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.

The genomic complexity of profound copy number aberrations has prevented effective molecular stratification of ovarian cancers. Here, to decode this complexity, we derived copy number signatures from shallow whole-genome sequencing of 117 high-grade serous ovarian cancer (HGSOC) cases, which were validated on 527 independent cases. We show that HGSOC comprises a continuum of genomes shaped by multiple mutational processes that result in known patterns of genomic aberration. Copy number signature exposures at diagnosis predict both overall survival and the probability of platinum-resistant relapse. Measurement of signature exposures provides a rational framework to choose combination treatments that target multiple mutational processes.

Genomic complexity from profound copynumber aberration has prevented effective molecular stratification of ovarian and other cancers. Here we present a method for copynumber signature identification that decodes this complexity. We derived eight signatures using 117 shallow wholegenome sequenced highgrade serous ovarian cancer cases, which were validated on a further 497 cases. Mutational processes underlying the copynumber signatures were identified, including breakagefusionbridge cycles, homologous recombination deficiency and wholegenome duplication. We show that most tumours are heterogeneous and harbour multiple signature exposures. We also demonstrate that copy number signatures predict overall survival and changes in signature exposure observed in response to chemotherapy suggest potential treatment strategies. 2. CC-BY-NC-ND 4.0 International license not peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was . http://dx.doi.org/10.1101/174201 doi: bioRxiv preprint first posted online Aug. 9, 2017;The discrete mutational processes that drive copynumber change in human cancers are not readily identifiable from genomewide sequence data. This presents a major challenge for the development of precision medicine for cancers that are strongly dominated by copynumber changes, including highgrade serous ovarian (HGSOC), oesophageal, nonsmallcell lung and triple negative breast cancers 1 . These tumours have low frequency of recurrent oncogenic mutations, few recurrent copy number alterations and highly complex genomic profiles 2 .HGSOCs are poor prognosis carcinomas with ubiquitous TP53 mutation 3 . Despite efforts to discover new molecular subtypes and targeted therapies, overall survival has not improved over two decades 4 . Current genomic stratification is limited to defining homologous recombinationdeficient (HRD) tumours 57 , and classification using gene expression does not currently have clinical utility 8,9 . Detailed genomic analysis using whole genome sequencing has shown frequent loss of RB1, NF1 and PTEN by gene breakage events 10 and enrichment of amplification associated foldback inversions in nonHRD tumours 11 . However, none of these approaches has provided a broad mechanistic understanding of HGSOC, reflecting the challenges of detecting classifiers in extreme genomic complexity.Recent algorithmic advances have enabled interpretation of complex genomic changes by identifying mutational signatures genomic patterns that are the imprint of mutagenic processes accumulated over the lifetime of a cancer cell . Importantly, these studies show that tumours typically harbour multiple mutational processes requiring computational approaches that can robustly identify coexistent mutational signatures. Quantification of the exposure of a tumour to specific mutational signatures provides a rational framework to personalise therapy 14 but currently is not readily applicable to copynumber driven tumours. We hypothesized that specific feat...

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Cells are a fundamental unit of life, and the ability to study the phenotypes and behaviors of individual cells is crucial to understanding the workings of complex biological systems. Cell phenotypes (epigenomic, transcriptomic, proteomic, and metabolomic) exhibit dramatic heterogeneity between and within the different cell types and states underlying cellular functional diversity. Cell genotypes can also display heterogeneity throughout an organism, in the form of somatic genetic variation—most notably in the emergence and evolution of tumors. Recent technical advances in single‐cell isolation and the development of omics approaches sensitive enough to reveal these aspects of cell identity have enabled a revolution in the study of multicellular systems. In this review, we discuss the technologies available to resolve the genomes, epigenomes, transcriptomes, proteomes, and metabolomes of single cells from a wide variety of living systems.