Lampros Dimitrakopoulos1, Ioannis Prassas2, Anieta M Sieuwerts3, Eleftherios P Diamandis4, John W M Martens5, George S Charames6. 1. 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. 2. 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. 3. Department of Medical Oncology and Cancer Genomics Netherlands, Erasmus MC Cancer Institute, Erasmus University Medical Center, Wytemaweg 80, 3015 CN Rotterdam, The Netherlands. 4. 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 Clinical Biochemistry, University Health Network, 190 Elizabeth Street, Toronto, ON M5G 2C4, Canada. 5. Department of Medical Oncology and Cancer Genomics Netherlands, Erasmus MC Cancer Institute, Erasmus University Medical Center, Wytemaweg 80, 3015 CN Rotterdam, The Netherlands. Electronic address: j.martens@erasmusmc.nl. 6. 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. Electronic address: george.charames@sinaihealthsystem.ca.
Abstract
BACKGROUND: 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). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Renee Salz; Robbin Bouwmeester; Ralf Gabriels; Sven Degroeve; Lennart Martens; Pieter-Jan Volders; Peter A C 't Hoen Journal: J Proteome Res Date: 2021-05-17 Impact factor: 4.466