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Aug 14, 2023;15 (16):

A Comprehensive Benchmark of Transcriptomic Biomarkers for Immune Checkpoint Blockades.

Hongen Kang, Xiuli Zhu, Ying Cui, Zhuang Xiong, Wenting Zong, Yiming Bao, Peilin Jia
Author Information
  1. Hongen Kang: CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China. ORCID
  2. Xiuli Zhu: CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China.
  3. Ying Cui: CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China.
  4. Zhuang Xiong: University of Chinese Academy of Sciences, Beijing 100049, China.
  5. Wenting Zong: University of Chinese Academy of Sciences, Beijing 100049, China.
  6. Yiming Bao: University of Chinese Academy of Sciences, Beijing 100049, China. ORCID
  7. Peilin Jia: CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China. ORCID
PMID: 37627121 DOI: 10.3390/cancers15164094

Abstract

Immune checkpoint blockades (ICBs) have revolutionized cancer therapy by inducing durable clinical responses, but only a small percentage of patients can benefit from ICB treatments. Many studies have established various biomarkers to predict ICB responses. However, different biomarkers were found with diverse performances in practice, and a timely and unbiased assessment has yet to be conducted due to the complexity of ICB-related studies and trials. In this study, we manually curated 29 published datasets with matched transcriptome and clinical data from more than 1400 patients, and uniformly preprocessed these datasets for further analyses. In addition, we collected 39 sets of transcriptomic biomarkers, and based on the nature of the corresponding computational methods, we categorized them into the gene-set-like group (with the self-contained design and the competitive design, respectively) and the deconvolution-like group. Next, we investigated the correlations and patterns of these biomarkers and utilized a standardized workflow to systematically evaluate their performance in predicting ICB responses and survival statuses across different datasets, cancer types, antibodies, biopsy times, and combinatory treatments. In our benchmark, most biomarkers showed poor performance in terms of stability and robustness across different datasets. Two scores (TIDE and CYT) had a competitive performance for ICB response prediction, and two others (PASS-ON and EIGS_ssGSEA) showed the best association with clinical outcome. Finally, we developed ICB-Portal to host the datasets, biomarkers, and benchmark results and to implement the computational methods for researchers to test their custom biomarkers. Our work provided valuable resources and a one-stop solution to facilitate ICB-related research.

Keywords

ICB-portal TIDE benchmark immune checkpoint blockade (ICB) transcriptomic biomarkers

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Grants

  1. XDB38010400/Strategic Priority Research Program of the Chinese Academy of Sciences
  2. 32270706/National Natural Science Foundation of China
  3. ANSO-PA-2020-07/The Professional Association of the Alliance of International Science Organizations
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