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Integrative analysis of risk factors for immune-related adverse events of checkpoint blockade therapy in cancer

Nature Cancer volume 4, pages 844–859 (2023)Cite this article

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Abstract

Immune-related adverse events (irAEs) induced by checkpoint inhibitors involve a multitude of different risk factors. Here, to interrogate the multifaceted underlying mechanisms, we compiled germline exomes and blood transcriptomes with clinical data, before and after checkpoint inhibitor treatment, from 672 patients with cancer. Overall, irAE samples showed a substantially lower contribution of neutrophils in terms of baseline and on-therapy cell counts and gene expression markers related to neutrophil function. Allelic variation of HLA-B correlated with overall irAE risk. Analysis of germline coding variants identified a nonsense mutation in an immunoglobulin superfamily protein, TMEM162. In our cohort and the Cancer Genome Atlas (TCGA) data, TMEM162 alteration was associated with higher peripheral and tumor-infiltrating B cell counts and suppression of regulatory T cells in response to therapy. We developed machine learning models for irAE prediction, validated using additional data from 169 patients. Our results provide valuable insights into risk factors of irAE and their clinical utility.

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Fig. 1: Summary of cohort data and workflow of data integration and predictive modeling.
Fig. 2: Basal cell counts and ICB-induced functional changes indicating the implications of neutrophils.
Fig. 3: Characterization of gene expression programs responding to ICB in irAE versus control samples.
Fig. 4: HLA variation associated with irAE pathogenesis.
Fig. 5: Characterization of integrative irAE prediction models incorporating germline variants.
Fig. 6: Functional and evolutionary characterization of the TMEM162 variant (rs541169) in association with irAE.

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Data availability

Our whole-exome and transcriptome data are available under BioProject PRJNA795330. Expression data used in this work are available at the Human Protein Atlas (https://www.proteinatlas.org/), the GTEx Portal (https://gtexportal.org/) and the Gene Expression Omnibus under accession number GSE107011. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

Our source codes for the prediction of irAE are available at https://github.com/kaistomics/GERMirAE.

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Acknowledgements

This research was supported by the Bio and Medical Technology Development Program of the National Research Foundation of Korea (NRF) funded by the Korean government (Ministry of Science and ICT) (NRF-2017M3A9A7050612 to J.K.C. and NRF-2019M3E5D4064636 to S.R.P.) and by the Korea Health Technology R&D Project funded by the Ministry of Health and Welfare (HR21C0198 to J.K.C.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Changhwan Sung, Jinhyeon An.

Authors and Affiliations

  1. Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea

    Changhwan Sung, Jinhyeon An, Jaesoon Park, Kang Seon Lee & Jung Kyoon Choi

  2. Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea

    Changhwan Sung

  3. Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Changhwan Sung

  4. Division of Oncology–Hematology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea

    Soohyeon Lee

  5. Department of Oncology, Haeundae Paik Hospital, Cancer Center, Inje University College of Medicine, Busan, Republic of Korea

    Il-Hwan Kim

  6. Center for Lung Cancer, National Cancer Center, Goyang, Republic of Korea

    Ji-Youn Han

  7. Division of Hematology–Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Yeon Hee Park

  8. Division of Hematology and Medical Oncology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea

    Jee Hyun Kim

  9. Division of Oncology, Department of Internal Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea

    Eun Joo Kang

  10. Division of Medical Oncology, Department of Internal Medicine, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea

    Min Hee Hong

  11. Division of Hematology and Medical Oncology, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea

    Tae-Yong Kim

  12. Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Jae Cheol Lee, Jae Lyun Lee, Shinkyo Yoon, Chang-Min Choi, Dae Ho Lee, Changhoon Yoo, Sang-We Kim, Jae Ho Jeong, Seyoung Seo, Sun Young Kim & Sook Ryun Park

  13. Targeted Therapy Branch, Research Institute, National Cancer Center, Goyang, Republic of Korea

    Sun-Young Kong

  14. Department of Cancer Biomedical Science, National Cancer Center Graduate School of Cancer Science and Policy, Goyang, Republic of Korea

    Sun-Young Kong

  15. Department of Laboratory Medicine, Hospital, National Cancer Center, Goyang, Republic of Korea

    Sun-Young Kong

  16. Penta Medix Co., Ltd., Seongnam, Republic of Korea

    Jung Kyoon Choi

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  1. Changhwan Sung
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Contributions

J.K.C. and S.R.P. conceived and designed this study. S.L., I.-H.K., J.-Y.H., Y.H.P., J.H.K., E.J.K., M.H.H., T.-Y.K., J.C.L., J.L.L., S.Y., C.-M.C., D.H.L., C.Y., S.-W.K., J.H.J., S.S., S.Y.K. and S.R.P. enrolled patients and collected data. C.S., J.A. and K.S.L. were responsible for statistical analysis, and all authors participated in data interpretation. The manuscript was drafted by C.S. and J.P. and was reviewed and revised by all authors. C.S., J.A., S.-Y.K. and J.K.C. had access to and verified the data. All authors had full access to all the data in the article.

Corresponding authors

Correspondence to Jung Kyoon Choi or Sook Ryun Park.

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The authors declare no competing interests.

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Nature Cancer thanks Zlatko Trajanoski for his contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 irAE co-occurrence map and PCA of gene expression data.

(a) Connection map constructed among 9 major irAE labels based on their co-occurrence frequencies using visNetwork R package. The size of the nodes indicates the number of patients with each irAE type. The width of edges between two irAE labels indicates the number of patients with both irAE types. Any, Multiple (any grade), and Multiple (grade ≥ 2) irAE were excluded from the map. Patients with more than one irAE onset (among the 9 major labels) were used to construct the map (N = 275 patients). (b) Principal component analysis performed with the expression of protein-coding genes (N = 19,951 genes) as input. Each colored group represents (left) the institution from which the RNA sequencing data was produced or (right) the time point when the data was produced (PRE vs EDT) (N = 259 patients, PRE and EDT matched).

Source data

Extended Data Fig. 2 Additional analysis of ICB-induced gene expression changes.

(a) Bar plot showing the expression of ANKRD22 in 18 immune cell types. The expression data was retrieved from the Human Protein Atlas (HPA). All expression data was normalized into nTPM calculated by the internal pipeline of HPA. I-monocyte, Intermediate monocyte; C-monocyte, Classical monocyte; NC-monocyte, Non-classical monocyte; CD4+ naive, Naïve CD4+ T-Cell; mDC, myeloid dendritic cell; NK, Natural killer cell; pDC, plasmacytoid dendritic cell; CD4+ memory, Memory CD4+ T cell; γδT, Gamma delta T cell; CD8+ memory, Memory CD8+ T cell; Treg, Regulatory T cell. (b) Box plot showing the expression of ANKRD22 in 29 immune cell types (N = 127 samples from 13 healthy individuals). Boxplot displays the 25th and 75th percentiles with the median represented by the center bar, and the whiskers show the farthest outliers within 1.5 times the interquartile range. The expression data was retrieved from the study by Monaco et al. (Cell Rep. 26:1627-1640). SM-B cell, Switched memory B cell; NSM-B cell, Non-switched memory B cell; EM-B cell, Exhausted memory B cell; Th1, T helper 1 cell; TEM CD4+ T cell, Terminal effector memory CD4+ T cell; Th17, T helper 17 cell; EM CD8+ T cell, Effector memory CD8+ T cell; MAIT, Mucosal-associated invariant T cell; CD8+ naive, Naïve CD8+ T-Cell; Tfh, Follicular helper T cell; Th2, T helper 2 cell; CM-CD8+ T cell, Central memory CD8+ T cell; TEM-CD8+ T cell, Terminal effector memory CD8+ T cell. (c, d) Pathway enrichment for all genes that were (c) up-regulated and (d) down-regulated in response to ICB therapy in the irAE (orange) or control (yellow) samples (Any irAE, N = 137 patients, Control, N = 122 patients, PRE and EDT matched). P values adjusted by Benjamini-Hochberg method were obtained from the two-sided Fisher exact test implemented by EnrichR.

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Extended Data Fig. 3 Further information of the association of HLA-B alleles with Any irAEs.

(a) Bar plot showing the frequency of each HLA-B allele in the Any irAE (red) or control (blue) groups (Any irAE, N = 354 patients, Control, N = 252 patients). *P < 0.05, **P < 0.01, ***P < 0.005. Nominal P values were obtained from two-sided multivariable regression. The exact p-value can be found in Extended Data Fig. 3b. (b) Forest plot showing the odds ratio (OR) with 95% confidence interval (CI) of the five most frequent HLA-B alleles for their association with Any irAE risk on the log scale. Nominal P values were obtained from two-sided multivariable regression. (c) Comparison of HLA typing results based on whole-exome sequencing and SBT. For the validation of exome-based typing, 20 samples with HLA-B*35:01 and 20 samples with HLA-B*40:02 were selected and subjected to SBT experiments. D−S9−069 was selected for HLA-B*40:02 and turned out to carry HLA-B*35:01 as well.

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Extended Data Fig. 4 Additional feature and model analysis.

(a) Prediction performance as the function of the number of SNVs. Average precision of the multivariable regression model for increasing numbers of common (upper) and rare (lower) variants. For each major irAE label, the irAE and control groups of samples were divided into a training and valid set by 8:2. MSK, Musculoskeletal; GI, Gastrointestinal; Multiple, Multiple (any grade); Multiple2, Multiple (grade ≥ 2) (N = 564 patients). (b) Correlation matrix among the 12 major types of irAE based on regression by the selected 859 features included in the prediction models. The color scale of the cells corresponds to the two-sided pearson correlation coefficient. Statistically significant correlations (Nominal P < 0.05) are indicated in color. (c) Comparison of the performance of the DNN model with that of the support vector machine (upper) and gradient boosting decision trees implemented by XGBoost (lower) in terms of ROC-AUC. (d) ROC curves for external validation by the DNN model, support vector machine, and gradient boosting decision trees implemented by XGBoost (N = 169 patients).

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Extended Data Fig. 5 Additional analysis of TMEM162 and rs541169.

(a) Stacked bar plots showing the proportions of the given types of irAEs according to the rs541169 genotype (Carrier, N = 158 patients, Non-carrier, N = 435 patients). Non-carriers are homozygous wildtype (C/C) whereas carriers are heterozygous or homozygous mutant (C/T or T/T). Nominal P values below the irAE labels were calculated by the two-sided Chi-squared test. MSK, Musculoskeletal; GI, Gastrointestinal; Multiple, Multiple (any grade); Multiple2, Multiple (grade ≥ 2). (b) Gene expression of TMEM162 broken down by tissue types according to the Illumina Human BodyMap data set (Genome Res. 22:1760-1774) (N = 48 samples). The data was obtained from https://www.ebi.ac.uk/gxa/experiments/E-MTAB-513/Results. (c, d) Gene expression of (c) TMEM162 and (d) BTN2A1 broken down by cell types according to the Genotype-Tissue Expression Project (GTEx) data set (Science 348:648–660; Science 348:660–665) (N = 948 samples). Boxplot displays the 25th and 75th percentiles with the median represented by the center bar, and the whiskers show the farthest outliers within 1.5 times the interquartile range. The data was obtained from https://gtexportal.org/home/. Tissues of origin (x-axis) are sorted by the alphabetical order.

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Extended Data Fig. 6 Comparison of irAEs and clinical responses to ICB.

(a) Coincidence of irAE onset and clinical benefit across the major 12 irAE types. P values adjusted by FDR were obtained from the two-sided Chi-squared test (N = 357 patients). (b) Comparison of Any irAEs and ICB responses in terms of differential gene expression in the PRE and EDT samples. Nominal P values of differential gene expression were obtained between Any irAE cases and control samples and between responders and non-responders (Any irAE, N = 105 patients, Control, N = 53 patients, Responder, N = 32 patients, Non-responder, N = 126 patients). (c) Comparison of the expression levels of neutrophil marker genes between responders and non-responders in the PRE and EDT samples (N = 158 patients, PRE and EDT matched). Nominal P values were calculated for normalized mean values by the two-sided Mann-Whitney U test. *P < 0.05. (d) Comparison of Any irAEs and ICB responses in terms of associations with germline variants (N = 321 patients). Only common SNVs were included here. Nominal P values were obtained from two-sided multivariable regression. (e) Manhattan plot for germline variants and HLA types associated with the ICB response. The red and blue horizontal dashed lines indicate a P value of 0.01 and 0.05, respectively. Orange and blue dots indicate SNVs and CNVs, respectively, with P < 0.01. Red dots represent the top 10 SNVs with the highest Shapley value. Nominal P values were obtained from two-sided multivariable regression (N = 321 patients).

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Supplementary information

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STROBE Statement—checklist of items that should be included in reports of observational studies.

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Supplementary Tables 1–12.

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Sung, C., An, J., Lee, S. et al. Integrative analysis of risk factors for immune-related adverse events of checkpoint blockade therapy in cancer. Nat Cancer 4, 844–859 (2023). https://doi.org/10.1038/s43018-023-00572-5

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