Esperança-Martins Miguel, Vasques Hugo, Ravasqueira Manuel Sokolov, Lemos Maria Manuel, Fonseca Filipa, Coutinho Diogo, López Jorge Antonio, Huang Richard S P, Dias Sérgio, Gallego-Paez Lina, Costa Luís, Abecasis Nuno, Gonçalves Emanuel, Fernandes Isabel
Medical Oncology Department, Unidade Local de Saúde de Santa Maria, 1649-028 Lisboa, Portugal.
Gulbenkian Institute for Molecular Medicine, 1649-035 Lisboa, Portugal.
Cancers (Basel). 2025 Aug 30;17(17):2861. doi: 10.3390/cancers17172861.
Soft tissue sarcomas (STSs) histopathological classification system and the clinical and molecular-based tools that are currently employed to estimate its prognosis have several limitations, impacting prognostication and treatment. Clinically driven molecular profiling studies may cover these gaps and offer alternative tools with superior prognostication capability and enhanced precision and personalized treatment approaches identification ability. We performed DNA sequencing (DNA-seq) and RNA sequencing (RNA-seq) to portray the molecular profile of 102 samples of high-grade STS, comprising the three most common STS histotypes. The analysis of RNA-seq data using unsupervised machine learning models revealed previously unknown molecular patterns, identifying four transcriptomic subtypes/clusters (TCs). This TC-based classification has a clear prognostic value (in terms of overall survival (OS) and disease-free survival (DFS)), a finding that was externally validated using independent patient cohorts. The prognostic value of this TC-based classification outperforms the prognostic accuracy of clinical-based (SARCULATOR nomograms) and molecular-based (CINSARC) prognostication tools, being one of the first molecular-based classifications capable of predicting OS in STS. The analysis of DNA-seq data from the same cohort revealed numerous and, in some cases, never documented molecular targets for precision treatment across different transcriptomic subtypes. The functional and predictive value of each genomic variant was analyzed using the Molecular Tumor Board Portal. This newly identified TC-based classification offers a superior prognostic value when compared with current gold-standard clinical and molecular-based prognostication tools, and identifies novel molecular targets for precision treatment, representing a cutting-edge tool for predicting prognosis and guiding treatment across different stages of STS.
软组织肉瘤（STSs）的组织病理学分类系统以及目前用于评估其预后的临床和基于分子的工具存在若干局限性，影响了预后判断和治疗。临床驱动的分子谱分析研究可能会弥补这些不足，并提供具有卓越预后评估能力、更高精准度以及更强个性化治疗方法识别能力的替代工具。我们进行了DNA测序（DNA-seq）和RNA测序（RNA-seq），以描绘102例高级别STSs样本的分子谱，这些样本涵盖了三种最常见的STSs组织学类型。使用无监督机器学习模型对RNA-seq数据进行分析，揭示了先前未知的分子模式，识别出四种转录组亚型/簇（TCs）。这种基于TC的分类具有明确的预后价值（就总生存期（OS）和无病生存期（DFS）而言），这一发现通过独立患者队列进行了外部验证。这种基于TC的分类的预后价值优于基于临床（SARCULATOR列线图）和基于分子（CINSARC）的预后评估工具的预后准确性，是首批能够预测STSs中OS的基于分子的分类之一。对同一队列的DNA-seq数据进行分析，揭示了不同转录组亚型中众多且在某些情况下从未记录过的精准治疗分子靶点。使用分子肿瘤委员会门户网站分析了每个基因组变异的功能和预测价值。与当前的金标准临床和基于分子的预后评估工具相比，这种新确定的基于TC的分类具有更高的预后价值，并识别出了精准治疗的新分子靶点，代表了一种用于预测预后和指导STSs不同阶段治疗的前沿工具。
