Quantitative Proteomics of the Cancer Cell Line Encyclopedia

TOP2A modulates signaling via the AKT/mTOR pathway to promote ovarian cancer cell proliferation

Ovarian cancer (OC) is a form of gynecological malignancy that is associated with worse patient outcomes than any other cancer of the female reproductive tract. Topoisomerase II α (TOP2A) is commonly regarded as an oncogene that is associated with malignant disease progression in a variety of cancers, its mechanistic functions in OC have yet to be firmly established. We explored the role of TOP2A in OC through online databases, clinical samples, in vitro and in vivo experiments. And initial analyses of public databases revealed high OC-related TOP2A expression in patient samples that was related to poorer prognosis. This was confirmed by clinical samples in which TOP2A expression was elevated in OC relative to healthy tissue. Kaplan-Meier analyses further suggested that higher TOP2A expression levels were correlated with worse prognosis in OC patients. In vitro, TOP2A knockdown resulted in the inhibition of OC cell proliferation, with cells entering G1 phase arrest and undergoing consequent apoptotic death. In rescue assays, TOP2A was confirmed to regulate cell proliferation and cell cycle through AKT/mTOR pathway activity. Mouse model experiments further affirmed the key role that TOP2A plays as a driver of OC cell proliferation. These data provide strong evidence supporting TOP2A as an oncogenic mediator and prognostic biomarker related to OC progression and poor outcomes. At the mechanistic level, TOP2A can control tumor cell growth via AKT/mTOR pathway modulation. These preliminary results provide a foundation for future research seeking to explore the utility of TOP2A inhibitor-based combination treatment regimens in platinum-resistant recurrent OC patients.

FBLN2 is associated with basal cell markers Krt14 and ITGB1 in mouse mammary epithelial cells and has a preferential expression in molecular subtypes of human breast cancer

BACKGROUND

Fibulin-2 (FBLN2) is a secreted extracellular matrix (ECM) glycoprotein and has been identified in the mouse mammary gland, in cap cells of terminal end buds (TEBs) during puberty, and around myoepithelial cells during early pregnancy. It is required for basement membrane (BM) integrity in mammary epithelium, and its loss has been associated with human breast cancer invasion. Herein, we attempted to confirm the relevance of FBLN2 to myoepithelial phenotype in mammary epithelium and to assess its expression in molecular subtypes of human breast cancer.

METHODS

The relationship between FBLN2 expression and epithelial markers was investigated in pubertal mouse mammary glands and the EpH4 mouse mammary epithelial cell line using immunohistochemistry, immunocytochemistry, and immunoblotting. Human breast cancer mRNA data from the METABRIC and TCGA datasets from Bioportal were analyzed to assess the association of Fbln2 expression with epithelial markers, and with molecular subtypes. Survival curves were generated using data from the METABRIC dataset and the KM databases.

RESULTS

FBLN2 knockdown in mouse mammary epithelial cells was associated with a reduction in KRT14 and an increase in KRT18. Further, TGFβ3 treatment resulted in the upregulation of FBLN2 in vitro. Meta-analyses of human breast cancer datasets from Bioportal showed a higher expression of Fbln2 mRNA in claudin-low, LumA, and normal-like breast cancers compared to LumB, Her2 +, and Basal-like subgroups. Fbln2 mRNA levels were positively associated with mesenchymal markers, myoepithelial markers, and markers of epithelial-mesenchymal transition. Higher expression of Fbln2 mRNA was associated with better prognosis in less advanced breast cancer and this pattern was reversed in more advanced lesions.

CONCLUSION

With further validation, these observations may offer a molecular prognostic tool for human breast cancer for more personalized therapeutic approaches.

ETV4‑mediated transcriptional activation of SLC12A5 exacerbates ferroptosis resistance and glucose metabolism reprogramming in breast cancer cells

Solute carrier family 12 member 5 (SLC12A5) is an oncogene in numerous types of cancer, however its function in breast cancer (BC) remains elusive. ETS translocation variant 4 (ETV4) promotes BC. Therefore, the present study aimed to elucidate the role of SLC12A5 in ferroptosis and glucose metabolism in BC cells as well as to understand the underlying mechanism. Analysis of data from the UALCAN database demonstrated expression levels of SLC12A5 in BC and its association with prognosis. Reverse transcription‑quantitative PCR and western blotting were conducted to evaluate the expression levels of SLC12A5 and ETV4 in BC cells. The abilities of BC cells to proliferate, migrate and invade were assessed using Cell Counting Kit‑8, colony formation, wound healing and Transwell assays. Thiobarbituric acid reactive substances assay and a C11 BODIPY 581/591 probe were used to evaluate lipid peroxidation. Ferroptosis resistance was evaluated by the measurement of Fe2+ and ferroptosis‑related solute carrier family 7a member 11 (SLC7A11), glutathione peroxidase 4 (GPX4), acyl‑CoA synthetase long‑chain family member 4 (ACSL4) and transferrin receptor 1 (TFR1) protein levels. Glycolysis was assessed via evaluation of extracellular acidification rate, oxygen consumption rate, lactate production and glucose consumption. Finally, luciferase reporter and chromatin immunoprecipitation assay were used to verify the interaction between ETV4 and the SLC12A5 promoter. UALCAN database analysis indicated that SLC12A5 was upregulated in BC tissues and cells and that SLC12A5 elevation indicated a poor prognosis of patients with BC. SLC12A5 knockdown suppressed the BC cell proliferative, migratory and invasive capabilities. Moreover, SLC12A5 knockdown decreased BC cell ferroptosis resistance and glucose metabolism reprogramming. The transcription factor ETV4 was demonstrated to bind to the SLC12A5 promoter and upregulate its transcription. Furthermore, ETV4 overexpression counteracted the suppressive effect of SLC12A5 knockdown on the BC cell proliferative, migratory and invasive abilities, as well as on ferroptosis resistance and glucose metabolism reprogramming. Transcriptional activation of SLC12A5 by ETV4 modulated the migration, invasion, ferroptosis resistance and glucose metabolism reprogramming of BC cells.

Protein biomarkers for subtyping breast cancer and implications for future research: a 2024 update

INTRODUCTION

Breast cancer subtyping is used clinically for diagnosis, prognosis, and treatment decisions. Subtypes are categorized by cell of origin, histomorphology, gene expression signatures, hormone receptor status, and/or protein levels. Categorizing breast cancer based on gene expression signatures aids in assessing a patient's recurrence risk. Protein biomarkers, on the other hand, provide functional data for selecting therapies for primary and recurrent tumors. We provide an update on protein biomarkers in breast cancer subtypes and their application in prognosis and therapy selection.

AREAS COVERED

Protein pathways in breast cancer subtypes are reviewed in the context of current protein-targeted treatment options. PubMed, Science Direct, Scopus, and Cochrane Library were searched for relevant studies between 2017 and 17 August 2024.

EXPERT OPINION

Post-translationally modified proteins and their unmodified counterparts have become clinically useful biomarkers for defining breast cancer subtypes from a therapy perspective. Tissue heterogeneity influences treatment outcomes and disease recurrence. Spatial profiling has revealed complex cellular subpopulations within the breast tumor microenvironment. Deciphering the functional relationships between and within tumor clonal cell populations will further aid in defining breast cancer subtypes and create new treatment paradigms for recurrent, drug resistant, and metastatic disease.

Clinical proteomics towards multiomics in cancer

Recent technological advancements in mass spectrometry (MS)-based proteomics technologies have accelerated its application to study greater and greater numbers of human tumor specimens. Over the last several years, the Clinical Proteomic Tumor Analysis Consortium, the International Cancer Proteogenome Consortium, and others have generated MS-based proteomic profiling data combined with corresponding multiomics data on thousands of human tumors to date. Proteomic data sets in the public domain can be re-examined by other researchers with different questions in mind from what the original studies explored. In this review, we examine the increasing role of proteomics in studying cancer, along with the potential for previous studies and their associated data sets to contribute to improving the diagnosis and treatment of cancer in the clinical setting. We also explore publicly available proteomics and multi-omics data from cancer cell line models to show how such data may aid in identifying therapeutic strategies for cancer subsets.

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein's median copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26-29% of luminal tumors. By adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics.

Stearoyl-CoA desaturase inhibition is toxic to acute myeloid leukemia displaying high levels of the de novo fatty acid biosynthesis and desaturation

Identification of specific and therapeutically actionable vulnerabilities, ideally present across multiple mutational backgrounds, is needed to improve acute myeloid leukemia (AML) patients' outcomes. We identify stearoyl-CoA desaturase (SCD), the key enzyme in fatty acid (FA) desaturation, as prognostic of patients' outcomes and, using the clinical-grade inhibitor SSI-4, show that SCD inhibition (SCDi) is a therapeutic vulnerability across multiple AML models in vitro and in vivo. Multiomic analysis demonstrates that SCDi causes lipotoxicity, which induces AML cell death via pleiotropic effects. Sensitivity to SCDi correlates with AML dependency on FA desaturation regardless of mutational profile and is modulated by FA biosynthesis activity. Finally, we show that lipotoxicity increases chemotherapy-induced DNA damage and standard chemotherapy further sensitizes AML cells to SCDi. Our work supports developing FA desaturase inhibitors in AML while stressing the importance of identifying predictive biomarkers of response and biologically validated combination therapies to realize their full therapeutic potential.

Systematic mapping of mitochondrial calcium uniporter channel (MCUC)-mediated calcium signaling networks

The mitochondrial calcium uniporter channel (MCUC) mediates mitochondrial calcium entry, regulating energy metabolism and cell death. Although several MCUC components have been identified, the molecular basis of mitochondrial calcium signaling networks and their remodeling upon changes in uniporter activity have not been assessed. Here, we map the MCUC interactome under resting conditions and upon chronic loss or gain of mitochondrial calcium uptake. We identify 89 high-confidence interactors that link MCUC to several mitochondrial complexes and pathways, half of which are associated with human disease. As a proof-of-concept, we validate the mitochondrial intermembrane space protein EFHD1 as a binding partner of the MCUC subunits MCU, EMRE, and MCUB. We further show a MICU1-dependent inhibitory effect of EFHD1 on calcium uptake. Next, we systematically survey compensatory mechanisms and functional consequences of mitochondrial calcium dyshomeostasis by analyzing the MCU interactome upon EMRE, MCUB, MICU1, or MICU2 knockdown. While silencing EMRE reduces MCU interconnectivity, MCUB loss-of-function leads to a wider interaction network. Our study provides a comprehensive and high-confidence resource to gain insights into players and mechanisms regulating mitochondrial calcium signaling and their relevance in human diseases.

The Lipid Droplet Protein DHRS3 Is a Regulator of Melanoma Cell State

Lipid droplets are fat storage organelles composed of a protein envelope and lipid-rich core. Regulation of this protein envelope underlies differential lipid droplet formation and function. In melanoma, lipid droplet formation has been linked to tumor progression and metastasis, but it is unknown whether lipid droplet proteins play a role. To address this, we performed proteomic analysis of the lipid droplet envelope in melanoma. We found that lipid droplet proteins were differentially enriched in distinct melanoma states; from melanocytic to undifferentiated. DHRS3, which converts all-trans-retinal to all-trans-retinol, is upregulated in the MITFLO/undifferentiated/neural crest-like melanoma cell state and reduced in the MITFHI/melanocytic state. Increased DHRS3 expression is sufficient to drive MITFHI/melanocytic cells to a more undifferentiated/invasive state. These changes are due to retinoic acid-mediated regulation of melanocytic genes. Our data demonstrate that melanoma cell state can be regulated by expression of lipid droplet proteins which affect downstream retinoid signaling.

Nuclear p62 condensates stabilize the promyelocytic leukemia nuclear bodies by sequestering their ubiquitin ligase RNF4

Liquid-liquid phase separation has emerged as a crucial mechanism driving the formation of membraneless biomolecular condensates, which play important roles in numerous cellular processes. These condensates, found both in the nucleus and cytoplasm, are formed through multivalent, low-affinity interactions between various molecules. P62-containing condensates serve, among other functions, as proteolytic hubs for the ubiquitin-proteasome system. In this study, we investigated the dynamic interplay between nuclear p62 condensates and promyelocytic nuclear bodies (PML-NBs). We show that p62 condensates stabilize PML-NBs under both basal conditions and following exposure to arsenic trioxide which stimulates their degradation. We further show that this effect on the stability of PML-NBs is due to sequestration of their ubiquitin E3 ligase RNF4 in the p62 condensates with subsequent rapid degradation of the ligase. The sequestration of the ligase is made possible by association between the proline-rich domain of the PML protein and the PB1 domain of p62, which results in the formation of a PML-NB shell around the p62 condensates. Importantly, these hybrid structures do not undergo fusion and mixing of their contents which leaves unsolved the mechanism of sequestration of RNF4 in the condensates. These findings suggest an additional possible mechanism of PML-NB as a tumor suppressor which is mediated via interactions between different biomolecular condensates.

Expression-Driven Genetic Dependency Reveals Targets for Precision Medicine

Cancer cells are heterogeneous, each harboring distinct molecular aberrations and are dependent on different genes for their survival and proliferation. While successful targeted therapies have been developed based on driver DNA mutations, many patient tumors lack druggable mutations and have limited treatment options. Here, we hypothesize that new precision oncology targets may be identified through "expression-driven dependency", whereby cancer cells with high expression of a targeted gene are more vulnerable to the knockout of that gene. We introduce a Bayesian approach, BEACON, to identify such targets by jointly analyzing global transcriptomic and proteomic profiles with genetic dependency data of cancer cell lines across 17 tissue lineages. BEACON identifies known druggable genes, e.g., BCL2, ERBB2, EGFR, ESR1, MYC , while revealing new targets confirmed by both mRNA- and protein-expression driven dependency. Notably, the identified genes show an overall 3.8-fold enrichment for approved drug targets and enrich for druggable oncology targets by 7 to 10-fold. We experimentally validate that the depletion of GRHL2 , TP63 , and PAX5 effectively reduce tumor cell growth and survival in their dependent cells. Overall, we present the catalog of express-driven dependency targets as a resource for identifying novel therapeutic targets in precision oncology.

OMIBONE: Omics-driven computer model of bone regeneration for personalized treatment

Treatment of bone fractures are standardized according to the AO classification, which mainly refers to the mechanical stabilization required in a given situation but neglect individual differences due to patient's healing potential or accompanying diseases. Specially in elderly or immune-compromised patients, the complexity of individual constrains on a biological as well as mechanical level are hard to account for. Here, we introduce a novel framework that allows to predict bone regeneration outcome using combined proteomic and mechanical analyses in a computer model. The framework uses Ingenuity Pathway Analysis (IPA) software to link protein changes to alterations in biological processes and integrates these in an Agent-Based Model (ABM) of bone regeneration. This combined framework allows to predict bone formation and the potential of an individual to heal a given fracture setting. The performance of the framework was evaluated by replicating the experimental setup of a mouse femur fracture stabilized with an intramedullary pin. The model was informed by serum derived proteomics data. The tissue formation patterns were compared against experimental data based on x-ray and histology images. The results indicate the framework potential in predicting an individual's bone formation potential and hold promise as a concept to enable personalized bone healing predictions for a chosen fracture fixation.

Mass Spectrometry-Based Proteomics for Assessing Epitranscriptomic Regulations

Epitranscriptomics is a rapidly evolving field that explores chemical modifications in RNA and how they contribute to dynamic and reversible regulations of gene expression. These modifications, for example, N6-methyladenosine (m6A), are crucial in various RNA metabolic processes, including splicing, stability, subcellular localization, and translation efficiency of mRNAs. Mass spectrometry-based proteomics has become an indispensable tool in unraveling the complexities of epitranscriptomics, offering high-throughput, precise protein identification, and accurate quantification of differential protein expression. Over the past two decades, advances in mass spectrometry, including the improvement of high-resolution mass spectrometers and innovative sample preparation methods, have allowed researchers to perform in-depth analyses of epitranscriptomic regulations. This review focuses on the applications of bottom-up proteomics in the field of epitranscriptomics, particularly in identifying and quantifying epitranscriptomic reader, writer, and eraser (RWE) proteins and in characterizing their functions, posttranslational modifications, and interactions with other proteins. Together, by leveraging modern proteomics, researchers can gain deep insights into the intricate regulatory networks of RNA modifications, advancing fundamental biology, and fostering potential therapeutic applications.

P16INK4A drives RB1 degradation by UTP14A-catalyzed K810 ubiquitination

P16INK4A expression is inversely associated with RB1 expression in cancer cells, and P16INK4A inhibits CDK4-catalyzed RB1 phosphorylation. How P16INK4A and RB1 coordinately express and regulate the cell cycle remains to be studied. In the present study, we found that P16INK4A upregulated the E3 ligase UTP14A, which led to the ubiquitination of RB1 at K810 and RB1 degradation. P16INK4A loss consistently disrupted the UTP14A-mediated degradation of RB1 and caused RB1 accumulation. Functionally, P16INK4A loss inhibited RB1 ubiquitination in a cell cycle progression-independent fashion and inhibited proteome-scale ubiquitination in a cell cycle progression-dependent manner. Our findings indicate that there is a negative feedback loop between P16INK4A and RB1 expression and that disruption of this loop may partially rescue the biological outcomes of P16INK4A loss. We also revealed a hitherto unknown function for P16 INK4A in regulating proteome-scale ubiquitination by inhibiting cell proliferation, which may be useful for the development of anticancer drugs.

Protocol for generating high-fidelity proteomic profiles using DROPPS

Deep mass spectrometry-based proteomic profiling of rare cell populations has been constrained by sample input requirements. Here, we present a protocol for droplet-based one-pot preparation for proteomic samples (DROPPS), an accessible low-input platform that generates high-fidelity proteomic profiles of 100-2,500 cells. We describe steps for depositing cellular material, cell lysis, and digesting proteins in the same microliter-droplet well. We anticipate DROPPS will accelerate biology-driven proteomic research for a multitude of rare cell populations. For complete details on the use and execution of this protocol, please refer to Waas et al.1.

An Extensive Atlas of Proteome and Phosphoproteome Turnover Across Mouse Tissues and Brain Regions

Understanding how proteins in different mammalian tissues are regulated is central to biology. Protein abundance, turnover, and post-translational modifications like phosphorylation, are key factors that determine tissue-specific proteome properties. However, these properties are challenging to study across tissues and remain poorly understood. Here, we present Turnover-PPT, a comprehensive resource mapping the abundance and lifetime of 11,000 proteins and 40,000 phosphosites across eight mouse tissues and various brain regions, using advanced proteomics and stable isotope labeling. We revealed tissue-specific short- and long-lived proteins, strong correlations between interacting protein lifetimes, and distinct impacts of phosphorylation on protein turnover. Notably, we discovered that peroxisomes are regulated by protein turnover across tissues, and that phosphorylation regulates the stability of neurodegeneration-related proteins, such as Tau and α-synuclein. Thus, Turnover-PPT provides new fundamental insights into protein stability, tissue dynamic proteotypes, and the role of protein phosphorylation, and is accessible via an interactive web-based portal at https://yslproteomics.shinyapps.io/tissuePPT.

Distinct tumor architectures and microenvironments for the initiation of breast cancer metastasis in the brain

Brain metastasis, a serious complication of cancer, hinges on the initial survival, microenvironment adaptation, and outgrowth of disseminated cancer cells. To understand the early stages of brain colonization, we investigated two prevalent sources of cerebral relapse, triple-negative (TNBC) and HER2+ (HER2BC) breast cancers. Using mouse models and human tissue samples, we found that these tumor types colonize the brain, with a preference for distinctive tumor architectures, stromal interfaces, and autocrine programs. TNBC models tend to form perivascular sheaths with diffusive contact with astrocytes and microglia. In contrast, HER2BC models tend to form compact spheroids driven by autonomous tenascin C production, segregating stromal cells to the periphery. Single-cell transcriptomics of the tumor microenvironment revealed that these architectures evoke differential Alzheimer's disease-associated microglia (DAM) responses and engagement of the GAS6 receptor AXL. The spatial features of the two modes of brain colonization have relevance for leveraging the stroma to treat brain metastasis.

Compensatory activity of the PC-ME1 metabolic axis underlies differential sensitivity to mitochondrial complex I inhibition

Deficiencies in the electron transport chain (ETC) lead to mitochondrial diseases. While mutations are distributed across the organism, cell and tissue sensitivity to ETC disruption varies, and the molecular mechanisms underlying this variability remain poorly understood. Here we show that, upon ETC inhibition, a non-canonical tricarboxylic acid (TCA) cycle upregulates to maintain malate levels and concomitant production of NADPH. Our findings indicate that the adverse effects observed upon CI inhibition primarily stem from reduced NADPH levels, rather than ATP depletion. Furthermore, we find that Pyruvate carboxylase (PC) and ME1, the key mediators orchestrating this metabolic reprogramming, are selectively expressed in astrocytes compared to neurons and underlie their differential sensitivity to ETC inhibition. Augmenting ME1 levels in the brain alleviates neuroinflammation and corrects motor function and coordination in a preclinical mouse model of CI deficiency. These studies may explain why different brain cells vary in their sensitivity to ETC inhibition, which could impact mitochondrial disease management.

Enhanced Sample Multiplexing-Based Targeted Proteomics with Intelligent Data Acquisition

Targeted proteomics has been playing an increasingly important role in hypothesis-driven protein research and clinical biomarker discovery. We previously created a workflow, Tomahto, to enable real-time targeted pathway proteomics assays using two-dimensional multiplexing technology. Coupled with the TMT 11-plex reagent, hundreds of proteins of interest from up to 11 samples can be targeted and accurately quantified in a single-shot experiment with remarkable sensitivity. However, room remains to further improve the sensitivity, accuracy, and throughput, especially for targeted studies demanding a high peptide-level success rate. Here, bearing in mind the goal to improve peptide-level targeting, we introduce several new functionalities in Tomahto, featuring the integration of gas-phase fractionation using the FAIMS device, an accompanying software program (TomahtoPrimer) to customize fragmentation for each peptide target, and support for higher multiplexing capacity with the latest TMTpro reagent. We demonstrate that adding these features to the Tomahto platform significantly improves overall success rate from 89% to 98% in a single 60 min targeted assay of 290 peptides across human cell lines, while boosting quantitative accuracy via reducing TMT reporter ion interference.

Efficient Strategy to Discover DNA Aptamers Against Low Abundance Cell Surface Proteins in Scarce Samples

Molecular recognition probes targeting cell surface proteins such as aptamers play crucial roles in precise diagnostics and therapy. However, the selection of aptamers against low-abundance proteins in situ on the cell surface, especially in scarce samples, remains an unmet challenge. In this study, we present a single-round, single-cell aptamer selection method by employing a digital DNA sequencing strategy, termed DiDS selection, to address this dilemma. This approach incorporates a molecular identification card for each DNA template, thereby mitigating biases introduced by multiple PCR amplifications and ensuring the accurate identification of aptamer candidates. Through DiDS selection, we successfully obtained a series of high-quality aptamers against cell lines, clinical specimens, and neurons. Subsequent analyses for target identification revealed that aptamers derived from DiDS selection exhibit recognition capabilities for proteins with varying abundance levels. In contrast, multiple rounds of selection resulted in the enrichment of only one aptamer targeting a high-abundance target. Moreover, the comprehensive profiling of cell surfaces at the single-cell level, utilizing an enriched aptamer pool, revealed unique molecular patterns for each cell line. This streamlined approach holds promise for the rapid generation of specific recognition molecules targeting cell surface proteins across a broad range of expression levels and expands its applications in cell profiling, specific probe identification, biomarker discovery, etc.

A protein expression atlas on tissue samples and cell lines from cancer patients provides insights into tumor heterogeneity and dependencies

The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) are foundational resources in cancer research, providing extensive molecular and phenotypic data. However, large-scale proteomic data across various cancer types for these cohorts remain limited. Here, we expand upon our previous work to generate high-quality protein expression data for approximately 8,000 TCGA patient samples and around 900 CCLE cell line samples, covering 447 clinically relevant proteins, using reverse-phase protein arrays. These protein expression profiles offer profound insights into intertumor heterogeneity and cancer dependency and serve as sensitive functional readouts for somatic alterations. We develop a systematic protein-centered strategy for identifying synthetic lethality pairs and experimentally validate an interaction between protein kinase A subunit α and epidermal growth factor receptor. We also identify metastasis-related protein markers with clinical relevance. This dataset represents a valuable resource for advancing our understanding of cancer mechanisms, discovering protein biomarkers and developing innovative therapeutic strategies.

Defining the heterogeneous molecular landscape of lung cancer cell responses to epigenetic inhibition

Epigenetic inhibitors exhibit powerful antiproliferative and anticancer activities. However, cellular responses to small-molecule epigenetic inhibition are heterogenous and dependent on factors such as the genetic background, metabolic state, and on-/off-target engagement of individual small-molecule compounds. The molecular study of the extent of this heterogeneity often measures changes in a single cell line or using a small number of compounds. To more comprehensively profile the effects of small-molecule perturbations and their influence on these heterogeneous cellular responses, we present a molecular resource based on the quantification of chromatin, proteome, and transcriptome remodeling due to histone deacetylase inhibitors (HDACi) in non-isogenic cell lines. Through quantitative molecular profiling of 10,621 proteins, these data reveal coordinated molecular remodeling of HDACi treated cancer cells. HDACi-regulated proteins differ greatly across cell lines with consistent (JUN, MAP2K3, CDKN1A) and divergent (CCND3, ASF1B, BRD7) cell-state effectors. Together these data provide valuable insight into cell-type driven and heterogeneous responses that must be taken into consideration when monitoring molecular perturbations in culture models.

Rapid profiling of transcription factor-cofactor interaction networks reveals principles of epigenetic regulation

Transcription factor (TF)-cofactor (COF) interactions define dynamic, cell-specific networks that govern gene expression; however, these networks are understudied due to a lack of methods for high-throughput profiling of DNA-bound TF-COF complexes. Here, we describe the Cofactor Recruitment (CoRec) method for rapid profiling of cell-specific TF-COF complexes. We define a lysine acetyltransferase (KAT)-TF network in resting and stimulated T cells. We find promiscuous recruitment of KATs for many TFs and that 35% of KAT-TF interactions are condition specific. KAT-TF interactions identify NF-κB as a primary regulator of acutely induced histone 3 lysine 27 acetylation (H3K27ac). Finally, we find that heterotypic clustering of CBP/P300-recruiting TFs is a strong predictor of total promoter H3K27ac. Our data support clustering of TF sites that broadly recruit KATs as a mechanism for widespread co-occurring histone acetylation marks. CoRec can be readily applied to different cell systems and provides a powerful approach to define TF-COF networks impacting chromatin state and gene regulation.

DCLK1 induces a pro-tumorigenic phenotype to drive gastric cancer progression

Doublecortin-like kinase 1 (DCLK1) is a proposed driver of gastric cancer (GC) that phosphorylates serine and threonine residues. Here, we showed that the kinase activity of DCLK1 orchestrated cancer cell-intrinsic and-extrinsic processes that led to pro-invasive and pro-metastatic reprogramming of GC cells. Inhibition of the kinase activity of DCLK1 reduced the growth of subcutaneous xenograft tumors formed from MKN1 human gastric carcinoma cells in mice and decreased the abundance of the stromal markers α-Sma, vimentin, and collagen. Similar effects were seen in mice with xenograft tumors formed from MKN1 cells expressing a kinase-inactive DCLK1 mutant (MKN1D511N). MKN1D511N cells also had reduced in vitro migratory potential and stemness compared with control cells. Mice orthotopically grafted with MKN1 cells overexpressing DCLK1 (MKN1DCLK1) showed increased invasiveness and had a greater incidence of lung metastases compared with those grafted with control MKN1 cells. Mechanistically, we showed that the chemokine CXCL12 acted downstream of DCLK1 in cultured MKN1 cells and in mice subcutaneously implanted with gastric tumors formed by MKN1DCLK1 cells. Moreover, inhibition of the kinase activity of DCLK1 or the expression of DCLK1D511N reversed the pro-tumorigenic and pro-metastatic phenotype. Together, this study establishes DCLK1 as a broadly acting and potentially targetable promoter of GC.

moPepGen: Rapid and Comprehensive Identification of Non-canonical Peptides

Gene expression is a multi-step transformation of biological information from its storage form (DNA) into functional forms (protein and some RNAs). Regulatory activities at each step of this transformation multiply a single gene into a myriad of proteoforms. Proteogenomics is the study of how genomic and transcriptomic variation creates this proteomic diversity, and is limited by the challenges of modeling the complexities of gene-expression. We therefore created moPepGen, a graph-based algorithm that comprehensively generates non-canonical peptides in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it enumerates previously unobservable noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization. By enabling efficient detection and quantitation of previously hidden proteins in both existing and new proteomic data, moPepGen facilitates all proteogenomics applications. It is available at: https://github.com/uclahs-cds/package-moPepGen.

Prognostic Protein Biomarker Screening for Thyroid Carcinoma Based on Cancer Proteomics Profiles

Thyroid carcinoma (THCA) ranks among the most prevalent cancers globally. Integrating advanced genomic and proteomic analyses to construct a protein-based prognostic model promises to identify effective biomarkers and explore new therapeutic avenues. In this study, proteomic data from The Cancer Proteomics Atlas (TCPA) and clinical data from The Cancer Genome Atlas (TCGA) were utilized. Using Kaplan-Meier, Cox regression, and LASSO penalized Cox analyses, we developed a prognostic risk model comprising 13 proteins (S100A4, PAI1, IGFBP2, RICTOR, B7-H3, COLLAGENVI, PAR, SNAIL, FAK, Connexin-43, Rheb, EVI1, and P90RSK_pT359S363). The protein prognostic model was validated as an independent predictor of survival time in THCA patients, based on risk curves, survival analysis, receiver operating characteristic curves and independent prognostic analysis. Additionally, we explored the immune cell infiltration and tumor mutational burden (TMB) related to these features. Notably, our study proved a novel approach for predicting treatment responses in THCA patients, including those undergoing chemotherapy and targeted therapy.

Human aneuploid cells depend on the RAF/MEK/ERK pathway for overcoming increased DNA damage

Aneuploidy is a hallmark of human cancer, yet the molecular mechanisms to cope with aneuploidy-induced cellular stresses remain largely unknown. Here, we induce chromosome mis-segregation in non-transformed RPE1-hTERT cells and derive multiple stable clones with various degrees of aneuploidy. We perform a systematic genomic, transcriptomic and proteomic profiling of 6 isogenic clones, using whole-exome DNA, mRNA and miRNA sequencing, as well as proteomics. Concomitantly, we functionally interrogate their cellular vulnerabilities, using genome-wide CRISPR/Cas9 and large-scale drug screens. Aneuploid clones activate the DNA damage response and are more resistant to further DNA damage induction. Aneuploid cells also exhibit elevated RAF/MEK/ERK pathway activity and are more sensitive to clinically-relevant drugs targeting this pathway, and in particular to CRAF inhibition. Importantly, CRAF and MEK inhibition sensitize aneuploid cells to DNA damage-inducing chemotherapies and to PARP inhibitors. We validate these results in human cancer cell lines. Moreover, resistance of cancer patients to olaparib is associated with high levels of RAF/MEK/ERK signaling, specifically in highly-aneuploid tumors. Overall, our study provides a comprehensive resource for genetically-matched karyotypically-stable cells of various aneuploidy states, and reveals a therapeutically-relevant cellular dependency of aneuploid cells.

The proteomic landscape and temporal dynamics of mammalian gastruloid development

Gastrulation is the highly coordinated process by which the early embryo breaks symmetry, establishes germ layers and a body plan, and sets the stage for organogenesis. As early mammalian development is challenging to study in vivo, stem cell-derived models have emerged as powerful surrogates, e.g. human and mouse gastruloids. However, although single cell RNA-seq (scRNA-seq) and high-resolution imaging have been extensively applied to characterize such in vitro embryo models, a paucity of measurements of protein dynamics and regulation leaves a major gap in our understanding. Here, we sought to address this by applying quantitative proteomics to human and mouse gastruloids at four key stages of their differentiation (naïve ESCs, primed ESCs, early gastruloids, late gastruloids). To the resulting data, we perform network analysis to map the dynamics of expression of macromolecular protein complexes and biochemical pathways, including identifying cooperative proteins that associate with them. With matched RNA-seq and phosphosite data from these same stages, we investigate pathway-, stage- and species-specific aspects of translational and post-translational regulation, e.g. finding peri-gastrulation stages of human and mice to be discordant with respect to the mitochondrial transcriptome vs. proteome, and nominating novel kinase-substrate relationships based on phosphosite dynamics. Finally, we leverage correlated dynamics to identify conserved protein networks centered around congenital disease genes. Altogether, our data (https://gastruloid.brotmanbaty.org/) and analyses showcase the potential of intersecting in vitro embryo models and proteomics to advance our understanding of early mammalian development in ways not possible through transcriptomics alone.

Modelling radiobiology

Radiotherapy has played an essential role in cancer treatment for over a century, and remains one of the best-studied methods of cancer treatment. Because of its close links with the physical sciences, it has been the subject of extensive quantitative mathematical modelling, but a complete understanding of the mechanisms of radiotherapy has remained elusive. In part this is because of the complexity and range of scales involved in radiotherapy-from physical radiation interactions occurring over nanometres to evolution of patient responses over months and years. This review presents the current status and ongoing research in modelling radiotherapy responses across these scales, including basic physical mechanisms of DNA damage, the immediate biological responses this triggers, and genetic- and patient-level determinants of response. Finally, some of the major challenges in this field and potential avenues for future improvements are also discussed.

The role of cytidine 5'-triphosphate synthetase 1 in metabolic rewiring during epithelial-to-mesenchymal transition in non-small-cell lung cancer

Epithelial-to-mesenchymal transition (EMT) contributes to the poor prognosis of patients with cancer by promoting distant metastasis and anti-cancer drug resistance. Several distinct metabolic alterations have been identified as key EMT phenotypes. In the present study, we further characterize the role of transforming growth factor-β (TGF-β)-induced EMT in non-small-cell lung cancer. Our study revealed that TGF-β plays a role in EMT functions by upregulation of cytidine 5'-triphosphate synthetase 1 (CTPS), a vital enzyme for CTP biosynthesis in the pyrimidine metabolic pathway. Both knockdown and enzymatic inhibition of CTPS reduced TGF-β-induced changes in EMT marker expression, chemoresistance and migration in vitro. Moreover, CTPS knockdown counteracted the TGF-β-mediated downregulation of UDP-glucuronate, glutarate, creatine, taurine and nicotinamide. These findings indicate that CTPS plays a multifaceted role in EMT metabolism, which is crucial for the malignant transformation of cancer through EMT, and underline its potential as a promising therapeutic target for preventing drug resistance and metastasis in non-small-cell lung cancer.

Multi-omics revealed anti-fatigue property of polyphenol from areca nut

BACKGROUND

Areca nut polyphenols (AP) that extracted from areca nut, have been demonstrated for their potential of anti-fatigue effects. However, the underlying mechanisms for the anti-fatigue properties of AP has not been fully elucidated to date. Previous studies have predominantly concentrated on single aspects, such as antioxidation and anti-inflammation, yet have lacked comprehensive multi-dimensional analyses.

PURPOSE

To explore the underlying mechanism of AP in exerting anti-fatigue effects.

METHODS

In this study, we developed a chronic sleep deprivation-induced fatigue model and used physiological, hematological, and biochemical indicators to evaluate the anti- fatigue efficacy of AP. Additionally, a multi-omics approach was employed to reveal the anti-fatigue mechanisms of AP from the perspective of microbiome, metabolome, and proteome.

RESULTS

The detection of physiology, hematology and biochemistry index indicated that AP markedly alleviate mice fatigue state induced by sleep deprivation. The 16S rRNA sequencing showed the AP promoted the abundance of probiotics (Odoribacter, Dubosiella, Marvinbryantia, and Eubacterium) and suppressed harmful bacteria (Ruminococcus). On the other hand, AP was found to regulate the expression of colonic proteins, such as increases of adenosine triphosphate (ATP) synthesis and mitochondrial function related proteins, including ATP5A1, ATP5O, ATP5L, ATP5H, NDUFA, NDUFB, NDUFS, and NDUFV. Serum metabolomic analysis revealed AP upregulated the levels of anti-fatigue amino acids, such as taurine, leucine, arginine, glutamine, lysine, and l-proline. Hepatic proteins express levels, especially tricarboxylic acid (TCA) cycle (CS, SDHB, MDH2, and DLST) and redox-related proteins (SOD1, SOD2, GPX4, and PRDX3), were significantly recovered by AP administration. Spearman correlation analysis uncovered the strong correlation between microbiome, metabolome and proteome, suggesting the anti-fatigue effects of AP is attribute to the energy homeostasis and redox balance through gut-liver axis.

CONCLUSION

AP increased colonic ATP production and improve mitochondrial function by regulating gut microbiota, and further upregulated anti-fatigue amino acid levels in the blood. Based on the gut-liver axis, AP upregulated the hepatic tricarboxylic acid cycle and oxidoreductase-related protein expression, regulating energy homeostasis and redox balance, and ultimately exerting anti-fatigue effects. This study provides insights into the anti-fatigue mechanisms of AP, highlighting its potential as a therapeutic agent.

Multi-omics profiling of longitudinal samples reveals early genomic changes in follicular lymphoma

Follicular lymphoma (FL) is the most common indolent type of B-cell non-Hodgkin lymphoma. Advances in treatment have improved overall survival, but early relapse or transformation to aggressive disease is associated with inferior outcome. To identify early genetic events and track tumor clonal evolution, we performed multi-omics analysis of 94 longitudinal biopsies from 44 FL patients; 22 with transformation (tFL) and 22 with relapse without transformation (nFL). Deep whole-exome sequencing confirmed recurrent mutations in genes encoding epigenetic regulators (CREBBP, KMT2D, EZH2, EP300), with similar mutational landscape in nFL and tFL patients. Calculation of genomic distances between longitudinal samples revealed complex evolutionary patterns in both subgroups. CREBBP and KMT2D mutations were identified as genetic events that occur early in the disease course, and cases with CREBBP KAT domain mutations had low risk of transformation. Gains in chromosomes 12 and 18 (TCF4), and loss in 6q were identified as early and stable copy number alterations. Identification of such early and stable genetic events may provide opportunities for early disease detection and disease monitoring. Integrative analysis revealed that tumors with EZH2 mutations exhibited reduced gene expression of numerous histone genes, including histone linker genes. This might contribute to the epigenetic dysregulation in FL.

Metabolic reprogramming in the pathogenesis and progression of nasopharyngeal carcinoma: molecular mechanisms and therapeutic implications

Nasopharyngeal carcinoma (NPC) is a unique head and neck cancer with a complex etiology involving genetic predispositions, environmental factors, and Epstein-Barr virus (EBV) infection. Despite progress in radiotherapy and chemotherapy, the prognosis for advanced NPC is still unfavorable, prompting the need for innovative therapeutic approaches. Metabolic reprogramming plays a crucial role in the development and progression of NPC, marked by substantial changes in glycolysis, lipid, and amino acid metabolism. These alterations aid tumor cell proliferation, survival under stress, and immune evasion, with features such as enhanced aerobic glycolysis (Warburg effect) and shifts in lipid and amino acid pathways. Oncogenic drivers like MYC, RAS, EGFR, and the loss of tumor suppressors such as TP53 and PTEN, along with key signaling pathways including mTOR, AMPK, and HIF-1α, orchestrate these metabolic changes. This review discusses the molecular mechanisms of metabolic reprogramming in NPC and outlines potential therapeutic targets within these pathways. Advances in metabolic imaging and biomarker discovery are also enhancing the precision of diagnostics and treatment monitoring, fostering personalized medicine in NPC treatment. This manuscript aims to provide a detailed overview of the current research and its implications for improving NPC management and patient outcomes through targeted metabolic therapies.

Constraint-based modelling predicts metabolic signatures of low and high-grade serous ovarian cancer

Ovarian cancer is an aggressive, heterogeneous disease, burdened with late diagnosis and resistance to chemotherapy. Clinical features of ovarian cancer could be explained by investigating its metabolism, and how the regulation of specific pathways links to individual phenotypes. Ovarian cancer is of particular interest for metabolic research due to its heterogeneous nature, with five distinct subtypes having been identified, each of which may display a unique metabolic signature. To elucidate metabolic differences, constraint-based modelling (CBM) represents a powerful technology, inviting the integration of 'omics' data, such as transcriptomics. However, many CBM methods have not prioritised accurate growth rate predictions, and there are very few ovarian cancer genome-scale studies. Here, a novel method for CBM has been developed, employing the genome-scale model Human1 and flux balance analysis, enabling the integration of in vitro growth rates, transcriptomics data and media conditions to predict the metabolic behaviour of cells. Using low- and high-grade ovarian cancer, subtype-specific metabolic differences have been predicted, which have been supported by publicly available CRISPR-Cas9 data from the Cancer Cell Line Encyclopaedia and an extensive literature review. Metabolic drivers of aggressive, invasive phenotypes, as well as pathways responsible for increased chemoresistance in low-grade cell lines have been suggested. Experimental gene dependency data has been used to validate areas of the pentose phosphate pathway as essential for low-grade cellular growth, highlighting potential vulnerabilities for this ovarian cancer subtype.

mTORC1 phosphorylates and stabilizes LST2 to negatively regulate EGFR

TORC1 (target of rapamycin complex 1) is a highly conserved protein kinase that plays a central role in regulating cell growth. Given the role of mammalian TORC1 (mTORC1) in metabolism and disease, understanding mTORC1 downstream signaling and feedback loops is important. mTORC1 recognizes some of its substrates via a five amino acid binding sequence called the TOR signaling (TOS) motif. mTORC1 binding to a TOS motif facilitates phosphorylation of a distinct, distal site. Here, we show that LST2, also known as ZFYVE28, contains a TOS motif (amino acids 401 to 405) and is directly phosphorylated by mTORC1 at serine 670 (S670). mTORC1-mediated S670 phosphorylation promotes LST2 monoubiquitination on lysine 87 (K87). Monoubiquitinated LST2 is stable and displays a broad reticular distribution. When mTORC1 is inactive, unphosphorylated LST2 is degraded by the proteasome. The absence of LST2 enhances EGFR (epidermal growth factor receptor) signaling. We propose that mTORC1 negatively feeds back on its upstream receptor EGFR via LST2.

Antitumor Effects and the Potential Mechanism of 10-HDA against SU-DHL-2 Cells

10-hydroxy-2-decenoic acid (10-HDA), which is a unique bioactive fatty acid of royal jelly synthesized by nurse bees for larvae and adult queen bees, is recognized for its dual utility in medicinal and nutritional applications. Previous research has indicated that 10-HDA exerts antitumor effects on numerous tumor cell lines, including colon cancer cells, A549 human lung cancer cells, and human hepatoma cells. The present study extends this inquiry to lymphoma, specifically evaluating the impact of 10-HDA on the SU-DHL-2 cell line. Our findings revealed dose-dependent suppression of SU-DHL-2 cell survival, with an IC50 of 496.8 μg/mL at a density of 3 × 106 cells/well after 24 h. For normal liver LO2 cells and human fibroblasts (HSFs), the IC50 values were approximately 1000 μg/mL and over 1000 μg/mL, respectively. The results of label-free proteomics revealed 147 upregulated and 347 downregulated differentially expressed proteins that were significantly enriched in the complement and coagulation cascades pathway (adjusted p-value = 0.012), including the differentially expressed proteins prothrombin, plasminogen, plasminogen, carboxypeptidase B2, fibrinogen beta chain, fibrinogen gamma chain, and coagulation factor V. The top three hub proteins, ribosomal protein L5, tumor protein p53, and ribosomal protein L24, were identified via protein-protein interaction (PPI) analysis. This result showed that the complement and coagulation cascade pathways might play a key role in the antitumor process of 10-HDA, suggesting a potential therapeutic avenue for lymphoma treatment. However, the specificity of the effect of 10-HDA on SU-DHL-2 cells warrants further investigation.

Multiomics2Targets identifies targets from cancer cohorts profiled with transcriptomics, proteomics, and phosphoproteomics

The availability of data from profiling of cancer patients with multiomics is rapidly increasing. However, integrative analysis of such data for personalized target identification is not trivial. Multiomics2Targets is a platform that enables users to upload transcriptomics, proteomics, and phosphoproteomics data matrices collected from the same cohort of cancer patients. After uploading the data, Multiomics2Targets produces a report that resembles a research publication. The uploaded matrices are processed, analyzed, and visualized using the tools Enrichr, KEA3, ChEA3, Expression2Kinases, and TargetRanger to identify and prioritize proteins, genes, and transcripts as potential targets. Figures and tables, as well as descriptions of the methods and results, are automatically generated. Reports include an abstract, introduction, methods, results, discussion, conclusions, and references and are exportable as citable PDFs and Jupyter Notebooks. Multiomics2Targets is applied to analyze version 3 of the Clinical Proteomic Tumor Analysis Consortium (CPTAC3) pan-cancer cohort, identifying potential targets for each CPTAC3 cancer subtype. Multiomics2Targets is available from https://multiomics2targets.maayanlab.cloud/.

Integration of proteomics and transcriptomics to construct a prognostic signature of renal clear cell carcinoma

Background: Protein information is often replaced by RNA data in studies to understand cancer-related biological processes or molecular functions, and proteins of prognostic significance in Kidney clear cell carcinoma (KIRC) remain to be mined. Methods: The cancer genome atlas program (TCGA) data was utilized to screen for proteins that are prognostically significant in KIRC. Machine learning algorithms were employed to develop protein prognostic models. Additionally, immune infiltration abundance, somatic mutation differences, and immunotherapeutic responses were analyzed in various protein risk subgroups. Ultimately, the validation of protein-coding genes was confirmed by utilizing an online database and implementing quantitative real-time PCR (qRT-PCR). Results: The patients were divided into two risk categories based on prognostic proteins, and notable disparities in both overall survival (OS) and progression free interval (PFI) were observed between the two groups. The OS was more unfavorable in the high-risk group, and there was a noteworthy disparity in the level of immune infiltration observed between the two groups. In addition, the nomogram showed high accuracy in predicting survival in KIRC patients. Conclusion: In this research, we elucidated the core proteins associated with prognosis in terms of survival prediction, immunotherapeutic response, somatic mutation, and immune microenvironment. Additionally, we have developed a reliable prognostic model with excellent predictive capabilities.

Multiple omics integrative analysis identifies GARS1 as a novel prognostic and immunological biomarker: from pan-cancer to bladder cancer

Glycyl-tRNA synthetase (GARS1) is differentially expressed across cancers. In this study, the value of GARS1 in the diagnosis and prognosis of various cancers was comprehensively evaluated by multiple omics integrative pan-cancer analysis and experimental verification. Through Kaplan-Meier, ROC and multiple databases, we explored GARS1 expression and prognostic and diagnostic patterns across cancers. The GARS1 relative reaction network was identified in PPI, GO, KEGG, methylation models and the genetic mutation atlas. Further research on the GARS1 value in bladder urothelial carcinoma (BLCA) was conducted by regression and nomogram models. We further analyzed the correlation between GARS1 and immune markers and cells in BLCA. Finally, in vitro experiments were used to validate GARS1 the oncogenic function of GARS1 in BLCA. We found that GARS1 was highly expressed across cancers, especially in BLCA. GARS1 expression was correlated with poor survival and had high diagnostic value in most tumor types. GARS1 is significantly associated with tRNA-related pathways whose mutation sites are mainly located on tRNA synthetase. In addition, Upregulation of GARS1 was connected with immune cell infiltration and five key MMR genes. M2 macrophages, TAMs, Th1 and T-cell exhaustion, and marker sets associated with GARS1 expression indicated specific immune infiltration in BLCA. Finally, in vitro experiments validated that GARS1 expression promotes BLCA cell proliferation and metastasis and inhibits apoptosis. Overall, GARS1 can be a novel prognostic and immunological biomarker through multiple omics integrative pan-cancer analysis. The expression of GARS1 in BLCA was positively correlated with specific immune infiltration, indicating that GARS1 might be related to the tumor immune microenvironment.

The deubiquitinase USP9X regulates RIT1 protein abundance and oncogenic phenotypes

RIT1 is a rare and understudied oncogene in lung cancer. Despite structural similarity to other RAS GTPase proteins such as KRAS, oncogenic RIT1 activity does not appear to be tightly regulated by nucleotide exchange or hydrolysis. Instead, there is a growing understanding that the protein abundance of RIT1 is important for its regulation and function. We previously identified the deubiquitinase USP9X as a RIT1 dependency in RIT1-mutant cells. Here, we demonstrate that both wild-type and mutant forms of RIT1 are substrates of USP9X. Depletion of USP9X leads to decreased RIT1 protein stability and abundance and resensitizes cells to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors in vitro and in vivo. Our work expands upon the current understanding of RIT1 protein regulation and presents USP9X as a key regulator of RIT1-driven oncogenic phenotypes.

Therapeutic potential of cis-targeting bispecific antibodies

The growing clinical success of bispecific antibodies (bsAbs) has led to rapid interest in leveraging dual targeting in order to generate novel modes of therapeutic action beyond mono-targeting approaches. While bsAbs that bind targets on two different cells (trans-targeting) are showing promise in the clinic, the co-targeting of two proteins on the same cell surface through cis-targeting bsAbs (cis-bsAbs) is an emerging strategy to elicit new functionalities. This includes the ability to induce proximity, enhance binding to a target, increase target/cell selectivity, and/or co-modulate function on the cell surface with the goal of altering, reversing, or eradicating abnormal cellular activity that contributes to disease. In this review, we focus on the impact of cis-bsAbs in the clinic, their emerging applications, and untangle the intricacies of improving bsAb discovery and development.

Translation efficiency covariation across cell types is a conserved organizing principle of mammalian transcriptomes

Characterization of shared patterns of RNA expression between genes across conditions has led to the discovery of regulatory networks and novel biological functions. However, it is unclear if such coordination extends to translation, a critical step in gene expression. Here, we uniformly analyzed 3,819 ribosome profiling datasets from 117 human and 94 mouse tissues and cell lines. We introduce the concept of Translation Efficiency Covariation (TEC), identifying coordinated translation patterns across cell types. We nominate potential mechanisms driving shared patterns of translation regulation. TEC is conserved across human and mouse cells and helps uncover gene functions. Moreover, our observations indicate that proteins that physically interact are highly enriched for positive covariation at both translational and transcriptional levels. Our findings establish translational covariation as a conserved organizing principle of mammalian transcriptomes.

Pan-cancer proteogenomics expands the landscape of therapeutic targets

Fewer than 200 proteins are targeted by cancer drugs approved by the Food and Drug Administration (FDA). We integrate Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteogenomics data from 1,043 patients across 10 cancer types with additional public datasets to identify potential therapeutic targets. Pan-cancer analysis of 2,863 druggable proteins reveals a wide abundance range and identifies biological factors that affect mRNA-protein correlation. Integration of proteomic data from tumors and genetic screen data from cell lines identifies protein overexpression- or hyperactivation-driven druggable dependencies, enabling accurate predictions of effective drug targets. Proteogenomic identification of synthetic lethality provides a strategy to target tumor suppressor gene loss. Combining proteogenomic analysis and MHC binding prediction prioritizes mutant KRAS peptides as promising public neoantigens. Computational identification of shared tumor-associated antigens followed by experimental confirmation nominates peptides as immunotherapy targets. These analyses, summarized at https://targets.linkedomics.org, form a comprehensive landscape of protein and peptide targets for companion diagnostics, drug repurposing, and therapy development.

Multi-omics and pharmacological characterization of patient-derived glioma cell lines

Glioblastoma (GBM) is the most common brain tumor and remains incurable. Primary GBM cultures are widely used tools for drug screening, but there is a lack of genomic and pharmacological characterization for these primary GBM cultures. Here, we collect 50 patient-derived glioma cell (PDGC) lines and characterize them by whole genome sequencing, RNA sequencing, and drug response screening. We identify three molecular subtypes among PDGCs: mesenchymal (MES), proneural (PN), and oxidative phosphorylation (OXPHOS). Drug response profiling reveals that PN subtype PDGCs are sensitive to tyrosine kinase inhibitors, whereas OXPHOS subtype PDGCs are sensitive to histone deacetylase inhibitors, oxidative phosphorylation inhibitors, and HMG-CoA reductase inhibitors. PN and OXPHOS subtype PDGCs stably form tumors in vivo upon intracranial transplantation into immunodeficient mice, whereas most MES subtype PDGCs fail to form tumors in vivo. In addition, PDGCs cultured by serum-free medium, especially long-passage PDGCs, carry MYC/MYCN amplification, which is rare in GBM patients. Our study provides a valuable resource for understanding primary glioma cell cultures and clinical translation and highlights the problems of serum-free PDGC culture systems that cannot be ignored.

Prion-like domain mediated phase separation of ARID1A promotes oncogenic potential of Ewing's sarcoma

Liquid-liquid phase separation (LLPS) facilitates the formation of membraneless organelles within cells, with implications in various biological processes and disease states. AT-rich interactive domain-containing protein 1A (ARID1A) is a chromatin remodeling factor frequently associated with cancer mutations, yet its functional mechanism remains largely unknown. Here, we find that ARID1A harbors a prion-like domain (PrLD), which facilitates the formation of liquid condensates through PrLD-mediated LLPS. The nuclear condensates formed by ARID1A LLPS are significantly elevated in Ewing's sarcoma patient specimen. Disruption of ARID1A LLPS results in diminished proliferative and invasive abilities in Ewing's sarcoma cells. Through genome-wide chromatin structure and transcription profiling, we identify that the ARID1A condensate localizes to EWS/FLI1 target enhancers and induces long-range chromatin architectural changes by forming functional chromatin remodeling hubs at oncogenic target genes. Collectively, our findings demonstrate that ARID1A promotes oncogenic potential through PrLD-mediated LLPS, offering a potential therapeutic approach for treating Ewing's sarcoma.

Increasing the Throughput and Reproducibility of Activity-Based Proteome Profiling Studies with Hyperplexing and Intelligent Data Acquisition

Intelligent data acquisition (IDA) strategies, such as a real-time database search (RTS), have improved the depth of proteome coverage for experiments that utilize isobaric labels and gas phase purification techniques (i.e., SPS-MS3). In this work, we introduce inSeqAPI, an instrument application programing interface (iAPI) program that enables construction of novel data acquisition algorithms. First, we analyze biotinylated cysteine peptides from ABPP experiments to demonstrate that a real-time search method within inSeqAPI performs similarly to an equivalent vendor method. Then, we describe PairQuant, a method within inSeqAPI designed for the hyperplexing approach that utilizes protein-level isotopic labeling and peptide-level TMT labeling. PairQuant allows for TMT analysis of 36 conditions in a single sample and achieves ∼98% coverage of both peptide pair partners in a hyperplexed experiment as well as a 40% improvement in the number of quantified cysteine sites compared with non-RTS acquisition. We applied this method in the ABPP study of ligandable cysteine sites in the nucleus leading to an identification of additional druggable sites on protein- and DNA-interaction domains of transcription regulators and on nuclear ubiquitin ligases.

M6A-methylated circPOLR2B forms an R-loop and regulates the biological behavior of glioma stem cells through positive feedback loops

Glioma is the most common primary brain tumor, and targeting glioma stem cells (GSCs) has become a key aspect of glioma treatment. In this study, we discovered a molecular network in which circRNA forms an R-loop structure with its parental gene to regulate the biological behavior of GSCs. Genes with abnormal expression in GSCs were screened using RNA-seq and circRNA microarray analyses. The study results showed that high expression of YTHDC1 in GSCs promoted the transportation of N6-methyladenosine (m6A)-modified circPOLR2B from the nucleus to the cytoplasm. Decreased circPOLR2B levels in the nucleus resulted in fewer R-loop structures formed with its parental gene POLR2B. This reduction in R-loop structures relieved the inhibitory effect on POLR2B transcription and upregulated PBX1 expression through alternative polyadenylation (APA) action, thereby promoting the malignant biological behavior of GSCs. Knockdown of YTHDC1, POLR2B, and PBX1 reduced xenograft tumor volume and prolonged the survival of nude mice. The YTHDC1/circPOLR2B/POLR2B/PBX1 axis plays a regulatory role in the biological behavior of GSCs, offering potential targets and novel strategies for the treatment of glioma.

A Novel AMPK Inhibitor Sensitizes Pancreatic Cancer Cells to Ferroptosis Induction

Cancer cells must develop strategies to adapt to the dynamically changing stresses caused by intrinsic or extrinsic processes, or therapeutic agents. Metabolic adaptability is crucial to mitigate such challenges. Considering metabolism as a central node of adaptability, it is focused on an energy sensor, the AMP-activated protein kinase (AMPK). In a subtype of pancreatic ductal adenocarcinoma (PDAC) elevated AMPK expression and phosphorylation is identified. Using drug repurposing that combined screening experiments and chemoproteomic affinity profiling, it is identified and characterized PF-3758309, initially developed as an inhibitor of PAK4, as an AMPK inhibitor. PF-3758309 shows activity in pre-clinical PDAC models, including primary patient-derived organoids. Genetic loss-of-function experiments showed that AMPK limits the induction of ferroptosis, and consequently, PF-3758309 treatment restores the sensitivity toward ferroptosis inducers. The work established a chemical scaffold for the development of specific AMPK-targeting compounds and deciphered the framework for the development of AMPK inhibitor-based combination therapies tailored for PDAC.

Exploring and clinical validation of prognostic significance and therapeutic implications of copper homeostasis-related gene dysregulation in acute myeloid leukemia

The patterns and biological functions of copper homeostasis-related genes (CHRGs) in acute myeloid leukemia (AML) remain unclear. We explored the patterns and biological functions of CHRGs in AML. Using independent cohorts, including TCGA-GTEx, GSE114868, GSE37642, and clinical samples, we identified 826 common differentially expressed genes. Specifically, 12 cuproptosis-related genes (e.g., ATP7A, ATP7B) were upregulated, while 17 cuproplasia-associated genes (e.g., ATOX1, ATP7A) were downregulated in AML. We used LASSO-Cox, Kaplan-Meier, and Nomogram analyses to establish prognostic risk models, effectively stratifying patients with AML into high- and low-risk groups. Subgroup analysis revealed that high-risk patients exhibited poorer overall survival and involvement in fatty acid metabolism, apoptosis, and glycolysis. Immune infiltration analysis indicated differences in immune cell composition, with notable increases in B cells, cytotoxic T cells, and memory T cells in the low-risk group, and increased monocytes and neutrophils in the high-risk group. Single-cell sequencing analysis corroborated the expression characteristics of critical CHRGs, such as MAPK1 and ATOX1, associated with the function of T, B, and NK cells. Drug sensitivity analysis suggested potential therapeutic agents targeting copper homeostasis, including Bicalutamide and Sorafenib. PCR validation confirmed the differential expression of 4 cuproptosis-related genes (LIPT1, SLC31A1, GCSH, and PDHA1) and 9 cuproplasia-associated genes (ATOX1, CCS, CP, MAPK1, SOD1, COA6, PDK1, DBH, and PDE3B) in AML cell line. Importantly, these genes serve as potential biomarkers for patient stratification and treatment. In conclusion, we shed light on the expression patterns and biological functions of CHRGs in AML. The developed risk models provided prognostic implications for patient survival, offering valuable information on the regulatory characteristics of CHRGs and potential avenues for personalized treatment in AML.

SLC45A4 encodes a mitochondrial putrescine transporter that promotes GABA de novo synthesis

Solute carriers (SLC) are membrane proteins that facilitate the transportation of ions and metabolites across either the plasma membrane or the membrane of intracellular organelles. With more than 450 human genes annotated as SLCs, many of them are still orphan transporters without known biochemical functions. We developed a metabolomic-transcriptomic association analysis, and we found that the expression of SLC45A4 has a strong positive correlation with the cellular level of γ-aminobutyric acid (GABA). Using mass spectrometry and the stable isotope tracing approach, we demonstrated that SLC45A4 promotes GABA de novo synthesis through the Arginine/Ornithine/Putrescine (AOP) pathway. SLC45A4 functions as a putrescine transporter localized to the mitochondrial membrane to facilitate GABA production. Taken together, our results revealed a new biochemical mechanism where SLC45A4 controls GABA production.