Tuba N Gide, Yizhe Mao, Richard A Scolyer, Georgina V Long, James S Wilmott
{"title":"Tissue-based Profiling Techniques to Achieve Precision Medicine in Cancer: Opportunities and Challenges in Melanoma.","authors":"Tuba N Gide, Yizhe Mao, Richard A Scolyer, Georgina V Long, James S Wilmott","doi":"10.1158/1078-0432.CCR-24-1109","DOIUrl":null,"url":null,"abstract":"<p><p>Immunotherapies targeting the programmed cell death 1 (PD-1) and cytotoxic T lymphocyte antigen 4 (CTLA-4) checkpoint receptors have revolutionised the treatment of metastatic melanoma. However, half of treated patients do not respond to or eventually progress on standard therapies and many experience adverse events as a result of drug toxicity. The identification of accurate biomarkers of clinical outcomes are required in order to move away from the one-size-fits-all treatment approach of standard clinical practice, and towards a more personalised approach to enable the administration of the optimal therapy for any given patient and further improve patient outcomes. Recent clinical trials have proven the potential of multi-omics analyses, including genomic, gene expression, and tumour immune profiling, of patients' tumour biopsies, to predict a patient's response to subsequently administered immunotherapies. However, reproducibility of such multi-omics analyses, tissue requirements, and clinical validation have limited the practical application of these approaches in routine clinical workflows. In this review, we discuss several pivotal tissue-based profiling techniques that can be utilised to identify potential genomic, transcriptomic and immune biomarkers predictive of clinical outcomes following treatment with immune checkpoint inhibitors in melanoma. Furthermore, we highlight the key opportunities and challenges associated with the use of each of these techniques. The development and implementation of multimodal predictive models which combine data derived from these various methods is the future for achieving precision medicine for patients with melanoma.</p>","PeriodicalId":10279,"journal":{"name":"Clinical Cancer Research","volume":null,"pages":null},"PeriodicalIF":10.0000,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Cancer Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1158/1078-0432.CCR-24-1109","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Immunotherapies targeting the programmed cell death 1 (PD-1) and cytotoxic T lymphocyte antigen 4 (CTLA-4) checkpoint receptors have revolutionised the treatment of metastatic melanoma. However, half of treated patients do not respond to or eventually progress on standard therapies and many experience adverse events as a result of drug toxicity. The identification of accurate biomarkers of clinical outcomes are required in order to move away from the one-size-fits-all treatment approach of standard clinical practice, and towards a more personalised approach to enable the administration of the optimal therapy for any given patient and further improve patient outcomes. Recent clinical trials have proven the potential of multi-omics analyses, including genomic, gene expression, and tumour immune profiling, of patients' tumour biopsies, to predict a patient's response to subsequently administered immunotherapies. However, reproducibility of such multi-omics analyses, tissue requirements, and clinical validation have limited the practical application of these approaches in routine clinical workflows. In this review, we discuss several pivotal tissue-based profiling techniques that can be utilised to identify potential genomic, transcriptomic and immune biomarkers predictive of clinical outcomes following treatment with immune checkpoint inhibitors in melanoma. Furthermore, we highlight the key opportunities and challenges associated with the use of each of these techniques. The development and implementation of multimodal predictive models which combine data derived from these various methods is the future for achieving precision medicine for patients with melanoma.
期刊介绍:
Clinical Cancer Research is a journal focusing on groundbreaking research in cancer, specifically in the areas where the laboratory and the clinic intersect. Our primary interest lies in clinical trials that investigate novel treatments, accompanied by research on pharmacology, molecular alterations, and biomarkers that can predict response or resistance to these treatments. Furthermore, we prioritize laboratory and animal studies that explore new drugs and targeted agents with the potential to advance to clinical trials. We also encourage research on targetable mechanisms of cancer development, progression, and metastasis.