Recent genomic analyses of pathologically-defined tumor types identify “within-a-tissue” disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head & neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multi-platform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All datasets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies.

The pan-cancer analysis of whole genomes The expansion of whole-genome sequencing studies from individual ICGC and TCGA working groups presented the opportunity to undertake a meta-analysis of genomic features across tumour types. To achieve this, the PCAWG Consortium was established. A Technical Working Group implemented the informatics analyses by aggregating the raw sequencing data from different working groups that studied individual tumour types, aligning the sequences to the human genome and delivering a set of high-quality somatic mutation calls for downstream analysis (Extended Data Fig. 1). Given the recent meta-analysis

SUMMARY Here we describe a multiplexed immunohistochemical platform, with computational image processing workflows including image cytometry, enabling simultaneous evaluation of 12 biomarkers in one formalin-fixed paraffin-embedded tissue section. To validate this platform, we used tissue microarrays containing 38 archival head and neck squamous cell carcinomas, and revealed differential immune profiles based on lymphoid and myeloid cell densities, correlating with human papilloma virus status and prognosis. Based on these results, we investigated 24 pancreatic ductal adenocarcinomas from patients who received neoadjuvant GVAX vaccination, and revealed that response to therapy correlated with degree of mono-myelocytic cell density, and percentages of CD8+ T cells expressing T cell exhaustion markers. These data highlight the utility of in situ immune monitoring for patient stratification, and provide digital image processing pipelines (https://github.com/multiplexIHC/cppipe) to the community for examining immune complexity in precious tissue sections, where phenotype and tissue architecture are preserved to thus improve biomarker discovery and assessment.

For nearly a century developmental biologists have recognized that cells from embryos can differ in their potential to differentiate into distinct cell types. Recently, it has been recognized that embryonic stem cells derived from both mice and humans display two stable yet epigenetically distinct states of pluripotency, naïve and primed. We now show that nicotinamide-N-methyl transferase (NNMT) and metabolic state regulate pluripotency in hESCs. Specifically, in naïve hESCs NNMT and its enzymatic product 1-methylnicotinamide (1-MNA) are highly upregulated, and NNMT is required for low SAM levels and H3K27me3 repressive state. NNMT consumes SAM in naïve cells, making it unavailable for histone methylation that represses Wnt and activates HIF pathway in primed hESCs. These data support the hypothesis that the metabolome regulates the epigenetic landscape of the earliest steps in human development.

The detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.

Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, miRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We found that incorporating molecular data with clinical variables yielded statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2–23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data.

Gain-of-function mutations in exon 3 of beta-catenin (CTNNB1) are specific for Wilms' tumors that have lost WT1, but 50% of WT1-mutant cases lack such "hot spot" mutations. To ask whether stabilization of beta-catenin might be essential after WT1 loss, and to identify downstream target genes, we compared expression profiles in WT1-mutant versus WT1 wild-type Wilms' tumors. Supervised and nonsupervised hierarchical clustering of the expression data separated these two classes of Wilms' tumor. The WT1-mutant tumors overexpressed genes encoding myogenic and other transcription factors (MOX2, LBX1, SIM2), signaling molecules (TGFB2, FST, BMP2A), extracellular Wnt inhibitors (WIF1, SFRP4), and known beta-catenin/TCF targets (FST, CSPG2, CMYC). Beta-Catenin/TCF target genes were overexpressed in the WT1-mutant tumors even in the absence of CTNNB1 exon 3 mutations, and complete sequencing revealed gain-of-function mutations elsewhere in the CTNNB1 gene in some of these tumors, increasing the overall mutation frequency to 75%. Lastly, we identified and validated a novel direct beta-catenin target gene, GAD1, among the WT1-mutant signature genes. These data highlight two molecular classes of Wilms' tumor, and indicate strong selection for stabilization of beta-catenin in the WT1-mutant class. Beta-Catenin stabilization can initiate tumorigenesis in other systems, and this mechanism is likely critical in tumor formation after loss of WT1.

Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks–DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models.