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Impact of STAT1 polymorphisms on crizotinib-induced hepatotoxicity in ALK-positive non-small cell lung cancer patients

  • Original Article – Cancer Research
  • Published:
Journal of Cancer Research and Clinical Oncology Aims and scope Submit manuscript

Abstract

Purpose

Crizotinib is the first-line small molecule tyrosine kinase inhibitor for ALK-positive non-small cell lung cancer. In this study, a retrospective pharmacogenomics investigation was conducted to explore the relationship between genes related to RTK downstream signaling pathways and crizotinib-induced hepatic toxicity in ALK-positive NSCLC patients.

Methods

The variable importance analysis of random forest algorithm was applied to identify the significant features which contribute to the crizotinib sensitivity in Cancer Cell Line Encyclopedia (CCLE) database. The KEGG and reactome pathway enrichment analysis were conducted with EnrichR. The differential expression genes were identified with R package DESeq2 in CCLE liver derived cell lines between crizotinib sensitive and resistant groups. From 2012 to 2015, 42 NSCLC patients were enrolled in this study. 90 polymorphisms were genotyped using the Sequenom Massarray system. Sequencing of HGFR (c-Met) genes was carried out on the Ion Torrent Proton.

Results

In total, 66.7% NSCLC patients suffered from crizotinib-induced liver toxicity and 11.9% progressed to severe hepatic toxicity. The features with the top importance from classification and regression random forest model were enriched in RTK downstream signaling pathways (JAK/STAT, RAS/RAF/MAPK, PI3K/AKT pathways) and immune system-related pathways. Collagen family genes (COL1A1, COL1A2, COL6A1, COL5A1) and other extracellular matrix protein (TNC, TAGLN, TENM2, EDIL3, VCAN, CNN1, SH3BP4, TAGLN), which were closely related to MAPK-ERK signaling pathways, were significantly enriched in crizotinib resistant cell lines. In multiple logistic regression, STAT1 rs10208033 (T > C) was significantly associated with crizotinib-induced liver toxicity (OR = 6.733, 95% CI 1.406–32.24, P = 0.017). Compared with non-CC, OR is 5.5 (95% CI 1.219–24.81, P = 0.027) for STAT1 rs10208033 CC genotype to develop crizotinib-induced liver toxicity. Further cell viability test in human fetal hepatocyte line, L-02, reveals that the STAT1 inhibitor might protect hepatocyte cells from the toxicity caused by crizotinib.

Conclusion

Polymorphism of rs10208033 is a potential biomarker for predicting crizotinib-induced hepatotoxicity. These results suggest that STAT1 plays an important role in crizotinib-induced hepatotoxicity. Further studies are needed to confirm our finding and understand the underlying mechanisms.

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Data availability

The Cancer Cell Line encyclopedia (CCLE) RNAseq gene expression data (RPKM) and the pharmacologic profile for 24 anticancer drugs across 504 (24 Feb 2015) were downloaded from http://portals.broadinstitute.org/ccle. In this work, those who carried out the original analysis and collection of the CCLE Data bear no responsibility for the further analysis or interpretation of it. The datasets generated and/or analyzed during the current clinical cohort are not publicly available due to the China Human Genetic Resources Regulations, but are available from the corresponding author on reasonable email request.

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Funding

This work was supported by the National Nature Science Foundation of China (No. 81572270, 81730103, 81473283, 81973398); and the Natural Major Projects for Science and Technology Development from Science and Technology Ministry of China (No.2012ZX09506001-004), and the National Key Research and Development Program (No. 2016YFC0905001) and the 111 project (No.B16047).

Author information

Author notes

  1. Shuang Xin, Wenfeng Fang, Jianwen Li, and Delan Li contributed equally to this work.

Authors and Affiliations

  1. Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510060, People’s Republic of China

    Shuang Xin, Min Huang, Wei Zhuang & Xueding Wang

  2. Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People’s Republic of China

    Shuang Xin, Wenfeng Fang & Likun Chen

  3. CapitalBio Genomics Co., Ltd., Dongguan, 523808, China

    Jianwen Li & Quanfei Huang

  4. Chemotherapy Department 2, Zhongshan City People’s Hospital, Zhongshan, 528403, People’s Republic of China

    Delan Li

  5. Department of Computer Science, City University of Hong Kong, Hong Kong, China

    Changzheng Wang

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  1. Shuang Xin
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Contributions

SX, XDW, LKC, and MH, conceived and planned the experiments. WFF, JWL, and ZW carried out the experiments. JWL and CZW planned and carried out the data analysis. DLL and WFF contributed to sample preparation and clinical data collection. QFH contributed to the interpretation of the results. SX took the lead in writing the manuscript and the results. All authors provided critical feedback and helped shape the research, analysis and manuscript.

Corresponding authors

Correspondence to Xueding Wang or Likun Chen.

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Conflict of interest

The authors have declared no conflicts of interest.

Ethical approval

The study was approved by the Ethical Committee of Sun Yat-Sen University Cancer Center (B2015-080-01).

Informed consent

Written informed consent was obtained from all participating subjects. This trial was registered at ClinicalTrials.gov. (NCT02708667).

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Electronic supplementary material

Below is the link to the electronic supplementary material.

432_2020_3476_MOESM1_ESM.tiff

Supplementary Figure1: The epigenomic landscape of SNP rs10208033 in adult liver cells visualized by the Human Epigenome Browser (TIFF 739 KB)

432_2020_3476_MOESM2_ESM.tif

Supplementary Figure 2: Agarose gel electrophoresis was adopted to detect the STAT1 gene expression under different crizotinib concentration (TIF 396 KB)

Supplementary Table 1. Adverse drug reaction induced by crizotinib (XLSX 1218 KB)

432_2020_3476_MOESM4_ESM.xlsx

Supplementary Table 2. IncNode Purity for each feature in the regression and classification Random Forest models (XLSX 70 KB)

Supplementary Table 3. KEGG and reactome enrichment analysis (CSV 3056 KB)

432_2020_3476_MOESM6_ESM.docx

Supplementary Table 4. The result of RNA differential expression genes by DESeq2. Table 5. The association between genetic polymorphisms and crizotinib-induced Grade≥2 severe hepatic toxicity by multivariate logsitic regression. Table 6. The correlation between genetic polymorphisms and crizotinib-induced elevations of ALT. Table 7. The Locus Score of rs10208033 analysed from Goshifter (DOCX 52 KB)

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Xin, S., Fang, W., Li, J. et al. Impact of STAT1 polymorphisms on crizotinib-induced hepatotoxicity in ALK-positive non-small cell lung cancer patients. J Cancer Res Clin Oncol 147, 725–737 (2021). https://doi.org/10.1007/s00432-020-03476-4

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  • DOI: https://doi.org/10.1007/s00432-020-03476-4

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