Abstract
Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.
Key points
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Researchers are developing various immunogenomic tools to predict response to treatment in patients with cancer who are receiving immune-checkpoint inhibitors (ICIs), based on cancer-intrinsic and cancer-extrinsic features that can be identified with sequencing, including tumour mutational burden, neoantigens and the presence of immune cells.
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Computational tools for HLA genotyping from whole-genome sequencing, whole-exome sequencing and RNA sequencing have been well established; long-read sequencing is a promising technology that is expected to improve the performance of HLA genotyping.
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Several approaches have been developed to identify immunogenic neoantigens, with a major focus on somatic single-nucleotide variants; however, the identification of neoantigens from non-canonical sources is crucial for a comprehensive understanding of neoantigen load.
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Deconvolution tools provide estimates of the immune cell proportions in the tumour microenvironment but have limitations in identifying low-abundance cell types and subsets; therefore, the use of these tools requires careful consideration of the underlying technical and biological factors.
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Multi-omic machine learning models trained on molecular and clinical features from large cohorts of tumour samples could improve the prediction of patient responses to immunotherapy and reveal key predictive features.
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Functionally verified approaches that integrate genomic intratumour heterogeneity, HLA genotypes and neoantigen trafficking, and expression and immunogenicity, among other features, could improve prediction of response to ICIs.
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Acknowledgements
N.W. is supported by the Australian National Health and Medical Research Council Research Fellowship. The authors thank K. Tran, R. L. Johnston and M. W. L. Teng (all at QIMR Berghofer Medical Research Institute) for intellectual input and helpful discussions.
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Addala, V., Newell, F., Pearson, J.V. et al. Computational immunogenomic approaches to predict response to cancer immunotherapies. Nat Rev Clin Oncol 21, 28–46 (2024). https://doi.org/10.1038/s41571-023-00830-6
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DOI: https://doi.org/10.1038/s41571-023-00830-6
