23rd Human Proteome Organization World Congress

Prof

Protein expression regulation in health and disease

Keynote speaker

Keynote lecture

Assoc. Professor

Multi-omics analysis in CTC-based liquid biopsy

Keynote Lecture

A specialized mechanism that reprograms anchorage dependency of solid tumor cells into circulating tumor cells (CTCs) during the metastatic cascade remains elusive. Using multi-omics approaches in liquid biopsy, we discovered a biological phenomenon referred to as Adherent-to-Suspension Transition (AST) that reprograms adherent cells into suspension cells via aberrant induction of hematopoietic transcriptional regulators, IKZF1, NFE2, BTG2, and IRF8, which are hijacked by solid tumor cells to disseminate into CTCs. During dissemination, tumor microenvironment triggers the epigenetic alteration of AST factors in the invasive front of solid tumors to evoke spontaneous cell-matrix dissociation, acquire anoikis resistance, and bypass immune surveillance of CTCs, however, in the absence of lineage differentiation and EMT. By performing scRNAseq in a cohort of de novo metastatic breast cancer patients, which consists tissue- and liquid biopsy paired specimens of primary tumor, CTCs, and metastatic lesions, we uncovered how Adherent-Suspension Plasticity (ASP) regulated anchorage plasticity during the dissemination and colonization process within the metastatic cascade. Finally, we demonstrate therapeutic strategies that target AST factors to specifically abrogate CTC formation and suppress distant metastases.Together, our finding underscores the mechanism that reprograms anchorage dependency of cancer cells and highlights undescribed hematopoietic targets for the development of anti-metastatic therapy. Furthermore, ASP opens unexplored scenarios of how reprogramming anchorage dependency in diverse physiological events orchestrates development, organ morphogenesis, and immune cell activation.

Prof. Dr.

An integrated landscape of mRNA and protein isoforms

Multiomics Approaches

Cellular processes such as alternative splicing (AS) and proteolytic processing generate various protein isoforms from a single protein-coding gene. Studies using high-throughput sequencing indicate that &gt; 95% of multi-exon genes in humans undergo AS [1,2]. In addition, proteolytic processing contributes to proteome complexity and is involved in processes like cell cycle regulation, cell signaling, and apoptosis. Current proteomic methods are not well suited to detect protein isoforms. Standard shotgun (bottom-up) proteomics involves digesting proteins into peptides. Although this approach identifies many proteins, it results in a loss of isoform information [3]. In contrast, mass spectrometric analysis of intact proteins (top-down proteomics) can distinguish protein isoforms but only covers a small subset of the proteome [4]. Here, we developed peptide correlation profiling (PepCP) as a method to obtain protein-level information from peptide-centric (bottom-up proteomic) data: First, proteins are fractionated by SDS-PAGE into polypeptides of different lengths. Second, individual protein fractions are digested into peptides. Third, peptides are identified and quantified across all fractions using quantitative mass spectrometry-based proteomics. Finally, peptide abundance profiles across fractions are analyzed to obtain protein-level information. We combined PepCP with long-read RNA sequencing to provide an integrated landscape of mRNA and protein isoforms in human RPE-1 cells. We established a reference set of 45,223 full-length transcripts and developed a computational framework to automatically detect different proteoforms at both the mRNA level (32612 protein isoforms from 12,400 unique genes) and protein level (16,983 protein isoforms from 8,168 unique genes), providing the largest available integrated dataset. Our data captures many well-known protein isoforms created by AS, alternative translation, and protein processing. Most of the detectable alternatively spliced transcripts are indeed detected at the protein level. In addition, we provide evidence for numerous new protein isoforms presumably generated by protein processing that cannot be observed at the mRNA level. Our data provides a unique resource of integrated mRNA and protein isoforms and paves the way for a deeper understanding of proteome complexity. 1. Pan, Q. et al. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet 40, 1413–1415 (2008). 2. Wang, E. T. et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008). 3. Sinitcyn, P. et al. Global detection of human variants and isoforms by deep proteome sequencing. Nat Biotechnol 41, 1776–1786 (2023). 4. Tran, J. C. et al. Mapping intact protein isoforms in discovery mode using top-down proteomics. Nature 480, 254-258 (2011).

Oral presentation

Multiomics characterization of in vivo resistance to anti-VEGF therapy in ovarian carcinoma

Epithelial ovarian cancer (OC) is the most lethal gynecologic malignancy in developed countries. Due to the lack of a screening strategy, the disease is usually diagnosed at an advanced stage and has a poor prognosis. In the advanced stage, antiangiogenic therapy combined with platinum-based chemotherapy represents a first-line clinical treatment option, but despite initial clinical benefit, resistance eventually occurs. The aim of this study is to investigate the mechanisms behind anti-VEGF therapy resistance in OC, by generating and analyzing multiomics data obtained from patient-derived xenografts (PDXs). We used a panel of seven PDXs, originally derived from the ascitic fluid of high-grade serous OC (HGSOC) patients and propagated in immune deficient mice. To a subset of the mice in each PDX group, we administered bevacizumab, a monoclonal antibody targeting VEGFA. To comprehensively profile the treated and wild-type mice within each group, we performed RNA-Seq, mass spectrometry (MS) proteomics, and whole exome sequencing (WES). In line with clinical data, the in vivo experiment reveals two mechanisms of resistance: three PDXs were intrinsically resistant to anti-VEGF therapy and the other four PDXs responded in the beginning but eventually developed resistance. To understand the reasons of these two mechanisms, we explore and integrate the three different layers of omics data. We analyze the mutational profiles of the different samples and identify genes differentially mutated in treated vs wild-type samples, and employ a proteogenomics approach to enhance the accuracy of peptide identification from MS data. We also examine the transcriptomics and proteomics profiles across tumors and replicates and identify genes potentially involved in the mechanisms of resistance. Genomic data reveals high mutational rates in all PDX models, as described in other studies on HGSOC. Mutations are particularly enriched in MUC, HLA and T53 genes. No significant mutations are enriched or selected during antiangiogenic therapy, suggesting that methylation might play an important role. Transcriptomics and proteomics data show that non-responders&quot; models have a poor modulation at the transcriptome and proteome levels. Conversely, acquired resistant tumors display a wide transcriptome and proteome rewiring.Our data indicates that acquired resistance models have a strong upregulation of interferon signatures at both transcriptomics and proteomics levels. In addition, acquired resistance models downregulate cell cycle and DNA packaging signatures as a mechanism of drug adaptation. RNA splicing analysis uncovers that one model is producing a splicing VEGFA variant as a mechanism to escape VEGFA inhibition, while other models adapt to antiangiogenic therapy by modulating their metabolism. After disclosing the mechanisms of resistance, we will validate our findings in vivo through combination treatment and survival analysis.

Prof.

Multi-omics biomarker approaches in precision medicine

IntroductionExponential developments in molecular omics technologies such as mass spectrometry and next generation sequencing have enabled us to obtain increasing insights in the molecular components of human biology and their interactions. Novel personalized diagnostics and high precision therapies that modulate selected disease mechanisms are now driving the new paradigm of precision medicine. However, we are rediscovering that human biology is a highly complex system and that we need multiple viewing angles to begin to understand disease mechanisms, identify its key nodes and define optimal personalized therapies. Measuring health using multiple biomarkers in a multi-omics approach can be a powerful way to achieve this. Proteomics takes up a unique part in multi-omics approaches, as it provides insights in enzymatic and structural components of biological pathways, yielding candidate biomarkers for diagnosis, prognosis, therapy prediction and monitoring. Optimally, multi-omics biomarker discovery, validation and development to clinical applications should be done in an integrated manner.MethodsIn the selected case studies that will be presented, we applied an LC-MS proteomics workflow using the Evosep One LC (Evosep) and timsTOF Pro MS (Bruker Daltonics), in combination with the PaSER (Parallel Search Engine in Real-time; Bruker Daltonics) platform that enables real-time processing of PASEF DDA and dia-PASEF spectral data streamed from the timsTOF Pro to the PaSER box. Proteomics analysis was aimed at either expression proteomics or glycoproteomics in untargeted or targeted (glyco)peptide analysis. Other omics analysis (genomics by WES, methylomics by ME-Seq, transcriptomics by RNA-Seq, metabolomics by (un)targeted LC-MS, lipidomics by (un)targeted LC-MS, glycoproteomics by (un)targeted LC-MS) were performed at laboratories that are part of the Netherlands X-omics Consortium or as part of the EATRIS Biomarker Platform. Omics data analyses using various workflows including Multi-Omics Factor Analysis and Similarity Network Fusion were done at the X-omics data team at Radboudumc. ResultsLessons learned from two multi-omics case studies will be presented. A cohort of 120 healthy individuals was analysed on 7 omics levels, yielding information on natural variation and inter-individual differences of analytes in a healthy population. Induced pluripotent stem cells derived from patients with three types of inherited diseases were analysed by 8 omics platforms, yielding information on differences between patients and between iPSC clones. Two examples will be discussed where targeted proteomics analysis of (glyco)peptides is being translated to certified clinical diagnostics.

Conference room 1-2

Session

Multiomics approaches and data integration

Session
Multiomics approaches and data integration

Appointment

Chairs

Program

SessionMultiomics approaches and data integration

Appointment

Chairs

Program

Session
Multiomics approaches and data integration