Protective and Susceptibility Effects of Human Leukocyte Antigen on Melanoma Prevalence and their Implications for Predicting Checkpoint Blockade Immunotherapy Outcomes

Lisa M. James1,2,3, Apostolos P. Georgopoulos1,2,3,4*

1The HLA Research Group, Brain Sciences Center, Department of Veterans Affairs Health Care System, Minneapolis, MN, 55417, USA

2Department of Neuroscience, University of Minnesota Medical School, Minneapolis, MN 55455, USA

3Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN 55455, USA

4Department of Neurology, University of Minnesota Medical School, Minneapolis, MN 55455, USA


The association of Human Leukocyte Antigen (HLA) with melanoma has been well documented. Similarly, the outcome of checkpoint blockade immunotherapy (CBI) in melanoma depends, to some extent, on the HLA genotype of the patient. Although specific favorable (or unfavorable) HLA alleles for CBI outcome for melanoma have been identified, there is currently no reliable way to predict a positive, neutral or negative melanoma CBI outcome for other alleles. Here we used an immunogenetic epidemiological approach to identify HLA alleles whose frequency is negatively (or positively) associated with melanoma prevalence (protective or susceptibility alleles, respectively). The findings demonstrated that, indeed, HLA alleles that are negatively associated with melanoma prevalence in the population have been associated with good CBI outcome at the individual level and, conversely, HLA alleles that are positively associated with melanoma prevalence have been associated with poor CBI outcome in individuals. Given this good prediction of CBI cancer immunotherapy by specific immunogenetically discovered HLA alleles, we used this epidemiologic immunogenetic approach to identify more HLA Class I and II alleles protective (or susceptibility) for melanoma which would thus be good predictors of CBI outcomes in those cancers. This is a new approach to successfully (a) identify HLA protective or susceptibility alleles for melanoma, and (b) use that information in anticipating outcomes in CBI cancer immunotherapy.


Introduction

The association of human leukocyte antigen (HLA) with cancer in general1,2 and melanoma in particular3 has been well documented. Research has been mainly focused on the role of HLA class I and associated engagement of CD8+ cytotoxic T lymphocytes in eliminating tumor cells under the hypothesis that novel antigens produced by tumor cells (“neoantigens”)4,5 attach to HLA class I molecules, forming a complex that moves to the cell surface, where it is recognized by CD8+ T lymphocytes resulting in cell death and apoptosis. This mechanism is thought to be suppressed by substances secreted by tumor cells which suppress T cell activation6,7. In fact, CBI is thought to act by blocking this tumor-induced T lymphocyte suppression, thus allowing CD8+ T lymphocytes to recognize HLA class I molecule – neoantigen complexes and kill the tumor cell. Under those conditions, the therapeutic effectiveness of CBI would depend on how well the HLA-neoantigen complex can engage the CD8+ lymphocyte in the first place. In that context, it was found8 that CBI produced a good outcome in melanoma patients with certain HLA class I alleles (B*18:01, B*44:02, B*44:03, B*44:05, B*50:01), whereas it had a poor outcome in patients with the B*15:01 allele. This differential therapeutic effect was attributed to how well those alleles may bind to melanoma tumor neoantigens8. It is reasonable to suppose that HLA binding affinity to melanoma tumor neoantigens would have consequences for the general population, outside of melanoma CBI therapy. More specifically, we hypothesized that such a mechanism could operate at the population level with the consequence that alleles that have been shown to have a positive effect on CBI would be associated with lower melanoma prevalence (protective alleles), whereas alleles that have been shown to have a negative effect on CBI would be associated with higher melanoma prevalence (susceptibility alleles). We tested this hypothesis by evaluating the correspondence between the population frequency of HLA alleles that have been shown to influence the outcome of CBI prevalence and the population prevalence of melanoma in 14 Continental Western European (CWE) countries. In addition, we extended those analyses to evaluate the association of additional HLA class I and class II allele frequencies in CWE (127 alleles in total) with the population prevalence of melanoma to identify HLA alleles that, at the population level, are associated with susceptibility to or protection against melanoma.

With respect to HLA class II alleles, they have also been involved in cancer in general9 and melanoma in particular3. Two main explanations have been advanced regarding the role of HLA class II in cancer. One is direct, involving the production of antibodies against tumor neoantigens and subsequent elimination of tumor cells; the other is indirect, based on the finding that CD4+ T lymphocytes activated by the HLA class II molecule – neoantigen complex induce proliferation of CD8+ T lymphocytes through the release of IL-26,7,9. In addition, HLA alleles may influence cancer via elimination of cancer-inducing viruses and bacteria10,11. Both the production of antibodies against tumor neoantigens and the elimination of cancer-inducing pathogens rests on the ability of HLA alleles to first bind with antigens. Subtle alterations in the HLA binding groove alter binding affinity12,13, thereby influencing the scope of antigens that can be bound and eliminated. Thus, characterization of HLA alleles as protective or susceptible with regard to the population prevalence of melanoma is presumed to reflect, in part, binding, and therefore elimination of, antigens that could otherwise contribute to melanoma.

Materials and Methods

Melanoma prevalence

The population prevalence of melanoma was calculated for 14 CWE countries including Austria, Belgium, Denmark, Finland, France, Germany, Greece, Italy, Netherlands, Norway, Portugal, Spain, Sweden, and Switzerland. Specifically, the 2016 melanoma case counts as determined by the Global Burden of Disease study14 were divided by the 2016 total population for each country15.

HLA

The population frequencies of class I (A, B, C) and class II (DRB1, DQB1, DPB1) classical genes in each of the 14 CWE countries were retrieved from Allele Frequency Net Database16,17, a public repository of immune gene frequencies worldwide. Of the 844 distinct alleles, 127 occurred in 9 or more of the 14 CWE countries above and were used in subsequent analyses. The distribution of the alleles used to Class and Gene is given in Table 1.

Table 1: Distribution of 127 HLA alleles analyzed to Class and Gene.

 

Class I (N = 69 alleles)

Class II (N = 58 alleles)

Gene

A

B

C

DPB1

DQB1

DRB1

Count

20

36

13

15

14

29

HLA Class I supertypes

Alleles of Class I A and B genes were assigned to a supertype18. (Supertypes for gene C of Class I or any gene of Class II have not been described.) Of a total of 56 alleles of Class I A and B genes, 53 alleles could be assigned to supertypes based on the assignments provided by Sidney et al.18, namely all 20 A gene alleles and 33/36 B gene alleles; B*13:02, B*47:01 and B*49:01 were unassigned (Fig. 2 in Sidney et al.18). The distribution of the 56 HLA Class I alleles to supertypes is given in Table 2. The individual alleles used and their assignments to Class, Gene and Supertype are given in Table 3.

Table 2: Distribution of 56 Class I A and B alleles in supertypes

Supertype

Count

A01

6

A02

3

A03

6

A24

3

A103

1

A124

1

B07

8

B08

1

B27

8

B44

11

B58

3

B62

2

Unassigned

3

Total

56

Table 3: The 127 HLA alleles used and their Class, gene and supertype assignments.

Index

Allele

Class

Gene

Supertype

1

A*01:01

I

A

A01

2

A*02:01

I

A

A02

3

A*02:05

I

A

A02

4

A*03:01

I

A

A03

5

A*11:01

I

A

A03

6

A*23:01

I

A

A24

7

A*24:02

I

A

A24

8

A*25:01

I

A

A01

9

A*26:01

I

A

A01

10

A*29:01

I

A

A24

11

A*29:02

I

A

A01 A24

12

A*30:01

I

A

A01 A03

13

A*30:02

I

A

A01

14

A*31:01

I

A

A03

15

A*32:01

I

A

A01

16

A*33:01

I

A

A03

17

A*33:03

I

A

A03

18

A*36:01

I

A

A01

19

A*68:01

I

A

A03

20

A*68:02

I

A

A02

21

B*07:02

I

B

B07

22

B*08:01

I

B

B08

23

B*13:02

I

B

Unassigned

24

B*14:01

I

B

B27

25

B*14:02

I

B

B27

26

B*15:01

I

B

B62

27

B*15:17

I

B

B58

28

B*15:18

I

B

B27

29

B*18:01

I

B

B44

30

B*27:02

I

B

B27

31

B*27:05

I

B

B27

32

B*35:01

I

B

B07

33

B*35:02

I

B

B07

34

B*35:03

I

B

B07

35

B*35:08

I

B

B07

36

B*37:01

I

B

B44

37

B*38:01

I

B

B27

38

B*39:01

I

B

B27

39

B*39:06

I

B

B27

40

B*40:01

I

B

B44

41

B*40:02

I

B

B44

42

B*41:01

I

B

B44

43

B*41:02

I

B

B44

44

B*44:02

I

B

B44

45

B*44:03

I

B

B44

46

B*44:05

I

B

B44

47

B*45:01

I

B

B44

48

B*47:01

I

B

Unassigned

49

B*49:01

I

B

Unassigned

50

B*50:01

I

B

B44

51

B*51:01

I

B

B07

52

B*52:01

I

B

B62

53

B*55:01

I

B

B07

54

B*56:01

I

B

B07

55

B*57:01

I

B

B58

56

B*58:01

I

B

B58

57

C*01:02

I

C

 

58

C*03:03

I

C

59

C*04:01

I

C

60

C*05:01

I

C

61

C*06:02

I

C

62

C*07:01

I

C

63

C*07:02

I

C

64

C*07:04

I

C

65

C*12:02

I

C

66

C*12:03

I

C

67

C*14:02

I

C

68

C*15:02

I

C

69

C*16:01

I

C

70

DPB1*01:01

II

DPB1

71

DPB1*02:01

II

DPB1

72

DPB1*02:02

II

DPB1

73

DPB1*03:01

II

DPB1

74

DPB1*04:01

II

DPB1

75

DPB1*04:02

II

DPB1

76

DPB1*05:01

II

DPB1

77

DPB1*06:01

II

DPB1

78

DPB1*09:01

II

DPB1

79

DPB1*10:01

II

DPB1

80

DPB1*11:01

II

DPB1

81

DPB1*13:01

II

DPB1

82

DPB1*14:01

II

DPB1

83

DPB1*17:01

II

DPB1

84

DPB1*19:01

II

DPB1

85

DQB1*02:01

II

DQB1

86

DQB1*02:02

II

DQB1

87

DQB1*03:01

II

DQB1

88

DQB1*03:02

II

DQB1

89

DQB1*03:03

II

DQB1

90

DQB1*04:02

II

DQB1

91

DQB1*05:01

II

DQB1

92

DQB1*05:02

II

DQB1

93

DQB1*05:03

II

DQB1

94

DQB1*06:01

II

DQB1

95

DQB1*06:02

II

DQB1

96

DQB1*06:03

II

DQB1

97

DQB1*06:04

II

DQB1

98

DQB1*06:09

II

DQB1

99

DRB1*01:01

II

DRB1

100

DRB1*01:02

II

DRB1

101

DRB1*01:03

II

DRB1

102

DRB1*03:01

II

DRB1

103

DRB1*04:01

II

DRB1

104

DRB1*04:02

II

DRB1

105

DRB1*04:03

II

DRB1

106

DRB1*04:04

II

DRB1

107

DRB1*04:05

II

DRB1

108

DRB1*04:07

II

DRB1

109

DRB1*04:08

II

DRB1

110

DRB1*07:01

II

DRB1

111

DRB1*08:01

II

DRB1

112

DRB1*08:03

II

DRB1

113

DRB1*09:01

II

DRB1

114

DRB1*10:01

II

DRB1

115

DRB1*11:01

II

DRB1

116

DRB1*11:02

II

DRB1

117

DRB1*11:03

II

DRB1

118

DRB1*11:04

II

DRB1

119

DRB1*12:01

II

DRB1

120

DRB1*13:01

II

DRB1

121

DRB1*13:02

II

DRB1

122

DRB1*13:03

II

DRB1

123

DRB1*13:05

II

DRB1

124

DRB1*14:01

II

DRB1

125

DRB1*15:01

II

DRB1

126

DRB1*15:02

II

DRB1

127

DRB1*16:01

II

DRB1

Data analysis

The primary analysis was the calculation of the raw melanoma score, namely the Pearson correlation coefficient  between the population prevalence of melanoma and the population frequency of each of the 127 HLA alleles. Fisher’s z-transformation19 was applied to  normalize its distribution:

JISS-22-1238-fig1

We have used this measure in previous studies of immunogenetic epidemiology of neurodegenerative diseases20-25, type 1 diabetes26, and various types of cancers27,28. The effects of HLA class and genes on the proportion of protective alleles in the population were evaluated using the Wald test for a single proportion (2-sided) and by constructing Agresti-Coull 95% confidence intervals (CI) for the proportions.

Since each individual carries 2 alleles for each one of the 6 genes, for a total of 12 alleles, we also derived an expected estimate, R', of melanoma protection/susceptibility for each allele as follows. For a given allele, we retained its r' and obtained the remaining eleven r' allele values by randomly drawing with replacement from the pools of alleles of each gene. This process was repeated 1000 times, and an average R' was calculated from 12 x 1000 = 12000 r' values (i.e. the value for the specific allele under consideration and the 11 remaining randomly drawn values, x 1000). Finally, R' was computed for each one of the 127 alleles. Analyses were conducted using SPSS (Version 27) and Intel Fortran (version 16.8.3).

Results

Melanoma immunogenetic scores: Individual alleles

All IMS values are plotted against their rank in Fig. 1 together with scatter plots of two protective and two susceptibility alleles to illustrate the dependence of melanoma prevalence on allele frequency (negative for protective and positive for susceptibility alleles, color-coded in blue and red, respectively). Of the 127 alleles investigated, the frequencies of 79 alleles were negatively associated with melanoma prevalence, indicating a protective effect, whereas the frequencies of 48 alleles were positively associated with melanoma prevalence, indicating a susceptibility effect. The IMS scores of the protective alleles are given in Table 4 and those of the susceptibility alleles are given in Table 5. It can be seen that both types of alleles can be found in both HLA classes and all 6 classical genes.

The results of the statistical analysis of the proportions are given in Table 6. We found the following. (a) The overall proportion of protective alleles (79/127 = 0.622) was statistically significantly higher than the null hypothesis of the proportion = 0.5, P = 0.005, Wald test). (b) Of the 69 class I alleles, 47 were protective (proportion = 0.681, P = 0.001). (c) Within class I, alleles of gene B had an overall statistically significant protective effect (proportion = 25/36 = 0.694, P = 0.011). With respect to class II, there were no statistically significant overall effects (Table 6).

JISS-22-1238-fig2

Figure 1: The HLA-melanoma risk scores are plotted against their rank. Arrows indicate the position in the graph of the plotted allele. Blue scatter plots illustrate the relation between the frequency of two protective alleles and melanoma frequency in CWE countries; for allele B*38:01, r = −0.799, r' = −1.095, P = 0.0098; for allele DPB1*10:01, r = −0.728, r' = −0.924, P = 0.017. Red scatter plots illustrate the relation between the frequency of two susceptibility alleles and melanoma frequency in CWE countries; for allele C*07:02, r = 0.851, r' = 1.259, P = 0.0036; for allele B*15:01, r = 0.777, r' = 1.037, P = 0.0082.

Table 4: Melanoma immunogenetic scores for the 79 HLA protective alleles ranked from high to low protection.

Index

Allele

Class

Gene

IMS

1

B*38:01 

I

B

−1.095

2

DPB1*10:01 

II

DPB1

−0.924

3

B*49:01 

I

B

−0.898

4

DRB1*01:02 

II

DRB1

−0.876

5

B*18:01 

I

B

−0.855

6

DPB1*02:01 

II

DPB1

−0.826

7

B*51:01 

I

B

−0.799

8

DPB1*13:01 

II

DPB1

−0.777

9

C*04:01 

I

C

−0.775

10

B*35:08 

I

B

−0.773

11

DPB1*14:01 

II

DPB1

−0.747

12

B*41:02 

I

B

−0.734

13

C*12:03 

I

C

−0.732

14

A*23:01 

I

A

−0.722

15

DRB1*11:03 

II

DRB1

−0.705

16

B*35:03 

I

B

−0.698

17

DRB1*04:03 

II

DRB1

−0.694

18

A*32:01 

I

A

−0.674

19

C*14:02 

I

C

−0.662

20

DRB1*13:05 

II

DRB1

−0.659

21

B*50:01 

I

B

−0.649

22

DQB1*05:02 

II

 

−0.648

23

DQB1*03:01 

II

DQB1

−0.625

24

DRB1*04:02 

II

DRB1

−0.622

25

A*11:01 

I

A

−0.614

26

DRB1*13:03 

II

DRB1

−0.610

27

DRB1*15:02 

II

DRB1

−0.608

28

A*33:03 

I

A

−0.607

29

A*26:01 

I

A

−0.590

30

B*35:02 

I

B

−0.572

31

C*15:02 

I

C

−0.554

32

DRB1*11:02 

II

DRB1

−0.530

33

C*12:02 

I

C

−0.514

34

B*44:05 

I

B

−0.512

35

B*15:18 

I

B

−0.495

36

DRB1*07:01 

II

DRB1

−0.452

37

B*14:02 

I

B

−0.448

38

DRB1*11:04 

II

DRB1

−0.444

39

DRB1*16:01 

II

DRB1

−0.437

40

A*36:01 

I

A

−0.435

41

B*58:01 

I

B

−0.432

42

A*33:01 

I

A

−0.422

43

A*02:05 

I

A

−0.421

44

DQB1*05:03 

II

DQB1

−0.413

45

B*14:01 

I

B

−0.404

46

DPB1*02:02 

II

DPB1

−0.400

47

B*52:01 

I

B

−0.389

48

A*30:02 

I

A

−0.379

49

A*29:02 

I

A

−0.371

50

DRB1*11:01 

II

DRB1

−0.369

51

B*44:03 

I

B

−0.365

52

B*41:01 

I

B

−0.362

53

DRB1*01:03 

II

DRB1

−0.354

54

DQB1*06:01 

II

DQB1

−0.325

55

B*47:01 

I

B

−0.316

56

DQB1*02:02 

II

DQB1

−0.305

57

B*45:01 

I

B

−0.299

58

DPB1*17:01 

II

DPB1

−0.275

59

DRB1*14:01 

II

DRB1

−0.265

60

DRB1*04:05 

II

DRB1

−0.261

61

C*07:04 

I

C

−0.245

62

A*01:01 

I

A

−0.240

63

DRB1*04:07 

II

DRB1

−0.228

64

DPB1*09:01 

II

DPB1

−0.227

65

DQB1*06:09 

II

DQB1

−0.226

66

B*15:17 

I

B

−0.218

67

B*39:06 

I

B

−0.217

68

DRB1*08:03 

II

DRB1

−0.206

69

B*39:01 

I

B

−0.174

70

B*57:01 

I

B

−0.139

71

DPB1*06:01 

II

DPB1

−0.135

72

C*16:01 

I

C

−0.116

73

A*68:02 

I

A

−0.112

74

B*27:02 

I

B

−0.108

75

B*35:01 

I

B

−0.101

76

A*30:01 

I

A

−0.088

77

DRB1*03:01 

II

DRB1

−0.026

78

C*06:02 

I

C

−0.022

79

A*29:01 

I

A

−0.015

Table 5: Melanoma immunogenetic scores for the 48 HLA susceptibility alleles ranked from high to low susceptibility.

Index

Allele

Class

Gene

IMS

1

C*07:02 

I

C

1.259

2

B*37:01 

I

B

1.181

3

DRB1*04:01 

II

DRB1

1.093

4

B*15:01 

I

B

1.037

5

B*07:02 

I

B

1.013

6

A*31:01 

I

A

0.982

7

DRB1*15:01 

II

DRB1

0.965

8

B*40:01 

I

B

0.957

9

DPB1*01:01 

II

DPB1

0.885

10

A*03:01 

I

A

0.779

11

DQB1*03:02 

II

DQB1

0.717

12

DPB1*04:01 

II

DPB1

0.627

13

DRB1*04:04 

II

DRB1

0.603

14

DQB1*03:03 

II

DQB1

0.585

15

DQB1*06:02 

II

DQB1

0.584

16

DRB1*01:01 

II

DRB1

0.576

17

DRB1*04:08 

II

DQB1

0.560

18

DQB1*02:01 

II

DQB1

0.555

19

DQB1*06:04 

II

DQB1

0.543

20

C*03:03 

I

C

0.542

21

DRB1*13:02 

II

DRB1

0.536

22

B*27:05 

I

B

0.502

23

B*55:01 

I

B

0.494

24

DQB1*04:02 

II

DQB1

0.437

25

DRB1*08:01 

II

DRB1

0.424

26

DRB1*09:01 

II

DRB1

0.423

27

B*08:01 

I

B

0.420

28

DRB1*12:01 

II

DRB1

0.372

29

DRB1*13:01 

II

DRB1

0.340

30

A*24:02 

I

A

0.282

31

DPB1*05:01 

II

DPB1

0.268

32

B*40:02 

I

B

0.256

33

DQB1*06:03 

II

DQB1

0.254

34

A*02:01 

I

A

0.252

35

B*56:01 

I

B

0.232

36

DPB1*19:01 

II

DPB1

0.204

37

C*05:01 

I

C

0.181

38

C*07:01 

I

C

0.118

39

B*13:02 

I

B

0.115

40

DPB1*03:01 

II

DPB1

0.109

41

B*44:02 

I

B

0.108

42

C*01:02 

I

C

0.102

43

A*68:01 

I

A

0.087

44

A*25:01 

I

A

0.082

45

DRB1*10:01 

II

DRB1

0.068

46

DQB1*05:01 

II

DQB1

0.052

47

DPB1*11:01 

II

DPB1

0.008

48

DPB1*04:02 

II

DPB1

0.003

Table 6: Number of melanoma protective and susceptibility alleles in HLA Class I, II and their classical genes. Confidence intervals are Agresti-Coull. Statistically significant results are marked by*.

 

Gene

Total

N

N

protective

N susceptibility

Proportion protective

Lower

95% CI

Upper 95% CI

Z

Wald Test

(2 sided P)

Class I

A

20

14

6

0.700

0.479

0.857

1.952

0.051

B

36

25

11

0.694

0.530

0.821

2.533

0.011*

C

13

8

5

0.615

0.354

0.824

0.855

0.392

Total

69

47

22

0.681

0.564

0.779

3.229

0.001*

Class II

DPB1

15

8

7

0.533

0.301

0.752

0.259

0.796

DQB1

14

6

8

0.429

0.213

0.674

0.540

0.589

DRB1

29

18

11

0.621

0.439

0.774

1.339

0.180

Total

58

32

26

0.552

0.424

0.673

0.792

0.428

Total

 

127

79

48

0.622

0.535

0.702

2.837

0.005*

Melanoma immunogenetic scores: Supertypes

The results of the statistical analysis of the proportions for 5 supertypes with N alleles > 5 (A01, A03, B07, B27, B44; Table 2) are given in Table 7. It can be seen that of the 5 supertypes tested, all but A03 comprised more protective than susceptibility alleles, although only in A01 and B27 this protective preponderance reached statistical significance.

Table 7: Number of melanoma protective and susceptibility alleles in 5 HLA Class I supertypes with N > 5. Confidence intervals are Agresti-Couli. Statistically significant results are colored red and marked by*.

Supertype

N

N

protective

N susceptibility

Proportion protective

Lower

95% CI

Upper 95% CI

Z

Wald Test

(2 sided P)

A01

6

5

1

0.833

0.448

0.989

2.191

0.028*

A03

6

3

3

0.500

0.188

0.912

0.000

1.000

B07

8

5

3

0.625

0.304

0.865

0.730

0.465

B27

8

7

1

0.875

0.508

0.999

3.207

0.001*

B44

11

7

7

0.636

0.362

0.950

0.940

0.347

Melanoma immunogenetic score : Application to individuals

The IMS scores analyzed above (Tables 4 and 5) refer to particular alleles. Given that an individual carries a total of 12 alleles (2 of each 6 HLA genes), the overall protection/susceptibility to melanoma for a specific individual is given by the average of the 12 IMS scores, one for each one of the 12 HLA alleles carried by that individual:

JISS-22-1238-fig3

where the subscripts on genes denote the 2 pairs of alleles carried by an individual for each classical HLA gene.

Melanoma immunogenetic score : Application to populations and CBI assessment

A second IMS estimate is the expected HLA-melanoma P/S  score, which is relevant to a population, where a given allele can be present in individuals together with 11 other alleles. More specifically,  is an estimate of the P/S influence of a specific allele in the presence of random combinations of any additional 11 alleles. The estimates of the expected (long-term) values of  are given in Table 8. It can be seen that most estimates are negative (i.e., protective), due to the fact that the IMS of most alleles are negative. This measure is especially relevant when evaluating effects of a given HLA allele on CBI outcomes because the individuals carrying the allele also carry 11 additional alleles which could also influence the outcome. The incorporation of the combined effect of 1000 random selections of these 11 alleles (from the total allele pool) in the derivation of  (see Methods) makes this estimate a solid and realistic measure by which to gauge the effect of the allele on CBI outcome.

Table 8: : Expected P/S estimates R'for the 127 alleles investigated in alphabetical order. The 6 alleles for which an effect on melanoma CBI immunotherapy outcome has been reported 8 are in bold and colored red to indicate poor outcome and blue to indicate beneficial outcome. Notice that these CBI-based attributes correspond to susceptibility and protective R', respectively

 

Allele

Expected

P/S estimate

1

 A*01:01 

-0.09810

2

 A*02:01 

-0.06601

3

 A*02:05 

-0.11706

4

 A*03:01 

-0.02287

5

 A*11:01 

-0.13304

6

 A*23:01 

-0.14128

7

 A*24:02 

-0.06191

8

 A*25:01 

-0.06935

9

 A*26:01 

-0.13616

10

 A*29:01 

-0.08055

11

 A*29:02 

-0.10928

12

 A*30:01 

-0.08787

13

 A*30:02 

-0.11728

14

 A*31:01 

0.00081

15

 A*32:01 

-0.12997

16

 A*33:01 

-0.11542

17

 A*33:03 

-0.12972

18

 A*36:01 

-0.11615

19

 A*68:01 

-0.06584

20

 A*68:02 

-0.08632

21

 B*07:02 

0.00323

22

 B*08:01 

-0.05063

23

 B*13:02 

-0.07745

24

 B*14:01 

-0.11786

25

 B*14:02 

-0.11589

26

 B*15:01 

0.00777

27

 B*15:17 

-0.10458

28

 B*15:18 

-0.12400

29

 B*18:01 

-0.15214

30

 B*27:02 

-0.09829

31

 B*27:05 

-0.04446

32

 B*35:01 

-0.09081

33

 B*35:02 

-0.13615

34

 B*35:03 

-0.14199

35

 B*35:08 

-0.15024

36

 B*37:01 

0.01478

37

 B*38:01 

-0.17444

38

 B*39:01 

-0.09179

39

 B*39:06 

-0.10054

40

 B*40:01 

-0.00427

41

 B*40:02 

-0.06165

42

 B*41:01 

-0.11417

43

 B*41:02 

-0.1469

44

 B*44:02 

-0.07697

45

 B*44:03 

-0.11213

46

 B*44:05 

-0.12451

47

 B*45:01 

-0.10965

48

 B*47:01 

-0.10636

49

 B*49:01 

-0.15750

50

 B*50:01 

-0.13779

51

 B*51:01 

-0.14575

52

 B*52:01 

-0.12171

53

 B*55:01 

-0.03946

54

 B*56:01 

-0.06161

55

 B*57:01 

-0.09192

56

 B*58:01 

-0.12279

57

 C*01:02 

-0.08364

58

 C*03:03 

-0.04563

59

 C*04:01 

-0.15210

60

 C*05:01 

-0.07476

61

 C*06:02 

-0.08138

62

 C*07:01 

-0.07739

63

 C*07:02 

0.01269

64

 C*07:04 

-0.12142

65

 C*12:02 

-0.12683

66

 C*12:03 

-0.15016

67

 C*14:02 

-0.14688

68

 C*15:02 

-0.13256

69

 C*16:01 

-0.09218

70

 DPB1*01:01 

-0.00218

71

 DPB1*02:01 

-0.14851

72

 DPB1*02:02 

-0.11065

73

 DPB1*03:01 

-0.07555

74

 DPB1*04:01 

-0.03509

75

 DPB1*04:02 

-0.08794

76

 DPB1*05:01 

-0.06835

77

 DPB1*06:01 

-0.09915

78

 DPB1*09:01 

-0.10201

79

 DPB1*10:01 

-0.15335

80

 DPB1*11:01 

-0.08137

81

 DPB1*13:01 

-0.14686

82

 DPB1*14:01 

-0.14786

83

 DPB1*17:01 

-0.11460

84

 DPB1*19:01 

-0.06244

85

 DQB1*02:01 

-0.05403

86

 DQB1*02:02 

-0.12581

87

 DQB1*03:01 

-0.16130

88

 DQB1*03:02 

-0.04474

89

 DQB1*03:03 

-0.04820

90

 DQB1*04:02 

-0.06698

91

 DQB1*05:01 

-0.09846

92

 DQB1*05:02 

-0.15057

93

 DQB1*05:03 

-0.13746

94

 DQB1*06:01 

-0.13615

95

 DQB1*06:02 

-0.05035

96

 DQB1*06:03 

-0.08316

97

 DQB1*06:04 

-0.05670

98

 DQB1*06:09 

-0.12162

99

 DRB1*01:01 

-0.04449

100

 DRB1*01:02 

-0.17229

101

 DRB1*01:03 

-0.12101

102

 DRB1*03:01 

-0.09058

103

 DRB1*04:01 

0.00772

104

 DRB1*04:02 

-0.14398

105

 DRB1*04:03 

-0.15780

106

 DRB1*04:04 

-0.04321

107

 DRB1*04:05 

-0.10505

108

 DRB1*04:07 

-0.10413

109

 DRB1*04:08 

-0.03819

110

 DRB1*07:01 

-0.13275

111

 DRB1*08:01 

-0.06066

112

 DRB1*08:03 

-0.11381

113

 DRB1*09:01 

-0.05353

114

 DRB1*10:01 

-0.09050

115

 DRB1*11:01 

-0.12326

116

 DRB1*11:02 

-0.13365

117

 DRB1*11:03 

-0.14712

118

 DRB1*11:04 

-0.13190

119

 DRB1*12:01 

-0.05823

120

 DRB1*13:01 

-0.06099

121

 DRB1*13:02 

-0.04509

122

 DRB1*13:03 

-0.14444

123

 DRB1*13:05 

-0.14817

124

 DRB1*14:01 

-0.11612

125

 DRB1*15:01 

0.00101

126

 DRB1*15:02 

-0.13911

127

 DRB1*16:01 

-0.12667

Relation to findings from CBI cancer immunotherapy

Chowell et al.8 reported on an association between HLA supertypes and degree of success of CBI in melanoma (see Table 1 in ref.8). Of 12 supertypes tested, statistically significant favorable effects with respect to CBI outcomes were found for 2 supertypes (B62 and B44). In our study, supertype B62 comprised only 2 alleles (Table 2) and, hence, could not be tested, whereas B44 (with 11 alleles) showed a preponderance of protective alleles but did not reach statistical significance (Table 7). However, a more clear picture is obtained with regard to individual alleles. Specifically, it was reported8 that alleles B*18:01, B*44:02, B*44:03, B*44:05 and B*50:01 were associated with favorable CBI response, whereas B*15:01 was associated with poor response. It can be seen in Table 7 that there was a complete congruence between this effect on CBI outcome and the P/S property of  of these alleles, namely that (a) 5 alleles with beneficial outcome had protective (negative)  scores (in blue), and (b) one allele with poor outcome had susceptibility (positive) score (in red).

Discussion

HLA is instrumental in immunosurveillance and T cell activation aimed at protection against foreign antigens. Neoantigens, a product of genetic mutations resulting from carcinogenesis or viral infections, stimulate the immune system to attack cancer cells4,5. Indeed, tumor specific neoantigens, which are selectively expressed on tumor cells and are therefore considered non-self by the immune system and are unaffected by immune tolerance, have become an increasingly promising target for personalized cancer immunotherapy29,30. However, a prerequisite of neoantigen presentation to the cancer cell’s surface and subsequent stimulation of immune system activation is that the neoantigen possesses a binding motif that is recognized by an individual’s HLA31. With regard to CBI, for instance, HLA-related differences in treatment effectiveness ae attributed to varying ability to bind melanoma tumor neoantigens8. Presumably, B*18:01, B*44:02, B*44:03, B*44:05 and B*50:01, which were shown to have a beneficial effect on survival under CBI therapy bind with greater efficiency than alleles that were found to have a negative effect on survival such as B*15:01. Remarkably, there was a complete congruence between the effects of those alleles on survival outcome and their expected IMS value (color-coded alleles Table 7). Specifically, allele B*15:01, which had a negative effect on survival8 had a susceptibility  score (red), whereas all alleles with positive effects on survival (B*18:01, B*44:02, B*44:03, B*44:05, B*50:01) had a protective IMS score (blue). These results suggest that HLA alleles that influence melanoma treatment (positively or negatively) also broadly influence melanoma protection or risk.

Finally, a word of caution regarding the interpretation of effects of HLA supertypes. Although the assignment of an allele to a supertype is based on sound biophysical principles18, this does not ensure a homogeneity of biological effect by the various alleles of a given supertype, given that even a single amino acid difference in a HLA molecule can result in a major difference in biological action12. In fact, the diversity of action of melanoma-related HLA molecules of the same supertype becomes evident from an examination of the confidence intervals of the hazard ratios (HR) of the effects of 12 HLA supertypes on melanoma CBI outcome8. Of these 12 supertypes, 10 did not show a statistically significant effect but had a wide range of 95% CI indicating the presence of mixed effects, i.e. of alleles with beneficial, neutral or detrimental effect on CBI outcome. For example, the lower and upper 95% CI of HR for supertype A01A03 (Table 1 in ref.8) were 0.43 and 2.94, respectively, with a fairly wide Confidence Limit Ratio32

JISS-22-1238-fig5

, indicating the inclusion in A01A03 of alleles with very different effects on CBI outcome. In all 10 supertypes above, HR 95% CI straddled the critical value of 1, thus indicating that all of these supertypes contained alleles with opposite effects on CBI outcome (beneficial/poor). Strictly speaking, the nonsignificant P values for the HR of these supertypes mean that the null hypothesis that HR = 1 cannot be rejected at α = 0.05, but the range in the 95% CI indicates the presence of mixed effects. The same considerations apply to our findings with respect to protective/susceptibility alleles and supertypes (Table 7), namely that supertypes comprise alleles with diverse effects. This brings to focus the point that the important unit for measuring HLA-related effects, in practically any application, is the individual allele, and not an aggregate of alleles, and, more specifically, the allele determined at 4-digit (high) resolution which distinguishes alleles encoding amino acid differences. This has been shown to be of critical importance in a recent large study of HLA associations in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)33.

 

In this study, we found other class I and class II alleles to have even more robust protective and susceptibility effects than those previously shown to influence CBI effectiveness8. Future studies are warranted to evaluate the extent to which the alleles that contribute to population protection or susceptibility translate to the individual level. Nonetheless, the current findings add to the literature documenting the importance not only of class I, but also class II alleles, on melanoma survival3,34,35 and extend those effects to the population. We hypothesize that a similar mechanism, namely antigen elimination, underlies the influence of HLA on melanoma at the individual and population level, as illustrated schematically in Fig. 2. Cancer cells notoriously evade the immune system via loss or alteration of HLA36. It is possible that some HLA alleles may be more vulnerable to cancer immune escape mechanisms, resulting in increased susceptibility, although that remains to be investigated.

JISS-22-1238-fig4

Figure 2: Schematic diagram to illustrate the hypothesized dependence of melanoma prevalence and survival on protective (blue) and susceptibility (red) HLA molecules. HLA alleles that are protective at the population level may facilitate melanoma binding and immunogenicity, potentially eliminating cancer cells even prior to detection; therefore, those protective alleles would be associated with low prevalence of melanoma. In contrast, HLA alleles that are associated with susceptibility may be unable to sufficiently bind and eliminate melanoma neoantigens, thereby promoting continued proliferation of cancerous cells and reduced survival.

The present study documents the influence of a large number of high-resolution HLA alleles on the population prevalence of melanoma in Continental Western Europe. While compelling, the findings must be considered in the context of several qualifications. First, the present findings come from data in 14 CWE countries and may not extend to populations in other earth locations. Second, HLA is the most highly polymorphic region of the human genome; thus, despite evaluating the influence of 127 HLA alleles on melanoma prevalence, many additional alleles not captured here may influence melanoma prevalence and immunotherapy outcome. Third, the present study exclusively focused on the influence of HLA on melanoma. Other factors including additional genetic contributors, host microbiome, and environmental factors have been shown to affect the appearance of melanoma and cancer immunity in general31and were not investigated here. Future studies evaluating potential moderating effects of HLA on factors that have been linked to cancer immunity are warranted but beyond the scope of the present study. Finally, the HLA-melanoma associations identified here are likely specific to melanoma. Analyses are underway to evaluate HLA associations with the prevalence of other cancers.

Acknowledgements

This paper is dedicated in loving memory to David Alan James. Funding: Partial funding for this study was provided by the University of Minnesota (the Anita Kunin Chair in Women's Healthy Brain Aging, the Brain and Genomics Fund, the McKnight Presidential Chair of Cognitive Neuroscience, and the American Legion Brain Sciences Chair) and the U.S. Department of Veterans Affairs. The sponsors had no role in the current study design, analysis or interpretation, or in the writing of this paper. The contents do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.

Author contributions: A.P.G. conceived the study; L.M.J. and A.P.G. contributed to data retrieval and analysis and writing the paper.

Competing interests: Authors declare no competing interests.

Data and materials availability: Data are publicly available from the websites mentioned in the Materials and Methods section. More specifically, here is the website information about the data that were retrieved to be analyzed:

1) Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2016 (GBD 2016) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2020. Available from http://ghdx.healthdata.org/gbd-results-tool. Data retrieved July 5, 2021.

2) Population Reference Bureau. 2016 world population data sheet with a special focus on human needs and sustainable resources. Population Reference Bureau, Washington, DC, 2016. https://www. prb.org/2016−world−population−data−sheet/. Accessed February 5, 2019.

3) Allele*Frequencies in Worldwide Populations [Internet]. Allele frequency net database (AFND) 2020 update. Liverpool, UK. Available from: http://allelefrequencies.net/hla6006a.asp. Accessed October 18, 2019.

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Article Info

Article Notes

  • Published on: June 29, 2022

Keywords

  • Melanoma
  • HLA
  • Cancer epidemiology
  • Immune blockade immunotherapy

*Correspondence:

Dr. Apostolos P. Georgopoulos,
The HLA Research Group, Brain Sciences Center, Department of Veterans Affairs Health Care System, Minneapolis, MN, 55417, USA;
Email: omega@umn.edu

Copyright: ©2022 Georgopoulos AP. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.