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Rationale The workup and longitudinal monitoring for subjects presenting with pulmonary nodules is a pressing clinical problem. A blood-based biomarker panel potentially has utility for identifying subjects at higher risk for harboring a malignant nodule for whom additional workup would be indicated or subjects at reduced risk for whom imaging-based follow-up would be indicated. Objectives To assess whether a previously described four-protein biomarker panel, reported to improve assessment of lung cancer risk compared with a smoking-based lung cancer risk model, can provide discrimination between benign and malignant indeterminate pulmonary nodules. Methods A previously validated multiplex enzyme-linked immunoassay was performed on matched case and control samples from each cohort. Measurements The biomarker panel was tested in two case-control cohorts of patients presenting with indeterminate pulmonary nodules at the University of Pittsburgh Medical Center and the University of Texas Southwestern. Main Results In both cohorts, the biomarker panel resulted in improved prediction of lung cancer risk over a model on the basis of nodule size alone. Of particular note, the addition of the marker panel to nodule size greatly improved sensitivity at a high specificity in both cohorts. Conclusions A four-marker biomarker panel, previously validated to improve lung cancer risk prediction, was found to also have utility in distinguishing benign from malignant indeterminate pulmonary nodules. Its performance in improving sensitivity at a high specificity indicates potential utility of the marker panel in assessing likelihood of malignancy in otherwise indeterminate nodules.

PURPOSE The combination chemotherapy of fluorouracil, leucovorin, irinotecan, and oxaliplatin (FOLFIRINOX) has provided clinically meaningful improvement for pancreatic ductal adenocarcinoma (PDAC). We previously uncovered a role for the serine hydrolase carboxylesterase 2 (CES2) in mediating intratumoral activation of the prodrug irinotecan, a constituent of FOLFIRINOX. We aimed to further test the predictive value of CES2 for response to irinotecan using patient-derived xenograft (PDX) models and to elucidate the determinants of CES2 expression and response to FOLFIRINOX treatment among patients with PDAC. METHODS PDXs were engrafted subcutaneously into nude mice and treated for 4 weeks with either saline control or irinotecan. CES2 and hepatocyte nuclear factor 4 alpha (HNF4A) expression in PDAC tissues was evaluated by immunohistochemical and Western blot analysis. Kaplan-Meier and Cox regression analyses were applied to assess the association between overall survival and hemoglobin A1C (HbA1C) levels in patients who underwent neoadjuvant FOLFIRINOX treatment. RESULTS High CES2 activity in PDAC PDXs was associated with increased sensitivity to irinotecan. Integrated gene expression, proteomic analyses, and in vitro genetic experiments revealed that nuclear receptor HNF4A, which is upregulated in diabetes, is the upstream transcriptional regulator of CES2 expression. Elevated CES2 protein expression in PDAC tissues was positively associated with a history of type 2 diabetes (odds ratio, 4.84; P = .02). High HbA1C levels were associated with longer overall survival in patients who received neoadjuvant FOLFIRINOX treatment ( P = .04). CONCLUSION To our knowledge, we provide, for the first time, evidence that CES2 expression is associated with a history of type 2 diabetes in PDAC and that elevated HbA1C, by predicting tumor CES2 expression, may represent a novel marker for stratifying patients most likely to respond to FOLFIRINOX therapy.

Ribonuclease (RNase) H2 is a key enzyme for the removal of RNA found in DNA-RNA hybrids, playing a fundamental role in biological processes such as DNA replication, telomere maintenance, and DNA damage repair. RNase H2 is a trimer composed of three subunits, RNASEH2A being the catalytic subunit. RNASEH2A expression levels have been shown to be upregulated in transformed and cancer cells. In this study, we used a bioinformatics approach to identify RNASEH2A co-expressed genes in different human tissues to underscore biological processes associated with RNASEH2A expression. Our analysis shows functional networks for RNASEH2A involvement such as DNA replication and DNA damage response and a novel putative functional network of cell cycle regulation. Further bioinformatics investigation showed increased gene expression in different types of actively cycling cells and tissues, particularly in several cancers, supporting a biological role for RNASEH2A but not for the other two subunits of RNase H2 in cell proliferation. Mass spectrometry analysis of RNASEH2A-bound proteins identified players functioning in cell cycle regulation. Additional bioinformatic analysis showed that RNASEH2A correlates with cancer progression and cell cycle related genes in Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) Pan Cancer datasets and supported our mass spectrometry findings.

Background We applied a training and testing approach to develop and validate a plasma metabolite panel for the detection of early-stage pancreatic ductal adenocarcinoma (PDAC) alone and in combination with a previously validated protein panel for early-stage PDAC. Methods A comprehensive metabolomics platform was initially applied to plasmas collected from 20 PDAC cases and 80 controls. Candidate markers were filtered based on a second independent cohort that included nine invasive intraductal papillary mucinous neoplasm cases and 51 benign pancreatic cysts. Blinded validation of the resulting metabolite panel was performed in an independent test cohort consisting of 39 resectable PDAC cases and 82 matched healthy controls. The additive value of combining the metabolite panel with a previously validated protein panel was evaluated. Results Five metabolites (acetylspermidine, diacetylspermine, an indole-derivative, and two lysophosphatidylcholines) were selected as a panel based on filtering criteria. A combination rule was developed for distinguishing between PDAC and healthy controls using the Training Set. In the blinded validation study with early-stage PDAC samples and controls, the five metabolites yielded areas under the curve (AUCs) ranging from 0.726 to 0.842, and the combined metabolite model yielded an AUC of 0.892 (95% confidence interval [CI] = 0.828 to 0.956). Performance was further statistically significantly improved by combining the metabolite panel with a previously validated protein marker panel consisting of CA 19–9, LRG1, and TIMP1 (AUC = 0.924, 95% CI = 0.864 to 0.983, comparison DeLong test one-sided P= .02). Conclusions A metabolite panel in combination with CA19-9, TIMP1, and LRG1 exhibited substantially improved performance in the detection of early-stage PDAC compared with a protein panel alone.

FeatureCorr is an R package that aids in association and network analysis of data obtained from preliminary bioinformatic analysis of next-generation sequencing(NGS) or microarray experiments. These experiments are widely used for various applications like mutation and expression profiles, detection of epigenetic changes in the genomic DNA, etc. FeatureCorr enables users in cleaning and preprocessing data to minimize batch effects and for background noise removal. FeatureCorr can help in the analysis of feature correlation in different ways: Correlation of one Feature vs multiple Features, pairwise correlation of multiple features against multiple features, and in-depth correlation and distributions of two features.

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.

Harnessing the immune response to tumor antigens in the form of autoantibodies, which occurs early during tumor development, has relevance to the detection of cancer at early stages. We conducted an initial screen of antigens associated with an autoantibody response in serous ovarian cancer using recombinant protein arrays. The top 25 recombinants that exhibited increased reactivity with cases compared to controls revealed TP53 and MYC, which are ovarian cancer driver genes, as major network nodes. A mass spectrometry based independent analysis of circulating immunoglobulin (Ig)-bound proteins in ovarian cancer and of ovarian cancer cell surface MHC-II bound peptides also revealed a TP53&ndash, MYC related network of antigens. Our findings support the occurrence of a humoral immune response to antigens linked to ovarian cancer driver genes that may have utility for early detection applications.

IMPORTANCE: There is an urgent need to improve lung cancer risk assessment because current screening criteria miss a large proportion of cases. OBJECTIVE: To investigate whether a lung cancer risk prediction model based on a panel of selected circulating protein biomarkers can outperform a traditional risk prediction model and current US screening criteria. DESIGN, SETTING, AND PARTICIPANTS: Prediagnostic samples from 108 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and samples from 216 smoking-matched controls from the Carotene and Retinol Efficacy Trial (CARET) cohort were used to develop a biomarker risk score based on 4 proteins (cancer antigen 125 [CA125], carcinoembryonic antigen [CEA], cytokeratin-19 fragment [CYFRA 21-1], and the precursor form of surfactant protein B [Pro-SFTPB]). The biomarker score was subsequently validated blindly using absolute risk estimates among 63 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and 90 matched controls from 2 large European population-based cohorts, the European Prospective Investigation into Cancer and Nutrition (EPIC) and the Northern Sweden Health and Disease Study (NSHDS). MAIN OUTCOMES AND MEASURES: Model validity in discriminating between future lung cancer cases and controls. Discrimination estimates were weighted to reflect the background populations of EPIC and NSHDS validation studies (area under the receiver-operating characteristics curve [AUC], sensitivity, and specificity). RESULTS: In the validation study of 63 ever-smoking patients with lung cancer and 90 matched controls (mean [SD] age, 57.7 [8.7] years; 68.6% men) from EPIC and NSHDS, an integrated risk prediction model that combined smoking exposure with the biomarker score yielded an AUC of 0.83 (95% CI, 0.76-0.90) compared with 0.73 (95% CI, 0.64-0.82) for a model based on smoking exposure alone (P = .003 for difference in AUC). At an overall specificity of 0.83, based on the US Preventive Services Task Force screening criteria, the sensitivity of the integrated risk prediction (biomarker) model was 0.63 compared with 0.43 for the smoking model. Conversely, at an overall sensitivity of 0.42, based on the US Preventive Services Task Force screening criteria, the integrated risk prediction model yielded a specificity of 0.95 compared with 0.86 for the smoking model. CONCLUSIONS AND RELEVANCE: This study provided a proof of principle in showing that a panel of circulating protein biomarkers may improve lung cancer risk assessment and may be used to define eligibility for computed tomography screening.

The epithelial-mesenchymal transition (EMT) plays a central role in cancer metastasis and drug resistance—two persistent clinical challenges. Epithelial cells can undergo a partial or full EMT, attaining either a hybrid epithelial/mesenchymal (E/M) or mesenchymal phenotype, respectively. Recent studies have emphasized that hybrid E/M cells may be more aggressive than their mesenchymal counterparts. However, mechanisms driving hybrid E/M phenotypes remain largely elusive. Here, to better characterize the hybrid E/M phenotype (s) and tumor aggressiveness, we integrate two computational methods—(a) RACIPE—to identify the robust gene expression patterns emerging from the dynamics of a given gene regulatory network, and (b) EMT scoring metric—to calculate the probability that a given gene expression profile displays a hybrid E/M phenotype. We apply the EMT scoring metric to RACIPE-generated gene expression data generated from a core EMT regulatory network and classify the gene expression profiles into relevant categories (epithelial, hybrid E/M, mesenchymal). This categorization is broadly consistent with hierarchical clustering readouts of RACIPE-generated gene expression data. We also show how the EMT scoring metric can be used to distinguish between samples composed of exclusively hybrid E/M cells and those containing mixtures of epithelial and mesenchymal subpopulations using the RACIPE-generated gene expression data.

Background: CA19-9, which is currently in clinical use as a pancreatic ductal adenocarcinoma (PDAC) biomarker, has limited performance in detecting early-stage disease. We and others have identified protein biomarker candidates that have the potential to complement CA19-9. We have carried out sequential validations starting with 17 protein biomarker candidates to determine which markers and marker combination would improve detection of early-stage disease compared with CA19-9 alone. Methods: Candidate biomarkers were subjected to enzyme-linked immunosorbent assay based sequential validation using independent multiple sample cohorts consisting of PDAC cases (n = 187), benign pancreatic disease (n = 93), and healthy controls (n = 169). A biomarker panel for early-stage PDAC was developed based on a logistic regression model. All statistical tests for the results presented below were one-sided. Results: Six out of the 17 biomarker candidates and CA19-9 were validated in a sample set consisting of 75 PDAC patients, 27 healthy subjects, and 19 chronic pancreatitis patients. A second independent set of 73 early-stage PDAC patients, 60 healthy subjects, and 74 benign pancreatic disease patients (combined validation set) yielded a model that consisted of TIMP1, LRG1, and CA19-9. Additional blinded testing of the model was done using an independent set of plasma samples from 39 resectable PDAC patients and 82 matched healthy subjects (test set). The model yielded areas under the curve (AUCs) of 0.949 (95% confidence interval [CI] = 0.917 to 0.981) and 0.887 (95% CI = 0.817 to 0.957) with sensitivities of 0.849 and 0.667 at 95% specificity in discriminating early-stage PDAC vs healthy subjects in the combined validation and test sets, respectively. The performance of the biomarker panel was statistically significantly improved compared with CA19-9 alone (P < .001, combined validation set; P = .008, test set). Conclusion: The addition of TIMP1 and LRG1 immunoassays to CA19-9 statistically significantly improves the detection of early-stage PDAC.