Computational immunogenomic approaches to predict response to cancer immunotherapies

Addala, Venkateswar; Newell, Felicity; Pearson, John V.; Redwood, Alec; Robinson, Bruce W.; Creaney, Jenette; Waddell, Nicola

doi:10.1038/s41571-023-00830-6

Cited by 18 publications

(12 citation statements)

References 306 publications

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“…We have demonstrated that tumors may adapt to high GEB by modulating their immune microenvironment. The exact mechanisms of these changes remain unclear but could include genetic mechanisms of immune escape, including HLA loss of heterozygosity or somatic mutations in the antigen-presentation pathway ( 64 , 65 ). Moreover, although the current study focuses on breast cancer, we anticipate that germline-mediated immunoediting extends to other cancers.…”

Section: Discussionmentioning

confidence: 99%

Germline-mediated immunoediting sculpts breast cancer subtypes and metastatic proclivity

Houlahan,

Khan,

Greenwald

et al. 2024

Science

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Tumors with the same diagnosis can have different molecular profiles and response to treatment. It remains unclear when and why these differences arise. Somatic genomic aberrations occur within the context of a highly variable germline genome. Interrogating 5870 breast cancer lesions, we demonstrated that germline-derived epitopes in recurrently amplified genes influence somatic evolution by mediating immunoediting. Individuals with a high germline-epitope burden in human epidermal growth factor receptor 2 (HER2/ ERBB2 ) are less likely to develop HER2-positive breast cancer compared with other subtypes. The same holds true for recurrent amplicons defining three aggressive estrogen receptor (ER)–positive subgroups. Tumors that overcome such immune-mediated negative selection are more aggressive and demonstrate an “immune cold” phenotype. These data show that the germline genome plays a role in dictating somatic evolution.

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Section: Discussionmentioning

confidence: 99%

Germline-mediated immunoediting sculpts breast cancer subtypes and metastatic proclivity

Houlahan,

Khan,

Greenwald

et al. 2024

Science

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“…The combination and development of multiple functional approaches, including large-scale genomic sequencing, single-cell transcriptomic techniques, multi-omics, and computational immunogenomics, which integrate intratumour heterogeneity, tumor mutational burden, neoantigen expression, and immunogenicity, could improve the prediction of response to ICIs. 1325 Second, although ICIs initially exhibited strong efficiency against tumor growth, patients still have relapse and/or develop acquired resistance. Combinational therapeutic strategies based on a deep understanding of the tumor and TME, and coordination of systemic and local intratumor immune responses enable to improve and maximize the potential benefit to more tumor patients.…”

Section: Tumor Biomarker-based Cancer Therapymentioning

confidence: 99%

Tumor biomarkers for diagnosis, prognosis and targeted therapy

Zhou,

Tao,

Qiu

et al. 2024

Sig Transduct Target Ther

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Tumor biomarkers, the substances which are produced by tumors or the body’s responses to tumors during tumorigenesis and progression, have been demonstrated to possess critical and encouraging value in screening and early diagnosis, prognosis prediction, recurrence detection, and therapeutic efficacy monitoring of cancers. Over the past decades, continuous progress has been made in exploring and discovering novel, sensitive, specific, and accurate tumor biomarkers, which has significantly promoted personalized medicine and improved the outcomes of cancer patients, especially advances in molecular biology technologies developed for the detection of tumor biomarkers. Herein, we summarize the discovery and development of tumor biomarkers, including the history of tumor biomarkers, the conventional and innovative technologies used for biomarker discovery and detection, the classification of tumor biomarkers based on tissue origins, and the application of tumor biomarkers in clinical cancer management. In particular, we highlight the recent advancements in biomarker-based anticancer-targeted therapies which are emerging as breakthroughs and promising cancer therapeutic strategies. We also discuss limitations and challenges that need to be addressed and provide insights and perspectives to turn challenges into opportunities in this field. Collectively, the discovery and application of multiple tumor biomarkers emphasized in this review may provide guidance on improved precision medicine, broaden horizons in future research directions, and expedite the clinical classification of cancer patients according to their molecular biomarkers rather than organs of origin.

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“…Currently, STS immunotherapy has achieved certain efficacy in some histological types, but the overall situation remains concerning ( 8 ). There is an urgent need to further explore the STS tumor immune microenvironment and identify more effective immunotherapy strategies and prognostic biomarkers ( 9 , 10 ).…”

Section: Introductionmentioning

confidence: 99%

Identifying specific TLS-associated genes as potential biomarkers for predicting prognosis and evaluating the efficacy of immunotherapy in soft tissue sarcoma

Wang,

Liu,

et al. 2024

Front. Immunol.

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BackgroundThe tertiary lymphatic structure (TLS) is an important component of the tumor immune microenvironment and has important significance in patient prognosis and response to immune therapy. However, the underlying mechanism of TLS in soft tissue sarcoma remains unclear.MethodsA total of 256 RNAseq and 7 single-cell sequencing samples were collected from TCGA-SARC and GSE212527 cohorts. Based on published TLS-related gene sets, four TLS scores were established by GSVA algorithm. The immune cell infiltration was calculated via TIMER2.0 and “MCPcounter” algorithms. In addition, the univariate, LASSO, and multivariate-Cox analyses were used to select TLS-related and prognosis-significant hub genes. Single-cell sequencing dataset, clinical immunohistochemical, and cell experiments were utilized to validate the hub genes.ResultsIn this study, four TLS-related scores were identified, and the total-gene TLS score more accurately reflected the infiltration level of TLS in STS. We further established two hub genes (DUSP9 and TNFSF14) prognosis markers and risk scores associated with soft tissue sarcoma prognosis and immune therapy response. Flow cytometry analysis showed that the amount of CD3, CD8, CD19, and CD11c positive immune cell infiltration in the tumor tissue dedifferentiated liposarcoma patients was significantly higher than that of liposarcoma patients. Cytological experiments showed that soft tissue sarcoma cell lines overexpressing TNFSF14 could inhibit the proliferation and migration of sarcoma cells.ConclusionThis study systematically explored the TLS and related genes from the perspectives of bioinformatics, clinical features and cytology experiments. The total-gene TLS score, risk score and TNFSF14 hub gene may be useful biomarkers for predicting the prognosis and immunotherapy efficacy of soft tissue sarcoma.

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Computational immunogenomic approaches to predict response to cancer immunotherapies

Cited by 18 publications

References 306 publications

Germline-mediated immunoediting sculpts breast cancer subtypes and metastatic proclivity

Germline-mediated immunoediting sculpts breast cancer subtypes and metastatic proclivity

Tumor biomarkers for diagnosis, prognosis and targeted therapy

Identifying specific TLS-associated genes as potential biomarkers for predicting prognosis and evaluating the efficacy of immunotherapy in soft tissue sarcoma

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