Dickkopf-related protein 3 is a novel biomarker for chronic GVHD after allogeneic hematopoietic cell transplantation.

To identify plasma biomarkers associated with fibrotic mechanisms of chronic graft-versus-host disease (GVHD), we used multiplex mass spectrometry with pooled samples for biomarker discovery in comparing proteomic profiles between patients with newly diagnosed sclerotic chronic GVHD (n = 21), those with newly diagnosed nonsclerotic chronic GVHD (n = 33), and those without chronic GVHD (n = 20). Immunoassay was used to measure protein concentrations of individual discovery samples and 186 independent verification samples. The discovery mass spectrometry analysis identified 2 candidate proteins with at least 1.5-fold difference in sclerotic GVHD: Dickkopf-related protein 3 (DKK3) and interleukin-1 receptor accessory protein (IL1RAP). Analysis of individual discovery samples by immunoassay showed that DKK3, a modulator of the Wnt signaling pathway, was a biomarker for both sclerotic and nonsclerotic chronic GVHD. Verification analysis of 186 patients confirmed that elevated plasma DKK3 concentrations were associated with chronic GVHD, regardless of the presence or absence of sclerosis, and that the area under the receiver operating characteristic curve was 0.85 for association of DKK3 concentrations with chronic GVHD. Multiple linear regression analysis showed that chronic GVHD with or without steroid treatment and patient age were independently associated with DKK3 concentrations. Patients with high DKK3 concentrations had a higher nonrelapse mortality than those with low concentrations. The lower IL1RAP concentrations in patients with sclerotic GVHD compared with other conditions in the discovery cohort were not confirmed in the verification cohort. DKK3 is a novel biomarker for chronic GVHD. Further studies are needed to determine the biological functions of DKK3 in the pathogenesis of chronic GVHD.

Immune-Complexome Analysis Identifies Immunoglobulin-Bound Biomarkers That Predict the Response to Chemotherapy of Pancreatic Cancer Patients.

Pancreatic Ductal Adenocarcinoma (PDA) is an aggressive malignancy with a very poor outcome. Although chemotherapy (CT) treatment has poor efficacy, it can enhance tumor immunogenicity. Tumor-Associated Antigens (TAA) are self-proteins that are overexpressed in tumors that may induce antibody production and can be PDA theranostic targets. However, the prognostic value of TAA-antibody association as Circulating Immune Complexes (CIC) has not yet been elucidated, mainly due to the lack of techniques that lead to their identification. In this study, we show a novel method to separate IgG, IgM, and IgA CIC from sera to use them as prognostic biomarkers of CT response. The PDA Immune-Complexome (IC) was identified using a LTQ-Orbitrap mass spectrometer followed by computational analysis. The analysis of the IC of 37 PDA patients before and after CT revealed differential associated antigens (DAA) for each immunoglobulin class. Our method identified different PDA-specific CIC in patients that were associated with poor prognosis patients. Finally, CIC levels were significantly modified by CT suggesting that they can be used as effective prognostic biomarkers to follow CT response in PDA patients.

Integrative Analysis of Novel Metabolic Subtypes in Pancreatic Cancer Fosters New Prognostic Biomarkers.

Background: Most of the patients with Pancreatic Ductal Adenocarcinoma (PDA) are not eligible for a curative surgical resection. For this reason there is an urgent need for personalized therapies. PDA is the result of complex interactions between tumor molecular profile and metabolites produced by its microenvironment. Despite recent studies identified PDA molecular subtypes, its metabolic classification is still lacking. Methods: We applied an integrative analysis on transcriptomic and genomic data of glycolytic genes in PDA. Data were collected from public datasets and molecular glycolytic subtypes were defined using hierarchical clustering. The grade of purity of the cancer samples was assessed estimating the different amount of stromal and immunological infiltrate among the identified PDA subtypes. Analyses of metabolomic data from a subset of PDA cell lines allowed us to identify the different metabolites produced by the metabolic subtypes. Sera of a cohort of 31 PDA patients were analyzed using Q-TOF mass spectrometer to measure the amount of metabolic circulating proteins present before and after chemotherapy. Results: Our integrative analysis of glycolytic genes identified two glycolytic and two non-glycolytic metabolic PDA subtypes. Glycolytic patients develop disease earlier, have poor prognosis, low immune-infiltrated tumors, and are characterized by a gain in chr12p13 genomic region. This gain results in the over-expression of GAPDH, TPI1, and FOXM1. PDA cell lines with the gain of chr12p13 are characterized by an higher lipid uptake and sensitivity to drug targeting the fatty acid metabolism. Our sera proteomic analysis confirms that TPI1 serum levels increase in poor prognosis gemcitabine-treated patients. Conclusions: We identify four metabolic PDA subtypes with different prognosis outcomes which may have pivotal role in setting personalized treatments. Moreover, our data suggest TPI1 as putative prognostic PDA biomarker.

Exosomes harbor B cell targets in pancreatic adenocarcinoma and exert decoy function against complement-mediated cytotoxicity.

Although B cell response is frequently found in cancer, there is little evidence that it alters tumor development or progression. The process through which tumor-associated antigens trigger humoral response is not well delineated. We investigate the repertoire of antigens associated with humoral immune response in pancreatic ductal adenocarcinoma (PDAC) using in-depth proteomic profiling of immunoglobulin-bound proteins from PDAC patient plasmas and identify tumor antigens that induce antibody response together with exosome hallmark proteins. Additional profiling of PDAC cell-derived exosomes reveals significant overlap in their protein content with immunoglobulin-bound proteins in PDAC plasmas, and significant autoantibody reactivity is observed between PDAC cell-derived exosomes and patient plasmas compared to healthy controls. Importantly, PDAC-derived exosomes induce a dose-dependent inhibition of PDAC serum-mediated complement-dependent cytotoxicity towards cancer cells. In summary, we provide evidence that exosomes display a large repertoire of tumor antigens that induce autoantibodies and exert a decoy function against complement-mediated cytotoxicity.

Association of Plasma CD163 Concentration with De Novo-Onset Chronic Graft-versus-Host Disease.

Chronic graft-versus-host disease (GVHD) is the leading cause of long-term morbidity and mortality after allogeneic hematopoietic cell transplantation. To identify prognostic plasma proteins associated with de novo- or quiescent-onset chronic GVHD (cGVHD), we performed a discovery and validation proteomic study. The total study cohort included 167 consecutive patients who had no clinical evidence of GVHD under minimum glucocorticoid administration and had available plasma samples obtained at 80 ± 14 days after transplantation. We first used high-throughput mass spectrometry to screen pooled plasma using 20 cases with subsequent cGVHD and 20 controls without it, and we identified 20 candidate proteins. We then measured 12 of the 20 candidate proteins by ELISA on the same individual samples and identified 4 proteins for further verification (LGALS3BP, CD5L, CD163, and TXN for de novo onset, and LGALS3BP and CD5L for quiescent onset). The verification cohort included 127 remaining patients. The cumulative incidence of de novo-onset cGVHD was higher in patients with higher plasma soluble CD163 concentrations at day 80 than those with lower concentrations (75% versus 40%, P = .018). The cumulative incidence of de novo- or quiescent-onset cGVHD did not differ statistically according to concentrations of the 3 other proteins at day 80. CD163 is a macrophage scavenger receptor and is elevated in oxidative conditions. These results suggest that monocyte or macrophage activation or increased oxidative stress may contribute to the pathogenesis of cGVHD.

Lysyl Hydroxylase 2 Is Secreted by Tumor Cells and Can Modify Collagen in the Extracellular Space.

Lysyl hydroxylase 2 (LH2) catalyzes the hydroxylation of lysine residues in the telopeptides of fibrillar collagens, which leads to the formation of stable collagen cross-links. Recently we reported that LH2 enhances the metastatic propensity of lung cancer by increasing the amount of stable hydroxylysine aldehyde-derived collagen cross-links (HLCCs), which generate a stiffer tumor stroma (Chen, Y., et al. (2015) J. Clin. Invest. 125, 125, 1147-1162). It is generally accepted that LH2 modifies procollagen α chains on the endoplasmic reticulum before the formation of triple helical procollagen molecules. Herein, we report that LH2 is also secreted and modifies collagen in the extracellular space. Analyses of lung cancer cell lines demonstrated that LH2 is present in the cell lysates and the conditioned media in a dimeric, active form in both compartments. LH2 co-localized with collagen fibrils in the extracellular space in human lung cancer specimens and in orthotopic lung tumors generated by injection of a LH2-expressing human lung cancer cell line into nude mice. LH2 depletion in MC3T3 osteoblastic cells impaired the formation of HLCCs, resulting in an increase in the unmodified lysine aldehyde-derived collagen cross-link (LCC), and the addition of recombinant LH2 to the media of LH2-deficient MC3T3 cells was sufficient to rescue HLCC formation in the extracellular matrix. The finding that LH2 modifies collagen in the extracellular space challenges the current view that LH2 functions solely on the endoplasmic reticulum and could also have important implications for cancer biology.

Latent Feature Decompositions for Integrative Analysis of Multi-Platform Genomic Data.

Increased availability of multi-platform genomics data on matched samples has sparked research efforts to discover how diverse molecular features interact both within and between platforms. In addition, simultaneous measurements of genetic and epigenetic characteristics illuminate the roles their complex relationships play in disease progression and outcomes. However, integrative methods for diverse genomics data are faced with the challenges of ultra-high dimensionality and the existence of complex interactions both within and between platforms. We propose a novel modeling framework for integrative analysis based on decompositions of the large number of platform-specific features into a smaller number of latent features. Subsequently we build a predictive model for clinical outcomes accounting for both within- and between-platform interactions based on Bayesian model averaging procedures. Principal components, partial least squares and non-negative matrix factorization as well as sparse counterparts of each are used to define the latent features, and the performance of these decompositions is compared both on real and simulated data. The latent feature interactions are shown to preserve interactions between the original features and not only aid prediction but also allow explicit selection of outcome-related features. The methods are motivated by and applied to a glioblastoma multiforme data set from The Cancer Genome Atlas to predict patient survival times integrating gene expression, microRNA, copy number and methylation data. For the glioblastoma data, we find a high concordance between our selected prognostic genes and genes with known associations with glioblastoma. In addition, our model discovers several relevant cross-platform interactions such as copy number variation associated gene dosing and epigenetic regulation through promoter methylation. On simulated data, we show that our proposed method successfully incorporates interactions within and between genomic platforms to aid accurate prediction and variable selection. Our methods perform best when principal components are used to define the latent features.

ETS2 mediated tumor suppressive function and MET oncogene inhibition in human non-small cell lung cancer.

PURPOSE: The ETS2 transcription factor is an evolutionarily conserved gene that is deregulated in cancer. We analyzed the transcriptome of lung adenocarcinomas and normal lung tissue by expression profiling and found that ETS2 was significantly downregulated in adenocarcinomas. In this study, we probed the yet unknown functional role of ETS2 in lung cancer pathogenesis. EXPERIMENTAL DESIGN: Lung adenocarcinomas (n = 80) and normal lung tissues (n = 30) were profiled using the Affymetrix Human Gene 1.0 ST platform. Immunohistochemical (IHC) analysis was conducted to determine ETS2 protein expression in non-small cell lung cancer (NSCLC) histologic tissue specimens (n = 201). Patient clinical outcome, based on ETS2 IHC expression, was statistically assessed using the log-rank and Kaplan-Meier tests. RNA interference and overexpression strategies were used to assess the effects of ETS2 expression on the transcriptome and on various malignant phenotypes. RESULTS: ETS2 expression was significantly reduced in lung adenocarcinomas compared with normal lung (P < 0.001). Low ETS2 IHC expression was a significant predictor of shorter time to recurrence in NSCLC (P = 0.009, HR = 1.89) and adenocarcinoma (P = 0.03, HR = 1.86). Moreover, ETS2 was found to significantly inhibit lung cancer cell growth, migration, and invasion (P < 0.05), and microarray and pathways analysis revealed significant (P < 0.001) activation of the HGF pathway following ETS2 knockdown. In addition, ETS2 was found to suppress MET phosphorylation and knockdown of MET expression significantly attenuated (P < 0.05) cell invasion mediated by ETS2-specific siRNA. Furthermore, knockdown of ETS2 augmented HGF-induced MET phosphorylation, cell migration, and invasion. CONCLUSION(S): Our findings point to a tumor suppressor role for ETS2 in human NSCLC pathogenesis through inhibition of the MET proto-oncogene.

Estrogen-regulated genes in rat testes and their relationship to recovery of spermatogenesis after irradiation.

Despite numerous observations of the effects of estrogens on spermatogenesis, identification of estrogen-regulated genes in the testis is limited. Using rats in which irradiation had completely blocked spermatogonial differentiation, we previously showed that testosterone suppression with gonadotropin-releasing hormone-antagonist acyline and the antiandrogen flutamide stimulated spermatogenic recovery and that addition of estradiol (E2) to this regimen accelerated this recovery. We report here the global changes in testicular cell gene expression induced by the E2 treatment. By minimizing the changes in other hormones and using concurrent data on regulation of the genes by these hormones, we were able to dissect the effects of estrogen on gene expression, independent of gonadotropin or testosterone changes. Expression of 20 genes, largely in somatic cells, was up- or downregulated between 2- and 5-fold by E2. The unexpected and striking enrichment of transcripts not corresponding to known genes among the E2-downregulated probes suggested that these might represent noncoding mRNAs; indeed, we have identified several as miRNAs and their potential target genes in this system. We propose that genes for which expression levels are altered in one direction by irradiation and in the opposite direction by both testosterone suppression and E2 treatment are candidates for controlling the block in differentiation. Several genes, including insulin-like 3 (Insl3), satisfied those criteria. If they are indeed involved in the inhibition of spermatogonial differentiation, they may be candidate targets for treatments to enhance recovery of spermatogenesis following gonadotoxic exposures, such as those resulting from cancer therapy.

A method for visualization of "omic" datasets for sphingolipid metabolism to predict potentially interesting differences.

Sphingolipids are structurally diverse and their metabolic pathways highly complex, which makes it difficult to follow all of the subspecies in a biological system, even using "lipidomic" approaches. This report describes a method to use transcriptomic data to visualize and predict potential differences in sphingolipid composition, and it illustrates its use with published data for cancer cell lines and tumors. In addition, several novel sphingolipids that were predicted to differ between MDA-MB-231 and MCF7 cells based on published microarray data for these breast cancer cell lines were confirmed by mass spectrometry. For the data that we were able to find for these comparisons, there was a significant match between the gene expression data and sphingolipid composition (P < 0.001 by Fisher's exact test). Upon considering the large number of gene expression datasets produced in recent years, this simple integration of two types of "omic" technologies ("transcriptomics" to direct "sphingolipidomics") might facilitate the discovery of useful relationships between sphingolipid metabolism and disease, such as the identification of new biomarkers.

Characterization of mutant serine palmitoyltransferase 1 in LY-B cells.

CHO-LY-B cells have been useful in studies of sphingolipid metabolism and function because they lack serine palmitoyltransferase (SPT) activity. Cloning and sequencing of the SPT1 transcript of LY-B cells identified the mutation as a guanine to adenine change at nucleotide 738, causing a G246R transformation. Western blots revealed low expression of the mutant SPT1 peptide, but activity was not detectable by mass spectrometric analysis of [(13)C]-palmitate incorporation into sphinganine, sphingosine, 1-deoxysphinganine, or 1-desoxymethylsphinganine. Treatment of LY-B cells with chemical chaperones (DMSO or glycerol) increased the amounts of mutant SPT1 as well as SPT2, but SPT activity was not restored. This study has established that G246R mutation in hamster SPT1 results in the loss of SPT activity.