Qian Shi

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

BackgroundLike microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data.ResultsWe developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments.ConclusionThe proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features.

Mst1 Is an Interacting Protein that Mediates PHLPPs' Induced Apoptosis

PHLPP1 and PHLPP2 phosphatases exert their tumor-suppressing functions by dephosphorylation and inactivation of Akt in several breast cancer and glioblastoma cells. However, Akt, or other known targets of PHLPPs that include PKC and ERK, may not fully elucidate the physiological role of the multifunctional phosphatases, especially their powerful apoptosis induction function. Here, we show that PHLPPs induce apoptosis in cancer cells independent of the known targets of PHLPPs. We identified Mst1 as a binding partner that interacts with PHLPPs both in vivo and in vitro. PHLPPs dephosphorylate Mst1 on the T387 inhibitory site, which activate Mst1 and its downstream effectors p38 and JNK to induce apoptosis. The same T387 site can be phosphorylated by Akt. Thus, PHLPP, Akt, and Mst1 constitute an autoinhibitory triangle that controls the fine balance of apoptosis and proliferation that is cell type and context dependent.

Crossroads of estrogen receptor and NF-kappaB signaling.

Cellular homeostasis in higher organisms is maintained by balancing cell growth, differentiation, and death. Two important systems that transmit extracellular signals into the machinery of the cell nucleus are the signaling pathways that activate nuclear factor κB (NF-κΒ) and estrogen receptor (ER). These two transcription factors induce expression of genes that control cell fates, including proliferation and cell death (apoptosis). However, ER has anti-inflammatory effects, whereas activated NF-κB initiates and maintains cellular inflammatory responses. Recent investigations elucidated a nonclassical and nongenomic effect of ER: inhibition of NF-κB activation and the inflammatory response. In breast cancer, antiestrogen therapy might cause reactivation of NF-κB, potentially rerouting a proliferative signal to breast cancer cells and contributing to hormone resistance. Thus, ER ligands that selectively block NF-κB activation could provide specific potential therapy for hormone-resistant ER-positive breast cancers.

Nuclear factor-κB activation: A molecular therapeutic target for estrogen receptor-negative and epidermal growth factor receptor family receptor-positive human breast cancer

Nuclear factor-κB (NF-κB), a transcription factor with pleotropic effects, is a downstream mediator of growth signaling in estrogen receptor (ER)-negative and erbB family particularly erbB2 (HER-2/neu) receptor–positive cancer. We previously reported activation of NF-κB in ER-negative breast cancer cells and breast tumor specimens, but the consequence of inhibiting NF-κB activation in this subclass of breast cancer has not been shown. In this study, we investigated the role of NF-κB activation by studying the tumorigenic potential of cells expressing genetically manipulated, inducible, dominant-negative inhibitory κB kinase (IKK) β in xenograft tumor model. Conditional inhibition of NF-κB activation by the inducible expression of dominant-negative IKKβ simultaneously blocked cell proliferation, reinstated apoptosis, and dramatically blocked xenograft tumor formation. Secondly, the humanized anti-erbB2 antibody trastuzumab (Herceptin) and the specific IKK inhibitor NF-κB essential modifier–binding domain peptide both blocked NF-κB activation and cell proliferation and reinstated apoptosis in two ER-negative and erbB2-positive human breast cancer cell lines that are used as representative model systems. Combinations of these two target-specific inhibitors synergistically blocked cell proliferation at concentrations that were singly ineffective. Inhibition of NF-κB activation with two other low molecular weight compounds, PS1145 and PS341, which inhibited IKK activity and proteasome-mediated phosphorylated inhibitory κB protein degradation, respectively, blocked erbB2-mediated cell growth and reversed antiapoptotic machinery. These results implicate NF-κB activation in the tumorigenesis and progression of ER-negative breast cancer. It is postulated that this transcription factor and its activation cascade offer therapeutic targets for erbB2-positive and ER-negative breast cancer. [Mol Cancer Ther 2007;6(7):1973–82]

Quantitative proteomics study of breast cancer cell lines isolated from a single patient: Discovery of TIMM17A as a marker for breast cancer

The proteins involved in breast cancer initiation and progression are still largely elusive. To gain insights into these processes, we conducted quantitative proteomic analyses with 21T series of breast cell lines, which include a normal, primary tumor and a metastatic tumor that were isolated from a single patient. Stable isotope labeling of amino acid in cell culture followed by LC‐MS/MS analysis was performed and deregulated proteins were identified using statistical analysis. Gene ontology analysis revealed that proteins involved in metabolic processes were the most deregulated in both tumorigenesis and metastasis. Interaction network analysis indicated that ERBB2 signaling played a critical role in tumorigenesis. In addition to known markers such as ERBB2 and E‐cadherin, novel markers, including BRP44L, MTHFD2 and TIMM17A, were found to be overexpressed in 21T breast cancer cells and verified in additional breast cell lines. mRNA expression analysis as well as immunohistochemistry analysis in breast cancer tissues indicated that expression level of TIMM17A was directly correlated with tumor progression, and survival analysis suggested that TIMM17A was a powerful prognosis factor in breast cancer. More interestingly, overexpression and siRNA knockdown experiments indicated an oncogenic activity of TIMM17A in breast cancer. Our study provides a list of potential novel markers for breast cancer tumorigenesis and metastasis using a unique cell model. Further studies on TIMM17A as well as other markers on the list may reveal mechanisms that result in more effective therapeutics for cancer treatment.

Serum GFAP autoantibody as an ELISA-detectable glioma marker

Glioma is the most common primary brain tumor, yet the high cost of diagnostic imaging has made early detection of asymptomatic glioma a formidable challenge. Thus, the development of a convenient, sensitive, and cost-effective diagnostic strategy, such as enzyme-linked immunosorbent assay (ELISA) based on glioma-specific and World Health Organization (WHO) grade-specific autoantibody serum markers, is necessary. To this end, a comparative proteomic analysis based on two-dimensional western blotting was carried out with the sera of glioma patients and normal controls. Of the 11 novel glioma-expressed autoantibodies, the autoantibody against glial fibrillary acidic protein (GFAP) showed the highest differential expression. To investigate the potential clinical utility of the GFAP autoantibody as an early diagnostic marker for glioma, an ELISA-based assay was developed and validated with sera from glioma patients with WHO grades II (n = 19), III (n = 17), and IV (n = 24). The GFAP autoantibody level directly correlated with WHO grade and tumor volume. Sera from patients of non-glioma brain tumors, as well as non-brain tumors, showed much lower levels of GFAP autoantibody than those of the glioma patients, indicating that elevated GFAP autoantibody is specific to glioma patients. Analysis of the receiver operating characteristics curve suggested that the new ELISA has good distinguishing power and sensitivity for diagnosing glioma patients. This is the first ELISA assay developed for an autoantibody of a glioma antigen and may prove valuable for the clinical detection of glioma.