Browsing by Author "Brennan, Paul"

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    Plasma microRNAs as biomarkers of pancreatic cancer risk in a prospective cohort study
    (Wiley, 2017-09-01) Duell, Eric J.; Lujan-Barroso, Leila; Sala, Nuria; McElyea, Samantha Deitz; Overvad, Kim; Tjonneland, Anne; Olsen, Anja; Weiderpass, Elisabete; Busund, Lill-Tove; Moi, Line; Muller, David; Vineis, Paolo; Aune, Dagfinn; Matullo, Giuseppe; Naccarati, Alessio; Panico, Salvatore; Tagliabue, Giovanna; Tumino, Rosario; Palli, Domenico; Kaaks, Rudolf; Katzke, Verena A.; Boeing, Heiner; H.B.(as), Bueno-de-Mesquita; Peeters, Petra H.; Trichopoulou, Antonia; Lagiou, Pagona; Kotanidou, Anastasia; Travis, Ruth C.; Wareham, Nick; Khaw, Kay-Tee; Quiros, Jose Ramon; Rodriguez-Barranco, Miguel; Dorronsoro, Miren; Chirlaque, Maria-Dolores; Ardanaz, Eva; Severi, Gianluca; Boutron-Rault, Marie-Christine; Rebours, Vinciane; Brennan, Paul; Gunter, Marc; Scelo, Ghislaine; Cote, Greg; Sherman, Stuart; Korc, Murray; Medicine, School of Medicine
    Noninvasive biomarkers for early pancreatic ductal adenocarcinoma (PDAC) diagnosis and disease risk stratification are greatly needed. We conducted a nested case-control study within the Prospective Investigation into Cancer and Nutrition (EPIC) cohort to evaluate prediagnostic microRNAs (miRs) as biomarkers of subsequent PDAC risk. A panel of eight miRs (miR-10a, -10b, -21-3p, -21-5p, -30c, -106b, -155 and -212) based on previous evidence from our group was evaluated in 225 microscopically confirmed PDAC cases and 225 controls matched on center, sex, fasting status and age/date/time of blood collection. MiR levels in prediagnostic plasma samples were determined by quantitative RT-PCR. Logistic regression was used to model levels and PDAC risk, adjusting for covariates and to estimate area under the receiver operating characteristic curves (AUC). Plasma miR-10b, -21-5p, -30c and -106b levels were significantly higher in cases diagnosed within 2 years of blood collection compared to matched controls (all p-values <0.04). Based on adjusted logistic regression models, levels for six miRs (miR-10a, -10b, -21-5p, -30c, -155 and -212) overall, and for four miRs (-10a, -10b, -21-5p and -30c) at shorter follow-up time between blood collection and diagnosis (≤5 yr, ≤2 yr), were statistically significantly associated with risk. A score based on the panel showed a linear dose-response trend with risk (p-value = 0.0006). For shorter follow-up (≤5 yr), AUC for the score was 0.73, and for individual miRs ranged from 0.73 (miR-212) to 0.79 (miR-21-5p).
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    A Plasma-Derived Protein-Metabolite Multiplexed Panel for Early-Stage Pancreatic Cancer
    (Oxford University Press, 2019-04-01) Fahrmann, Johannes F.; Bantis, Leonidas E.; Capello, Michela; Scelo, Ghislaine; Dennison, Jennifer B.; Patel, Nikul; Murage, Eunice; Vykoukal, Jody; Kundnani, Deepali L.; Foretova, Lenka; Fabianova, Eleonora; Holcatova, Ivana; Janout, Vladimir; Feng, Ziding; Yip-Schneider, Michele; Zhang, Jianjun; Brand, Randall; Taguchi, Ayumu; Maitra, Anirban; Brennan, Paul; Max Schmidt, C.; Hanash, Samir; Surgery, School of Medicine
    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.