Abstract：
BACKGROUND: Formalin-fixed paraffin-embedded (FFPE) tissue has been the gold standard for routine pathology for general and cancer postoperative diagnostics. Despite robust histopathology, immunohistochemistry, and molecular methods, accurate diagnosis remains difficult for certain cases. Overall, the entire process can be time consuming, labor intensive, and does not reach over 90% diagnostic sensitivity and specificity. There is a growing need in onco-pathology for adjunct novel rapid, accurate, reliable, diagnostically sensitive, and specific methods for high-throughput biomolecular identification. Lipids have long been considered only as building blocks of cell membranes or signaling molecules, but have recently been introduced as central players in cancer. Due to sample processing, which limits their detection, lipid analysis directly from unprocessed FFPE tissues has never been reported.
METHODS: We present a proof-of-concept with direct analysis of tissue-lipidomic signatures from FFPE tissues without dewaxing and minimal sample preparation using water-assisted laser desorption ionization mass spectrometry and deep-learning.
RESULTS: On a cohort of difficult canine and human sarcoma cases, classification for canine sarcoma subtyping was possible with 99.1% accuracy using \"5-fold\" and 98.5% using \"leave-one-patient out,\" and 91.2% accuracy for human sarcoma using 5-fold and 73.8% using leave-one-patient out. The developed classification model enabled stratification of blind samples in <5 min and showed >95% probability for discriminating 2 human sarcoma blind samples.
CONCLUSIONS: It is possible to create a rapid diagnostic platform to screen clinical FFPE tissues with minimal sample preparation for molecular pathology.
METHODS: We present a proof-of-concept with direct analysis of tissue-lipidomic signatures from FFPE tissues without dewaxing and minimal sample preparation using water-assisted laser desorption ionization mass spectrometry and deep-learning.
RESULTS: On a cohort of difficult canine and human sarcoma cases, classification for canine sarcoma subtyping was possible with 99.1% accuracy using \"5-fold\" and 98.5% using \"leave-one-patient out,\" and 91.2% accuracy for human sarcoma using 5-fold and 73.8% using leave-one-patient out. The developed classification model enabled stratification of blind samples in <5 min and showed >95% probability for discriminating 2 human sarcoma blind samples.
CONCLUSIONS: It is possible to create a rapid diagnostic platform to screen clinical FFPE tissues with minimal sample preparation for molecular pathology.
摘要：
背景：福尔马林固定石蜡包埋（FFPE）组织已成为常规病理学和癌症术后诊断的金标准。尽管有强大的组织病理学，免疫组织化学，和分子方法，对于某些病例，准确诊断仍然很困难。总的来说,整个过程可能很耗时，劳动密集型，诊断灵敏度和特异度不超过90%。在临床病理学上对辅助新的快速，准确,可靠,诊断敏感，和高通量生物分子鉴定的具体方法。长期以来，脂质一直被认为只是作为细胞膜或信号分子的组成部分，但最近被介绍为癌症的核心参与者。由于样品处理，这限制了它们的检测，从未报道过直接从未加工的FFPE组织进行脂质分析。
方法：我们提出了一个概念验证，使用水辅助激光解吸电离质谱和深度学习直接分析来自FFPE组织的组织脂质组学特征，无需脱蜡和最少的样品制备。
结果：在一组困难的犬和人类肉瘤病例中，犬肉瘤亚型的分类使用“5倍”可以达到99.1%的准确率，使用“留一病人”可以达到98.5%的准确率，“使用5倍和73.8%的人肉瘤准确率为91.2%。所开发的分类模型使得能够在<5分钟内对盲样本进行分层,并且显示用于区分2个人类肉瘤盲样本的>95%概率。
结论：有可能创建一个快速诊断平台，以筛选临床FFPE组织，只需最少的分子病理学样品制备。
方法：我们提出了一个概念验证，使用水辅助激光解吸电离质谱和深度学习直接分析来自FFPE组织的组织脂质组学特征，无需脱蜡和最少的样品制备。
结果：在一组困难的犬和人类肉瘤病例中，犬肉瘤亚型的分类使用“5倍”可以达到99.1%的准确率，使用“留一病人”可以达到98.5%的准确率，“使用5倍和73.8%的人肉瘤准确率为91.2%。所开发的分类模型使得能够在<5分钟内对盲样本进行分层,并且显示用于区分2个人类肉瘤盲样本的>95%概率。
结论：有可能创建一个快速诊断平台，以筛选临床FFPE组织，只需最少的分子病理学样品制备。
