In:
Science, American Association for the Advancement of Science (AAAS), Vol. 376, No. 6596 ( 2022-05-27)
Abstract:
Untreated prostate cancers rely on androgen receptor (AR) signaling for growth and survival, forming the basis for the initial efficacy of androgen deprivation therapy (ADT). Yet the disease can relapse and progress to a lethal stage termed castration-resistant prostate cancer (CRPC). Reactivation of AR signaling represents the most common driver of CRPC growth, and next-generation AR signaling inhibitors (ARSIs) are now used in combination with ADT as a first-line therapy. However, ARSIs can result in selective pressure, thereby generating AR-independent tumors. The transition from AR dependence frequently accompanies a change in phenotype resembling developmental transdifferentiation or “lineage plasticity.” Neuroendocrine prostate cancer, which lacks a defined pathologic classification, is the most studied type of lineage plasticity. However, most AR-null tumors do not exhibit neuroendocrine features and are classified as “double-negative prostate cancer,” the drivers of which are poorly defined. RATIONALE Lineage plasticity studies in CRPC are limited by the lack of genetically defined patient-derived models that recapitulate the disease spectrum. To address this, we developed a biobank of organoids generated from patient biopsies to study the landscape of metastatic CRPC and allow for functional validation assays. Proteins called transcription factors (TFs) are drivers of tumor lineage plasticity. To identify the key TFs that drive the growth of AR-independent tumors, we integrated epigenetic and transcriptomic data generated from CRPC models. RESULTS We generated ATAC-seq (assay for transposase-accessible chromatin sequencing) and RNA-seq data from 22 metastatic human prostate cancer organoids, six patient-derived xenografts (PDXs), and 12 derived or traditional cell lines. We classified the 40 models into four subtypes and predicted key TFs of each subtype. We identified the well-characterized AR-dependent (CRPC-AR) and neuroendocrine subtypes (CRPC-NE) as well as two AR-negative/low groups, including a Wnt-dependent subtype (CRPC-WNT), driven by TCF/LEF TFs, and a stem cell–like (SCL) subtype (CRPC-SCL), driven by the AP-1 family of TFs. We applied RNA-seq signatures derived from the organoids to 366 patient samples from two independent CRPC datasets, which recapitulated the four-subtype classification. We found that CRPC-SCL is the second most prevalent group and is associated with shorter time under ARSI treatment compared to CRPC-AR. Additional chromatin immunoprecipitation sequencing (ChIP-seq) analysis indicated that AP-1 works together with the proteins YAP, TAZ, and TEAD, revealing YAP/TAZ and AP-1 as potential actionable targets in CRPC-SCL. Using overexpression assays in AR-high cells, we revealed how AP-1 functions as a pioneering factor and master regulator for CRPC-SCL. CONCLUSION By using a diverse biobank of organoids, PDXs, and cell lines that recapitulate the heterogeneity of metastatic prostate cancer, we created a map of the chromatin accessibility and transcriptomic landscape of CRPC. We validated the CRPC-AR and CRPC-NE subtypes and report two subtypes of AR-negative/low samples as well as their respective key TFs. Additional analysis revealed a model in which YAP, TAZ, TEAD, and AP-1 function together and drive oncogenic growth in CRPC-SCL samples. Overall, our results show how stratification of CRPC patients into four subtypes using their transcriptomes can potentially inform appropriate clinical decisions. Identification of four subtypes of castration-resistant prostate cancer (CRPC) by integration of chromatin accessibility and transcriptomic data from organoids, patient-derived xenografts (PDXs), and cell lines. TF, transcription factor; AR, androgen receptor; NE, neuroendocrine; SCL, stem cell–like. YAP/TAZ/TEAD/AP-1 cooperation in CRPC-SCL suggests actionable targets. Application of RNA-seq signatures derived from the models to 366 patient samples recapitulates the four-subtype classification.
Type of Medium:
Online Resource
ISSN:
0036-8075
,
1095-9203
DOI:
10.1126/science.abe1505
Language:
English
Publisher:
American Association for the Advancement of Science (AAAS)
Publication Date:
2022
detail.hit.zdb_id:
128410-1
detail.hit.zdb_id:
2066996-3
detail.hit.zdb_id:
2060783-0
SSG:
11
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