Next generation pan-cancer blood proteome profiling using proximity extension assay

Álvez, María Bueno; Edfors, Fredrik; Feilitzen, Kalle von; Zwahlén, Martin; Mardinoğlu, Adil; Edqvist, Per‐Henrik; Sjöblom, Tobias; Lundin, Emma; Rameika, Natallia; Enblad, Gunilla; Lindman, Henrik; Höglund, Martin; Hesselager, Göran; Stålberg, Karin; Enblad, Malin; Simonson, Oscar E.; Häggman, Michael; Axelsson, Tomas; Åberg, Mikael; Nordlund, Jessica; Zhong, Wen; Karlsson, Max; Gyllensten, Ulf; Pontén, Fredrik; Fagerberg, Linn; Uhlén, Mathias

doi:10.1038/s41467-023-39765-y

Cited by 18 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also did not replicate protein risk associations previously reported for pancreatic cancer 30 . Nonetheless, our findings are in-line with some of those reported in a cross-cancer case-control study (with blood collected at or after diagnosis) within the Uppsala-Umeå Comprehensive Cancer Consortium biobank; we replicated the reported association of GFAP with glioma and the associations of CNTN5, SLAMF7, MZB1, QPCT and TNFRSF13B with multiple myeloma 31 .…”

Section: Discussionsupporting

confidence: 90%

Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank

Papier,

Atkins,

Tong

et al. 2024

Nat Commun

View full text Add to dashboard Cite

The availability of protein measurements and whole exome sequence data in the UK Biobank enables investigation of potential observational and genetic protein-cancer risk associations. We investigated associations of 1463 plasma proteins with incidence of 19 cancers and 9 cancer subsites in UK Biobank participants (average 12 years follow-up). Emerging protein-cancer associations were further explored using two genetic approaches, cis-pQTL and exome-wide protein genetic scores (exGS). We identify 618 protein-cancer associations, of which 107 persist for cases diagnosed more than seven years after blood draw, 29 of 618 were associated in genetic analyses, and four had support from long time-to-diagnosis ( > 7 years) and both cis-pQTL and exGS analyses: CD74 and TNFRSF1B with NHL, ADAM8 with leukemia, and SFTPA2 with lung cancer. We present multiple blood protein-cancer risk associations, including many detectable more than seven years before cancer diagnosis and that had concordant evidence from genetic analyses, suggesting a possible role in cancer development.

show abstract

Section: Discussionsupporting

confidence: 90%

Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank

Papier,

Atkins,

Tong

et al. 2024

Nat Commun

View full text Add to dashboard Cite

show abstract

“…Then EVs were directly lysed for protein and RNA coanalysis by Co-PAR (Figure A). Primer extension assay (PEA), , used for protein detection, consisted of five steps [Figure B(i)]. The first step was protein capture.…”

Section: Resultsmentioning

confidence: 99%

Codetection of Proteins and RNAs on Extracellular Vesicles for Pancreatic Cancer Early Diagnosis

He,

Long,

Zhai

et al. 2024

Anal. Chem.

View full text Add to dashboard Cite

Tumor-derived extracellular vesicles (EVs) carry tumor-specific proteins and RNAs, thus becoming prevalent targets for early cancer diagnosis. However, low expression of EV cargos and insufficient diagnostic power of individual biomarkers hindered EVs application in clinical practice. Herein, we propose a multiplex Codetection platform of proteins and RNAs (Co-PAR) for EVs. Co-PAR adopted a pair of antibody-DNA probes to recognize the same target protein, which in turn formed a double-stranded DNA. Thus, the target protein could be quantified by detecting the double-stranded DNA via qPCR. Meanwhile, qRT-PCR simultaneously quantified the target RNAs. Thus, with a regular qPCR instrument, Co-PAR enabled the codetection of multiplex proteins and RNAs, with the sensitivity of 102 EVs/μL (targeting CD63) and 1 EV/μL (targeting snRNA U6). We analyzed the coexpressions of three protein markers (CD63, GPC-1, HER2) and three RNA markers (snRNA U6, GPC-1 mRNA, miR-10b) on EVs from three pancreatic cell lines and 30 human plasma samples using Co-PAR. The diagnostic accuracy of the 6-biomarker combination reached 92.9%, which was at least 6.2% higher than that of 3-biomarker combinations and at least 13.5% higher than that of 6 single biomarkers. Co-PAR, as a multiparameter detection platform for EVs, has great potential in early disease diagnosis.

show abstract

“…The Disease section allows exploration of protein levels and proteome signatures in blood in patients with different diseases. In the first release of this new section, plasma profiles of 1463 proteins from 1400 cancer patients representing 12 major cancer types were measured in blood plasma using proximity extension assay (Olink) and targeted proteomics . Differential expression profiling and machine learning-based disease prediction highlighted proteins associated with each of the analyzed cancer types, constituting panels of proteins suitable for future diagnostic studies.…”

Section: Antibody Pillarmentioning

confidence: 99%

The 2023 Report on the Proteome from the HUPO Human Proteome Project

Omenn,

Lane,

Overall

et al. 2024

J. Proteome Res.

View full text Add to dashboard Cite

Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1–4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

show abstract

Next generation pan-cancer blood proteome profiling using proximity extension assay

Cited by 18 publications

References 39 publications

Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank

Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank

Codetection of Proteins and RNAs on Extracellular Vesicles for Pancreatic Cancer Early Diagnosis

The 2023 Report on the Proteome from the HUPO Human Proteome Project

Contact Info

Product

Resources

About