Global Proteome Analysis of the NCI-60 Cell Line Panel

Evaluation of Protein Identification and Quantification by the diaPASEF Method on timsTOF SCP

Accurate and precise quantification is crucial in modern proteomics, particularly in the context of exploring low-amount samples. While the innovative 4D-data-independent acquisition (DIA) quantitative proteomics facilitated by timsTOF mass spectrometers gives enhanced sensitivity and selectivity for protein identification, the diaPASEF (parallel accumulation-serial fragmentation combined with data-independent acquisition) parameters have not been systematically optimized, and a comprehensive evaluation of the quantification is currently lacking. In this study, we conducted a thorough optimization of key parameters on a timsTOF SCP instrument, including sample loading amount (50 ng), ramp/accumulation time (140 ms), isolation window width (20 m/z), and gradient time (60 min). To further improve the identification of proteins in low-amount samples, we utilized different column settings and introduced 0.02% n-dodecyl-β-d-maltoside (DDM) in the sample reconstitution solution, resulting in a remarkable 19-fold increase in protein identification at the single-cell-equivalent level. Moreover, a comprehensive comparison of protein quantification using a tandem mass tag reporter (TMT-reporter), complement TMT ions (TMTc), and diaPASEF revealed a strong correlation between these methods. Both diaPASEF and TMTc have effectively addressed the issue of ratio compression, highlighting the diaPASEF method's effectiveness in achieving accurate quantification data compared to TMT reporter quantification. Additionally, an in-depth analysis of in-group variation positioned diaPASEF between the TMT-reporter and TMTc methods. Therefore, diaPASEF quantification on the timsTOF SCP instrument emerges as a precise and accurate methodology for quantitative proteomics, especially for samples with small amounts.

TurboID-Based IRE1 Interactome Reveals Participants of the Endoplasmic Reticulum-Associated Protein Degradation Machinery in the Human Mast Cell Leukemia Cell Line HMC-1.2

The unfolded protein response is an intricate system of sensor proteins in the endoplasmic reticulum (ER) that recognizes misfolded proteins and transmits information via transcription factors to either regain proteostasis or, depending on the severity, to induce apoptosis. The main transmembrane sensor is IRE1α, which contains cytoplasmic kinase and RNase domains relevant for its activation and the mRNA splicing of the transcription factor XBP1. Mast cell leukemia (MCL) is a severe form of systemic mastocytosis. The inhibition of IRE1α in the MCL cell line HMC-1.2 has anti-proliferative and pro-apoptotic effects, motivating us to elucidate the IRE1α interactors/regulators in HMC-1.2 cells. Therefore, the TurboID proximity labeling technique combined with MS analysis was applied. Gene Ontology and pathway enrichment analyses revealed that the majority of the enriched proteins are involved in vesicle-mediated transport, protein stabilization, and ubiquitin-dependent ER-associated protein degradation pathways. In particular, the AAA ATPase VCP and the oncoprotein MTDH as IRE1α-interacting proteins caught our interest for further analyses. The pharmacological inhibition of VCP activity resulted in the increased stability of IRE1α and MTDH as well as the activation of IRE1α. The interaction of VCP with both IRE1α and MTDH was dependent on ubiquitination. Moreover, MTDH stability was reduced in IRE1α-knockout cells. Hence, pharmacological manipulation of IRE1α-MTDH-VCP complex(es) might enable the treatment of MCL.

Novel sulfonamide-indolinone hybrids targeting mitochondrial respiration of breast cancer cells

Breast cancer (BC) still poses a threat worldwide which demands continuous efforts to present safer and efficacious treatment options via targeted therapy. Beside kinases' aberrations as Aurora B kinase which controls cell division, BC adopts distinct metabolic profiles to meet its high energy demands. Accordingly, targeting both aurora B kinase and/or metabolic vulnerability presents a promising approach to tackle BC. Based on a previously reported indolinone-based Aurora B kinase inhibitor (III), and guided by structural modification and SAR investigation, we initially synthesized 11 sulfonamide-indolinone hybrids (5a-k), which showed differential antiproliferative activities against the NCI-60 cell line panel with BC cells displaying preferential sensitivity. Nonetheless, modest activity against Aurora B kinase (18-49% inhibition) was noted at 100 nM. Screening of a representative derivative (5d) against 17 kinases, which are overexpressed in BC, failed to show significant activity at 1 μM concentration, suggesting that kinase inhibitory activity only played a partial role in targeting BC. Bioinformatic analyses of genome-wide transcriptomics (RNA-sequencing), metabolomics, and CRISPR loss-of-function screens datasets suggested that indolinone-completely responsive BC cell lines (MCF7, MDA-MB-468, and T-47D) were more dependent on mitochondrial oxidative phosphorylation (OXPHOS) compared to partially responsive BC cell lines (MDA-MB-231, BT-549, and HS 578 T). An optimized derivative, TC11, obtained by molecular hybridization of 5d with sunitinib polar tail, manifested superior antiproliferative activity and was used for further investigations. Indeed, TC11 significantly reduced/impaired the mitochondrial respiration, as well as mitochondria-dependent ROS production of MCF7 cells. Furthermore, TC11 induced G0/G1 cell cycle arrest and apoptosis of MCF7 BC cells. Notably, anticancer doses of TC11 did not elicit cytotoxic effects on normal cardiomyoblasts and hepatocytes. Altogether, these findings emphasize the therapeutic potential of targeting the metabolic vulnerability of OXPHOS-dependent BC cells using TC11 and its related sulfonamide-indolinone hybrids. Further investigation is warranted to identify their precise/exact molecular target.

Organelle resolved proteomics uncovers PLA2R1 as a novel cell surface marker required for chordoma growth

Chordomas are clinically aggressive tumors with a high rate of disease progression despite maximal therapy. Given the limited therapeutic options available, there remains an urgent need for the development of novel therapies to improve clinical outcomes. Cell surface proteins are attractive therapeutic targets yet are challenging to profile with common methods. Four chordoma cell lines were analyzed by quantitative proteomics using a differential ultracentrifugation organellar fractionation approach. A subtractive proteomics strategy was applied to select proteins that are plasma membrane enriched. Systematic data integration prioritized PLA2R1 (secretory phospholipase A2 receptor-PLA2R1) as a chordoma-enriched surface protein. The expression profile of PLA2R1 was validated across chordoma cell lines, patient surgical tissue samples, and normal tissue lysates via immunoblotting. PLA2R1 expression was further validated by immunohistochemical analysis in a richly annotated cohort of 25-patient tissues. Immunohistochemistry analysis revealed that elevated expression of PLA2R1 is correlated with poor prognosis. Using siRNA- and CRISPR/Cas9-mediated knockdown of PLA2R1, we demonstrated significant inhibition of 2D, 3D and in vivo chordoma growth. PLA2R1 depletion resulted in cell cycle defects and metabolic rewiring via the MAPK signaling pathway, suggesting that PLA2R1 plays an essential role in chordoma biology. We have characterized the proteome of four chordoma cell lines and uncovered PLA2R1 as a novel cell-surface protein required for chordoma cell survival and association with patient outcome.

Mitochondrial ATP generation is more proteome efficient than glycolysis

Metabolic efficiency profoundly influences organismal fitness. Nonphotosynthetic organisms, from yeast to mammals, derive usable energy primarily through glycolysis and respiration. Although respiration is more energy efficient, some cells favor glycolysis even when oxygen is available (aerobic glycolysis, Warburg effect). A leading explanation is that glycolysis is more efficient in terms of ATP production per unit mass of protein (that is, faster). Through quantitative flux analysis and proteomics, we find, however, that mitochondrial respiration is actually more proteome efficient than aerobic glycolysis. This is shown across yeast strains, T cells, cancer cells, and tissues and tumors in vivo. Instead of aerobic glycolysis being valuable for fast ATP production, it correlates with high glycolytic protein expression, which promotes hypoxic growth. Aerobic glycolytic yeasts do not excel at aerobic growth but outgrow respiratory cells during oxygen limitation. We accordingly propose that aerobic glycolysis emerges from cells maintaining a proteome conducive to both aerobic and hypoxic growth.

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.

MMCL-CDR: enhancing cancer drug response prediction with multi-omics and morphology images contrastive representation learning

MOTIVATION

Cancer is a complex disease that results in a significant number of global fatalities. Treatment strategies can vary among patients, even if they have the same type of cancer. The application of precision medicine in cancer shows promise for treating different types of cancer, reducing healthcare expenses, and improving recovery rates. To achieve personalized cancer treatment, machine learning models have been developed to predict drug responses based on tumor and drug characteristics. However, current studies either focus on constructing homogeneous networks from single data source or heterogeneous networks from multiomics data. While multiomics data have shown potential in predicting drug responses in cancer cell lines, there is still a lack of research that effectively utilizes insights from different modalities. Furthermore, effectively utilizing the multimodal knowledge of cancer cell lines poses a challenge due to the heterogeneity inherent in these modalities.

RESULTS

To address these challenges, we introduce MMCL-CDR (Multimodal Contrastive Learning for Cancer Drug Responses), a multimodal approach for cancer drug response prediction that integrates copy number variation, gene expression, morphology images of cell lines, and chemical structure of drugs. The objective of MMCL-CDR is to align cancer cell lines across different data modalities by learning cell line representations from omic and image data, and combined with structural drug representations to enhance the prediction of cancer drug responses (CDR). We have carried out comprehensive experiments and show that our model significantly outperforms other state-of-the-art methods in CDR prediction. The experimental results also prove that the model can learn more accurate cell line representation by integrating multiomics and morphological data from cell lines, thereby improving the accuracy of CDR prediction. In addition, the ablation study and qualitative analysis also confirm the effectiveness of each part of our proposed model. Last but not least, MMCL-CDR opens up a new dimension for cancer drug response prediction through multimodal contrastive learning, pioneering a novel approach that integrates multiomics and multimodal drug and cell line modeling.

AVAILABILITY AND IMPLEMENTATION

MMCL-CDR is available at https://github.com/catly/MMCL-CDR.

Global detection of human variants and isoforms by deep proteome sequencing

An average shotgun proteomics experiment detects approximately 10,000 human proteins from a single sample. However, individual proteins are typically identified by peptide sequences representing a small fraction of their total amino acids. Hence, an average shotgun experiment fails to distinguish different protein variants and isoforms. Deeper proteome sequencing is therefore required for the global discovery of protein isoforms. Using six different human cell lines, six proteases, deep fractionation and three tandem mass spectrometry fragmentation methods, we identify a million unique peptides from 17,717 protein groups, with a median sequence coverage of approximately 80%. Direct comparison with RNA expression data provides evidence for the translation of most nonsynonymous variants. We have also hypothesized that undetected variants likely arise from mutation-induced protein instability. We further observe comparable detection rates for exon-exon junction peptides representing constitutive and alternative splicing events. Our dataset represents a resource for proteoform discovery and provides direct evidence that most frame-preserving alternatively spliced isoforms are translated.

Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation - A meta-analysis

BACKGROUND

Although interest in the role of extracellular vesicles (EV) in oncology is growing, not all potential aspects have been investigated. In this meta-analysis, data regarding (i) the EV proteome and (ii) the invasion and proliferation capacity of the NCI-60 tumor cell lines (60 cell lines from nine different tumor types) were analyzed using machine learning methods.

METHODS

On the basis of the entire proteome or the proteins shared by all EV samples, 60 cell lines were classified into the nine tumor types using multiple logistic regression. Then, utilizing the Least Absolute Shrinkage and Selection Operator, we constructed a discriminative protein panel, upon which the samples were reclassified and pathway analyses were performed. These panels were validated using clinical data (n = 4,665) from Human Protein Atlas.

RESULTS

Classification models based on the entire proteome, shared proteins, and discriminative protein panel were able to distinguish the nine tumor types with 49.15%, 69.10%, and 91.68% accuracy, respectively. Invasion and proliferation capacity of the 60 cell lines were predicted with R2 = 0.68 and R2 = 0.62 (p < 0.0001). The results of the Reactome pathway analysis of the discriminative protein panel suggest that the molecular content of EVs might be indicative of tumor-specific biological processes.

CONCLUSION

Integrating in vitro EV proteomic data, cell physiological characteristics, and clinical data of various tumor types illuminates the diagnostic, prognostic, and therapeutic potential of EVs. Video Abstract.

Proteomic Dynamics of Breast Cancer Cell Lines Identifies Potential Therapeutic Protein Targets

Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We identified 6091 proteins of 76 human BC cell lines using data-independent acquisition (DIA). Integrating our proteomic findings with prior multi-omics datasets, we found that including proteomics data improved drug sensitivity predictions and provided insights into the mechanisms of action. We subsequently profiled the proteomic changes in nine cell lines (five TNBC and four non-TNBC) treated with EGFR/AKT/mTOR inhibitors. In TNBC, metabolism pathways were dysregulated after EGFR/mTOR inhibitor treatment, while RNA modification and cell cycle pathways were affected by AKT inhibitor. This systematic multi-omics and in-depth analysis of the proteome of BC cells can help prioritize potential therapeutic targets and provide insights into adaptive resistance in TNBC.

Deep Bottom-up Proteomics Enabled by the Integration of Liquid-Phase Ion Trap

In bottom-up proteomics, the complexity of the proteome requires advanced peptide separation and/or fractionation methods to acquire an in-depth understanding of protein profiles. Proposed earlier as a solution-phase ion manipulation device, liquid phase ion traps (LPITs) were used in front of mass spectrometers to accumulate target ions for improved detection sensitivity. In this work, an LPIT-reversed phase liquid chromatography-tandem mass spectrometry (LPIT-RPLC-MS/MS) platform was established for deep bottom-up proteomics. LPIT was used here as a robust and effective method for peptide fractionation, which also shows good reproducibility and sensitivity on both qualitative and quantitative levels. LPIT separates peptides based on their effective charges and hydrodynamic radii, which is orthogonal to that of RPLC. With excellent orthogonality, the integration of LPIT with RPLC-MS/MS could effectively increase the number of peptides and proteins being detected. When HeLa cells were analyzed, peptide and protein coverages were increased by ∼89.2% and 50.3%, respectively. With high efficiency and low cost, this LPIT-based peptide fraction method could potentially be used in routine deep bottom-up proteomics.

Proteomic Characterization of Acute Myeloid Leukemia for Precision Medicine

Acute myeloid leukemia (AML) is a highly heterogeneous cancer of the hematopoietic system with no cure for most patients. In addition to chemotherapy, treatment options for AML include recently approved therapies that target proteins with roles in AML pathobiology, such as FLT3, BLC2, and IDH1/2. However, due to disease complexity, these therapies produce very diverse responses, and survival rates are still low. Thus, despite considerable advances, there remains a need for therapies that target different aspects of leukemic biology and for associated biomarkers that define patient populations likely to respond to each available therapy. To meet this need, drugs that target different AML vulnerabilities are currently in advanced stages of clinical development. Here, we review proteomics and phosphoproteomics studies that aimed to provide insights into AML biology and clinical disease heterogeneity not attainable with genomic approaches. To place the discussion in context, we first provide an overview of genetic and clinical aspects of the disease, followed by a summary of proteins targeted by compounds that have been approved or are under clinical trials for AML treatment and, if available, the biomarkers that predict responses. We then discuss proteomics and phosphoproteomics studies that provided insights into AML pathogenesis, from which potential biomarkers and drug targets were identified, and studies that aimed to rationalize the use of synergistic drug combinations. When considered as a whole, the evidence summarized here suggests that proteomics and phosphoproteomics approaches can play a crucial role in the development and implementation of precision medicine for AML patients.

Mapping the Protein Kinome: Current Strategy and Future Direction

The kinome includes over 500 different protein kinases, which form an integrated kinase network that regulates cellular phosphorylation signals. The kinome plays a central role in almost every cellular process and has strong linkages with many diseases. Thus, the evaluation of the cellular kinome in the physiological environment is essential to understand biological processes, disease development, and to target therapy. Currently, a number of strategies for kinome analysis have been developed, which are based on monitoring the phosphorylation of kinases or substrates. They have enabled researchers to tackle increasingly complex biological problems and pathological processes, and have promoted the development of kinase inhibitors. Additionally, with the increasing interest in how kinases participate in biological processes at spatial scales, it has become urgent to develop tools to estimate spatial kinome activity. With multidisciplinary efforts, a growing number of novel approaches have the potential to be applied to spatial kinome analysis. In this paper, we review the widely used methods used for kinome analysis and the challenges encountered in their applications. Meanwhile, potential approaches that may be of benefit to spatial kinome study are explored.

Mitochondrial RNA methyltransferase TRMT61B is a new, potential biomarker and therapeutic target for highly aneuploid cancers

Despite being frequently observed in cancer cells, chromosomal instability (CIN) and its immediate consequence, aneuploidy, trigger adverse effects on cellular homeostasis that need to be overcome by anti-stress mechanisms. As such, these safeguard responses represent a tumor-specific Achilles heel, since CIN and aneuploidy are rarely observed in normal cells. Recent data have revealed that epitranscriptomic marks catalyzed by RNA-modifying enzymes change under various stress insults. However, whether aneuploidy is associated with such RNA modifying pathways remains to be determined. Through an in silico search for aneuploidy biomarkers in cancer cells, we found TRMT61B, a mitochondrial RNA methyltransferase enzyme, to be associated with high levels of aneuploidy. Accordingly, TRMT61B protein levels are increased in tumor cell lines with an imbalanced karyotype as well as in different tumor types when compared to control tissues. Interestingly, while TRMT61B depletion induces senescence in melanoma cell lines with low levels of aneuploidy, it leads to apoptosis in cells with high levels. The therapeutic potential of these results was further validated by targeting TRMT61B in transwell and xenografts assays. We show that TRM61B depletion reduces the expression of several mitochondrial encoded proteins and limits mitochondrial function. Taken together, these results identify a new biomarker of aneuploidy in cancer cells that could potentially be used to selectively target highly aneuploid tumors.

Small Extracellular Vesicles from Hypoxic Triple-Negative Breast Cancer Cells Induce Oxygen-Dependent Cell Invasion

Hypoxia, a condition of low oxygenation frequently found in triple-negative breast tumors (TNBC), promotes extracellular vesicle (EV) secretion and favors cell invasion, a complex process in which cell morphology is altered, dynamic focal adhesion spots are created, and ECM is remodeled. Here, we investigated the invasive properties triggered by TNBC-derived hypoxic small EV (SEVh) in vitro in cells cultured under hypoxic (1% O₂) and normoxic (20% O₂) conditions, using phenotypical and proteomic approaches. SEVh characterization demonstrated increased protein abundance and diversity over normoxic SEV (SEVn), with enrichment in pro-invasive pathways. In normoxic cells, SEVh promotes invasive behavior through pro-migratory morphology, invadopodia development, ECM degradation, and matrix metalloprotease (MMP) secretion. The proteome profiling of 20% O₂-cultured cells exposed to SEVh determined enrichment in metabolic processes and cell cycles, modulating cell health to escape apoptotic pathways. In hypoxia, SEVh was responsible for proteolytic and catabolic pathway inducement, interfering with integrin availability and gelatinase expression. Overall, our results demonstrate the importance of hypoxic signaling via SEV in tumors for the early establishment of metastasis.

Multiomics surface receptor profiling of the NCI-60 tumor cell panel uncovers novel theranostics for cancer immunotherapy

BACKGROUND

Immunotherapy with immune checkpoint inhibitors (ICI) has revolutionized cancer therapy. However, therapeutic targeting of inhibitory T cell receptors such as PD-1 not only initiates a broad immune response against tumors, but also causes severe adverse effects. An ideal future stratified immunotherapy would interfere with cancer-specific cell surface receptors only.

METHODS

To identify such candidates, we profiled the surface receptors of the NCI-60 tumor cell panel via flow cytometry. The resulting surface receptor expression data were integrated into proteomic and transcriptomic NCI-60 datasets applying a sophisticated multiomics multiple co-inertia analysis (MCIA). This allowed us to identify surface profiles for skin, brain, colon, kidney, and bone marrow derived cell lines and cancer entity-specific cell surface receptor biomarkers for colon and renal cancer.

RESULTS

For colon cancer, identified biomarkers are CD15, CD104, CD324, CD326, CD49f, and for renal cancer, CD24, CD26, CD106 (VCAM1), EGFR, SSEA-3 (B3GALT5), SSEA-4 (TMCC1), TIM1 (HAVCR1), and TRA-1-60R (PODXL). Further data mining revealed that CD106 (VCAM1) in particular is a promising novel immunotherapeutic target for the treatment of renal cancer.

CONCLUSION

Altogether, our innovative multiomics analysis of the NCI-60 panel represents a highly valuable resource for uncovering surface receptors that could be further exploited for diagnostic and therapeutic purposes in the context of cancer immunotherapy.

Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies

Hypothesis-free high-throughput profiling allows relative quantification of thousands of proteins or transcripts across samples and thereby identification of differentially expressed genes. It is used in many biological contexts to characterize differences between cell lines and tissues, identify drug mode of action or drivers of drug resistance, among others. Changes in gene expression can also be due to confounding factors that were not accounted for in the experimental plan, such as change in cell proliferation. We combined the analysis of 1,076 and 1,040 cell lines in five proteomics and three transcriptomics data sets to identify 157 genes that correlate with cell proliferation rates. These include actors in DNA replication and mitosis, and genes periodically expressed during the cell cycle. This signature of cell proliferation is a valuable resource when analyzing high-throughput data showing changes in proliferation across conditions. We show how to use this resource to help in interpretation of in vitro drug screens and tumor samples. It informs on differences of cell proliferation rates between conditions where such information is not directly available. The signature genes also highlight which hits in a screen may be due to proliferation changes; this can either contribute to biological interpretation or help focus on experiment-specific regulation events otherwise buried in the statistical analysis.

Opportunities for pharmacoproteomics in biomarker discovery

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape. This article is protected by copyright. All rights reserved.

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.

A review of machine learning approaches for drug synergy prediction in cancer

Combinational pharmacotherapy with the synergistic/additive effect is a powerful treatment strategy for complex diseases such as malignancies. Identifying synergistic combinations with various compounds and structures requires testing a large number of compound combinations. However, in practice, examining different compounds by in vivo and in vitro approaches is costly, infeasible and challenging. In the last decades, significant success has been achieved by expanding computational methods in different pharmacological and bioinformatics domains. As promising tools, computational approaches such as machine learning algorithms (MLAs) are used for prioritizing combinational pharmacotherapies. This review aims to provide the models developed to predict synergistic drug combinations in cancer by MLAs with various information, including gene expression, protein-protein interactions, metabolite interactions, pathways and pharmaceutical information such as chemical structure, molecular descriptor and drug-target interactions.

Machine learning for multi-omics data integration in cancer

Multi-omics data analysis is an important aspect of cancer molecular biology studies and has led to ground-breaking discoveries. Many efforts have been made to develop machine learning methods that automatically integrate omics data. Here, we review machine learning tools categorized as either general-purpose or task-specific, covering both supervised and unsupervised learning for integrative analysis of multi-omics data. We benchmark the performance of five machine learning approaches using data from the Cancer Cell Line Encyclopedia, reporting accuracy on cancer type classification and mean absolute error on drug response prediction, and evaluating runtime efficiency. This review provides recommendations to researchers regarding suitable machine learning method selection for their specific applications. It should also promote the development of novel machine learning methodologies for data integration, which will be essential for drug discovery, clinical trial design, and personalized treatments.

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Featuring a balance of both biological and technical content

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Categorizing the reviewed tools into general purpose and task-specific

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Performing an independent benchmarking analysis using a publicly available dataset

MYC dosage compensation is mediated by miRNA-transcription factor interactions in aneuploid cancer

We hypothesize that dosage compensation of critical genes arises from systems-level properties for cancer cells to withstand the negative effects of aneuploidy. We identified several candidate genes in cancer multiomics data and developed a biocomputational platform to construct a mathematical model of their interaction network with micro-RNAs and transcription factors, where the property of dosage compensation emerged for MYC and was dependent on the kinetic parameters of its feedback interactions with three micro-RNAs. These circuits were experimentally validated using a genetic tug-of-war technique to overexpress an exogenous MYC, leading to overexpression of the three microRNAs involved and downregulation of endogenous MYC. In addition, MYC overexpression or inhibition of its compensating miRNAs led to dosage-dependent cytotoxicity in MYC-amplified colon cancer cells. Finally, we identified negative correlation of MYC dosage compensation with patient survival in TCGA breast cancer patients, highlighting the potential of this mechanism to prevent aneuploid cancer progression.

Nucleoporin TPR promotes tRNA nuclear export and protein synthesis in lung cancer cells

The robust proliferation of cancer cells requires vastly elevated levels of protein synthesis, which relies on a steady supply of aminoacylated tRNAs. Delivery of tRNAs to the cytoplasm is a highly regulated process, but the machinery for tRNA nuclear export is not fully elucidated. In this study, using a live cell imaging strategy that visualizes nascent transcripts from a specific tRNA gene in yeast, we identified the nuclear basket proteins Mlp1 and Mlp2, two homologs of the human TPR protein, as regulators of tRNA export. TPR expression is significantly increased in lung cancer tissues and correlated with poor prognosis. Consistently, knockdown of TPR inhibits tRNA nuclear export, protein synthesis and cell growth in lung cancer cell lines. We further show that NXF1, a well-known mRNA nuclear export factor, associates with tRNAs and mediates their transport through nuclear pores. Collectively, our findings uncover a conserved mechanism that regulates nuclear export of tRNAs, which is a limiting step in protein synthesis in eukaryotes.

Reinspection of a Clinical Proteomics Tumor Analysis Consortium (CPTAC) Dataset with Cloud Computing Reveals Abundant Post-Translational Modifications and Protein Sequence Variants

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number of sequence variants and post-translational modifications that can be considered. The original CPTAC studies limited the search for PTMs to only samples that were chemically enriched for those modified peptides. Similarly, the only sequence variants considered were those with strong evidence at the exon or transcript level. In this multi-institutional collaborative reanalysis, we utilized unbiased protein databases containing millions of human sequence variants in conjunction with hundreds of common post-translational modifications. Using these tools, we identified tens of thousands of high-confidence PTMs and sequence variants. We identified 4132 phosphorylated peptides in nonenriched samples, 93% of which were confirmed in the samples which were chemically enriched for phosphopeptides. In addition, our results also cover 90% of the high-confidence variants reported by the original proteogenomics study, without the need for sample specific next-generation sequencing. Finally, we report fivefold more somatic and germline variants that have an independent evidence at the peptide level, including mutations in ERRB2 and BCAS1. In this reanalysis of CPTAC proteomic data with cloud computing, we present an openly available and searchable web resource of the highest-coverage proteomic profiling of human tumors described to date.

Functional genomics approaches to improve pre-clinical drug screening and biomarker discovery

Advances in sequencing technology have enabled the genomic and transcriptomic characterization of human malignancies with unprecedented detail. However, this wealth of information has been slow to translate into clinically meaningful outcomes. Different models to study human cancers have been established and extensively characterized. Using these models, functional genomic screens and pre-clinical drug screening platforms have identified genetic dependencies that can be exploited with drug therapy. These genetic dependencies can also be used as biomarkers to predict response to treatment. For many cancers, the identification of such biomarkers remains elusive. In this review, we discuss the development and characterization of models used to study human cancers, RNA interference and CRISPR screens to identify genetic dependencies, large-scale pharmacogenomics studies and drug screening approaches to improve pre-clinical drug screening and biomarker discovery.

A proteomic platform to identify off-target proteins associated with therapeutic modalities that induce protein degradation or gene silencing

Novel modalities such as PROTAC and RNAi have the ability to inadvertently alter the abundance of endogenous proteins. Currently available in vitro secondary pharmacology assays, which evaluate off-target binding or activity of small molecules, do not fully assess the off-target effects of PROTAC and are not applicable to RNAi. To address this gap, we developed a proteomics-based platform to comprehensively evaluate the abundance of off-target proteins. First, we selected off-target proteins using genetics and pharmacology evidence. This process yielded 2813 proteins, which we refer to as the “selected off-target proteome” (SOTP). An iterative algorithm was then used to identify four human cell lines out of 932. The 4 cell lines collectively expressed ~ 80% of the SOTP based on transcriptome data. Second, we used mass spectrometry to quantify the intracellular and extracellular proteins from the selected cell lines. Among over 10,000 quantifiable proteins identified, 1828 were part of the predefined SOTP. The SOTP was designed to be easily modified or expanded, owing to the rational selection process developed and the label free LC–MS/MS approach chosen. This versatility inherent to our platform is essential to design fit-for-purpose studies that can address the dynamic questions faced in investigative toxicology.

MicroRNA-324-5p-CUEDC2 Axis Mediates Gain-of-Function Mutant p53-Driven Cancer Stemness

Regulation of cancer stemness has recently emerged as a new gain-of-function (GOF) property of mutant p53. In this study, we identify miR-324-5p as a critical epigenetic regulator of cancer stemness and demonstrate its role in mediating GOF-mutant p53-driven stemness phenotypes. We report that miR-324-5p is upregulated in human cancer cell lines and non-small cell lung carcinoma (NSCLC) tumors carrying TP53 GOF mutations. Mechanistically, we show that GOF mutant p53 upregulates miR-324-5p expression via c-Myc, an oncogenic transcription factor in cancer cells. Our experimental results suggest that miR-324-5p-induced CSC phenotypes stem from the downregulation of CUEDC2, a downstream target gene of miR-324-5p. Accordingly, CUEDC2 complementation diminishes elevated CSC marker expression in miR-324-5p-overexpressing cancer cells. We further demonstrate that mutant p53 cancer cells maintain a low level of CUEDC2 that is rescued upon miR-324-5p inhibition. Importantly, we identify CUEDC2 downregulation as a novel characteristic feature of TP53-mutated human cancers. We show that activation of NF-κB due to downregulation of CUEDC2 by miR-324-5p imparts stemness in GOF mutant p53 cancer cells. Finally, we provide evidence that TP53 mutations coupled with high miR-324-5p expression predict poor prognosis in patients with lung adenocarcinoma. Thus, our study delineates an altered miR-324-5p-CUEDC2-NF-κB pathway as a novel regulator of GOF mutant p53-driven cancer stemness. IMPLICATIONS: Our findings implicate miRNA-324-5p as a novel epigenetic modifier of human cancer stemness.

Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models

Metabolic heterogeneity is a hallmark of cancer and can distinguish a normal phenotype from a cancer phenotype. In the systems biology domain, context-specific models facilitate extracting physiologically relevant information from high-quality data. Here, to utilize the heterogeneity of metabolic patterns to discover biomarkers of all cancers, we benchmarked thousands of context-specific models using well-established algorithms for the integration of omics data into the generic human metabolic model Recon3D. By analyzing the active reactions capable of carrying flux and their magnitude through flux balance analysis, we proved that the metabolic pattern of each cancer is unique and could act as a cancer metabolic fingerprint. Subsequently, we searched for proper feature selection methods to cluster the flux states characterizing each cancer. We employed PCA-based dimensionality reduction and a random forest learning algorithm to reveal reactions containing the most relevant information in order to effectively identify the most influential fluxes. Conclusively, we discovered different pathways that are probably the main sources for metabolic heterogeneity in cancers. We designed the GEMbench website to interactively present the data, methods, and analysis results.

A Systematic Evaluation of Semispecific Peptide Search Parameter Enables Identification of Previously Undescribed N-Terminal Peptides and Conserved Proteolytic Processing in Cancer Cell Lines

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become the most commonly used technique in explorative proteomic research. A variety of open-source tools for peptide-spectrum matching have become available. Most analyses of explorative MS data are performed using conventional settings, such as fully specific enzymatic constraints. Here we evaluated the impact of the fragment mass tolerance in combination with the enzymatic constraints on the performance of three search engines. Three open-source search engines (Myrimatch, X! Tandem, and MSGF+) were evaluated concerning the suitability in semi- and unspecific searches as well as the importance of accurate fragment mass spectra in non-specific peptide searches. We then performed a semispecific reanalysis of the published NCI-60 deep proteome data applying the most suited parameters. Semi- and unspecific LC-MS/MS data analyses particularly benefit from accurate fragment mass spectra while this effect is less pronounced for conventional, fully specific peptide-spectrum matching. Search speed differed notably between the three search engines for semi- and non-specific peptide-spectrum matching. Semispecific reanalysis of NCI-60 proteome data revealed hundreds of previously undescribed N-terminal peptides, including cases of proteolytic processing or likely alternative translation start sites, some of which were ubiquitously present in all cell lines of the reanalyzed panel. Highly accurate MS2 fragment data in combination with modern open-source search algorithms enable the confident identification of semispecific peptides from large proteomic datasets. The identification of previously undescribed N-terminal peptides in published studies highlights the potential of future reanalysis and data mining in proteomic datasets.

Deciphering the signaling network of breast cancer improves drug sensitivity prediction

One goal of precision medicine is to tailor effective treatments to patients' specific molecular markers of disease. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data-on more than 80 million single cells from 4,000 conditions-were used to fit mechanistic signaling network models that provide insight into how cancer cells process information. Our dynamic single-cell-based models accurately predicted drug sensitivity and identified genomic features associated with drug sensitivity, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. We observed similar trends in genotype-drug sensitivity associations in patient-derived xenograft mouse models. This work provides proof of principle that patient-specific single-cell measurements and modeling could inform effective precision medicine strategies.

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.

Proteome Discoverer-A Community Enhanced Data Processing Suite for Protein Informatics

Proteomics researchers today face an interesting challenge: how to choose among the dozens of data processing and analysis pipelines available for converting tandem mass spectrometry files to protein identifications. Due to the dominance of Orbitrap technology in proteomics in recent history, many researchers have defaulted to the vendor software Proteome Discoverer. Over the fourteen years since the initial release of the software, it has evolved in parallel with the increasingly complex demands faced by proteomics researchers. Today, Proteome Discoverer exists in two distinct forms with both powerful commercial versions and fully functional free versions in use in many labs today. Throughout the 11 main versions released to date, a central theme of the software has always been the ability to easily view and verify the spectra from which identifications are made. This ability is, even today, a key differentiator from other data analysis solutions. In this review I will attempt to summarize the history and evolution of Proteome Discoverer from its first launch to the versions in use today.

Integrative pan cancer analysis reveals epigenomic variation in cancer type and cell specific chromatin domains

Epigenetic mechanisms contribute to the initiation and development of cancer, and epigenetic variation promotes dynamic gene expression patterns that facilitate tumor evolution and adaptation. While the NCI-60 panel represents a diverse set of human cancer cell lines that has been used to screen chemical compounds, a comprehensive epigenomic atlas of these cells has been lacking. Here, we report an integrative analysis of 60 human cancer epigenomes, representing a catalog of activating and repressive histone modifications. We identify genome-wide maps of canonical sharp and broad H3K4me3 domains at promoter regions of tumor suppressors, H3K27ac-marked conventional enhancers and super enhancers, and widespread inter-cancer and intra-cancer specific variability in H3K9me3 and H4K20me3-marked heterochromatin domains. Furthermore, we identify features of chromatin states, including chromatin state switching along chromosomes, correlation of histone modification density with genetic mutations, DNA methylation, enrichment of DNA binding motifs in regulatory regions, and gene activity and inactivity. These findings underscore the importance of integrating epigenomic maps with gene expression and genetic variation data to understand the molecular basis of human cancer. Our findings provide a resource for mining epigenomic maps of human cancer cells and for identifying epigenetic therapeutic targets.

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.

Molecular genomic features associated with in vitro response of the NCI-60 cancer cell line panel to natural products

Natural products remain a significant source of anticancer chemotherapeutics. The search for targeted drugs for cancer treatment includes consideration of natural products, which may provide new opportunities for antitumor cytotoxicity as single agents or in combination therapy. We examined the association of molecular genomic features in the well-characterized NCI-60 cancer cell line panel with in vitro response to treatment with 1302 small molecules which included natural products, semisynthetic natural product derivatives, and synthetic compounds based on a natural product pharmacophore from the Developmental Therapeutics Program of the US National Cancer Institute's database. These compounds were obtained from a variety of plant, marine, and microbial species. Molecular information utilized for the analysis included expression measures for 23059 annotated transcripts, lncRNAs, and miRNAs, and data on protein-changing single nucleotide variants in 211 cancer-related genes. We found associations of expression of multiple genes including SLFN11, CYP2J2, EPHX1, GPC1, ELF3, and MGMT involved in DNA damage repair, NOTCH family members, ABC and SLC transporters, and both mutations in tyrosine kinases and BRAF V600E with NCI-60 responses to specific categories of natural products. Hierarchical clustering identified groups of natural products, which correlated with a specific mechanism of action. Specifically, several natural product clusters were associated with SLFN11 gene expression, suggesting that potential action of these compounds may involve DNA damage. The associations between gene expression or genome alterations of functionally relevant genes with the response of cancer cells to natural products provide new information about potential mechanisms of action of these identified clusters of compounds with potentially similar biological effects. This information will assist in future drug discovery and in design of new targeted cancer chemotherapy agents.

Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models

Research on new cancer drugs is performed either through gene knockout studies or phenotypic screening of drugs in cancer cell-lines. Both of these approaches are costly and time-consuming. Computational framework, e.g., genome-scale metabolic models (GSMMs), could be a good alternative to find potential drug targets. The present study aims to investigate the applicability of gene knockout strategies to be used as the finding of drug targets using GSMMs. We performed single-gene knockout studies on existing GSMMs of the NCI-60 cell-lines obtained from 9 tissue types. The metabolic genes responsible for the growth of cancerous cells were identified and then ranked based on their cellular growth reduction. The possible growth reduction mechanisms, which matches with the gene knockout results, were described. Gene ranking was used to identify potential drug targets, which reduce the growth rate of cancer cells but not of the normal cells. The gene ranking results were also compared with existing shRNA screening data. The rank-correlation results for most of the cell-lines were not satisfactory for a single-gene knockout, but it played a significant role in deciding the activity of drug against cell proliferation, whereas multiple gene knockout analysis gave better correlation results. We validated our theoretical results experimentally and showed that the drugs mitotane and myxothiazol can inhibit the growth of at least four cell-lines of NCI-60 database.

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.

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.

Cysteine alkylation methods in shotgun proteomics and their possible effects on methionine residues

In order to optimize sample preparation for shotgun proteomics, we compared four cysteine alkylating agents: iodoacetamide, chloroacetamide, 4-vinylpyridine and methyl methanethiosulfonate, and estimated their effects on the results of proteome analysis. Because alkylation may result in methionine modification in vitro, proteomics data were searched for methionine to isothreonine conversions, which may mimic genomic methionine to threonine substitutions found in proteogenomic analyses. We found that chloroacetamide was superior to the other reagents in terms of the number of identified peptides and undesirable off-site reactions. Among the reagents evaluated, iodoacetamide increased the rate of methionine-to-isothreonine conversion, especially if the sample was prepared in gel. The presence of proline following methionine in a protein sequence increased the modification rate as well. Generally, the methionine-to-isothreonine conversion events were relatively rare, but should be taken into account in proteogenomic studies when searching for single nucleotide polymorphism events at the protein level. Additionally, we have evaluated other methionine modifications, such as oxidation and carbamidomethylation. We found that carbamidomethylation may affect up to 80% of peptides containing methionine under the condition of iodoacetamide alkylation. In this case, carbamidomethylation of methionine is more common than oxidation and should be accounted for as a variable modification during proteomic search. SIGNIFICANCE: One of the most trending questions in bottom-up proteomics is the depth of proteome profiling, in other words, the coverage of proteins by identified tryptic peptides. In proteogenomics, where the identification of a single peptide, e.g. bearing an amino acid substitution, may be of interest, high sequence coverage is especially important. Chemical modifications during sample preparation may mimic biologically significant coding mutations at the proteome level. A typical example of such modification is methionine to isothreonine conversion during alkylation, which mimics methionine to threonine substitution in protein sequences due to respective genomic mutations. Therefore, the studies on the proper selection of alkylating reagents which balance the cysteine alkylation efficiency and the extent of methionine conversion upon conventional proteomic sample preparation workflow are crucial for the outcome of proteogenomic analyses and should present a general interest for the proteomic community.

Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology

Data-independent acquisition mass spectrometry (DIA-MS) is a next generation proteomic methodology that generates permanent digital proteome maps offering highly reproducible retrospective analysis of cellular and tissue specimens. The adoption of this technology has ushered a new wave of oncology studies across a wide range of applications including its use in molecular classification, oncogenic pathway analysis, drug and biomarker discovery and unravelling mechanisms of therapy response and resistance. In this review, we provide an overview of the experimental workflows commonly used in DIA-MS, including its current strengths and limitations versus conventional data-dependent acquisition mass spectrometry (DDA-MS). We further summarise a number of key studies to illustrate the power of this technology when applied to different facets of oncology. Finally we offer a perspective of the latest innovations in DIA-MS technology and machine learning-based algorithms necessary for driving the development of high-throughput, in-depth and reproducible proteomic assays that are compatible with clinical diagnostic workflows, which will ultimately enable the delivery of precision cancer medicine to achieve optimal patient outcomes.

A Three-Way Combinatorial CRISPR Screen for Analyzing Interactions among Druggable Targets

We present a CRISPR-based multi-gene knockout screening system and toolkits for extensible assembly of barcoded high-order combinatorial guide RNA libraries en masse. We apply this system for systematically identifying not only pairwise but also three-way synergistic therapeutic target combinations and successfully validate double- and triple-combination regimens for suppression of cancer cell growth and protection against Parkinson's disease-associated toxicity. This system overcomes the practical challenges of experimenting on a large number of high-order genetic and drug combinations and can be applied to uncover the rare synergistic interactions between druggable targets.

Proteome activity landscapes of tumor cell lines determine drug responses

Integrated analysis of genomes, transcriptomes, proteomes and drug responses of cancer cell lines (CCLs) is an emerging approach to uncover molecular mechanisms of drug action. We extend this paradigm to measuring proteome activity landscapes by acquiring and integrating quantitative data for 10,000 proteins and 55,000 phosphorylation sites (p-sites) from 125 CCLs. These data are used to contextualize proteins and p-sites and predict drug sensitivity. For example, we find that Progesterone Receptor (PGR) phosphorylation is associated with sensitivity to drugs modulating estrogen signaling such as Raloxifene. We also demonstrate that Adenylate kinase isoenzyme 1 (AK1) inactivates antimetabolites like Cytarabine. Consequently, high AK1 levels correlate with poor survival of Cytarabine-treated acute myeloid leukemia patients, qualifying AK1 as a patient stratification marker and possibly as a drug target. We provide an interactive web application termed ATLANTiC (http://atlantic.proteomics.wzw.tum.de), which enables the community to explore the thousands of novel functional associations generated by this work.

Quantitative Proteomics of the Cancer Cell Line Encyclopedia

Proteins are essential agents of biological processes. To date, large-scale profiling of cell line collections including the Cancer Cell Line Encyclopedia (CCLE) has focused primarily on genetic information whereas deep interrogation of the proteome has remained out of reach. Here, we expand the CCLE through quantitative profiling of thousands of proteins by mass spectrometry across 375 cell lines from diverse lineages to reveal information undiscovered by DNA and RNA methods. We observe unexpected correlations within and between pathways that are largely absent from RNA. An analysis of microsatellite instable (MSI) cell lines reveals the dysregulation of specific protein complexes associated with surveillance of mutation and translation. These and other protein complexes were associated with sensitivity to knockdown of several different genes. These data in conjunction with the wider CCLE are a broad resource to explore cellular behavior and facilitate cancer research.

ProteomicsDB: a multi-omics and multi-organism resource for life science research

ProteomicsDB (https://www.ProteomicsDB.org) started as a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. The data types and contents grew over time to include RNA-Seq expression data, drug-target interactions and cell line viability data. In this manuscript, we summarize new developments since the previous update that was published in Nucleic Acids Research in 2017. Over the past two years, we have enriched the data content by additional datasets and extended the platform to support protein turnover data. Another important new addition is that ProteomicsDB now supports the storage and visualization of data collected from other organisms, exemplified by Arabidopsis thaliana. Due to the generic design of ProteomicsDB, all analytical features available for the original human resource seamlessly transfer to other organisms. Furthermore, we introduce a new service in ProteomicsDB which allows users to upload their own expression datasets and analyze them alongside with data stored in ProteomicsDB. Initially, users will be able to make use of this feature in the interactive heat map functionality as well as the drug sensitivity prediction, but ultimately will be able to use all analytical features of ProteomicsDB in this way.

Proteomics and functional study reveal marginal zone B and B1 cell specific protein as a candidate marker of multiple myeloma

Multiple myeloma (MM) is a plasma cell‑associated cancer and accounts for 13% of all hematological malignancies, worldwide. MM still remains an incurable plasma cell malignancy with a poor prognosis due to a lack of suitable markers. Therefore, discovering novel markers and targets for diagnosis and therapeutics of MM is essential. The present study aims to identify markers associated with MM malignancy using patient‑derived MM mononuclear cells (MNCs). Label‑free quantitative proteomics analysis revealed a total of 192 differentially regulated proteins, in which 79 proteins were upregulated and 113 proteins were found to be downregulated in MM MNCs as compared to non‑hematological malignant samples. The identified differentially expressed candidate proteins were analyzed using various bioinformatics tools, including Ingenuity Pathway Analysis (IPA), Protein Analysis THrough Evolutionary Relationships (PANTHER), Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Database for Annotation, Visualization and Integrated Discovery (DAVID) to determine their biological context. Among the 192 candidate proteins, marginal zone B and B1 cell specific protein (MZB1) was investigated in detail using the RPMI-8226 cell line model of MM. The functional studies revealed that higher expression of MZB1 is associated with promoting the progression of MM pathogenesis and could be established as a potential target for MM in the future.

[Modification of cysteine residues for mass spectrometry-based proteomic analysis: facts and artifacts]

Mass spectrometric proteomic analysis at the sample preparation stage involves the artificial reduction of disulfide bonds in proteins formed between cysteine residues. Such bonds, when preserved in their native state, complicate subsequent enzymatic hydrolysis and interpretation of the research results. To prevent the re-formation of the disulfide bonds, cysteine residues are protected by special groups, most often by alkylation. In this review, we consider the methods used to modify cysteine residues during sample preparation, as well as possible artifacts of this stage. Particularly, adverse reactions of the alkylating agents with other amino acid residues are described. The most common alkylating compound used to protect cysteine residues in mass spectrometric proteomic analysis is iodoacetamide. However, an analysis of the literature in this area indicates that this reagent causes more adverse reactions than other agents used, such as chloroacetamide and acrylamide. The latter can be recommended for wider use. In the review we also discuss the features of the cysteine residue modifications and their influence on the efficiency of the search for post-translational modifications and protein products of single nucleotide substitutions.