Supplementary Table 4 from Tumor Immunobiological Differences in Prostate Cancer between African-American and European-American Men

Supplementary Table 5 from Tumor Immunobiological Differences in Prostate Cancer between African-American and European-American Men

Supplementary Table 1. Characteristics of the African-American prostate cancer patients in the Cleveland Clinic Cohort according to overall survival; Supplementary Table 2. IRDS Probesets (n=49) for the Affymetrix U133A 2.0 array

Supplementary Table 10: Genes differently expressed between nicotine-treated and untreated LNCaP cells.

Supplementary Figure Legends 1-3 from Tumor Immunobiological Differences in Prostate Cancer between African-American and European-American Men

Supplementary Table 1 from Tumor Immunobiological Differences in Prostate Cancer between African-American and European-American Men

Legends for Supplementary Figures 1-6 from Genomic Profiling of MicroRNA and Messenger RNA Reveals Deregulated MicroRNA Expression in Prostate Cancer

Supplementary Table 3 from Tumor Immunobiological Differences in Prostate Cancer between African-American and European-American Men

Supplementary Table S1: Clinical characteristics of the study population by smoking status. Supplementary Table S2: Immunoglobulin expression in prostate tumors by smoking status of the patients. Supplementary Table S3: Genes differently expressed between current and never smokers in prostate tumors. Supplementary Table S4: Genes differently expressed between current and never/past smokers in 67 prostate tumors.

Supplemetary Table 8: Genes differently expressed between current and never in microdissected tumors using the Bioconductor limma R package.

Supplementary Figure S1: Weight curves for untreated TRAMP mice and TRAMP mice treated with nicotine for 80 days. Supplementary Figure S2: Ig lambda†and Ig kappaâ€positive B lymphocytes in cancerous prostate tissue by inâ€situ hybridization. Supplementary Figure S3: qRTâ€PCR validation of six genes upâ€regulated among current smokers. Supplementary Figure S4: Analysis of prostate tumors using Bioconductor limma R. Supplementary Figure S5: Gene Set Enrichment Analysis (GSEA) highlighting common features between smokingâ€related gene signatures in prostate tumors, nicotineâ€induced gene signatures in human prostate cancer cells, and gene signatures archived in the GSEA database. Supplementary Figure S6: Expression of various nicotinic acetylcholine receptor (AChR) subunits in human prostate cancer cell lines, prostate tumors, and adjacent nonâ€tumor prostate tissues. Supplementary Figure S7: Nicotineâ€mediated Akt activation in human immortalized prostate epithelial cells and prostate cancer cell lines. Supplementary Figure S8: Effect of nicotine on the mobility of human prostate cancer cell lines. Supplementary Figure S9: Nicotine enhances cell surface integrin expression and extracellular matrix binding (ECM) of 22Rv1 human prostate cancer cells. Supplementary Figure S10: Nicotine enhances proliferation of RWPEâ€1 in complete Kâ€SFM medium but not in Kâ€SFM medium without EGF and bovine pituitary extract. Supplementary Figure S11: Nicotine did not enhance proliferation of 22Rv1 and PCâ€3 human prostate cancer cells. Supplementary Figure S12: Lymphotoxinâ€Î² plasma levels in prostate cancer patients (Case) and populationâ€based controls (Control) by smoking status.

Supplementary Figure 1. Expression of IFNL4 after transfection of human prostate cancer cell lines with the IFNL4-Halo construct; Supplementary Figure 2. Expression of two interferon signatures, IRG and IRDS, in cultured prostate cancer epithelial cells from 14 African-American and 13 European-American men; Supplementary Figure 3. Association of IFNL4 SNP rs12979860-T allele with decreased overall survival among African-American prostate cancer patients in the Cleveland Clinic cohort (n = 197).

Supplementary Notes from Genomic Profiling of MicroRNA and Messenger RNA Reveals Deregulated MicroRNA Expression in Prostate Cancer

Patient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H&E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of large human H&E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000 PDX and paired human progenitor H&E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions and to predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site. This repository enables PDX-specific, investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues.

Supplementary Figure 2 from Tumor Immunobiological Differences in Prostate Cancer between African-American and European-American Men

Patient-derived xenografts (PDXs) recapitulate intratumoral spatial heterogeneity and simulate a tumor microenvironment in which human immune and stromal cells in the PDX are replaced over passages by murine cells partially lacking immune function. Histological imaging enables exploring the spatial heterogeneity and dynamics of cancer, stromal, and immune cell interactions as correlates of tumor stage and therapeutic response over passages. We created a repository of curated, haematoxylin and eosin (H&E) images as a community resource for addressing these questions. Images were generated at five sites within the NCI’s PDX Development and Trial Centers Research Network (PDXNet) and the NCI Patient-Derived Models Repository. Over 900 images, including 739 from PDXs and 190 from paired patients, are hosted on the Seven Bridges Genomics Cancer Genomics Cloud. They represent 42 cancer subtypes, including breast cancer (n=134), colon adenocarcinoma (COAD; n=94), pancreatic cancer (n=87), lung adenocarcinoma (LUAD; n=80), melanoma (n=71), and squamous cell lung cancer (LUSC; n=65). Paired human/PDX images are available for each of these cancers. Human and/or PDX images generated following patient treatment are available for 37 of the subtypes. Most images are from early passages (P0: 158; P1: 292; P2: 152; P3: 69; >P3: 55). Annotations include sex, age, race, ethnicity, and, for most images, pathological assessment of tissue-level percent cancer, stromal, and necrotic cell content (n=639) and tumor stage (n=650). RNA and exome sequencing data are available for 99 and 228 images, respectively, matched at the patient or sample level. Quality control was performed using HistoQC. Cells were segmented and labeled as neoplastic, necrotic, immune, stromal, or other using Hover-Net and predictions of total neoplastic cell area correlated with whole-slide pathological assessment of cancer cell percentage (COAD: r=0.51; LUSC: r=0.59). HD-Staining, another classification approach, was applied to a subset of images and our clinical annotations will facilitate validation of this and related methods. Features of 512 x 512 pixel tiles were computed using the Inception V3 convolutional neural network pre-trained on ImageNet. Unsupervised clustering of these features demonstrate inter-patient heterogeneity within pathologist-annotated tumor regions. A classifier developed using pathologist-annotated cancer, stromal, and necrotic regions and trained on the features in LUSC images (n=10 images) achieved a cross-validation accuracy of 96% for cancer tiles across (n=5) LUAD images. Accuracy was lower for stromal classification (90%), likely reflecting current limitations of our small, but growing, labeled training set. Our repository of clinically-annotated PDX H&E images should aid the community in studying spatial heterogeneity and in training deep learning-based image analysis methods. Citation Format: Brian S. White, Xingyi Woo, Soner Koc, Todd Sheridan, Steven B. Neuhauser, Akshat M. Savaliya, Lacey E. Dobrolecki, John D. Landua, Matthew H. Bailey, Maihi Fujita, Kurt W. Evans, Bingliang Fang, Junya Fujimoto, Maria Gabriela Raso, Shidan Wang, Guanghua Xiao, Yang Xie, Sherri R. Davies, Ryan C. Fields, R Jay Mashl, Jacqueline L. Mudd, Yeqing Chen, Min Xiao, Xiaowei Xu, Melinda G. Hollingshead, Shahanawaz Jiwani, PDXNet Consortium, Yvonne A. Evrard, Tiffany A. Wallace, Jeffrey A. Moscow, James H. Doroshow, Nicholas Mitsiades, Salma Kaochar, Chong-xian Pan, Moon S. Chen, Luis G. Carvajal-Carmona, Alana L. Welm, Bryan E. Welm, Michael T. Lewis, Ramaswamy Govindan, Li Ding, Shunqiang Li, Meenhard Herlyn, Michael A. Davies, Jack A. Roth, Funda Meric-Bernstam, Carol J. Bult, Brandi Davis-Dusenbery, Dennis A. Dean, Jeffrey H. Chuang. A repository of PDX histology images for exploring spatial heterogeneity and cancer dynamics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1202.

We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols and training materials for processing PDX data. Currently, the portal contains PDXNet model information and data resources from 334 newmodels across 33 cancer types. Tissue samples of these models were deposited in the NCI's Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials.

Abstract We created the PDX Network (PDXNet) Portal to provide an intuitive way for researchers to explore and understand the models, sequencing data, and bioinformatics workflows generated by NCI's PDXNet consortium for research access (https://portal.pdxnetwork.org/). The portal also provides metrics for PDXNet's activities, data processing protocols, and training materials for processing PDX data. The PDXNet Portal highlights model and data resources that include 216 new models across 29 cancer types. The most prevalent cancers represented in the PDX model dataset include invasive breast carcinoma (30.6%), melanoma (18.1%), and adenocarcinoma (14.4%). PDXNet teams have provided 2263 sequencing files from 356 samples across 204 patients, comprising whole exome (82.9%) and RNA seq files (17.1%). The most prevalent cancers represented in the PDXNet sequencing data set include Breast Pleural Effusion (27.2%), Breast Poorly Differentiated (12.5%), and Lung Adenocarcinoma (9.6%). The portal also provides access to 9492 sequencing files across 78 disease types that include 2594 samples across 463 patients uploaded from the NCI Patient-Derived Model Repository. The dataset includes both whole exomes (52.8%) and RNA seq (47.2%) data. The PDMR samples include PDX (82.7%), primary tumor (5.7%), normal germline (5.5), organoid culture (3.2), and Mixed Tumor Culture (2.9). The PDMR dataset also has multiple passages: P0 (21.8%), P1(39.5%), P2 (25.6%), and P3 (8.5%). These models and data resources support ten PDXNet Pilot activities, multiple publications, and international collaborations. PDXNet has also developed a set of 13 robust, validated, and standardized workflows for processing PDXNet whole-exome and RNA seq data. Collectively, these workflows allow for the standardized processing of PDX and complementary human tissues (normal and tumor). Our plan is to continuously update the model and data lists on the PDX portal as resources are generated. We expect that the PDXNet generated models, scheduled to grow to 1000 new models by 2022, will support multi-agent treatment studies, determination of mechanisms of sensitivity and resistance, and pre-clinical trials for example through the COMBO-MATCH program. The robust standard workflows currently processing all PDX sequencing data may also facilitate harmonizing data across studies. Lastly, we expect that the generated sequencing data will support computational approaches for studying cancer evolution and the mechanisms underlying cancer treatments. Citation Format: Soner Koc, Mike Lloyd, Steven Neuhauser, Javad Noodbakhsh, Anuj Srivastava, Xing Yi Woo, Ryan Jeon, Jeffrey Grover, Sara Seepo, Christian Frech, Jack DiGiovanna, PDXNet Consortium, Yvonne A. Evard, Tiffany Wallace, Jeffrey Moscow, James H. Doroshow, Nicholas Mitsuade, Salma Kaochar, Chong-xian Pan, Moon S. Chen, Luis Carvarjal-Carmona, Alana Welm, Bryan Welm, Michael T. Lewis, Govindan Ramaswamy, Li Ding, Shunquang Li, Meenherd Herlyn, Mike Davies, Jack Roth, Funda Meric-Bernstam, Peter Robinson, Carol J. Bult, Brandi Davis-Dusenbery, Dennis A. Dean, Jeffrey H. Chuang. Advancing PDX research through model, data, and bioinformatics with the PDXNet Portal [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3017.

Abstract Purpose: Men of African ancestry experience an excessive prostate cancer mortality that could be related to an aggressive tumor biology. We previously described an immune-inflammation signature in prostate tumors of African-American (AA) patients. Here, we further deconstructed this signature and investigated its relationships with tumor biology, survival, and a common germline variant in the IFNλ4 (IFNL4) gene. Experimental Design: We analyzed gene expression in prostate tissue datasets and performed genotype and survival analyses. We also overexpressed IFNL4 in human prostate cancer cells. Results: We found that a distinct interferon (IFN) signature that is analogous to the previously described “IFN-related DNA damage resistance signature” (IRDS) occurs in prostate tumors. Evaluation of two independent patient cohorts revealed that IRDS is detected about twice as often in prostate tumors of AA than European-American men. Furthermore, analysis in TCGA showed an association of increased IRDS in prostate tumors with decreased disease-free survival. To explain these observations, we assessed whether IRDS is associated with an IFNL4 germline variant (rs368234815-ΔG) that controls production of IFNλ4, a type III IFN, and is most common in individuals of African ancestry. We show that the IFNL4 rs368234815-ΔG allele was significantly associated with IRDS in prostate tumors and overall survival of AA patients. Moreover, IFNL4 overexpression induced IRDS in three human prostate cancer cell lines. Conclusions: Our study links a germline variant that controls production of IFNλ4 to the occurrence of a clinically relevant IFN signature in prostate tumors that may predominantly affect men of African ancestry. Clin Cancer Res; 24(21); 5471–81. ©2018 AACR.

Abstract Not all segments of the U.S. population have equally benefited from the advances in our knowledge and treatment of cancer. As a result, African-American (AA) men still have the highest prostate cancer mortality among all U.S. racial and ethnic groups. In a recent study, we examined the tumor biology of prostate cancer comparing AA patients with European-American (EA) patients using large scale gene expression profiling and found significant differences in gene expression that are consistent with race/ethnic differences in the tumor microenvironment and immunobiology. Intriguingly, an interferon gene signature was detected in AA prostate that may relate to an unknown etiologic agent in disease pathology. This signature occurred commonly in AA men in two independent patient cohorts and may influence therapeutic outcome because it is analogous to a recently discovered interferon-related DNA damage resistance signature (termed IRDS), which predicts resistance to chemotherapy and radiation in breast cancer and perhaps other epithelial cancers. Here, we assessed whether the development of this signature could be functionally linked to a germline variant allele (rs368234815-ΔG) that is frequently found in subjects of African ancestry (~65% allele frequency) but is less common in subjects of European ancestry (~30% allele frequency). Carriers of ΔG can express a recently discovered interferon, termed interferon lambda 4 (IFNL4), while the rs368234815-TT allele eliminates expression. We genotyped DNA from tumors that have previously been characterized for presence of the interferon signature and found that the ability to generate IFNL4 (in carriers of the ΔG allele) was significantly associated with the presence of this signature (P < 0.001, n = 44), indicating that IFNL4 expression may be involved. In summary, our study links a germline genetic variant in IFNL4 to the occurrence of a clinically relevant interferon signature in prostate tumors of African-American men. Future research will assess how this genetic variant may influence disease outcome. Citation Format: Symone Jordan, Wei Tang, Tiffany Wallace, Tiffany Dorsey, Ming Yi, Robert Stephens, Ludmila Prokunina-Olsson, Stefan Ambs. An interferon λ 4 genotype is linked to a gene expression signature in prostate tumors of African American men. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr B51.