CHD1 loss sensitizes prostate cancer to DNA damaging therapy by promoting error-prone double-strand break repair.

BACKGROUND: Deletion of the chromatin remodeler chromodomain helicase DNA-binding protein 1 (CHD1) is a common genomic alteration found in human prostate cancers (PCas). CHD1 loss represents a distinct PCa subtype characterized by SPOP mutation and higher genomic instability. However, the role of CHD1 in PCa development in vivo and its clinical utility remain unclear. PATIENTS AND METHODS: To study the role of CHD1 in PCa development and its loss in clinical management, we generated a genetically engineered mouse model with prostate-specific deletion of murine Chd1 as well as isogenic CHD1 wild-type and homozygous deleted human benign and PCa lines. We also developed patient-derived organoid cultures and screened patients with metastatic PCa for CHD1 loss. RESULTS: We demonstrate that CHD1 loss sensitizes cells to DNA damage and causes a synthetic lethal response to DNA damaging therapy in vitro, in vivo, ex vivo, in patient-derived organoid cultures and in a patient with metastatic PCa. Mechanistically, CHD1 regulates 53BP1 stability and CHD1 loss leads to decreased error-free homologous recombination (HR) repair, which is compensated by increased error-prone non-homologous end joining (NHEJ) repair for DNA double-strand break (DSB) repair. CONCLUSIONS: Our study provides the first in vivo and in patient evidence supporting the role of CHD1 in DSB repair and in response to DNA damaging therapy. We uncover mechanistic insights that CHD1 modulates the choice between HR and NHEJ DSB repair and suggest that CHD1 loss may contribute to the genomic instability seen in this subset of PCas.

Androgen receptor expression in circulating tumour cells from castration-resistant prostate cancer patients treated with novel endocrine agents.

BACKGROUND: Abiraterone and enzalutamide are novel endocrine treatments that abrogate androgen receptor (AR) signalling in castration-resistant prostate cancer (CRPC). Here, we developed a circulating tumour cells (CTCs)-based assay to evaluate AR expression in real-time in CRPC and investigated nuclear AR expression in CTCs in patients treated with enzalutamide and abiraterone. METHODS: CTCs were captured and characterised using the CellSearch system. An automated algorithm to identify CTCs and quantify AR expression was employed. The primary aim was to evaluate the association between CTC AR expression and prior treatment with abiraterone or enzalutamide. RESULTS: AR expression in CTCs was evaluated in 94 samples from 48 metastatic CRPC patients. We observed large intra-patient heterogeneity of AR expression in CTCs. Prior exposure to abiraterone or enzalutamide was not associated with a change in CTCs AR expression (median intensity and distribution of AR-positive classes). In support of this, we also confirmed maintained nuclear AR expression in tissue samples collected after progression on abiraterone. AR staining also identified additional AR-positive CD45-negative circulating cells that were CK-negative/weak and therefore missed using standard protocols. The number of these events correlated with traditional CTCs and was associated with worse outcome on univariate analysis. CONCLUSIONS: We developed a non-invasive method to monitor AR nuclear expression in CTCs. Our studies confirm nuclear AR expression in CRPC patients progressing on novel endocrine treatments. Owing to the significant heterogeneity of AR expression in CTCs, studies in larger cohorts of patients are required to identify associations with outcome.

Validation and utilisation of high-coverage next-generation sequencing to deliver the pharmacological audit trail.

BACKGROUND: Predictive biomarker development is a key challenge for novel cancer therapeutics. We explored the feasibility of next-generation sequencing (NGS) to validate exploratory genomic biomarkers that impact phase I trial selection. METHODS: We prospectively enrolled 158 patients with advanced solid tumours referred for phase I clinical trials at the Royal Marsden Hospital (October 2012 to March 2013). After fresh and/or archived tumour tissue were obtained, 93 patients remained candidates for phase I trials. Results from tumour sequencing on the Illumina MiSeq were cross-validated in 27 out of 93 patients on the Ion Torrent Personal Genome Machine (IT-PGM) blinded to results. MiSeq validation with Sequenom MassARRAY OncoCarta 1.0 (Sequenom Inc., San Diego, CA, USA) was performed in a separate cohort. RESULTS: We found 97% concordance of mutation calls by MiSeq and IT-PGM at a variant allele frequency ⩾13% and ⩾500 × depth coverage, and 91% concordance between MiSeq and Sequenom. Common 'actionable' mutations involved deoxyribonucleic acid (DNA) repair (51%), RAS-RAF-MEK (35%), Wnt (26%), and PI3K-AKT-mTOR (24%) signalling. Out of 53, 29 (55%) patients participating in phase I trials were recommended based on identified actionable mutations. CONCLUSIONS: Targeted high-coverage NGS panels are a highly feasible single-centre technology well-suited to cross-platform validation, enrichment of trials with molecularly defined populations and hypothesis testing early in drug development.

Sting_RDB: a relational database of structural parameters for protein analysis with support for data warehousing and data mining.

An effective strategy for managing protein databases is to provide mechanisms to transform raw data into consistent, accurate and reliable information. Such mechanisms will greatly reduce operational inefficiencies and improve one's ability to better handle scientific objectives and interpret the research results. To achieve this challenging goal for the STING project, we introduce Sting_RDB, a relational database of structural parameters for protein analysis with support for data warehousing and data mining. In this article, we highlight the main features of Sting_RDB and show how a user can explore it for efficient and biologically relevant queries. Considering its importance for molecular biologists, effort has been made to advance Sting_RDB toward data quality assessment. To the best of our knowledge, Sting_RDB is one of the most comprehensive data repositories for protein analysis, now also capable of providing its users with a data quality indicator. This paper differs from our previous study in many aspects. First, we introduce Sting_RDB, a relational database with mechanisms for efficient and relevant queries using SQL. Sting_rdb evolved from the earlier, text (flat file)-based database, in which data consistency and integrity was not guaranteed. Second, we provide support for data warehousing and mining. Third, the data quality indicator was introduced. Finally and probably most importantly, complex queries that could not be posed on a text-based database, are now easily implemented. Further details are accessible at the Sting_RDB demo web page: http://www.cbi.cnptia.embrapa.br/StingRDB.

Sting_RDB: a relational database of structural parameters for protein analysis with support for data warehousing and data mining

An effective strategy for managing protein databases is to provide mechanisms to transform raw data into consistent, accurate and reliable information. Such mechanisms will greatly reduce operational inefficiencies and improve one’s ability to better handle scientific objectives and interpret the research results. To achieve this challenging goal for the STING project, we introduce Sting_RDB, a relational database of structural parameters for protein analysis with support for data warehousing and data mining. In this article, we highlight the main features of Sting_RDB and show how a user can explore it for efficient and biologically relevant queries. Considering its importance for molecular biologists, effort has been made to advance Sting_RDB toward data quality assessment. To the best of our knowledge, Sting_RDB is one of the most comprehensive data repositories for protein analysis, now also capable of providing its users with a data quality indicator. This paper differs from our previous study in many aspects. First, we introduce Sting_RDB, a relational database with mechanisms for efficient and relevant queries using SQL. Sting_rdb evolved from the earlier, text (flat file)-based database, in which data consistency and integrity was not guaranteed. Second, we provide support for data warehousing and mining. Third, the data quality indicator was introduced. Finally and probably most importantly, complex queries that could not be posed on a text-based database, are now easily implemented. Further details are accessible at the Sting_RDB demo web page: http://www.cbi.cnptia.embrapa.br/StingRDB.

The Star STING server: a multiplatform environment for protein structure analysis.

Star STING is the latest version of the STING suite of programs and corresponding database. We report on five important aspects of this package that have acquired some new characteristics, designed to add key advantages to the whole suite: 1) availability for most popular platforms and browsers, 2) introduction of the STING_DB quality assessment, 3) improvement in algorithms for calculation of three STING parameters, 4) introduction of five new STING modules, and 5) expansion of the existing modules. Star STING is freely accessible at: http://sms.cbi.cnptia.embrapa.br/SMS/, http://trantor.bioc.columbia.edu/SMS, http://www.es.embnet.org/SMS/, http://gibk26.bse.kyutech.ac.jp/SMS/ and http://www.ar.embnet.org/SMS.

The Star STING server: a multiplatform environment for protein structure analysis

Star STING is the latest version of the STING suite of programs and corresponding database. We report on five important aspects of this package that have acquired some new characteristics, designed to add key advantages to the whole suite: 1) availability for most popular platforms and browsers, 2) introduction of the STING_DB quality assessment, 3) improvement in algorithms for calculation of three STING parameters, 4) introduction of five new STING modules, and 5) expansion of the existing modules. Star STING is freely accessible at: http://sms.cbi.cnptia.embrapa.br/SMS/, http://trantor.bioc.columbia.edu/SMS, http://www.es.embnet.org/SMS/, http://gibk26.bse.kyutech.ac.jp/SMS/ and http://www.ar.embnet.org/SMS.