Habtom W. Ressom

The properties of high-dimensional data spaces: implications for exploring gene and protein expression data

High-throughput genomic and proteomic technologies are widely used in cancer research to build better predictive models of diagnosis, prognosis and therapy, to identify and characterize key signalling networks and to find new targets for drug development. These technologies present investigators with the task of extracting meaningful statistical and biological information from high-dimensional data spaces, wherein each sample is defined by hundreds or thousands of measurements, usually concurrently obtained. The properties of high dimensionality are often poorly understood or overlooked in data modelling and analysis. From the perspective of translational science, this Review discusses the properties of high-dimensional data spaces that arise in genomic and proteomic studies and the challenges they can pose for data analysis and interpretation.

Progenitor/stem cells give rise to liver cancer due to aberrant TGF-β and IL-6 signaling

Cancer stem cells (CSCs) are critical for the initiation, propagation, and treatment resistance of multiple cancers. Yet functional interactions between specific signaling pathways in solid organ “cancer stem cells,” such as those of the liver, remain elusive. We report that in regenerating human liver, two to four cells per 30,000–50,000 cells express stem cell proteins Stat3, Oct4, and Nanog, along with the prodifferentiation proteins TGF-β-receptor type II (TBRII) and embryonic liver fodrin (ELF). Examination of human hepatocellular cancer (HCC) reveals cells that label with stem cell markers that have unexpectedly lost TBRII and ELF. elf+/− mice spontaneously develop HCC; expression analysis of these tumors highlighted the marked activation of the genes involved in the IL-6 signaling pathway, including IL-6 and Stat3, suggesting that HCC could arise from an IL-6-driven transformed stem cell with inactivated TGF-β signaling. Similarly, suppression of IL-6 signaling, through the generation of mouse knockouts involving a positive regulator of IL-6, Inter-alpha-trypsin inhibitor-heavy chain-4 (ITIH4), resulted in reduction in HCC in elf+/− mice. This study reveals an unexpected functional link between IL-6, a major stem cell signaling pathway, and the TGF-β signaling pathway in the modulation of mammalian HCC, a lethal cancer of the foregut. These experiments suggest an important therapeutic role for targeting IL-6 in HCCs lacking a functional TGF-β pathway.

Activation of p53-Dependent Growth Suppression in Human Cells by Mutations in PTEN or PIK3CA

ABSTRACT In an effort to identify genes whose expression is regulated by activated phosphatidylinositol 3-kinase (PI3K) signaling, we performed microarray analysis and subsequent quantitative reverse transcription-PCR on an isogenic set of PTEN gene-targeted human cancer cells. Numerous p53 effectors were upregulated following PTEN deletion, including p21, GDF15, PIG3, NOXA, and PLK2. Stable depletion of p53 led to reversion of the gene expression program. Western blots revealed that p53 was stabilized in HCT116 PTEN−/− cells via an Akt1-dependent and p14ARF-independent mechanism. Stable depletion of PTEN in untransformed human fibroblasts and epithelial cells also led to upregulation of p53 and senescence-like growth arrest. Simultaneous depletion of p53 rescued this phenotype, enabling PTEN-depleted cells to continue proliferating. Next, we tested whether oncogenic PIK3CA, like inactivated PTEN, could activate p53. Retroviral expression of oncogenic human PIK3CA in MCF10A cells led to activation of p53 and upregulation of p53-regulated genes. Stable depletion of p53 reversed these PIK3CA-induced expression changes and synergized with oncogenic PIK3CA in inducing anchorage-independent growth. Finally, targeted deletion of an endogenous allele of oncogenic, but not wild-type, PIK3CA in a human cancer cell line led to a reduction in p53 levels and a decrease in the expression of p53-regulated genes. These studies demonstrate that activation of PI3K signaling by mutations in PTEN or PIK3CA can lead to activation of p53-mediated growth suppression in human cells, indicating that p53 can function as a brake on phosphatidylinositol (3, 4, 5)-triphosphate-induced mitogenesis during human cancer pathogenesis.

Detection of Hepatocellular Carcinoma Using Glycomic Analysis

Purpose: Hepatocellular carcinoma (HCC) represents an increasing health problem in the United States. Serum α-fetoprotein, the currently used clinical marker, is elevated in only ∼60% of HCC patients; therefore, the identification of additional markers is expected to have significant public health impact. The objective of our study was to quantitatively assess N-glycans originating from serum glycoproteins as alternative markers for the detection of HCC. Experimental Design: We used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry for quantitative comparison of 83 N-glycans in serum samples of 202 participants (73 HCC cases, 77 age- and gender-matched cancer-free controls, and 52 patients with chronic liver disease). N-glycans were enzymatically released from serum glycoproteins and permethylated before mass spectrometric quantification. Results: The abundance of 57 N-glycans was significantly altered in HCC patients compared with controls. The sensitivity of six individual glycans evaluated for separation of HCC cases from population controls ranged from 73% to 90%, and the specificity ranged from 36% to 91%. A combination of three selected N-glycans was sufficient to classify HCC with 90% sensitivity and 89% specificity in an independent validation set of patients with chronic liver disease. The three N-glycans remained associated with HCC after adjustment for chronic viral infection and other known covariates, whereas the other glycans increased significantly at earlier stages of the progression of chronic viral infection to HCC. Conclusion: A set of three identified N-glycans is sufficient for the detection of HCC with 90% prediction accuracy in a population with high rates of hepatitis C viral infection. Further evaluation of a wider clinical utility of these candidate markers is warranted.

Peak selection from MALDI-TOF mass spectra using ant colony optimization

MOTIVATION Due to the large number of peaks in mass spectra of low-molecular-weight (LMW) enriched sera, a systematic method is needed to select a parsimonious set of peaks to facilitate biomarker identification. We present computational methods for matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) spectral data preprocessing and peak selection. In particular, we propose a novel method that combines ant colony optimization (ACO) with support vector machines (SVM) to select a small set of useful peaks. RESULTS The proposed hybrid ACO-SVM algorithm selected a panel of eight peaks out of 228 candidate peaks from MALDI-TOF spectra of LMW enriched sera. An SVM classifier built with these peaks achieved 94% sensitivity and 100% specificity in distinguishing hepatocellular carcinoma from cirrhosis in a blind validation set of 69 samples. Area under the receiver operating characteristic (ROC) curve was 0.996. The classification capability of these peaks is compared with those selected by the SVM-recursive feature elimination method. AVAILABILITY Supplementary material and MATLAB scripts to implement the methods described in this article are available at http://microarray.georgetown.edu/web/files/bioinf.htm. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Mutational Inactivation of PTPRD in Glioblastoma Multiforme and Malignant Melanoma

An additional tumor suppressor gene on chromosome 9p telomeric to the CDKN2A/B locus has long been postulated to exist. Using Affymetrix 250K single nucleotide polymorphism arrays to screen for copy number changes in glioblastoma multiforme (GBM), we detected a high frequency of deletions of the PTPRD gene, which encodes a receptor protein tyrosine phosphatase at chromosome 9p23-24.1. Missense and nonsense mutations of PTPRD were identified in a subset of the samples lacking deletions, including an inherited mutation with somatic loss of the wild-type allele. We then sequenced the gene in melanoma and identified 10 somatic mutations in 7 of 57 tumors (12%). Reconstitution of PTPRD expression in GBM and melanoma cells harboring deletions or mutations led to growth suppression and apoptosis that was alleviated by both the somatic and constitutional mutations. These data implicate PTPRD in the pathogenesis of tumors of neuroectodermal origin and, when taken together with other recent reports of PTPRD mutations in adenocarcinoma of the colon and lung, suggest that PTPRD may be one of a select group of tumor suppressor genes that are inactivated in a wide range of common human tumor types.

Utilization of metabolomics to identify serum biomarkers for hepatocellular carcinoma in patients with liver cirrhosis

Characterizing the metabolic changes pertaining to hepatocellular carcinoma (HCC) in patients with liver cirrhosis is believed to contribute towards early detection, treatment, and understanding of the molecular mechanisms of HCC. In this study, we compare metabolite levels in sera of 78 HCC cases with 184 cirrhotic controls by using ultra performance liquid chromatography coupled with a hybrid quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from patients with cirrhosis are selected by parametric and non-parametric statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. Verification of the identities of selected metabolites is conducted by comparing their MS/MS fragmentation patterns and retention time with those from authentic compounds. Quantitation of these metabolites is performed in a subset of the serum samples (10 HCC and 10 cirrhosis) using isotope dilution by selected reaction monitoring (SRM) on triple quadrupole linear ion trap (QqQLIT) and triple quadrupole (QqQ) mass spectrometers. The results of this analysis confirm that metabolites involved in sphingolipid metabolism and phospholipid catabolism such as sphingosine-1-phosphate (S-1-P) and lysophosphatidylcholine (lysoPC 17:0) are up-regulated in sera of HCC vs. those with liver cirrhosis. Down-regulated metabolites include those involved in bile acid biosynthesis (specifically cholesterol metabolism) such as glycochenodeoxycholic acid 3-sulfate (3-sulfo-GCDCA), glycocholic acid (GCA), glycodeoxycholic acid (GDCA), taurocholic acid (TCA), and taurochenodeoxycholate (TCDCA). These results provide useful insights into HCC biomarker discovery utilizing metabolomics as an efficient and cost-effective platform. Our work shows that metabolomic profiling is a promising tool to identify candidate metabolic biomarkers for early detection of HCC cases in high risk population of cirrhotic patients.

Analysis of mass spectral serum profiles for biomarker selection

MOTIVATION Mass spectrometric profiles of peptides and proteins obtained by current technologies are characterized by complex spectra, high dimensionality and substantial noise. These characteristics generate challenges in the discovery of proteins and protein-profiles that distinguish disease states, e.g. cancer patients from healthy individuals. We present low-level methods for the processing of mass spectral data and a machine learning method that combines support vector machines, with particle swarm optimization for biomarker selection. RESULTS The proposed method identified mass points that achieved high prediction accuracy in distinguishing liver cancer patients from healthy individuals in SELDI-QqTOF profiles of serum. AVAILABILITY MATLAB scripts to implement the methods described in this paper are available from the HWRs lab website http://lombardi.georgetown.edu/labpage

LC-MS based serum metabolomics for identification of hepatocellular carcinoma biomarkers in Egyptian cohort.

Although hepatocellular carcinoma (HCC) has been subjected to continuous investigation and its symptoms are well-known, early stage diagnosis of this disease remains difficult and the survival rate after diagnosis is typically very low (3-5%). Early and accurate detection of metabolic changes in the sera of patients with liver cirrhosis can help improve the prognosis of HCC and lead to a better understanding of its mechanism at the molecular level, thus providing patients with in-time treatment of the disease. In this study, we compared metabolite levels in sera of 40 HCC patients and 49 cirrhosis patients from Egypt by using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometer (UPLC-QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from cirrhotic controls are selected by statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. The identities of some of the putative identifications are verified by comparing their MS/MS fragmentation patterns and retention times with those from authentic compounds. Finally, the serum samples are reanalyzed for quantitation of selected metabolites as candidate biomarkers of HCC. This quantitation was performed using isotope dilution by selected reaction monitoring (SRM) on a triple quadrupole linear ion trap (QqQLIT) coupled to UPLC. Statistical analysis of the UPLC-QTOF data identified 274 monoisotopic ion masses with statistically significant differences in ion intensities between HCC cases and cirrhotic controls. Putative identifications were obtained for 158 ions by mass based search against databases. We verified the identities of selected putative identifications including glycholic acid (GCA), glycodeoxycholic acid (GDCA), 3β, 6β-dihydroxy-5β-cholan-24-oic acid, oleoyl carnitine, and Phe-Phe. SRM-based quantitation confirmed significant differences between HCC and cirrhotic controls in metabolite levels of bile acid metabolites, long chain carnitines and small peptide. Our study provides useful insight into appropriate experimental design and computational methods for serum biomarker discovery using LC-MS/MS based metabolomics. This study has led to the identification of candidate biomarkers with significant changes in metabolite levels between HCC cases and cirrhotic controls. This is the first MS-based metabolic biomarker discovery study on Egyptian subjects that led to the identification of candidate metabolites that discriminate early stage HCC from patients with liver cirrhosis.

MetaboSearch: Tool for Mass-Based Metabolite Identification Using Multiple Databases

Searching metabolites against databases according to their masses is often the first step in metabolite identification for a mass spectrometry-based untargeted metabolomics study. Major metabolite databases include Human Metabolome DataBase (HMDB), Madison Metabolomics Consortium Database (MMCD), Metlin, and LIPID MAPS. Since each one of these databases covers only a fraction of the metabolome, integration of the search results from these databases is expected to yield a more comprehensive coverage. However, the manual combination of multiple search results is generally difficult when identification of hundreds of metabolites is desired. We have implemented a web-based software tool that enables simultaneous mass-based search against the four major databases, and the integration of the results. In addition, more complete chemical identifier information for the metabolites is retrieved by cross-referencing multiple databases. The search results are merged based on IUPAC International Chemical Identifier (InChI) keys. Besides a simple list of m/z values, the software can accept the ion annotation information as input for enhanced metabolite identification. The performance of the software is demonstrated on mass spectrometry data acquired in both positive and negative ionization modes. Compared with search results from individual databases, MetaboSearch provides better coverage of the metabolome and more complete chemical identifier information. Availability: The software tool is available at http://omics.georgetown.edu/MetaboSearch.html.