Browsing by Author "Bowers, Jeremiah"

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    Detection of Hepatocellular Carcinoma in Hepatitis C Patients: Biomarker Discovery by LC-MS
    (Elsevier, 2014-09-01) Bowers, Jeremiah; Hughes, Emma; Skill, Nicholas; Maluccio, Mary; Raftery, Daniel; Department of Surgery, IU School of Medicine
    Hepatocellular carcinoma (HCC) accounts for most cases of liver cancer worldwide; contraction of hepatitis C (HCV) is considered a major risk factor for liver cancer even when individuals have not developed formal cirrhosis. Global, untargeted metabolic profiling methods were applied to serum samples from patients with either HCV alone or HCC (with underlying HCV). The main objective of the study was to identify metabolite based biomarkers associated with cancer risk, with the long term goal of ultimately improving early detection and prognosis. Serum global metabolite profiles from patients with HCC (n=37) and HCV (n=21) were obtained using high performance liquid chromatography-mass spectrometry (HPLC-MS) methods. The selection of statistically significant metabolites for partial least-squares discriminant analysis (PLS-DA) model creation based on biological and statistical significance was contrasted to that of a traditional approach utilizing p-values alone. A PLS-DA model created using the former approach resulted in a model with 92% sensitivity, 95% specificity, and an AUROC of 0.93. A series of PLS-DA models iteratively utilizing three to seven metabolites that were altered significantly (p<0.05) and sufficiently (FC≤0.7 or FC≥1.3) showed the best performance using p-values alone, the PLS-DA model was capable of generating 73% sensitivity, 95% specificity, and an AUROC of 0.92. Metabolic profiles derived from LC-MS readily distinguish patients with HCC and HCV from those with HCV only. Differences in the metabolic profiles between highrisk individuals and HCC indicate the possibility of identifying the early development of liver cancer in at risk patients. The use of biological significance as a selection process prior to PLSDA modeling may offer improved probabilities for translation of newly discovered biomarkers to clinical application.
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    Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer
    (Wiley, 2013-06) Wei, Siwei; Liu, Lingyan; Zhang, Jian; Bowers, Jeremiah; Gowda, G.A. Nagana; Seeger, Harald; Fehm, Tanja; Neubauer, Hans J.; Vogel, Ulrich; Clare, Susan E.; Raftery, Daniel; Surgery, School of Medicine
    Breast cancer is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. As an example, only some women will benefit from chemotherapy. Identifying patients who will respond to chemotherapy and thereby improve their long‐term survival has important implications to treatment protocols and outcomes, while identifying non responders may enable these patients to avail themselves of other investigational approaches or other potentially effective treatments. In this study, serum metabolite profiling was performed to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy for breast cancer. Metabolic profiles of serum from patients with complete (n = 8), partial (n = 14) and no response (n = 6) to chemotherapy were studied using a combination of nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography–mass spectrometry (LC–MS) and statistical analysis methods. The concentrations of four metabolites, three (threonine, isoleucine, glutamine) from NMR and one (linolenic acid) from LC–MS were significantly different when comparing response to chemotherapy. A prediction model developed by combining NMR and MS derived metabolites correctly identified 80% of the patients whose tumors did not show complete response to chemotherapy. These results show promise for larger studies that could result in more personalized treatment protocols for breast cancer patients., ► Metabolomics differentiates response to neoadjuvant breast cancer chemotherapy.► Four serum metabolites are found to correlate with response to chemotherapy.► A 4‐metabolite model identifies 80% of the patients not showing complete response.► Additional studies on larger patient cohorts are needed to validate the findings.