Genomic Determinants of Protein Abundance Variation in Colorectal Cancer Cells

The Role of Proteomics and Genomics in the Development of Colorectal Cancer Diagnostic Tools and Potential New Treatments

The complex molecular mechanisms involving genetic and epigenetic modifications contribute to colorectal cancer (CRC), which remains a significant threat to world health. This review elucidates the role of proteomics and genomics in the progression, diagnosis, and treatment of colorectal cancers. All potential key pathways involved in CRC, including WNT, MAPK, PI3K, and TGF-β pathways, are reviewed with a systematic analysis, concluding with their involvement in tumorigenesis and therapeutic resistance. Emerging next-generation sequencing technologies revealed critical mutations that are relevant to CRC development. Proteomics has contributed to identifying biomarkers and post-translational modifications that hold promise for targeted therapies. Recent technological advances have provided functional insights into protein signaling networks and pathways through mass spectrometry and integrated proteogenomic approaches. This work emphasizes biomarker-driven translational efforts that integrate genomic insights with protein expression profiles to refine personalized treatments. The application of innovations in liquid biopsy and computational biology advocates for precision medicine paths to improve the outcomes for CRC. Now, pharmacoproteomics offers novel domains for drug discovery and resistance management and serves as a foundation for comprehensive CRC treatment paradigms.

A tissue-specific atlas of protein-protein associations enables prioritization of candidate disease genes

Despite progress in mapping protein-protein interactions, their tissue specificity is understudied. Here, given that protein coabundance is predictive of functional association, we compiled and analyzed protein abundance data of 7,811 proteomic samples from 11 human tissues to produce an atlas of tissue-specific protein associations. We find that this method recapitulates known protein complexes and the larger structural organization of the cell. Interactions of stable protein complexes are well preserved across tissues, while cell-type-specific cellular structures, such as synaptic components, are found to represent a substantial driver of differences between tissues. Over 25% of associations are tissue specific, of which <7% are because of differences in gene expression. We validate protein associations for the brain through cofractionation experiments in synaptosomes, curation of brain-derived pulldown data and AlphaFold2 modeling. We also construct a network of brain interactions for schizophrenia-related genes, indicating that our approach can functionally prioritize candidate disease genes in loci linked to brain disorders.

PBRM1 directs PBAF to pericentromeres and protects centromere integrity

The specialised structure of the centromere is critical for effective chromosome segregation, but its repetitive nature makes it vulnerable to rearrangements. Centromere fragility can drive tumorigenesis, but protective mechanisms preventing fragility are still not fully understood. The PBAF chromatin remodelling complex is frequently misregulated in cancer, but its role in cancer is incompletely characterized. Here, we identify PBAF as a protector of centromere and pericentromere structure with profound consequences for genome stability. A conserved feature of isogenic cell lines lacking PBRM1, a subunit of PBAF, is compromised centromere and pericentromere integrity. PBAF is present at these regions, and binding patterns of PBAF and H3K9 methylation change when PBRM1 is absent. PBRM1 loss creates a dependence on the spindle assembly checkpoint, which represents a therapeutic vulnerability. Importantly, we find that even in the absence of any perturbations, PBRM1 loss leads to centromere fragility, thus identifying a key player in centromere protection.

Spatial deconvolution from bulk DNA methylation profiles determines intratumoral epigenetic heterogeneity

BACKGROUND

Intratumoral heterogeneity emerges from accumulating genetic and epigenetic changes during tumorigenesis, which may contribute to therapeutic failure and drug resistance. However, the lack of a quick and convenient approach to determine the intratumoral epigenetic heterogeneity (eITH) limit the application of eITH in clinical settings. Here, we aimed to develop a tool that can evaluate the eITH using the DNA methylation profiles from bulk tumors.

METHODS

Genomic DNA of three laser micro-dissected tumor regions, including digestive tract surface, central bulk, and invasive front, was extracted from formalin-fixed paraffin-embedded sections of colorectal cancer patients. The genome-wide methylation profiles were generated with methylation array. The most variable methylated probes were selected to construct a DNA methylation-based heterogeneity (MeHEG) estimation tool that can deconvolve the proportion of each reference tumor region with the support vector machine model-based method. A PCR-based assay for quantitative analysis of DNA methylation (QASM) was developed to specifically determine the methylation status of each CpG in MeHEG assay at single-base resolution to realize fast evaluation of epigenetic heterogeneity.

RESULTS

In the discovery set with 79 patients, the differentially methylated CpGs among the three tumor regions were found. The 7 most representative CpGs were identified and subsequently selected to develop the MeHEG algorithm. We validated its performance of deconvolution of tumor regions in an independent cohort. In addition, we showed the significant association of MeHEG-based epigenetic heterogeneity with the genomic heterogeneity in mutation and copy number variation in our in-house and TCGA cohorts. Besides, we found that the patients with higher MeHEG score had worse disease-free and overall survival outcomes. Finally, we found dynamic change of epigenetic heterogeneity based on MeHEG score in cancer cells under the treatment of therapeutic drugs.

CONCLUSION

By developing a 7-loci panel using a machine learning approach combined with the QASM assay for PCR-based application, we present a valuable method for evaluating intratumoral heterogeneity. The MeHEG algorithm offers novel insights into tumor heterogeneity from an epigenetic perspective, potentially enriching current knowledge of tumor complexity and providing a new tool for clinical and research applications in cancer biology.

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

PhoXplex: Combining Phospho-enrichable Cross-Linking with Isobaric Labeling for Quantitative Proteome-Wide Mapping of Protein Interfaces

Integrating cross-linking mass spectrometry (XL-MS) into structural biology workflows provides valuable information about the spatial arrangement of amino acid stretches, which can guide elucidation of protein assembly architecture. Additionally, the combination of XL-MS with peptide quantitation techniques is a powerful approach to delineate protein interface dynamics across diverse conditions. While XL-MS is increasingly effective with isolated proteins or small complexes, its application to whole-cell samples poses technical challenges related to analysis depth and throughput. The use of enrichable cross-linkers has greatly improved the detectability of protein interfaces in a proteome-wide scale, facilitating global protein-protein interaction mapping. Therefore, bringing together enrichable cross-linking and multiplexed peptide quantification is an appealing approach to enable comparative characterization of structural attributes of proteins and protein interactions. Here, we combined phospho-enrichable cross-linking with TMT labeling to develop a streamline workflow (PhoXplex) for the detection of differential structural features across a panel of cell lines in a global scale. We achieved deep coverage with quantification of over 9000 cross-links and long loop-links in total including potentially novel interactions. Overlaying AlphaFold predictions and disorder protein annotations enables exploration of the quantitative cross-linking data set, to reveal possible associations between mutations and protein structures. Lastly, we discuss current shortcomings and perspectives for deep whole-cell profiling of protein interfaces at large-scale.

Miniaturized protein profiling permits targeted signaling pathway analysis in individual circulating tumor cells to improve personalized treatment

BACKGROUND

Traditional genomic profiling and mutation analysis of single cells like Circulating Tumor Cells (CTCs) fails to capture post-translational and functional alterations of proteins, often leading to limited treatment efficacy. To overcome this gap, we developed a miniaturized 'protein analysis on the single cell level' workflow-baptized ZeptoCTC. It integrates established technologies for single-cell isolation with sensitive Reverse Phase Protein Array (RPPA) analysis, thus enabling the comprehensive assessment of multiple protein expression and activation in individual CTCs.

METHODS

The ZeptoCTC workflow involves several critical steps. Firstly, individual cells are labeled and isolated. This is followed by cell lysis and the printing of true single cell lysate preparations onto a ZeptoChip using a modified micromanipulator, CellCelector™. The printed lysates then undergo fluorescence immunoassay RPPA protein detection using a ZeptoReader. Finally, signal quantification is carried out with Image J software, ensuring precise measurement of multiple protein levels.

RESULTS

The efficacy of ZeptoCTC was demonstrated through various applications. Initially, it was used for measuring EpCAM protein expression, a standard marker for CTC detection, revealing higher levels in single MCF-7 over MDA-MB-231 tumor cells. Furthermore, in Capivasertib (Akt-inhibitor)-treated MCF-7 single cells, ZeptoCTC detected a 2-fold increase in the pAkt/Akt ratio compared to control cells, and confirmed co-performed bulk-cell western blot analysis results. Notably, when applied to individual CTCs from metastasized breast cancer patients, ZeptoCTC revealed significant differences in protein activation levels, particularly in measured pAkt and pErk levels, compared to patient-matched WBCs. Moreover, it successfully differentiated between CTCs from patients with different Akt1 genotypes, highlighting its potential to determine the activation status of druggable cancer driving proteins for individual and targeted treatment decision making.

CONCLUSIONS

The ZeptoCTC workflow represents a valuable tool in single cell cancer research, crucial for personalized medicine. It permits detailed analysis of key proteins and their activation status of targeted, cancer-driven signaling pathways in single cell samples, aiding in understanding tumor response, progression, and treatment efficacy beyond bulk analysis. The method significantly advances clinical investigations in cancer, improving treatment precision and effectiveness. The workflow will be applicable to protein analysis on other types of single cells like relevant in stem cell, neuropathology and hemopoietic cell research.

From Crypts to Cancer: A Holistic Perspective on Colorectal Carcinogenesis and Therapeutic Strategies

Colorectal cancer (CRC) represents a significant global health burden, with high incidence and mortality rates worldwide. Recent progress in research highlights the distinct clinical and molecular characteristics of colon versus rectal cancers, underscoring tumor location's importance in treatment approaches. This article provides a comprehensive review of our current understanding of CRC epidemiology, risk factors, molecular pathogenesis, and management strategies. We also present the intricate cellular architecture of colonic crypts and their roles in intestinal homeostasis. Colorectal carcinogenesis multistep processes are also described, covering the conventional adenoma-carcinoma sequence, alternative serrated pathways, and the influential Vogelstein model, which proposes sequential APC, KRAS, and TP53 alterations as drivers. The consensus molecular CRC subtypes (CMS1-CMS4) are examined, shedding light on disease heterogeneity and personalized therapy implications.

Proceedings of the EuBIC-MS developers meeting 2023

The 2023 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers Meeting was held from January 15th to January 20th, 2023, in Congressi Stefano Franscin at Monte Verità in Ticino, Switzerland. The participants were scientists and developers working in computational mass spectrometry (MS), metabolomics, and proteomics. The 5-day program was split between introductory keynote lectures and parallel hackathon sessions focusing on "Artificial Intelligence in proteomics" to stimulate future directions in the MS-driven omics areas. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts and actively contribute to highly relevant research projects. We successfully produced several new tools applicable to the proteomics community by improving data analysis and facilitating future research.

mTORC1-Driven Protein Translation Correlates with Clinical Benefit of Capivasertib within a Genetically Preselected Cohort of PIK3CA-Altered Tumors

Capivasertib is a potent selective inhibitor of AKT. It was recently FDA approved in combination with fulvestrant to treat HR+, HER2-negative breast cancers with certain genetic alteration(s) activating the PI3K pathway. In phase I trials, heavily pretreated patients with tumors selected for activating PI3K pathway mutations treated with capivasertib monotherapy demonstrated objective response rates of <30%. We investigated the proteomic profile associated with capivasertib response in genetically preselected patients and cancer cell lines. We analyzed samples from 16 PIK3CA-mutated patient tumors collected prior to capivasertib monotherapy in the phase I trial. PI3K pathway proteins were precisely quantified with immuno-Matrix-Assisted Laser Desorption/Ionization-mass spectrometry (iMALDI-MS). Global proteomic profiles were also obtained. Patients were classified according to response to capivasertib monotherapy: "clinical benefit (CB)" (≥12 weeks without progression, n = 7) or "no clinical benefit (NCB)" (progression in <12 weeks, n = 9). Proteins that differed between the patient groups were subsequently quantified in AKT1- or PIK3CA-altered breast cancer cell lines with varying capivasertib sensitivity. The measured concentrations of AKT1 and AKT2 varied among the PIK3CA-mutated tumors but did not differ between the CB and NCB groups. However, analysis of the global proteome data showed that translational activity was higher in tumors of the NCB vs. CB group. When reproducibly quantified by validated LC-MRM-MS assays, the same proteins of interest similarly distinguished between capivasertib-sensitive versus -resistant cell lines. The results provide further evidence that increased mTORC1-driven translation functions as a mechanism of resistance to capivasertib monotherapy. Protein concentrations may offer additional insights for patient selection for capivasertib, even among genetically preselected patients.

SIGNIFICANCE

Capivasertib's first-in-class FDA approval demonstrates its promise, yet there remains an opportunity to optimize its use. Our results provide new evidence that proteomics can stratify genetically preselected patients on clinical benefit. Characterization of the same profile in cell lines furnishes additional validation. Among PIK3CA-altered tumors, increased mTORC1-driven translation appears to confer intrinsic resistance. Assessing mTORC1 activation could therefore prove a useful complement to the existing genetic selection strategy for capivasertib.

Illuminating phenotypic drug responses of sarcoma cells to kinase inhibitors by phosphoproteomics

Kinase inhibitors (KIs) are important cancer drugs but often feature polypharmacology that is molecularly not understood. This disconnect is particularly apparent in cancer entities such as sarcomas for which the oncogenic drivers are often not clear. To investigate more systematically how the cellular proteotypes of sarcoma cells shape their response to molecularly targeted drugs, we profiled the proteomes and phosphoproteomes of 17 sarcoma cell lines and screened the same against 150 cancer drugs. The resulting 2550 phenotypic profiles revealed distinct drug responses and the cellular activity landscapes derived from deep (phospho)proteomes (9-10,000 proteins and 10-27,000 phosphorylation sites per cell line) enabled several lines of analysis. For instance, connecting the (phospho)proteomic data with drug responses revealed known and novel mechanisms of action (MoAs) of KIs and identified markers of drug sensitivity or resistance. All data is publicly accessible via an interactive web application that enables exploration of this rich molecular resource for a better understanding of active signalling pathways in sarcoma cells, identifying treatment response predictors and revealing novel MoA of clinical KIs.

A proteomic landscape of pharmacologic perturbations for functional relevance

Pharmacological perturbation studies based on protein-level signatures are fundamental for drug discovery. In the present study, we used a mass spectrometry (MS)-based proteomic platform to profile the whole proteome of the breast cancer MCF7 cell line under stress induced by 78 bioactive compounds. The integrated analysis of perturbed signal abundance revealed the connectivity between phenotypic behaviors and molecular features in cancer cells. Our data showed functional relevance in exploring the novel pharmacological activity of phenolic xanthohumol, as well as the noncanonical targets of clinically approved tamoxifen, lovastatin, and their derivatives. Furthermore, the rational design of synergistic inhibition using a combination of histone methyltransferase and topoisomerase was identified based on their complementary drug fingerprints. This study provides rich resources for the proteomic landscape of drug responses for precision therapeutic medicine.

Detection of early prognostic biomarkers for metastasis of Ewing's sarcoma in pediatric patients

AIMS

Ewing's Sarcoma is an extremely aggressive tumor in children. The disease is associated with highly metastatic rate, especially at the time of diagnosis, contributing to a lower survival rate and poor prognosis. The study aimed to identify predictive biomarkers for metastatic Ewing's sarcoma through in-depth analysis of the plasma proteome profile of pediatric Ewing's sarcoma patients.

MAIN METHODS

Plasma samples from Ewing's sarcoma patients and control individuals were profiled using both shotgun and dimethyl-labeled proteomics analysis. Subsequently, Ewing's sarcoma patients were further stratified according to their metastatic state and chemotherapy response. Western blot was used for validation. Univariate and multivariate analyses were performed to determine proteome metastasis predictors. Receiver operating characteristic (ROC) analysis was done to assess the diagnostic significance of the potential plasma Ewing's sarcoma biomarkers.

KEY FINDINGS

Our results revealed a set of proteins significantly associated with the metastatic Ewing's sarcoma disease profile. These proteins include ceruloplasmin and several immunoglobulins. Additionally, our study disclosed significant differentially expressed proteins in pediatric Ewing's sarcoma, including CD5 antigen-like, clusterin, and dermcidin. Stable isotope dimethyl labeling and western blot further confirmed our results, strengthening the impact of such proteins in disease development. Furthermore, an unbiased ROC curve evaluated and confirmed the predictive power of these biomarker candidates.

SIGNIFICANCE

This study presented potential empirical predictive circulating biomarkers for determining the disease status of pediatric Ewing's sarcoma, which is vital for early prediction.

Whole transcriptome analysis in offspring whose fathers were exposed to a developmental insult: a novel avian model

Although the effects of paternal exposure to insults on the offspring received limited attention in the past, it is currently gaining interest especially after understanding the mechanisms which may mediate such exposure effects. In the current study, the well-controlled avian model (Fayoumi) was utilized to investigate the effects of paternal exposure to the developmental insult, chlorpyrifos on the offspring's gene expression via mRNA and small RNA sequencing. Numerous mRNA gene expression changes were detected in the offspring after paternal exposure to the developmental insult, especially in genes related to neurogenesis, learning and memory. qPCR analysis of several genes, that were significantly changed in mRNA sequencing, confirmed the results obtained in mRNA sequencing. On the other hand, small RNA sequencing did not identify significant microRNA genes expression changes in the offspring after paternal exposure to the developmental insult. The effects of the paternal exposure were more pronounced in the female offspring compared to the male offspring. The results identified expression alterations in major genes (some of which were pertinent to the functional changes observed in other forms of early developmental exposure) after paternal insult exposure and provided a direction for future studies involving the most affected genes.

A computational framework for the inference of protein complex remodeling from whole-proteome measurements

Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at defined quantitative ratios. Typically, global changes in protein complex composition are assessed with experimental approaches that tend to be time consuming. Here, we have developed a computational algorithm for the detection of altered protein complexes based on the systematic assessment of subunit ratios from quantitative proteomic measurements. We applied it to measurements from breast cancer cell lines and patient biopsies and were able to identify strong remodeling of HDAC2 epigenetic complexes in more aggressive forms of cancer. The presented algorithm is available as an R package and enables the inference of changes in protein complex states by extracting functionally relevant information from bottom-up proteomic datasets.

Unbiased differential proteomic profiling between cancer-associated fibroblasts and cancer cell lines

Cancer-associated fibroblasts (CAFs) are a key component of tumors. We aimed to profile the proteome of cancer cell lines representing three common cancer types (lung, colorectal and pancreatic) and a representative CAF cell line from each tumor type to gain insight into CAF function and novel CAF biomarkers. We used isobaric labeling, liquid chromatography and mass spectrometry to evaluate the proteome of 9 cancer and 3 CAF cell lines. Of the 9460 proteins evaluated, functional enrichment analysis revealed an upregulation of N-glycan biosynthesis and extracellular matrix proteins in CAFs. 85 proteins had 16-fold higher expression in CAFs compared to cancer cells, including previously known CAF markers like fibroblast activation protein (FAP). Novel overexpressed CAF biomarkers included heat shock protein β-6 (HSPB6/HSP20) and cyclooxygenase 1 (PTGS1/COX1). SiRNA knockdown of the genes encoding these proteins did not reduce contractility in lung CAFs, suggesting they were not crucial to this function. Immunohistochemical analysis of 30 tumor samples (10 lung, 10 colorectal and 10 pancreatic) showed restricted HSPB6 and PTGS1 expression in the stroma. Therefore, we describe an unbiased differential proteome analysis of CAFs compared to cancer cells, which revealed higher expression of HSPB6 and PTGS1 in CAFs. Data are available via ProteomeXchange (PXD040360). SIGNIFICANCE: Cancer-associated fibroblasts (CAFs) are highly abundant stromal cells present in tumors. CAFs are known to influence tumor progression and drug resistance. Characterizing the proteome of CAFs could give potential insights into new stromal drug targets and biomarkers. Mass spectrometry-based analysis comparing proteomic profiles of CAFs and cancers characterized 9460 proteins of which 85 proteins had 16-fold higher expression in CAFs compared to cancer cells. Further interrogation of this rich resource could provide insight into the function of CAFs and could reveal putative stromal targets. We describe for the first time that heat shock protein β-6 (HSPB6/HSP20) and cyclooxygenase 1 (PTGS1/COX1) are overexpressed in CAFs compared to cancer cells.

Integrative analysis of cancer dependency data and comprehensive phosphoproteomics data revealed the EPHA2-PARD3 axis as a cancer vulnerability in KRAS-mutant colorectal cancer

Colorectal cancer (CRC), a common malignant tumour of the gastrointestinal tract, is a life-threatening cancer worldwide. Mutations in KRAS and BRAF, the major driver mutation subtypes in CRC, activate the RAS pathway, contribute to tumorigenesis in CRC and are being investigated as potential therapeutic targets. Despite recent advances in clinical trials targeting KRASG12C or RAS downstream signalling molecules for KRAS-mutant CRC, there is a lack of effective therapeutic interventions. Therefore, understanding the unique molecular characteristics of KRAS-mutant CRC is essential for identifying molecular targets and developing novel therapeutic interventions. We obtained in-depth proteomics and phosphoproteomics quantitative data for over 7900 proteins and 38 700 phosphorylation sites in cells from 35 CRC cell lines and performed informatic analyses, including proteomics-based coexpression analysis and correlation analysis between phosphoproteomics data and cancer dependency scores of the corresponding phosphoproteins. Our results revealed novel dysregulated protein-protein associations enriched specifically in KRAS-mutant cells. Our phosphoproteomics analysis revealed activation of EPHA2 kinase and downstream tight junction signalling in KRAS-mutant cells. Furthermore, the results implicate the phosphorylation site Y378 in the tight junction protein PARD3 as a cancer vulnerability in KRAS-mutant cells. Together, our large-scale phosphoproteomics and proteomics data across 35 steady-state CRC cell lines represent a valuable resource for understanding the molecular characteristics of oncogenic mutations. Our approach to predicting cancer dependency from phosphoproteomics data identified the EPHA2-PARD3 axis as a cancer vulnerability in KRAS-mutant CRC.

Pan-Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival

Genetic alterations in cancer cells trigger oncogenic transformation, a process largely mediated by the dysregulation of kinase and transcription factor (TF) activities. While the mutational profiles of thousands of tumours have been extensively characterised, the measurements of protein activities have been technically limited until recently. We compiled public data of matched genomics and (phospho)proteomics measurements for 1,110 tumours and 77 cell lines that we used to estimate activity changes in 218 kinases and 292 TFs. Co-regulation of kinase and TF activities reflects previously known regulatory relationships and allows us to dissect genetic drivers of signalling changes in cancer. We find that loss-of-function mutations are not often associated with the dysregulation of downstream targets, suggesting frequent compensatory mechanisms. Finally, we identified the activities most differentially regulated in cancer subtypes and showed how these can be linked to differences in patient survival. Our results provide broad insights into the dysregulation of protein activities in cancer and their contribution to disease severity.

Genetics of mitochondrial diseases: Current approaches for the molecular diagnosis

Mitochondrial diseases are a genetically and phenotypically variable set of monogenic disorders. The main characteristic of mitochondrial diseases is a defective oxidative phosphorylation. Both nuclear and mitochondrial DNA encode the approximately 1500 mitochondrial proteins. Since identification of the first mitochondrial disease gene in 1988 a total of 425 genes have been associated with mitochondrial diseases. Mitochondrial dysfunctions can be caused both by pathogenic variants in the mitochondrial DNA or the nuclear DNA. Hence, besides maternal inheritance, mitochondrial diseases can follow all modes of Mendelian inheritance. The maternal inheritance and tissue specificity distinguish molecular diagnostics of mitochondrial disorders from other rare disorders. With the advances made in the next-generation sequencing technology, whole exome sequencing and even whole-genome sequencing are now the established methods of choice for molecular diagnostics of mitochondrial diseases. They reach a diagnostic rate of more than 50% in clinically suspected mitochondrial disease patients. Moreover, next-generation sequencing is delivering a constantly growing number of novel mitochondrial disease genes. This chapter reviews mitochondrial and nuclear causes of mitochondrial diseases, molecular diagnostic methodologies, and their current challenges and perspectives.

Phosphoproteomic analysis of metformin signaling in colorectal cancer cells elucidates mechanism of action and potential therapeutic opportunities

BACKGROUND

The biguanide drug metformin is a safe and widely prescribed drug for type 2 diabetes. Interestingly, hundreds of clinical trials have been set to evaluate the potential role of metformin in the prevention and treatment of cancer including colorectal cancer (CRC). However, the "metformin signaling" remains controversial.

AIMS AND METHODS

To interrogate cell signaling induced by metformin in CRC and explore the druggability of the metformin-rewired phosphorylation network, we performed integrative analysis of phosphoproteomics, bioinformatics, and cell proliferation assays on a panel of 12 molecularly heterogeneous CRC cell lines. Using the high-resolute data-independent analysis mass spectrometry (DIA-MS), we monitored a total of 10,142 proteins and 56,080 phosphosites (P-sites) in CRC cells upon a short- and a long-term metformin treatment.

RESULTS AND CONCLUSIONS

We found that metformin tended to primarily remodel cell signaling in the long-term and only minimally regulated the total proteome expression levels. Strikingly, the phosphorylation signaling response to metformin was highly heterogeneous in the CRC panel, based on a network analysis inferring kinase/phosphatase activities and cell signaling reconstruction. A "MetScore" was determined to assign the metformin relevance of each P-site, revealing new and robust phosphorylation nodes and pathways in metformin signaling. Finally, we leveraged the metformin P-site signature to identify pharmacodynamic interactions and confirmed a number of candidate metformin-interacting drugs, including navitoclax, a BCL-2/BCL-xL inhibitor. Together, we provide a comprehensive phosphoproteomic resource to explore the metformin-induced cell signaling for potential cancer therapeutics. This resource can be accessed at https://yslproteomics.shinyapps.io/Metformin/.

Characterisation of Colorectal Cancer Cell Lines through Proteomic Profiling of Their Extracellular Vesicles

Colorectal cancer (CRC) is one of the most prevalent cancers, driven by several factors including deregulations in intracellular signalling pathways. Small extracellular vesicles (sEVs) are nanosized protein-packaged particles released from cells, which are present in liquid biopsies. Here, we characterised the proteome landscape of sEVs and their cells of origin in three CRC cell lines HCT116, HT29 and SW620 to explore molecular traits that could be exploited as cancer biomarker candidates and how intracellular signalling can be assessed by sEV analysis instead of directly obtaining the cell of origin itself. Our findings revealed that sEV cargo clearly reflects its cell of origin with proteins of the PI3K-AKT pathway highly represented in sEVs. Proteins known to be involved in CRC were detected in both cells and sEVs including KRAS, ARAF, mTOR, PDPK1 and MAPK1, while TGFB1 and TGFBR2, known to be key players in epithelial cancer carcinogenesis, were found to be enriched in sEVs. Furthermore, the phosphopeptide-enriched profiling of cell lysates demonstrated a distinct pattern between cell lines and highlighted potential phosphoproteomic targets to be investigated in sEVs. The total proteomic and phosphoproteomics profiles described in the current work can serve as a source to identify candidates for cancer biomarkers that can potentially be assessed from liquid biopsies.

A NuRD for all seasons

The nucleosome-remodeling and deacetylase (NuRD) complex is an essential transcriptional regulator in all complex animals. All seven core subunits of the complex exist as multiple paralogs, raising the question of whether the complex might utilize paralog switching to achieve cell type-specific functions. We examine the evidence for this idea, making use of published quantitative proteomic data to dissect NuRD composition in 20 different tissues, as well as a large-scale CRISPR knockout screen carried out in >1000 human cancer cell lines. These data, together with recent reports, provide strong support for the idea that distinct permutations of the NuRD complex with tailored functions might regulate tissue-specific gene expression programs.

Network analysis of genome-wide association studies for drug target prioritisation

Over the past decades, genome-wide association studies (GWAS) have led to a dramatic expansion of genetic variants implicated with human traits and diseases. These advances are expected to result in new drug targets but the identification of causal genes and the cell biology underlying human diseases from GWAS remains challenging. Here, we review protein interaction network-based methods to analyse GWAS data. These approaches can rank candidate drug targets at GWAS-associated loci or among interactors of disease genes without direct genetic support. These methods identify the cell biology affected in common across diseases, offering opportunities for drug repurposing, as well as be combined with expression data to identify focal tissues and cell types. Going forward, we expect that these methods will further improve from advances in the characterisation of context specific interaction networks and the joint analysis of rare and common genetic signals.

Modelling premature cardiac aging with induced pluripotent stem cells from a hutchinson-gilford Progeria Syndrome patient

Hutchinson-Gilford Progeria Syndrome (HGPS) is a rare genetic disorder that causes accelerated aging and a high risk of cardiovascular complications. However, the underlying mechanisms of cardiac complications of this syndrome are not fully understood. This study modeled HGPS using cardiomyocytes (CM) derived from induced pluripotent stem cells (iPSC) derived from a patient with HGPS and characterized the biophysical, morphological, and molecular changes found in these CM compared to CM derived from a healthy donor. Electrophysiological recordings suggest that the HGPS-CM was functional and had normal electrophysiological properties. Electron tomography showed nuclear morphology alteration, and the 3D reconstruction of electron tomography images suggests structural abnormalities in HGPS-CM mitochondria, however, there was no difference in mitochondrial content as measured by Mitotracker. Immunofluorescence indicates nuclear morphological alteration and confirms the presence of Troponin T. Telomere length was measured using qRT-PCR, and no difference was found in the CM from HGPS when compared to the control. Proteomic analysis was carried out in a high-resolution system using Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS). The proteomics data show distinct group separations and protein expression differences between HGPS and control-CM, highlighting changes in ribosomal, TCA cycle, and amino acid biosynthesis, among other modifications. Our findings show that iPSC-derived cardiomyocytes from a Progeria Syndrome patient have significant changes in mitochondrial morphology and protein expression, implying novel mechanisms underlying premature cardiac aging.

Biological network topology features predict gene dependencies in cancer cell-lines

Motivation

Protein-protein interaction (PPI) networks have been shown to successfully predict essential proteins. However, such networks are derived generically from experiments on many thousands of different cells. Consequently, conventional PPI networks cannot capture the variation of genetic dependencies that exists across different cell types, let alone those that emerge as a result of the massive cell restructuring that occurs during carcinogenesis. Predicting cell-specific dependencies is of considerable therapeutic benefit, facilitating the use of drugs to inhibit those proteins on which the cancer cells have become specifically dependent. In order to go beyond the limitations of the generic PPI, we have attempted to personalise PPI networks to reflect cell-specific patterns of gene expression and mutation. By using 12 topological features of the resulting PPIs, together with matched gene dependency data from DepMap, we trained random-forest classifiers (DependANT) to predict novel gene dependencies.

Results

We found that DependANT improves the power of the baseline generic PPI models in predicting common gene dependencies, by up to 10.8% and is more sensitive than the baseline generic model when predicting genes on which only a small number of cell types are dependent.

Availability and implementation

Software available at https://bitbucket.org/bioinformatics_lab_sussex/dependant2.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Proteotype coevolution and quantitative diversity across 11 mammalian species

Evolutionary profiling has been largely limited to the nucleotide level. Using consistent proteomic methods, we quantified proteomic and phosphoproteomic layers in fibroblasts from 11 common mammalian species, with transcriptomes as reference. Covariation analysis indicates that transcript and protein expression levels and variabilities across mammals remarkably follow functional role, with extracellular matrix-associated expression being the most variable, demonstrating strong transcriptome-proteome coevolution. The biological variability of gene expression is universal at both interindividual and interspecies scales but to a different extent. RNA metabolic processes particularly show higher interspecies versus interindividual variation. Our results further indicate that while the ubiquitin-proteasome system is strongly conserved in mammals, lysosome-mediated protein degradation exhibits remarkable variation between mammalian lineages. In addition, the phosphosite profiles reveal a phosphorylation coevolution network independent of protein abundance.

Pan-cancer proteomic map of 949 human cell lines

The proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has restricted the identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types are analyzed by mass spectrometry. Deploying a workflow to quantify 8,498 proteins, these data capture evidence of cell-type and post-transcriptional modifications. Integrating multi-omics, drug response, and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline reveals thousands of protein biomarkers of cancer vulnerabilities that are not significant at the transcript level. The power of the proteome to predict drug response is very similar to that of the transcriptome. Further, random downsampling to only 1,500 proteins has limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger) is a comprehensive resource available at https://cellmodelpassports.sanger.ac.uk.

SimPLIT: Simplified Sample Preparation for Large-Scale Isobaric Tagging Proteomics

Large scale proteomic profiling of cell lines can reveal molecular signatures attributed to variable genotypes or induced perturbations, enabling proteogenomic associations and elucidation of pharmacological mechanisms of action. Although isobaric labeling has increased the throughput of proteomic analysis, the commonly used sample preparation workflows often require time-consuming steps and costly consumables, limiting their suitability for large scale studies. Here, we present a simplified and cost-effective one-pot reaction workflow in a 96-well plate format (SimPLIT) that minimizes processing steps and demonstrates improved reproducibility compared to alternative approaches. The workflow is based on a sodium deoxycholate lysis buffer and a single detergent cleanup step after peptide labeling, followed by quick off-line fractionation and MS2 analysis. We showcase the applicability of the workflow in a panel of colorectal cancer cell lines and by performing target discovery for a set of molecular glue degraders in different cell lines, in a 96-sample assay. Using this workflow, we report frequently dysregulated proteins in colorectal cancer cells and uncover cell-dependent protein degradation profiles of seven cereblon E3 ligase modulators (CRL4CRBN). Overall, SimPLIT is a robust method that can be easily implemented in any proteomics laboratory for medium-to-large scale TMT-based studies for deep profiling of cell lines.

Quantitative proteome remodeling characterization of two human reference pluripotent stem cell lines during neurogenesis and cardiomyogenesis

Human pluripotent stem cells (PSCs) have become popular tools within the research community to study developmental and model diseases. While many induced-PSCs (iPSCs) from various genetic background sources are currently available, scientific advancement has been hampered by the considerable phenotypic variations observed between different iPSC lines. A recent collaborative effort selected a novel iPSC line to address this and encourage the adoption of a standardized iPSC line termed KOLF2.1J. Here, leveraging the multiplexing power of isobaric labeling, we systematically investigate, at the 10k proteome level, the relative protein abundance profiles of the KOLF2.1J reference iPSC line upon two distinct cell state differentiation trajectories. In addition, we side-by-side systematically compare this line with the H9 line, an established embryonically derived PSC line that we previously characterized. We noticed differences in the basal proteome of the two cell lines and highlighted the differentially expressed proteins. While the difference between the cell line's proteome subsisted upon differentiation, the global proteome remodeling trajectory was highly similar during the tested differentiation routes. We thus conclude that the KOLF2.1J line performs well at the proteome level upon the neuro and cardiomyogenesis differentiation protocol used. We believe this dataset will serve as a resource of value for the research community.

Autoimmune Effect of Antibodies against the SARS-CoV-2 Nucleoprotein

COVID-19 caused by SARS-CoV-2 is continuing to spread around the world and drastically affect our daily life. New strains appear, and the severity of the course of the disease itself seems to be decreasing, but even people who have been ill on an outpatient basis suffer post-COVID consequences. Partly, it is associated with the autoimmune reactions, so debates about the development of new vaccines and the need for vaccination/revaccination continue. In this study we performed an analysis of the antibody response of patients with COVID-19 to linear and conformational epitopes of viral proteins using ELISA, chip array and western blot with analysis of correlations between antibody titer, disease severity, and complications. We have shown that the presence of IgG antibodies to the nucleoprotein can deteriorate the course of the disease, induce multiple direct COVID-19 symptoms, and contribute to long-term post-covid symptoms. We analyzed the cross reactivity of antibodies to SARS-CoV-2 with own human proteins and showed that antibodies to the nucleocapsid protein can bind to human proteins. In accordance with the possibility of HLA presentation, the main possible targets of the autoantibodies were identified. People with HLA alleles A01:01; A26:01; B39:01; B15:01 are most susceptible to the development of autoimmune processes after COVID-19.

Multi-omics approach reveals posttranscriptionally regulated genes are essential for human pluripotent stem cells

The effects of transcription factors on the maintenance and differentiation of human-induced or embryonic pluripotent stem cells (iPSCs/ESCs) have been well studied. However, the importance of posttranscriptional regulatory mechanisms, which cause the quantitative dissociation of mRNA and protein expression, has not been explored in detail. Here, by combining transcriptome and proteome profiling, we identified 228 posttranscriptionally regulated genes with strict upregulation of the protein level in iPSCs/ESCs. Among them, we found 84 genes were vital for the survival of iPSCs and HDFs, including 20 genes that were specifically necessary for iPSC survival. These 20 proteins were upregulated only in iPSCs/ESCs and not in differentiated cells derived from the three germ layers. Although there are still unknown mechanisms that downregulate protein levels in HDFs, these results reveal that posttranscriptionally regulated genes have a crucial role in iPSC survival.

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The posttranscriptionally regulated 20 genes are necessary for iPSC survival

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The proteins of HSPA8, EIF3D, and NCBP2 are quickly degraded in HDFs

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mRNA localization affects the protein amounts in most of the 20 genes

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Translation is repressed in HDFs despite mRNA binding to ribosomes

Aneuploidy tolerance caused by BRG1 loss allows chromosome gains and recovery of fitness

Aneuploidy results in decreased cellular fitness in many species and model systems. However, aneuploidy is commonly found in cancer cells and often correlates with aggressive growth, suggesting that the impact of aneuploidy on cellular fitness is context dependent. The BRG1 (SMARCA4) subunit of the SWI/SNF chromatin remodelling complex is frequently lost in cancer. Here, we use a chromosomally stable cell line to test the effect of BRG1 loss on the evolution of aneuploidy. BRG1 deletion leads to an initial loss of fitness in this cell line that improves over time. Notably, we find increased tolerance to aneuploidy immediately upon loss of BRG1, and the fitness recovery over time correlates with chromosome gain. These data show that BRG1 loss creates an environment where karyotype changes can be explored without a fitness penalty. At least in some genetic backgrounds, therefore, BRG1 loss can affect the progression of tumourigenesis through tolerance of aneuploidy.

EPEC-induced activation of the Ca2+ transporter TRPV2 leads to pyroptotic cell death

The enteropathogenic Escherichia coli (EPEC) type III secretion system effector Tir, which mediates intimate bacterial attachment to epithelial cells, also triggers Ca2+ influx followed by LPS entry and caspase-4-dependent pyroptosis, which could be antagonized by the effector NleF. Here we reveal the mechanism by which EPEC induces Ca2+ influx. We show that in the intestinal epithelial cell line SNU-C5, Tir activates the mechano/osmosensitive cation channel TRPV2 which triggers extracellular Ca2+ influx. Tir-induced Ca2+ influx could be blocked by siRNA silencing of TRPV2, pre-treatment with the TRPV2 inhibitor SET2 or by growing cells in low osmolality medium. Pharmacological activation of TRPV2 in the absence of Tir failed to initiate caspase-4-dependent cell death, confirming the necessity of Tir. Consistent with the model implicating activation on translocation of TRPV2 from the ER to plasma membrane, inhibition of protein trafficking by either brefeldin A or the effector NleA prevented TRPV2 activation and cell death. While infection with EPECΔnleA triggered pyroptotic cell death, this could be prevented by NleF. Taken together this study shows that while integration of Tir into the plasma membrane activates TRPV2, EPEC uses NleA to inhibit TRPV2 trafficking and NleF to inhibit caspase-4 and pyroptosis.

Decoding the cellular effects of genetic variation through interaction proteomics

It is often unclear how genetic variation translates into cellular phenotypes, including how much of the coding variation can be recovered in the proteome. Proteogenomic analyses of heterogenous cell lines revealed that the genetic differences impact mostly the abundance and stoichiometry of protein complexes, with the effects propagating post-transcriptionally via protein interactions onto other subunits. Conversely, large scale binary interaction analyses of missense variants revealed that loss of interaction is widespread and caused by about 50% disease-associated mutations, while deep scanning mutagenesis of binary interactions identified thousands of interaction-deficient variants per interaction. The idea that phenotypes arise from genetic variation through protein-protein interaction is therefore substantiated by both forward and reverse interaction proteomics. With improved methodologies, these two approaches combined can close the knowledge gap between nucleotide sequence variation and its functional consequences on the cellular proteome.

Loss of circadian gene Timeless induces EMT and tumor progression in colorectal cancer via Zeb1-dependent mechanism

The circadian gene Timeless (TIM) provides a molecular bridge between circadian and cell cycle/DNA replication regulatory systems and has been recently involved in human cancer development and progression. However, its functional role in colorectal cancer (CRC), the third leading cause of cancer-related deaths worldwide, has not been fully clarified yet. Here, the analysis of two independent CRC patient cohorts (total 1159 samples) reveals that loss of TIM expression is an unfavorable prognostic factor significantly correlated with advanced tumor stage, metastatic spreading, and microsatellite stability status. Genome-wide expression profiling, in vitro and in vivo experiments, revealed that TIM knockdown induces the activation of the epithelial-to-mesenchymal transition (EMT) program. Accordingly, the analysis of a large set of human samples showed that TIM expression inversely correlated with a previously established gene signature of canonical EMT markers (EMT score), and its ectopic silencing promotes migration, invasion, and acquisition of stem-like phenotype in CRC cells. Mechanistically, we found that loss of TIM expression unleashes ZEB1 expression that in turn drives the EMT program and enhances the aggressive behavior of CRC cells. Besides, the deranged TIM-ZEB1 axis sets off the accumulation of DNA damage and delays DNA damage recovery. Furthermore, we show that the aggressive and genetically unstable 'CMS4 colorectal cancer molecular subtype' is characterized by a lower expression of TIM and that patients with the combination of low-TIM/high-ZEB1 expression have a poorer outcome. In conclusion, our results as a whole suggest the engagement of an unedited TIM-ZEB1 axis in key pathological processes driving malignant phenotype acquisition in colorectal carcinogenesis. Thus, TIM-ZEB1 expression profiling could provide a robust prognostic biomarker in CRC patients, supporting targeted therapeutic strategies with better treatment selection and patients' outcomes.

Prognostic value of the PrPC-ILK-IDO1 axis in the mesenchymal colorectal cancer subtype

The CMS4 mesenchymal subtype of colorectal cancer (CRC) is associated with poor prognosis and resistance to treatment. The cellular prion protein PrP^C is overexpressed in CMS4 tumors and controls the expression of a panel of CMS4-specific genes in CRC cell lines. Here, we sought to investigate PrP^C downstream pathways that may underlie its role in CMS4 CRC. By combining gene set enrichment analyses and gain and loss of function approaches in CRC cell lines, we identify the integrin-linked kinase ILK as a proximal effector of PrP^C that mediates its control on the CMS4 phenotype. We further leveraged three independent large CRC cohorts to assess correlations in gene expression pattern with patient outcomes and found that ILK is overexpressed in CMS4 mesenchymal tumors and confers a poor prognosis, especially when combined with high expression of the PrP^C encoding gene PRNP. Of note, we discovered that the PrP^C-ILK signaling axis controls the expression and activity of the tryptophan metabolizing enzyme indoleamine 2,3 dioxygenase IDO1, a key player in immune tolerance. In addition, we monitored alterations in the levels of tryptophan and its metabolites of the kynurenine pathway in the plasma of metastatic CRC patients (n = 325) and we highlight their prognostic value in combination with plasma PrP^C levels. Thus, the PrP^C-ILK-IDO1 axis plays a key role in the mesenchymal subtype of CRC. PrP^C and IDO1-targeted strategies may represent new avenues for patient stratification and treatment in CRC.

Data-independent acquisition-based proteome and phosphoproteome profiling across six melanoma cell lines reveals determinants of proteotypes

Human cancer cell lines are widely used in pharmacological and systems biological studies. The rapid documentation of the steady-state gene expression landscape of the cells used in a particular experiment may help to improve the reproducibility of scientific research. Here we applied a data-independent acquisition mass spectrometry (DIA-MS) method, coupled with a peptide spectral-library-free data analysis workflow, to measure both the proteome and phosphoproteome of a melanoma cell line panel with different metastatic properties. For each cell line, the single-shot DIA-MS detected 8100 proteins and almost 40 000 phosphopeptides in the respective measurements of two hours. Benchmarking the DIA-MS data towards the RNA-seq data and tandem mass tag (TMT)-MS results from the same set of cell lines demonstrated comparable qualitative coverage and quantitative reproducibility. Our data confirmed the high but complex mRNA-protein and protein-phospsite correlations. The results successfully established DIA-MS as a strong and competitive proteotyping approach for cell lines. The data further showed that all subunits of the glycosylphosphatidylinositol (GPI)-anchor transamidase complex were overexpressed in metastatic melanoma cells and identified altered phosphoprotein modules such as the BAF complex and mRNA splicing between metastatic and primary cells. This study provides a high-quality resource for calibrating DIA-MS performance, benchmarking DIA bioinformatic algorithms, and exploring the metastatic proteotypes in melanoma cells.

PBRM1 Deficiency Confers Synthetic Lethality to DNA Repair Inhibitors in Cancer

Inactivation of Polybromo 1 (PBRM1), a specific subunit of the PBAF chromatin remodeling complex, occurs frequently in cancer, including 40% of clear cell renal cell carcinomas (ccRCC). To identify novel therapeutic approaches to targeting PBRM1-defective cancers, we used a series of orthogonal functional genomic screens that identified PARP and ATR inhibitors as being synthetic lethal with PBRM1 deficiency. The PBRM1/PARP inhibitor synthetic lethality was recapitulated using several clinical PARP inhibitors in a series of in vitro model systems and in vivo in a xenograft model of ccRCC. In the absence of exogenous DNA damage, PBRM1-defective cells exhibited elevated levels of replication stress, micronuclei, and R-loops. PARP inhibitor exposure exacerbated these phenotypes. Quantitative mass spectrometry revealed that multiple R-loop processing factors were downregulated in PBRM1-defective tumor cells. Exogenous expression of the R-loop resolution enzyme RNase H1 reversed the sensitivity of PBRM1-deficient cells to PARP inhibitors, suggesting that excessive levels of R-loops could be a cause of this synthetic lethality. PARP and ATR inhibitors also induced cyclic GMP-AMP synthase/stimulator of interferon genes (cGAS/STING) innate immune signaling in PBRM1-defective tumor cells. Overall, these findings provide the preclinical basis for using PARP inhibitors in PBRM1-defective cancers. SIGNIFICANCE: This study demonstrates that PARP and ATR inhibitors are synthetic lethal with the loss of PBRM1, a PBAF-specific subunit, thus providing the rationale for assessing these inhibitors in patients with PBRM1-defective cancer. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/81/11/2888/F1.large.jpg.

An integrated landscape of protein expression in human cancer

Using 11 proteomics datasets, mostly available through the PRIDE database, we assembled a reference expression map for 191 cancer cell lines and 246 clinical tumour samples, across 13 lineages. We found unique peptides identified only in tumour samples despite a much higher coverage in cell lines. These were mainly mapped to proteins related to regulation of signalling receptor activity. Correlations between baseline expression in cell lines and tumours were calculated. We found these to be highly similar across all samples with most similarity found within a given sample type. Integration of proteomics and transcriptomics data showed median correlation across cell lines to be 0.58 (range between 0.43 and 0.66). Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis focusing on cancer-related public proteomics datasets. We therefore also highlight shortcomings and limitations of such studies. All data is available through PRIDE dataset identifier PXD013455 and in Expression Atlas.

Proteogenomic interrogation of cancer cell lines: an overview of the field

Introduction: Cancer cell lines (CCLs) have been a major resource for cancer research. Over the past couple of decades, they have been instrumental in omic profiling method development and as model systems to generate new knowledge in cell and cancer biology. More recently, with the increasing amount of genomic, transcriptomic and proteomic data being generated in hundreds of CCLs, there is growing potential for integrative proteogenomic data analyses to be performed.Areas covered: In this review, we first describe the most commonly used proteome profiling methods in CCLs. We then discuss how these proteomics data can be integrated with genomics data for proteogenomics analyses. Finally, we highlight some of the recent biological discoveries that have arisen from proteogenomics analyses of CCLs.Expert opinion: Protegeonomics analyses of CCLs have so far enabled the discovery of novel proteins and proteoforms. It has also improved our understanding of biological processes including post-transcriptional regulation of protein abundance and the presentation of antigens by major histocompatibility complex alleles. With proteomics data to be generated in hundreds to thousands of CCLs in coming years, there will be further potential for large-scale proteogenomics analyses and data integration with the phenotypically well-characterized CCLs.

Werner Helicase Is a Synthetic-Lethal Vulnerability in Mismatch Repair-Deficient Colorectal Cancer Refractory to Targeted Therapies, Chemotherapy, and Immunotherapy

Targeted therapies, chemotherapy, and immunotherapy are used to treat patients with mismatch repair-deficient (dMMR)/microsatellite instability-high (MSI-H) colorectal cancer. The clinical effectiveness of targeted therapy and chemotherapy is limited by resistance and drug toxicities, and about half of patients receiving immunotherapy have disease that is refractory to immune checkpoint inhibitors. Loss of Werner syndrome ATP-dependent helicase (WRN) is a synthetic lethality in dMMR/MSI-H cells. To inform the development of WRN as a therapeutic target, we performed WRN knockout or knockdown in 60 heterogeneous dMMR colorectal cancer preclinical models, demonstrating that WRN dependency is an almost universal feature and a robust marker for patient selection. Furthermore, models of resistance to clinically relevant targeted therapy, chemotherapy, and immunotherapy retain WRN dependency. These data show the potential of therapeutically targeting WRN in patients with dMMR/MSI-H colorectal cancer and support WRN as a therapeutic option for patients with dMMR/MSI-H cancers refractory to current treatment strategies. SIGNIFICANCE: We found that a large, diverse set of dMMR/MSI-H colorectal cancer preclinical models, including models of treatment-refractory disease, are WRN-dependent. Our results support WRN as a promising synthetic-lethal target in dMMR/MSI-H colorectal cancer tumors as a monotherapy or in combination with targeted agents, chemotherapy, or immunotherapy.This article is highlighted in the In This Issue feature, p. 1861.

Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs

Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman's rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p < 0.005). Our results indicate that DRUML accurately ranks anti-cancer drugs by their efficacy across a wide range of pathologies.

Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor

Molecular and functional profiling of cancer cell lines is subject to laboratory-specific experimental practices and data analysis protocols. The current challenge therefore is how to make an integrated use of the omics profiles of cancer cell lines for reliable biological discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta-analysis of 53 omics studies across 12 research laboratories for 2,018 cell lines. To account for a relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. The multi-modal meta-analysis approach also identified synthetic lethal partners of cancer drivers, including a co-dependency of PTEN deficient endometrial cancer cells on RNA helicases.

Advancing Biomarker Development Through Convergent Engagement: Summary Report of the 2nd International Danube Symposium on Biomarker Development, Molecular Imaging and Applied Diagnostics; March 14-16, 2018; Vienna, Austria

Here, we report on the outcome of the 2nd International Danube Symposium on advanced biomarker development that was held in Vienna, Austria, in early 2018. During the meeting, cross-speciality participants assessed critical aspects of non-invasive, quantitative biomarker development in view of the need to expand our understanding of disease mechanisms and the definition of appropriate strategies both for molecular diagnostics and personalised therapies. More specifically, panelists addressed the main topics, including the current status of disease characterisation by means of non-invasive imaging, histopathology and liquid biopsies as well as strategies of gaining new understanding of disease formation, modulation and plasticity to large-scale molecular imaging as well as integrative multi-platform approaches. Highlights of the 2018 meeting included dedicated sessions on non-invasive disease characterisation, development of disease and therapeutic tailored biomarkers, standardisation and quality measures in biospecimens, new therapeutic approaches and socio-economic challenges of biomarker developments. The scientific programme was accompanied by a roundtable discussion on identification and implementation of sustainable strategies to address the educational needs in the rapidly evolving field of molecular diagnostics. The central theme that emanated from the 2nd Donau Symposium was the importance of the conceptualisation and implementation of a convergent approach towards a disease characterisation beyond lesion-counting "lumpology" for a cost-effective and patient-centric diagnosis, therapy planning, guidance and monitoring. This involves a judicious choice of diagnostic means, the adoption of clinical decision support systems and, above all, a new way of communication involving all stakeholders across modalities and specialities. Moreover, complex diseases require a comprehensive diagnosis by converging parameters from different disciplines, which will finally yield to a precise therapeutic guidance and outcome prediction. While it is attractive to focus on technical advances alone, it is important to develop a patient-centric approach, thus asking "What can we do with our expertise to help patients?"

Proteogenomics of Non-smoking Lung Cancer in East Asia Delineates Molecular Signatures of Pathogenesis and Progression

Lung cancer in East Asia is characterized by a high percentage of never-smokers, early onset and predominant EGFR mutations. To illuminate the molecular phenotype of this demographically distinct disease, we performed a deep comprehensive proteogenomic study on a prospectively collected cohort in Taiwan, representing early stage, predominantly female, non-smoking lung adenocarcinoma. Integrated genomic, proteomic, and phosphoproteomic analysis delineated the demographically distinct molecular attributes and hallmarks of tumor progression. Mutational signature analysis revealed age- and gender-related mutagenesis mechanisms, characterized by high prevalence of APOBEC mutational signature in younger females and over-representation of environmental carcinogen-like mutational signatures in older females. A proteomics-informed classification distinguished the clinical characteristics of early stage patients with EGFR mutations. Furthermore, integrated protein network analysis revealed the cellular remodeling underpinning clinical trajectories and nominated candidate biomarkers for patient stratification and therapeutic intervention. This multi-omic molecular architecture may help develop strategies for management of early stage never-smoker lung adenocarcinoma.

Clustering of Tir during enteropathogenic E. coli infection triggers calcium influx-dependent pyroptosis in intestinal epithelial cells

Clustering of the enteropathogenic Escherichia coli (EPEC) type III secretion system (T3SS) effector translocated intimin receptor (Tir) by intimin leads to actin polymerisation and pyroptotic cell death in macrophages. The effect of Tir clustering on the viability of EPEC-infected intestinal epithelial cells (IECs) is unknown. We show that EPEC induces pyroptosis in IECs in a Tir-dependent but actin polymerisation-independent manner, which was enhanced by priming with interferon gamma (IFNγ). Mechanistically, Tir clustering triggers rapid Ca2+ influx, which induces lipopolysaccharide (LPS) internalisation, followed by activation of caspase-4 and pyroptosis. Knockdown of caspase-4 or gasdermin D (GSDMD), translocation of NleF, which blocks caspase-4 or chelation of extracellular Ca2+, inhibited EPEC-induced cell death. IEC lines with low endogenous abundance of GSDMD were resistant to Tir-induced cell death. Conversely, ATP-induced extracellular Ca2+ influx enhanced cell death, which confirmed the key regulatory role of Ca2+ in EPEC-induced pyroptosis. We reveal a novel mechanism through which infection with an extracellular pathogen leads to pyroptosis in IECs.

Why Tumor Genetic Heterogeneity May Require Rethinking Cancer Genesis and Treatment

Tumor genetic heterogeneity, in which individual tumors contain both multiple variant cancer-associated and normal genes, has been widely reported, although its significance has yet to be fully understood. We propose a genetic heterogeneity-based selection-centric hypothesis in which genetic heterogeneity, caused by the temporary reduction of DNA repair efficiency, occurs very early in human development, resulting in a small minority of cells in normal tissues acquiring cancer-associated genes that remain dormant. Cancer develops when precancer cells are selected for by altered tissue microenvironments; similar scenarios occur with development of metastases and therapeutic resistance in established cancer. This suggests that a normal cell selection treatment approach based on preferentially selecting normal cells within tumors may be effective in treating cancer.

Using Deep Learning to Extrapolate Protein Expression Measurements

Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for imputations of proteins measured in at least some of the samples. In silico methods for comprehensively estimating abundances across all proteins are still missing. Here, a novel method is proposed using deep learning to extrapolate the observed protein expression values in label-free MS experiments to all proteins, leveraging gene functional annotations and RNA measurements as key predictive attributes. This method is tested on four datasets, including human cell lines and human and mouse tissues. This method predicts the protein expression values with average R 2 scores between 0.46 and 0.54, which is significantly better than predictions based on correlations using the RNA expression data alone. Moreover, it is demonstrated that the derived models can be "transferred" across experiments and species. For instance, the model derived from human tissues gave a R 2 = 0.51 when applied to mouse tissue data. It is concluded that protein abundances generated in label-free MS experiments can be computationally predicted using functional annotated attributes and can be used to highlight aberrant protein abundance values.

RALB GTPase: a critical regulator of DR5 expression and TRAIL sensitivity in KRAS mutant colorectal cancer

RAS mutant (MT) metastatic colorectal cancer (mCRC) is resistant to MEK1/2 inhibition and remains a difficult-to-treat group. Therefore, there is an unmet need for novel treatment options for RASMT mCRC. RALA and RALB GTPases function downstream of RAS and have been found to be key regulators of several cell functions implicated in KRAS-driven tumorigenesis. However, their role as regulators of the apoptotic machinery remains to be elucidated. Here, we found that inhibition of RALB expression, but not RALA, resulted in Caspase-8-dependent cell death in KRASMT CRC cells, which was not further increased following MEK1/2 inhibition. Proteomic analysis and mechanistic studies revealed that RALB depletion induced a marked upregulation of the pro-apoptotic cell surface TRAIL Death Receptor 5 (DR5) (also known as TRAIL-R2), primarily through modulating DR5 protein lysosomal degradation. Moreover, DR5 knockdown or knockout attenuated siRALB-induced apoptosis, confirming the role of the extrinsic apoptotic pathway as a regulator of siRALB-induced cell death. Importantly, TRAIL treatment resulted in the association of RALB with the death-inducing signalling complex (DISC) and targeting RALB using pharmacologic inhibition or RNAi approaches triggered a potent increase in TRAIL-induced cell death in KRASMT CRC cells. Significantly, high RALB mRNA levels were found in the poor prognostic Colorectal Cancer Intrinsic Subtypes (CRIS)-B CRC subgroup. Collectively, this study provides to our knowledge the first evidence for a role for RALB in apoptotic priming and suggests that RALB inhibition may be a promising strategy to improve response to TRAIL treatment in poor prognostic RASMT CRIS-B CRC.