Dimitrakopoulos Lampros, Prassas Ioannis, Sieuwerts Anieta M, Diamandis Eleftherios P, Martens John W M, Charames George S
Department of Laboratory Medicine and Pathobiology, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada; Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, 600 University Avenue, Toronto, ON M5G 1X5, Canada; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Avenue, Toronto, ON M5G 1X5, Canada.
Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, 600 University Avenue, Toronto, ON M5G 1X5, Canada; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Avenue, Toronto, ON M5G 1X5, Canada.
Clin Biochem. 2019 Apr;66:63-75. doi: 10.1016/j.clinbiochem.2019.01.005. Epub 2019 Jan 23.
Recent advances in mass spectrometric instrumentation and bioinformatics have critically contributed to the field of proteogenomics. Nonetheless, whether that integrative approach has reached the point of maturity to effectively reveal the flow of genetic variants from DNA to proteins still remains elusive. The objective of this study was to detect somatically acquired protein variants in breast cancer specimens for which full genome and transcriptome data was already available (BASIS cohort).
LC-MS/MS shotgun proteomic results of 21 breast cancer tissues were coupled to DNA sequencing data to identify variants at the protein level and finally were used to associate protein expression with gene expression levels.
Here we report the observation of three sequencing-predicted single amino acid somatic variants. The sensitivity of single amino acid variant (SAAV) detection based on DNA sequencing-predicted single nucleotide variants was 0.4%. This sensitivity was increased to 0.6% when all the predicted variants were filtered for MS "compatibility" and was further increased to 2.9% when only proteins with at least one wild type peptide detected were taken into account. A correlation of mRNA abundance and variant peptide detection revealed that transcripts for which variant proteins were detected ranked among the top 6.3% most abundant transcripts. The variants were detected in highly abundant proteins as well, thus establishing transcript and protein abundance and MS "compatibility" as the main factors affecting variant onco-proteogenomic identification.
While proteomics fails to identify the vast majority of exome DNA variants in the resulting proteome, its ability to detect a small subset of SAAVs could prove valuable for precision medicine applications.
质谱仪器和生物信息学的最新进展对蛋白质基因组学领域做出了至关重要的贡献。尽管如此，这种整合方法是否已达到成熟阶段，能够有效地揭示从DNA到蛋白质的遗传变异流动，仍然难以确定。本研究的目的是在已经获得全基因组和转录组数据的乳腺癌标本（BASIS队列）中检测体细胞获得的蛋白质变异。
将21个乳腺癌组织的液相色谱-串联质谱鸟枪法蛋白质组学结果与DNA测序数据相结合，以识别蛋白质水平的变异，最后用于将蛋白质表达与基因表达水平相关联。
在此我们报告观察到三个测序预测的单氨基酸体细胞变异。基于DNA测序预测的单核苷酸变异检测单氨基酸变异（SAAV）的灵敏度为0.4%。当所有预测变异经过质谱“兼容性”筛选时，该灵敏度提高到0.6%，当仅考虑检测到至少一种野生型肽段的蛋白质时，灵敏度进一步提高到2.9%。mRNA丰度与变异肽段检测的相关性表明，检测到变异蛋白的转录本位列最丰富转录本的前6.3%。这些变异也在高丰度蛋白质中被检测到，从而确定转录本和蛋白质丰度以及质谱“兼容性”是影响变异肿瘤蛋白质基因组识别的主要因素。
虽然蛋白质组学未能在所得蛋白质组中识别绝大多数外显子DNA变异，但其检测一小部分SAAV的能力可能对精准医学应用具有重要价值。
