INDIVIDUALIZED VACCINES FOR CANCER

• US 20140178438A1
• Filed: 05/23/2012
• Published: 06/26/2014
• Est. Priority Date: 05/24/2011
• Status: Active Grant

• Current Assignee: BioNTech SE, TRANSLATIONALE ONKOLOGIE AN DER UNIVERSITATSMEDIZIN DER JOHANNES GUTENBERG-UNIVERSITAT MAINZ GEMEINNUTZIGE GMBH
• Original Assignee: BioNTech RNA Pharmaceuticals GmbH (BioNTech SE)
• Inventors: Sahin, Ugur, Kreiter, Sebastian, Omokoko, Tana, Diken, Mustafa, Diekmann, Jan, Koslowski, Michael, Britten, Cedrik, Castle, John Christopher, Lower, Martin, Renard, Bernhard, De Graaf, Johannes Hendrikus
• Granted Patent: US 10,738,355 B2
• Time in Patent Office: Days
• US Class Current: 424/277.1
• CPC Class Codes: A61K 2039/80 Vaccine for a specifically defined cancer
• CPC Class Codes: A61K 39/00 Medicinal preparations containing antigens or antibodies materials for immunoassay G01N33/53
• CPC Class Codes: A61K 39/0011 Cancer antigens
• CPC Class Codes: A61P 35/00 Antineoplastic agents
• CPC Class Codes: A61P 37/04 Immunostimulants
• CPC Class Codes: C12Q 1/6869 Methods for sequencing
• CPC Class Codes: C12Q 1/6886 for cancer immunoassay for cancer G01N33/574
• CPC Class Codes: C12Q 2600/156 Polymorphic or mutational markers
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

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. 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 a further aspect, the present invention relates to vaccines which are obtainable by said method.

Claims

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.

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 obtainable by the method according to any one of claims 1 to 17.

21. A method of treating a cancer patient comprising the steps:

(a) providing an individualized cancer vaccine by the method according to any one of claims 1 to 17; and

(b) administering said vaccine to the patient.

22. A method of treating a cancer patient comprising administering the vaccine according to any one of claims 18 to 20 to the patient.

3. The method according to 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.

19. 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.

20. The vaccine according to claim 19, wherein the polypeptide further comprises epitopes not containing cancer specific somatic mutations which are expressed by cancer cells.

23. 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.

24. The method of claim 23 wherein said genetic material is a DNA.

25. The method of claim 23 wherein Q is determined by:

establishing a set of quality properties S=(s₁, . . . , s_n) wherein S is preferable to T=(t₁, . . . , t_n), denoted by S>

T, when s_i>

t_i for all i=1, . . . , n;

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 top values out of said value range;

creating each possible combination of sampled quality values which results in pⁿ 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.

26. The method of claim 24 wherein said second DNA sample from an animal or human is allogenic to said first DNA sample from an animal or human.

27. The method of claim 24 wherein said second DNA sample from an animal or human is autologous to said first DNA sample from an animal or human.

28. The method of claim 24 wherein said second DNA sample from an animal or human is xenogenic to said first DNA sample from an animal or human.

29. The method of claim 23 wherein said genetic material is a RNA.

30. The method of claim 29 wherein Q is determined by:

establishing a set of quality properties S=(s₁, . . . , s_n) wherein S is preferable to T=(t₁, . . . , t_n), denoted by S>

T, when s_i>

t_i for all i=1, . . . , n;

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 in 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 pⁿ 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.

31. The method of claim 30 wherein said second RNA sample from an animal or human is allogenic to said first RNA sample from an animal or human.

32. The method of claim 30 wherein said second RNA sample from an animal or human is autologous to said first RNA sample from an animal or human.

33. The method of claim 30 wherein said second RNA sample from an animal or human is xenogenic to said first RNA sample from an animal or human.

34. The method of claim 23 wherein said false discovery rate is used to make a vaccine formulation.

35. The method of claim 34 wherein said vaccine is deliverable intravenously.

36. The method of claim 34 wherein said vaccine is deliverable dermally.

37. The method of claim 34 wherein said vaccine is deliverable muscularly.

38. The method of claim 34 wherein said vaccine is deliverable subcutaneously.

39. The method of claim 34 wherein said vaccine is tailored for a specific patient.

40. The method of claim 39 wherein 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.

41. The method of claim 23 wherein 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.

42. The method of claim 41 wherein 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.

43. The method of claim 41 wherein 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.

44. The method of claim 41 wherein 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.

45. 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; and

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.