Proteomic Profiling of Colorectal Adenomas Identifies a Predictive Risk Signature for Development of Metachronous Advanced Colorectal Neoplasia

Bech, Jacob Mathias; Terkelsen, Thilde; Bartels, Annette Snejbjerg; Coscia, Fabian; Doll, Sophia; Zhao, Siqi; Zhang, Zhaojun; Brünner, Nils; Lindebjerg, Jan; Madsen, Gunvor Iben; Fang, Xiangdong; Mann, Matthias; Moreira, José M.A.

doi:10.1053/j.gastro.2023.03.208

Cited by 16 publications

(4 citation statements)

References 41 publications

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“…To investigate molecular protein signatures associated with disease progression in CRA, we employed a retrospective study design that comprised a cohort of male individuals who had undergone polypectomy procedures for CRA removal (Bech et al, 2023). We selected a sub-cohort of nine patients that all had high-grade (HG) dysplasia adenomas as verified by a pathologist using morphological criteria and immunohistochemical (IHC) analysis, but had heterogeneous clinical outcomes.…”

Section: Resultsmentioning

confidence: 99%

Spatial characterization and stratification of colorectal adenomas by Deep Visual Proteomics

Kabatnik,

Post,

Drici

et al. 2023

Preprint

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Colorectal adenomas (CRA) are precursor lesions of the colon that may progress to adenocarcinomas, with patients currently categorized into risk groups by morphological features. We aimed to establish a molecular feature-based risk allocation framework towards improved patient stratification. Deep Visual Proteomics (DVP) is a novel approach that combines image-based artificial intelligence with automated microdissection and ultra-high sensitive mass spectrometry. Here we used DVP on formalin-fixed, paraffin-embedded (FFPE) CRA tissues from nine patients, immunohistologically stained for Caudal-type homeobox 2 (CDX2), a protein implicated in colorectal cancer, enabling the characterization of cellular heterogeneity within distinct tissue regions and across patients. DVP seamlessly integrated with current pathology workflows and equipment, identifying DMBT1, MARCKS and CD99 as correlated with disease recurrence history, making them potential markers of risk stratification. DVP uncovered a metabolic switch towards anaerobic glycolysis in areas of high dysplasia, which was specific for cells with high CDX2 expression. Our findings underscore the potential of spatial proteomics to refine early-stage detection and contribute to personalized patient management strategies and provided novel insights into metabolic reprogramming.

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Section: Resultsmentioning

confidence: 99%

Spatial characterization and stratification of colorectal adenomas by Deep Visual Proteomics

Kabatnik,

Post,

Drici

et al. 2023

Preprint

Self Cite

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“…The normalized data were then log2 transformed. Since this data had multiple MS runs and missing values, we used limma 59 – 61 package (version 3.58.1) to identify proteins whose mean levels are significantly different between LTNPs and RPs. We first used lmFit function in limma package to fit an additive linear model with a fixed group effect and a fixed MS run effect.…”

Section: Methodsmentioning

confidence: 99%

RPLP1 restricts HIV-1 transcription by disrupting C/EBPβ binding to the LTR

Yang,

Wang,

et al. 2024

Nat Commun

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Long-term non-progressors (LTNPs) of HIV-1 infection may provide important insights into mechanisms involved in viral control and pathogenesis. Here, our results suggest that the ribosomal protein lateral stalk subunit P1 (RPLP1) is expressed at higher levels in LTNPs compared to regular progressors (RPs). Functionally, RPLP1 inhibits transcription of clade B HIV-1 strains by occupying the C/EBPβ binding sites in the viral long terminal repeat (LTR). This interaction requires the α-helixes 2 and 4 domains of RPLP1 and is evaded by HIV-1 group M subtype C and group N, O and P strains that do not require C/EBPβ for transcription. We further demonstrate that HIV-1-induced translocation of RPLP1 from the cytoplasm to the nucleus is essential for antiviral activity. Finally, knock-down of RPLP1 promotes reactivation of latent HIV-1 proviruses. Thus, RPLP1 may play a role in the maintenance of HIV-1 latency and resistance to RPLP1 restriction may contribute to the effective spread of clade C HIV-1 strains.

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“…Additionally, the use of formalin-fixed paraffin-embedded (FFPE) tissues has expanded access to larger cohorts [68]. In this regard, mass spectrometry-based proteomics combined with machine learning analysis of FFPE tissues has been able to distinguish groups of proteins capable of predicting the future appearance of high-grade adenomas or CRC development [50].…”

Section: Diagnostic Biomarkermentioning

confidence: 99%

Biomarker Identification through Proteomics in Colorectal Cancer

Martín-García,

García-Aranda,

Redondo

2024

IJMS

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Colorectal cancer (CRC) is a devastating disease that ranks third in diagnosis and as the second leading cause of cancer-related deaths. The early detection of CRC has been shown to be the most effective strategy to improve treatment outcomes and patient survival. Therefore, current lines of research focus on the development of reliable diagnostic tools. Targeted therapies, in combination with standard chemotherapy and immune checkpoint inhibitors, have emerged as promising treatment protocols in CRC. However, their effectiveness is linked to the molecular characteristics of each patient. The importance of discovering biomarkers that help predict response to therapies and assess prognosis is evident as they allow for a fundamental step towards personalized care and successful treatments. Among the ongoing efforts to identify them, mass spectrometry-based translational proteomics presents itself as a unique opportunity as it enables the discovery and application of protein biomarkers that may revolutionize the early detection and treatment of CRC. Our objective is to show the most recent studies focused on the identification of CRC-related protein markers, as well as to provide an updated view of advances in the field of proteomics and cancer.

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Proteomic Profiling of Colorectal Adenomas Identifies a Predictive Risk Signature for Development of Metachronous Advanced Colorectal Neoplasia

Cited by 16 publications

References 41 publications

Spatial characterization and stratification of colorectal adenomas by Deep Visual Proteomics

Spatial characterization and stratification of colorectal adenomas by Deep Visual Proteomics

RPLP1 restricts HIV-1 transcription by disrupting C/EBPβ binding to the LTR

Biomarker Identification through Proteomics in Colorectal Cancer

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