Browsing by Author "Vykoukal, Jody"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Contributions of the Microbiome-Derived Metabolome for Risk Assessment and Prognostication of Pancreatic Cancer
    (Oxford University Press, 2024) León-Letelier, Ricardo A.; Dou, Rongzhang; Vykoukal, Jody; Yip-Schneider, Michele T.; Maitra, Anirban; Irajizad, Ehsan; Wu, Ranran; Dennison, Jennifer B.; Do, Kim-An; Zhang, Jianjun; Schmidt, C. Max; Hanash, Samir; Fahrmann, Johannes F.; Epidemiology, Richard M. Fairbanks School of Public Health
    Background: Increasing evidence implicates microbiome involvement in the development and progression of pancreatic ductal adenocarcinoma (PDAC). Studies suggest that reflux of gut or oral microbiota can lead to colonization in the pancreas, resulting in dysbiosis that culminates in release of microbial toxins and metabolites that potentiate an inflammatory response and increase susceptibility to PDAC. Moreover, microbe-derived metabolites can exert direct effector functions on precursors and cancer cells, as well as other cell types, to either promote or attenuate tumor development and modulate treatment response. Content: The occurrence of microbial metabolites in biofluids thereby enables risk assessment and prognostication of PDAC, as well as having potential for design of interception strategies. In this review, we first highlight the relevance of the microbiome for progression of precancerous lesions in the pancreas and, using liquid chromatography-mass spectrometry, provide supporting evidence that microbe-derived metabolites manifest in pancreatic cystic fluid and are associated with malignant progression of intraductal papillary mucinous neoplasm(s). We secondly summarize the biomarker potential of microbe-derived metabolite signatures for (a) identifying individuals at high risk of developing or harboring PDAC and (b) predicting response to treatment and disease outcomes. Summary: The microbiome-derived metabolome holds considerable promise for risk assessment and prognostication of PDAC.
  • Thumbnail Image
    Item
    Lead-Time Trajectory of CA19-9 as an Anchor Marker for Pancreatic Cancer Early Detection
    (Elsevier, 2021) Fahrmann, Johannes F.; Schmidt, C. Max; Mao, Xiangying; Irajizad, Ehsan; Loftus, Maureen; Zhang, Jinming; Patel, Nikul; Vykoukal, Jody; Dennison, Jennifer B.; Long, James P.; Do, Kim-Anh; Zhang, Jianjun; Chabot, John A.; Kluger, Michael D.; Kastrinos, Fay; Brais, Lauren; Babic, Ana; Jajoo, Kunal; Lee, Linda S.; Clancy, Thomas E.; Ng, Kimmie; Bullock, Andrea; Genkinger, Jeanine; Yip-Schneider, Michele T.; Maitra, Anirban; Wolpin, Brian M.; Hanash, Samir; Surgery, School of Medicine
    Background & Aims There is substantial interest in liquid biopsy approaches for cancer early detection among subjects at risk, using multi-marker panels. CA19-9 is an established circulating biomarker for pancreatic cancer; however, its relevance for pancreatic cancer early detection or for monitoring subjects at risk has not been established. Methods CA19-9 levels were assessed in blinded sera from 175 subjects collected up to 5 years before diagnosis of pancreatic cancer and from 875 matched controls from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. For comparison of performance, CA19-9 was assayed in blinded independent sets of samples collected at diagnosis from 129 subjects with resectable pancreatic cancer and 275 controls (100 healthy subjects; 50 with chronic pancreatitis; and 125 with noncancerous pancreatic cysts). The complementary value of 2 additional protein markers, TIMP1 and LRG1, was determined. Results In the PLCO cohort, levels of CA19-9 increased exponentially starting at 2 years before diagnosis with sensitivities reaching 60% at 99% specificity within 0 to 6 months before diagnosis for all cases and 50% at 99% specificity for cases diagnosed with early-stage disease. Performance was comparable for distinguishing newly diagnosed cases with resectable pancreatic cancer from healthy controls (64% sensitivity at 99% specificity). Comparison of resectable pancreatic cancer cases to subjects with chronic pancreatitis yielded 46% sensitivity at 99% specificity and for subjects with noncancerous cysts, 30% sensitivity at 99% specificity. For prediagnostic cases below cutoff value for CA19-9, the combination with LRG1 and TIMP1 yielded an increment of 13.2% in sensitivity at 99% specificity ( P = .031) in identifying cases diagnosed within 1 year of blood collection. Conclusion CA19-9 can serve as an anchor marker for pancreatic cancer early detection applications.
  • Thumbnail Image
    Item
    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.