Edward Curry

Bio: Edward Curry is an academic researcher from Imperial College London. The author has contributed to research in topics: DNA methylation & Chromatin. The author has an hindex of 16, co-authored 34 publications receiving 1832 citations. Previous affiliations of Edward Curry include St Thomas' Hospital.

Whole-genome characterization of chemoresistant ovarian cancer

Abstract: Patients with high-grade serous ovarian cancer (HGSC) have experienced little improvement in overall survival, and standard treatment has not advanced beyond platinum-based combination chemotherapy, during the past 30 years. To understand the drivers of clinical phenotypes better, here we use whole-genome sequencing of tumour and germline DNA samples from 92 patients with primary refractory, resistant, sensitive and matched acquired resistant disease. We show that gene breakage commonly inactivates the tumour suppressors RB1, NF1, RAD51B and PTEN in HGSC, and contributes to acquired chemotherapy resistance. CCNE1 amplification was common in primary resistant and refractory disease. We observed several molecular events associated with acquired resistance, including multiple independent reversions of germline BRCA1 or BRCA2 mutations in individual patients, loss of BRCA1 promoter methylation, an alteration in molecular subtype, and recurrent promoter fusion associated with overexpression of the drug efflux pump MDR1.

Poised epigenetic states and acquired drug resistance in cancer

Abstract: Epigenetic events, which are somatically inherited through cell division, are potential drivers of acquired drug resistance in cancer. The high rate of epigenetic change in tumours generates diversity in gene expression patterns that can rapidly evolve through drug selection during treatment, leading to the development of acquired resistance. This will potentially confound stratified chemotherapy decisions that are solely based on mutation biomarkers. Poised epigenetic states in tumour cells may drive multistep epigenetic fixation of gene expression during the acquisition of drug resistance, which has implications for clinical strategies to prevent the emergence of drug resistance.

A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer

Abstract: The five-year survival rate of epithelial ovarian cancer (EOC) is approximately 35-40% despite maximal treatment efforts, highlighting a need for stratification biomarkers for personalized treatment. Here we extract 657 quantitative mathematical descriptors from the preoperative CT images of 364 EOC patients at their initial presentation. Using machine learning, we derive a non-invasive summary-statistic of the primary ovarian tumor based on 4 descriptors, which we name "Radiomic Prognostic Vector" (RPV). RPV reliably identifies the 5% of patients with median overall survival less than 2 years, significantly improves established prognostic methods, and is validated in two independent, multi-center cohorts. Furthermore, genetic, transcriptomic and proteomic analysis from two independent datasets elucidate that stromal phenotype and DNA damage response pathways are activated in RPV-stratified tumors. RPV and its associated analysis platform could be exploited to guide personalized therapy of EOC and is potentially transferrable to other cancer types.

LARP1 post-transcriptionally regulates mTOR and contributes to cancer progression

Abstract: RNA-binding proteins (RBPs) bind to and post-transcriptionally regulate the stability of mRNAs. La-related protein 1 (LARP1) is a conserved RBP that interacts with poly-A-binding protein and is known to regulate 5′-terminal oligopyrimidine tract (TOP) mRNA translation. Here, we show that LARP1 is complexed to 3000 mRNAs enriched for cancer pathways. A prominent member of the LARP1 interactome is mTOR whose mRNA transcript is stabilized by LARP1. At a functional level, we show that LARP1 promotes cell migration, invasion, anchorage-independent growth and in vivo tumorigenesis. Furthermore, we show that LARP1 expression is elevated in epithelial cancers such as cervical and non-small cell lung cancers, where its expression correlates with disease progression and adverse prognosis, respectively. We therefore conclude that, through the post-transcriptional regulation of genes such as mTOR within cancer pathways, LARP1 contributes to cancer progression.

Epigenome-wide association study reveals decreased average methylation levels years before breast cancer diagnosis

Abstract: Interest in the potential of DNA methylation in peripheral blood as a biomarker of cancer risk is increasing. We aimed to assess whether epigenome-wide DNA methylation measured in peripheral blood samples obtained before onset of the disease is associated with increased risk of breast cancer. We report on three independent prospective nested case-control studies from the European Prospective Investigation into Cancer and Nutrition (EPIC-Italy; n = 162 matched case-control pairs), the Norwegian Women and Cancer study (NOWAC; n = 168 matched pairs), and the Breakthrough Generations Study (BGS; n = 548 matched pairs). We used the Illumina 450k array to measure methylation in the EPIC and NOWAC cohorts. Whole-genome bisulphite sequencing (WGBS) was performed on the BGS cohort using pooled DNA samples, combined to reach 50× coverage across ~16 million CpG sites in the genome including 450k array CpG sites. Mean β values over all probes were calculated as a measurement for epigenome-wide methylation. In EPIC, we found that high epigenome-wide methylation was associated with lower risk of breast cancer (odds ratio (OR) per 1 SD = 0.61, 95 % confidence interval (CI) 0.47–0.80; −0.2 % average difference in epigenome-wide methylation for cases and controls). Specifically, this was observed in gene bodies (OR = 0.51, 95 % CI 0.38–0.69) but not in gene promoters (OR = 0.92, 95 % CI 0.64–1.32). The association was not replicated in NOWAC (OR = 1.03 95 % CI 0.81–1.30). The reasons for heterogeneity across studies are unclear. However, data from the BGS cohort was consistent with epigenome-wide hypomethylation in breast cancer cases across the overlapping 450k probe sites (difference in average epigenome-wide methylation in case and control DNA pools = −0.2 %). We conclude that epigenome-wide hypomethylation of DNA from pre-diagnostic blood samples may be predictive of breast cancer risk and may thus be useful as a clinical biomarker.

Integrative analysis of 111 reference human epigenomes

Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Genomic analyses identify molecular subtypes of pancreatic cancer

Abstract: Integrated genomic analysis of 456 pancreatic ductal adenocarcinomas identified 32 recurrently mutated genes that aggregate into 10 pathways: KRAS, TGF-β, WNT, NOTCH, ROBO/SLIT signalling, G1/S transition, SWI-SNF, chromatin modification, DNA repair and RNA processing. Expression analysis defined 4 subtypes: (1) squamous; (2) pancreatic progenitor; (3) immunogenic; and (4) aberrantly differentiated endocrine exocrine (ADEX) that correlate with histopathological characteristics. Squamous tumours are enriched for TP53 and KDM6A mutations, upregulation of the TP63∆N transcriptional network, hypermethylation of pancreatic endodermal cell-fate determining genes and have a poor prognosis. Pancreatic progenitor tumours preferentially express genes involved in early pancreatic development (FOXA2/3, PDX1 and MNX1). ADEX tumours displayed upregulation of genes that regulate networks involved in KRAS activation, exocrine (NR5A2 and RBPJL), and endocrine differentiation (NEUROD1 and NKX2-2). Immunogenic tumours contained upregulated immune networks including pathways involved in acquired immune suppression. These data infer differences in the molecular evolution of pancreatic cancer subtypes and identify opportunities for therapeutic development.

Integrative Genomics Viewer

Abstract: Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Abstract: Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

A view on drug resistance in cancer

Abstract: The problem of resistance to therapy in cancer is multifaceted. Here we take a reductionist approach to define and separate the key determinants of drug resistance, which include tumour burden and growth kinetics; tumour heterogeneity; physical barriers; the immune system and the microenvironment; undruggable cancer drivers; and the many consequences of applying therapeutic pressures. We propose four general solutions to drug resistance that are based on earlier detection of tumours permitting cancer interception; adaptive monitoring during therapy; the addition of novel drugs and improved pharmacological principles that result in deeper responses; and the identification of cancer cell dependencies by high-throughput synthetic lethality screens, integration of clinico-genomic data and computational modelling. These different approaches could eventually be synthesized for each tumour at any decision point and used to inform the choice of therapy. A review of drug resistance in cancer analyses each biological determinant of resistance separately and discusses existing and new therapeutic strategies to combat the problem as a whole.