44 results on '"Weinstein, Alana S."'
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2. Genomic and transcriptomic features of androgen receptor signaling inhibitor resistance in metastatic castration-resistant prostate cancer
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Zhu, Xiaolin, Farsh, Tatyanah, Vis, Daniel, Yu, Ivan, Li, Haolong, Liu, Tianyi, Sjostrom, Martin, Shrestha, Raunak, Kneppers, Jeroen, Severson, Tesa, Zhang, Meng, Lundberg, Arian, Rodriguez, Thaidy Moreno, Weinstein, Alana S., Foye, Adam, Mehra, Niven, Aggarwal, Rahul R., Bergman, Andries M., Small, Eric J., Lack, Nathan A., Zwart, Wilbert, Quigley, David A., van der Heijden, Michiel S., and Feng, Felix Y.
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Oncology, Experimental ,Prostate cancer -- Development and progression -- Genetic aspects -- Drug therapy ,Metastasis -- Development and progression -- Genetic aspects -- Drug therapy ,Drug resistance -- Genetic aspects ,Cellular signal transduction -- Research ,Genetic transcription -- Research ,Cancer -- Research ,Health care industry - Abstract
BACKGROUND. Androgen receptor signaling inhibitors (ARSIs) have improved outcomes for patients with metastatic castration-resistant prostate cancer (mCRPC), but their clinical benefit is limited by treatment resistance. METHODS. To investigate the mechanisms of ARSI resistance, we analyzed the whole-genome (n = 45) and transcriptome (n = 31) sequencing data generated from paired metastatic biopsies obtained before initiation of first-line ARSI therapy for mCRPC and after radiographic disease progression. We investigated the effects of genetic and pharmacologic modulation of SSTR1 in 22Rv1 cells, a representative mCRPC cell line. RESULTS. We confirmed the predominant role of tumor genetic alterations converging on augmenting androgen receptor (AR) signaling and the increased transcriptional heterogeneity and lineage plasticity during the emergence of ARSI resistance. We further identified amplifications involving a putative enhancer downstream of the AR and transcriptional downregulation of SSTR1, encoding somatostatin receptor 1, in ARSI-resistant tumors. We found that patients with SSTR1-low mCRPC tumors derived less benefit from subsequent ARSI therapy in a retrospective cohort. We showed that SSTR1 was antiproliferative in 22Rv1 cells and that the FDA- approved drug pasireotide suppressed 22Rv1 cell proliferation. CONCLUSION. Our findings expand the knowledge of ARSI resistance and point out actionable next steps, exemplified by potentially targeting SSTR1, to improve patient outcomes. FUNDING. National Cancer Institute (NCI), NIH; Prostate Cancer Foundation; Conquer Cancer, American Society of Clinical Oncology Foundation; UCSF Benioff Initiative for Prostate Cancer Research; Netherlands Cancer Institute., Introduction Metastatic castration-resistant prostate cancer (mCRPC) is the lethal form of prostate cancer (PCa) (1). Androgen receptor signaling inhibitors (ARSIs), such as abiraterone and enzalutamide, have improved outcomes for patients [...]
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- 2024
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3. The genomic and epigenomic landscape of double-negative metastatic prostate cancer
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Lundberg, Arian, Zhang, Meng, Aggarwal, Rahul, Li, Haolong, Zhang, Li, Foye, Adam, Sjöström, Martin, Chou, Jonathan, Chang, Kevin, Moreno-Rodriguez, Thaidy, Shrestha, Raunak, Baskin, Avi, Zhu, Xiaolin, Weinstein, Alana S, Younger, Noah, Alumkal, Joshi J, Beer, Tomasz M, N., Kim, Evans, Christopher P, Gleave, Martin, Lara, Primo N, Reiter, Rob E, Rettig, Matthew B, Witte, Owen N, Wyatt, Alexander W, Feng, Felix Y, Small, Eric J, and Quigley, David A
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Biological Sciences ,Biomedical and Clinical Sciences ,Clinical Sciences ,Oncology and Carcinogenesis ,Genetics ,Cancer Genomics ,Cancer ,Urologic Diseases ,Human Genome ,Precision Medicine ,Biotechnology ,Prostate Cancer ,Good Health and Well Being ,Humans ,Male ,Prostatic Neoplasms ,Castration-Resistant ,Receptors ,Androgen ,Epigenomics ,Androgen Antagonists ,Androgens ,Genomics ,Neuroendocrine Tumors ,Oncology & Carcinogenesis ,Biochemistry and cell biology ,Oncology and carcinogenesis - Abstract
Systemic targeted therapy in prostate cancer is primarily focused on ablating androgen signaling. Androgen deprivation therapy and second-generation androgen receptor (AR)-targeted therapy selectively favor the development of treatment-resistant subtypes of metastatic castration-resistant prostate cancer (mCRPC), defined by AR and neuroendocrine (NE) markers. Molecular drivers of double-negative (AR-/NE-) mCRPC are poorly defined. In this study, we comprehensively characterized treatment-emergent mCRPC by integrating matched RNA sequencing, whole-genome sequencing, and whole-genome bisulfite sequencing from 210 tumors. AR-/NE- tumors were clinically and molecularly distinct from other mCRPC subtypes, with the shortest survival, amplification of the chromatin remodeler CHD7, and PTEN loss. Methylation changes in CHD7 candidate enhancers were linked to elevated CHD7 expression in AR-/NE+ tumors. Genome-wide methylation analysis nominated Krüppel-like factor 5 (KLF5) as a driver of the AR-/NE- phenotype, and KLF5 activity was linked to RB1 loss. These observations reveal the aggressiveness of AR-/NE- mCRPC and could facilitate the identification of therapeutic targets in this highly aggressive disease.SignificanceComprehensive characterization of the five subtypes of metastatic castration-resistant prostate cancer identified transcription factors that drive each subtype and showed that the double-negative subtype has the worst prognosis.
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- 2023
4. Transcriptional profiling of matched patient biopsies clarifies molecular determinants of enzalutamide-induced lineage plasticity.
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Westbrook, Thomas C, Guan, Xiangnan, Rodansky, Eva, Flores, Diana, Liu, Chia Jen, Udager, Aaron M, Patel, Radhika A, Haffner, Michael C, Hu, Ya-Mei, Sun, Duanchen, Beer, Tomasz M, Foye, Adam, Aggarwal, Rahul, Quigley, David A, Youngren, Jack F, Ryan, Charles J, Gleave, Martin, Wang, Yuzhuo, Huang, Jiaoti, Coleman, Ilsa, Morrissey, Colm, Nelson, Peter S, Evans, Christopher P, Lara, Primo, Reiter, Robert E, Witte, Owen, Rettig, Matthew, Wong, Christopher K, Weinstein, Alana S, Uzunangelov, Vlado, Stuart, Josh M, Thomas, George V, Feng, Felix Y, Small, Eric J, Yates, Joel A, Xia, Zheng, and Alumkal, Joshi J
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Cell Line ,Tumor ,Humans ,Benzamides ,Nitriles ,Phenylthiohydantoin ,Receptors ,Androgen ,RNA ,Biopsy ,Drug Resistance ,Neoplasm ,Male ,E2F1 Transcription Factor ,Androgen Receptor Antagonists ,Prostatic Neoplasms ,Castration-Resistant ,Prostate Cancer ,Genetics ,Urologic Diseases ,Cancer ,Clinical Research ,2.1 Biological and endogenous factors ,Aetiology - Abstract
The androgen receptor (AR) signaling inhibitor enzalutamide (enza) is one of the principal treatments for metastatic castration-resistant prostate cancer (CRPC). Several emergent enza clinical resistance mechanisms have been described, including lineage plasticity in which the tumors manifest reduced dependency on the AR. To improve our understanding of enza resistance, herein we analyze the transcriptomes of matched biopsies from men with metastatic CRPC obtained prior to treatment and at progression (n = 21). RNA-sequencing analysis demonstrates that enza does not induce marked, sustained changes in the tumor transcriptome in most patients. However, three patients' progression biopsies show evidence of lineage plasticity. The transcription factor E2F1 and pathways linked to tumor stemness are highly activated in baseline biopsies from patients whose tumors undergo lineage plasticity. We find a gene signature enriched in these baseline biopsies that is strongly associated with poor survival in independent patient cohorts and with risk of castration-induced lineage plasticity in patient-derived xenograft models, suggesting that tumors harboring this gene expression program may be at particular risk for resistance mediated by lineage plasticity and poor outcomes.
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- 2022
5. Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer
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Guan, Xiangnan, Sun, Duanchen, Lu, Eric, Urrutia, Joshua A, Reiter, Robert Evan, Rettig, Matthew, Evans, Christopher P, Lara, Primo, Gleave, Martin, Beer, Tomasz M, Thomas, George V, Huang, Jiaoti, Aggarwal, Rahul R, Quigley, David A, Foye, Adam, Chen, William S, Youngren, Jack, Weinstein, Alana S, Stuart, Joshua M, Feng, Felix Y, Small, Eric J, Xia, Zheng, and Alumkal, Joshi J
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Biomedical and Clinical Sciences ,Clinical Sciences ,Oncology and Carcinogenesis ,Human Genome ,Urologic Diseases ,Cancer ,Biotechnology ,Prostate Cancer ,Genetics ,Benzamides ,Biomarkers ,Tumor ,Biopsy ,Chromosomes ,Human ,Pair 17 ,DNA Copy Number Variations ,Disease-Free Survival ,Drug Resistance ,Neoplasm ,Humans ,Male ,Nitriles ,Phenylthiohydantoin ,Prostate ,Prostatic Neoplasms ,Castration-Resistant ,RNA-Seq ,Survival Analysis ,Oncology & Carcinogenesis ,Clinical sciences ,Oncology and carcinogenesis - Abstract
PurposeThe purpose of this study was to measure genomic changes that emerge with enzalutamide treatment using analyses of whole-genome sequencing and RNA sequencing.Experimental designOne hundred and one tumors from men with metastatic castration-resistant prostate cancer (mCRPC) who had not been treated with enzalutamide (n = 64) or who had enzalutamide-resistant mCRPC (n = 37) underwent whole genome sequencing. Ninety-nine of these tumors also underwent RNA sequencing. We analyzed the genomes and transcriptomes of these mCRPC tumors.ResultsCopy number loss was more common than gain in enzalutamide-resistant tumors. Specially, we identified 124 protein-coding genes that were more commonly lost in enzalutamide-resistant samples. These 124 genes included eight putative tumor suppressors located at nine distinct genomic regions. We demonstrated that focal deletion of the 17q22 locus that includes RNF43 and SRSF1 was not present in any patient with enzalutamide-naïve mCRPC but was present in 16% (6/37) of patients with enzalutamide-resistant mCRPC. 17q22 loss was associated with lower RNF43 and SRSF1 expression and poor overall survival from time of biopsy [median overall survival of 19.3 months in 17q22 intact vs. 8.9 months in 17q22 loss, HR, 3.44 95% confidence interval (CI), 1.338-8.867, log-rank P = 0.006]. Finally, 17q22 loss was linked with activation of several targetable factors, including CDK1/2, Akt, and PLK1, demonstrating the potential therapeutic relevance of 17q22 loss in mCRPC.ConclusionsCopy number loss is common in enzalutamide-resistant tumors. Focal deletion of chromosome 17q22 defines a previously unappreciated molecular subset of enzalutamide-resistant mCRPC associated with poor clinical outcome.
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- 2020
6. Transcriptional profiling identifies an androgen receptor activity-low, stemness program associated with enzalutamide resistance
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Alumkal, Joshi J, Sun, Duanchen, Lu, Eric, Beer, Tomasz M, Thomas, George V, Latour, Emile, Aggarwal, Rahul, Cetnar, Jeremy, Ryan, Charles J, Tabatabaei, Shaadi, Bailey, Shawna, Turina, Claire B, Quigley, David A, Guan, Xiangnan, Foye, Adam, Youngren, Jack F, Urrutia, Joshua, Huang, Jiaoti, Weinstein, Alana S, Friedl, Verena, Rettig, Matthew, Reiter, Robert E, Spratt, Daniel E, Gleave, Martin, Evans, Christopher P, Stuart, Joshua M, Chen, Yiyi, Feng, Felix Y, Small, Eric J, Witte, Owen N, and Xia, Zheng
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Urologic Diseases ,Genetics ,Prostate Cancer ,Aging ,Cancer ,Development of treatments and therapeutic interventions ,5.1 Pharmaceuticals ,Aged ,Aged ,80 and over ,Antineoplastic Agents ,Benzamides ,Drug Resistance ,Neoplasm ,Gene Expression Profiling ,Humans ,Male ,Middle Aged ,Nitriles ,Phenylthiohydantoin ,Prostate-Specific Antigen ,Prostatic Neoplasms ,Castration-Resistant ,Receptors ,Androgen ,enzalutamide ,resistance ,androgen receptor ,stemness - Abstract
The androgen receptor (AR) antagonist enzalutamide is one of the principal treatments for men with castration-resistant prostate cancer (CRPC). However, not all patients respond, and resistance mechanisms are largely unknown. We hypothesized that genomic and transcriptional features from metastatic CRPC biopsies prior to treatment would be predictive of de novo treatment resistance. To this end, we conducted a phase II trial of enzalutamide treatment (160 mg/d) in 36 men with metastatic CRPC. Thirty-four patients were evaluable for the primary end point of a prostate-specific antigen (PSA)50 response (PSA decline ≥50% at 12 wk vs. baseline). Nine patients were classified as nonresponders (PSA decline
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- 2020
7. Hydra: A mixture modeling framework for subtyping pediatric cancer cohorts using multimodal gene expression signatures.
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Pfeil, Jacob, Sanders, Lauren M, Anastopoulos, Ioannis, Lyle, A Geoffrey, Weinstein, Alana S, Xue, Yuanqing, Blair, Andrew, Beale, Holly C, Lee, Alex, Leung, Stanley G, Dinh, Phuong T, Shah, Avanthi Tayi, Breese, Marcus R, Devine, W Patrick, Bjork, Isabel, Salama, Sofie R, Sweet-Cordero, E Alejandro, Haussler, David, and Vaske, Olena Morozova
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Humans ,Neoplasms ,Neuroblastoma ,Cluster Analysis ,Models ,Statistical ,Gene Expression Profiling ,Computational Biology ,Gene Expression Regulation ,Neoplastic ,Child ,Tumor Microenvironment ,Transcriptome ,Biomarkers ,Tumor ,Precision Medicine ,Models ,Statistical ,Gene Expression Regulation ,Neoplastic ,Biomarkers ,Tumor ,Bioinformatics ,Mathematical Sciences ,Biological Sciences ,Information and Computing Sciences - Abstract
Precision oncology has primarily relied on coding mutations as biomarkers of response to therapies. While transcriptome analysis can provide valuable information, incorporation into workflows has been difficult. For example, the relative rather than absolute gene expression level needs to be considered, requiring differential expression analysis across samples. However, expression programs related to the cell-of-origin and tumor microenvironment effects confound the search for cancer-specific expression changes. To address these challenges, we developed an unsupervised clustering approach for discovering differential pathway expression within cancer cohorts using gene expression measurements. The hydra approach uses a Dirichlet process mixture model to automatically detect multimodally distributed genes and expression signatures without the need for matched normal tissue. We demonstrate that the hydra approach is more sensitive than widely-used gene set enrichment approaches for detecting multimodal expression signatures. Application of the hydra analysis framework to small blue round cell tumors (including rhabdomyosarcoma, synovial sarcoma, neuroblastoma, Ewing sarcoma, and osteosarcoma) identified expression signatures associated with changes in the tumor microenvironment. The hydra approach also identified an association between ATRX deletions and elevated immune marker expression in high-risk neuroblastoma. Notably, hydra analysis of all small blue round cell tumors revealed similar subtypes, characterized by changes to infiltrating immune and stromal expression signatures.
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- 2020
8. Using Transcriptional Signatures to Find Cancer Drivers with LURE.
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Haan, David, Tao, Ruikang, Friedl, Verena, Anastopoulos, Ioannis N, Wong, Christopher K, Weinstein, Alana S, and Stuart, Joshua M
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Humans ,Neoplasms ,Computational Biology ,Mutation ,Machine Learning ,Cancer ,Genomics ,Drivers ,Gene Expression ,Human Genome ,Genetics - Abstract
Cancer genome projects have produced multidimensional datasets on thousands of samples. Yet, depending on the tumor type, 5-50% of samples have no known driving event. We introduce a semi-supervised method called Learning UnRealized Events (LURE) that uses a progressive label learning framework and minimum spanning analysis to predict cancer drivers based on their altered samples sharing a gene expression signature with the samples of a known event. We demonstrate the utility of the method on the TCGA Pan-Cancer Atlas dataset for which it produced a high-confidence result relating 59 new connections to 18 known mutation events including alterations in the same gene, family, and pathway. We give examples of predicted drivers involved in TP53, telomere maintenance, and MAPK/RTK signaling pathways. LURE identifies connections between genes with no known prior relationship, some of which may offer clues for targeting specific forms of cancer. Code and Supplemental Material are available on the LURE website: https://sysbiowiki.soe.ucsc.edu/lure.
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- 2020
9. Clinical and Genomic Characterization of Treatment-Emergent Small-Cell Neuroendocrine Prostate Cancer: A Multi-institutional Prospective Study.
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Aggarwal, Rahul, Huang, Jiaoti, Alumkal, Joshi J, Zhang, Li, Feng, Felix Y, Thomas, George V, Weinstein, Alana S, Friedl, Verena, Zhang, Can, Witte, Owen N, Lloyd, Paul, Gleave, Martin, Evans, Christopher P, Youngren, Jack, Beer, Tomasz M, Rettig, Matthew, Wong, Christopher K, True, Lawrence, Foye, Adam, Playdle, Denise, Ryan, Charles J, Lara, Primo, Chi, Kim N, Uzunangelov, Vlado, Sokolov, Artem, Newton, Yulia, Beltran, Himisha, Demichelis, Francesca, Rubin, Mark A, Stuart, Joshua M, and Small, Eric J
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Prostate Cancer ,Clinical Research ,Human Genome ,Genetics ,Cancer ,Urologic Diseases ,Detection ,screening and diagnosis ,4.2 Evaluation of markers and technologies ,Aged ,Aged ,80 and over ,Carcinoma ,Neuroendocrine ,DNA Repair ,Humans ,Male ,Middle Aged ,Prospective Studies ,Prostatic Neoplasms ,Castration-Resistant ,Clinical Sciences ,Oncology and Carcinogenesis ,Oncology & Carcinogenesis - Abstract
Purpose The prevalence and features of treatment-emergent small-cell neuroendocrine prostate cancer (t-SCNC) are not well characterized in the era of modern androgen receptor (AR)-targeting therapy. We sought to characterize the clinical and genomic features of t-SCNC in a multi-institutional prospective study. Methods Patients with progressive, metastatic castration-resistant prostate cancer (mCRPC) underwent metastatic tumor biopsy and were followed for survival. Metastatic biopsy specimens underwent independent, blinded pathology review along with RNA/DNA sequencing. Results A total of 202 consecutive patients were enrolled. One hundred forty-eight (73%) had prior disease progression on abiraterone and/or enzalutamide. The biopsy evaluable rate was 79%. The overall incidence of t-SCNC detection was 17%. AR amplification and protein expression were present in 67% and 75%, respectively, of t-SCNC biopsy specimens. t-SCNC was detected at similar proportions in bone, node, and visceral organ biopsy specimens. Genomic alterations in the DNA repair pathway were nearly mutually exclusive with t-SCNC differentiation ( P = .035). Detection of t-SCNC was associated with shortened overall survival among patients with prior AR-targeting therapy for mCRPC (hazard ratio, 2.02; 95% CI, 1.07 to 3.82). Unsupervised hierarchical clustering of the transcriptome identified a small-cell-like cluster that further enriched for adverse survival outcomes (hazard ratio, 3.00; 95% CI, 1.25 to 7.19). A t-SCNC transcriptional signature was developed and validated in multiple external data sets with > 90% accuracy. Multiple transcriptional regulators of t-SCNC were identified, including the pancreatic neuroendocrine marker PDX1. Conclusion t-SCNC is present in nearly one fifth of patients with mCRPC and is associated with shortened survival. The near-mutual exclusivity with DNA repair alterations suggests t-SCNC may be a distinct subset of mCRPC. Transcriptional profiling facilitates the identification of t-SCNC and novel therapeutic targets.
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- 2018
10. TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal
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Newton, Yulia, Novak, Adam M, Swatloski, Teresa, McColl, Duncan C, Chopra, Sahil, Graim, Kiley, Weinstein, Alana S, Baertsch, Robert, Salama, Sofie R, Ellrott, Kyle, Chopra, Manu, Goldstein, Theodore C, Haussler, David, Morozova, Olena, and Stuart, Joshua M
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Biological Sciences ,Biomedical and Clinical Sciences ,Oncology and Carcinogenesis ,Cancer ,Genetics ,Biotechnology ,Human Genome ,Chromosome Mapping ,Computational Biology ,Gene Regulatory Networks ,Genetic Predisposition to Disease ,Genome ,Human ,Genomics ,Humans ,Mutation ,Neoplasms ,Reproducibility of Results ,Software ,User-Computer Interface ,Oncology & Carcinogenesis ,Biochemistry and cell biology ,Oncology and carcinogenesis - Abstract
Vast amounts of molecular data are being collected on tumor samples, which provide unique opportunities for discovering trends within and between cancer subtypes. Such cross-cancer analyses require computational methods that enable intuitive and interactive browsing of thousands of samples based on their molecular similarity. We created a portal called TumorMap to assist in exploration and statistical interrogation of high-dimensional complex "omics" data in an interactive and easily interpretable way. In the TumorMap, samples are arranged on a hexagonal grid based on their similarity to one another in the original genomic space and are rendered with Google's Map technology. While the important feature of this public portal is the ability for the users to build maps from their own data, we pre-built genomic maps from several previously published projects. We demonstrate the utility of this portal by presenting results obtained from The Cancer Genome Atlas project data. Cancer Res; 77(21); e111-4. ©2017 AACR.
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- 2017
11. Data from The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
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Lundberg, Arian, primary, Zhang, Meng, primary, Aggarwal, Rahul, primary, Li, Haolong, primary, Zhang, Li, primary, Foye, Adam, primary, Sjöström, Martin, primary, Chou, Jonathan, primary, Chang, Kevin, primary, Moreno-Rodriguez, Thaidy, primary, Shrestha, Raunak, primary, Baskin, Avi, primary, Zhu, Xiaolin, primary, Weinstein, Alana S., primary, Younger, Noah, primary, Alumkal, Joshi J., primary, Beer, Tomasz M., primary, Chi, Kim N., primary, Evans, Christopher P., primary, Gleave, Martin, primary, Lara, Primo N., primary, Reiter, Rob E., primary, Rettig, Matthew B., primary, Witte, Owen N., primary, Wyatt, Alexander W., primary, Feng, Felix Y., primary, Small, Eric J., primary, and Quigley, David A., primary
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- 2023
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12. Supplementary Figure 8. from The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
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Lundberg, Arian, primary, Zhang, Meng, primary, Aggarwal, Rahul, primary, Li, Haolong, primary, Zhang, Li, primary, Foye, Adam, primary, Sjöström, Martin, primary, Chou, Jonathan, primary, Chang, Kevin, primary, Moreno-Rodriguez, Thaidy, primary, Shrestha, Raunak, primary, Baskin, Avi, primary, Zhu, Xiaolin, primary, Weinstein, Alana S., primary, Younger, Noah, primary, Alumkal, Joshi J., primary, Beer, Tomasz M., primary, Chi, Kim N., primary, Evans, Christopher P., primary, Gleave, Martin, primary, Lara, Primo N., primary, Reiter, Rob E., primary, Rettig, Matthew B., primary, Witte, Owen N., primary, Wyatt, Alexander W., primary, Feng, Felix Y., primary, Small, Eric J., primary, and Quigley, David A., primary
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- 2023
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13. Supplementary Figure 4. from The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
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Lundberg, Arian, primary, Zhang, Meng, primary, Aggarwal, Rahul, primary, Li, Haolong, primary, Zhang, Li, primary, Foye, Adam, primary, Sjöström, Martin, primary, Chou, Jonathan, primary, Chang, Kevin, primary, Moreno-Rodriguez, Thaidy, primary, Shrestha, Raunak, primary, Baskin, Avi, primary, Zhu, Xiaolin, primary, Weinstein, Alana S., primary, Younger, Noah, primary, Alumkal, Joshi J., primary, Beer, Tomasz M., primary, Chi, Kim N., primary, Evans, Christopher P., primary, Gleave, Martin, primary, Lara, Primo N., primary, Reiter, Rob E., primary, Rettig, Matthew B., primary, Witte, Owen N., primary, Wyatt, Alexander W., primary, Feng, Felix Y., primary, Small, Eric J., primary, and Quigley, David A., primary
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- 2023
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14. Supplementary Figure 6. from The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
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Lundberg, Arian, primary, Zhang, Meng, primary, Aggarwal, Rahul, primary, Li, Haolong, primary, Zhang, Li, primary, Foye, Adam, primary, Sjöström, Martin, primary, Chou, Jonathan, primary, Chang, Kevin, primary, Moreno-Rodriguez, Thaidy, primary, Shrestha, Raunak, primary, Baskin, Avi, primary, Zhu, Xiaolin, primary, Weinstein, Alana S., primary, Younger, Noah, primary, Alumkal, Joshi J., primary, Beer, Tomasz M., primary, Chi, Kim N., primary, Evans, Christopher P., primary, Gleave, Martin, primary, Lara, Primo N., primary, Reiter, Rob E., primary, Rettig, Matthew B., primary, Witte, Owen N., primary, Wyatt, Alexander W., primary, Feng, Felix Y., primary, Small, Eric J., primary, and Quigley, David A., primary
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- 2023
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15. Supplementary Figure 1. from The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
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Lundberg, Arian, primary, Zhang, Meng, primary, Aggarwal, Rahul, primary, Li, Haolong, primary, Zhang, Li, primary, Foye, Adam, primary, Sjöström, Martin, primary, Chou, Jonathan, primary, Chang, Kevin, primary, Moreno-Rodriguez, Thaidy, primary, Shrestha, Raunak, primary, Baskin, Avi, primary, Zhu, Xiaolin, primary, Weinstein, Alana S., primary, Younger, Noah, primary, Alumkal, Joshi J., primary, Beer, Tomasz M., primary, Chi, Kim N., primary, Evans, Christopher P., primary, Gleave, Martin, primary, Lara, Primo N., primary, Reiter, Rob E., primary, Rettig, Matthew B., primary, Witte, Owen N., primary, Wyatt, Alexander W., primary, Feng, Felix Y., primary, Small, Eric J., primary, and Quigley, David A., primary
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- 2023
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16. Supplementary Figure 5. from The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
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Lundberg, Arian, primary, Zhang, Meng, primary, Aggarwal, Rahul, primary, Li, Haolong, primary, Zhang, Li, primary, Foye, Adam, primary, Sjöström, Martin, primary, Chou, Jonathan, primary, Chang, Kevin, primary, Moreno-Rodriguez, Thaidy, primary, Shrestha, Raunak, primary, Baskin, Avi, primary, Zhu, Xiaolin, primary, Weinstein, Alana S., primary, Younger, Noah, primary, Alumkal, Joshi J., primary, Beer, Tomasz M., primary, Chi, Kim N., primary, Evans, Christopher P., primary, Gleave, Martin, primary, Lara, Primo N., primary, Reiter, Rob E., primary, Rettig, Matthew B., primary, Witte, Owen N., primary, Wyatt, Alexander W., primary, Feng, Felix Y., primary, Small, Eric J., primary, and Quigley, David A., primary
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- 2023
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17. Supplementary Table 3. from The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
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Lundberg, Arian, primary, Zhang, Meng, primary, Aggarwal, Rahul, primary, Li, Haolong, primary, Zhang, Li, primary, Foye, Adam, primary, Sjöström, Martin, primary, Chou, Jonathan, primary, Chang, Kevin, primary, Moreno-Rodriguez, Thaidy, primary, Shrestha, Raunak, primary, Baskin, Avi, primary, Zhu, Xiaolin, primary, Weinstein, Alana S., primary, Younger, Noah, primary, Alumkal, Joshi J., primary, Beer, Tomasz M., primary, Chi, Kim N., primary, Evans, Christopher P., primary, Gleave, Martin, primary, Lara, Primo N., primary, Reiter, Rob E., primary, Rettig, Matthew B., primary, Witte, Owen N., primary, Wyatt, Alexander W., primary, Feng, Felix Y., primary, Small, Eric J., primary, and Quigley, David A., primary
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- 2023
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18. Supplementary Data 1. from The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
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Lundberg, Arian, primary, Zhang, Meng, primary, Aggarwal, Rahul, primary, Li, Haolong, primary, Zhang, Li, primary, Foye, Adam, primary, Sjöström, Martin, primary, Chou, Jonathan, primary, Chang, Kevin, primary, Moreno-Rodriguez, Thaidy, primary, Shrestha, Raunak, primary, Baskin, Avi, primary, Zhu, Xiaolin, primary, Weinstein, Alana S., primary, Younger, Noah, primary, Alumkal, Joshi J., primary, Beer, Tomasz M., primary, Chi, Kim N., primary, Evans, Christopher P., primary, Gleave, Martin, primary, Lara, Primo N., primary, Reiter, Rob E., primary, Rettig, Matthew B., primary, Witte, Owen N., primary, Wyatt, Alexander W., primary, Feng, Felix Y., primary, Small, Eric J., primary, and Quigley, David A., primary
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- 2023
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19. Supplementary Figure 3. from The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
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Lundberg, Arian, primary, Zhang, Meng, primary, Aggarwal, Rahul, primary, Li, Haolong, primary, Zhang, Li, primary, Foye, Adam, primary, Sjöström, Martin, primary, Chou, Jonathan, primary, Chang, Kevin, primary, Moreno-Rodriguez, Thaidy, primary, Shrestha, Raunak, primary, Baskin, Avi, primary, Zhu, Xiaolin, primary, Weinstein, Alana S., primary, Younger, Noah, primary, Alumkal, Joshi J., primary, Beer, Tomasz M., primary, Chi, Kim N., primary, Evans, Christopher P., primary, Gleave, Martin, primary, Lara, Primo N., primary, Reiter, Rob E., primary, Rettig, Matthew B., primary, Witte, Owen N., primary, Wyatt, Alexander W., primary, Feng, Felix Y., primary, Small, Eric J., primary, and Quigley, David A., primary
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- 2023
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20. Supplementary Figure 7. from The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
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Lundberg, Arian, primary, Zhang, Meng, primary, Aggarwal, Rahul, primary, Li, Haolong, primary, Zhang, Li, primary, Foye, Adam, primary, Sjöström, Martin, primary, Chou, Jonathan, primary, Chang, Kevin, primary, Moreno-Rodriguez, Thaidy, primary, Shrestha, Raunak, primary, Baskin, Avi, primary, Zhu, Xiaolin, primary, Weinstein, Alana S., primary, Younger, Noah, primary, Alumkal, Joshi J., primary, Beer, Tomasz M., primary, Chi, Kim N., primary, Evans, Christopher P., primary, Gleave, Martin, primary, Lara, Primo N., primary, Reiter, Rob E., primary, Rettig, Matthew B., primary, Witte, Owen N., primary, Wyatt, Alexander W., primary, Feng, Felix Y., primary, Small, Eric J., primary, and Quigley, David A., primary
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- 2023
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21. Supplementary Data 2. from The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
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Lundberg, Arian, primary, Zhang, Meng, primary, Aggarwal, Rahul, primary, Li, Haolong, primary, Zhang, Li, primary, Foye, Adam, primary, Sjöström, Martin, primary, Chou, Jonathan, primary, Chang, Kevin, primary, Moreno-Rodriguez, Thaidy, primary, Shrestha, Raunak, primary, Baskin, Avi, primary, Zhu, Xiaolin, primary, Weinstein, Alana S., primary, Younger, Noah, primary, Alumkal, Joshi J., primary, Beer, Tomasz M., primary, Chi, Kim N., primary, Evans, Christopher P., primary, Gleave, Martin, primary, Lara, Primo N., primary, Reiter, Rob E., primary, Rettig, Matthew B., primary, Witte, Owen N., primary, Wyatt, Alexander W., primary, Feng, Felix Y., primary, Small, Eric J., primary, and Quigley, David A., primary
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- 2023
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22. Supplementary Figure 2. from The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
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Lundberg, Arian, primary, Zhang, Meng, primary, Aggarwal, Rahul, primary, Li, Haolong, primary, Zhang, Li, primary, Foye, Adam, primary, Sjöström, Martin, primary, Chou, Jonathan, primary, Chang, Kevin, primary, Moreno-Rodriguez, Thaidy, primary, Shrestha, Raunak, primary, Baskin, Avi, primary, Zhu, Xiaolin, primary, Weinstein, Alana S., primary, Younger, Noah, primary, Alumkal, Joshi J., primary, Beer, Tomasz M., primary, Chi, Kim N., primary, Evans, Christopher P., primary, Gleave, Martin, primary, Lara, Primo N., primary, Reiter, Rob E., primary, Rettig, Matthew B., primary, Witte, Owen N., primary, Wyatt, Alexander W., primary, Feng, Felix Y., primary, Small, Eric J., primary, and Quigley, David A., primary
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- 2023
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23. Supplementary Table 1. from The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
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Lundberg, Arian, primary, Zhang, Meng, primary, Aggarwal, Rahul, primary, Li, Haolong, primary, Zhang, Li, primary, Foye, Adam, primary, Sjöström, Martin, primary, Chou, Jonathan, primary, Chang, Kevin, primary, Moreno-Rodriguez, Thaidy, primary, Shrestha, Raunak, primary, Baskin, Avi, primary, Zhu, Xiaolin, primary, Weinstein, Alana S., primary, Younger, Noah, primary, Alumkal, Joshi J., primary, Beer, Tomasz M., primary, Chi, Kim N., primary, Evans, Christopher P., primary, Gleave, Martin, primary, Lara, Primo N., primary, Reiter, Rob E., primary, Rettig, Matthew B., primary, Witte, Owen N., primary, Wyatt, Alexander W., primary, Feng, Felix Y., primary, Small, Eric J., primary, and Quigley, David A., primary
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- 2023
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24. Supplementary Table 2. from The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
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Lundberg, Arian, primary, Zhang, Meng, primary, Aggarwal, Rahul, primary, Li, Haolong, primary, Zhang, Li, primary, Foye, Adam, primary, Sjöström, Martin, primary, Chou, Jonathan, primary, Chang, Kevin, primary, Moreno-Rodriguez, Thaidy, primary, Shrestha, Raunak, primary, Baskin, Avi, primary, Zhu, Xiaolin, primary, Weinstein, Alana S., primary, Younger, Noah, primary, Alumkal, Joshi J., primary, Beer, Tomasz M., primary, Chi, Kim N., primary, Evans, Christopher P., primary, Gleave, Martin, primary, Lara, Primo N., primary, Reiter, Rob E., primary, Rettig, Matthew B., primary, Witte, Owen N., primary, Wyatt, Alexander W., primary, Feng, Felix Y., primary, Small, Eric J., primary, and Quigley, David A., primary
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- 2023
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25. Figure S4 from Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer
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Guan, Xiangnan, primary, Sun, Duanchen, primary, Lu, Eric, primary, Urrutia, Joshua A., primary, Reiter, Robert Evan, primary, Rettig, Matthew, primary, Evans, Christopher P., primary, Lara, Primo, primary, Gleave, Martin, primary, Beer, Tomasz M., primary, Thomas, George V., primary, Huang, Jiaoti, primary, Aggarwal, Rahul R., primary, Quigley, David A., primary, Foye, Adam, primary, Chen, William S., primary, Youngren, Jack, primary, Weinstein, Alana S., primary, Stuart, Joshua M., primary, Feng, Felix Y., primary, Small, Eric J., primary, Xia, Zheng, primary, and Alumkal, Joshi J., primary
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- 2023
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26. Supplementary Dataset S2 from Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer
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Guan, Xiangnan, primary, Sun, Duanchen, primary, Lu, Eric, primary, Urrutia, Joshua A., primary, Reiter, Robert Evan, primary, Rettig, Matthew, primary, Evans, Christopher P., primary, Lara, Primo, primary, Gleave, Martin, primary, Beer, Tomasz M., primary, Thomas, George V., primary, Huang, Jiaoti, primary, Aggarwal, Rahul R., primary, Quigley, David A., primary, Foye, Adam, primary, Chen, William S., primary, Youngren, Jack, primary, Weinstein, Alana S., primary, Stuart, Joshua M., primary, Feng, Felix Y., primary, Small, Eric J., primary, Xia, Zheng, primary, and Alumkal, Joshi J., primary
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- 2023
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27. Data from Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer
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Guan, Xiangnan, primary, Sun, Duanchen, primary, Lu, Eric, primary, Urrutia, Joshua A., primary, Reiter, Robert Evan, primary, Rettig, Matthew, primary, Evans, Christopher P., primary, Lara, Primo, primary, Gleave, Martin, primary, Beer, Tomasz M., primary, Thomas, George V., primary, Huang, Jiaoti, primary, Aggarwal, Rahul R., primary, Quigley, David A., primary, Foye, Adam, primary, Chen, William S., primary, Youngren, Jack, primary, Weinstein, Alana S., primary, Stuart, Joshua M., primary, Feng, Felix Y., primary, Small, Eric J., primary, Xia, Zheng, primary, and Alumkal, Joshi J., primary
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- 2023
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28. Supplemental Figure 3 from TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal
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Newton, Yulia, primary, Novak, Adam M., primary, Swatloski, Teresa, primary, McColl, Duncan C., primary, Chopra, Sahil, primary, Graim, Kiley, primary, Weinstein, Alana S., primary, Baertsch, Robert, primary, Salama, Sofie R., primary, Ellrott, Kyle, primary, Chopra, Manu, primary, Goldstein, Theodore C., primary, Haussler, David, primary, Morozova, Olena, primary, and Stuart, Joshua M., primary
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- 2023
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29. Supplemental Figure 5 from TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal
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Newton, Yulia, primary, Novak, Adam M., primary, Swatloski, Teresa, primary, McColl, Duncan C., primary, Chopra, Sahil, primary, Graim, Kiley, primary, Weinstein, Alana S., primary, Baertsch, Robert, primary, Salama, Sofie R., primary, Ellrott, Kyle, primary, Chopra, Manu, primary, Goldstein, Theodore C., primary, Haussler, David, primary, Morozova, Olena, primary, and Stuart, Joshua M., primary
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- 2023
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30. Data from TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal
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Newton, Yulia, primary, Novak, Adam M., primary, Swatloski, Teresa, primary, McColl, Duncan C., primary, Chopra, Sahil, primary, Graim, Kiley, primary, Weinstein, Alana S., primary, Baertsch, Robert, primary, Salama, Sofie R., primary, Ellrott, Kyle, primary, Chopra, Manu, primary, Goldstein, Theodore C., primary, Haussler, David, primary, Morozova, Olena, primary, and Stuart, Joshua M., primary
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- 2023
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31. Supplementary Figure Legend from Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer
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Guan, Xiangnan, primary, Sun, Duanchen, primary, Lu, Eric, primary, Urrutia, Joshua A., primary, Reiter, Robert Evan, primary, Rettig, Matthew, primary, Evans, Christopher P., primary, Lara, Primo, primary, Gleave, Martin, primary, Beer, Tomasz M., primary, Thomas, George V., primary, Huang, Jiaoti, primary, Aggarwal, Rahul R., primary, Quigley, David A., primary, Foye, Adam, primary, Chen, William S., primary, Youngren, Jack, primary, Weinstein, Alana S., primary, Stuart, Joshua M., primary, Feng, Felix Y., primary, Small, Eric J., primary, Xia, Zheng, primary, and Alumkal, Joshi J., primary
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- 2023
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32. Supplemental Figure 4 from TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal
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Newton, Yulia, primary, Novak, Adam M., primary, Swatloski, Teresa, primary, McColl, Duncan C., primary, Chopra, Sahil, primary, Graim, Kiley, primary, Weinstein, Alana S., primary, Baertsch, Robert, primary, Salama, Sofie R., primary, Ellrott, Kyle, primary, Chopra, Manu, primary, Goldstein, Theodore C., primary, Haussler, David, primary, Morozova, Olena, primary, and Stuart, Joshua M., primary
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- 2023
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33. Supplemental Methods from TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal
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Newton, Yulia, primary, Novak, Adam M., primary, Swatloski, Teresa, primary, McColl, Duncan C., primary, Chopra, Sahil, primary, Graim, Kiley, primary, Weinstein, Alana S., primary, Baertsch, Robert, primary, Salama, Sofie R., primary, Ellrott, Kyle, primary, Chopra, Manu, primary, Goldstein, Theodore C., primary, Haussler, David, primary, Morozova, Olena, primary, and Stuart, Joshua M., primary
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- 2023
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34. Supplemental Figure 2 from TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal
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Newton, Yulia, primary, Novak, Adam M., primary, Swatloski, Teresa, primary, McColl, Duncan C., primary, Chopra, Sahil, primary, Graim, Kiley, primary, Weinstein, Alana S., primary, Baertsch, Robert, primary, Salama, Sofie R., primary, Ellrott, Kyle, primary, Chopra, Manu, primary, Goldstein, Theodore C., primary, Haussler, David, primary, Morozova, Olena, primary, and Stuart, Joshua M., primary
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- 2023
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35. Supplemental Table 1 from TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal
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Newton, Yulia, primary, Novak, Adam M., primary, Swatloski, Teresa, primary, McColl, Duncan C., primary, Chopra, Sahil, primary, Graim, Kiley, primary, Weinstein, Alana S., primary, Baertsch, Robert, primary, Salama, Sofie R., primary, Ellrott, Kyle, primary, Chopra, Manu, primary, Goldstein, Theodore C., primary, Haussler, David, primary, Morozova, Olena, primary, and Stuart, Joshua M., primary
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- 2023
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36. Video 1 from TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal
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Newton, Yulia, primary, Novak, Adam M., primary, Swatloski, Teresa, primary, McColl, Duncan C., primary, Chopra, Sahil, primary, Graim, Kiley, primary, Weinstein, Alana S., primary, Baertsch, Robert, primary, Salama, Sofie R., primary, Ellrott, Kyle, primary, Chopra, Manu, primary, Goldstein, Theodore C., primary, Haussler, David, primary, Morozova, Olena, primary, and Stuart, Joshua M., primary
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- 2023
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37. Supplementary Table S1 from Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer
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Guan, Xiangnan, primary, Sun, Duanchen, primary, Lu, Eric, primary, Urrutia, Joshua A., primary, Reiter, Robert Evan, primary, Rettig, Matthew, primary, Evans, Christopher P., primary, Lara, Primo, primary, Gleave, Martin, primary, Beer, Tomasz M., primary, Thomas, George V., primary, Huang, Jiaoti, primary, Aggarwal, Rahul R., primary, Quigley, David A., primary, Foye, Adam, primary, Chen, William S., primary, Youngren, Jack, primary, Weinstein, Alana S., primary, Stuart, Joshua M., primary, Feng, Felix Y., primary, Small, Eric J., primary, Xia, Zheng, primary, and Alumkal, Joshi J., primary
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- 2023
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38. Supplemental Figure 1 from TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal
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Newton, Yulia, primary, Novak, Adam M., primary, Swatloski, Teresa, primary, McColl, Duncan C., primary, Chopra, Sahil, primary, Graim, Kiley, primary, Weinstein, Alana S., primary, Baertsch, Robert, primary, Salama, Sofie R., primary, Ellrott, Kyle, primary, Chopra, Manu, primary, Goldstein, Theodore C., primary, Haussler, David, primary, Morozova, Olena, primary, and Stuart, Joshua M., primary
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- 2023
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39. Down-regulation of ADRB2 expression is associated with small cell neuroendocrine prostate cancer and adverse clinical outcomes in castration-resistant prostate cancer
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Kwon, Daniel H., primary, Zhang, Li, additional, Quigley, David A., additional, Foye, Adam, additional, Chen, William S., additional, Wong, Christopher K., additional, Feng, Felix Y., additional, Bailey, Adina, additional, Huang, Jiaoti, additional, Stuart, Joshua M., additional, Friedl, Verena, additional, Weinstein, Alana S., additional, Beer, Tomasz M., additional, Alumkal, Joshi J., additional, Rettig, Matthew, additional, Gleave, Martin, additional, Lara, Primo N., additional, Thomas, George V., additional, Li, Patricia, additional, Lui, Austin, additional, Small, Eric J., additional, and Aggarwal, Rahul R., additional
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- 2020
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40. Using Transcriptional Signatures to Find Cancer Drivers with LURE
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Haan, David, primary, Tao, Ruikang, additional, Friedl, Verena, additional, Anastopoulos, Ioannis N, additional, Wong, Christopher K, additional, Weinstein, Alana S, additional, and Stuart, Joshua M, additional
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- 2019
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41. Genomic and transcriptomic features of androgen receptor signaling inhibitor resistance in metastatic castration-resistant prostate cancer.
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Xiaolin Zhu, Farsh, Tatyanah, Vis, Daniël, Ivan Yu, Haolong Li, Tianyi Liu, Sjöström, Martin, Shrestha, Raunak, Kneppers, Jeroen, Severson, Tesa, Meng Zhang, Lundberg, Arian, Rodriguez, Thaidy Moreno, Weinstein, Alana S., Foye, Adam, Mehra, Niven, Aggarwal, Rahul R., Bergman, Andries M., Small, Eric J., and Lack, Nathan A.
- Subjects
- *
ANDROGEN receptors , *SOMATOSTATIN receptors , *PROSTATE cancer , *CASTRATION-resistant prostate cancer , *DISEASE progression , *CELL proliferation - Abstract
BACKGROUND. Androgen receptor signaling inhibitors (ARSIs) have improved outcomes for patients with metastatic castration-resistant prostate cancer (mCRPC), but their clinical benefit is limited by treatment resistance. METHODS. To investigate the mechanisms of ARSI resistance, we analyzed the whole-genome (n = 45) and transcriptome (n = 31) sequencing data generated from paired metastatic biopsies obtained before initiation of first-line ARSI therapy for mCRPC and after radiographic disease progression. We investigated the effects of genetic and pharmacologic modulation of SSTR1 in 22Rv1 cells, a representative mCRPC cell line. RESULTS. We confirmed the predominant role of tumor genetic alterations converging on augmenting androgen receptor (AR) signaling and the increased transcriptional heterogeneity and lineage plasticity during the emergence of ARSI resistance. We further identified amplifications involving a putative enhancer downstream of the AR and transcriptional downregulation of SSTR1, encoding somatostatin receptor 1, in ARSI-resistant tumors. We found that patients with SSTR1-low mCRPC tumors derived less benefit from subsequent ARSI therapy in a retrospective cohort. We showed that SSTR1 was antiproliferative in 22Rv1 cells and that the FDA-approved drug pasireotide suppressed 22Rv1 cell proliferation. CONCLUSION. Our findings expand the knowledge of ARSI resistance and point out actionable next steps, exemplified by potentially targeting SSTR1, to improve patient outcomes. FUNDING. National Cancer Institute (NCI), NIH; Prostate Cancer Foundation; Conquer Cancer, American Society of Clinical Oncology Foundation; UCSF Benioff Initiative for Prostate Cancer Research; Netherlands Cancer Institute. [ABSTRACT FROM AUTHOR]
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- 2024
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42. Hydra: A mixture modeling framework for subtyping pediatric cancer cohorts using multimodal gene expression signatures
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Phuong T. Dinh, Holly C. Beale, Isabel Bjork, Alana S. Weinstein, Stanley G. Leung, E. Alejandro Sweet-Cordero, David Haussler, Ioannis N. Anastopoulos, Jacob Pfeil, Avanthi Tayi Shah, W. Patrick Devine, Yuanqing Xue, Lauren Sanders, Alex G. Lee, Sofie R. Salama, Olena M. Vaske, Marcus R. Breese, A. Geoffrey Lyle, Andrew Blair, Markowetz, Florian, Pfeil, Jacob [0000-0002-8773-8520], Anastopoulos, Ioannis [0000-0002-6279-0648], Lyle, A Geoffrey [0000-0002-3435-526X], Weinstein, Alana S [0000-0002-1563-9072], Xue, Yuanqing [0000-0003-1892-6787], Beale, Holly C [0000-0003-4091-538X], Dinh, Phuong T [0000-0002-0273-1603], Devine, W Patrick [0000-0003-4634-8830], Salama, Sofie R [0000-0001-6999-7193], Sweet-Cordero, E Alejandro [0000-0002-9787-9351], Vaske, Olena Morozova [0000-0002-1677-417X], and Apollo - University of Cambridge Repository
- Subjects
0301 basic medicine ,Hydra ,Gene Expression ,Pathology and Laboratory Medicine ,Pediatrics ,Mathematical Sciences ,Transcriptome ,Neuroblastoma ,0302 clinical medicine ,Animal Cells ,Models ,Neoplasms ,Gene expression ,Medicine and Health Sciences ,Tumor Microenvironment ,Blastomas ,Cluster Analysis ,Biology (General) ,Precision Medicine ,Child ,Immune Response ,Cancer ,Regulation of gene expression ,Pediatric ,Osteosarcoma ,Tumor ,Ecology ,Sarcomas ,Eukaryota ,Animal Models ,Statistical ,Biological Sciences ,Synovial sarcoma ,Gene Expression Regulation, Neoplastic ,Computational Theory and Mathematics ,Experimental Organism Systems ,Oncology ,Modeling and Simulation ,Lernaean Hydra ,Cellular Types ,Research Article ,Biotechnology ,Pediatric Research Initiative ,QH301-705.5 ,Pediatric Cancer ,Bioinformatics ,Immune Cells ,Ewing Sarcoma ,Immunology ,Computational biology ,Biology ,Research and Analysis Methods ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,Cnidaria ,Signs and Symptoms ,Rare Diseases ,Diagnostic Medicine ,Information and Computing Sciences ,medicine ,Biomarkers, Tumor ,Genetics ,Animals ,Humans ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,ATRX ,Inflammation ,Tumor microenvironment ,Neoplastic ,Models, Statistical ,Gene Expression Profiling ,Organisms ,Neurosciences ,Biology and Life Sciences ,Cancers and Neoplasms ,Computational Biology ,Cell Biology ,medicine.disease ,Pediatric cancer ,Invertebrates ,030104 developmental biology ,Orphan Drug ,Good Health and Well Being ,Gene Expression Regulation ,Animal Studies ,030217 neurology & neurosurgery ,Biomarkers - Abstract
Precision oncology has primarily relied on coding mutations as biomarkers of response to therapies. While transcriptome analysis can provide valuable information, incorporation into workflows has been difficult. For example, the relative rather than absolute gene expression level needs to be considered, requiring differential expression analysis across samples. However, expression programs related to the cell-of-origin and tumor microenvironment effects confound the search for cancer-specific expression changes. To address these challenges, we developed an unsupervised clustering approach for discovering differential pathway expression within cancer cohorts using gene expression measurements. The hydra approach uses a Dirichlet process mixture model to automatically detect multimodally distributed genes and expression signatures without the need for matched normal tissue. We demonstrate that the hydra approach is more sensitive than widely-used gene set enrichment approaches for detecting multimodal expression signatures. Application of the hydra analysis framework to small blue round cell tumors (including rhabdomyosarcoma, synovial sarcoma, neuroblastoma, Ewing sarcoma, and osteosarcoma) identified expression signatures associated with changes in the tumor microenvironment. The hydra approach also identified an association between ATRX deletions and elevated immune marker expression in high-risk neuroblastoma. Notably, hydra analysis of all small blue round cell tumors revealed similar subtypes, characterized by changes to infiltrating immune and stromal expression signatures., Author summary Pediatric cancers generally have few somatic mutations. To increase the number of actionable treatment leads, precision pediatric oncology initiatives also analyze tumor gene expression patterns. However, currently available approaches for gene expression data analysis in the clinical setting often use arbitrary thresholds for assessing overexpression and assume gene expression is normally distributed. These methods also rely on reference distributions of related cancer types or normal samples for assessing expression distributions. Often adequate normal samples are not available, and comparing matched cancer cohorts without accounting for subtype expression overestimates the uncertainty in the analysis. We developed a computational framework to automatically detect multimodal expression distributions within well-defined disease populations. Our analysis of small blue round cell tumors (including rhabdomyosarcoma, synovial sarcoma, neuroblastoma, Ewing sarcoma and osteosarcoma) discovered a significant number of multimodally expressed genes. Multimodally expressed genes were associated with proliferative signaling, extracellular matrix organization, and immune signaling pathways across cancer types. Expression signatures correlated with differences in patient outcomes for MYCN non-amplified neuroblastoma, osteosarcoma, and synovial sarcoma. The low mutation rate in pediatric cancers has led some to suggest that pediatric cancers are less immunogenic. However, our analysis suggests that immune infiltration can be identified across small blue round cell tumors. Thus, further research into modulating immune cells for patient benefit may be warranted.
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- 2020
43. An Atlas of Accessible Chromatin in Advanced Prostate Cancer Reveals the Epigenetic Evolution during Tumor Progression.
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Shrestha R, Chesner LN, Zhang M, Zhou S, Foye A, Lundberg A, Weinstein AS, Sjöström M, Zhu X, Moreno-Rodriguez T, Li H, Alumkal JJ, Aggarwal R, Small EJ, Lupien M, Quigley DA, and Feng FY
- Subjects
- Male, Humans, Gene Expression Regulation, Neoplastic, Transcription Factors genetics, Transcription Factors metabolism, Receptors, Androgen genetics, Receptors, Androgen metabolism, Chromatin genetics, Chromatin metabolism, Prostatic Neoplasms, Castration-Resistant genetics, Prostatic Neoplasms, Castration-Resistant pathology, Prostatic Neoplasms, Castration-Resistant metabolism, Epigenesis, Genetic, Disease Progression
- Abstract
Metastatic castration-resistant prostate cancer (mCRPC) is a lethal disease that resists therapy targeting androgen signaling, the primary driver of prostate cancer. mCRPC resists androgen receptor (AR) inhibitors by amplifying AR signaling or by evolving into therapy-resistant subtypes that do not depend on AR. Elucidation of the epigenetic underpinnings of these subtypes could provide important insights into the drivers of therapy resistance. In this study, we produced chromatin accessibility maps linked to the binding of lineage-specific transcription factors (TF) by performing assay for transposase-accessible chromatin sequencing on 70 mCRPC tissue biopsies integrated with transcriptome and whole-genome sequencing. mCRPC had a distinct global chromatin accessibility profile linked to AR function. Analysis of TF occupancy across accessible chromatin revealed 203 TFs associated with mCRPC subtypes. Notably, ZNF263 was identified as a putative prostate cancer TF with a significant impact on gene activity in the double-negative subtype (AR- neuroendocrine-), potentially activating MYC targets. Overall, this analysis of chromatin accessibility in mCRPC provides valuable insights into epigenetic changes that occur during progression to mCRPC. Significance: Integration of a large cohort of transcriptome, whole-genome, and ATAC sequencing characterizes the chromatin accessibility changes in advanced prostate cancer and identifies therapy-resistant prostate cancer subtype-specific transcription factors that modulate oncogenic programs., (©2024 American Association for Cancer Research.)
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- 2024
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44. Using Transcriptional Signatures to Find Cancer Drivers with LURE.
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Haan D, Tao R, Friedl V, Anastopoulos IN, Wong CK, Weinstein AS, and Stuart JM
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- Humans, Mutation, Computational Biology, Neoplasms genetics
- Abstract
Cancer genome projects have produced multidimensional datasets on thousands of samples. Yet, depending on the tumor type, 5-50% of samples have no known driving event. We introduce a semi-supervised method called Learning UnRealized Events (LURE) that uses a progressive label learning framework and minimum spanning analysis to predict cancer drivers based on their altered samples sharing a gene expression signature with the samples of a known event. We demonstrate the utility of the method on the TCGA Pan-Cancer Atlas dataset for which it produced a high-confidence result relating 59 new connections to 18 known mutation events including alterations in the same gene, family, and pathway. We give examples of predicted drivers involved in TP53, telomere maintenance, and MAPK/RTK signaling pathways. LURE identifies connections between genes with no known prior relationship, some of which may offer clues for targeting specific forms of cancer. Code and Supplemental Material are available on the LURE website: https://sysbiowiki.soe.ucsc.edu/lure.
- Published
- 2020
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