Farah Rahmatpanah

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.

Overexpression of periostin in stroma positively associated with aggressive prostate cancer.

Background Periostin is an important extracellular matrix protein involved in cell development and adhesion. Previously, we identified periostin to be up-regulated in aggressive prostate cancer (CaP) using quantitative glycoproteomics and mass spectrometry. The expression of periostin was further evaluated in primary radical prostatectomy (RP) prostate tumors and adjacent non-tumorous prostate tissues using immunohistochemistry (IHC). Our IHC results revealed a low background periostin levels in the adjacent non-tumorous prostate tissues, but overexpressed periostin levels in the peritumoral stroma of primary CaP tumors. Methods In this study, periostin expression in CaP was further examined on multiple tissue microarrays (TMAs), which were conducted in four laboratories. To achieve consistent staining, all TMAs were stained with same protocol and scored by same image computation tool to determine the total periostin staining intensities. The TMAs were further scored by pathologists to characterize the stromal staining and epithelial staining. Results The periostin staining was observed mainly in peritumoral stromal cells and in some cases in tumor epithelial cells though the stronger staining was found in peritumoral stromal cells. Both periostin stromal staining and epithelial staining can differentiate BPH from CaP including low grade CaP (Gleason score ≤6), with significant p-value of 2.2e-16 and 0.001, respectively. Periostin epithelial staining differentiated PIN from low grade CaP (Gleason score ≤6) (p=0.001), while periostin stromal staining differentiated low grade Cap (Gleason score ≤6) from high grade Cap (Gleason score ≤6) (p=1.7e-05). In addition, a positive correlation between total periostin staining and Gleason score was observed (r=0.87, p=0.002). Conclusions The results showed that periostin staining was positively correlated with increasing Gleason score and the aggressiveness of prostate disease.

Role of the Adjacent Stroma Cells in Prostate Cancer Development and Progression: Synergy between TGF-β and IGF Signaling

This review postulates the role of transforming growth factor-beta (TGF-β) and insulin-like growth factor (IGF-I/IGF-II) signaling in stromal cells during prostate carcinogenesis and progression. It is known that stromal cells have a reciprocal relationship to the adjacent epithelial cells in the maintenance of structural and functional integrity of the prostate. An interaction between TGF-β and IGF signaling occupies a central part in this stromal-epithelial interaction. An increase in TGF-β and IGF signaling will set off the imbalance of this relationship and will lead to cancer development. A continuous input from TGF-β and IGF in the tumor microenvironment will result in cancer progression. Understanding of these events can help prevention, diagnosis, and therapy of prostate cancer.

Correction: Overexpression of Periostin in Stroma Positively Associated with Aggressive Prostate Cancer

CORRECTION Correction: Overexpression of Periostin in Stroma Positively Associated with Aggressive Prostate Cancer Yuan Tian, Caitlin H. Choi, Qing Kay Li, Farah B. Rahmatpanah, Xin Chen, Sara Ruth Kim, Robert Veltri, David Chia, Zhen Zhang, Dan Mercola, Hui Zhang There are errors in the Funding section. The correct funding information is as follows: This work was supported by National Institutes of Health, National Cancer Institute, the Early De- tection Research Network (EDRN, U01CA152813) to HZ, the Prostate Cancer Biorepository Network, Department of Defense Grant Number W81XWH-10-2-0056, and Early Detection Research Network NCI CA86366 to DC. The funders had no role in study design, data collec- tion and analysis, decision to publish, or preparation of the manuscript. Reference Tian Y, Choi CH, Li QK, Rahmatpanah FB, Chen X, Kim SR, et al. (2015) Overexpression of Periostin in Stroma Positively Associated with Aggressive Prostate Cancer. PLoS ONE 10(3): e0121502. doi:10.1371/journal.pone.0121502 PMID: 25781169 OPEN ACCESS Citation: Tian Y, Choi CH, Li QK, Rahmatpanah FB, Chen X, Kim SR, et al. (2015) Correction: Overexpression of Periostin in Stroma Positively Associated with Aggressive Prostate Cancer. PLoS ONE 10(6): e0130333. doi:10.1371/journal. pone.0130333 Published: June 4, 2015 Copyright:

Airway epithelial cells prime plasmacytoid dendritic cells to respond to pathogens via secretion of growth factors

Plasmacytoid dendritic cells (PDCs) are critical for defense against respiratory viruses because of their propensity to secrete high levels of type I interferons (IFN). The functions of PDCs in the lung can be influenced by airway epithelial cells. We examined the effect of human primary bronchial epithelial cells (PBECs) on PDC functions by performing RNA-sequencing of PDCs after co-culture with air liquid interface differentiated PBECs. Functional analysis revealed that PDCs co-cultured with PBECs displayed upregulation of type I IFN production and response genes. Upregulated transcripts included those encoding cytosolic sensors of DNA, ZBP-1,IRF-3, and NFkB as well as genes involved in amplification of the IFN response, such as IFNAR1, JAK/STAT, ISG15. In keeping with the RNA-seq data, we observe increased secretion of type I IFN and other cytokines in response to influenza in PDCs co-cultured with PBECs. The PDCs also primed Th1 responses in T cells. The enhanced response of PDCs co-cultured with PBECs was due to the action of growth factors, GMCSF, GCSF, and VEGF, which were secreted by PBECs on differentiation. These data highlight possible mechanisms to enhance the production of type-I IFN in the airways, which is critical for host defense against respiratory infections.

Transcriptional Profiling of Age-Associated Gene Expression Changes in Human Circulatory CD1c+ Myeloid Dendritic Cell Subset

Immune dysfunction is a hallmark of aging and is thought to be responsible for the age-associated diseases. Dendritic cells (DCs) of the immune system function as initiators and regulators of the immune responses. Recent studies have highlighted the division of labor between various DC subsets. CD1c+ DC subset has emerged as a major inducer of CD4 T cell response. There is a scarcity of information regarding the age-associated changes in the functions of DC subsets in the elderly. Here, we investigated the changes in transcriptional profile of CD1c+ DC subset from healthy aged and young individuals using RNA sequencing. Our results suggest that majority of the genes in DCs are upregulated with age. Glucose transport, GPCR, and potassium channel genes are all upregulated in DCs from aged as compared to young indicating an enhanced activation state of DCs from aged individuals. The expression of histones, small nucleolar RNA H/ACA box (SNORA) and small nucleolar RNA C/D/box (SNORD), and long non-coding RNA (lncRNA) is also substantially upregulated in the DCs from aged. In contrast, the antigen-presenting and energy generating pathways are downregulated. In summary, DCs from aged subjects display an activated state coupled with reduced antigen presentation which may be responsible for age-associate immune dysfunction.

Abstract B04: The use of whole genome methylation scanning to define genes preferentially suppressed in African American Prostate Cancer

Background: The incidence of prostate cancer (PCa) is approximately 60% higher, and the mortality rate is 2 to 3 times greater, among African American men (AA) resident in the U.S. compared with American men with a European background (EA). Men of West African ancestry from the Caribbean and South America share similar incidence and mortality to AAs, suggesting a possible genetic and/or epigenetic contribution to these outcomes. We previously carried out a sensitive expression analysis comparing the expression of both tumor and tumor-adjacent stroma of AA and EA patients by Paired T-tests for matched (age, stage, grade, tumor content, stroma content) patients (1). Remarkably, 92% of expression differences between AA and EA occurred in the stroma component and of these 97% were in the direction of lower expression in AA compared to EA patients. The downregulated genes were dominated by immune regulatory and extracellular matrix genes. This suggests the possibility that decreased immune and inflammatory processes in tumor-adjacent stroma may be responsible for the aggressive nature of prostate cancer in AA patients (1). We have begun to test this possibility by examining whether the down regulated genes are methylated. Methods: Whole Genome FFPE Methylation and Transcriptome Profiling. We used archived FFPE samples where there is a potential to examine many patients. We are in the early methods-development stage of this project. One AA and two EA prostate cancer cases were matched for age, time to relapse/survival and Gleason score. A Norgen kit was used to obtain both DNA and RNA from the same sample. Methylated regions in each case were captured by methylated DNA ChIP using the human Methyl-CpG Binding Domain-2 (hMBD2) protein MBD2 then DNA purification followed by NGS. The transcriptome of tumor stroma was obtained using the TruSeq RNA access kit which is based on capture oligonucleotides to each exon to enrich RNA of coding regions. The method works for low amounts of highly degraded RNA isolated from low quality samples. The kit includes > 425,000 probes each constructed against NCBI377 hg19 reference genome, covering 98.3% of the RefSeq exome. The probes capture >210,000 targets, spanning 21,415 genes. Results: Our preliminary data show that one AA tumor-adjacent stroma has a greater number of hypermethylated regions than in tumor-adjacent stroma of two matched EA patients. Genome-wide methylation analysis revealed 5217 sites of significant (p Our studies provide the first joint methylation and transcriptome analysis of PCa patients using FFPE samples and support the possibility that epigenetic alterations might be responsible for deficiencies in tumor immunity in AA PCa. Reference: Kinseth M, A., Z. Jia, et al., 2014. Int. J. Cancer 134: 81-91. DOI: 10.1002/ijc.28326. Citation Format: Farah Rahmatpanah, Kathleen McGuire, Michael Lilly, Michael McClelland, Dan Mercola. The use of whole genome methylation scanning to define genes preferentially suppressed in African American Prostate Cancer. [abstract]. In: Proceedings of the Ninth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2016 Sep 25-28; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(2 Suppl):Abstract nr B04.

Abstract 1973: HER2 promotes super enhancer formation in breast cancer

Background. HER2 positive (HER2+) breast cancer (BCa) occurs in 25-30% of cases and is associated with an aggressive phenotype. Multiple HER2-stimulated pathways are known but the genes that are specifically controlled by HER2 are poorly understood. We previously used RNA polymerase II (POL II) immunoprecipitation (ChIP) to identify 737 genes that bind POL II in HER2+ but not in HER2- BCa cell lines. 51 of these genes were differentially expressed in a HER2+ dependent manner and 113 genes were differentially expressed in a HER2-dependent manner in breast tumors from 812 patients. The 113 genes are not differentially expressed in cell lines but expressed in BCa cases can be considered “poised” for expression, and activated when in the context of a breast tumor. Methods. The 737 genes were examined using pathway finding tools and the MIT database for Super-enhancers and the dbSUPER database (2). The expression of certain genes in attached cell cultures and in mammosphere cultures were examined by qPCR. Results. Some of the 113 genes that are “poised” for expression in cell lines in a HER2-dependent manner, and then expressed in breast cancers in a HER2-dependent manner, encode proteins that commonly occur in Super-enhancers, which are a variety of DNA regulatory structures formed by looping that associate multiple bound transcription and DNA-modifying factors in proximity to target promoters. Examples include Mediator12 (MED12) and the Bromdomain protein 2 (BRD2), and DNA binding factors such as HDAC and CREB1-cofactors. Moreover, many pluripotency genes are also found in the list of 113 genes; NANOG, OCT3/4, and SOX2 (NOS genes). These genes commonly interact with SEs as associated factors and as targets. We confirmed by qPCR that the three NOS genes exhibit increased expression in mammosphere of HER2+ MCF7 cells but not in control cells. Using the MIT and dbSUPER (2) database we determined that 70 genes of the 113 genes (62%) that are located in or near SEs, including some present in the HER2+ BCa cell line HCC1954. Similarly, 33 (65%) of the 51 differentially expressed genes in HER2+ cell lines were associated with SEs in many diverse cell types. For comparison, the class of 573 genes that are bound to POL II in HER2+ cell lines, but are not differentially expressed in cell lines or breast cancer tissues, have a much lower fraction of genes, 213 or 37%, that encode potential members or targets of SE. The difference is highly significant, p = 0.008. Pathway analyses of the 113 genes indicates that major pathways enriched in these genes (p Conclusion. These results suggest that the pathways associated with HER2 over-expression in BCa include genes associated with Super-enhancer assembly, as well as many genes encoded close to the location of Super-enhancers. 1.Rahmatpanah et al,. (2015) Oncotarget 2. Aziz Khan, Nucleic Acids Res., 2015 Citation Format: Farah Rahmatpanah, Zhenyu Jia, Yuanjie Hu, Frank Jones, Michael McClelland, Dan Mercola. HER2 promotes super enhancer formation in breast cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1973.

Abstract 1982: The HER2 Regulon:Identification of 113 genes that are directly controlled by HER2 and define four nodes of cancer stem cell networks

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA We recently developed a combination method using RNA polymerase II immunoprecipitation together with expression analysis in order to identify genes regulated by the HER2 oncogene: the HER2 Regulon. We identified 737 genes which are poised for transcription. 113 of these genes are differentially expressed (p < 0.05) in HER2+/- as determined by analysis of available expression arrays of 812 breast cancer tissues. Thus for these genes, expression may depend upon features of the in situ organization and/or features of the microenvironment in addition to the action of high HER2. We further noted that 58/113 genes are members of pathways related by four “nodes”: NANOG, SOX2, OCT3/4, and CREB1.We hypothesize that high HER2 expression and factors of breast cancer cells in situ, possibly including microenvironment factors, determine the concerted expression of the four node proteins and the 58 associated genes. These genes may participate in the formation of CSC cells or cells with enhanced “stemness” in high HER2-expressing breast cancer. Methods The HER2+/- breast cancer cell lines were grown in medium supplied with FBS, the sub-cultures were depleted from FBS and cultured on ultra-low adherent plate supplemented with EGF/FGF/B27. The formed spheres were collected and used for expression analysis of ALDH1, SOX2, NANOG and OCT3/4 and 58 microenvironment dependent genes employing quantitative real time PCR. Immunofluorescence staining was used to measure protein expression levels of ALDH1, NANOG, SOX2 and OCT3/4. Results We used PCR to compare the amount of transcripts formed in high- and low-HER2 expression cells both as attached cultures and as “mammospheres”. We observed all four node transcript levels were strikingly higher in mammospheres of HER2-expressing cells compared to attached cultures or compared to HER2-null control cells. We used immunofluorescence to confirm that the corresponding protein levels were also increased. Thus, in the context of mammospheres, node genes exhibit HER2-dependent and greatly increased expression analogous to the conditions required for the 113 genes. This observation suggests that the anchorage-independent growth mode of mammospheres growing in factor-supplemented medium may replicate key features of cancer cells in situ not observed in attached cultures. If so, mammospheres may provide an in vitro model for the dissection of the key in situ and microenvironment features influencing HER2-dependent expression in breast cancer. Conclusion A “poised” class of genes in HER2+ cell lines has POLII binding and low RNA expression and is differentially expressed in primary tumors in a HER2-dependent manner. These genes include key genes with a role in stem cell proliferation. These genes become expressed when cell lines are grown as three dimensional mammospheres. These observations strongly suggest a role of 3D structure. Note: This abstract was not presented at the meeting. Citation Format: Farah Rahmatpanah, Yuanjie Hu, Xin Chen, Michael McClelland, Frank Jones, Dan Mercola. The HER2 Regulon:Identification of 113 genes that are directly controlled by HER2 and define four nodes of cancer stem cell networks. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1982. doi:10.1158/1538-7445.AM2015-1982