Pathogenic Roles for RNASET2 in Clear Cell Renal Cell Carcinoma.

A specific splicing isoform of RNASET2 is associated with worse oncologic outcomes in clear cell renal cell carcinoma (ccRCC). However, the interplay between wild-type RNASET2 and its splice variant and how this might contribute to the pathogenesis of ccRCC remains poorly understood. We sought to better understand the relationship of RNASET2 in the pathogenesis of ccRCC and the interplay with a pathogenic splicing isoform (RNASET2-SV) and the tumor immune microenvironment. Using data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium, we correlated clinical variables to RNASET2 expression and the presence of a specific RNASET2-SV. Immunohistochemical staining with matched RNA sequencing of ccRCC patients was then utilized to understand the spatial relationships of RNASET2 with immune cells. Finally, in vitro studies were performed to demonstrate the oncogenic role of RNASET2 and highlight its potential mechanisms. RNASET2 gene expression is associated with higher grade tumors and worse overall survival in The Cancer Genome Atlas cohort. The presence of the RNASET2-SV was associated with increased expression of the wild-type RNASET2 protein and epigenetic modifications of the gene. Immunohistochemical staining revealed increased intracellular accumulation of RNASET2 in patients with increased RNA expression of RNASET2-SV. In vitro experiments reveal that this accumulation results in increased cell proliferation, potentially from altered metabolic pathways. RNASET2 exhibits a tumor-promoting role in the pathogenesis of ccRCC that is increased in the presence of a specific RNASET2-SV and associated with changes in the cellular localization of the protein.

Network models of protein phosphorylation, acetylation, and ubiquitination connect metabolic and cell signaling pathways in lung cancer.

We analyzed large-scale post-translational modification (PTM) data to outline cell signaling pathways affected by tyrosine kinase inhibitors (TKIs) in ten lung cancer cell lines. Tyrosine phosphorylated, lysine ubiquitinated, and lysine acetylated proteins were concomitantly identified using sequential enrichment of post translational modification (SEPTM) proteomics. Machine learning was used to identify PTM clusters that represent functional modules that respond to TKIs. To model lung cancer signaling at the protein level, PTM clusters were used to create a co-cluster correlation network (CCCN) and select protein-protein interactions (PPIs) from a large network of curated PPIs to create a cluster-filtered network (CFN). Next, we constructed a Pathway Crosstalk Network (PCN) by connecting pathways from NCATS BioPlanet whose member proteins have PTMs that co-cluster. Interrogating the CCCN, CFN, and PCN individually and in combination yields insights into the response of lung cancer cells to TKIs. We highlight examples where cell signaling pathways involving EGFR and ALK exhibit crosstalk with BioPlanet pathways: Transmembrane transport of small molecules; and Glycolysis and gluconeogenesis. These data identify known and previously unappreciated connections between receptor tyrosine kinase (RTK) signal transduction and oncogenic metabolic reprogramming in lung cancer. Comparison to a CFN generated from a previous multi-PTM analysis of lung cancer cell lines reveals a common core of PPIs involving heat shock/chaperone proteins, metabolic enzymes, cytoskeletal components, and RNA-binding proteins. Elucidation of points of crosstalk among signaling pathways employing different PTMs reveals new potential drug targets and candidates for synergistic attack through combination drug therapy.

A comparative Proteomics Analysis Identified Differentially Expressed Proteins in Pancreatic Cancer-Associated Stellate Cell Small Extracellular Vesicles.

Human pancreatic stellate cells (HPSCs) are an essential stromal component and mediators of pancreatic ductal adenocarcinoma (PDAC) progression. Small extracellular vesicles (sEVs) are membrane-enclosed nanoparticles involved in cell-to-cell communications and are released from stromal cells within PDAC. A detailed comparison of sEVs from normal pancreatic stellate cells (HPaStec) and from PDAC-associated stellate cells (HPSCs) remains a gap in our current knowledge regarding stellate cells and PDAC. We hypothesized there would be differences in sEVs secretion and protein expression that might contribute to PDAC biology. To test this hypothesis, we isolated sEVs using ultracentrifugation followed by characterization by electron microscopy and Nanoparticle Tracking Analysis. We report here our initial observations. First, HPSC cells derived from PDAC tumors secrete a higher volume of sEVs when compared to normal pancreatic stellate cells (HPaStec). Although our data revealed that both normal and tumor-derived sEVs demonstrated no significant biological effect on cancer cells, we observed efficient uptake of sEVs by both normal and cancer epithelial cells. Additionally, intact membrane-associated proteins on sEVs were essential for efficient uptake. We then compared sEV proteins isolated from HPSCs and HPaStecs cells using liquid chromatography-tandem mass spectrometry. Most of the 1481 protein groups identified were shared with the exosome database, ExoCarta. Eighty-seven protein groups were differentially expressed (selected by 2-fold difference and adjusted p value ≤0.05) between HPSC and HPaStec sEVs. Of note, HPSC sEVs contained dramatically more CSE1L (chromosome segregation 1-like protein), a described marker of poor prognosis in patients with pancreatic cancer. Based on our results, we have demonstrated unique populations of sEVs originating from stromal cells with PDAC and suggest that these are significant to cancer biology. Further studies should be undertaken to gain a deeper understanding that could drive novel therapy.

Deciphering Phenotypes from Protein Biomarkers for Translational Research with PIPER.

Liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM) has widespread clinical use for detection of inborn errors of metabolism, therapeutic drug monitoring, and numerous other applications. This technique detects proteolytic peptides as surrogates for protein biomarker expression, mutation, and post-translational modification in individual clinical assays and in cancer research with highly multiplexed quantitation across biological pathways. LC-MRM for protein biomarkers must be translated from multiplexed research-grade panels to clinical use. LC-MRM panels provide the capability to quantify clinical biomarkers and emerging protein markers to establish the context of tumor phenotypes that provide highly relevant supporting information. An application to visualize and communicate targeted proteomics data will empower translational researchers to move protein biomarker panels from discovery to clinical use. Therefore, we have developed a web-based tool for targeted proteomics that provides pathway-level evaluations of key biological drivers (e.g., EGFR signaling), signature scores (representing phenotypes) (e.g., EMT), and the ability to quantify specific drug targets across a sample cohort. This tool represents a framework for integrating summary information, decision algorithms, and risk scores to support Physician-Interpretable Phenotypic Evaluation in R (PIPER) that can be reused or repurposed by other labs to communicate and interpret their own biomarker panels.

Integrated proteomics identifies PARP inhibitor-induced prosurvival signaling changes as potential vulnerabilities in ovarian cancer.

BRCA1/2-deficient ovarian carcinoma (OC) has been shown to be particularly sensitive to poly (ADP-ribose) polymerase inhibitors (PARPis). Furthermore, BRCA1/2 mutation status is currently used as a predictive biomarker for PARPi therapy. Despite providing a major clinical benefit to the majority of patients, a significant proportion of BRCA1/2-deficient OC tumors do not respond to PARPis for reasons that are incompletely understood. Using an integrated chemical, phospho- and ADP-ribosylation proteomics approach, we sought here to develop additional mechanism-based biomarker candidates for PARPi therapy in OC and identify new targets for combination therapy to overcome primary resistance. Using chemical proteomics with PARPi baits in a BRCA1-isogenic OC cell line pair, as well as patient-derived BRCA1-proficient and BRCA1-deficient tumor samples, and subsequent validation by coimmunoprecipitation, we showed differential PARP1 and PARP2 protein complex composition in PARPi-sensitive, BRCA1-deficient UWB1.289 (UWB) cells compared to PARPi-insensitive, BRCA1-reconstituted UWB1.289+BRCA1 (UWB+B) cells. In addition, global phosphoproteomics and ADP-ribosylation proteomics furthermore revealed that the PARPi rucaparib induced the cell cycle pathway and nonhomologous end joining (NHEJ) pathway in UWB cells but downregulated ErbB signaling in UWB+B cells. In addition, we observed AKT PARylation and prosurvival AKT-mTOR signaling in UWB+B cells after PARPi treatment. Consistently, we found the synergy of PARPis with DNAPK or AKT inhibitors was more pronounced in UWB+B cells, highlighting these pathways as actionable vulnerabilities. In conclusion, we demonstrate the combination of chemical proteomics, phosphoproteomics, and ADP-ribosylation proteomics can identify differential PARP1/2 complexes and diverse, but actionable, drug compensatory signaling in OC.

Dose-escalation trial of combination dabrafenib, trametinib, and AT13387 in patients with BRAF-mutant solid tumors.

BACKGROUND: Combination BRAF and MEK inhibitor therapy is an active regimen in patients who have BRAF V600E-mutated tumors; however, the clinical efficacy of this therapy is limited by resistance. Preclinically, the addition of heat shock protein 90 (HSP90) inhibition improves the efficacy of BRAF inhibitor therapy in both BRAF inhibitor-sensitive and BRAF inhibitor-resistant mutant cell lines. METHODS: Cancer Therapy Evaluation Program study 9557 (ClinicalTrials.gov identifier NCT02097225) is a phase 1 study that was designed to assess the safety and efficacy of the small-molecule HSP90 inhibitor, AT13387, in combination with dabrafenib and trametinib in BRAF V600E/K-mutant solid tumors. Correlative analyses evaluated the expression of HSP90 client proteins and chaperones. RESULTS: Twenty-two patients with metastatic, BRAF V600E-mutant solid tumors were enrolled using a 3 + 3 design at four dose levels, and 21 patients were evaluable for efficacy assessment. The most common tumor type was colorectal cancer (N = 12). Dose-limiting toxicities occurred in one patient at dose level 3 and in one patient at dose level 4; specifically, myelosuppression and fatigue, respectively. The maximum tolerated dose was oral dabafenib 150 mg twice daily, oral trametinib 2 mg once daily, and intravenous AT13387 260 mg/m2 on days 1, 8, and 15. The best response was a partial response in two patients and stable disease in eight patients, with an overall response rate of 9.5% (90% exact confidence interval [CI], 2%-27%), a disease control rate of 47.6% (90% CI, 29%-67%), and a median overall survival of 5.1 months (90% CI, 3.4-7.6 months). There were no consistent proteomic changes associated with response or resistance, although responders did have reductions in BRAF expression, and epidermal growth factor receptor downregulation using HSP90 inhibition was observed in one patient who had colorectal cancer. CONCLUSIONS: HSP90 inhibition combined with BRAF/MEK inhibition was safe and produced evidence of modest disease control in a heavily pretreated population. Additional translational work may identify tumor types and resistance mechanisms that are most sensitive to this approach.

Differential Chemoproteomics Reveals MARK2/3 as Cell Migration-Relevant Targets of the ALK Inhibitor Brigatinib.

Metastasis poses a major challenge in cancer management, including EML4-ALK-rearranged non-small cell lung cancer (NSCLC). As cell migration is a critical step during metastasis, we assessed the anti-migratory activities of several clinical ALK inhibitors in NSCLC cells and observed differential anti-migratory capabilities despite similar ALK inhibition, with brigatinib displaying superior anti-migratory effects over other ALK inhibitors. Applying an unbiased in situ mass spectrometry-based chemoproteomics approach, we determined the proteome-wide target profile of brigatinib in EML4-ALK+ NSCLC cells. Dose-dependent and cross-competitive chemoproteomics suggested MARK2 and MARK3 as relevant brigatinib kinase targets. Functional validation showed that combined pharmacological inhibition or genetic modulation of MARK2/3 inhibited cell migration. Consistently, brigatinib treatment induced inhibitory YAP1 phosphorylation downstream of MARK2/3. Collectively, our data suggest that brigatinib exhibits unusual cross-phenotype polypharmacology as, despite similar efficacy for inhibiting EML4-ALK-dependent cell proliferation as other ALK inhibitors, it more effectively prevented migration of NSCLC cells due to co-targeting of MARK2/3.

Associations of gut microbiome with endogenous estrogen levels in healthy postmenopausal women.

PURPOSE: The gut microbiome is a potentially important contributor to endogenous estrogen levels after menopause. In healthy postmenopausal women, we examined associations of fecal microbiome composition with levels of urinary estrogens, their metabolites, and relevant metabolic pathway ratios implicated in breast cancer risk. METHODS: Eligible postmenopausal women (n = 164) had a body mass index (BMI) ≤ 35 kg/m2 and no history of hormone use (previous 6 months) or cancer/metabolic disorders. Estrogens were quantified in spot urine samples with liquid chromatography-high resolution mass spectrometry (corrected for creatinine). Bacterial DNA was isolated from fecal samples and the V1-V2 hypervariable regions of 16S rRNA were sequenced on the Illumina MiSeq platform. We examined associations of gut microbiome's indices of within-sample (alpha) diversity (i.e., Shannon, Chao1, and Inverse Simpson), phylogenetic diversity, and the ratio of the two main phyla (Firmicutes and Bacteroidetes; F/B ratio) with individual estrogens and metabolic ratios, adjusted for age and BMI. RESULTS: In this sample of 164 healthy postmenopausal women, the mean age was 62.9 years (range 47.0-86.0). We found significant inverse associations of observed species with 4-pathway:total estrogens (p = 0.04) and 4-pathway:2-pathway (p = 0.01). Shannon index was positively associated with 2-catechols: methylated 2-catechols (p = 0.04). Chao1 was inversely associated with E1:total estrogens (p = 0.04), and 4-pathway:2-pathway (p = 0.02) and positively associated with 2-pathway:parent estrogens (p = 0.01). Phylogenetic diversity was inversely associated with 4-pathway:total estrogens (p = 0.02), 4-pathway:parent estrogens (p = 0.03), 4-pathway:2-pathway (p = 0.01), and 4-pathway:16-pathway (p = 0.03) and positively associated with 2-pathway:parent estrogens (p = 0.01). F/B ratio was not associated with any of the estrogen measures. CONCLUSION: Microbial diversity was associated with several estrogen metabolism ratios implicated in breast cancer risk. Further studies are warranted to confirm these findings in a larger and more representative sample of postmenopausal women, particularly with enrichment of minority participants.

Detectable Lipidomes and Metabolomes by Different Plasma Exosome Isolation Methods in Healthy Controls and Patients with Advanced Prostate and Lung Cancer.

Circulating exosomes in the blood are promising tools for biomarker discovery in cancer. Due to their heterogeneity, different isolation methods may enrich distinct exosome cargos generating different omic profiles. In this study, we evaluated the effects of plasma exosome isolation methods on detectable multi-omic profiles in patients with non-small cell lung cancer (NSCLC), castration-resistant prostate cancer (CRPC), and healthy controls, and developed an algorithm to quantify exosome enrichment. Plasma exosomes were isolated from CRPC (n = 10), NSCLC (n = 14), and healthy controls (n = 10) using three different methods: size exclusion chromatography (SEC), lectin binding, and T-cell immunoglobulin domain and mucin domain-containing protein 4 (TIM4) binding. Molecular profiles were determined by mass spectrometry of extracted exosome fractions. Enrichment analysis of uniquely detected molecules was performed for each method with MetaboAnalyst. The exosome enrichment index (EEI) scores methods based on top differential molecules between patient groups. The lipidomic analysis detected 949 lipids using exosomes from SEC, followed by 246 from lectin binding and 226 from TIM4 binding. The detectable metabolites showed SEC identifying 191 while lectin binding and TIM4 binding identified 100 and 107, respectively. When comparing uniquely detected molecules, different methods showed preferential enrichment of different sets of molecules with SEC enriching the greatest diversity. Compared to controls, SEC identified 28 lipids showing significant difference in NSCLC, while only 1 metabolite in NSCLC and 5 metabolites in CRPC were considered statistically significant (FDR < 0.1). Neither lectin-binding- nor TIM4-binding-derived exosome lipids or metabolites demonstrated significant differences between patient groups. We observed the highest EEI from SEC in lipids (NSCLC: 871.33) which was also noted in metabolites. These results support that the size exclusion method of exosome extraction implemented by SBI captures more heterogeneous exosome populations. In contrast, lectin-binding and TIM4-binding methods bind surface glycans or phosphatidylserine moieties of the exosomes. Overall, these findings suggest that specific isolation methods select subpopulations which may significantly impact cancer biomarker discovery.

Associations of established breast cancer risk factors with urinary estrogens in postmenopausal women.

PURPOSE: Circulating estrogens are an established risk factor for postmenopausal breast cancer (BCa). We describe the distribution of urinary estrogens, their metabolites, and relevant metabolic pathway ratios among healthy postmenopausal women and examine associations of several known BCa factors with these estrogen measures. METHODS: Eligible postmenopausal women (n = 167) had no history of hormone use (previous 6 months) and cancer/metabolic disorders and had a body mass index (BMI) ≤ 35 kg/m2. Estrogens were quantified in spot urine samples with liquid chromatography-high-resolution mass spectrometry and corrected for creatinine. We assessed overall distributions of estrogens and associations of age, BMI, race/ethnicity, parity/age at first birth, age at menarche, alcohol, and smoking with log-transformed estrogen measures using multivariate regression. RESULTS: BMI was positively associated with estrone (ß per unit = 0.04, 95% Confidence Interval [CI] 0.00; 0.07), combined parent estrogens (ß = 0.04, 95% CI 0.01; 0.07), and E2:total estrogens (ß = 0.04, 95% CI 0.02; 0.06), and inversely associated with 4-MeOE1 (ß = - 0.17, 95% CI - 0.33; - 0.02), E3:parent estrogens (ß = - 0.04, 95% CI - 0.07; - 0.00), and 16-pathway:parent (ß = - 0.04, 95% CI - 0.07; - 0.01). Being African American vs. white was associated with higher levels of 4-MeOE1 (ß = 3.41, 95% CI 0.74; 6.08), 17-epiE3 (ß = 1.19, 95% CI 0.07; 2.31), 2-pathway:parent (ß = 0.54, 95% CI 0.04; 1.04), and lower levels of E2:total estrogens (ß = - 0.48, 95% CI - 0.83; - 0.13). Having < 7 alcohol drinks/week vs. none was associated with higher levels of 16-ketoE2 (ß = 1.32, 95% CI 0.36; 2.27), 16-epiE3 (ß = 1.02, 95% CI 0.24; 1.79), and 17-epiE3 (ß = 0.55, 95% CI 0.02; 1.08). Smoking was positively associated with E3:parent (ß = 0.29, 95% CI 0.01; 0.57), 16-pathway:parent (ß = 0.25, 95% CI 0.01; 0.49), and inversely associated with estradiol (ß = - 0.52, 95% CI - 0.93; - 0.10). As compared to nulliparous, parous women with age at first birth ≥ 25 years had lower levels of estrone, combined parent estrogens, 2-OHE1, and 2-OHE2. CONCLUSION: Our findings suggest that BMI, race/ethnicity, and some reproductive and lifestyle factors may contribute to postmenopausal BCa through their effects on circulating estrogens.

Cereblon harnesses Myc-dependent bioenergetics and activity of CD8+ T lymphocytes.

Immunomodulatory drugs, such as thalidomide and related compounds, potentiate T-cell effector functions. Cereblon (CRBN), a substrate receptor of the DDB1-cullin-RING E3 ubiquitin ligase complex, is the only molecular target for this drug class, where drug-induced, ubiquitin-dependent degradation of known "neosubstrates," such as IKAROS, AIOLOS, and CK1α, accounts for their biological activity. Far less clear is whether these CRBN E3 ligase-modulating compounds disrupt the endogenous functions of CRBN. We report that CRBN functions in a feedback loop that harnesses antigen-specific CD8+ T-cell effector responses. Specifically, Crbn deficiency in murine CD8+ T cells augments their central metabolism manifested as elevated bioenergetics, with supraphysiological levels of polyamines, secondary to enhanced glucose and amino acid transport, and with increased expression of metabolic enzymes, including the polyamine biosynthetic enzyme ornithine decarboxylase. Treatment with CRBN-modulating compounds similarly augments central metabolism of human CD8+ T cells. Notably, the metabolic control of CD8+ T cells by modulating compounds or Crbn deficiency is linked to increased and sustained expression of the master metabolic regulator MYC. Finally, Crbn-deficient T cells have augmented antigen-specific cytolytic activity vs melanoma tumor cells, ex vivo and in vivo, and drive accelerated and highly aggressive graft-versus-host disease. Therefore, CRBN functions to harness the activation of CD8+ T cells, and this phenotype can be exploited by treatment with drugs.

HDAC11 deficiency disrupts oncogene-induced hematopoiesis in myeloproliferative neoplasms.

Protein acetylation is an important contributor to cancer initiation. Histone deacetylase 6 (HDAC6) controls JAK2 translation and protein stability and has been implicated in JAK2-driven diseases best exemplified by myeloproliferative neoplasms (MPNs). By using novel classes of highly selective HDAC inhibitors and genetically deficient mouse models, we discovered that HDAC11 rather than HDAC6 is necessary for the proliferation and survival of oncogenic JAK2-driven MPN cells and patient samples. Notably, HDAC11 is variably expressed in primitive stem cells and is expressed largely upon lineage commitment. Although Hdac11is dispensable for normal homeostatic hematopoietic stem and progenitor cell differentiation based on chimeric bone marrow reconstitution, Hdac11 deficiency significantly reduced the abnormal megakaryocyte population, improved splenic architecture, reduced fibrosis, and increased survival in the MPLW515L-MPN mouse model during primary and secondary transplantation. Therefore, inhibitors of HDAC11 are an attractive therapy for treating patients with MPN. Although JAK2 inhibitor therapy provides substantial clinical benefit in MPN patients, the identification of alternative therapeutic targets is needed to reverse MPN pathogenesis and control malignant hematopoiesis. This study establishes HDAC11 as a unique type of target molecule that has therapeutic potential in MPN.

HDAC6 deacetylates and ubiquitinates MSH2 to maintain proper levels of MutSα.

MutS protein homolog 2 (MSH2) is a key DNA mismatch repair protein. It forms the MSH2-MSH6 (MutSα) and MSH2-MSH3 (MutSß) heterodimers, which help to ensure genomic integrity. MutSα not only recognizes and repairs mismatched nucleotides but also recognizes DNA adducts induced by DNA-damaging agents, and triggers cell-cycle arrest and apoptosis. Loss or depletion of MutSα from cells leads to microsatellite instability (MSI) and resistance to DNA damage. Although the level of MutSα can be reduced by the ubiquitin-proteasome pathway, the detailed mechanisms of this regulation remain elusive. Here we report that histone deacetylase 6 (HDAC6) sequentially deacetylates and ubiquitinates MSH2, leading to MSH2 degradation. In addition, HDAC6 significantly reduces cellular sensitivity to DNA-damaging agents and decreases cellular DNA mismatch repair activities by downregulation of MSH2. Overall, these findings reveal a mechanism by which proper levels of MutSα are maintained.

Translational pathology, genomics and the development of systemic therapies for acral melanoma.

Acral melanomas arise on the non-hair bearing skin of the palms, soles and in the nail beds. These rare tumors comprise 2-3 % of all melanomas, are not linked to UV-exposure, and represent the most frequent subtype of melanomas in patients of Asian, African and Hispanic origin. Although recent work has revealed candidate molecular events that underlie acral melanoma development, this knowledge is not yet been translated into efficacious local, regional, or systemic therapies. In the current review, we describe the clinical characteristics of acral melanoma and outline the genetic basis of acral melanoma development. Further discussion is given to the current status of systemic therapy for acral melanoma with a focus on ongoing developments in both immunotherapy and targeted therapy for the treatment of advanced disease.

Metabolic Changes Are Associated with Melphalan Resistance in Multiple Myeloma.

Multiple myeloma is an incurable hematological malignancy that impacts tens of thousands of people every year in the United States. Treatment for eligible patients involves induction, consolidation with stem cell rescue, and maintenance. High-dose therapy with a DNA alkylating agent, melphalan, remains the primary drug for consolidation therapy in conjunction with autologous stem-cell transplantation; as such, melphalan resistance remains a relevant clinical challenge. Here, we describe a proteometabolomic approach to examine mechanisms of acquired melphalan resistance in two cell line models. Drug metabolism, steady-state metabolomics, activity-based protein profiling (ABPP, data available at PRIDE: PXD019725), acute-treatment metabolomics, and western blot analyses have allowed us to further elucidate metabolic processes associated with melphalan resistance. Proteometabolomic data indicate that drug-resistant cells have higher levels of pentose phosphate pathway metabolites. Purine, pyrimidine, and glutathione metabolisms were commonly altered, and cell-line-specific changes in metabolite levels were observed, which could be linked to the differences in steady-state metabolism of naïve cells. Inhibition of selected enzymes in purine synthesis and pentose phosphate pathways was evaluated to determine their potential to improve melphalan's efficacy. The clinical relevance of these proteometabolomic leads was confirmed by comparison of tumor cell transcriptomes from newly diagnosed MM patients and patients with relapsed disease after treatment with high-dose melphalan and autologous stem-cell transplantation. The observation of common and cell-line-specific changes in metabolite levels suggests that omic approaches will be needed to fully examine melphalan resistance in patient specimens and define personalized strategies to optimize the use of high-dose melphalan.

GMSimpute: a generalized two-step Lasso approach to impute missing values in label-free mass spectrum analysis.

MOTIVATION: Missingness in label-free mass spectrometry is inherent to the technology. A computational approach to recover missing values in metabolomics and proteomics datasets is important. Most existing methods are designed under a particular assumption, either missing at random or under the detection limit. If the missing pattern deviates from the assumption, it may lead to biased results. Hence, we investigate the missing patterns in free mass spectrometry data and develop an omnibus approach GMSimpute, to allow effective imputation accommodating different missing patterns. RESULTS: Three proteomics datasets and one metabolomics dataset indicate missing values could be a mixture of abundance-dependent and abundance-independent missingness. We assess the performance of GMSimpute using simulated data (with a wide range of 80 missing patterns) and metabolomics data from the Cancer Genome Atlas breast cancer and clear cell renal cell carcinoma studies. Using Pearson correlation and normalized root mean square errors between the true and imputed abundance, we compare its performance to K-nearest neighbors' type approaches, Random Forest, GSimp, a model-based method implemented in DanteR and minimum values. The results indicate GMSimpute provides higher accuracy in imputation and exhibits stable performance across different missing patterns. In addition, GMSimpute is able to identify the features in downstream differential expression analysis with high accuracy when applied to the Cancer Genome Atlas datasets. AVAILABILITY AND IMPLEMENTATION: GMSimpute is on CRAN: https://cran.r-project.org/web/packages/GMSimpute/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Lowering Sample Requirements to Study Tyrosine Kinase Signaling Using Phosphoproteomics with the TMT Calibrator Approach.

Analysis of tyrosine kinase signaling is critical for the development of targeted cancer therapy. Currently, immunoprecipitation of phosphotyrosine (pY) peptides prior to liquid chromatography-tandem mass spectrometry (LC-MS/MS) is used to profile tyrosine kinase substrates. A typical protocol requests 10 mg of total protein from ≈108 cells or 50-100 mg of tissue. Large sample requirements can be cost prohibitive or not feasible for certain experiments. Sample multiplexing using chemical labeling reduces the protein amount required for each sample, and newer approaches use a material-rich reference channel as a calibrator to trigger detection and quantification for smaller samples. Here, it is demonstrated that the tandem mass tag (TMT) calibrator approach reduces the sample input for pY profiling tenfold (to ≈1 mg total protein per sample from 107 cells grown in one plate), while maintaining the depth of pY proteome sampling and the biological content of the experiment. Data are available through PRIDE (PXD019764 for label-free and PXD018952 for TMT). This strategy opens more opportunities for pY profiling of large sample cohorts and samples with limited protein quantity such as immune cells, xenograft models, and human tumors.

Functional Proteomics and Deep Network Interrogation Reveal a Complex Mechanism of Action of Midostaurin in Lung Cancer Cells.

Lung cancer is associated with high prevalence and mortality, and despite significant successes with targeted drugs in genomically defined subsets of lung cancer and immunotherapy, the majority of patients currently does not benefit from these therapies. Through a targeted drug screen, we found the recently approved multi-kinase inhibitor midostaurin to have potent activity in several lung cancer cells independent of its intended target, PKC, or a specific genomic marker. To determine the underlying mechanism of action we applied a layered functional proteomics approach and a new data integration method. Using chemical proteomics, we identified multiple midostaurin kinase targets in these cells. Network-based integration of these targets with quantitative tyrosine and global phosphoproteomics data using protein-protein interactions from the STRING database suggested multiple targets are relevant for the mode of action of midostaurin. Subsequent functional validation using RNA interference and selective small molecule probes showed that simultaneous inhibition of TBK1, PDPK1 and AURKA was required to elicit midostaurin's cellular effects. Immunoblot analysis of downstream signaling nodes showed that combined inhibition of these targets altered PI3K/AKT and cell cycle signaling pathways that in part converged on PLK1. Furthermore, rational combination of midostaurin with the potent PLK1 inhibitor BI2536 elicited strong synergy. Our results demonstrate that combination of complementary functional proteomics approaches and subsequent network-based data integration can reveal novel insight into the complex mode of action of multi-kinase inhibitors, actionable targets for drug discovery and cancer vulnerabilities. Finally, we illustrate how this knowledge can be used for the rational design of synergistic drug combinations with high potential for clinical translation.

Polypharmacology-based ceritinib repurposing using integrated functional proteomics.

Targeted drugs are effective when they directly inhibit strong disease drivers, but only a small fraction of diseases feature defined actionable drivers. Alternatively, network-based approaches can uncover new therapeutic opportunities. Applying an integrated phenotypic screening, chemical and phosphoproteomics strategy, here we describe the anaplastic lymphoma kinase (ALK) inhibitor ceritinib as having activity across several ALK-negative lung cancer cell lines and identify new targets and network-wide signaling effects. Combining pharmacological inhibitors and RNA interference revealed a polypharmacology mechanism involving the noncanonical targets IGF1R, FAK1, RSK1 and RSK2. Mutating the downstream signaling hub YB1 protected cells from ceritinib. Consistent with YB1 signaling being known to cause taxol resistance, combination of ceritinib with paclitaxel displayed strong synergy, particularly in cells expressing high FAK autophosphorylation, which we show to be prevalent in lung cancer. Together, we present a systems chemical biology platform for elucidating multikinase inhibitor polypharmacology mechanisms, subsequent design of synergistic drug combinations, and identification of mechanistic biomarker candidates.

Comparison of Quantitative Mass Spectrometry Platforms for Monitoring Kinase ATP Probe Uptake in Lung Cancer.

Recent developments in instrumentation and bioinformatics have led to new quantitative mass spectrometry platforms including LC-MS/MS with data-independent acquisition (DIA) and targeted analysis using parallel reaction monitoring mass spectrometry (LC-PRM), which provide alternatives to well-established methods, such as LC-MS/MS with data-dependent acquisition (DDA) and targeted analysis using multiple reaction monitoring mass spectrometry (LC-MRM). These tools have been used to identify signaling perturbations in lung cancers and other malignancies, supporting the development of effective kinase inhibitors and, more recently, providing insights into therapeutic resistance mechanisms and drug repurposing opportunities. However, detection of kinases in biological matrices can be challenging; therefore, activity-based protein profiling enrichment of ATP-utilizing proteins was selected as a test case for exploring the limits of detection of low-abundance analytes in complex biological samples. To examine the impact of different MS acquisition platforms, quantification of kinase ATP uptake following kinase inhibitor treatment was analyzed by four different methods: LC-MS/MS with DDA and DIA, LC-MRM, and LC-PRM. For discovery data sets, DIA increased the number of identified kinases by 21% and reduced missingness when compared with DDA. In this context, MRM and PRM were most effective at identifying global kinome responses to inhibitor treatment, highlighting the value of a priori target identification and manual evaluation of quantitative proteomics data sets. We compare results for a selected set of desthiobiotinylated peptides from PRM, MRM, and DIA and identify considerations for selecting a quantification method and postprocessing steps that should be used for each data acquisition strategy.