Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale^1–3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter⁴; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation^5,6; analyses timings and patterns of tumour evolution⁷; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity^8,9; and evaluates a range of more-specialized features of cancer genomes^8,10–18.

Age-related macular degeneration (AMD) is the most common cause of irreversible vision loss in the developed world and has a strong genetic predisposition. A locus at human chromosome 10q26 affects the risk of AMD, but the precise gene(s) have not been identified. We genotyped 581 AMD cases and 309 normal controls in a Caucasian cohort in Utah. We demonstrate that a single-nucleotide polymorphism, rs11200638, in the promoter region of HTRA1 is the most likely causal variant for AMD at 10q26 and is estimated to confer a population attributable risk of 49.3%. The HTRA1 gene encodes a secreted serine protease. Preliminary analysis of lymphocytes and retinal pigment epithelium from four AMD patients revealed that the risk allele was associated with elevated expression levels of HTRA1 mRNA and protein. We also found that drusen in the eyes of AMD patients were strongly immunolabeled with HTRA1 antibody. Together, these findings support a key role for HTRA1 in AMD susceptibility and identify a potential new pathway for AMD pathogenesis.

Emerging data suggest that signaling by heparin-binding growth factors is influenced by the sulfation state of N-acetylglucosamine residues of heparan sulfate proteoglycans (HSPGs). Here we report that the recently identified protein HSulf-1, a heparin-degrading endosulfatase, encodes a cell surface-associated enzyme that diminishes sulfation of cell surface HSPGs. The message encoding this enzyme is readily detectable in a variety of normal tissues, including normal ovarian surface epithelial cells, but is undetectable in 5 of 7 ovarian carcinoma cell lines and markedly diminished or undetectable in ∼75% of ovarian cancers. Similar down-regulation is also observed in breast, pancreatic, renal cells, and hepatocellular carcinoma lines. Re-expression of HSulf-1 in ovarian cancer cell lines resulted in diminished HSPG sulfation, diminished phosphorylation of receptor tyrosine kinases that require sulfated HSPGs as co-receptors for their cognate ligands, and diminished downstream signaling through the extracellular signal-regulated kinase pathway after treatment with fibroblast growth factor-2 or heparin-binding epidermal growth factor. Consistent with these changes, HSulf-1 re-expression resulted in reduced proliferation as well as sensitivity to induction of apoptosis by the broad spectrum kinase inhibitor staurosporine and the chemotherapeutic agent cisplatin. Collectively, these observations provide evidence that HSulf-1 modulates signaling by heparin-binding growth factors, and HSulf-1 down-regulation represents a novel mechanism by which cancer cells can enhance growth factor signaling.

Familial macular degeneration is a clinically and genetically heterogeneous group of disorders characterized by progressive central vision loss. Here we show that an R373C missense mutation in the prominin 1 gene (PROM1) causes 3 forms of autosomal-dominant macular degeneration. In transgenic mice expressing R373C mutant human PROM1, both mutant and endogenous PROM1 were found throughout the layers of the photoreceptors, rather than at the base of the photoreceptor outer segments, where PROM1 is normally localized. Moreover, the outer segment disk membranes were greatly overgrown and misoriented, indicating defective disk morphogenesis. Immunoprecipitation studies showed that PROM1 interacted with protocadherin 21 (PCDH21), a photoreceptor-specific cadherin, and with actin filaments, both of which play critical roles in disk membrane morphogenesis. Collectively, our results identify what we believe to be a novel complex involved in photoreceptor disk morphogenesis and indicate a possible role for PROM1 and PCDH21 in macular degeneration.

Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by or . This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are initially sensitive to chemotherapy. Net-Cox toolbox is available at http://compbio.cs.umn.edu/Net-Cox/. Network-based computational models are attracting increasing attention in studying cancer genomics because molecular networks provide valuable information on the functional organizations of molecules in cells. Survival analysis mostly with the Cox proportional hazard model is widely used to predict or correlate gene expressions with time to an event of interest (outcome) in cancer genomics. Surprisingly, network-based survival analysis has not received enough attention. In this paper, we studied resistance to chemotherapy in ovarian cancer with a network-based Cox model, called Net-Cox. The experiments confirm that networks representing gene co-expression or functional relations can be used to improve the accuracy and the robustness of survival prediction of outcome in ovarian cancer treatment. The study also revealed subnetwork signatures that are enriched by extracellular matrix receptors and modulators and the downstream nuclear signaling components of extracellular signal-regulators, respectively. In particular, FBN1, which was detected as a signature gene of high confidence by Net-Cox with network information, was validated as a biomarker for predicting early recurrence in platinum-sensitive ovarian cancer patients in laboratory.

Ovarian cancer is a clinically and molecularly heterogeneous disease. The driving forces behind this variability are unknown. Here, we report wide variation in the expression of the DNA cytosine deaminase APOBEC3B, with elevated expression in the majority of ovarian cancer cell lines (three SDs above the mean of normal ovarian surface epithelial cells) and high-grade primary ovarian cancers. APOBEC3B is active in the nucleus of several ovarian cancer cell lines and elicits a biochemical preference for deamination of cytosines in 5′-TC dinucleotides. Importantly, examination of whole-genome sequence from 16 ovarian cancers reveals that APOBEC3B expression correlates with total mutation load as well as elevated levels of transversion mutations. In particular, high APOBEC3B expression correlates with C-to-A and C-to-G transversion mutations within 5′-TC dinucleotide motifs in early-stage high-grade serous ovarian cancer genomes, suggesting that APOBEC3B-catalyzed genomic uracil lesions are further processed by downstream DNA "repair" enzymes including error-prone translesion polymerases. These data identify a potential role for APOBEC3B in serous ovarian cancer genomic instability. Cancer Res; 73(24); 7222–31. ©2013 AACR.