? ? Hepatocellular carcinoma (HCC) is a common cancer worldwide with as many as 500,000 new cases each year. Between 1981 to 1998, the 5-year patient survival rate with HCC only rose from 2% to 5%. This poor survival rate is in part related to the diagnosis of HCC at advanced stages, where effective therapies are lacking. Early detection of HCC improves patient survival. Patients with cirrhosis are typically the ones to develop HCC. Hence, monitoring cirrhotic patients can potentially decrease the cancer-related mortality rate. The poor sensitivity and specificity of currently available tools has prevented widespread implementation of HCC surveillance. Therefore, additional serum markers that provide higher sensitivity and specificity are needed to improve the detection rate of early HCC. The goal of this collaborative project is to identify a panel of serum biomarkers for early diagnosis of HCC. The long-term goal is to find and validate markers that would help identify HCC at a treatable stage in high-risk population of cirrhotic patients. This project will lead to the development of innovative mass spectral data preprocessing and biomarker selection methods that for the identification of candidate biomarkers specific to HCC by using matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry (MS) of low-molecular-weight (LMW) enriched sera.
The specific aims of the project are the following:
Aim 1 : To develop algorithms for improved MALDI-TOF mass spectral data preprocessing including outlier screening, binning, smoothing, baseline correction, normalization, peak detection, and peak calibration. The proposed algorithms will enable us to reduce run-to-run variability in replicate spectra of a standard serum and to enhance the prediction accuracy in distinguishing HCC patients from cirrhotic patients or healthy individuals.
Aim 2 : To develop a novel algorithm that is superior to currently used biomarker selection methods by combining two popular machine learning methods, particle swarm optimization (PSO) and support vector machines (SVMs). The proposed algorithm will be used to identify HCC-specific markers from the preprocessed MALDI-TOF spectra. To avoid confounding effects, peaks will be removed prior to biomarker selection if they are associated with viral infection or covariates such as age, gender, smoking status, drinking status, and residency (urban or rural). From the remaining peaks, a small set of candidate biomarkers that accurately distinguishes HCC patients from cirrhotic patients will be identified. The capability of the algorithm to identify a small set of markers with high sensitivity and specificity is critical for establishment of clinical tests. Additionally, the algorithm will identify markers that distinguish various pairs (normal vs. cirrhosis, normal vs. HCC, cirrhosis vs. early-stage HCC, and cirrhosis vs. late-stage HCC). This will enable us to isolate HCC- specific markers and identify disease progression markers. Furthermore, the peptides represented by the selected candidate biomarkers will be identified. Finally, the performance of the algorithm will be compared with existing methods. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Small Research Grants (R03)
Project #
1R03CA119313-01A2
Application #
7265522
Study Section
Special Emphasis Panel (ZCA1-SRRB-F (J1))
Program Officer
Kagan, Jacob
Project Start
2007-03-01
Project End
2009-02-28
Budget Start
2007-03-01
Budget End
2008-02-29
Support Year
1
Fiscal Year
2007
Total Cost
$77,600
Indirect Cost
Name
Georgetown University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
049515844
City
Washington
State
DC
Country
United States
Zip Code
20057
Tang, Zhiqun; Varghese, Rency S; Bekesova, Slavka et al. (2010) Identification of N-glycan serum markers associated with hepatocellular carcinoma from mass spectrometry data. J Proteome Res 9:104-12
Befekadu, Getachew K; Tadesse, Mahlet G; Ressom, Habtom W (2009) A Bayesian based functional mixed-effects model for analysis of LC-MS data. Conf Proc IEEE Eng Med Biol Soc 2009:6743-6
Ressom, Habtom W; Varghese, Rency S; Zhang, Zhen et al. (2008) Classification algorithms for phenotype prediction in genomics and proteomics. Front Biosci 13:691-708
Varghese, Rency S; Goldman, Lenka; An, Yanming et al. (2008) Integrated peptide and glycan biomarker discovery using MALDI-TOF mass spectrometry. Conf Proc IEEE Eng Med Biol Soc 2008:3791-4
Befekadu, Getachew K; Tadesse, Mahlet G; Hathout, Yetrib et al. (2008) Multi-class alignment of LC-MS data using probabilistic-based mixture regression models. Conf Proc IEEE Eng Med Biol Soc 2008:4094-7
Ressom, Habtom W; Varghese, Rency S; Goldman, Lenka et al. (2008) Analysis of MALDI-TOF mass spectrometry data for detection of glycan biomarkers. Pac Symp Biocomput :216-27
Clarke, Robert; Ressom, Habtom W; Wang, Antai et al. (2008) The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nat Rev Cancer 8:37-49
Ressom, Habtom W; Varghese, Rency S; Goldman, Lenka et al. (2008) Analysis of MALDI-TOF mass spectrometry data for discovery of peptide and glycan biomarkers of hepatocellular carcinoma. J Proteome Res 7:603-10
Goldman, Radoslav; Ressom, Habtom W; Abdel-Hamid, Mohamed et al. (2007) Candidate markers for the detection of hepatocellular carcinoma in low-molecular weight fraction of serum. Carcinogenesis 28:2149-53
McNamara 2nd, James O; Andrechek, Eran R; Wang, Yong et al. (2006) Cell type-specific delivery of siRNAs with aptamer-siRNA chimeras. Nat Biotechnol 24:1005-15