the effect of the coverage on exon capture data, we
downsampled the number of aligned sequence reads for every 1x50 nt library to generate an
approximate coverage of 5, 10 and 20 fold, respectively, and then reapplied the mutation call
algorithms. As expected, a higher coverage results in a better (i.e. fewer false positives)
somatic mutation set, although the improvement from the 20 fold coverage to the maximum is
marginal (Fig. 19B).
It is straightforward to simulate and rank different experimental settings using the available
data and framework. Comparing duplicates to triplicates, triplicates do not offer a benefit
compared to the duplicates (Fig. 19C), while duplicates offer a clear improvement compared
to a study without any replicates. In terms of the ratio of somatic mutations in the given sets,
we see enrichment at a FDR of 5% from 24.2% for a run without replicates to 71.2% for
duplicates and 85.8% for triplicates. Despite the enrichment, using the intersection of
triplicates removes more mutations with a low FDR than ones with a high FDR, as indicated
by the lower ROC AUC and the shift of the curve to the left (Fig. 19C): the specificity is
slightly increased at the cost of a lower sensitivity.
The additionally sequenced 2x100 nt library was used to simulate a 1x100, two 2x50 and two
1x50 nt libraries, respectively, by in silicio removal of the second read and/or the 3’ and 5’
ends of the reads, resulting in a total of 5 simulated libraries. These libraries were compared
using the calculated FDRs of predicted mutations (Fig. 19D). Despite the much higher mean
coverage (more than 77 vs. 38), the somatic mutations found using the 2x50 5’ and 1x100 nt
libraries have a lower ROC AUC and thus a worse FDR distribution than the 1x50 nt library.
This phenomenon results from the accumulation of high FDR mutations in low coverage
regions as the sets of low FDR mutations found are highly similar. The consequence is that
the optimal sequencing length is either small so that the sequenced bases are concentrated
around the capture probe sequences (potentially losing information on the somatic status of
mutations in non-covered regions, though) or should be close to the fragment length (2x100 nt
= 200 nt total length for ~250 nt fragments in our case), effectively filling up the coverage
gaps. This is also supported by the ROC AUC of the 2x50 nt 3’ library (simulated by using
only the 3’ ends of the 2x100 nt library) which is higher than the one of the 2x50 nt 5’ library
(simulated by using only the 5’ ends of the 2x100 nt library) despite the lower base quality of
the 3’ read ends.
These observations allow us to define best practice procedures for the discovery of somatic
mutations. Across all evaluated parameters, 20 fold coverage in both samples and using a
technical duplicate achieves close to the optimum results in these relatively homogeneous
samples, while also considering costs. A 1x50 nt library resulting in approximately 100
million reads seems to be the most pragmatic choice to achieve this coverage. This remains
true across all possible dataset pairings. We retrospectively applied those parameter settings,
used no additional filtering of the raw variant calls, and calculated the FDRs for 50 selected
mutations from the intersection of all three methods as shown in Figure 17. All mutations
were confirmed by a combination of Sanger resequencing and the B16 RNA-Seq sequence
reads. 44 of those mutations would have been found using a FDR cutoff of 5% (Fig. 20). As a
negative control, we re-sequenced the loci of 44 predicted mutations with high FDRs (> 50%)
and examined the respective sequences in the RNA-Seq data. We found 37 of these mutations
to be not validated while the remaining seven loci of potential mutations were both not
covered by RNA-Seq reads and yielded in not sequencing reaction.
While we show application of the framework to four specific questions, it is by no means
limited to these parameters, but can be applied to study the influence of all experimental or
algorithmic parameters, e.g. the influence of the alignment software, the choice of a mutation
metric, or the choice of vendor for exome selection.
We performed all experiments on a set of B16 melanoma cell experiments; however, the
method is not restricted to these data. The only requirement is the availability of a 'same-vs-
same' reference data set, meaning at least a single technical repeat of a non-tumorous sample
should be performed for each new protocol. While our experiments indicate that the method is
robust with regard to the choice of the technical repeat within certain limits, so that a repeat is
not necessarily required in every single experiment. However, the method does require that
the various quality measures are comparable between the reference data set and remaining
datasets.
Within this contribution, we have pioneered a statistical framework for a false-discovery-rate
driven detection of somatic mutations. This framework is not only applicable for the
diagnostic or therapeutic target selection, but also allows a generic comparison of
experimental and computational protocol steps on a generated quasi ground truth data. Here,
we applied this idea to make protocol decisions with regard to software tools, coverage,
replicates as well as paired end sequencing.
Methods
Library capture and sequencing
Next-generation sequencing, DNA sequencing: Exome capture for DNA resequencing was
performed using the Agilent Sure-Select solution-based capture assay [Gnirke, A., et al., Nat.
Biotechnol. 27, 182-189 (2009)], in this case designed to capture all known mouse exons.
3 μg purified genomic DNA was fragmented to 150-200 nt using a Covaris S2 ultrasound
device. gDNA fragments were end repaired using T4 DNA polymerase, Klenow DNA
polymerase and 5’ phosphorylated using T4 polynucleotide kinase. Blunt ended gDNA
fragments were 3’ adenylated using Klenow fragment (3’ to 5’ exo minus). 3’ single T-
overhang Illumina paired end adapters were ligated to the gDNA fragments using a 10:1
molar ratio of adapter to genomic DNA insert using T4 DNA ligase. Adapter ligated gDNA
fragments were enriched pre capture and flow cell specific sequences were added using
Illumina PE PCR primers 1.0 and 2.0 and Herculase II polymerase (Agilent) using 4 PCR
cycles.
500 ng of adapter ligated, PCR enriched gDNA fragments were hybridized to Agilent’s
SureSelect biotinylated mouse whole exome RNA library baits for 24 hrs at 65 °C.
Hybridized gDNA/RNA bait complexes where removed using streptavidin coated magnetic
beads. gDNA/RNA bait complexes were washed and the RNA baits cleaved off during
elution in SureSelect elution buffer leaving the captured adapter ligated, PCR enriched gDNA
fragments. gDNA fragments were PCR amplified post capture using Herculase II DNA
polymerase (Agilent) and SureSelect GA PCR Primers for 10 cycles.
Cleanups were performed using 1.8x volume of AMPure XP magnetic beads (Agencourt). For
quality controls we used Invitrogen’s Qubit HS assay and fragment size was determined using
Agilent’s 2100 Bioanalyzer HS DNA assay.
Exome enriched gDNA libraries were clustered on the cBot using Truseq SR cluster kit v2.5
using 7 pM and sequenced on the Illumina HiSeq2000 using Truseq SBS kit.
Exome data analysis
Sequence reads were aligned using bwa (version 0.5.8c) [Li, H. Durbin, R., Bioinformatics
, 1754-1760 (2009)] using default options to the reference mouse genome assembly mm9
[Mouse Genome Sequencing Consortium, Nature 420, 520-562 (2002)]. Ambiguous reads –
those reads mapping to multiple locations of the genome as provided by the bwa output - were
removed. The remaining alignments were sorted, indexed and converted to a binary and
compressed format (BAM) and the read quality scores converted from the Illumina standard
phred+64 to standard Sanger quality scores using shell scripts.
For each sequencing lane, mutations were identified using three software programs:
SAMtools pileup (version 0.1.8) [Li, H. et al., Bioinformatics 25, 2078-2079 (2009)], GATK
(version 1.0.4418) [DePristo, M.A. et al., Nature Genetics 43, 491–498 (2011)], and
SomaticSniper [Ding, L. et al., Hum. Mol. Genet (2010) first published online September 15,
2010]. For SAMtools, the author-recommend options and filter criteria were used
(http://sourceforge.net/apps/mediawiki/SAMtools/index.php?title=SAM_FAQ; accessed
September 2011), including first round filtering, maximum coverage 200. For SAMtools
second round filtering, the minimum indel quality score was 50, the point mutation minimum
quality was 30. For GATK mutation calling, we followed the author-designed best practice
guidelines presented on the GATK user manual
(http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit; accessed
October 2010). For each sample a local realignment around indel sites followed by a base
quality recalibration was performed. The UnifiedGenotyper module was applied to the
resultant alignment data files. When needed, the known polymorphisms of the dbSNP
[Sherry, S.T. et al., Nucleic Acids Res. 29, 308-311 (2009)] (version 128 for mm9) were
supplied to the individual steps. The variant score recalibration step was omitted and replaced
by the hard-filtering option. For SomaticSniper mutation calling, the default options were
used and only predicted mutations with a “somatic score” of 30 or more were considered
further. Additionally, for each potentially mutated locus we required a non-zero coverage in
the normal tissue and removed all mutations located in repetitive sequences as defined by the
RepeatMasker track of the UCSC Genome Browser for the mouse genome assembly mm9
[Fujita, P.A. et al., Nucleic Acids Res. 39, 876-882 (2011)].
RNA-Seq
Barcoded mRNA-seqcDNA libraries were prepared from 5 ug of total RNA using a modified
version of the Illumina mRNA-seq protocol. mRNA was isolated using SeramagOligo(dT)
magnetic beads (Thermo Scientific). Isolated mRNA was fragmented using divalent cations
and heat resulting in fragments ranging from 160-200 bp. Fragmented mRNA was converted
to cDNA using random primers and SuperScriptII (Invitrogen) followed by second strand
synthesis using DNA polymerase I and RNaseH. cDNA was end repaired using T4 DNA
polymerase, Klenow DNA polymerase and 5’ phosphorylated using T4 polynucleotide kinase.
Blunt ended cDNA fragments were 3’ adenylated using Klenow fragment (3’ to 5’ exo
minus). 3’ single T-overhang Illumina multiplex specific adapters were ligated on the cDNA
fragments using T4 DNA ligase. cDNA libraries were purified and size selected at 300 bp
using the E-Gel 2 % SizeSelect gel (Invitrogen). Enrichment, adding of Illumina six base
index and flow cell specific sequences was done by PCR using Phusion DNA polymerase
(Finnzymes). All cleanups were performed using 1,8x volume of Agencourt AMPure XP
magnetic beads.
Barcoded RNA-seq libraries were clustered on the cBot using Truseq SR cluster kit v2.5
using 7 pM and sequenced on the Illumina HiSeq2000 using Truseq SBS kit.
The raw output data of the HiSeq was processed according to the Illumina standard protocol,
including removal of low quality reads and demultiplexing. Sequence reads were then aligned
to the reference genome sequence [Mouse Genome Sequencing Consortium, Nature 420, 520-
562 (2002)] using bowtie [Langmead, B. et al., Genome Biol. 10, R25 (2009)]. The alignment
coordinates were compared to the exon coordinates of the RefSeq transcripts [Pruitt, K.D. et
al., Nucleic Acids Res. 33, 501-504 (2005)] and for each transcript the counts of overlapping
alignments were recorded. Sequence reads not aligning to the genomic sequence were aligned
to a database of all possible exon-exon junction sequences of the RefSeq transcripts [Pruitt,
K.D. et al., Nucleic Acids Res. 33, 501-504 (2005)]. The alignment coordinates were
compared to RefSeq exon and junction coordinates, reads counted, and normalized to RPKM
(number of reads which map per nucleotide kilobase of transcript per million mapped reads
[Mortazavi, A. et al., Nat. Methods 5, 621-628 (2008)]) for each transcript.
Validation of SNVs
We selected SNVs for validation by Sanger re-sequencing and RNA. SNVs were identified
which were predicted by all three programs, non-synonymous, and found in transcripts having
a minimum 10 RPKM. Of these, we selected the 50 with the highest SNP quality scores as
provided by the programs. As a negative control, 44 SNVs were selected which have a FDR
of 50% or more, are present in only one cell line sample and are predicted by only one
mutation calling program. Using DNA, the selected variants were validated by PCR
amplification of the regions using 50 ng of DNA, followed by Sanger sequencing (Eurofins
MWG Operon, Ebersberg, Germany). The reactions were successful for 50 and 32 loci of
positive and negative controls, respectively. Validation was also done by examination of the
tumor RNA-Seq reads.
Calculation of FDRs and machine learning
Random Forest Quality Score Computation: Commonly-used mutation calling algorithms
(DePristo, M.A. et al., Nature Genetics 43, 491–498 (2011), Li, H. et al., Bioinformatics 25,
2078-2079 (2009), Ding, L. et al., Hum. Mol. Genet (2010) first published online September
, 2010) output multiple scores, which all are potentially influential for the quality of the
mutation call. These include - but are not limited to - the quality of the base of interest as
assigned by the instrument, the quality alignment for this position, the number of reads
covering this position or a score for the difference between the two genomes compared at this
position. For the computation of the false discovery rate we require an ordering of mutations,
however this is not directly feasible for all mutations since we might have contradicting
information from the various quality scores.
We use the following strategy to achieve a complete ordering. In a first step, we apply a very
rigorous definition of superiority by assuming that a mutation has better quality than another
if and only if it is superior in all categories. So a set of quality properties S=(s ,…,s ) is
preferable to T=(t ,…,t ), denoted by S>T, iff s > t for all i=1,…,n. We define an
1 n i i
intermediate FDR (IFDR) as follows
However, we regard the IFDR only as an intermediate step since in many closely related
cases, no comparison is feasible and we are thus not benefitting from the vast amount of data
available. Thus, we take advantage of the good generalization property of random forest
regression [Breiman, L., Statist. Sci. 16, 199-231 (2001)] and train a random forest as
implemented in R (R Development Core Team. R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria, 2010, Liaw, A., Wiener,
M., R News 2, 18-22 (2002)).
For m input mutations with n quality properties each, the value range for each property was
determined and up to p values were sampled with uniform spacing out of this range; when the
set of values for a quality property was smaller than p, this set was used instead of the
sampled set. Then each possible combination of sampled or selected quality values is created,
which results in a maximum of p data points in the n-dimensional quality space. A random
sample of 1% of these points and the corresponding IFDR values were used as predictor and
response, respectively, for the random forest training.
The resulting regression score is our generalized quality score Q; it can be regarded as a
locally weighted combination of the individual quality scores. It allows direct, single value
comparison of any two mutations and the computation of the actual false discovery rate:
For the training of the random forest model used to create the results for this study, we
calculate the sample IFDR on the somatic mutations of all samples before selecting the
random 1% subset. This ensures the mapping of the whole available quality space to FDR
values. We used the quality properties “SNP quality”, “coverage depth”, “consensus quality”
and “RMS mapping quality” (SAMtools, p = 20); “SNP quality”, “coverage depth”, “Variant
confidence/unfiltered depth” and “RMS mapping quality” (GATK, p = 20); or SNP quality”,
“coverage depth”, “consensus quality”, “RMS mapping quality” and “somatic score”
(SomaticSNiPer, p = 12), respectively. The different values of p ensure a set size of
comparable magnitude.
Common coverage computation: The number of possible mutation calls can introduce a major
bias in the definition of a false discovery rate. Only if we have the same number of possible
locations for mutations to occur for our tumor comparison and for our same vs. same
comparison, the number of called mutations is comparable and can serve as a basis for a false
discovery rate computation. To correct for this potential bias, we use the common coverage
ratio. As common coverage we define the number of bases with coverage of at least one in
both samples which are used for the mutation calling. We compute the common coverage
individually for the tumor comparison as well as for the same vs. same comparison.
ROC estimation
Receiver operating characteristic (ROC) curves and the corresponding area under curve
(AUC) are useful for organizing classifiers and visualizing their performance [Fawcett, T.,
Pattern Recogn. Lett. 27, 861–874 (2006)]. We extend this concept for evaluating the
performance of experimental and computational procedures. However, plotting ROC graphs
requires knowledge of all true and false positive (TP and FP) examples in a dataset,
information which is usually not given and hard to establish for high throughput data (such as
NGS data). Thus, we use the calculated FDRs to estimate the respective TP and FP rates and
plot a ROC graph and calculate an AUC. The central idea is that the FDR of a single mutation
in the dataset gives the proportion how much this mutation contributes to the sum of TP/FP
mutations, respectively. Also, for a list of random assignments to TP and FP, the resultant
ROC AUC will be equal to 0.5 with our method, indicating a completely random prediction.
We start with two conditions:
FDR =
FPR+ TPR
FPR+TPR=1 [2]
with FPR and TPR being the needed false positive true positive ratios, respectively, for the
given mutation, defining the corresponding point in ROC space. [1] and [2] can be rearranged
TPR = 1− FPR [3]
FPR = FDR [4]
To obtain an estimated ROC curve, the mutations in dataset are sorted by FDR and for each
mutation a point is plotted at the cumulative TPR and FPR values up to this mutation, divided
by the sum of all TPR and TPR values, respectively. The AUC is calculated by summing up
the areas of all consecutive trapezoids between the curve and the x-axis.
Claims (21)
1. A method for providing an individualized cancer vaccine comprising the steps: (a) identifying cancer specific somatic mutations in a tumor specimen of a cancer patient to provide a cancer mutation signature of the patient; and (b) providing a vaccine featuring the cancer mutation signature obtained in step (a).
2. The method according to claim 1, wherein the step of identifying cancer specific somatic mutations comprises identifying the cancer mutation signature of the exome of one or more cancer cells.
3. The method according to claim 1 or 2, wherein the step of identifying cancer specific somatic mutations comprises single cell sequencing of one or more cancer cells.
4. The method according to claim 3, wherein the cancer cells are circulating tumor cells.
5. The method according to any one of claims 1 to 4, wherein the step of identifying cancer specific somatic mutations involves using next generation sequencing (NGS).
6. The method according to any one of claims 1 to 5, wherein the step of identifying cancer specific somatic mutations comprises sequencing genomic DNA and/or RNA of the tumor specimen.
7. The method according to claim 6, wherein the step of identifying cancer specific somatic mutations is replicated at least in duplicates.
8. The method according to any one of claims 1 to 7, comprising the further step of determining the usability of the identified mutations in epitopes for cancer vaccination.
9. The method according to any one of claims 1 to 8, wherein the vaccine featuring the mutation signature of the patient comprises a polypeptide comprising mutation based neo- epitopes, or a nucleic acid encoding said polypeptide.
10. The method according to claim 9, wherein the polypeptide comprises up to 30 mutation based neo-epitopes.
11. The method according to claim 9 or 10, wherein the polypeptide further comprises epitopes not containing cancer specific somatic mutations which are expressed by cancer cells.
12. The method according to any one of claims 9 to 11, wherein the epitopes are in their natural sequence context so as to form a vaccine sequence.
13. The method according to claim 12, wherein the vaccine sequence is about 30 amino acids long.
14. The method according to any one of claims 9 to 13, wherein the neo-epitopes, epitopes and/or vaccine sequences are lined up head-to-tail.
15. The method according to any one of claims 9 to 14, wherein the neo-epitopes, epitopes and/or vaccine sequences are spaced by linkers.
16. The method according to any one of claims 1 to 15, wherein the vaccine is an RNA vaccine.
17. The method according to any one of claims 1 to 15, wherein the vaccine is a prophylactic and/or therapeutic vaccine.
18. A vaccine which is obtained by the method according to any one of claims 1 to 17 wherein the vaccine, when administered to a patient, provides a collection of MHC presented epitopes incorporating sequence changes based on the identified mutations.
19. Use of an individualized cancer vaccine in the preparation of a medicament for treating cancer in a patient, wherein the individualized cancer vaccine is provided by the method according to any one of claims 1 to 17.
20. Use of a vaccine according to claim 18 in the preparation of a medicament for treating cancer in a patient.
21. The vaccine according to claim 18 wherein the MHC presented epitopes are MHC class II-presented epitopes that are capable of eliciting a CD4+ helper T cell response against cells expressing antigens from which the MHC presented epitopes are derived and optionally MHC class I-presented epitopes that are capable of eliciting a CD8+ T cell response against cells expressing antigens from which the MHC presented epitopes are derived. 1//2 25 5
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
NZ718326A NZ718326B2 (en) | 2011-05-24 | 2012-05-23 | Individualized vaccines for cancer |
Applications Claiming Priority (5)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/EP2011/002576 WO2012159643A1 (en) | 2011-05-24 | 2011-05-24 | Individualized vaccines for cancer |
EPPCT/EP2011/002576 | 2011-05-24 | ||
EPPCT/EP2012/000006 | 2012-01-02 | ||
EP2012000006 | 2012-01-02 | ||
PCT/EP2012/002209 WO2012159754A2 (en) | 2011-05-24 | 2012-05-23 | Individualized vaccines for cancer |
Publications (2)
Publication Number | Publication Date |
---|---|
NZ617217A NZ617217A (en) | 2016-04-29 |
NZ617217B2 true NZ617217B2 (en) | 2016-08-02 |
Family
ID=
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11248264B2 (en) | Individualized vaccines for cancer | |
AU2020230292B2 (en) | Individualized vaccines for cancer | |
EP3892295B1 (en) | Individualized vaccines for cancer | |
RU2779946C2 (en) | Individualized anti-tumor vaccines | |
NZ617217B2 (en) | Individualized vaccines for cancer | |
NZ718326B2 (en) | Individualized vaccines for cancer |