Anne Sawyers

Bcl-B Expression in Human Epithelial and Nonepithelial Malignancies

Purpose: Apoptosis plays an important role in neoplastic processes. Bcl-B is an antiapoptotic Bcl-2 family member, which is known to change its phenotype upon binding to Nur77/TR3. The expression pattern of this protein in human malignancies has not been reported. Experimental Design: We investigated Bcl-B expression in normal human tissues and several types of human epithelial and nonepithelial malignancy by immunohistochemistry, correlating results with tumor stage, histologic grade, and patient survival. Results: Bcl-B protein was strongly expressed in all normal plasma cells but found in only 18% of multiple myelomas (n = 133). Bcl-B immunostaining was also present in normal germinal center centroblasts and centrocytes and in approximately half of diffuse large B-cell lymphoma (n = 48) specimens, whereas follicular lymphomas (n = 57) did not contain Bcl-B. In breast (n = 119), prostate (n = 66), gastric (n = 180), and colorectal (n = 106) adenocarcinomas, as well as in non–small cell lung cancers (n = 82), tumor-specific overexpression of Bcl-B was observed. Bcl-B expression was associated with variables of poor prognosis, such as high tumor grade in breast cancer (P = 0.009), microsatellite stability (P = 0.0002), and left-sided anatomic location (P = 0.02) of colorectal cancers, as well as with greater incidence of death from prostate cancer (P = 0.005) and shorter survival of patients with small cell lung cancer (P = 0.009). Conversely, although overexpressed in many gastric cancers, Bcl-B tended to correlate with better outcome (P = 0.01) and more differentiated tumor histology (P < 0.0001). Conclusions: Tumor-specific alterations in Bcl-B expression may define subsets of nonepithelial and epithelial neoplasms with distinct clinical behaviors.

Diagnosis of prostate cancer using differentially expressed genes in stroma.

More than one million prostate biopsies are performed in the United States every year. A failure to find cancer is not definitive in a significant percentage of patients due to the presence of equivocal structures or continuing clinical suspicion. We have identified gene expression changes in stroma that can detect tumor nearby. We compared gene expression profiles of 13 biopsies containing stroma near tumor and 15 biopsies from volunteers without prostate cancer. About 3,800 significant expression changes were found and thereafter filtered using independent expression profiles to eliminate possible age-related genes and genes expressed at detectable levels in tumor cells. A stroma-specific classifier for nearby tumor was constructed on the basis of 114 candidate genes and tested on 364 independent samples including 243 tumor-bearing samples and 121 nontumor samples (normal biopsies, normal autopsies, remote stroma, as well as stroma within a few millimeters of tumor). The classifier predicted the tumor status of patients using tumor-free samples with an average accuracy of 97% (sensitivity = 98% and specificity = 88%) whereas classifiers trained with sets of 100 randomly generated genes had no diagnostic value. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for categorizing the presence of tumor in patients when a prostate sample is derived from near the tumor but does not contain any recognizable tumor.

Expression differences between African American and Caucasian prostate cancer tissue reveals that stroma is the site of aggressive changes

In prostate cancer, race/ethnicity is the highest risk factor after adjusting for age. African Americans have more aggressive tumors at every clinical stage of the disease, resulting in poorer prognosis and increased mortality. A major barrier to identifying crucial gene activity differences is heterogeneity, including tissue composition variation intrinsic to the histology of prostate cancer. We hypothesized that differences in gene expression in specific tissue types would reveal mechanisms involved in the racial disparities of prostate cancer. We examined 17 pairs of arrays for AAs and Caucasians that were formed by closely matching the samples based on the known tissue type composition of the tumors. Using pair‐wise t‐test we found significantly altered gene expression between AAs and CAs. Independently, we performed multiple linear regression analyses to associate gene expression with race considering variation in percent tumor and stroma tissue. The majority of differentially expressed genes were associated with tumor‐adjacent stroma rather than tumor tissue. Extracellular matrix, integrin family and signaling mediators of the epithelial‐to‐mesenchymal transition (EMT) pathways were all downregulated in stroma of AAs. Using MetaCore (GeneGo) analysis, we observed that 35% of significant (p < 10−3) pathways identified EMT and 25% identified immune response pathways especially for interleukins‐2, ‐4, ‐5, ‐6, ‐7, ‐10, ‐13, ‐15 and ‐22 as the major changes. Our studies reveal that altered immune and EMT processes in tumor‐adjacent stroma may be responsible for the aggressive nature of prostate cancer in AAs.

Expression Changes in the Stroma of Prostate Cancer Predict Subsequent Relapse

Biomarkers are needed to address overtreatment that occurs for the majority of prostate cancer patients that would not die of the disease but receive radical treatment. A possible barrier to biomarker discovery may be the polyclonal/multifocal nature of prostate tumors as well as cell-type heterogeneity between patient samples. Tumor-adjacent stroma (tumor microenvironment) is less affected by genetic alteration and might therefore yield more consistent biomarkers in response to tumor aggressiveness. To this end we compared Affymetrix gene expression profiles in stroma near tumor and identified a set of 115 probe sets for which the expression levels were significantly correlated with time-to-relapse. We also compared patients that chemically relapsed shortly after prostatectomy (<1 year), and patients that did not relapse in the first four years after prostatectomy. We identified 131 differentially expressed microarray probe sets between these two categories. 19 probe sets (15 genes overlapped between the two gene lists with p<0.0001). We developed a PAM-based classifier by training on samples containing stroma near tumor: 9 rapid relapse patient samples and 9 indolent patient samples. We then tested the classifier on 47 different samples, containing 90% or more stroma. The classifier predicted the risk status of patients with an average accuracy of 87%. This is the first general tumor microenvironment-based prognostic classifier. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for predicting outcomes for patients.

An Accurate Prostate Cancer Prognosticator Using a Seven-Gene Signature Plus Gleason Score and Taking Cell Type Heterogeneity into Account

One of the major challenges in the development of prostate cancer prognostic biomarkers is the cellular heterogeneity in tissue samples. We developed an objective Cluster-Correlation (CC) analysis to identify gene expression changes in various cell types that are associated with progression. In the Cluster step, samples were clustered (unsupervised) based on the expression values of each gene through a mixture model combined with a multiple linear regression model in which cell-type percent data were used for decomposition. In the Correlation step, a Chi-square test was used to select potential prognostic genes. With CC analysis, we identified 324 significantly expressed genes (68 tumor and 256 stroma cell expressed genes) which were strongly associated with the observed biochemical relapse status. Significance Analysis of Microarray (SAM) was then utilized to develop a seven-gene classifier. The Classifier has been validated using two independent Data Sets. The overall prediction accuracy and sensitivity is 71% and 76%, respectively. The inclusion of the Gleason sum to the seven-gene classifier raised the prediction accuracy and sensitivity to 83% and 76% respectively based on independent testing. These results indicated that our prognostic model that includes cell type adjustments and using Gleason score and the seven-gene signature has some utility for predicting outcomes for prostate cancer for individual patients at the time of prognosis. The strategy could have applications for improving marker performance in other cancers and other diseases.

Generation of ''Virtual'' Control Groups for Single Arm Prostate Cancer Adjuvant Trials

It is difficult to construct a control group for trials of adjuvant therapy (Rx) of prostate cancer after radical prostatectomy (RP) due to ethical issues and patient acceptance. We utilized 8 curve-fitting models to estimate the time to 60%, 65%, … 95% chance of progression free survival (PFS) based on the data derived from Kattan post-RP nomogram. The 8 models were systematically applied to a training set of 153 post-RP cases without adjuvant Rx to develop 8 subsets of cases (reference case sets) whose observed PFS times were most accurately predicted by each model. To prepare a virtual control group for a single-arm adjuvant Rx trial, we first select the optimal model for the trial cases based on the minimum weighted Euclidean distance between the trial case set and the reference case set in terms of clinical features, and then compare the virtual PFS times calculated by the optimum model with the observed PFSs of the trial cases by the logrank test. The method was validated using an independent dataset of 155 post-RP patients without adjuvant Rx. We then applied the method to patients on a Phase II trial of adjuvant chemo-hormonal Rx post RP, which indicated that the adjuvant Rx is highly effective in prolonging PFS after RP in patients at high risk for prostate cancer recurrence. The method can accurately generate control groups for single-arm, post-RP adjuvant Rx trials for prostate cancer, facilitating development of new therapeutic strategies.

A stroma-based 15 gene profile for prostate cancer suggests increased DNA methylation and senescence in the stroma of patients with poor prognosis

Biomarkers are needed to address overtreatment that occurs for the majority of prostate cancer patients that would not die of the disease but receive radical treatment. Barriers to biomarker discover include the polyclonal/multifocal nature of prostate tumors and the cell-type heterogeneity between patient samples. Tumor-adjacent stroma (tumor microenvironment) is much less affected by these problems but exhibit hundreds of gene expression changes compared to normal stroma (1). We performed Affymetrix gene expression profiles of tumor-adjacent stroma for asymptomatic organ-confined disease with negative surgical margins. We identified a set of 115 probe sets for which the expression levels were significantly correlated with time-to-relapse following prostatectomy. Next, we compared expression in patients that chemically relapsed shortly after prostatectomy ( 1. Jia, . et al. Canc. Res. 2011;71 :2476-2487. 2. Gabai, V. et al. Oncogene. 2010 ;2129 :1952-1962. 3. Banerjee, J. et al. Oncogene 2013 ; Epub., PMC accession no. 24141771. Citation Format: Zhenyu Jia, Farah Rahmatpanah, Xin Chen, Waldemar Lernhardt, Yipeng Wang, Xiao-Qin Xia, Anne Sawyers, Michael McClelland, Dan Mercola. A stroma-based 15 gene profile for prostate cancer suggests increased DNA methylation and senescence in the stroma of patients with poor prognosis. [abstract]. In: Abstracts: AACR Special Conference on Cellular Heterogeneity in the Tumor Microenvironment; 2014 Feb 26-Mar 1; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(1 Suppl):Abstract nr A63. doi:10.1158/1538-7445.CHTME14-A63

Diagnosis of prostate cancer using differentially expressed genes in stroma

Over 1 million prostate biopsies are performed in the U.S. every year. A failure to find cancer is not definitive in a significant percentage of patients due to the presence of equivocal structures or continuing clinical suspicion. We have identified gene expression changes in stroma that can detect tumor nearby. We compared gene expression profiles of 13 biopsies containing stroma near tumor and 15 biopsies from volunteers without prostate cancer. More than a thousand significant expression changes were found and thereafter filtered using large numbers of other expression profiles to eliminate possible age-related genes and genes expressed at detectable levels in tumor cells. A stroma-specific classifier was constructed based on 114 candidate genes and tested on 364 independent samples, including 243 tumor-bearing samples and 121 non-tumor samples (normal biopsies, normal autopsies, remote stroma, as well as stroma within a few millimeters of tumor). The classifier predicted the tumor status of patients using tumor-free samples with an average accuracy of 97.2% (sensitivity = 97.9% and specificity = 88.0%) whereas classifiers trained with sets of 100 randomly generated genes had no diagnostic value. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for categorizing the presence of tumor in patients when a prostate sample is derived from near the tumor but does not contain any recognizable tumor.

Abstract 2735: Diagnosis of prostate cancer without tumor cells using differentially expressed genes in the tumor microenvironment

Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC Over one million prostate biopsies are performed in the U.S. every year. However, pathology examination is not definitive in a significant percentage of cases due to missing the tumor or equivocal structures. Given that the tissue near tumors will experience expression changes, biopsies could reveal the presence of a nearby tumor even if the biopsy contains no tumor. In order to identify reliable examples of such genes we compared gene expression profiles (Affymetrix U133plus2) from 15 volunteer biopsy specimens to 13 specimens containing largely tumor-adjacent stroma. More than a thousand significant expression changes were found and thereafter filtered to eliminate possible aging-related genes and genes also expressed at detectable levels in tumor cells. Classifiers were constructed based on 114 unique candidate genes (131 Affymetrix probe sets) and tested on 380 independent cases, including 255 tumor-bearing cases, 125 nontumor cases (normal biopsies, normal autopsies, remote stroma as well as pure tumor adjacent stroma). The classifier predicted the tumor status of patients with an average accuracy of 97.4% (sensitivity = 98.0% and specificity = 89.7%). These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for detecting “presence of tumor”. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2735.

Abstract 1988: In silico estimates of cell components in cancer tissue based on expression profiling data

Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC Cancer gene expression profiling studies often measure samples that vary widely in the mixtures of cell types they contain. Such variation could confound efforts to correlate expression with clinical parameters. In principle, the proportion of each major tissue type can be estimated from the profiling data and used to triage samples before using the data to study correlations with disease parameters. Four large gene expression microarray data sets from prostate tissue whose cell components were estimated by pathologists were used to test the performance of in silico prediction of tissue components. Multi-variate linear regression models were developed for in silico prediction of major cell components of prostate cancer tissue. 10-fold cross-validation within each data set gave average differences between the pathologist and in silico predictions of 8∼14% for the tumor component and 13∼17% for stroma component. Across data sets that used similar platforms and fresh frozen samples, the average differences were 11∼12% for tumor and 12∼17% for stroma. Prediction models were applied to expression data on the same platform from 219 other tumor-enriched prostate cancer samples for which tissue proportions were not known. The tumor “enriched” samples were predicted to have a wide range of tumor percentages; 0 to 87%. Furthermore, there was a 10.5% difference in the average predicted tumor percentages between 37 recurrent and 42 non-recurrent cancer patients. This systematic difference in tumor content would likely cause tissue-specific gene changes to falsely appear to be correlated with recurrence unless some samples were excluded to remove this bias or unless tissue percentages were incorporated into the prediction model. Similar circumstances may arise in other sets of clinical samples. A web service, CellPred, has been designed for the in silico prediction of prostate cancer sample cell components. While this site is currently based on microarray data, it could equally well use high-throughput sequencing data. The approach presented here can be generalized to other tissue mixtures once data on both tissue content and expression profiles are obtained for a training set. CellPred is freely available at http://www.webarraydb.org/. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1988.