Expanding the landscape of lysine acetylation stoichiometry and clinical impact.

Lysine acetylation, once viewed primarily as a histone mark, is now recognized as a widespread regulator of protein function. Recent breakthroughs in chemical labeling, isotopic tagging workflows, and data-independent acquisition mass spectrometry enable precise, site-specific quantification of acetylation stoichiometry. This quantitative "acetylomics" approach reveals a "rheostat" model, where most acetylation sites exhibit low occupancy, acting as subtle modulators, while a subset of highly acetylated lysines (e.

A perspective on integrating digital pathology, proteomics, clinical data and AI analytics in cancer research.

Nearly 40 % of individuals will be diagnosed with cancer in their lifetime, translating to an estimated 20 million new cases annually. Despite remarkable therapeutic advances, only 15-20 % of patients achieve durable responses to immunotherapy, and the high cost of treatment (illustrated by immune checkpoint inhibitors like pembrolizumab and nivolumab, totaling roughly $191,000 per year) remains a formidable global challenge. The convergence of digital pathology, high-throughput molecular profiling, and advanced computational strategies has the potential to transform cancer research.

Mitochondrial proteome landscape unveils key insights into melanoma severity and treatment strategies.

Background: Melanoma, the deadliest form of skin cancer, exhibits resistance to conventional therapies, particularly in advanced and metastatic stages. Mitochondrial pathways, including oxidative phosphorylation and mitochondrial translation, have emerged as critical drivers of melanoma progression and therapy resistance. This study investigates the mitochondrial proteome in melanoma to uncover novel therapeutic vulnerabilities.

KChIP3 fosters neuroinflammation and synaptic dysfunction in the 5XFAD mouse model of Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by β-amyloid (βA) accumulation, neuroinflammation, excessive synaptic pruning, and cognitive decline. Despite extensive research, effective treatments remain elusive. Here, we identify potassium channel-interacting protein 3 (KChIP3) as a key driver of AD pathology using the 5XFAD mouse model.

The melanoma MEGA-study: Integrating proteogenomics, digital pathology, and AI-analytics for precision oncology.

Melanoma remains the most aggressive form of skin cancer, characterized by high metastatic potential, genetic heterogeneity, and resistance to conventional therapies. The Melanoma MEGA-Study is a multi-center initiative designed to address these clinical challenges by integrating advanced proteogenomic profiling, clinical metadata, with AI-driven digital pathology and machine learning analytics, aiming to enhance personalized treatment strategies and improve patient outcomes. Between 2013 and 2022, a cohort of 1653 melanoma patients each contributed a primary tumor sample, with 361 providing 819 metastatic tumor samples.

Melanoma Proteomics Unveiled: Harmonizing Diverse Data Sets for Biomarker Discovery and Clinical Insights via MEL-PLOT.

Using several melanoma proteomics data sets we created a single analysis platform that enables the discovery, knowledge build, and validation of diagnostic, predictive, and prognostic biomarkers at the protein level. Quantitative mass-spectrometry-based proteomic data was obtained from five independent cohorts, including 489 tissue samples from 394 patients with accompanying clinical metadata. We established an interactive R-based web platform that enables the comparison of protein levels across diverse cohorts, and supports correlation analysis between proteins and clinical metadata including survival outcomes.

Comprehensive biobanking strategy with clinical impact at the European Cancer Moonshot Lund Center.

This white paper presents a comprehensive biobanking framework developed at the European Cancer Moonshot Lund Center that merges rigorous sample handling, advanced automation, and multi-omic analyses to accelerate precision oncology. Tumor and blood-based workflows, supported by automated fractionation systems and standardized protocols, ensure the collection of high-quality biospecimens suitable for proteomic, genomic, and metabolic studies. A robust informatics infrastructure, integrating LIMS, barcoding, and REDCap, supports end-to-end traceability and realtime data synchronization, thereby enriching each sample with critical clinical metadata.

Proteogenomic Profiling of Treatment-Naïve Metastatic Malignant Melanoma.

Background: Melanoma is a highly heterogeneous disease, and a deeper molecular classification is essential for improving patient stratification and treatment approaches. Here, we describe the histopathology-driven proteogenomic landscape of 142 treatment-naïve metastatic melanoma samples to uncover molecular subtypes and clinically relevant biomarkers.

Methods: We performed an integrative proteogenomic analysis to identify proteomic subtypes, assess the impact of BRAF V600 mutations, and study the molecular profiles and cellular composition of the tumor microenvironment.

TMEM160 Promotes Tumor Growth in Lung Adenocarcinoma and Cervical Adenocarcinoma Cell Lines.

The Chromosome-Centric Human Proteome Project (C-HPP) is an international initiative. It aims to create a protein list expressed in human cells by each chromosomal and mitochondrial DNA to enhance our understanding of disease mechanisms, akin to the gene list generated by the Human Genome Project. Transmembrane protein 160 (TMEM160) is a member of the transmembrane proteins (TMEM) family.

Unbiased Drug Target Prediction Reveals Sensitivity to Ferroptosis Inducers, HDAC and RTK Inhibitors in Melanoma Subtypes.

Background: The utilization of PD1 and CTLA4 inhibitors has revolutionized the treatment of malignant melanoma (MM). However, resistance to targeted and immune-checkpoint-based therapies still poses a significant problem.

Objective: Here, we mine large-scale MM proteogenomic data to identify druggable targets and forecast treatment efficacy and resistance.

Identification of genetic fingerprint of type I interferon therapy in visceral metastases of melanoma.

Malignant melanoma is a difficult-to-treat skin cancer with increasing incidence worldwide. Although type-I interferon (IFN) is no longer part of guidelines, several melanoma patients are treated with type-I interferon (IFN) at some point of the disease, potentially affecting its genetic progression. We run genome-wide copy number variation (CNV) analysis on previously type-I IFN-treated (n = 17) and control (n = 11) visceral metastases of melanoma patients.

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts.

Introduction: While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy.

Methods: Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders.

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts.

While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy. Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders.

Identifying Ferroptosis Inducers, HDAC, and RTK Inhibitor Sensitivity in Melanoma Subtypes through Unbiased Drug Target Prediction.

Unlabelled: The utilization of PD1 and CTLA4 inhibitors has revolutionized the treatment of malignant melanoma (MM). However, resistance to targeted and immune-checkpoint-based therapies still poses a significant problem. Here we mine large scale MM proteogenomic data integrating it with MM cell line dependency screen, and drug sensitivity data to identify druggable targets and forecast treatment efficacy and resistance.

Dynamics of Mitochondrial Proteome and Acetylome in Glioblastoma Cells with Contrasting Metabolic Phenotypes.

Glioblastoma, a type of cancer affecting the central nervous system, is characterized by its poor prognosis and the dynamic alteration of its metabolic phenotype to fuel development and progression. Critical to cellular metabolism, mitochondria play a pivotal role, where the acetylation of lysine residues on mitochondrial enzymes emerges as a crucial regulatory mechanism of protein function. This post-translational modification, which negatively impacts the mitochondrial proteome's functionality, is modulated by the enzyme sirtuin 3 (SIRT3).

Proteomic analysis across patient iPSC-based models and human post-mortem hippocampal tissue reveals early cellular dysfunction and progression of Alzheimer's disease pathogenesis.

The hippocampus is a primary region affected in Alzheimer's disease (AD). Because AD postmortem brain tissue is not available prior to symptomatic stage, we lack understanding of early cellular pathogenic mechanisms. To address this issue, we examined the cellular origin and progression of AD pathogenesis by comparing patient-based model systems including iPSC-derived brain cells transplanted into the mouse brain hippocampus.

CFN42 proteomes showed isoenzymes in free-living and symbiosis with a different transcriptional regulation inferred from a transcriptional regulatory network.

A comparative proteomic study at 6 h of growth in minimal medium (MM) and bacteroids at 18 days of symbiosis of CFN42 with the leguminous plant was performed. A gene ontology classification of proteins in MM and bacteroid, showed 31 and 10 pathways with higher or equal than 30 and 20% of proteins with respect to genome content per pathway, respectively. These pathways were for energy and environmental compound metabolism, contributing to understand how is adapted to the different conditions.

Lipid Metabolic Reprogramming Extends beyond Histologic Tumor Demarcations in Operable Human Pancreatic Cancer.

Unlabelled: Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest malignancies and potentially curable only with radical surgical resection at early stages. The tumor microenvironment has been shown to be central to the development and progression of PDAC. A better understanding of how early human PDAC metabolically communicates with its environment and differs from healthy pancreas could help improve PDAC diagnosis and treatment.

The Prognostic Relevance of PMCA4 Expression in Melanoma: Gender Specificity and Implications for Immune Checkpoint Inhibition.

PMCA4 is a critical regulator of Ca homeostasis in mammalian cells. While its biological and prognostic relevance in several cancer types has already been demonstrated, only preclinical investigations suggested a metastasis suppressor function in melanoma. Therefore, we studied the expression pattern of PMCA4 in human skin, nevus, as well as in primary and metastatic melanoma using immunohistochemistry.

Quantitative Proteomic Analysis of Cervical Cancer Tissues Identifies Proteins Associated With Cancer Progression.

Background/aim: To date, several proteomics studies in cervical cancer (CC) have focused mainly on squamous cervical cancer (SCC). Our study aimed to discover and clarify differences in SCC and CAD that may provide valuable information for the identification of proteins involved in tumor progression, in CC as a whole, or specific for SCC or CAD.

Materials And Methods: Total protein extracts from 15 individual samples corresponding to 5 different CC tissue types were compared with a non-cancerous control group using bidimensional liquid chromatography-mass spectrometry (2D LC-MS/MS), isobaric tags for relative and absolute quantitation (ITRAQ), principal component analysis (PCA) and gene set enrichment analysis (GSEA).

Distinct subcellular autophagy impairments in induced neurons from patients with Huntington's disease.

Huntington's disease is a neurodegenerative disorder caused by CAG expansions in the huntingtin (HTT) gene. Modelling Huntington's disease is challenging, as rodent and cellular models poorly recapitulate the disease as seen in ageing humans. To address this, we generated induced neurons through direct reprogramming of human skin fibroblasts, which retain age-dependent epigenetic characteristics.