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Ronglai Shen

Publications and source records attributed to Ronglai Shen.

16 recordsLinked to original sources

Pan-cancer analysis of biallelic inactivation in tumor suppressor genes identifies KEAP1 zygosity as a predictive biomarker in lung cancer.

The canonical model of tumor suppressor gene (TSG)-mediated oncogenesis posits that loss of both alleles is necessary for inactivation. Here, through allele-specific analysis of sequencing data from 48,179 cancer patients, we define the prevalence, selective pressure for, and functional consequences of biallelic inactivation across TSGs. TSGs largely assort into distinct classes associated with either pan-cancer (Class 1) or lineage-specific (Class 2) patterns of selection for biallelic loss, although some TSGs are predominantly monoallelically inactivated (Class 3/4). We demonstrate that selection for biallelic inactivation can be utilized to identify driver genes in non-canonical contexts, including among variants of unknown significance (VUSs) of several TSGs such as KEAP1. Genomic, functional, and clinical data collectively indicate that KEAP1 VUSs phenocopy established KEAP1 oncogenic alleles and that zygosity, rather than variant classification, is predictive of therapeutic response. TSG zygosity is therefore a fundamental determinant of disease etiology and therapeutic sensitivity.

Kelch-Like ECH-Associated Protein 1↗

Eigengene-based linear discriminant model for tumor classification using gene expression microarray data.

MOTIVATION: The nearest shrunken centroids classifier has become a popular algorithm in tumor classification problems using gene expression microarray data. Feature selection is an embedded part of the method to select top-ranking genes based on a univariate distance statistic calculated for each gene individually. The univariate statistics summarize gene expression profiles outside of the gene co-regulation network context, leading to redundant information being included in the selection procedure. RESULTS: We propose an Eigengene-based Linear Discriminant Analysis (ELDA) to address gene selection in a multivariate framework. The algorithm uses a modified rotated Spectral Decomposition (SpD) technique to select 'hub' genes that associate with the most important eigenvectors. Using three benchmark cancer microarray datasets, we show that ELDA selects the most characteristic genes, leading to substantially smaller classifiers than the univariate feature selection based analogues. The resulting de-correlated expression profiles make the gene-wise independence assumption more realistic and applicable for the shrunken centroids classifier and other diagonal linear discriminant type of models. Our algorithm further incorporates a misclassification cost matrix, allowing differential penalization of one type of error over another. In the breast cancer data, we show false negative prognosis can be controlled via a cost-adjusted discriminant function. AVAILABILITY: R code for the ELDA algorithm is available from author upon request.

Artificial Intelligence↗

Metastasis suppressor gene Raf kinase inhibitor protein (RKIP) is a novel prognostic marker in prostate cancer.

BACKGROUND: Diminished expression of Raf kinase inhibitor protein (RKIP), an inhibitor of the Raf signaling cascade, promotes prostate cancer (PCa) metastasis in a murine model, suggesting that it is a metastasis suppressor gene. However, the prognostic significance of RKIP expression and its association with metastasis in PCa patients is unknown. METHODS: To investigate RKIP protein expression is a prognostic marker in PCa we performed immunohistochemical staining for RKIP expression in tissue microarrays consisting of 758 non-neoplastic prostate tissues, primary tumors and metastases from 134 PCa patients. The Cox proportional-hazards model was used to adjust for covariates including Gleason score, tumor volume, tumor weight, clinical stage, digital rectal exam findings, serum PSA level and surgical margins. RESULTS: RKIP expression was low in approximately 5%, 48%, and 89% of non-neoplastic prostate, primary tumors and metastases, respectively. Low RKIP expression in primary tumors was a strong positive predictive factor for PCa recurrence based on PSA levels. In patients whose primary tumors expressed high RKIP levels, the 7-year PSA recurrence rate was <10%; whereas in patients with tumors with low RKIP expression the recurrence rate was 50% (P<0.001). Multivariate analysis revealed RKIP was an independent prognostic factor (P<0.001). CONCLUSION: In contrast to increased expression of pro-tumorigenic genes, these results demonstrate decreased protein expression of a gene, for example, RKIP, can serve as a prognostic marker in PCa patients.

Biomarkers, Tumor↗

Internet-based Profiler system as integrative framework to support translational research.

BACKGROUND: Translational research requires taking basic science observations and developing them into clinically useful tests and therapeutics. We have developed a process to develop molecular biomarkers for diagnosis and prognosis by integrating tissue microarray (TMA) technology and an internet-database tool, Profiler. TMA technology allows investigators to study hundreds of patient samples on a single glass slide resulting in the conservation of tissue and the reduction in inter-experimental variability. The Profiler system allows investigator to reliably track, store, and evaluate TMA experiments. Here within we describe the process that has evolved through an empirical basis over the past 5 years at two academic institutions. RESULTS: The generic design of this system makes it compatible with multiple organ system (e.g., prostate, breast, lung, renal, and hematopoietic system,). Studies and folders are restricted to authorized users as required. Over the past 5 years, investigators at 2 academic institutions have scanned 656 TMA experiments and collected 63,311 digital images of these tissue samples. 68 pathologists from 12 major user groups have accessed the system. Two groups directly link clinical data from over 500 patients for immediate access and the remaining groups choose to maintain clinical and pathology data on separate systems. Profiler currently has 170 K data points such as staining intensity, tumor grade, and nuclear size. Due to the relational database structure, analysis can be easily performed on single or multiple TMA experimental results. The TMA module of Profiler can maintain images acquired from multiple systems. CONCLUSION: We have developed a robust process to develop molecular biomarkers using TMA technology and an internet-based database system to track all steps of this process. This system is extendable to other types of molecular data as separate modules and is freely available to academic institutions for licensing.

Automation↗

Identification of GATA3 as a breast cancer prognostic marker by global gene expression meta-analysis.

GATA binding protein 3 (GATA3) is a transcriptional activator highly expressed by the luminal epithelial cells in the breast. Here we did a meta-analysis of the available breast cancer cDNA data sets on a cohort of 305 patients and found that GATA3 was one of the top genes with low expression in invasive carcinomas with poor clinical outcome. To validate its prognostic utility, we did a tissue microarray analysis on a cohort of 139 consecutive invasive carcinomas (n = 417 tissue samples) immunostained with a monoclonal antibody against GATA3. Low GATA3 expression was associated with higher histologic grade (P < 0.001), positive nodes (P = 0.002), larger tumor size (P = 0.03), negative estrogen receptor and progesterone receptor (P < 0.001 for both), and HER2-neu overexpression (P = 0.03). Patients whose tumors expressed low GATA3 had significantly shorter overall and disease-free survival when compared with those whose tumors had high GATA3 levels. The hazard ratio of metastasis or recurrence according to the GATA3 status was 0.31 (95% confidence interval, 0.13-0.74; P = 0.009). Cox multivariate analysis showed that GATA3 had independent prognostic significance above and beyond conventional variables. Our data suggest that immunohistochemical analysis of GATA3 may be the basis for a new clinically applicable test to predict tumor recurrence early in the progression of breast cancer.

Biomarkers, Tumor↗

Autoantibody signatures in prostate cancer.

BACKGROUND: New biomarkers, such as autoantibody signatures, may improve the early detection of prostate cancer. METHODS: With a phage-display library derived from prostate-cancer tissue, we developed and used phage protein microarrays to analyze serum samples from 119 patients with prostate cancer and 138 controls, with the samples equally divided into training and validation sets. A phage-peptide detector that was constructed from the training set was evaluated on an independent validation set of 128 serum samples (60 from patients with prostate cancer and 68 from controls). RESULTS: A 22-phage-peptide detector had 88.2 percent specificity (95 percent confidence interval, 0.78 to 0.95) and 81.6 percent sensitivity (95 percent confidence interval, 0.70 to 0.90) in discriminating between the group with prostate cancer and the control group. This panel of peptides performed better than did prostate-specific antigen (PSA) in distinguishing between the group with prostate cancer and the control group (area under the curve for the autoantibody signature, 0.93; 95 percent confidence interval, 0.88 to 0.97; area under the curve for PSA, 0.80; 95 percent confidence interval, 0.71 to 0.88). Logistic-regression analysis revealed that the phage-peptide panel provided additional discriminative power over PSA (P<0.001). Among the 22 phage peptides used as a detector, 4 were derived from in-frame, named coding sequences. The remaining phage peptides were generated from untranslated sequences. CONCLUSIONS: Autoantibodies against peptides derived from prostate-cancer tissue could be used as the basis for a screening test for prostate cancer.

Aged↗

Alpha-methylacyl-CoA racemase protein expression is associated with the degree of differentiation in breast cancer using quantitative image analysis.

Alpha-methylacyl-CoA racemase (AMACR) is an enzyme involved in the metabolism of fatty acids and is an important tissue biomarker in the prostate to distinguish normal glands from prostate cancer. Here, for the first time, we evaluated the expression of AMACR protein in normal breast, ductal carcinoma in situ, and invasive carcinomas. By immunofluorescence and immunohistochemistry, AMACR was seen in cytoplasmic granules consistent with a mitochondrial and peroxisomal localization. AMACR expression was determined by immunohistochemistry on 160 invasive carcinomas with long follow-up, using a high-density tissue microarray, and evaluated by two methods: standard pathology review and quantitative image analysis. AMACR was overexpressed in 42 of 160 (26%) invasive carcinomas, and it was associated with a decrease in tumor differentiation, a feature of aggressive breast cancer. Quantitative analysis allowed for better discrimination and more accurate evaluation of low-intensity staining. In conclusion, AMACR protein is expressed in normal breast and its expression seems to increase in invasive carcinomas. We observed stronger AMACR protein expression in high-grade carcinomas when compared with low-grade ones. Quantitative image analysis is a novel way to accurately and reproducibly evaluate immunohistochemistry in breast tissue samples using high-density tissue microarrays.

Adult↗

Decreased alpha-methylacyl CoA racemase expression in localized prostate cancer is associated with an increased rate of biochemical recurrence and cancer-specific death.

Alpha-methylacyl CoA racemase (AMACR) is overexpressed in prostate cancer relative to benign prostatic tissue. AMACR expression is highest in localized prostate cancer and decreases in metastatic prostate cancer. Herein, we explored the use of AMACR as a biomarker for aggressive prostate cancer. AMACR protein expression was determined by immunohistochemistry using an image analysis system on two localized prostate cancer cohorts consisting of 204 men treated by radical prostatectomy and 188 men followed expectantly. The end points for the cohorts were time to prostate-specific antigen (PSA) failure (i.e., elevation >0.2 ng/mL) and time to prostate cancer death in the watchful waiting cohort. Using a regression tree method, optimal AMACR protein expression cut-points were determined to best differentiate prostate cancer outcome in each of the cohorts separately. Cox proportional hazard models were then employed to examine the effect of the AMACR cut-point on prostate cancer outcome, and adjusted for clinical variables. Lower AMACR tissue expression was associated with worse prostate cancer outcome, independent of clinical variables (hazard ratio, 3.7 for PSA failure; P = 0.018; hazard ratio, 4.1 for prostate cancer death, P = 0.0006). Among those with both low AMACR expression and high Gleason score, the risk of prostate cancer death was 18-fold higher (P = 0.006). The AMACR cut-point developed using prostate cancer-specific death as the end point predicted PSA failures independent of Gleason score, PSA, and margin status. This is the first study to show that AMACR expression is significantly associated with prostate cancer progression and suggests that not all surrogate end points may be optimal to define biomarkers of aggressive prostate cancer.

Biomarkers, Tumor↗

Positional signaling mediated by a receptor-like kinase in Arabidopsis.

The position-dependent specification of root epidermal cells in Arabidopsis provides an elegant paradigm for cell patterning during development. Here, we describe a new gene, SCRAMBLED (SCM), required for cells to appropriately interpret their location within the developing root epidermis. SCM encodes a receptor-like kinase protein with a predicted extracellular domain of six leucine-rich repeats and an intracellular serine-threonine kinase domain. SCM regulates the expression of the GLABRA2, CAPRICE, WEREWOLF, and ENHANCER OF GLABRA3 transcription factor genes that define the cell fates. Further, the SCM gene is expressed throughout the developing root. Therefore, SCM likely enables developing epidermal cells to detect positional cues and establish an appropriate cell-type pattern.

Amino Acid Sequence↗

Androgen-independent prostate cancer is a heterogeneous group of diseases: lessons from a rapid autopsy program.

Understanding the biology of prostate cancer metastasis has been limited by the lack of tissue for study. We studied the clinical data, distribution of prostate cancer involvement, morphology, immunophenotypes, and gene expression from 30 rapid autopsies of men who died of hormone-refractory prostate cancer. A tissue microarray was constructed and quantitatively evaluated for expression of prostate-specific antigen, androgen receptor, chromogranin, synaptophysin, MIB-1, and alpha-methylacylCoA-racemase markers. Hierarchical clustering of 16 rapid autopsy tumor samples was performed to evaluate the cDNA expression pattern associated with the morphology. Comparisons were made between patients as well as within the same patient. Metastatic hormone-refractory prostate cancer has a heterogeneous morphology, immunophenotype, and genotype, demonstrating that "metastatic disease" is a group of diseases even within the same patient. An appreciation of this heterogeneity is critical to evaluating diagnostic and prognostic biomarkers as well as to designing therapeutic targets for advanced disease.

Aged↗

Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data.

BACKGROUND: An increasing number of studies have profiled tumor specimens using distinct microarray platforms and analysis techniques. With the accumulating amount of microarray data, one of the most intriguing yet challenging tasks is to develop robust statistical models to integrate the findings. RESULTS: By applying a two-stage Bayesian mixture modeling strategy, we were able to assimilate and analyze four independent microarray studies to derive an inter-study validated "meta-signature" associated with breast cancer prognosis. Combining multiple studies (n = 305 samples) on a common probability scale, we developed a 90-gene meta-signature, which strongly associated with survival in breast cancer patients. Given the set of independent studies using different microarray platforms which included spotted cDNAs, Affymetrix GeneChip, and inkjet oligonucleotides, the individually identified classifiers yielded gene sets predictive of survival in each study cohort. The study-specific gene signatures, however, had minimal overlap with each other, and performed poorly in pairwise cross-validation. The meta-signature, on the other hand, accommodated such heterogeneity and achieved comparable or better prognostic performance when compared with the individual signatures. Further by comparing to a global standardization method, the mixture model based data transformation demonstrated superior properties for data integration and provided solid basis for building classifiers at the second stage. Functional annotation revealed that genes involved in cell cycle and signal transduction activities were over-represented in the meta-signature. CONCLUSION: The mixture modeling approach unifies disparate gene expression data on a common probability scale allowing for robust, inter-study validated prognostic signatures to be obtained. With the emerging utility of microarrays for cancer prognosis, it will be important to establish paradigms to meta-analyze disparate gene expression data for prognostic signatures of potential clinical use.

Bayes Theorem↗

Usefulness of basal cell cocktail (34betaE12 + p63) in the diagnosis of atypical prostate glandular proliferations.

We evaluated the diagnostic usefulness of the 34betaE12-p63 cocktail, compared with 34betaE12 and p63 used alone, in 34 prostate needle biopsy (NBXs) and 3 transurethral resection specimens containing atypical glandular proliferations and in 18 NBXs containing unequivocal prostate carcinoma (PCa). Staining intensity; percentage of basal cells staining in benign, atypical, and malignant glands; number of benign glands lacking basal cell staining; and staining variance were analyzed. All NBXs with unequivocal PCa were negative with all 3 markers. Diagnoses were as follows for the atypical cases after staining for the 3 markers: PCa, 9; postatrophic hyperplasia, 12; high-grade prostatic intraepithelial neoplasia (HGPIN), 5; atypical adenomatous hyperplasia, 6; benign atypical proliferations, 4; and HGPIN with adjacent small atypical acinar proliferation suggestive of PCa, 1. The cocktail demonstrated consistently strong staining intensity and improved basal cell staining in morphologically benign and benign atypical glands compared with p63 and 34betaE12 alone; it had the smallest staining variance compared with 34betaE12 (F < 0.0001) and p63 (F = 0.31), although its advantage for resolving individual atypical cases was limited compared with 34betaE12 and p63 alone. Of 37 atypical cases, 1 (3%) additionally was resolved as benign using the cocktail and p63. Because the diagnosis of PCa is supported by lack of basal cell staining, the immunohistochemical analysis with highest possible sensitivity and lowest possible variability is critical to ensure that a negative reaction is true. The cocktail provides a simple, cost-effective improvement in basal cell immunohistochemical analysis of difficult prostate lesions.

Biomarkers, Tumor↗

EZH2 is a marker of aggressive breast cancer and promotes neoplastic transformation of breast epithelial cells.

The Polycomb Group Protein EZH2 is a transcriptional repressor involved in controlling cellular memory and has been linked to aggressive prostate cancer. Here we investigate the functional role of EZH2 in cancer cell invasion and breast cancer progression. EZH2 transcript and protein were consistently elevated in invasive breast carcinoma compared with normal breast epithelia. Tissue microarray analysis, which included 917 samples from 280 patients, demonstrated that EZH2 protein levels were strongly associated with breast cancer aggressiveness. Overexpression of EZH2 in immortalized human mammary epithelial cell lines promotes anchorage-independent growth and cell invasion. EZH2-mediated cell invasion required an intact SET domain and histone deacetylase activity. This study provides compelling evidence for a functional link between dysregulated cellular memory, transcriptional repression, and neoplastic transformation.

Breast↗

Basal cell cocktail (34betaE12 + p63) improves the detection of prostate basal cells.

Antibodies against high molecular weight cytokeratin (34betaE12) and p63 are frequently used basal cell markers to aid in the diagnosis of prostate cancer (Pca). Absence of a basal cell marker in an atypical lesion histologically suspicious for cancer supports a diagnosis of Pca. However, absence of basal cells demonstrable by basal cell immunohistochemistry (IHC) is not always conclusive for PCa. Some benign prostatic lesions may have inconspicuous or even lack basal cell lining focally. Technical factors such as tissue fixation and antigen retrieval techniques may also make the detection of basal cells difficult. Improving the sensitivity of current basal cell markers is critical if these tests are being used to help make diagnostic decisions in conjunction with standard histology. In this study, we test the hypothesis that that inclusion of both 34betaE12 and p63 in the same IHC reaction (basal cell cocktail) is advantageous over either marker used alone. One thousand three hundred fifty glands from 9 trans-urethral resectioned of prostate specimens with benign prostatic hypertrophy were used to study the immunostaining intensity and pattern for 34betaE12, p63, and the basal cell cocktail. Basal cell marker expression was scored as strong, moderate, weak, or negative. Basal cell staining was considered complete if 75% of the gland's circumference was positive for the basal cell marker and partial if <25% of the circumference was stained. The mean staining intensity and variance were calculated for 34betaE12, p63, and the basal cell cocktail. A paired test was used to evaluate whether the overall basal cell staining was significantly different between 34betaE12, p63, and the basal cell cocktail. F-test was used to assess the variances for 34betaE12, p63, and the basal cell cocktail. A high-density tissue microarray (TMA) comprising prostate tissue from 103 tumors from men with clinically localized Pca and a separate TMA comprising metastatic hormone-refractory Pca samples from 23 rapid autopsy cases were used to study the aberrant expression of 34betaE12 and p63 in clinically localized and poorly differentiated Pca. The prostate glands in transition zone have variable basal cell staining intensity and pattern with 34betaE12, p63, or the cocktail. Histologically, benign glands lack basal cell lining in 2%, 6%, and 2% of glands with cocktail, 34betaE12, and p63 staining, respectively. The staining variance for the cocktail is significantly smaller than that for 34betaE12 (0.0100 vs 0.1559, p = 0.0008). It is also smaller than that for p63, although a statistical significance has not been reached (0.0100 vs 0.0345, p = 0.099). The basal cell cocktail stains the basal cell layers more intensely than either 34betaE12 or p63 alone, with complete and partial strong basal cell staining in 93% and 1% of benign glands, compared with 55% and 4% with 34betaE12 and 81% and 1% with p63. Complete and partial weak staining is seen in 0% and 0% of benign glands with basal cell cocktail, compared with 8% and 7% with 34betaE12 and 4% and 1% with p63 (p = 0.007 and 0.014 for cocktail vs 34betaE12 and cocktail vs p63, respectively). A total of 2.8% clinically localized Pca had positive 34betaE12 staining and 0.3% had positive p63 staining. Five (22%) of the metastatic Pca is positive for 34betaE12. However, none had p63 expression. The basal cell cocktail had a staining pattern identical to that of 34betaE12. IHC of the prostatic glands from the transition zone is subjected to staining variability that results in frequent variable and occasional negative basal cell staining in histologically benign glands; 34betaE12 is most susceptible, and basal cell cocktail is least susceptible to such variability. Basal cell cocktail not only increases the sensitivity of the basal cell detection, but also reduces the staining variability and therefore renders the basal cell immunostaining more consistent. We recommend this basal cell cocktail for routine Pca diagnostic work-up.

Biomarkers, Tumor↗

Changes in differential gene expression because of warm ischemia time of radical prostatectomy specimens.

The expression of thousands of genes can be monitored simultaneously using cDNA microarray technology. This technology is being used to understand the complexity of human disease. One significant technical concern regards potential alterations in gene expression because of the effect of tissue ischemia. This study evaluates the increase in the differential gene expression because of tissue processing time. To evaluate differential gene expression because of ischemia time, prostate samples were divided into five time points (0, 0.5, 1, 3, and 5 hours). Each time point consisted of a homogeneous mixture of 12 to 15 prostate tissue cubes (5 mm(3)). These tissues were maintained at room temperature until at the assigned time point the tissue was placed in OCT, flash frozen in liquid nitrogen, and stored at -80 degrees C until RNA extraction. RNA from each time point was hybridized against an aliquot of 0 time point RNA from the same prostate. Four prostate glands were used in parallel studies. M-A plots were graphed to compare variability between time point sample hybridizations. Statistical Analysis of Microarray software was used to identify genes overexpressed at the 1-hour time point versus the 0-hour time with statistically significance. Microarray analysis revealed only a small percentage of genes (<0.6%) from more than 9000 to demonstrate overexpression at the 1-hour time point. Among the 41 statistically significant named overexpressed genes at the 1-hour time point were early growth response 1 (EGR1), jun B proto-oncogene (jun B), jun D proto-oncogene (jun D), and activating transcription factor 3 (ATF3). Genes previously associated with prostate cancer did not have significantly altered expression with ischemia time. Increased EGR1 protein expression was confirmed by Western blot analysis. Microarray technology has opened the possibility of evaluating the expression of a multitude of genes simultaneously, however, the interpretation of this complex data needs to be assessed circumspectly using refined statistical methods. Because RNA expression represents the tissue response to insults such as ischemia, and is also sensitive to degradation, investigators need be mindful of confounding artifacts secondary to tissue processing. All attempts should be made to process tissue rapidly to ensure that the microarray gene profile accurately represents the state of the cells and confirmatory studies should be performed using alternative methods (eg, Northern blot analysis, Western blot, immunohistochemistry).

Artifacts↗

Expression of the platelet-derived growth factor receptor in prostate cancer and treatment implications with tyrosine kinase inhibitors.

The platelet-derived growth factor receptor (PDGFR) is a receptor tyrosine kinase overexpressed in a subset of solid tumors and therefore is the target of drugs inhibiting this function such as imatinib mesylate (Gleevec). Thus far, drug therapy has played a limited role in the treatment of localized prostate cancer (PCa). This study characterizes PDGFR-beta expression in a wide spectrum of PCa samples to provide empirical data as part of a rational treatment strategy. A survey of five published prostate expression array studies, including 100 clinically localized PCa, did not identify tumors with increased PDGFR-beta expression level. Protein expression of PDGFR-beta, as determined by immunohistochemistry, revealed 5% of clinically localized PCa and 16% of metastatic PCa cases to show moderate or strong expression. To develop a strategy to detect patients most likely to profit from Gleevec treatment, we analyzed cDNA expression array data from 10,000 transcripts for PDGFR-beta expression and divided tumors in groups based on PDGFR-beta expression level. Performing a supervised analysis to identify potential comarkers of PDGFR-beta in PCa, we identified a set of genes whose expression was associated with PDGFR-beta status including early growth response 1 (Egr1), an upstream effector of PDGF (4.2-fold upregulation), alpha-methylacyl-CoA racemase, as well as v-Maf and neuroblastoma suppressor of tumorigenicity (both with a 2.2-fold downregulation). Taken together, this study suggests that only a small subset of PCas may be amenable to tyrosine kinase inhibitors specific for PDGFR.

Antineoplastic Agents↗