| 1. |
- Branca, Rui M. M., et al.
(författare)
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HiRIEF LC-MSMS enables deep proteome coverage and unbiased proteogenomics
- 2014
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Ingår i: Nature Methods. - 1548-7091 .- 1548-7105. ; 11:1, s. 59-
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Tidskriftsartikel (refereegranskat)abstract
- We present a liquid chromatography-mass spectrometry (LC-MSMS)-based method permitting unbiased (gene prediction-independent) genome-wide discovery of protein-coding loci in higher eukaryotes. Using high-resolution isoelectric focusing (HiRIEF) at the peptide level in the 3.7-5.0 pH range and accurate peptide isoelectric point (pI) prediction, we probed the six-reading-frame translation of the human and mouse genomes and identified 98 and 52 previously undiscovered protein-coding loci, respectively. The method also enabled deep proteome coverage, identifying 13,078 human and 10,637 mouse proteins.
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| 2. |
- Johansson, Henrik J., et al.
(författare)
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Breast cancer quantitative proteome and proteogenomic landscape
- 2019
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Ingår i: Nature Communications. - : NATURE PUBLISHING GROUP. - 2041-1723. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- In the preceding decades, molecular characterization has revolutionized breast cancer (BC) research and therapeutic approaches. Presented herein, an unbiased analysis of breast tumor proteomes, inclusive of 9995 proteins quantified across all tumors, for the first time recapitulates BC subtypes. Additionally, poor-prognosis basal-like and luminal B tumors are further subdivided by immune component infiltration, suggesting the current classification is incomplete. Proteome-based networks distinguish functional protein modules for breast tumor groups, with co-expression of EGFR and MET marking ductal carcinoma in situ regions of normal-like tumors and lending to a more accurate classification of this poorly defined subtype. Genes included within prognostic mRNA panels have significantly higher than average mRNA-protein correlations, and gene copy number alterations are dampened at the protein-level; underscoring the value of proteome quantification for prognostication and phenotypic classification. Furthermore, protein products mapping to non-coding genomic regions are identified; highlighting a potential new class of tumor-specific immunotherapeutic targets.
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| 3. |
- Lehtiö, Janne, et al.
(författare)
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Proteogenomics of non-small cell lung cancer reveals molecular subtypes associated with specific therapeutic targets and immune-evasion mechanisms
- 2021
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Ingår i: Nature Cancer. - : Springer Science and Business Media LLC. - 2662-1347. ; 2:11, s. 1224-1242
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Tidskriftsartikel (refereegranskat)abstract
- Despite major advancements in lung cancer treatment, long-term survival is still rare and a deeper understanding of molecular phenotypes would allow the identification of specific cancer dependencies and immune-evasion mechanisms. Here we performed in-depth mass-spectrometry-based proteogenomic analysis of 141 tumors representing all major histologies of non-small cell lung cancer (NSCLC). We identified six distinct proteome subtypes with striking differences in immune cell composition and subtype-specific expression of immune checkpoints. Unexpectedly, high neoantigen burden was linked to global hypomethylation and complex neoantigens mapped to genomic regions, such as endogenous retroviral elements and introns, in immune-cold subtypes. Further, we linked immune evasion with LAG-3 via STK11 mutation-dependent HNF1A activation and FGL1 expression. Finally, we develop a data-independent acquisition mass-spectrometry-based NSCLC subtype classification method, validate it in an independent cohort of 208 NSCLC cases and demonstrate its clinical utility by analyzing an additional cohort of 84 late-stage NSCLC biopsy samples.
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| 4. |
- Zhu, Yafeng, et al.
(författare)
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Discovery of coding regions in the human genome by integrated proteogenomics analysis workflow
- 2018
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Proteogenomics enable the discovery of novel peptides (from unannotated genomic protein-coding loci) and single amino acid variant peptides (derived from single-nucleotide polymorphisms and mutations). Increasing the reliability of these identifications is crucial to ensure their usefulness for genome annotation and potential application as neoantigens in cancer immunotherapy. We here present integrated proteogenomics analysis workflow (IPAW), which combines peptide discovery, curation, and validation. IPAW includes the SpectrumAI tool for automated inspection of MS/MS spectra, eliminating false identifications of single-residue substitution peptides. We employ IPAW to analyze two proteomics data sets acquired from A431 cells and five normal human tissues using extended (pH range, 3-10) high-resolution isoelectric focusing (HiRIEF) pre-fractionation and TMT-based peptide quantitation. The IPAW results provide evidence for the translation of pseudogenes, lncRNAs, short ORFs, alternative ORFs, N-terminal extensions, and intronic sequences. Moreover, our quantitative analysis indicates that protein production from certain pseudogenes and lncRNAs is tissue specific.
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