Prediction of coexisting invasive carcinoma on ductal carcinoma in situ (DCIS) lesions by mass spectrometry imaging
Prediction of coexisting invasive carcinoma on ductal carcinoma in situ (DCIS) lesions by mass spectrometry imaging
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DOI：
10.1002/path.6154
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发表时间：
2023-08-09
影响因子：
7.3
通讯作者：
Bu，Hong
中科院分区：
文献类型：
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作者：
Chen，Hong;Li，Xin;Bu，Hong
由于活检样本有限，约20%经活检证实的DCIS病变在手术切除后升级为浸润性导管癌（IDC）。在一个不鼓励DCIS过度治疗的时代，在诊断DCIS时避免低估IDC已成为一个紧迫的挑战。在这项研究中，使用解吸电喷雾电离质谱（DESI-MS）成像分析了284份新鲜冷冻乳腺样本的代谢谱，包括肿瘤组织和邻近良性组织（ABT）和远端周围组织（DST）。使用DESI-MS数据进行的代谢组学分析显示，纯DCIS和IDC之间的代谢物水平存在显著差异，包括小分子抗氧化剂、长链多不饱和脂肪酸（PUFA）和磷脂。然而，DCIS中浸润性癌组分的代谢谱明显地向邻近IDC组分转移。例如，与纯DCIS相比，含有浸润性癌成分的DCIS显示出较低水平的抗氧化剂和较高水平的游离脂肪酸。此外，长链PUFA和含有PUFA残基的磷脂酰肌醇（PI）的蓄积也可能与DCIS的进展相关。这些独特的代谢特征可能为研究DCIS的恶性潜能提供有价值的指示。通过将DESI-MS数据与机器学习（ML）方法相结合，可以区分各种乳腺病变。重要的是，通过Lasso预测模型成功区分了纯DCIS组分与术后标本中具有浸润的样本中的DCIS组分，实现了0.851的AUC值。此外，基于DESI-MS数据的像素级预测实现了整个组织切片中组织特性的自动可视化。总之，组织病理学切片的DESI-MS成像可以提供有关乳腺病变的丰富代谢信息。通过分析组织切片中的空间代谢特征，该技术有可能通过推断DCIS病变周围IDC组分的存在来促进DCIS的准确诊断和个体化治疗。© 2023大不列颠和爱尔兰病理学会。
Due to limited biopsy samples, ~20% of DCIS lesions confirmed by biopsy are upgraded to invasive ductal carcinoma (IDC) upon surgical resection. Avoiding underestimation of IDC when diagnosing DCIS has become an urgent challenge in an era discouraging overtreatment of DCIS. In this study, the metabolic profiles of 284 fresh frozen breast samples, including tumor tissues and adjacent benign tissues (ABTs) and distant surrounding tissues (DSTs), were analyzed using desorption electrospray ionization‐mass spectrometry (DESI‐MS) imaging. Metabolomics analysis using DESI‐MS data revealed significant differences in metabolite levels, including small‐molecule antioxidants, long‐chain polyunsaturated fatty acids (PUFAs) and phospholipids between pure DCIS and IDC. However, the metabolic profile in DCIS with invasive carcinoma components clearly shifts to be closer to adjacent IDC components. For instance, DCIS with invasive carcinoma components showed lower levels of antioxidants and higher levels of free fatty acids compared to pure DCIS. Furthermore, the accumulation of long‐chain PUFAs and the phosphatidylinositols (PIs) containing PUFA residues may also be associated with the progression of DCIS. These distinctive metabolic characteristics may offer valuable indications for investigating the malignant potential of DCIS. By combining DESI‐MS data with machine learning (ML) methods, various breast lesions were discriminated. Importantly, the pure DCIS components were successfully distinguished from the DCIS components in samples with invasion in postoperative specimens by a Lasso prediction model, achieving an AUC value of 0.851. In addition, pixel‐level prediction based on DESI‐MS data enabled automatic visualization of tissue properties across whole tissue sections. Summarily, DESI‐MS imaging on histopathological sections can provide abundant metabolic information about breast lesions. By analyzing the spatial metabolic characteristics in tissue sections, this technology has the potential to facilitate accurate diagnosis and individualized treatment of DCIS by inferring the presence of IDC components surrounding DCIS lesions. © 2023 The Pathological Society of Great Britain and Ireland.