Biomedical subjects

Steve Horvath

Publications and source records attributed to Steve Horvath.

At least 37 records · Page 2Linked to original sources

PubMed2005

Prostate stem cell antigen is overexpressed in prostate cancer metastases.

PURPOSE: Prostate stem cell antigen (PSCA) is expressed by a majority of prostate cancers and is a promising therapeutic target. PSCA protein and mRNA expression was examined in prostate cancer bone, lymph node, and visceral metastases to assess the potential of PSCA as an immunotherapeutic target in advanced prostate cancer. EXPERIMENTAL DESIGN: Immunohistochemical analysis of PSCA protein expression and quantitative mRNA expression analysis of PSCA was done on clinical specimens of prostate cancer bone, lymph node, and visceral metastases. PSCA protein and mRNA expression levels were quantified and compared between available matched pairs of bone and lymph node or visceral metastases. RESULTS: Bone metastases stained with higher intensity of PSCA compared with lymph node or liver metastases in seven of eight (87.5%) matched pairs (P = 0.035). PSCA mRNA expression was equal or greater than that of LAPC-9, a PSCA expressing xenograft, in 12 of 24 (50%) cases of prostate cancer metastases and was significantly correlated with PSCA protein expression (sigma = 0.84, P = 0.0019). Overall, PSCA protein expression was detected in 41 of 47 (87.2%), four of six (66.7%), and two of three (66.7%) cases of bone, lymph node, and liver metastases, respectively. Mean PSCA staining intensity was significantly higher in prostate cancer bone metastases compared with lymph node metastases (2.0 +/- 0.02 versus 0.83 +/- 0.31, P = 0.014). CONCLUSIONS: Prostate cancer metastases express PSCA. However, greater PSCA staining intensity and level of PSCA mRNA expression was associated with bone metastases compared with lymph node metastases. This study suggests that PSCA is a promising tumor marker and potential therapeutic target for patients with metastatic prostate cancer.

Antigens, Neoplasm↗

PubMed2005

Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinoma.

We describe a novel strategy (random forest clustering) for tumor profiling based on tissue microarray data. Random forest clustering is attractive for tissue microarray and other immunohistochemistry data since it handles highly skewed tumor marker expressions well and weighs the contribution of each marker according to its relatedness with other tumor markers. This is the first tumor class discovery analysis of renal cell carcinoma patients based on protein expression profiles. The tissue array data contained at least three tumor samples from each of 366 renal cell carcinoma patients. The eight tumor markers explore tumor proliferation, cell cycle abnormalities, cell mobility, and the hypoxia pathway. Since the procedure is unsupervised, no clinicopathological data or traditional classifications are used a priori. To explore whether the tissue microarray data can be used to identify fundamental subtypes of renal cell carcinoma patients, we first carried out random forest clustering of all 366 patients. By analyzing the tumor markers simultaneously, the procedure automatically detected classes that correspond to clear- vs non-clear cell tumors (demonstration of proof-of-principle). The resulting molecular grouping provides better prediction of survival (logrank P=0.000090) than this classical pathological grouping (logrank P=0.023). We then sought to extend the class discovery by searching for finer subclasses of clear cell patients. The procedure automatically discovered: (a) two classes corresponding to low- and high-grade patients (demonstration of proof-of-principle); (b) a subgroup of long-surviving clear cell patients with a distinct molecular profile and (c) two novel tumor subclasses in low-grade clear cell patients that could not be explained by any clinicopathological variables (demonstration of discovery).

Biomarkers, Tumor↗

PubMed2005

p53 is an independent predictor of tumor recurrence and progression after nephrectomy in patients with localized renal cell carcinoma.

PURPOSE: We determined which clinical and molecular variables can predict cancer recurrence in patients following surgical management for localized renal cell carcinoma (RCC). MATERIALS AND METHODS: From a custom kidney cancer tissue microarray containing tumors specimens from 350 patients 193 undergoing nephrectomy for localized RCC at our institution between 1989 and 2000 were identified. The array was then analyzed by immunohistochemistry for certain molecular markers, namely CA9, CA12, Ki67, gelsolin, p53, EpCAM, pTEN and vimentin. The medical records of these patients were then reviewed for age, sex, TNM stage, tumor size, nuclear grade, Eastern Cooperative Oncology Group (ECOG) performance status, recurrence status and, when applicable, time to recurrence. Cox regression analyses were done to determine clinical and molecular predictors of time to tumor recurrence. RESULTS: Of the patients 15% demonstrated evidence of tumor recurrence following nephrectomy (29 of 193). Univariate Cox regression demonstrated that tumor size, T stage, grade, ECOG performance status, Ki67, EpCAM and p53 were significantly associated with recurrence (p <0.05). A multivariate Cox regression model showed that T stage (p = 0.018), ECOG (p = 0.004) and p53 (p = 0.003) were the 3 most significant predictors. p53 expression correlated significantly with nuclear grade (Pearson correlation 0.22, p = 0.023) but not with any other clinical factors. Patients with localized tumors demonstrating mean p53 staining values above and below 20% of cells had recurrence rates of 37.7% and 14.4%, respectively (RR = 3.28, p = 0.018). CONCLUSIONS: p53 is a significant molecular predictor of tumor recurrence, as identified in patients undergoing treatment for localized RCC.

Adult↗

PubMed2005

Using tumor markers to predict the survival of patients with metastatic renal cell carcinoma.

PURPOSE: Approximately 30% of renal cell carcinomas (RCCs) present as metastatic disease. Molecular markers have the potential to characterize accurately the biological behavior of tumors and they may be useful for determining prognosis. MATERIALS AND METHODS: A custom tissue array was constructed using clear cell RCC from 150 patients with metastatic RCC who underwent nephrectomy prior to immunotherapy. The tissue array was stained for 8 molecular markers, namely Ki67, p53, gelsolin, carbonic anhydrase (CA)9, CA12, PTEN (phosphatase and tensin homologue deleted on chromosome 10), epithelial cell adhesion molecule and vimentin. Marker status and established clinical predictors of prognosis were considered when developing a prognostic model for disease specific survival. RESULTS: On univariate Cox regression analysis certain markers were statistically significant predictors of survival, namely CA9 (p <0.00001), p53 (p = 0.0072), gelsolin (p = 0.030), Ki67 (p = 0.036) and CA12 (p = 0.043). On multivariate Cox regression analysis that included all markers and clinical variables CA9 (p = 0.00002), PTEN (p <0.0001), vimentin (p = 0.0032), p53 (p = 0.028), T category (p = 0.0025) and performance status (p = 0.0013) were significant independent predictors of disease specific survival and they were used to construct a combined molecular and clinical prognostic model. The bias corrected concordance index (C-index) of this combined prognostic model was C = 0.68, which was significantly higher (p = 0.0033) than that of a multivariate clinical predictor model (C = 0.62) based on the UCLA Integrated Staging System (T category, histological grade and performance status). CONCLUSIONS: In patients with clear cell RCC a prognostic model for survival that includes molecular and clinical predictors is significantly more accurate than a standard clinical model using the combination of stage, histological grade and performance status.

Adult↗

PubMed2005

Expression of transcription factor Yin Yang 1 in prostate cancer.

The transcription repressor Yin Yang 1 (YY1) is expressed in several human cancer cell lines and its expression correlates with resistance to immune-mediated apoptosis. This study used tissue microarrays to investigate the expression and localization of YY1 in 1364 representative tissue samples from 246 hormone naive prostate cancer patients who underwent radical prostatectomy. Staining intensity and frequency measures for both YY1 nuclear and cytoplasmic expression were higher in neoplastic tissues and in PIN samples compared to matched benign cells (p < 0.0001 for all comparisons). Expression of YY1 is predominantly elevated in early malignancy (PIN), as well as in tumors of intermediate to high morphologic grade (Gleason's grade 3-5). Using multivariate Cox proportional hazards analysis, we observed that low nuclear YY1 staining is an independent predictor of a shorter time to recurrence (p = 0.012). Based on these results, we hypothesize that YY1 may play a role in prostate cancer development; however, decreased YY1 may give metastatic cells a survival advantage. These results may also implicate YY1 as a useful diagnostic and prognostic marker.

Aged↗

PubMed2004

Antibody-based profiling of the phosphoinositide 3-kinase pathway in clinical prostate cancer.

PURPOSE: As kinase inhibitors transition from the laboratory to patients, it is imperative to develop biomarkers that can be used in the clinic. The primary objectives are to identify patients most likely to benefit from molecularly targeted therapies and to document modulation of the drug target. Constitutive activation of the phosphoinositide 3-kinase (PI3K) pathway and its downstream effectors, as a result of PTEN loss or by other mechanisms, occurs in a high proportion of prostate cancers, making it an ideal template for the design of clinical trials involving PI3K pathway inhibitors. Prostate cancers also present unique organ-specific challenges, in that tumors are heterogeneous and diagnostic tissue is extremely limited. EXPERIMENTAL DESIGN: Working within these limitations, we have developed a set of immunohistochemical assays that define activation of the PI3K pathway in clinical samples. RESULTS AND CONCLUSIONS: Using both univariate and multivariate analyses, we show that loss of PTEN is highly correlated with the activation of AKT, and this, in turn, is associated with the phosphorylation of S6, one of its main effectors. These three antibodies are potentially able to define a molecular signature of PTEN loss and/or AKT pathway activation in prostate cancer.

Antibodies, Monoclonal↗

PubMed2004

Trefoil factor 3 is overexpressed in human prostate cancer.

The trefoil factors are secreted peptides produced by normal intestinal mucosa. Members of the trefoil family are overexpressed in a variety of cancers and are associated with tumor invasion, resistance to apoptosis, and metastasis. Recent cDNA array analyses suggest that human intestinal trefoil factor 3 (TFF3) may be overexpressed in human prostate cancer. Immunohistochemistry was performed on a prostate cancer tissue microarray containing tumor tissue samples from 246 primary radical retropubic prostatectomy cases with antibodies specific for TFF3. Prostatic intraepithelial neoplasia (PIN), benign prostatic hyperplasia (BPH), and morphologically normal prostatic epithelium were represented on this array. Additionally, 18 metastatic lesions were also stained. Two independent pathologists scored the tissue arrays, with positive cases defined as those containing TFF3 staining in a majority of target cells within any spots representing the appropriate designated histology. Forty-two percent of 236 cases containing prostate cancer stained positive for TFF3, while only 10% of 145 cases containing normal tissue and 18% of 91 cases containing BPH, stained positive. Seven of 18 (39%) metastatic lesions analyzed stained positive. Although TFF3 expression correlates significantly with prostate cancer, TFF3 expression did not correlate with Gleason grade, tumor stage, or rate of recurrence. These studies validate that TFF3 is overexpressed in a subset of primary and metastic prostate cancers.

Aged↗

PubMed2004

Gene expression profiling of gliomas strongly predicts survival.

In current clinical practice, histology-based grading of diffuse infiltrative gliomas is the best predictor of patient survival time. Yet histology provides little insight into the underlying biology of gliomas and is limited in its ability to identify and guide new molecularly targeted therapies. We have performed large-scale gene expression analysis using the Affymetrix HG U133 oligonucleotide arrays on 85 diffuse infiltrating gliomas of all histologic types to assess whether a gene expression-based, histology-independent classifier is predictive of survival and to determine whether gene expression signatures provide insight into the biology of gliomas. We found that gene expression-based grouping of tumors is a more powerful survival predictor than histologic grade or age. The poor prognosis samples could be grouped into three different poor prognosis groups, each with distinct molecular signatures. We further describe a list of 44 genes whose expression patterns reliably classify gliomas into previously unrecognized biological and prognostic groups: these genes are outstanding candidates for use in histology-independent classification of high-grade gliomas. The ability of the large scale and 44 gene set expression signatures to group tumors into strong survival groups was validated with an additional external and independent data set from another institution composed of 50 additional gliomas. This demonstrates that large-scale gene expression analysis and subset analysis of gliomas reveals unrecognized heterogeneity of tumors and is efficient at selecting prognosis-related gene expression differences which are able to be applied across institutions.

Adolescent↗

PubMed2004

Using protein expressions to predict survival in clear cell renal carcinoma.

PURPOSE: An accurate system for predicting survival for patients with solid tumors will allow for better patient selection for both established and novel therapies. We propose a staging system for clear cell variants of renal cell carcinoma (RCC) that includes molecular predictors and standard clinical predictors such as tumor-node-metastasis (TNM) stage, histological grade, and performance status (PS). EXPERIMENTAL DESIGN: A custom tissue array was constructed using clear cell RCC from 318 patients, representing all stages of localized and metastatic RCC, and immunohistochemically stained for molecular markers Ki67, p53, gelsolin, CA9, CA12, PTEN, EpCAM, and vimentin. We present a strategy for evaluating individual candidate markers for prognostic information and integrating informative markers into a multivariate prognostic system. RESULTS: The overall median follow-up and the median follow-up for surviving patients were 28 and 55 months, respectively. A prognostic model based primarily on molecular markers included metastasis status, p53, CA9, gelsolin, and vimentin as predictors and had high discriminatory power: its statistically validated concordance index (C-index) was found to be 0.75. A prognostic model based on a combination of clinical and molecular predictors included metastasis status, T stage, Eastern Cooperative Oncology Group PS, p53, CA9, and vimentin as predictors and had a C-index of 0.79, which was significantly higher (P < 0.05) than that of prognostic models based on grade alone (C = 0.65), TNM stage alone (C = 0.73), or the University of California Los Angeles integrated staging system (C = 0.76). CONCLUSIONS: Protein expressions obtained using widely available technology can complement standard clinical predictors such as TNM stage, histological grade, and PS.

Adenocarcinoma, Clear Cell↗

PubMed2004

Family-based tests for associating haplotypes with general phenotype data: application to asthma genetics.

We provide a general purpose family-based testing strategy for associating disease phenotypes with haplotypes when phase may be ambiguous and parental genotype data may be missing. These tests for linkage and association can be used in candidate gene studies with tightly linked markers. Our proposed weighted conditional approach extends the method described in Rabinowitz and Laird to multiple markers. It is attractive because it provides haplotype tests for family-based studies that are efficient and robust to population admixture, phenotype distribution specification, and ascertainment based on phenotypes. It can handle missing parental genotypes and/or missing phase in both offspring and parents. It yields either haplotype-specific (univariate) tests or multi-haplotype (global) tests. This extension has been implemented in the freely available software haplotype FBAT. We used the haplotype FBAT program to test for associations between asthma phenotypes and single nucleotide polymorphisms (SNPs) in the beta-2 adrenergic receptor gene. Whereas no single SNP showed significant association with asthma diagnosis or bronchodilator responsiveness (quantitative trait), a haplotype-based global test found a highly significant association with asthma diagnosis (P value <0.00005) and the measure of bronchodilator responsiveness (P value =0.016).

Algorithms↗

PubMed2004

Statistical methods for analyzing tissue microarray data.

Tissue microarrays (TMAs) are a new high-throughput tool for the study of protein expression patterns in tissues and are increasingly used to evaluate the diagnostic and prognostic importance of biomarkers. TMA data are rather challenging to analyze. Covariates are highly skewed, non-normal, and may be highly correlated. We present statistical methods for relating TMA data to censored time-to-event data. We review methods for evaluating the predictive power of Cox regression models and show how to test whether biomarker data contain predictive information above and beyond standard pathology covariates. We use nonparametric bootstrap methods to validate model fitting indices such as the concordance index. We also present data mining methods for characterizing high risk patients with simple biomarker rules. Since researchers in the TMA community routinely dichotomize biomarker expression values, survival trees are a natural choice. We also use bump hunting (patient rule induction method), which we adapt to the use with survival data. The proposed methods are applied to a kidney cancer tissue microarray data set.

Algorithms↗

PubMed2004

Prostate stem cell antigen expression is associated with gleason score, seminal vesicle invasion and capsular invasion in prostate cancer.

PURPOSE: Few successful therapeutic options exist for men who present with metastatic prostate cancer (CaP) or for the 30% with recurrence. The development and characterization of molecular markers are vital to the development of prognostic and therapeutic modalities in CaP. We investigated the expression and potential clinical usefulness of prostate stem cell antigen (PSCA) in CaP using tissue microarrays. MATERIALS AND METHODS: Immunohistochemical analysis using a PSCA monoclonal antibody was performed on tissue microarrays constructed from paraffin embedded specimens from 246 patients who underwent radical retropubic prostatectomy. PSCA staining was correlated with established prognostic factors, such as Gleason score, prostate specific antigen (PSA), and seminal vesicle invasion. In addition, recurrence-free survival was analyzed. RESULTS: A high PSCA intensity of 3 was associated with adverse prognostic features, such as Gleason score 7 and above (p = 0.001), seminal vesicle invasion (p = 0.005) and capsular involvement (p = 0.033). On univariate analysis tumors with a PSCA intensity of 3 carried an increased risk of PSA recurrence (p = 0.031, HR 1.77, 95% CI 1.05 to 2.96). However, after adjusting for these variables a PSCA intensity of 3 was no longer an independent predictor of PSA recurrence. CONCLUSIONS: We found that high PSCA intensity is significantly associated with adverse prognostic features such as high Gleason score and extra-organ disease. The results of this study suggest that PSCA is a promising tumor marker for the selection of patients at high risk but additional studies are necessary to assess the usefulness of PSCA in patient biopsies.

Aged↗

PubMed2004

Prognostic value of carbonic anhydrase IX and KI67 as predictors of survival for renal clear cell carcinoma.

PURPOSE: The natural history of renal cell carcinoma (RCC) is complex and not entirely explained by conventional prognostic factors. In this study we evaluated the prognostic value of carbonic anhydrase IX (CAIX) and Ki67 to predict survival in RCC. MATERIALS AND METHODS: Immunohistochemical analysis using a CAIX and a Ki67 monoclonal antibody was performed on tissue microarrays constructed from paraffin embedded specimens from 224 patients treated with nephrectomy for clear cell renal carcinoma. CAIX and Ki67 staining were correlated with clinical factors, pathological features and survival. Median followup was 34 months (range 0.3 to 117) and disease specific survival was the primary end point assessed. RESULTS: Univariate statistical analysis showed that high Ki67 staining and low CAIX staining correlated significantly with poor median survival (21 months, p < 0.001 and 22 months, p = 0.011, respectively). Each marker was highly significant for stratifying patient groups defined by T stage, Fuhrman grade, nodal status, metastatic status and performance status. On multivariate analysis CAIX and Ki67 were significant predictors of survival with an HR of 1.78 (p = 0.014) and 1.75 (p = 0.009), respectively. Although CAIX and Ki67 staining were inversely correlated (p = 0.009), Ki67 significantly substratified patient subgroups defined by high or low CAIX staining (p = 0.001 and 0.003, respectively). When Ki67 and CAIX were combined into a single parameter, RCC tumors could be stratified into low, intermediate and high risk groups with a median survival of greater than 101, 31 and 9 months, respectively (p <0.001). On multivariate analysis the combined parameter consisting of Ki67 and CAIX was a significant predictor of survival (p <0.001) and it was able to displace histological grade. CONCLUSIONS: Ki67and CAIX are useful prognostic biomarkers for RCC that improve the survival prediction and classification of kidney cancer.

Adult↗

PubMed2004

Kidney transplant rejection and tissue injury by gene profiling of biopsies and peripheral blood lymphocytes.

A major challenge for kidney transplantation is balancing the need for immunosuppression to prevent rejection, while minimizing drug-induced toxicities. We used DNA microarrays (HG-U95Av2 GeneChips, Affymetrix) to determine gene expression profiles for kidney biopsies and peripheral blood lymphocytes (PBLs) in transplant patients including normal donor kidneys, well-functioning transplants without rejection, kidneys undergoing acute rejection, and transplants with renal dysfunction without rejection. We developed a data analysis schema based on expression signal determination, class comparison and prediction, hierarchical clustering, statistical power analysis and real-time quantitative PCR validation. We identified distinct gene expression signatures for both biopsies and PBLs that correlated significantly with each of the different classes of transplant patients. This is the most complete report to date using commercial arrays to identify unique expression signatures in transplant biopsies distinguishing acute rejection, acute dysfunction without rejection and well-functioning transplants with no rejection history. We demonstrate for the first time the successful application of high density DNA chip analysis of PBL as a diagnostic tool for transplantation. The significance of these results, if validated in a multicenter prospective trial, would be the establishment of a metric based on gene expression signatures for monitoring the immune status and immunosuppression of transplanted patients.

Adult↗

PubMed2003

Multivariate variance-components analysis of longitudinal blood pressure measurements from the Framingham Heart Study.

Multivariate variance-components analysis provides several advantages over univariate analysis when studying correlated traits. It can test for pleiotropy or (in the longitudinal context) gene x age interaction. It can also have more power than univariate analyses to detect a quantitative trait locus influencing several traits. We apply multivariate variance components to longitudinal systolic blood pressure data from the Framingham Heart Study. We find evidence for a polygenic influence on blood pressure (heritabilities at different ages range from 27% to 38%). Tests based on a factor-analytic parameterization of the polygenic variance find significant (p < 2 x 10(-3)) evidence that different genes affect blood pressure at different ages. Still, estimates for the proportion of polygenic variance due to shared genes ran as high as 85% for some trait pairs. Univariate and multivariate linkage analyses replicate previous linkage results on chromosome 17 (maximum LOD scores of 2.2 and 2.4, respectively). In this study, multivariate analysis provides no increase in power; this is likely due to the strong positive correlation in systolic blood pressure measured at different ages.

Adult↗

PubMed2003

Sexually dimorphic gene expression in mouse brain precedes gonadal differentiation.

The classic view of brain sexual differentiation and behavior is that gonadal steroid hormones act directly to promote sex differences in neural and behavioral development. In particular, the actions of testosterone and its metabolites induce a masculine pattern of brain development, while inhibiting feminine neural and behavioral patterns of differentiation. However, recent evidence indicates that gonadal hormones may not solely be responsible for sex differences in brain development and behavior between males and females. Here we examine an alternative hypothesis that genes, by directly inducing sexually dimorphic patterns of neural development, can influence the sexual differences between male and female brains. Using microarrays and RT-PCR, we have detected over 50 candidate genes for differential sex expression, and confirmed at least seven murine genes which show differential expression between the developing brains of male and female mice at stage 10.5 days post coitum (dpc), before any gonadal hormone influence. The identification of genes differentially expressed between male and female brains prior to gonadal formation suggests that genetic factors may have roles in influencing brain sexual differentiation.

Animals↗

PubMed2003

High concentrations of long interspersed nuclear element sequence distinguish monoallelically expressed genes.

Genes subject to monoallelic expression are expressed from only one of the two alleles either selected at random (random monoallelic genes) or in a parent-of-origin specific manner (imprinted genes). Because high densities of long interspersed nuclear element (LINE)-1 transposon sequence have been implicated in X-inactivation, we asked whether monoallelically expressed autosomal genes are also flanked by high densities of LINE-1 sequence. A statistical analysis of repeat content in the regions surrounding monoallelically and biallelically expressed genes revealed that random monoallelic genes were flanked by significantly higher densities of LINE-1 sequence, evolutionarily more recent and less truncated LINE-1 elements, fewer CpG islands, and fewer base-pairs of short interspersed nuclear elements (SINEs) sequence than biallelically expressed genes. Random monoallelic and imprinted genes were pooled and subjected to a clustering analysis algorithm, which found two clusters on the basis of aforementioned sequence characteristics. Interestingly, these clusters did not follow the random monoallelic vs. imprinted classifications. We infer that chromosomal sequence context plays a role in monoallelic gene expression and may involve the recognition of long repeats or other features. The sequence characteristics that distinguished the high-LINE-1 category were used to identify more than 1,000 additional genes from the human and mouse genomes as candidate genes for monoallelic expression.

Algorithms↗

PubMed2003

Gene expression profiling identifies molecular subtypes of gliomas.

Identification of distinct molecular subtypes is a critical challenge for cancer biology. In this study, we used Affymetrix high-density oligonucleotide arrays to identify the global gene expression signatures associated with gliomas of different types and grades. Here, we show that the global transcriptional profiles of gliomas of different types and grades are distinct from each other and from the normal brain. To determine whether our data could be used to uncover molecular subtypes without prior knowledge of pathologic type and grade, we performed K-means clustering analysis and found evidence for three clusters with the aid of multidimensional scaling plots. These clusters corresponded to glioblastomas, lower grade astrocytomas and oligodendrogliomas (P<0.00001). A predictor constructed from the 170 genes that are most differentially expressed between the subsets correctly identified the type and grade of all samples, indicating that a relatively small number of genes can be used to distinguish between these molecular subtypes. These results further define molecular subsets of gliomas which may potentially be used for patient stratification, and suggest potential targets for treatment.

Astrocytoma↗