Background: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.Materials and Methods/Results: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.Conclusions: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.
背景：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分子工具存在诸多局限性，影响了预后判断和治疗。临床驱动的分子谱研究可能填补这些空白，并提供具有更优预后能力、更高精确度及更强个性化治疗方案识别能力的替代工具。 材料与方法/结果：我们对102例高级别STS样本进行了DNA测序（DNA-seq）和RNA测序（RNA-seq），以描绘其分子谱特征，这些样本涵盖三种最常见的STS组织学亚型。通过无监督机器学习模型分析RNA-seq数据，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（在总生存期（OS）和无病生存期（DFS）方面），这一发现在独立患者队列中得到了外部验证。该TC分类的预后价值优于基于临床的（SARCULATOR列线图）和基于分子的（CINSARC）预后工具，成为首个能够预测STS总生存期的分子分类方法之一。对同一队列DNA-seq数据的分析揭示了大量且在某些情况下从未被记录的精准治疗分子靶点，这些靶点跨越不同的转录组亚型。通过分子肿瘤委员会门户网站对每个基因组变异的功能和预测价值进行了分析。 结论：与当前金标准的临床和分子预后工具相比，这种新发现的基于TC的分类方法提供了更优的预后价值，并识别出新的精准治疗分子靶点，成为预测STS不同阶段预后和指导治疗的尖端工具。
肿瘤(癌症)患者之家