IDENTIFICATION OF PATIENTS IN NEED OF PD-L1 INHIBITOR COTHERAPY

Abstract

The present invention relates to means and methods for determining whether a patient is in need of a PD-L1 inhibitor cotherapy. A patient is determined to be in need of the PD-L1 inhibitor cotherapy if a low or absent ER expression level and an expression level of programmed death ligand 1 (PD-L1) that is increased in comparison to a control is measured in vitro in a sample from the patient. The patient is undergoing therapy comprising a modulator of the HER2/neu (ErbB2) signaling pathway (like Trastuzumab) and a chemotherapeutic agent (like dodetaxel) or such a therapy is contemplated for the patient. Also provided herein are means and methods for treating a cancer in a cancer patient for whom therapy comprising a modulator of the HER2/neu (ErbB2) signaling pathway (like Trastuzumab) and a chemotherapeutic agent (like dodetaxel) is contemplated, wherein the patient is to receive PD-L1 inhibitor cotherapy.

Claims

1. A method of determining the need of a cancer patient for a PD-L1 inhibitor cotherapy, (i) wherein therapy comprising a modulator of the HER2/neu (ErbB2) signaling pathway and a chemotherapeutic agent is contemplated for the patient or (ii) wherein the patient is undergoing therapy comprising a modulator of the HER2/neu (ErbB2) signaling pathway and a chemotherapeutic agent, the method comprising the steps of a) measuring in vitro in a sample from said patient the expression level of Estrogen receptor (ER) and of programmed death ligand 1 (PD-L1), b) determining a patient as being in need of a PD-L1 inhibitor cotherapy if a low or absent ER expression level and an expression level of programmed death ligand 1 (PD-L1) that is increased in comparison to a control is measured in step (a).

2. A method of treating a cancer in a cancer patient for whom therapy comprising a modulator of the HER2/neu (ErbB2) signaling pathway and a chemotherapeutic agent is contemplated, the method comprising selecting a cancer patient whose cancer is determined to have a low or absent ER expression level and to have an increased expression level of programmed death ligand 1 (PD-L1) in comparison to a control, and administering to the patient an effective amount of a modulator of the HER2/neu (ErbB2) signaling pathway, of a chemotherapeutic agent and of a programmed death ligand 1 (PD-L1) inhibitor.

3. A method of treating a cancer in a cancer patient who is undergoing therapy comprising a modulator of the HER2/neu (ErbB2) signaling pathway and a chemotherapeutic agent, the method comprising selecting a cancer patient whose cancer is determined to have a low or absent ER expression level and to have an increased expression level of programmed death ligand 1 (PD-L1) in comparison to a control, and administering to the patient an effective amount of a programmed death ligand 1 (PD-L1) inhibitor.

4. The method of claim 1, further comprising measuring in vitro in a sample from said patient the expression level of interferon-gamma (IFNγ) and determining a patient as being in need of a PD-L1 inhibitor cotherapy if an expression level of interferon-gamma (IFNγ) that is decreased in comparison to a control is measured.

5. The method of claim 1; or the pharmaceutical composition of any one of claims 4, 5 and 7, wherein the ER expression level is ER(−).

6. The method of claim 1, wherein said modulator of the HER2/neu (ErbB2) signaling pathway is an inhibitor of HER shedding.

7. The method of claim 6, wherein said inhibitor of HER shedding is a HER2 shedding inhibitor.

8. The method of claim 6, wherein said inhibitor of HER shedding inhibits HER heterodimerization or HER homodimerization.

9. The method of claim 6, wherein said inhibitor of HER shedding is a HER antibody.

10. The method of claim 9, wherein said HER antibody binds to a HER receptor selected from the group consisting of EGFR, HER2 and HER3.

11. The method of claim 10, wherein said antibody binds to HER2.

12. The method of claim 11, wherein said HER2 antibody binds to sub-domain IV of the HER2 extracellular domain.

13. The method of claim 9, wherein said HER2 antibody is Herceptin/Trastuzumab.

14. The method of claim 1, wherein said modulator of the HER2/neu (ErbB2) signaling pathway is a HER dimerization/signaling inhibitor.

15. The method of claim 14, wherein said HER dimerization inhibitor is a HER2 dimerization inhibitor.

16. The method of claim 14, wherein said HER dimerization inhibitor inhibits HER heterodimerization or HER homodimerization.

17. The method of claim 14, wherein said HER dimerization inhibitor is a anti HER antibody.

18. The method of claim 17, wherein said HER antibody binds to a HER receptor selected from the group consisting of EGFR, HER2 and HER3.

19. The method of claim 18, wherein said antibody binds to HER2.

20. The method of claim 19, wherein said anti HER2 antibody binds to domain II of HER2 extracellular domain.

21. The method of claim 20, wherein said antibody binds to a junction between domains I, II and III of HER2 extracellular domain.

22. The method of claim 17, wherein said anti HER2 antibody is Pertuzumab.

23. The method of claim 1, wherein said chemotherapeutic agent is taxol or a taxol derivative.

24. The method of claim 23, wherein said taxol derivative is dodetaxel.

25. The method of claim 1, wherein said inhibitor of programmed death ligand 1 (PD-L1) is an antibody specifically binding to PD-L1 (anti-PD-L1 antibody).

26. The method of claim 25, wherein said antibody comprises an heavy chain variable region polypeptide comprising an HVR-H1, HVR-H2 and HVR-H3 sequence, wherein: TABLE-US-00047 (SEQ ID NO: 1) (a) the HVR-H1 sequence is GFTFSX1SWIH; (SEQ ID NO: 2) (b) the HVR-H2 sequence is AWIX2PYGGSX3YYADSVKG; (SEQ ID NO: 3) (c) the HVR-H3 sequence is RHWPGGFDY; further wherein: X1 is D or G; X2 is S or L; X3 is T or S.

27. The method of claim 26, wherein X1 is D; X2 is S and X3 is T.

28. The method of claim 26, wherein said polypeptide further comprises variable region heavy chain framework sequences juxtaposed between the HVRs according to the formula: (HC-FR1)-(HVR-H1)-(HC-FR2)-(HVR-H2)-(HC-FR3)-(HVR-H3)-(HC-FR4).

29. The method of claim 28, wherein the framework sequences are derived from human consensus framework sequences.

30. The method of claim 29, wherein the framework sequences are VH subgroup III consensus framework.

31. The method of claim 30, wherein one or more of the framework sequences is the following: TABLE-US-00048 (SEQ ID NO: 4) HC-FR1 is EVQLVESGGGLVQPGGSLRLSCAAS (SEQ ID NO: 5) HC-FR2 is WVRQAPGKGLEWV (SEQ ID NO: 6) HC-FR3 is RFTISADTSKNTAYLQMNSLRAEDTAVYYCAR (SEQ ID NO: 7) HC-FR4 is WGQGTLVTVSA.

32. The method of claim 26, wherein said heavy chain polypeptide is in combination with a variable region light chain comprising an HVR-L1, HVR-L2 and HVR-L3, wherein: TABLE-US-00049 (SEQ ID NOs: 8) (a) the HVR-L1 sequence is RASQX4X5X6TX7X8A; (SEQ ID NOs: 9) (b) the HVR-L2 sequence is SASX9LX10S,; and (SEQ ID NOs: 10) (c) the HVR-L3 sequence is QQX11X12X13X14PX15T; further wherein: X4 is D or V; X5 is V or I; X6 is S or N; X7 is A or F; X8 is V or L; X9 is F or T; X10 is Y or A; X11 is Y, G, F, or S; X12 is L, Y, F or W; X13 is Y, N, A, T, G, F or I; X14 is H, V, P, T or I; X15 is A, W, R, P or T.

33. The method of claim, wherein X4 is D; X5 is V; X6 is S; X7 is A; X8 is V; X9 is F; X10 is Y; X11 is Y; X12 is L; X13 is Y; X14 is H; X15 is A.

34. The method of claim 32, wherein said polypeptide further comprises variable region light chain framework sequences juxtaposed between the HVRs according to the formula: (LC-FR1)-(HVR-L1)-(LC-FR2)-(HVR-L2)-(LC-FR3)-(HVR-L3)-(LC-FR4).

35. The method of claim 34, wherein the framework sequences are derived from human consensus framework sequences.

36. The method of claim 34, wherein the framework sequences are VL kappa I consensus framework.

37. The method of claim 36, wherein one or more of the framework sequences is the following: TABLE-US-00050 (SEQ ID NO: 11) LC-FR1 is DIQMTQSPSSLSASVGDRVTITC; (SEQ ID NO: 12) LC-FR2 is WYQQKPGKAPKLLIY; (SEQ ID NO: 13) LC-FR3 is GVPSRFSGSGSGTDFTLTISSLQPEDFATYYC; (SEQ ID NO: 14) LC-FR4 is FGQGTKVEIKR.

38. The method of claim 26, wherein said anti-PD-L1 antibody comprises a heavy chain and a light chain variable region sequence, wherein: (a) the heavy chain comprises an HVR-H1, HVR-H2 and HVR-H3, wherein further: TABLE-US-00051 (SEQ ID NO: 1) (i) the HVR-H1 sequence is GFTFSX1SWIH; (SEQ ID NO: 2) (ii) the HVR-H2 sequence is AWIX2PYGGSX3YYADSVKG; (SEQ ID NO: 3) (iii) the HVR-H3 sequence is RHWPGGFDY,; and  (b) the light chain comprises an HVR-L1, HVR-L2 and HVR-L3, wherein further: TABLE-US-00052 (SEQ ID NOs: 8) (iv) the HVR-L1 sequence is RASQX4X5X6TX7X8A; (SEQ ID NOs: 9) (v) the HVR-L2 sequence is SASX9LX10S; (SEQ ID NOs: 10) (vi) the HVR-L3 sequence is QQX11X12X13X14PX15T; wherein: X1 is D or G; X2 is S or L; X3 is T or S; X4 may be D or V; X5 may be V or I; X6 may be S or N; X7 may be A or F; X8 may be V or L; X9 may be F or T; X10 may be Y or A; X11 may be Y, G, F, or S; X12 may be L, Y, F or W; X13 may be Y, N, A, T, G, F or I; X14 may be H, V, P, T or I; X15 may be A, W, R, P or T.

39. The method of claim 38, wherein X1 is D; X2 is S and X3 is T.

40. The method of claim 38, wherein X4=D, X5=V, X6=S, X7=A and X8=V, X9=F, and X10=Y, X11=Y, X12=L, X13=Y, X14=H and X15=A.

41. The method of claim 38, wherein X1=D, X2=S and X3=T, X4=D, X5=V, X6=S, X7=A and X8=V, X9=F, and X10=Y, X11=Y, X12=L, X13=Y, X14=H and X15=A.

42. The method of claim 38, wherein the antibody further comprises (a) variable region heavy chain framework sequences juxtaposed between the HVRs according to the formula: (HC-FR1)-(HVR-H1)-(HC-FR2)-(HVR-H2)-(HC-FR3)-(HVR-H3)-(HC-FR4), and (b) variable region light chain framework sequences juxtaposed between the HVRs according to the formula: (LC-FR1)-(HVR-L1)-(LC-FR2)-(HVR-L2)-(LC-FR3)-(HVR-L3)-(LC-FR4).

43. The method of claim 42, wherein the framework sequences are derived from human consensus framework sequences.

44. The method of claim 43, wherein the variable region heavy chain framework sequences are VH subgroup III consensus framework.

45. The method of claim 44, wherein one or more of the framework sequences is the following: TABLE-US-00053 (SEQ ID NO: 4) HC-FR1 is EVQLVESGGGLVQPGGSLRLSCAAS; (SEQ ID NO: 5) HC-FR2 is WVRQAPGKGLEWV; (SEQ ID NO: 6) HC-FR3 is RFTISADTSKNTAYLQMNSLRAEDTAVYYCAR; (SEQ ID NO: 7) HC-FR4 is WGQGTLVTVSA.

46. The method of claim 43, wherein the variable region light chain framework sequences are VL kappa I consensus framework.

47. The method of claim 46, wherein one or more of the framework sequences is the following: TABLE-US-00054 (SEQ ID NO: 11) LC-FR1 is DIQMTQSPSSLSASVGDRVTITC; (SEQ ID NO: 12) LC-FR2 is WYQQKPGKAPKLLIY; (SEQ ID NO: 13) LC-FR3 is GVPSRFSGSGSGTDFTLTISSLQPEDFATYYC,; and  (SEQ ID NO: 14) LC-FR4 is FGQGTKVEIKR.

48. The method of claim 43, wherein: (a) the variable heavy chain framework sequences are the following: TABLE-US-00055 (SEQ ID NO: 4) (i) HC-FR1 is EVQLVESGGGLVQPGGSLRLSCAAS; (SEQ ID NO: 5) (ii) HC-FR2 is WVRQAPGKGLEWV; (SEQ ID NO: 6) (iii) HC-FR3 is RFTISADTSKNTAYLQMNSLRAEDTAVYYCAR; (SEQ ID NO: 7) (iv) HC-FR4 is WGQGTLVTVSA;; and  (b) the variable light chain framework sequences are the following: TABLE-US-00056 (SEQ ID NO: 11) (i) LC-FR1 is DIQMTQSPSSLSASVGDRVTITC; (SEQ ID NO: 12) (ii) LC-FR2 is WYQQKPGKAPKLLIY; (SEQ ID NO: 13) (iii) LC-FR3 is GVPSRFSGSGSGTDFTLTISSLQPEDFATYYC; (SEQ ID NO: 14) (iv) LC-FR4 is FGQGTKVEIKR.

49. The method of claim, wherein the antibody further comprises a human constant region.

50. The method of claim 49, wherein the constant region is selected from the group consisting of IgG1, IgG2, IgG3 and IgG4.

51. The method of claim 50 wherein the constant region is IgG1.

52. The method of claim 48, wherein the antibody further comprises murine constant region.

53. The method of claim 52, wherein the constant region is selected from the group consisting of IgG1, IgG2A, IgG2B and IgG3.

54. The method of claim 53, wherein the constant region is IgG2A.

55. The method of claim 50, wherein said antibody has reduced or minimal effector function.

56. The method of claim 55, wherein the minimal effector function results from an effector-less Fc mutation.

57. The method of claim 56, wherein the effector-less Fc mutation is N297A.

58. The method of claim 56, wherein the effector-less Fc mutation is D265A/N297A.

59. Method of claim 55, wherein the minimal effector function results from aglycosylation.

60. The method of claim 26, wherein said antibody comprises a heavy chain and a light chain variable region sequence, wherein: (a) the heavy chain comprises an HVR-H1, HVR-H2 and an HVR-H3, having at least 85% overall sequence identity to GFTFSDSWIH (SEQ ID NO:15), AWISPYGGSTYYADSVKG (SEQ ID NO:16) and RHWPGGFDY (SEQ ID NO:3), respectively, and (b) the light chain comprises an HVR-L1, HVR-L2 and an HVR-L3, having at least 85% overall sequence identity to RASQDVSTAVA (SEQ ID NO:17), SASFLYS (SEQ ID NO:18) and QQYLYHPAT (SEQ ID NO:19), respectively.

61. The method of claim 60, wherein said sequence identity is at least 90%.

62. The method of claim 61, wherein said antibody further comprises: (a) variable region heavy chain (VH) framework sequences juxtaposed between the HVRs according to the formula: (HC-FR1)-(HVR-H1)-(HC-FR2)-(HVR-H2)-(HC-FR3)-(HVR-H3)-(HC-FR4), and (b) variable region light chain (VL) framework sequences juxtaposed between the HVRs according to the formula: (LC-FR1)-(HVR-L1)-(LC-FR2)-(HVR-L2)-(LC-FR3)-(HVR-L3)-(LC-FR4).

63. The method of claim 62, wherein said antibody further comprises a VH and VL framework region derived from a human consensus sequence.

64. The method of claim 63, wherein the VH framework sequence is derived from a Kabat subgroup I, II, or III sequence.

65. The method of claim 64, wherein the VH framework sequence is a Kabat subgroup III consensus framework sequence.

66. The method of claim 65, wherein the VH framework sequences are the following: TABLE-US-00057 (SEQ ID NO: 4) HC-FR1 is EVQLVESGGGLVQPGGSLRLSCAAS; (SEQ ID NO: 5) HC-FR2 is WVRQAPGKGLEWV; (SEQ ID NO: 6) HC-FR3 is RFTISADTSKNTAYLQMNSLRAEDTAVYYCAR; (SEQ ID NO: 7) HC-FR4 is WGQGTLVTVSA.

67. The method of claim 63, wherein the VL framework sequence is derived from a Kabat kappa I, II, III or IV subgroup sequence.

68. The method of claim 67, wherein the the VL framework sequence is a Kabat kappa I consensus framework sequence.

69. The method of claim 68, wherein the VL framework sequences are the following: TABLE-US-00058 (SEQ ID NO: 11) LC-FR1 is DIQMTQSPSSLSASVGDRVTITC; (SEQ ID NO: 12) LC-FR2 is WYQQKPGKAPKLLIY; (SEQ ID NO: 13) LC-FR3 is GVPSRFSGSGSGTDFTLTISSLQPEDFATYYC; (SEQ ID NO: 14) LC-FR4 is FGQGTKVEIKR.

70. The method of claim 26, wherein said antibody comprises a heavy chain and a light chain variable region sequence, wherein: (a) the heavy chain sequence has at least 85% sequence identity to the heavy chain sequence: TABLE-US-00059 (SEQ ID NO: 20) EVQLVESGGGLVQPGGSLRLSCAASGFTFSDSWIHWVRQAPGKGLEWVAW ISPYGGSTYYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCARRH WPGGFDYWGQGTLVTVSA, and (b) the light chain sequence has at least 85% sequence identity to the light chain sequence: TABLE-US-00060 (SEQ ID NO: 21) DIQMTQSPSSLSASVGDRVTITCRASQDVSTAVAWYQQKPGKAPKLLIYS ASFLYSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYLYHPATFGQ GTKVEIKR.

71. The method of claim 70, wherein the sequence identity is at least 90%.

72. The method of claim 26, wherein said antibody comprises a heavy chain and light chain variable region sequence, wherein: (a) the heavy chain comprises the sequence: EVQLVESGGGLVQPGGSLRLS CAASGFTF SDSWIHWVRQAPGKGLEWVAWISPYGGSTYYADSVKGRFTISADTSKNTAYL QMNSLRAEDTAVYYCARRHWPGGFDYWGQGTLVTVSA (SEQ ID NO:20), and (b) the light chain comprises the sequence: DIQMTQSPSSLSASVGDRVTITC RASQDVSTAVAWYQQKPGKAPKLLIYSASFLYSGVPSRF SGSGSGTDFTLTISSLQPEDFAT YYCQQYLYHPATFGQGTKVEIKR (SEQ ID NO:21).

73. The method of claim 1, wherein said cancer is a solid cancer.

74. The method of claim 73, wherein said solid cancer is breast cancer or gastric cancer.

75. The method of claim 73, wherein said solid cancer is breast cancer.

76. The method of claim 1, wherein the expression level of PD-L1 is higher or equal to 5.3 determined by routine methods like Affymetrix.

77. The method claim 1, wherein the expression level of PD-L1 is the mRNA expression level.

78. The method of claim 76, wherein the mRNA expression level of PD-L1 is assessed by in situ hybridization, micro-arrays, or RealTime PCR.

79. The method of claim 1, wherein the expression level of PD-L1 is the protein expression level.

80. The method of claim 78, wherein said protein expression level of PD-L1 is assessed by immunoassay, gel- or blot-based methods, IHC, mass spectrometry, flow cytometry, or FACS.

81. The method of claim 1, wherein the patient to be treated is a human.

82. The method of claim 1, wherein said modulator of the HER2/neu (ErbB2) signaling pathway, said chemotherapeutic agent and said inhibitor of programmed death ligand 1 (PD-L1) are to be administered in a neoadjuvant setting or adjuvant setting or metastatic setting.

83. A method for treating cancer comprising administering an effective amount of a modulator of the HER2/neu (ErbB2) signaling pathway, a chemotherapeutic agent and an inhibitor of programmed death ligand 1 (PD-L1) to a subject in need thereof.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0233] FIG. 1 provides a schematic of the HER2 protein structure, and amino acid sequences for Domains I-IV, respectively) of the extracellular domain thereof (SEQ ID NOS. 22-25, respectively, in order of appearance).

[0234] FIGS. 2A and 2B depict alignments of the amino acid sequences of the variable light (VL) (FIG. 2A) and variable heavy (VH) (FIG. 2B) domains of murine monoclonal antibody 2C4 (SEQ ID Nos. 26 and 27, respectively); VL and VH domains of variant 574/Pertuzumab (SEQ ID Nos. 28 and 29, respectively), and human VL and VH consensus frameworks (hum κ1, light kappa subgroup I; humIII, heavy subgroup III) (SEQ ID Nos. 30 and 31, respectively). Asterisks identify differences between variable domains of Pertuzumab and murine monoclonal antibody 2C4 or between variable domains of Pertuzumab and the human framework. Complementarity Determining Regions (CDRs) are in brackets.

[0235] FIGS. 3A and 3B show the amino acid sequences of Pertuzumab light chain (FIG. 3A; SEQ ID NO: 32) and heavy chain (FIG. 3B; SEQ ID NO: 33). CDRs are shown in bold. Calculated molecular mass of the light chain and heavy chain are 23,526.22 Da and 49,216.56 Da (cysteines in reduced form). The carbohydrate moiety is attached to Asn 299 of the heavy chain.

[0236] FIGS. 4A and 4B show the amino acid sequences of Trastuzumab light chain (FIG. 4A; SEQ ID NO: 34) and heavy chain (FIG. 4B; SEQ ID NO: 35), respectively. Boundaries of the variable light and variable heavy domains are indicated by arrows.

[0237] FIGS. 5A and 5B depict a variant Pertuzumab light chain sequence (FIG. 5A; SEQ ID NO: 36) and a variant Pertuzumab heavy chain sequence (FIG. 5B; SEQ ID NO: 37), respectively.

[0238] FIG. 6: FIGS. 6A and 6B show known mRNA transcripts and position of the relevant AFFYMETRIX probe set target regions for gene CD274. Exons are shown as grey bold rectangles, junction regions are indicated by thin horizontal lines. Probe sets with their sequence mapped against mRNA sequences are shown as black bold rectangles. Provided coordinates are genomic coordinates on chromosome 9.

[0239] FIG. 7: FIGS. 7A and 7B show known mRNA transcripts and position of the relevant AFFYMETRIX probe set target regions for gene IFNG. Exons are shown as grey bold rectangles, junction regions are indicated by thin horizontal lines. Probe sets with their sequence mapped against mRNA sequences are shown as black bold rectangles. Provided coordinates are genomic coordinates on chromosome 12.

[0240] FIG. 8 shows the distribution of the expression of genes IFNG and CD274 in the samples of ER- and ER-30 populations. Symbol types correspond to the final pCR status (solid: pCR achieved, open—pCR not achieved).

[0241] FIG. 9A is a box plot of expression of gene CD274 for ER− responders (pCR=YES) and nonresponders (pCR=NO). On the right the histograms of expression for both categories are provided. FIG. 9B shows a distribution of t-test statistics (HO hypothesis of no difference). The vertical mark indicates the actual value found in the involved sample. The area of the shaded regions corresponds to the alpha level.

[0242] FIG. 10A is a box plot of expression of gene IFNG for ER− responders (pCR=YES) and nonresponders (pCR=NO). On the right the histograms of expression for both categories are provided. FIG. 10B shows the distribution of t-test statistics (HO hypothesis of no difference). The vertical mark indicates the actual value found in the involved sample. The area of the shaded regions corresponds to the alpha level.

[0243] FIG. 11A is a box plot of expression of gene CD274 for ER+ responders (pCR=YES) and nonresponders (pCR=NO). On the right the histograms of expression for both categories are provided. FIG. 11B shows a distribution of t-test statistics (HO hypothesis of no difference). The vertical mark indicates the actual value found in the involved sample. The area of the shaded regions corresponds to the alpha level.

[0244] FIG. 12A is a box plot of expression of gene IFNG for ER+ responders (pCR=YES) and nonresponders (pCR=NO). On the right the histograms of expression for both categories are provided. FIG. 12B shows a distribution of t-test statistics (HO hypothesis of no difference). The vertical mark indicates the actual value found in the involved sample. The area of the shaded regions corresponds to the alpha level.

[0245] FIG. 13 is the receiver operating characteristic of the final logistic regression model for ER-population. Positive level is taken to be the positive response status (pCR=YES).

[0246] FIG. 14 is a LIFT curve of the final logistic regression model for ER− population. Positive level is taken to be the positive response status (pCR=YES). The Y-axis displays the ratio of how rich the portion of the population is in the chosen response level (upper curve corresponds to pCR=YES) compared to the rate of that response level as a whole.

[0247] FIG. 15 is an example of predicted clinical response status for ER− population. Shown is predicted profile of response as controlled by patient age, Cancer type, pN status, and expression of both genes involved. The actual predicted pCR probability (which is equal to 0.443) is given for NO LABC patient around 60 y. old and with expression in both genes around median values.

[0248] FIG. 16 is the receiver operating characteristic of the final logistic regression model for ER+ population. Positive level is taken to be the positive response status (pCR=YES).

[0249] FIG. 17 shows the distribution of age of ER− patients.

[0250] FIG. 18 shows the distribution of age of ER+ patients.

[0251] FIG. 19 shows a decision tree view on expression of IFNG and CD274 genes predicting clinical response in ER patients. The first two splits required to explain pCR are the ones wrt to IFNG and CD274.

[0252] The Example illustrates the invention.

Example 1: Cancer Patients Undergoing HER2 Targeted Therapy and Chemotherapy Benefit from PD-L1 Inhibitor Cotherapy, if the Expression Level of ER is Low or Absent (ER Negative) and if PD-L1 Expression Level is Increased

[0253] Estimation of gene expression was performed with the help of R Bioconductor package ‘affy’, R version 2.15.0. All exploratory analyses and predictive models were made using SAS JAR′ ver. 10.0 48 HER2+, ER+ and 39 HER2+, ER− breast cancer biopsies were obtained from NeoSphere clinical trial. The samples had been taken at diagnosis from patients afterwards treated with Docetaxel and Trastuzumab in a neo-adjuvant setting. The distribution of main clinical covariates at base line, as well as of clinical response (as assessed at the surgery) in the involved population is as follows:

ER Negative Samples:

[0254]

TABLE-US-00018 Patient Age (see FIG. 17) Quantiles 100.0% maximum 72  99.5% 72  97.5% 71.55  90.0% 64  75.0% quartile 54  50.0% median 50.5  25.0% quartile 44.25  10.0% 39  2.5% 34.675  0.5% 34  0.0% minimum 34 Cancer Type Level Count Prob IBC 2 0.04167 LABC 22 0.45833 OPERABLE 24 0.50000 Total 48 1.00000 pT (pathologic staging of Tumor) Level Count Prob T2 18 0.37500 T3 15 0.31250 T4 15 0.31250 Total 48 1.00000 pN (pathologic staging of nodes) Level Count Prob N0 12 0.25000 N1 36 0.75000 Total 48 1.00000 G (Grade) Level Count Prob G1 1 0.02083 G2 15 0.31250 G3 16 0.33333 NA 16 0.33333 Total 48 1.00000

ER Positive Samples:

[0255]

TABLE-US-00019 Patient Age (see FIG. 18) Quantiles 100.0% maximum 74  99.5% 74  97.5% 74  90.0% 65  75.0% quartile 57  50.0% median 50  25.0% quartile 43  10.0% 40  2.5% 32  0.5% 32  0.0% minimum 32 Cancer Type Level Count Prob IBC 5 0.12821 LABC 8 0.20513 OPERABLE 26 0.66667 Total 39 1.00000 pT Level Count Prob T2 15 0.38462 T3 16 0.41026 T4 8 0.20513 Total 39 1.00000 pN Level Count Prob N0 11 0.28205 N1 28 0.71795 Total 39 1.00000 G Level Count Prob G2 13 0.33333 G3 10 0.25641 NA 16 0.41026 Total 39 1.00000
Contingency Analysis of Pathological Complete Response (pCR) by Estrogen Receptor Status (ER)

TABLE-US-00020 Count Row % pCR = NO pCR = YES ER = ER− 27 21 48 56.25 43.75 ER = ER+ 33 6 39 84.62 15.38 60 27 87

Gene Expression Profiling

[0256] The tumor biopsy samples were profiled for gene expression on AFFYMETRIX HG-U133Plus 2 whole Human Genome microarray platform. Roche HighPure RNA extraction, NuGen amplification and standard AFFYMETRIX hybridization and scanning protocols were used. All array scans passed standard AFFYMETRIX QC.

[0257] Robust Multiarray algorithm (RMA) was used for preprocessing of raw signals (Irizarry et al, 2003. available at ncbi.nlm.nih.gov/pubmed/12925520). All probe sets available for the genes of interest were retrieved as reported below. For gene CD274, when several probe sets were available to represent this gene, the probe set with the probe set with the highest average expression value (defined as an arithmetical average of expression of a given probe set) was selected to represent the gene:

CD274 (PDL1)

[0258] 223834_at selected for PDL1
227458_at

[0259] The selected probe set corresponds to the last exon/3′UTR of the gene and captures all known RefSEq mRNAs (see FIGS. 6A and 6B)

IFNG

[0260] 210354_at

[0261] This probe set also represents the last exon/3′UTR of the gene and captures all known RefSEq mRNAs (see FIGS. 7A and 7B)

[0262] FIG. 8 shows joint distribution of the expression of the above genes in the samples of both ER− and ER− populations. Symbol types correspond to the final pCR status (solid: pCRachieved, open—pCR not achieved).

[0263] More details on distribution of CD274 and IFNG expression across ER and pCR strata can be found in Appendix I.

[0264] For every ER subpopulation, a logistic regression model was constructed that relates expression of the selected genes with clinical response adjusted for patient age, cancer type, and nodal status:

Response˜Patient.Age+Cancer.Type+pN+CD274+IFNG

1. ER− Population.

[0265] Summarized model output is given below. Odds ratios are (OR) provided per unit change of biomarker value. As the expression values are given on log 2 scale, one unit change would correspond to 2-fold overexpression. For details see Appendix.

TABLE-US-00021 ER− population LR test Term OR (95% CI) p-value CD274 5.2 (1.5; 26.7) 0.008 IFNG 0.30 (0.10; 0.74) 0.007 Patient 0.24 Age Cancer 0.91 Type pN 0.87

[0266] The final model for predicting probability for a particular patient to respond to the treatment includes expression of CD274 and IFNG and looks like:

p(pCR)=−3.737+1.607*CD274−1.069*IFNG

2. Er+ Population.

[0267] Summarized model output is given below. Odds ratios are (OR) provided per unit change of biomarker value. As the expression values are given on log 2 scale, one unit change would correspond to 2-fold overexpression. For details see Appendix.

TABLE-US-00022 ER+ population LR test Term OR (95% CI) p-value CD274 0.93 IFNG 0.23 Patient 0.34 Age Cancer 0.39 Type pN 0.92

[0268] The role of PDL1 expression is evident in ER− subpopulation of HER2+ breast cancer patients that underwent combinational treatment with Trastuzumab and chemotherapy in the neoadjuvant setting. Namely, overexpression of PDL1 at diagnosis corresponds to a lower rate of response to neoadjuvant therapy (i.e. a lower rate of response to combinational treatment with Trastuzumab and chemotherapy). This holds irrespective of patient age, cancer type, or lymph node status. A baseline assessment of gene expression of either of the two biomarkers, PDL1 and INFG, respectively, allows to identify if a patient is likely to experience a greater benefit if a PDL-1 targeted therapy is added to Trastuzumab and chemotherapy.

[0269] The following relates to a cut-off value allowing determining a patient as being in need of a PD-L1 inhibitor cotherapy in accordance with the present invention.

[0270] If a gene expression analysis gives a result for IFNG expression higher or equal to 4.8 no combination treatment (HER2-targeted and PDL1-targeted) is recommended and no further PDL1 assessment would be necessary. If a gene expression analysis gives a result for IFNG lower than 4.8 a parallel assessment of PDL-1 is necessary. If PDL-1 gene expression analysis then gives a result of higher or equal to 5.3 a combination treatment (HER2-targeted and PDL1-targeted) is recommended (see FIG. 19).

Appendix I

ER− Subpopulation

Oneway Analysis of CD274 Expression By pCR ER=ERneg (see FIG. 9A)

t Test

YES-NO

Assuming Unequal Variances

[0271]

TABLE-US-00023 Difference −0.32948 t Ratio −1.94171 Std Err Dif 0.16969 DF 45.11513 Upper CL Dif 0.01226 Prob > |t|  0.0584  Lower CL Dif −0.67122 Prob > t  0.9708  Confidence 0.95 Prob < t  0.0292*

[0272] The results are also shown in FIG. 9B.

Oneway Analysis of IFNG Expression By pCR ER=ERneg

[0273] The results are shown in FIG. 10A.

t Test

YES-NO

Assuming Unequal Variances

[0274]

TABLE-US-00024 Difference 0.58405 t Ratio  2.044225 Std Err Dif 0.28571 DF 30.21429 Upper CL Dif 1.16737 Prob > |t|  0.0497* Lower CL Dif 0.00073 Prob > t  0.0249* Confidence 0.95 Prob < t 0.9751

[0275] The results are shown in FIG. 10B.

ER+ Subpopulation

Oneway Analysis of CD274 Expression By pCR ER=ERpos

[0276] The results are shown in FIG. 11A.

t Test

YES-NO

Assuming Unequal Variances

[0277]

TABLE-US-00025 Difference 0.25169 t Ratio 0.898709 Std Err Dif 0.28006 DF 6.542171 Upper CL Dif 0.92345 Prob > |t| 0.4007 Lower CL Dif −0.42006 Prob > t 0.2003 Confidence 0.95 Prob < t 0.7997

[0278] The results are shown in FIG. 11B.

Oneway Analysis of IFNG Expression By pCR ER=ERpos

[0279] The results are shown in FIG. 12A.

t Test

YES-NO

Assuming Unequal Variances

[0280]

TABLE-US-00026 Difference 0.5931 t Ratio 1.501336 Std Err Dif 0.3951 DF 7.109044 Upper CL Dif 1.5244 Prob > |t| 0.1763 Lower CL Dif −0.3382 Prob > t 0.0882 Confidence 0.95 Prob < t 0.9118

[0281] The results are shown in FIG. 12B.

Appendix II

Nominal Logistic Fit for pCR ER=ERneg

[0282] Converged in Gradient, 5 iterations

Whole Model Test

[0283]

TABLE-US-00027 Model -LogLikelihood DF ChiSquare Prob > ChiSq Difference 6.784783 6 13.56957 0.0348* Full 26.110299 Reduced 32.895082

TABLE-US-00028 RSquare (U) 0.2063 AICc 69.0206 BIC 79.319 Observations (or Sum Wgts) 48

TABLE-US-00029 Measure Training Definition Entropy RSquare 0.2063 1-Loglike(model)/Loglike(0) Generalized RSquare 0.3301 (1-(L(0)/L(model)){circumflex over ( )}(2/n))/(1-L(0){circumflex over ( )}(2/n)) Mean -Log p 0.5440 Σ -Log(ρ[j])/n RMSE 0.4278 √ Σ(y[j]-ρ[j]).sup.2/n Mean Abs Dev 0.3665 Σ |y[j]-ρ[j]|/n Misclassification Rate 0.2292 Σ (ρ[j]≠ρMax)/n N 48 n

Lack Of Fit

[0284]

TABLE-US-00030 Source DF -LogLikelihood Chi Square Lack Of Fit 41 26.110299 52.2206 Saturated 47  0.000000 Prob > ChiSq Fitted  6 26.110299  0.1125

Parameter Estimates

[0285]

TABLE-US-00031 Prob > Lower Upper Term Estimate Std Error ChiSquare ChiSq 95% 95% Intercept −5.9688255 4.1632695 2.06 0.1517 −15.115329 1.70408281 Patient Age 0.04906238 0.0425045 1.33 0.2484 −0.0324034 0.13829525 Cancer Type[IBC] −0.0943023 1.0982289 0.01 0.9316 −2.5407977 2.23824618 Cancer −0.1514945 0.6544424 0.05 0.8169 −1.5051269 1.21757158 Type[LABC] 0.08157636 0.4979574 0.03 0.8699 −0.8986622 1.09707358 pN[N0] CD274 Expression 1.64979222 0.7194762 5.26 0.0218* 0.39533833 3.2836052 IFNG Expression −1.1882978 0.5122023 5.38 0.0203* −2.3323039 −0.2889168
For log odds of NO/YES

Effect Likelihood Ratio Tests

[0286]

TABLE-US-00032 Source Nparm DF L-R ChiSquare Prob > ChiSq Patient Age 1 1 1.38574446 0.2391 Cancer Type 2 2 0.19781033 0.9058 pN 1 1 0.02690704 0.8697 CD274 1 1 7.09800433  0.0077* Expression IFNG 1 1 7.15387723  0.0075* Expression

Odds Ratios

[0287] For pCR odds of NO versus YES
Tests and confidence intervals on odds ratios are likelihood ratio based.

Unit Odds Ratios

[0288] Per unit change in regressor

TABLE-US-00033 Term Odds Ratio Lower 95% Upper 95% Reciprocal Patient Age 1.050286 0.968116 1.148315 0.9521217 CD274 5.205898 1.484886 26.67176    0.1920898 Expression IFNG 0.30474  0.097072 0.749074 3.2814908 Expression

Odds Ratios for Cancer Type

[0289]

TABLE-US-00034 Prob > Level1 /Level2 Odds Ratio Chisq Lower 95% Upper 95% LABC IBC 0.9444125 0.9722 0.0282989 35.902054 OPER- IBC 1.405087  0.8471 0.0357479 68.159191 ABLE OPER- LABC 1.4877895 0.6568 0.2518769 9.0216463 ABLE IBC LABC 1.0588593 0.9722 0.0278536 35.337072 IBC OPER- 0.7116997 0.8471 0.0146715 27.973694 ABLE LABC OPER- 0.6721381 0.6568 0.1108445 3.9701934 ABLE

Odds Ratios for pN

[0290]

TABLE-US-00035 Prob > Level1 /Level2 Odds Ratio Chisq Lower 95% Upper 95% N1 N0 0.8494615 0.8697 0.1114536 6.033483  N0 N1 1.1772165 0.8697 0.1657417 8.9723459

Receiver Operating Characteristic

(see FIG. 13)

[0291] Using pCR=′YES' to be the positive level [0292] AUC [0293] 0.79718

Confusion Matrix

Actual

Predicted

[0294]

TABLE-US-00036 Training NO YES NO 22  5 YES  6 15

Lift Curve (see FIG. 14)

[0295] pCR [0296] NO [0297] YES

Prediction Profiler (see FIG. 15)

Nominal Logistic Fit for pCR ER=ERpos

[0298] Converged in Gradient, 19 iterations

Whole Model Test

[0299]

TABLE-US-00037 Model -LogLikelihood DF ChiSquare Prob > ChiSq Difference  2.400597 6 4.801193 0.5696 Full 14.343001 Reduced 16.743598

TABLE-US-00038 RSquare (U) 0.1434 AICc 46.2989 BIC 54.3309 Observations (or Sum Wgts) 39

TABLE-US-00039 Measure Training Definition Entropy RSquare 0.1434 1-Loglike(model)/Loglike(0) Generalized RSquare 0.2010 (1-(L(0)/L(model)){circumflex over ( )}(2/n))/ (1-L(0){circumflex over ( )}(2/n)) Mean -Log p 0.3678 Σ -Log(ρ[j])/n RMSE 0.3462 √ Σ(y[j]-ρ[j]).sup.2/n Mean Abs Dev 0.2351 Σ |y[j]-ρ[j]|/n Misclassification Rate 0.1795 Σ (ρ[j] ≠ ρMax)/n N 39 n

Lack Of Fit

[0300]

TABLE-US-00040 Source DF -LogLikelihood Chi Square Lack Of Fit 32 14.343001 28.686  Saturated 38  0.000000 Prob > ChiSq Fitted  6 14.343001 0.6351

Parameter Estimates

[0301]

TABLE-US-00041 Prob > Lower Upper Term Estimate Std Error ChiSquare ChiSq 95% 95% Intercept Unstable 7.20306909 3597.5107 0.00 0.9984 −7043.7884 7058.1945 Patient Age 0.0578149 0.0628112 0.85 0.3573 −0.0560483 0.19608254 Cancer Unstable 12.0092513 7195.0139 0.00 0.9987 −14089.959 14113.9773 Type[IBC] Unstable −6.5864683 3597.507 0.00 0.9985 −7057.5706 7044.39766 Cancer Type[LABC] −0.0542869 0.5572904 0.01 0.9224 −1.1698378 1.117206 pN[N0] CD274 0.08485271 0.9859164 0.01 0.9314 −1.8704698 2.14104768 Expression IFNG −0.7334678 0.6191817 1.40 0.2362 −2.0476903 0.45985303 Expression
For log odds of NO/YES

Effect Likelihood Ratio Tests

[0302]

TABLE-US-00042 Source Nparm DF L-R ChiSquare Prob > ChiSq Patient Age 1 1 0.92588732 0.3359 Cancer Type 2 2 1.89140212 0.3884 pN 1 1 0.00946444 0.9225 CD274 Expression 1 1 0.00742213 0.9313 IFNG Expression 1 1 1.45693945 0.2274

Odds Ratios

[0303] For pCR odds of NO versus YES
Tests and confidence intervals on odds ratios are likelihood ratio based.

Unit Odds Ratios

[0304] Per unit change in regressor

TABLE-US-00043 Term Odds Ratio Lower 95% Upper 95% Reciprocal Patient Age 1.059519 0.945493 1.216627 0.9438246 CD274 1.088557 0.154051 8.508347 0.9186476 Expression IFNG 0.480241 0.129033 1.583841 2.0822891 Expression

Odds Ratios for Cancer Type

[0305]

TABLE-US-00044 Level1 /Level2 Odds Ratio Prob > Chisq Lower 95% Upper 95% LABC IBC 8.3942e−9 0.2128 0        5.1523961 OPERABLE IBC 2.6876e−8 0.4499 0      20.868673 OPERABLE LABC      3.2017112 0.3193 0.2999262 36.429388 IBC LABC 119129251     0.2128 0.1940845 . IBC OPERABLE  37207993     0.4499 0.0479187 . LABC OPERABLE     0.312333 0.3193 0.0274504   3.3341535

Odds Ratios for pN

[0306]

TABLE-US-00045 Level1 /Level2 Odds Ratio Prob > Chisq Lower 95% Upper 95% N1 N0 1.1146872 0.9225 0.1070551 10.377869   N0 N1 0.8971126 0.9225 0.0963589 9.3409878

Receiver Operating Characteristic

(see FIG. 16)

[0307] Using pCR=′YES' to be the positive level [0308] AUC [0309] 0.77273

Confusion Matrix

Actual

Predicted

[0310]

TABLE-US-00046 Training NO YES NO 32 1 YES  6 0

[0311] The present invention refers to the following nucleotide and amino acid sequences:

[0312] The sequences provided herein are, inter alia, available in the NCBI database and disclosed in WO 2010/077634 and can be retrieved from world wide web at ncbi.nlm.nih.gov/sites/entrez?db=gene; Theses sequences also relate to annotated and modified sequences. The present invention also provides techniques and methods wherein homologous sequences, and variants of the concise sequences provided herein are used.

[0313] SEQ ID NOS: 1-21 define the anti-PD-L1 antibody to be used in accordance with the present invention. SEQ ID NOS: 1-21 are shown in the sequence listing.

[0314] SEQ ID No. 22 to 37 show sequences of amino acid sequences for Domains I-IV of the HER2 protein (SEQ ID NO. 22-25, see also FIG. 1) and sequences of anti-HER2-antibodies. (SEQ ID NOS: 26 to 37; see also FIGS. 2A, 2B, 3A, 3B, 4A, 4B, 5A, and 5B).

[0315] SEQ ID No. 26:

[0316] Amino acid sequence of the variable

[0317] light (Vr) (FIG. 2A) domain of murine monoclonal antibody 2C4 (SEQ ID NOS: 26 and 27, respectively) as shown in FIGS. 2A and 2B.

[0318] SEQ ID No. 27:

[0319] Amino acid sequence of the variable heavy (VH) (FIG. 2B) domain of murine monoclonal antibody 2C4 as shown in FIGS. 2A and 2B.

[0320] SEQ ID No. 28:

[0321] Amino acid sequence of the variable light (VL) (FIG. 2A) domain of variant 574/Pertuzumab as shown in FIGS. 2A and 2B.

[0322] SEQ ID No. 29:

[0323] Amino acid sequence of the variable heavy (VH) (FIG. 2B) domain of variant 574/Pertuzumab as shown in FIGS. 2A and 2B.

[0324] SEQ ID No. 30:

[0325] human V.sub.L consensus frameworks (hum Ki, light kappa subgroup I; humIII, heavy subgroup III) as shown in FIGS. 2A and 2B.

[0326] SEQ ID No. 31:

[0327] human V.sub.H consensus frameworks (hum Ki, light kappa subgroup I; humIII, heavy subgroup III) as shown in FIGS. 2A and 2B.

[0328] SEQ ID No. 32:

[0329] Amino acid sequences of Pertuzumab light chain as shown in FIG. 3A.

[0330] SEQ ID No. 33:

[0331] Amino acid sequences of Pertuzumab heavy chain as shown in FIG. 3B.

[0332] SEQ ID No. 34:

[0333] Amino acid sequence of Trastuzumab light chain domain as shown in FIG. 4A Boundaries of the variable light domain are indicated by arrows.

[0334] SEQ ID No. 35:

[0335] Amino acid sequence of Trastuzumab heavy chain as shown in FIG. 4B. Boundaries of the variable heavy domain are indicated by arrows.

[0336] SEQ ID No. 36:

[0337] Amino acid sequence of variant Pertuzumab light chain sequence (FIG. 5A).

[0338] SEQ ID No. 37:

[0339] Amino acid sequence of variant Pertuzumab heavy chain sequence (FIG. 5B).

[0340] SEQ ID NO. 38:

[0341] Nucleotide sequence encoding Homo sapiens Progesterone Receptor (PR)

[0342] NCBI Reference Sequence: NC 000011.9

[0343] >gi|224589802:c101000544-100900355 Homo sapiens chromosome 11, GRCh37.p10 Primary Assembly

[0344] SEQ ID No. 39:

[0345] Amino acid sequence of Homo sapiens Progesterone Receptor (PR)

[0346] PRGR_HUMAN Length: 933 Dec. 7, 2012 15:10 Type: P Check: 6067.

[0347] SEQ ID NO. 40:

[0348] Nucleotide sequence encoding Homo sapiens Estrogen Receptor (ER) (NM_000125.3)

[0349] SEQ ID NO. 41:

[0350] Nucleotide sequence encoding Homo sapiens Estrogen Receptor (ER)

[0351] NCBI Reference Sequence: NC 000006.11

[0352] >gi|224589818:152011631-152424409 Homo sapiens chromosome 6, GRCh37.p10 Primary Assembly

[0353] SEQ ID No. 42:

[0354] Amino acid sequence of Homo sapiens Estrogen Receptor (ER)

[0355] >ENST00000206249_6

[0356] SEQ ID No. 43:

[0357] Nucleotide sequence encoding Homo sapiens programmed death ligand 1 (PD-L1)

[0358] NCBI Reference Sequence: NC 000009.11

[0359] >gi|224589821:5450503-5470567 Homo sapiens chromosome 9, GRCh37.p10 Primary Assembly

[0360] SEQ ID NO. 44

[0361] Nucleotide sequence encoding Homo sapiens programmed death ligand 1(PD-L1) (CD274), transcript variant 1, mRNA

[0362] NCBI Reference Sequence: NM 014143.3

[0363] >gi|292658763|ref|NM_014143.3|Homo sapiens CD274 molecule (CD274), transcript variant 1, mRNA

[0364] SEQ ID No.45:

[0365] Amino acid sequence of Homo sapiens programmed death ligand 1(PD-L1) (programmed cell death 1 ligand 1 isoform a precursor [Homo sapiens])

[0366] NCBI Reference Sequence: NP 054862.1

[0367] >gi|7661534|ref|NP_054862.1|programmed cell death 1 ligand 1 isoform a precursor [Homo sapiens]

[0368] SEQ ID No. 46:

[0369] Nucleotide sequence encoding Homo sapiens programmed death ligand 1(PD-L1) (CD274), transcript variant 2, mRNA

[0370] NCBI Reference Sequence: NM 001267706.1

[0371] >gi|390979638|ref|NM_001267706.1|Homo sapiens CD274 molecule (CD274), transcript variant 2, mRNA

[0372] SEQ ID No. 47:

[0373] Amino acid sequence of Homo sapiens programmed death ligand 1(PD-L1) (programmed cell death 1 ligand 1 isoform b precursor [Homo sapiens])

[0374] NCBI Reference Sequence: NP 001254635.1

[0375] >gi|390979639|ref|NP_001254635.1|programmed cell death 1 ligand 1 isoform b precursor [Homo sapiens]

[0376] SEQ ID No. 48:

[0377] Nucleotide sequence encoding Homo sapiens programmed death ligand 1(PD-L1) (Homo sapiens CD274 molecule (CD274), transcript variant 3, non-coding RNA)

[0378] NCBI Reference Sequence: NR_052005.1

[0379] >gi|390979640|ref|NR_052005.1|Homo sapiens CD274 molecule (CD274), transcript variant 3, non-coding RNA

[0380] SEQ ID No. 49:

[0381] Nucleotide sequence encoding Homo sapiens interferon gamma (Homo sapiens chromosome 12, GRCh37.p10 Primary Assembly)

[0382] NCBI Reference Sequence: NC_000012.11

[0383] >gi|224589803:c68553521-68548550 Homo sapiens chromosome 12, GRCh37.p10 Primary Assembly

[0384] SEQ ID No. 50:

[0385] Nucleotide sequence encoding Homo sapiens interferon gamma, mRNA

[0386] NCBI Reference Sequence: NM 000619.2

[0387] >gi|56786137|ref|NM 000619.21Homo sapiens interferon, gamma (IFNG), mRNA

[0388] SEQ ID No. 51:

[0389] Amino acid sequence of Homo sapiens interferon gamma, interferon gamma precursor [Homo sapiens]

[0390] NCBI Reference Sequence: NP 000610.2

[0391] >gi|56786138|ref|NP_000610.2| interferon gamma precursor [Homo sapiens]

[0392] All references cited herein are fully incorporated by reference. Having now fully described the invention, it will be understood by a person skilled in the art that the invention may be practiced within a wide and equivalent range of conditions, parameters and the like, without affecting the spirit or scope of the invention or any embodiment thereof.