Prostate tumours are highly variable in their response to therapies, but clinically available prognostic factors can explain only a fraction of this heterogeneity. Here we analysed 200 whole-genome sequences and 277 additional whole-exome sequences from localized, non-indolent prostate tumours with similar clinical risk profiles, and carried out RNA and methylation analyses in a subset. These tumours had a paucity of clinically actionable single nucleotide variants, unlike those seen in metastatic disease. Rather, a significant proportion of tumours harboured recurrent non-coding aberrations, large-scale genomic rearrangements, and alterations in which an inversion repressed transcription within its boundaries. Local hypermutation events were frequent, and correlated with specific genomic profiles. Numerous molecular aberrations were prognostic for disease recurrence, including several DNA methylation events, and a signature comprised of these aberrations outperformed well-described prognostic biomarkers. We suggest that intensified treatment of genomically aggressive localized prostate cancer may improve cure rates.

Herein we provide a detailed molecular analysis of the spatial heterogeneity of clinically localized, multifocal prostate cancer to delineate new oncogenes or tumor suppressors. We initially determined the copy number aberration (CNA) profiles of 74 patients with index tumors of Gleason score 7. Of these, 5 patients were subjected to whole-genome sequencing using DNA quantities achievable in diagnostic biopsies, with detailed spatial sampling of 23 distinct tumor regions to assess intraprostatic heterogeneity in focal genomics. Multifocal tumors are highly heterogeneous for single-nucleotide variants (SNVs), CNAs and genomic rearrangements. We identified and validated a new recurrent amplification of MYCL, which is associated with TP53 deletion and unique profiles of DNA damage and transcriptional dysregulation. Moreover, we demonstrate divergent tumor evolution in multifocal cancer and, in some cases, tumors of independent clonal origin. These data represent the first systematic relation of intraprostatic genomic heterogeneity to predicted clinical outcome and inform the development of novel biomarkers that reflect individual prognosis.

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include a myriad of properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

Objectives: Lack of clarity on the definition of "patient engagement" has been highlighted as a barrier to fully implementing patient engagement in research. This study identified themes within existing definitions related to patient engagement and proposes a consensus definition of "patient engagement in research." Methods: A systematic review was conducted to identify definitions of patient engagement and related terms in published literature (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018). Definitions were extracted and qualitatively analyzed to identify themes and characteristics. A multistakeholder approach, including academia, industry, and patient representation, was taken at all stages. A proposed definition is offered based on a synthesis of the findings.Results: Of 1821 abstracts identified and screened for eligibility, 317 were selected for full-text review. Of these, 169 articles met inclusion criteria, from which 244 distinct definitions were extracted for analysis. The most frequently defined terms were: "patient-centered" (30.5%), "patient engagement" (15.5%), and "patient participation" (13.4%). The majority of definitions were specific to the healthcare delivery setting (70.5%); 11.9% were specific to research. Among the definitions of "patient engagement," the most common themes were "active process," "patient involvement," and "patient as participant." In the research setting, the top themes were "patient as partner," "patient involvement," and "active process"; these did not appear in the top 3 themes of nonresearch definitions. Conclusion:Distinct themes are associated with the term "patient engagement" and with engagement in the "research" setting. Based on an analysis of existing literature and review by patient, industry, and academic stakeholders, we propose a scalable consensus definition of "patient engagement in research."