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    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2022
    In:  Cancer Research Vol. 82, No. 12_Supplement ( 2022-06-15), p. 5858-5858
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 5858-5858
    Abstract: Background: Effective biomarkers are urgently needed in the clinical settings. However, most biomarkers are currently developed from single type of omics data. The goal of this study is to identify prognostic prostate cancer signatures using transcriptomics and metabolomics profiles jointly aiming to capture wider spectrum of biological information. Methods: In this study, we included 94 tumor and 48 adjacency normal samples with both transcriptomics and metabolomics profiles from Dana-Farber/Harvard Cancer Center SPORE Prostate Cancer Cohort. There were 85 patients being followed up with median length of 2.02 years including 3 lethal and 8 progression cases. We first constructed a multi-omics covering network that contained minimal set of variable pairs but sufficiently rich to account for observed inter-patient variations. The network was built on known gene-metabolite interaction pairs from Pathway Commons as prior knowledge. Next, we used a diffusion process with each of connected gene-metabolite pairs as seeds to identify candidate signatures in the network. Signature sizes were controlled under 5 by adjusting the tree height during pruning. Hierarchical clustering and survival analyses were then performed to stratify patients into two risk groups for disease-free survival probability. Only the prognostic signatures with power higher than 0.9 from bootstrapping were kept for external validation and functional analyses. Since to our best knowledge, there is no publicly available dataset with both transcriptomics and metabolomics to validate the multi-omics signature we identified. We thus trained a rank-based kTSP classifier using gene expression data in SPORE cohort as a surrogate signature and validated it in TCGA prostate cancer cohort independently. Results: Constructed covering network consisted of 12 metabolites and 54 genes with inter-patient heterogeneities being captured efficiently. We identified one high-powered multi-omics signature (Gene: EGLN3; Metabolites: succinate, trans-4-hydroxyproline) that exhibited good prognostic value where high-risk patients had significant less time of disease-free survival (log rank test: p=0.019, power: 0.974). In addition, genomic variations were observed in different percentages of patients in high and low risk groups including NCOR1(SNV, 70.6% vs 96.3% p=0.025), NKX3.1(CNV, 29.6% vs 64.7% p=0.031) and TNFRSF10C (CNV, 29.6% vs 64.7%, p=0.031). No evidence indicated the patient grouping by the signature depend on Gleason scores (p=0.44). The surrogated gene signature contained 6 pairs of genes that can effectively classify TCGA patients into two prognostic groups (log rank test: p=0.048). Still, no evidence indicated the surrogate gene signature is associated with Gleason score (p=0.23) in TCGA dataset. Conclusions: We identified a prognostic multi-omics signature (EGLN3, succinate, trans-4-hydroxyproline) with high statistical power. Citation Format: Zhuoran Xu, Elisa Benedetti, Ryan Carelli, Jacob Rosenthal, Hubert Pakula, Mohamed Omar, Renato Umeton, David Brundage, Jan Krumsiek, Massimo Loda, Luigi Marchionni. A multi-omics signature for patients’ risk classification in prostate cancer [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 5858.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 2
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 1343-1343
    Abstract: Mesenchymal cells in the prostate cancer (PCa) tumor microenvironment (TME) contribute to the biological and clinical history of PCa. Indeed, mesenchymal cells heavily interact with cancer cells, immune cells, and the other cellular and non-cellular components of the TME to favor or hinder carcinogenesis and tumor progression. Using a comprehensive array of genetically engineered mouse models (GEMMs) of prostate cancer, 8 mesenchymal populations with different transcriptional programs are preferentially enriched in specific GEMMs at different stages of PCa. Here, we determine the transferability of this mesenchymal cluster designation from mice PCa models to human PCa cases. To this end, we compared: a) Tmprss2-ERG (T-ERG) mouse and ERG+ human cases; b) Pb4-Cre+/-;Ptenf/f;LSL-MYCN+/+;Rb1f/f (PRN) mouse and PCa bone metastasis. We generated scRNA-seq data for & gt; 8000 mesenchymal cells from ERG+ (n=6) and ERG- (n=3) PCa patients, and we retrieved data for bone metastasis mesenchymal cells (osteoblasts, osteoclasts, endothelial cells, pericytes; 1,872 total cells) from GSE143791. To transfer the stromal mouse clusters’ labels to human data, human gene symbols were converted to their mouse counterparts, then both datasets were restricted to overlapping genes. For the human PCa cases, label transfer was performed through ‘ingest’ using the scRNA-seq data from the mouse T-ERG model as reference for the human ERG+ cases and data from the remaining GEMMs as reference for the human ERG- cases. For bone metastases cases, mouse stromal data from all GEMMs were used to project the 8 stromal clusters to the mesenchymal cells in the bone metastases microenvironment. Not surprisingly, ERG+ human samples were enriched ( & gt; 60% of total stromal cells) in mouse stroma clusters predominantly present in T-ERG mouse model, characterized by the expression of Wnt regulators and AR. Common populations to all murine models, representing myofibroblasts and immunomodulatory fibroblasts (expressing Gpx3, C3, C7, Cfh), were also commonly present in patients, irrespectively to the ERG status. In the PCa bone metastases, mesenchymal clusters enriched in the PRN model were strongly represented in human bone metastases, comprising & gt; 60% of total stromal cells. These cells were characterized by high expression of POSTN and MKI67, as well as bone-specific genes like BGN. Altogether, these findings suggest that our mesenchymal cluster designation developed using GEMMs can be meaningfully applied to human PCa, and that the different transcriptional programs we identified in distinct mesenchymal population are conserved across species. This lays the foundation for the utilization of defined genetically-engineered models in defining the interactions and cross-talks between different mesenchymal populations in relation to cancer and immune cells and other components of the TME in human prostate cancer. Citation Format: Mohamed Omar, Hubert Pakula, Filippo Pederzoli, Giuseppe N. Fanelli, Tania Panellinni, Ryan Carelli, Silvia Rodrigues, Caroline Fidalgo-Ribeiro, Pier V. Nuzzo, Lucie V. Emmenis, Mohammad Mohammad, Madhavi Jere, Caitlin Unkenholz, David Rickman, Christopher Barbieri, Brian Robinson, Luigi Marchionni, Massimo Loda. Mesenchymal cell populations associated with different stages of prostate cancer progression in mice and human [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1343.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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    Location Call Number Limitation Availability
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  • 3
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 3816-3816
    Abstract: Prostate cancer has a heterogeneous prognosis, and genetic alterations alone do not fully explain clinical behavior. We previously characterized the stroma of localized human prostates by Laser Capture Microdissection, and found that stroma was substantially different in prostates with and without tumor. Furthermore, a stromal gene signature reflecting bone remodeling was upregulated in high compared to low Gleason grade cases. To determine how stromal cells contribute to carcinogenesis and progression we study whether specific genetic alterations in the epithelium induce unique stromal changes. To do this, we utilized Genetically Engineered Mouse Models (GEMMs) representing common prostate cancer mutations and compared these to their wild-type conterparts: the Tmprss2-ERG fusion knock-in murine model induces histological alterations in the stroma in the absence of an epithelial phenotype; the Pten deletion mouse model (PtenKO) results in prostate intraepithelial neoplasia (PIN) but not invasive cancer; the Hi-Myc GEMM, leads to PIN and subsequently invasion; and the Pb4-Cre +/-;Pten f/f; LSL-MYCN +/+; Rb1 f/f (MNRPDKO) mouse model that leads to neuroendocrine prostate cancer (NEPC). We generated a comprehensive single-cell transcriptomic atlas of the mouse prostate cancer mesenchyme in these models. Using deep generative modeling followed by graph-based clustering and gene regulatory network inference, six (6) distinct subsets of fibroblasts and two (2) subsets of smooth muscle cells (myofibroblasts and pericytes) were identified. Notably, some subsets were common across all GEMMs and WT mice, while others aligned with specific genotypes. Moreover, we found a variable pattern of positive and negative Ar expressing cells between genotypes. Analysis by CellphoneDB of mesenchymal-epithelial communications revealed the complex cross-talk between mutated epithelial cells and the tumor microenvironment. Multiplex immunofluorescence phenotyping of mesenchymal cell confirmed the cluster subtypes by both expression and spatial location. Finally, stromal transcripts defining mesenchymal cluster subtypes associated with Tmprss2-ERG were conserved between mouse and human genotypes.This study lays the groundwork for understanding and ultimately targeting stromal-epithelial interactions in prostate cancer. Citation Format: Hubert Pakula, Ryan Carelli, Nicolo Fanelli, Madhavi Jere, Caitlin Unkenholz, Mohamed Omar, Caroline Ribeiro- Fidalgo, Filippo Pederzoli, Cory Abate-Shen, David S. Rickman, Brian Robinson, Luigi Marchionni, Massimo Loda. Functional atlas of prostate mesenchyme [abstract]. In: Proceedings of the Ame rican Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3816.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Location Call Number Limitation Availability
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