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- Alvez, Maria Bueno, et al.
(author)
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Next generation pan-cancer blood proteome profiling using proximity extension assay
- 2023
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In: Nature Communications. - : Springer Nature. - 2041-1723. ; 14:1
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Journal article (peer-reviewed)abstract
- A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed.
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| 3. |
- Gyllensten, Ulf B., et al.
(author)
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Next Generation Plasma Proteomics Identifies High-Precision Biomarker Candidates for Ovarian Cancer
- 2022
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In: Cancers. - : MDPI AG. - 2072-6694. ; 14:7
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Journal article (peer-reviewed)abstract
- Simple Summary Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30-50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. The aim of our study was to broadly measure protein biomarkers to find tests for the early detection of ovarian cancer. We found that combinations of 4-7 protein biomarkers can provide highly accurate detection of early- and late-stage ovarian cancer compared to benign conditions. The performance of the tests was then validated in a second independent cohort. Background: Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30-50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. Methods: We employed the Explore PEA technology for high-precision analysis of 1463 plasma proteins and conducted a discovery and replication study using two clinical cohorts of previously untreated patients with benign or malignant ovarian tumours (N = 111 and N = 37). Results: The discovery analysis identified 32 proteins that had significantly higher levels in malignant cases as compared to benign diagnoses, and for 28 of these, the association was replicated in the second cohort. Multivariate modelling identified three highly accurate models based on 4 to 7 proteins each for separating benign tumours from early-stage and/or late-stage ovarian cancers, all with AUCs above 0.96 in the replication cohort. We also developed a model for separating the early-stage from the late-stage achieving an AUC of 0.81 in the replication cohort. These models were based on eleven proteins in total (ALPP, CXCL8, DPY30, IL6, IL12, KRT19, PAEP, TSPAN1, SIGLEC5, VTCN1, and WFDC2), notably without MUCIN-16. The majority of the associated proteins have been connected to ovarian cancer but not identified as potential biomarkers. Conclusions: The results show the ability of using high-precision proteomics for the identification of novel plasma protein biomarker candidates for the early detection of ovarian cancer.
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| 4. |
- Thrane, Kim, 1984-
(author)
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Exploring Biological Systems using Spatial Transcriptomic Technologies
- 2022
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Doctoral thesis (other academic/artistic)abstract
- The transcriptome and the cells’ spatial organization are important determinants for the functions of biological systems, such as a tumor, brain, or skin tissue. Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for profiling the transcriptome of individual cells. The nuanced characterization of cell types and states enabled by scRNA-seq has revolutionized our understanding of biological systems. However, these methods rely on the dissociation of tissues into single cells whereby spatial context is lost. Recent advancements have resulted in technologies that retain and associate spatial information with the gene expression of tissues, which has permitted the delineation of biological systems at an unprecedented level. The Spatial Transcriptomics (ST) technology offers transcriptome profiling across thousands of subareas of a tissue section by capturing mRNA in situ and sequencing ex situ.In Paper I, ST was used to explore heterogeneity in lymph node metastases of human cutaneous malignant melanoma. A data-driven analysis approach revealed inter- and intratumor heterogeneity in the examined tumor tissue, whereas the stromal tissue exhibited similar gene expression across patients. Paper II presents an integration of ST, scRNA-seq, and spatial protein analysis to characterize human cutaneous squamous cell carcinoma. The spatial resolution of ST is not at the single-cell level; however, this multimodal approach allowed for the identification of tumor subpopulations and revealed the niches in which they reside. In Paper III, ST and scRNA-seq data were generated to build an atlas of human skin. The combined data was used to map cell-type abundance and intercellular communications in homeostasis. Moreover, cell-of-origin analysis allowed for the identification of candidate cell types accountable for human genetic skin diseases. Paper IV introduces Spatial VDJ, a technique for spatial analysis of B and T cell antigen receptor transcripts, hence determining the position of lymphocyte clones. The spatial VDJ technique was applied to human tonsil and human breast cancer tissues, and this revealed enrichment of immunoglobulin clones in distinct spatial regions. Finally, Paper V explores an alternative protocol for ST that uses long-read sequencing to enable spatial isoform profiling in tissue sections. The protocol was applied to mouse brain and identified genes with spatially distinct alternative isoform expression. Additionally, the full-length transcript information was used to explore RNA editing events across different anatomical regions of the mouse brain.
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| 5. |
- Yang, Zhijian, et al.
(author)
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Genetic Landscape of the ACE2 Coronavirus Receptor
- 2022
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In: Circulation. - : Ovid Technologies (Wolters Kluwer Health). - 0009-7322 .- 1524-4539. ; 30:SUPPL 1, s. 36-36
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Journal article (peer-reviewed)abstract
- Background: SARS-CoV-2, the causal agent of COVID-19, enters human cells using the ACE2 (angiotensin-converting enzyme 2) protein as a receptor. ACE2 is thus key to the infection and treatment of the coronavirus. ACE2 is highly expressed in the heart and respiratory and gastrointestinal tracts, playing important regulatory roles in the cardiovascular and other biological systems. However, the genetic basis of the ACE2 protein levels is not well understood.Methods: We have conducted the largest genome-wide association meta-analysis of plasma ACE2 levels in >28 000 individuals of the SCALLOP Consortium (Systematic and Combined Analysis of Olink Proteins). We summarize the cross-sectional epidemiological correlates of circulating ACE2. Using the summary statistics-based high-definition likelihood method, we estimate relevant genetic correlations with cardiometabolic phenotypes, COVID-19, and other human complex traits and diseases. We perform causal inference of soluble ACE2 on vascular disease outcomes and COVID-19 severity using mendelian randomization. We also perform in silico functional analysis by integrating with other types of omics data.Results: We identified 10 loci, including 8 novel, capturing 30% of the heritability of the protein. We detected that plasma ACE2 was genetically correlated with vascular diseases, severe COVID-19, and a wide range of human complex diseases and medications. An X-chromosome cis-protein quantitative trait loci-based mendelian randomization analysis suggested a causal effect of elevated ACE2 levels on COVID-19 severity (odds ratio, 1.63 [95% CI, 1.10-2.42]; P=0.01), hospitalization (odds ratio, 1.52 [95% CI, 1.05-2.21]; P=0.03), and infection (odds ratio, 1.60 [95% CI, 1.08-2.37]; P=0.02). Tissue- and cell type-specific transcriptomic and epigenomic analysis revealed that the ACE2 regulatory variants were enriched for DNA methylation sites in blood immune cells.Conclusions: Human plasma ACE2 shares a genetic basis with cardiovascular disease, COVID-19, and other related diseases. The genetic architecture of the ACE2 protein is mapped, providing a useful resource for further biological and clinical studies on this coronavirus receptor.
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