Katz Lauren, Tata Alessandra, Woolman Michael, Zarrine-Afsar Arash
Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON M5G 1L7, Canada.
Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, ON M5G 1P5, Canada.
Metabolites. 2021 Sep 28;11(10):660. doi: 10.3390/metabo11100660.
Untargeted lipid fingerprinting with hand-held ambient mass spectrometry (MS) probes without chromatographic separation has shown promise in the rapid characterization of cancers. As human cancers present significant molecular heterogeneities, careful molecular modeling and data validation strategies are required to minimize late-stage performance variations of these models across a large population. This review utilizes parallels from the pitfalls of conventional protein biomarkers in reaching bedside utility and provides recommendations for robust modeling as well as validation strategies that could enable the next logical steps in large scale assessment of the utility of ambient MS profiling for cancer diagnosis. Six recommendations are provided that range from careful initial determination of clinical added value to moving beyond just statistical associations to validate lipid involvements in disease processes mechanistically. Further guidelines for careful selection of suitable samples to capture expected and unexpected intragroup variance are provided and discussed in the context of demographic heterogeneities in the lipidome, further influenced by lifestyle factors, diet, and potential intersect with cancer lipid pathways probed in ambient mass spectrometry profiling studies.
使用无需色谱分离的手持式常压质谱（MS）探针进行非靶向脂质指纹分析，已显示出在癌症快速表征方面的前景。由于人类癌症存在显著的分子异质性，需要谨慎的分子建模和数据验证策略，以尽量减少这些模型在大量人群中的后期性能差异。本综述借鉴了传统蛋白质生物标志物在实现床边应用过程中的陷阱，并为稳健建模以及验证策略提供建议，这些策略可以推动在大规模评估常压质谱分析用于癌症诊断的实用性方面迈出下一步合理的步伐。提出了六项建议，范围从仔细初步确定临床附加值到超越单纯的统计关联，以机械地验证脂质在疾病过程中的参与情况。还提供了关于仔细选择合适样本以捕获预期和意外组内差异的进一步指导方针，并在脂质组中的人口统计学异质性背景下进行了讨论，脂质组的异质性还受到生活方式因素、饮食的进一步影响，以及与常压质谱分析研究中探测的癌症脂质途径的潜在交叉影响。
