Università Vita-Salute San Raffaele, Milan, Italy; Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy.
Department of Bioinformatics, Semmelweis University, Tűzoltó Utca 7-9, 1094 Budapest, Hungary; Research Centre for Natural Sciences, Oncology Biomarker Research Group, Institute of Molecular Life Sciences, Eötvös Loránd Research Network, Magyar Tudósok Körútja 2, 1117 Budapest, Hungary.
Eur J Cancer. 2023 Dec;195:113379. doi: 10.1016/j.ejca.2023.113379. Epub 2023 Oct 11.
Antibody-drug conjugates (ADCs) are a rapidly expanding class of compounds in oncology. Our goal was to assess the expression of ADC targets and potential downstream determining factors of activity across pan-cancer and normal tissues.
ADCs in clinical trials (n = 121) were identified through ClinicalTrials.gov, corresponding to 54 targets. Genes potentially implicated in treatment response were identified in the literature. Gene expression from The Cancer Genome Atlas (9000+ cancers of 31 cancer types), the Genotype-Tissue Expression database (n = 19,000 samples from 31 normal tissue types), and the TNMplot.com (n = 12,494 unmatched primary and metastatic samples) were used in this analysis. To compare relative expression across and within tumour types we used pooled normal tissues as reference.
For most ADC targets, mRNA levels correlated with protein expression. Pan-cancer target expression distributions identified appealing cancer types for each ADC development. Co-expression of multiple targets was common and suggested opportunities for ADC combinations. Expression levels of genes potentially implicated in ADC response downstream of the target might provide additional information (e.g. TOP1 was highly expressed in many tumour types, including breast and lung cancers). Metastatic compared to primary tissues overexpressed some ADCs targets. Single sample "targetgram" plots were generated to visualise the expression of potentially competing ADC targets and resistance/sensitivity markers highlighting high inter-patient heterogeneity. Off-cancer target expression only partially explains adverse events, while expression of determinants of payload activity explained more of the observed toxicities.
Our findings draw attention to new therapeutic opportunities for ADCs that can be tested in the clinic and our web platform (https://tnmplot.com) can assist in prioritising upcoming ADC targets for clinical development.
抗体药物偶联物(ADCs)是肿瘤学中迅速发展的一类化合物。我们的目标是评估 ADC 靶点在泛癌和正常组织中的表达,以及潜在的下游活性决定因素。
通过 ClinicalTrials.gov 鉴定了临床试验中的 ADC(n=121),对应 54 个靶点。从文献中确定了可能与治疗反应相关的基因。使用了来自癌症基因组图谱(31 种癌症类型的 9000 多个癌症)、基因型组织表达数据库(31 种正常组织类型的 19000 个样本)和 TNMplot.com(n=12494 个未匹配的原发和转移样本)的基因表达数据。为了比较肿瘤类型之间和内部的相对表达,我们使用了混合的正常组织作为参考。
对于大多数 ADC 靶点,mRNA 水平与蛋白表达相关。泛癌靶点表达分布确定了每个 ADC 开发有吸引力的癌症类型。多个靶点的共表达很常见,提示了 ADC 组合的机会。潜在靶点下游与 ADC 反应相关的基因的表达水平可能提供额外的信息(例如,TOP1 在许多肿瘤类型中高表达,包括乳腺癌和肺癌)。与原发组织相比,转移组织过表达了一些 ADC 靶点。生成了单个样本“targetgram”图,以可视化潜在竞争的 ADC 靶点和耐药/敏感性标志物的表达,突出了高患者间异质性。除癌症靶点的表达仅部分解释不良事件外,有效载荷活性决定因素的表达解释了更多观察到的毒性。
我们的发现引起了对 ADC 新治疗机会的关注,这些机会可以在临床上进行测试,我们的网络平台(https://tnmplot.com)可以帮助确定即将进行临床开发的 ADC 靶标优先级。