Artificial intelligence for prediction of response to cancer immunotherapy

Yang, Yuhan; Zhao, Ying; Liu, Xici; Huang, Juan

doi:10.1016/j.semcancer.2022.11.008

Cited by 17 publications

(10 citation statements)

References 103 publications

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“…Though biomarkers have guided ICI precision therapy to some extent, they still only benefit a restricted number of patients (6). Computer models can simulate and reproduce dynamic changes in the immune system during immunotherapy, conferring unparalleled advantages in inferring the law of immunotherapy and predicting tumor ICI immunotherapy response (7,8).…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Furthermore, this digital method has the advantages of high efficiency and is not limited by sequencing. However, it is still difficult to achieve uniform batch variation due to time differences, reagent differences, and staining method differences [ 28 ].…”

Section: Ai In Lung Cancer Pd-l1 and Tmb Predictionmentioning

confidence: 99%

The artificial intelligence and machine learning in lung cancer immunotherapy

Gao

Yang

et al. 2023

J Hematol Oncol

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Since the past decades, more lung cancer patients have been experiencing lasting benefits from immunotherapy. It is imperative to accurately and intelligently select appropriate patients for immunotherapy or predict the immunotherapy efficacy. In recent years, machine learning (ML)-based artificial intelligence (AI) was developed in the area of medical-industrial convergence. AI can help model and predict medical information. A growing number of studies have combined radiology, pathology, genomics, proteomics data in order to predict the expression levels of programmed death-ligand 1 (PD-L1), tumor mutation burden (TMB) and tumor microenvironment (TME) in cancer patients or predict the likelihood of immunotherapy benefits and side effects. Finally, with the advancement of AI and ML, it is believed that "digital biopsy" can replace the traditional single assessment method to benefit more cancer patients and help clinical decision-making in the future. In this review, the applications of AI in PD-L1/TMB prediction, TME prediction and lung cancer immunotherapy are discussed.

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“…AI encompasses the use of machines to replicate intelligent actions, addressing complex tasks with minimal human intervention. This involves concepts like ML and DL [ 27 ]. AI's integration into the field of medicine is enhancing healthcare systems across various domains, including confirming diagnoses, assessing risk, analyzing data, forecasting outcomes, monitoring treatments, and offering virtual health support [ 28 ].…”

Section: Reviewmentioning

confidence: 99%

Artificial Intelligence Applications for Biomedical Cancer Research: A Review

Weerarathna,

Kamble,

Luharia

2023

Cureus

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Artificial intelligence (AI) has rapidly evolved and demonstrated its potential in transforming biomedical cancer research, offering innovative solutions for cancer diagnosis, treatment, and overall patient care. Over the past two decades, AI has played a pivotal role in revolutionizing various facets of cancer clinical research. In this comprehensive review, we delve into the diverse applications of AI across the cancer care continuum, encompassing radiodiagnosis, radiotherapy, chemotherapy, immunotherapy, targeted therapy, surgery, and nanotechnology. AI has revolutionized cancer diagnosis, enabling early detection and precise characterization through advanced image analysis techniques. In radiodiagnosis, AI-driven algorithms enhance the accuracy of medical imaging, making it an invaluable tool for clinicians in the detection and assessment of cancer. AI has also revolutionized radiotherapy, facilitating precise tumor boundary delineation, optimizing treatment planning, and enabling real-time adjustments to improve therapeutic outcomes while minimizing collateral damage to healthy tissues. In chemotherapy, AI models have emerged as powerful tools for predicting patient responses to different treatment regimens, allowing for more personalized and effective strategies. In immunotherapy, AI analyzes genetic and imaging data to select ideal candidates for treatment and predict responses. Targeted therapy has seen great advancements with AI, aiding in the identification of specific molecular targets for tailored treatments. AI plays a vital role in surgery by offering real-time navigation and support, enhancing surgical precision. Moreover, the synergy between AI and nanotechnology promises the development of personalized nanomedicines, offering more efficient and targeted cancer treatments. While challenges related to data quality, interpretability, and ethical considerations persist, the future of AI in cancer research holds tremendous promise for improving patient outcomes through advanced and individualized care.

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Artificial intelligence for prediction of response to cancer immunotherapy

Cited by 17 publications

References 103 publications

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy

The artificial intelligence and machine learning in lung cancer immunotherapy

Artificial Intelligence Applications for Biomedical Cancer Research: A Review

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