Papillary thyroid cancer immune phenotypes via tumor-infiltrating lymphocyte spatial analysis.

IF 4.1 2区 医学 Q2 ENDOCRINOLOGY & METABOLISM Endocrine-related cancer Pub Date : 2023-07-28 Print Date: 2023-09-01 DOI:10.1530/ERC-23-0110
Myungwoo Nam, Woojung Yang, Hye Sung Kim, Jewel Park, Gahee Park, Sukjun Kim, Sanghoon Song, Chan-Young Ock, Victor G Wang, Jeffrey H Chuang, Young Kwang Chae
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Abstract

Standard-of-care treatment options provide an excellent prognosis for papillary thyroid cancers (PTCs); however, approximately 10% of cases are advanced PTCs, resulting in less than 50% 5-year survival rates. Understanding the tumor microenvironment is essential for understanding cancer progression and investigating potential biomarkers for treatment, such as immunotherapy. Our study focused on tumor-infiltrating lymphocytes (TILs), which are the main effectors of antitumor immunity and related to the mechanism of immunotherapy. Using an artificial intelligence model, we analyzed the density of intratumoral and peritumoral TILs in the pathologic slides of The Cancer Genome Atlas PTC cohort. Tumors were classified into three immune phenotypes (IPs) based on the spatial distribution of TILs: immune-desert (48%), immune-excluded (34%), and inflamed (18%). Immune-desert IP was mostly characterized by RAS mutations, high thyroid differentiation score, and low antitumor immune response. Immune-excluded IP predominantly consisted of BRAF V600E-mutated tumors and had a higher rate of lymph node metastasis. Inflamed IP was characterized by a high antitumor immune response, as demonstrated by a high cytolytic score, immune-related cell infiltrations, expression of immunomodulatory molecules (including immunotherapy target molecules), and enrichment of immune-related pathways. This study is the first to investigate IP classification using TILs in PTC through a tissue-based approach. Each IP had unique immune and genomic profiles. Further studies are warranted to assess the predictive value of IP classification in advanced PTC patients treated with immunotherapy.

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通过肿瘤浸润淋巴细胞空间分析的乳头状甲状腺癌症免疫表型。
标准护理治疗方案为甲状腺乳头状癌(PTC)提供了良好的预后;然而,大约10%的病例是晚期PTC,导致5年生存率低于50%。了解肿瘤微环境对于了解癌症进展和研究潜在的治疗生物标志物(如免疫疗法)至关重要。我们的研究重点是肿瘤浸润淋巴细胞(TILs),它是抗肿瘤免疫的主要效应物,并与免疫治疗机制有关。使用人工智能模型,我们分析了癌症基因组图谱PTC队列病理切片中肿瘤内和肿瘤周围TIL的密度。根据TIL的空间分布,肿瘤被分为三种免疫表型(IP):免疫沙漠(48%)、免疫排斥(34%)和炎症(18%)。免疫沙漠IP的主要特征是RAS突变、甲状腺分化评分高和抗肿瘤免疫反应低。免疫排斥性IP主要由BRAF V600E突变肿瘤组成,淋巴结转移率较高。炎症IP的特征是高抗肿瘤免疫反应,表现为高细胞溶解评分、免疫相关细胞浸润、免疫调节分子(包括免疫治疗靶分子)的表达和免疫相关途径的富集。本研究首次通过基于组织的方法研究PTC中使用TIL的IP分类。每个IP都有独特的免疫和基因组图谱。需要进一步的研究来评估IP分类对接受免疫治疗的晚期PTC患者的预测价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Endocrine-related cancer
Endocrine-related cancer 医学-内分泌学与代谢
CiteScore
7.80
自引率
2.60%
发文量
138
审稿时长
6-12 weeks
期刊介绍: Endocrine-Related Cancer is an official flagship journal of the Society for Endocrinology and is endorsed by the European Society of Endocrinology, the United Kingdom and Ireland Neuroendocrine Society, and the Japanese Hormones and Cancer Society. Endocrine-Related Cancer provides a unique international forum for the publication of high quality original articles describing novel, cutting edge basic laboratory, translational and clinical investigations of human health and disease focusing on endocrine neoplasias and hormone-dependent cancers; and for the publication of authoritative review articles in these topics. Endocrine neoplasias include adrenal cortex, breast, multiple endocrine neoplasia, neuroendocrine tumours, ovary, prostate, paraganglioma, parathyroid, pheochromocytoma pituitary, testes, thyroid and hormone-dependent cancers. Neoplasias affecting metabolism and energy production such as bladder, bone, kidney, lung, and head and neck, are also considered.
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