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10.1080/1750743X.2024.2353539
2353539
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Rapid Communication
Short Communication
Association between specific human leukocyte antigen alleles and development of thyroid immune-related adverse event
Sasaki Eisaku a
https://orcid.org/0000-0002-9535-5298
Natori Yutaka * a
Tokuda Emi a
Kimura-Tsuchiya Reiko a
Suga Junko b
Kanazawa Kenya c
Koguchi Tomoyuki d
Kikuchi Nobuyuki e
Okabe Naoyuki f
Murono Shigeyuki g
Tachibana Kazunoshin h
Soeda Shu i
Shimabukuro Michio j
https://orcid.org/0000-0002-6732-8030
Saji Shigehira a
a Department of Medical Oncology, Fukushima Medical University, Fukushima, 960-1295, Japan
b Division of Cancer Genome Medicine, Cancer Center, Kyoto University Hospital, Kyoto, 606-8507, Japan
c Department of Pulmonary Medicine, Fukushima Medical University, Fukushima, 960-1295, Japan
d Department of Urology, Fukushima Medical University, Fukushima, 960-1295, Japan
e Department of Dermatology, Fukushima Medical University, Fukushima, 960-1295, Japan
f Department of Chest Surgery, Fukushima Medical University, Fukushima, 960-1295, Japan
g Department of Otorhinolaryngology, Fukushima Medical University, Fukushima, 960-1295, Japan
h Department of Breast Surgery, Fukushima Medical University, Fukushima, 960-1295, Japan
i Department of Obstetrics & Gynecology, Fukushima Medical University, Fukushima, 960-1295, Japan
j Department of Diabetes, Endocrinology & Metabolism, Fukushima Medical University, Fukushima, 960-1295, Japan
* CONTACT: natori2000@gmail.com
18 6 2024
2024
18 6 2024
16 11 723732
Aptara8 5 2024
17 6 2024
14 1 2024
03 5 2024
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Aim: Inherent variations in human leukocyte antigen (HLA) alleles have been revealed epidemiologically to influence the development of autoimmune diseases. HLA alleles may thus also be associated with the development of immune-related adverse events (irAEs), such as thyroid irAE.

Materials & methods: In this case–control study, 71 cancer patients who received immune checkpoint inhibitors were enrolled and HLA-genotyped and the frequency of HLA alleles was compared.

Results: A*26:01, DPA1*01:03 and DPB1*02:01 were significantly more frequent in patients with thyroid irAE than in patients without any irAEs (35.0 vs 3.2% [p = 0.004], 80.0 vs 45.2% [p = 0.020] and 55.0 vs 25.8% [p = 0.044], respectively).

Conclusion: A*26:01, DPA1*01:03 and DPB1*02:01 appear to be associated with thyroid irAE.

Plain Language Summary

Everyone has a unique combination of human leukocyte antigens (HLAs) in their body that help the immune system identify threats. HLAs were named from the fact that they were first identified on the surface of human leukocytes. Afterward, HLAs were also found on all human cells. HLAs present antigens to immune cells. These HLAs also influence how the immune system attacks cancer cells. Immune checkpoint inhibitors are drugs that can help the immune system fight cancer, but they sometimes cause severe adverse events. In this study, we investigated whether specific HLA genes are related to the development of an adverse event that affects the thyroid in cancer patients treated with immune checkpoint inhibitors. We found an association between three HLA genes (A*26:01, DPA1*01:03 and DPB1*02:01) and the development of the thyroid adverse event. However, larger studies are needed to confirm and generalize these initial exploratory findings.

Tweetable Abstract

Variations in HLA alleles may be associated with the development of immune-related adverse events (irAEs). We found specific HLA alleles that were highly frequent in patients with thyroid irAE.

Article highlights

Background

Human leukocyte antigen (HLA) alleles may influence the development of immune-related adverse events (irAEs) affecting the thyroid.

Patients & methods

In the present case–control study, 71 cancer patients who received immune-checkpoint inhibitors (ICIs) were divided into groups and the frequency of the HLA alleles in the groups was compared.

Highly frequent HLA alleles in patients with thyroid irAE

A*26:01 was significantly more frequent in the thyroid-irAE group than in the no-thyroid-irAE group (35.0 vs 7.8% [p = 0.009]).

A*26:01, DPA1*01:03 and DPB1*02:01 were significantly more frequent in the thyroid-irAE group than in the no-irAE group (35.0 vs 3.2% [p = 0.004], 80.0 vs 45.2% [p = 0.020] and 55.0 vs 25.8% [p = 0.044], respectively).

DPA1*01:03 was significantly more frequent in the other-irAE group than in the no-irAE group (75.0 vs 45.2% [p = 0.046]).

A*26:01 and DPB1*02:01 appeared to be specific for the thyroid irAE group; in contrast, DPA1*01:03 was found to be in common between the thyroid-irAE group and the other-irAE group.

Highly frequent combinations of HLA alleles in patients with thyroid irAE

The frequency of the combination of A*26:01 and DPA1*01:03 was significantly higher in the thyroid-irAE group than in the no-thyroid-irAE group (25.0 vs 3.9% [p = 0.016]).

Odds ratio for the risk of thyroid irAE among patients with different HLA alleles

The odds ratio of A*26:01, DPA1*01:03 and DPB1*02:01 for the development of thyroid irAE was 16.2, 4.9 and 3.5, respectively.

The combination of A*26:01 and DPA1*01:03 showed the highest odds ratio of 20.3.

Median time to onset of thyroid irAE among patients with different HLA alleles

The median time to onset of thyroid irAE was shorter in patients with A*26:01 than in all patients overall (53 vs 72 days).

Conclusions

Our results indicate that HLA genotyping prior to the administration of ICIs may help predict the risk of thyroid irAE development.

Keywords: 

adverse event
allele
cancer
case–control study
genotyping
HLA
immune checkpoint inhibitor
immunotherapy
irAE
thyroid
Japan Society for the Promotion of Science 10.13039/501100001691 22K07256, 22K08697 Fundamental Research for Establishment of Standard Treatment for Adult Solid Tumors 2020-J-3 This study was supported by grants from the Japan Society for the Promotion of Science KAKENHI (numbers 22K07256 and 22K08697) and Fundamental Research for Establishment of Standard Treatment for Adult Solid Tumors (2020-J-3).
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pmc1. Background

Human leukocyte antigen (HLA) is a polymorphic cell-surface protein, and each individual has a unique combination of HLAs. HLAs on cancer cells present neoantigens to T cells [1–3]. This process activates the T cells to attack the cancer cells. However, immune checkpoint proteins, such as PD-1 [4–6], PD-L1 [7] and CTLA-4 [8,9], prevent the activation of the T cells and these proteins thus enable cancer cells to evade the host immune system. Immune checkpoint inhibitors (ICIs) interfere with the functions of the checkpoint proteins. Currently, ICIs that prevent the functions of PD-1, PD-L1 and CTLA-4 are clinically available, and they can reactivate T cells to attack cancer cells [10–12]. However, the reactivated T cells also attack healthy cells and various organs, causing immune-related adverse events (irAEs) in the human body [13,14].

Approximately 30,000 polymorphisms of HLA alleles have been found (http://hla.alleles.org/). Specific HLA alleles have been revealed epidemiologically to influence the development of various autoimmune diseases [15,16]. For example, the odds ratio for developing ankylosing spondylitis was 171 among people who carried HLA-B27 [17], and HLA-DRB1*01:03 was 3.63-times more common in ulcerative colitis patients than in healthy controls [18]. Since the symptoms and mechanisms of autoimmune diseases and irAEs are similar, HLA alleles may also be involved in irAEs.

Recently, associations between HLA alleles and irAEs have been reported [19,20]. The frequency of HLA-DQB1*03:01 was reported to be higher in patients with irAE colitis [21], and the frequencies of HLA-Cw12, DR15, DQ7 and DPw9 were reported to be significantly higher in patients with isolated adrenocorticotropic hormone (ACTH) deficiency as an irAE [22]. We previously reported a case of narcolepsy as an irAE in a patient that had a set of specific HLA alleles that conferred a risk of developing narcolepsy type 1 with an odds ratio of 251 [23]. Thus, specific HLA alleles may be associated with specific irAEs.

We have been focusing on thyroid irAE, which is very frequent among irAEs (7.1–42.0%) [24–26]. High frequencies of DPA1*01:03 and DPB1*02:01 and their haplotypes have been reported in patients with thyroid irAE [27]; however, in that study, the control group consisted of healthy people from a database, and not cancer patients who had not received ICIs.

In the present study, we investigated the associations between HLA alleles and the development of thyroid irAE. We attempted to obtain more accurate associations by including appropriate control groups for reliable comparisons. In this study, a control group of cancer patients who had received ICIs was established. In addition, several control groups were established to distinguish specific HLA alleles for thyroid irAE and common HLA alleles for irAEs. Our study results provide useful data that can help guide the establishment of predictive markers for the development of thyroid irAE. As HLA alleles are genetically determined, and they do not change over the course of an individual's lifetime, HLA genotyping prior to the administration of ICIs is expected to contribute to the early detection of thyroid irAE, and improve patient safety.

2. Materials & methods

2.1. Patients

This study was performed in line with the principles of the Declaration of Helsinki and the Ethical Guidelines for Medical and Health Research Involving Human Subjects (Ministry of Health, Labour and Welfare of Japan). The study was approved by the Ethics Committee of Fukushima Medical University (approval No. 2019–221). This case–control study targeted cancer patients who were treated with at least one ICI at Fukushima Medical University Hospital between October 2019 and November 2021. All cancers were eligible for inclusion. The ICIs included anti-PD-1 antibody, anti-PD-L1 antibody and anti-CTLA-4 antibody. The combination of these ICIs with other ICIs or cytotoxic anticancer drugs was also allowed. Patients with a history of thyroid disease or who had received thyroid surgery were excluded. Patients (n = 71) who provided informed consent for inclusion in this study were enrolled. The 71 patients were divided into two groups: those who developed thyroid irAE (the case group; n = 20) and those who did not develop thyroid irAE (the control group; n = 51).

2.2. Diagnosis of irAEs

We retrospectively and prospectively searched patient clinical records for irAEs after the registration of the patients. The data lock point for the search was December 2021. Thyroid irAE was defined as a new thyroid dysfunction that developed after ICI treatment. Two or more endocrinologists diagnosed the thyroid irAE. Thyroid irAE is similar to thyroiditis, and is subcategorized into thyrotoxicosis and hypothyroidism. Thyrotoxicosis was diagnosed by a low serum thyrotropin level below 0.50 μIU/ml with an elevated free thyroxine (FT4) level above 1.7 ng/dl [28–30]. Hypothyroidism, which often occurs after thyrotoxicosis, was diagnosed by a high thyrotropin level above 4.2 μIU/ml with a low serum FT4 level below 0.95 ng/dl [28–30]. Institutional reference ranges were used to evaluate the levels of the hormones. Thyroid disorders secondary to pituitary irAEs were classified as other irAEs. Patients who did not develop thyroid irAE during the observation period were defined as patients without thyroid irAE. Patients who developed irAEs (Common Terminology Criteria for Adverse Events [CTCAE] grade ≥1) other than thyroid irAE were defined as patients with other irAEs. The time from the initial treatment with ICIs to the onset of thyroid irAE was determined for each specific HLA allele.

2.3. HLA genotyping

Blood samples were taken from patients, and the HLA alleles were identified from blood cell DNA at the HLA Foundation Laboratory (Kyoto, Japan). DNA was purified from patient blood using the QIAamp DNA Blood Midi Kit (QIAGEN, MD, USA). DNA samples were sequenced by next-generation sequencing using MiSeq technology (Illumina, CA, USA). DNA sequences were genotyped using an integrated allele typing system (Scisco Genetics, WA, USA). We performed high-resolution HLA genotyping for HLA-A, -B, -C, -DRB1, -DRB3/4/5, -DQA1, -DQB1, -DPA1 and -DPB1 at the DNA-sequence level. We used the HLA alleles at the amino acid-sequence level (2-field resolution).

2.4. Statistical analysis

The required sample size was calculated to be 64 patients (5% significance level, 80% power and 25% difference). The difference of 25% was taken from a previous study that reported a 25% difference in the frequency of DPA1*01:03 between the thyroid-irAE group and the control group [27]. Nominal variables of HLA frequencies (non-normal distribution) in each group were compared using Fisher's exact test (two-sided). Fisher's exact test and sensitivity and specificity analyses were conducted using R version 4.0.3 (R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ accessed on 10 November 2020). p-values <0.05 were considered to indicate statistical significance. The analyses were not blinded.

3. Results

3.1. Patient characteristics

71 cancer patients treated with ICIs were enrolled and HLA-genotyped. All patients were Japanese. The patients were divided into two groups: those who developed thyroid irAE (thyroid-irAE group; n = 20) and those who did not develop thyroid irAE (no-thyroid-irAE group; n = 51) (Figure 1). The no-thyroid-irAE group was further divided into two groups: those who did not develop any irAEs (no-irAE group; n = 31) and those who developed irAEs (CTCAE grade ≥1) other than thyroid irAE (other-irAE group; n = 20).

Figure 1. Flow chart for the categorization of patients.

The patient characteristics of each group are shown in Table 1. Lung cancer was the most common cancer (30.0–58.1%), followed by urological cancer (12.9–35.0%), malignant melanoma (9.7–20.0%), breast cancer (0.0–15.0%), head and neck cancer (5.0–10.0%) and other cancers (0.0–9.7%). All groups showed a similar trend in the proportions of the cancer types. The ICIs included anti-PD-1 antibody (pembrolizumab, nivolumab), anti-PD-L1 antibody (atezolizumab, durvalumab) and anti-CTLA-4 antibody (ipilimumab). The other irAEs (irAEs other than thyroid irAE) observed were interstitial pneumonia, isolated ACTH deficiency, hepatitis, dermatitis, thrombocytopenia, hypophysitis, colitis and others.

Table 1. Characteristics of the cancer patients in each group.

 	Thyroid-irAE	No-thyroid-irAE	No-irAE	Other-irAE	
 	n = 20	n = 51	n = 31	n = 20	
Age median (range)	67	(36–86)	68	(44–83)	67	(44–83)	69	(45–79)	
Sex (%)	
  Male	12	(60.0)	36	(70.6)	21	(67.7)	15	(75.0)	
  Female	8	(40.0)	15	(29.4)	10	(32.3)	5	(25.0)	
Malignancy (%)	
  Lung cancer	6	(30.0)	25	(49.0)	18	(58.1)	7	(35.0)	
  Urologic cancer	4	(20.0)	11	(21.6)	4	(12.9)	7	(35.0)	
  Malignant melanoma	4	(20.0)	7	(13.7)	3	(9.7)	4	(20.0)	
  Breast cancer	3	(15.0)	1	(2.0)	0	(0.0)	1	(5.0)	
  Head and neck cancer	2	(10.0)	4	(7.8)	3	(9.7)	1	(5.0)	
  Others	1	(5.0)	3	(5.9)	3	(9.7)	0	(0.0)	
History of ICI (%)	
  Anti-PD-1 antibody	13	(65.0)	35	(68.6)	24	(77.4)	11	(55.0)	
  Anti-PD-L1 antibody	6	(30.0)	9	(17.6)	6	(19.4)	3	(15.0)	
  Anti-PD-1 + CTLA4 antibodies	1	(5.0)	7	(13.7)	1	(3.2)	6	(30.0)	
irAE (>CTCAE grade1) (%)	
  Thyroid irAE	20	(100.0)	0	(0.0)	 	0	(0.0)	
  Interstitial pneumonia	2	(10.0)	10	(19.6)	 	10	(50.0)	
  Isolated ACTH deficiency	3	(15.0)	6	(11.8)	 	6	(30.0)	
  Hepatitis	2	(10.0)	6	(11.8)	 	6	(30.0)	
  Dermatitis	3	(15.0)	3	(5.9)	 	3	(15.0)	
  Thrombocytopenia	0	(0.0)	3	(5.9)	 	3	(15.0)	
  Hypophysitis	0	(0.0)	2	(3.9)	 	2	(10.0)	
  Colitis	1	(5.0)	2	(3.9)	 	2	(10.0)	
  Others	3	(15.0)	2	(3.9)	 	2	(10.0)	

The median observation period of all patients was 115 weeks (range: 18–485 weeks). The median duration of ICI treatment in the thyroid-irAE, no-thyroid-irAE, no-irAE and other-irAE groups was 48 weeks (range: 7–213 weeks), 89 weeks (range: 6–256 weeks), 118 weeks (range: 18–256 weeks) and 74 weeks (6–211 weeks), respectively. The median observation time in the no-thyroid-irAE group was 133 weeks (range: 27–314 weeks).

3.2. Highly frequent HLA alleles in patients with thyroid irAE

The percentage of patients with each HLA allele was analyzed. Among the 121 HLA alleles detected in all patients, A*26:01 showed a significantly higher frequency in the thyroid-irAE group than in the no-thyroid-irAE group (35.0 vs 7.8% [p = 0.009]) (Supplementary Table S1 & Figure 2A).

Figure 2. The proportion of cancer patients with each HLA allele in the different groups. In total, 121 HLA alleles were detected in the patients. p-values <0.05 were considered to indicate statistical significance. (A) A*26:01 was significantly more frequent in the thyroid-irAE group than in the no-thyroid-irAE group (35.0 vs. 7.8% [p = 0.009]). (B) The frequencies of A*26:01, DPA1*01:03, and DPB1*02:01 were significantly different between the thyroid-irAE group and the no-irAE group (35.0 vs 3.2% [p = 0.004], 80.0 vs 45.2% [p = 0.020] and 55.0 vs 25.8% [p = 0.044], respectively). (C) The frequency of DPA1*01:03 was significantly different between the other-irAE group and the no-irAE group (75.0 vs 45.2% [p = 0.046]).

We hypothesized that patients with thyroid irAE and patients with other irAEs would share some common HLA alleles, which might render it difficult to differentiate these patients during screening. For this reason, we decided to compare the patients with thyroid irAE to those without any irAEs. The thyroid-irAE group showed a significantly higher frequency of A*26:01, DPA1*01:03 and DPB1*02:01 than the no-irAE group (35.0 vs 3.2% [p = 0.004], 80.0 vs 45.2% [p = 0.020] and 55.0 vs 25.8% [p = 0.044], respectively) (Supplementary Table S2 & Figure 2B). The frequency of DPA1*01:03 was significantly higher in the other-irAE group than in the no-irAE group (75.0 vs 45.2% [p = 0.046]) (Supplementary Table S3 & Figure 2C). These results suggested that A*26:01, DPA1*01:03 and DPB1*02:01 were associated with the development of thyroid irAE. In particular, A*26:01 and DPB1*02:01 appeared to be specific for the thyroid irAE group. In contrast, DPA1*01:03 was found to be in common between the thyroid-irAE group and the other-irAE group.

3.3. Highly frequent combinations of HLA alleles in patients with thyroid irAE

The frequencies of combinations of the three HLA alleles (A*26:01, DPA1*01:03 and DPB1*02:01) were compared between the groups. The frequency of the combination of A*26:01 and DPA1*01:03 was significantly higher in the thyroid-irAE group than in the no-thyroid-irAE group (25.0% vs 3.9% [p = 0.016]) (Figure 3A). Similarly, the frequency of the combination of A*26:01 and DPA1*01:03 was significantly higher in the thyroid-irAE group than in the no-irAE group (25.0 vs 0.0% [p = 0.007]) (Figure 3B). In contrast, no significant difference was found in the frequency of the combination of A*26:01 and DPA1*01:03 between the other-irAE group and the no-irAE group (Figure 3C).

Figure 3. The proportion of cancer patients with each combination of the three HLA alleles (A*26:01, DPA1*01:03 and DPB1*02:01) in the different groups. p-values <0.05 were considered to indicate statistical significance. (A) The combination of A*26:01 and DPA1*01:03 was significantly more frequent in the thyroid-irAE group than in the no-thyroid-irAE group (25.0 vs 3.9% [p = 0.016]). (B) The combination of A*26:01 and DPA1*01:03 was significantly more frequent in the thyroid-irAE group than in the no-irAE group (25.0 vs 0.0% [p = 0.007]). (C) No significant difference in the HLA frequency was found for the combinations of the three HLAs between the other-irAE group and the no-irAE group.

3.4. Odds ratio for the risk of thyroid irAE among patients with different HLA alleles

The odds ratio for the development of thyroid irAE was 16.2, 4.9 and 3.5 in patients with A*26:01, DPA1*01:03 and DPB1*02:01, respectively. The odds ratio was the highest at 20.3 among those with the combination of A*26:01 and DPA1*01:03.

Although our results are limited due to the small number of cases, we calculated the sensitivity and specificity of the HLA alleles for detecting thyroid irAE. A*26:01 showed high specificity (96.8%), but low sensitivity (35.0%). In contrast, DPA1*01:03 showed low specificity (54.8%), but high sensitivity (80.0%). On the other hand, DPB1*02:01 showed moderate specificity (74.2%) and sensitivity (55.0%). The combination of A*26:01 and DPA1*01:03 showed the highest specificity (100.0%), but the lowest sensitivity (25.0%). The positive predictive value of A*26:01 and the combination of A*26:01 and DPA1*01:03 for the development of thyroid irAE was 87.5 and 100.0%, respectively.

3.5. Median time to onset of thyroid irAE among patients with different HLA alleles

In all 20 patients with thyroid irAE, the median time to onset of thyroid irAE was 72 days (range, 7–398 days) after ICI administration (Figure 4). The patients with A*26:01 showed a shorter time to onset of 53 days (range, 7–325 days). The patients with the combination of A*26:01 and DPA*01:03 also showed a shorter time to onset of 53 days, but the range was the shortest (range, 7–149 days).

Figure 4. The median time to onset of thyroid irAE from the initial treatment with ICIs in patients with each HLA allele. The median time to onset was 72 days (range, 7–398 days) in the 20 patients with thyroid irAE. Among these patients, those with A*26:01 showed a shorter median time of onset of 53 days (range, 7–325 days). Those with the combination of A*26:01 and DPA*01:03 also showed a shorter median time of onset of 53 days, but the range was the shortest (range: 7–149 days).

IQR: Interquartile range.

4. Discussion

A previous study suggested that DPA1*01:03 and DPB1*02:01 were associated with thyroid irAE [27]. However, in that study, the comparisons were not appropriate, because the control group consisted of healthy people from a database that were unrelated to cancer, ICIs and the presence of thyroid disorders. In the present study, we prepared appropriate control groups consisting of cancer patients receiving ICI treatment who had not developed thyroid irAE. The present study results revealed that the development of thyroid irAE is associated with specific HLA alleles, in other words, A*26:01, DPA1*01:03 and DPB1*02:01.

In the present study, there was a significant difference in the frequency of A*26:01 in the simple comparison between the thyroid-irAE and no-thyroid-irAE groups. Although A*26:01 may be one of the specific HLA alleles for the development of thyroid irAE, other specific HLA alleles are also thought to be present. Various irAEs occur in about 75–100% of patients treated with ICIs [31–33]. Therefore, the presence of common HLA alleles between those with thyroid irAE and those with other irAEs makes it difficult to identify HLA alleles specific for thyroid irAE. Thus, multiple comparisons were performed to exclude overlapping HLA alleles for the association of thyroid irAE and other irAEs. The thyroid-irAE group had significantly higher frequencies of A*26:01, DPA1*01:03 and DPB1*02:01 when compared with the no-irAE group. On the other hand, DPA1*01:03 was also associated with irAEs other than thyroid irAE (other-irAE group). Therefore, we concluded that A*26:01 and DPB1*02:01 are specific for thyroid irAE and that DPA1*01:03 is associated not only with thyroid irAE, but also with various irAEs.

A single HLA allele, A*26:01, had a high odds ratio of 16.2 for the development of thyroid irAE. The combination of A*26:01 and DPA1*01:03 had the highest odds ratio of 20.3. The time to onset of thyroid irAE varied according to the HLA alleles of the patients; the median time to onset was 53 days in those with the combination of A*26:01 and DPA1*01:03 and it was 72 days in those with DPA1*01:03. If these HLA alleles are used for the prediction marker, these results indicate that the required period of increased surveillance may differ depending on the HLA alleles of the patient. In this study, patients having an HLA allele with a higher odds ratio showed earlier onset. This indicates that the possible association between the odds ratio and the time of thyroid irAE onset may be linked to the strength of the molecular function of the HLA.

The molecular mechanism by which specific HLA alleles predispose patients to specific autoimmune diseases remains unclear even for the most well-studied association between spondylarthritis and HLA-B*27 [34]. Many different peptides that are commonly derived from self-proteins are bound to HLA-B27 [35]. Therefore, it is possible that A*26:01, DPA1*01:03 and DPB1*02:01 may also bind to self-proteins, especially peptides derived from the thyroid.

This study has several limitations. As a screening study, this study had a more appropriate design than the previous study. However, a larger validation study is still needed for the generalization of the results. One limitation of the study is the low number of cases, which may have led to underpowered comparisons. A larger sample size is needed to reflect the population of HLA alleles and the irAEs more accurately. In addition, this exploratory study aimed to detect even weakly associated HLA alleles and no correction to adjust for multiplicity was thus used in this analysis. Another limitation is that this study was conducted only in Japanese people. Racial differences in HLA alleles are known, and independent studies in different races or regions may identify different HLA alleles. Also, in patients with early disease progression, irAEs may not be observed even in those with high-risk HLA alleles due to the shorter duration of treatment; this might have an impact on the detection. Finally, the association between the treatment efficacy of ICIs and irAEs is also of interest; however, in the present study, we did not obtain any data on patient outcomes, such as progression-free survival, so we were unable to examine treatment efficacy.

5. Conclusion

In the present study, we statistically identified specific HLA alleles (A*26:01, DPA1*01:03 and DPB1*02:01) that were found at higher frequencies in patients with thyroid irAE. Although a larger validation study is needed for the generalization of our results, the results indicate that HLA genotyping prior to the administration of ICIs may help predict the risk of thyroid irAE development.

Supplementary Material

Supplementary Tables S1-S3

Acknowledgments

We would like to thank FORTE Science Communications (Tokyo, Japan) for language editing.

Supplemental material

Supplementary data for this article can be accessed at https://doi.org/10.1080/1750743X.2024.2353539

Author contributions

Conceptualization: E Sasaki, Y Natori and S Saji; data curation: E Sasaki, Y Natori, J Suga and M Shimabukuro; formal analysis: E Sasaki and Y Natori; funding acquisition: Y Natori and S Saji; investigation: E Sasaki and Y Natori; methodology: E Sasaki and Y Natori; project administration: Y Natori and S Saji; resources: E Sasaki, Y Natori, E Tokuda, R Kimura-Tsuchiya, K Kanazawa, T Koguchi, N Kikuchi, N Okabe, S Murono, K Tachibana and S Soeda; software: E Sasaki and Y Natori; supervision: S Saji; validation: E Sasaki and Y Natori; visualization: E Sasaki and Y Natori; writing – original draft: E Sasaki and Y Natori; writing – review & editing: E Sasaki, Y Natori and S Saji. All authors have read and agreed to the published version of the manuscript.

Financial disclosure

This study was supported by grants from the Japan Society for the Promotion of Science KAKENHI (numbers 22K07256 and 22K08697) and Fundamental Research for Establishment of Standard Treatment for Adult Solid Tumors (2020-J-3). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Competing interests disclosure

S Saji reports research grants from Taiho, Eisai, Chugai, Takeda, MSD, Astra Zeneca and Daiichi Sankyo. S Saji also reports personal fees (lecture fees) from Chugai, Kyowa Kirin, MSD, Novartis, Eisai, Takeda, Daiichi Sankyo, Eli Lilly, Astra Zeneca, Pfizer, Taiho, Ono and Nippon Kayaku. S Saji is an advisory board member of Chugai/Roche, Astra Zeneca, Eli Lilly, Pfizer, Kyowa Kirin, Daiichi Sankyo, MSD. S Saji is an executive board member of JBCRG, JBCS, JSMO and BIG. E Tokuda reports research grants from Eli Lilly. E Tokuda also reports personal fees (lecture fees) from Pfizer, Sanofi, Merck, Eisai, Taiho, Johnson & Johnson and Astra Zeneca. Kenya Kanazawa reports research grants from Ely Lilly. K Kanazawa also reports personal fees (lecture fees) from Chugai, Merck, Riken Genesis, Sysmex, Astra Zeneca, Novartis and Ely Lilly. M Shimabukuro reports personal fees (lecture fees) from Chugai, Daiichi Sankyo, Tanabe Mitsubishi, Taisho, Kowa, Ely Lilly, Sanofi, Ono, Kyowa Kirin and Nihon Boehringer Ingelheim. S Murono reports personal fees (lecture fees) from Ono and Bristol Myers Squibb. S Soeda reports personal fees (lecture fees) from Takeda. All grants and fees were for work outside of the submitted work. The authors have no other competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript apart from those disclosed.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The study was conducted in accordance with the Declaration of Helsinki and Ethical Guidelines for Medical and Health Research Involving Human Subjects (Ministry of Health, Labour and Welfare in Japan). This study was approved by the Ethics Committee of Fukushima Medical University (approval No. 2019-221).

Data availability statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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