The Society for Immunotherapy of Cancer Perspective on Tissue-Based Technologies for Immuno-Oncology Biomarker Discovery and Application

生物标志物发现 免疫疗法 癌症 生物标志物 癌症免疫疗法 计算机科学 精确肿瘤学 肿瘤科 医学 临床肿瘤学 透视图(图形) 癌症研究 内科学 蛋白质组学 生物 基因 人工智能 生物化学
作者
Anne Monette,Adriana Aguilar‐Mahecha,Emre Altınmakas,Mathew G. Angelos,Nima Assad,Gerald Batist,Praveen K. Bommareddy,Diana L. Bonilla,Christoph H. Borchers,S. Church,Gennaro Ciliberto,Alexandria P. Cogdill,Luigi Fattore,Nir Hacohen,Mohammad Haris,Vincent Lacasse,Wen‐Rong Lie,Arnav Mehta,Marco Ruella,Houssein Abdul Sater
出处
期刊:Clinical Cancer Research [American Association for Cancer Research]
卷期号:31 (3): 439-456 被引量:11
标识
DOI:10.1158/1078-0432.ccr-24-2469
摘要

With immuno-oncology becoming the standard of care for a variety of cancers, identifying biomarkers that reliably classify patient response, resistance, or toxicity becomes the next critical barrier toward improving care. Multiparametric, multi-omics, and computational platforms generating an unprecedented depth of data are poised to usher in the discovery of increasingly robust biomarkers for enhanced patient selection and personalized treatment approaches. Deciding which developing technologies to implement in clinical settings ultimately, applied either alone or in combination, relies on weighing pros and cons, from minimizing patient sampling to maximizing data outputs, and assessing the reproducibility and representativeness of findings, while lessening data fragmentation toward harmonization. These factors are all assessed while taking into consideration the shortest turnaround time. The Society for Immunotherapy of Cancer Biomarkers Committee convened to identify important advances in biomarker technologies and to address advances in biomarker discovery using multiplexed IHC and immunofluorescence, their coupling to single-cell transcriptomics, along with mass spectrometry-based quantitative and spatially resolved proteomics imaging technologies. We summarize key metrics obtained, ease of interpretation, limitations and dependencies, technical improvements, and outward comparisons of these technologies. By highlighting the most interesting recent data contributed by these technologies and by providing ways to improve their outputs, we hope to guide correlative research directions and assist in their evolution toward becoming clinically useful in immuno-oncology.
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