Browsing by Subject "early cancer detection"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Aberrant reduction of telomere repetitive sequences in plasma cell-free DNA for early breast cancer detection.
    (Impact Journals, 2015-10-06) Wu, Xi; Tanaka, Hiromi; Department of Medical & Molecular Genetics, IU School of Medicine
    Excessive telomere shortening is observed in breast cancer lesions when compared to adjacent non-cancerous tissues, suggesting that telomere length may represent a key biomarker for early cancer detection. Because tumor-derived, cell-free DNA (cfDNA) is often released from cancer cells and circulates in the bloodstream, we hypothesized that breast cancer development is associated with changes in the amount of telomeric cfDNA that can be detected in the plasma. To test this hypothesis, we devised a novel, highly sensitive and specific quantitative PCR (qPCR) assay, termed telomeric cfDNA qPCR, to quantify plasma telomeric cfDNA levels. Indeed, the internal reference primers of our design correctly reflected input cfDNA amount (R2 = 0.910, P = 7.82 × 10−52), implying accuracy of this assay. We found that plasma telomeric cfDNA levels decreased with age in healthy individuals (n = 42, R2 = 0.094, P = 0.048), suggesting that cfDNA is likely derived from somatic cells in which telomere length shortens with increasing age. Our results also showed a significant decrease in telomeric cfDNA level from breast cancer patients with no prior treatment (n = 47), compared to control individuals (n = 42) (P = 4.06 × 10−8). The sensitivity and specificity for the telomeric cfDNA qPCR assay was 91.49% and 76.19%, respectively. Furthermore, the telomeric cfDNA level distinguished even the Ductal Carcinoma In Situ (DCIS) group (n = 7) from the healthy group (n = 42) (P = 1.51 × 10−3). Taken together, decreasing plasma telomeric cfDNA levels could be an informative genetic biomarker for early breast cancer detection.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Multi-Modality Plasma-Based Detection of Minimal Residual Disease in Triple-Negative Breast Cancer
    (2019-07) Chen, Yu-Hsiang; Radovich, Milan; Medical & Molecular Genetics; Ivan, Mircea; Li, Lang; Liu, Yunlong; Schneider, Bryan P.; Skaar, Todd C.
    Triple-negative breast cancers (TNBCs) are pathologically defined by the absence of estrogen, progesterone, and HER2 receptors. Compared to other breast cancers, TNBC has a relatively high mortality. In addition, TNBC patients are more likely to relapse in the first few years after treatment, and experiencing a shorter median time from recurrence to death. Detecting the presence of tumor in patients who are technically “disease-free” after neoadjuvant chemotherapy and surgery as early as possible might be able to predict recurrence of patients, and then provide timely intervention for additional therapy. To this end, I applied the analysis of “liquid biopsies” for early detection of minimal residual disease (MRD) on early-stage TNBC patients using next-generation sequencing. For the first part of this study, I focused on detecting circulating tumor DNA (ctDNA) from TNBC patients after neoadjuvant chemotherapy and surgery. First, patient-specific somatic mutations were identified by sequencing primary tumors. From these data, 82% of the patients had at least one TP53 mutation, followed by 16% of the patients having at least one PIK3CA mutation. Next, I sequenced matched plasma samples collected after surgery to identify ctDNA with the same mutations. I observed that by detecting corresponding ctDNA I was able to predict rapid recurrence, but not distant recurrence. To increase the sensitivity of MRD detection, in the second part I developed a strategy to co-detect ctDNA along with circulating tumor RNA (ctRNA). An advantage of ctRNA is its active release into the circulation from living cancer cells. Preliminary data showed that more mutant molecules were identified after incorporating ctRNA with ctDNA detection in a metastatic breast cancer setting. A validation study in early-stage TNBC is in progress. In summary, my study suggests that co-detection of ctDNA and ctRNA could be a potential solution for the early detection of disease recurrence.