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

No abstract

Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations. We describe a new method, PhyloWGS, which can be applied to whole-genome sequencing data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutations and population frequencies of structural variations. We introduce a principled phylogenic correction for VAFs in loci affected by copy number alterations and we show that this correction greatly improves subclonal reconstruction compared to existing methods. PhyloWGS is free, open-source software, available at https://github.com/morrislab/phylowgs.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0602-8) contains supplementary material, which is available to authorized users.

Pancreas cancer (PC), a highly aggressive tumour type with uniformly poor prognosis, is an exemplar of the classical view of cancer development based on stepwise progression1. The current progression model, based on analyses of precursor lesions termed pancreatic intraepithelial neoplasm (PanINs) lesions, makes two predictions: 1) PC develops through a particular sequence of genetic alterations2–5 (KRAS > CDKN2A > TP53/SMAD4); and 2) the evolutionary trajectory of PC progression is gradual because each alteration is acquired independently. One shortcoming of this nearly two decade old contention is that clonally expanded precursor lesions have been identified that do not always belong to the tumour lineage2,5–9, indicating that the evolutionary trajectory of the tumour lineage and precursor lesions can be divergent. This prevailing view of tumourigenesis has contributed to the clinical notion that PC evolves slowly and presents at a late stage10. However, the propensity for this disease to rapidly metastasize and the inability to improve patient outcomes despite efforts aimed at early detection11, argue that PC progression is anything but gradual. By tracking DNA copy number changes and their associated rearrangements from tumour-enriched genomes using novel informatics tools, we found that PC tumourigenesis neither is gradual nor follows the accepted mutation order. Two-thirds of tumours harbour complex rearrangement patterns associated with mitotic errors, consistent with punctuated equilibrium as the principal evolutionary trajectory12. In a subset of cases, the consequence of such errors was the simultaneous, rather than sequential, knockout of canonical preneoplastic genetic drivers that likely set-off invasive cancer growth. These findings challenge the current model of PC tumourigenesis and provide novel insights into the mutational processes giving rise to these aggressive tumours.

Highlights d PDAC regional heterogeneity stems from sub-tumor microenvironments (subTMEs) d SubTMEs exhibit distinct immune phenotypes and CAF differentiation states d SubTMEs execute distinct tumor-promoting and chemoprotective functions d Intratumoral subTME co-occurrence links stromal heterogeneity to patient outcome

Signature-based subtyping may guide personalized therapy of PDAC in the context of biomarker-driven prospective trials.

Identification of clinically actionable molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcome. Intertumoral metabolic heterogeneity contributes to cancer survival and the balance between distinct metabolic pathways may influence PDAC outcome. We hypothesized that PDAC can be stratified into prognostic metabolic subgroups based on alterations in the expression of genes involved in glycolysis and cholesterol synthesis.Experimental Design: We performed bioinformatics analysis of genomic, transcriptomic, and clinical data in an integrated cohort of 325 resectable and nonresectable PDAC. The resectable datasets included retrospective The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) cohorts. The nonresectable PDAC cohort studies included prospective COMPASS, PanGen, and BC Cancer Personalized OncoGenomics program (POG).Results: On the basis of the median normalized expression of glycolytic and cholesterogenic genes, four subgroups were identified: quiescent, glycolytic, cholesterogenic, and mixed. Glycolytic tumors were associated with the shortest median survival in resectable (log-rank test P ¼ 0.018) and metastatic settings (log-rank test P ¼ 0.027). Patients with cholesterogenic tumors had the longest median survival. KRAS and MYC-amplified tumors had higher expression of glycolytic genes than tumors with normal or lost copies of the oncogenes (Wilcoxon rank sum test P ¼ 0.015). Glycolytic tumors had the lowest expression of mitochondrial pyruvate carriers MPC1 and MPC2. Glycolytic and cholesterogenic gene expression correlated with the expression of prognostic PDAC subtype classifier genes.Conclusions: Metabolic classification specific to glycolytic and cholesterogenic pathways provides novel biological insight into previously established PDAC subtypes and may help develop personalized therapies targeting unique tumor metabolic profiles.See related commentary by Mehla and Singh, p. 6

Graphical Abstract Highlights d Higher cell cycle progression in PDAC metastases; increases with driver gene loss d Half of PDACs are hypoxic and are associated with subtypes and treatment response d Paired tumors show molecular conservation and Halstedian progression d Multiple PDACs arising in the same pancreas are intraparenchymal metastases In Brief Connor et al. molecularly characterize primary and metastatic PDAC and show conserved alterations between primary and metastatic lesions. Clinical features outperform molecular alterations in survival analyses, but cell cycle progression and hypoxia signatures may inform clinical practice. SUMMARYWe integrated clinical, genomic, and transcriptomic data from 224 primaries and 95 metastases from 289 patients to characterize progression of pancreatic ductal adenocarcinoma (PDAC). Driver gene alterations and mutational and expression-based signatures were preserved, with truncations, inversions, and translocations most conserved. Cell cycle progression (CCP) increased with sequential inactivation of tumor suppressors, yet remained higher in metastases, perhaps driven by cell cycle regulatory gene variants. Half of the cases were hypoxic by expression markers, overlapping with molecular subtypes. Paired tumor heterogeneity showed cancer cell migration by Halstedian progression. Multiple PDACs arising synchronously and metachronously in the same pancreas were actually intra-parenchymal metastases, not independent primary tumors. Established clinical co-variates dominated survival analyses, although CCP and hypoxia may inform clinical practice. SignificancePancreatic ductal adenocarcinoma has dismal prognosis due to rapid metastatic dissemination. This rigorous study of paired and unpaired tumors informs both progression mechanisms and therapy. First, there was no evidence of discrete metastases enabling genes. Second, greater CCP in metastases may explain aggressive behavior and correspond to treatment response. Third, hypoxia signature was associated with chemotherapy resistance. Fourth, comparing mutations in paired samples revealed sequential progression from primary to lymph node to distant metastases, and sequencing synchronous and metachronous lesions distinguished these as recurrences rather than separate primaries, resolving this clinical conundrum. Finally, clinical features outperformed and were independent of molecular alterations in survival analyses, implying greater insight is needed before molecular profiling broadly informs therapy.