Abstract：
Hepatocellular carcinoma (HCC) mortality rates continue to increase faster than those of other cancer types due to high heterogeneity, which limits diagnosis and treatment. Pathological and molecular subtyping have identified that HCC tumors with poor outcomes are characterized by intratumoral collagenous accumulation. However, the translational and post-translational regulation of tumor collagen, which is critical to the outcome, remains largely unknown. Here, we investigate the spatial extracellular proteome to understand the differences associated with HCC tumors defined by Hoshida transcriptomic subtypes of poor outcome (Subtype 1; S1; n = 12) and better outcome (Subtype 3; S3; n = 24) that show differential stroma-regulated pathways. Collagen-targeted mass spectrometry imaging (MSI) with the same-tissue reference libraries, built from untargeted and targeted LC-MS/MS was used to spatially define the extracellular microenvironment from clinically-characterized, formalin-fixed, paraffin-embedded tissue sections. Collagen α-1(I) chain domains for discoidin-domain receptor and integrin binding showed distinctive spatial distribution within the tumor microenvironment. Hydroxylated proline (HYP)-containing peptides from the triple helical regions of fibrillar collagens distinguished S1 from S3 tumors. Exploratory machine learning on multiple peptides extracted from the tumor regions could distinguish S1 and S3 tumors (with an area under the receiver operating curve of ≥0.98; 95% confidence intervals between 0.976 and 1.00; and accuracies above 94%). An overall finding was that the extracellular microenvironment has a high potential to predict clinically relevant outcomes in HCC.
摘要：
由于高度异质性，肝细胞癌（HCC）死亡率继续比其他癌症类型增加更快，这限制了诊断和治疗。病理和分子分型已经确定，预后较差的HCC肿瘤以瘤内胶原积聚为特征。然而,肿瘤胶原的翻译和翻译后调节，这对结果至关重要，仍然很大程度上未知。这里,我们研究了空间细胞外蛋白质组，以了解与Hoshida转录组亚型定义的HCC肿瘤相关的差异，这些亚型具有不良结局（亚型1;S1;n=12）和较好的结局（亚型3;S3;n=24），它们显示出不同的基质调节途径。具有相同组织参考库的胶原蛋白靶向质谱成像（MSI），由非靶向和靶向LC-MS/MS构建，用于在空间上定义临床特征的细胞外微环境，福尔马林固定，石蜡包埋的组织切片。盘状结构域受体和整联蛋白结合的胶原α-1（I）链域在肿瘤微环境中显示出独特的空间分布。来自纤维状胶原蛋白的三螺旋区域的含羟基脯氨酸（HYP）的肽将S1与S3肿瘤区分开。从肿瘤区域提取的多种肽的探索性机器学习可以区分S1和S3肿瘤（受试者工作曲线下的面积≥0.98；在0.976和1.00之间的95％置信区间；准确性高于94％）。总体发现是细胞外微环境具有预测HCC临床相关结果的高潜力。
