Multi-Omics Approaches for the Prediction of Clinical Endpoints after Immunotherapy in Non-Small Cell Lung Cancer: A Comprehensive Review

Bourbonne, Vincent; Geier, M.; Schick, Ulrike; Lucia, François

doi:10.3390/biomedicines10061237

Cited by 7 publications

(4 citation statements)

References 167 publications

(189 reference statements)

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“…ML approaches can extract multi-dimensional latent features from biological multi-omics sequencing data, including genomic, epigenomic, transcriptomic, proteomic, and metabolomic data, to explore predictive markers of efficacy in ICI therapy (165,166). These ML approaches generally entail data collection and selection, feature engineering, model building, validation, optimization, and The computer models for predicting responses to immune checkpoint inhibitor therapy.…”

Section: Machine Learning (Ml) Modelsmentioning

confidence: 99%

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy

Qin,

Huo,

Liu

et al. 2024

Front. Immunol.

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Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

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Section: Machine Learning (Ml) Modelsmentioning

confidence: 99%

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy

Qin,

Huo,

Liu

et al. 2024

Front. Immunol.

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“…For example, several reports showed that mRNA vaccine could be applied for cancer treatment via regulation of TME ( Zhong et al, 2021 ; Zhao W. et al, 2022 ; Huang et al, 2022 ). Proteomics, genomics, and metabolomics might be good approaches to explore the mechanism of gene mutation-driven lung cancer and TME ( Zhou et al, 2019 ; Bourbonne et al, 2022 ). Recently, several studies used the single-cell profiling of lung cancer to determine the TME and immunotherapy ( Maynard et al, 2020 ; Wu et al, 2021 ; Hui et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Targeting immune cell types of tumor microenvironment to overcome resistance to PD-1/PD-L1 blockade in lung cancer

Wang

Zhu²,

Yang³

et al. 2023

Front. Pharmacol.

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Lung cancer is the common malignant tumor with the highest mortality rate. Lung cancer patients have achieved benefits from immunotherapy, including immune checkpoint inhibitors (ICIs) therapy. Unfortunately, cancer patients acquire adaptive immune resistance, leading to poor prognosis. Tumor microenvironment (TME) has been demonstrated to play a critical role in participating in acquired adaptive immune resistance. TME is associated with molecular heterogeneity of immunotherapy efficacy in lung cancer. In this article, we discuss how immune cell types of TME are correlated with immunotherapy in lung cancer. Moreover, we describe the efficacy of immunotherapy in driven gene mutations in lung cancer, including KRAS, TP53, EGFR, ALK, ROS1, KEAP1, ZFHX3, PTCH1, PAK7, UBE3A, TNF-α, NOTCH, LRP1B, FBXW7, and STK11. We also emphasize that modulation of immune cell types of TME could be a promising strategy for improving adaptive immune resistance in lung cancer.

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“…With the rapid development of omic methods (genomics, proteomics, transcriptomics, metabolomics), massive omics data have become available for clinical analysis ( 93 ). Rich et al.…”

Section: Cutting-edge Progress In Biomarker Explorationmentioning

confidence: 99%

Advances in efficacy prediction and monitoring of neoadjuvant immunotherapy for non-small cell lung cancer

Wang

Feng

et al. 2023

Front. Oncol.

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The use of immune checkpoint inhibitors (ICIs) has become mainstream in the treatment of non-small cell lung cancer (NSCLC). The idea of harnessing the immune system to fight cancer is fast developing. Neoadjuvant treatment in NSCLC is undergoing unprecedented change. Chemo-immunotherapy combinations not only seem to achieve population-wide treating coverage irrespective of PD-L1 expression but also enable achieving a pathological complete response (pCR). Despite these recent advancements in neoadjuvant chemo-immunotherapy, not all patients respond favorably to treatment with ICIs plus chemo and may even suffer from severe immune-related adverse effects (irAEs). Similar to selection for target therapy, identifying patients most likely to benefit from chemo-immunotherapy may be valuable. Recently, several prognostic and predictive factors associated with the efficacy of neoadjuvant immunotherapy in NSCLC, such as tumor-intrinsic biomarkers, tumor microenvironment biomarkers, liquid biopsies, microbiota, metabolic profiles, and clinical characteristics, have been described. However, a specific and sensitive biomarker remains to be identified. Recently, the construction of prediction models for ICI therapy using novel tools, such as multi-omics factors, proteomic tests, host immune classifiers, and machine learning algorithms, has gained attention. In this review, we provide a comprehensive overview of the different positive prognostic and predictive factors in treating preoperative patients with ICIs, highlight the recent advances made in the efficacy prediction of neoadjuvant immunotherapy, and provide an outlook for joint predictors.

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Multi-Omics Approaches for the Prediction of Clinical Endpoints after Immunotherapy in Non-Small Cell Lung Cancer: A Comprehensive Review

Cited by 7 publications

References 167 publications

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy

Targeting immune cell types of tumor microenvironment to overcome resistance to PD-1/PD-L1 blockade in lung cancer

Advances in efficacy prediction and monitoring of neoadjuvant immunotherapy for non-small cell lung cancer

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