We have refined our understanding of genetic events in myeloma and identified clinically relevant mutations that may be used to better stratify patients at presentation.

Liver cancer, which is most often associated with virus infection, is prevalent worldwide, and its underlying etiology and genomic structure are heterogeneous. Here we provide a whole-genome landscape of somatic alterations in 300 liver cancers from Japanese individuals. Our comprehensive analysis identified point mutations, structural variations (STVs), and virus integrations, in noncoding and coding regions. We discovered mutational signatures related to liver carcinogenesis and recurrently mutated coding and noncoding regions, such as long intergenic noncoding RNA genes (NEAT1 and MALAT1), promoters, CTCF-binding sites, and regulatory regions. STV analysis found a significant association with replication timing and identified known (CDKN2A, CCND1, APC, and TERT) and new (ASH1L, NCOR1, and MACROD2) cancer-related genes that were recurrently affected by STVs, leading to altered expression. These results emphasize the value of whole-genome sequencing analysis in discovering cancer driver mutations and understanding comprehensive molecular profiles of liver cancer, especially with regard to STVs and noncoding mutations.

Understanding the profile of oncogene and tumor suppressor gene mutations with their interactions and impact on the prognosis of multiple myeloma (MM) can improve the definition of disease subsets and identify pathways important in disease pathobiology. Using integrated genomics of 1273 newly diagnosed patients with MM, we identified 63 driver genes, some of which are novel, including ,, ,, and Oncogene mutations are significantly more clonal than tumor suppressor mutations, indicating they may exert a bigger selective pressure. Patients with more driver gene abnormalities are associated with worse outcomes, as are identified mechanisms of genomic instability. Oncogenic dependencies were identified between mutations in driver genes, common regions of copy number change, and primary translocation and hyperdiploidy events. These dependencies included associations with t(4;14) and mutations in, , and; t(11;14) with mutations in and; t(14;16) with mutations in ,, , and; and hyperdiploidy with gain 11q, mutations in , and rearrangements. These associations indicate that the genomic landscape of myeloma is predetermined by the primary events upon which further dependencies are built, giving rise to a nonrandom accumulation of genetic hits. Understanding these dependencies may elucidate potential evolutionary patterns and lead to better treatment regimens.

Patients with newly diagnosed multiple myeloma (NDMM) with high-risk disease are in need of new treatment strategies to improve the outcomes. Multiple clinical, cytogenetic, or gene expression features have been used to identify high-risk patients, each of which has significant weaknesses. Inclusion of molecular features into risk stratification could resolve the current challenges. In a genome-wide analysis of the largest set of molecular and clinical data established to date from NDMM, as part of the Myeloma Genome Project, we have defined DNA drivers of aggressive clinical behavior. Whole-genome and exome data from 1273 NDMM patients identified genetic factors that contribute significantly to progression free survival (PFS) and overall survival (OS) (cumulative R = 18.4% and 25.2%, respectively). Integrating DNA drivers and clinical data into a Cox model using 784 patients with ISS, age, PFS, OS, and genomic data, the model has a cumlative R of 34.3% for PFS and 46.5% for OS. A high-risk subgroup was defined by recursive partitioning using either a) bi-allelic TP53 inactivation or b) amplification (≥4 copies) of CKS1B (1q21) on the background of International Staging System III, comprising 6.1% of the population (median PFS = 15.4 months; OS = 20.7 months) that was validated in an independent dataset. Double-Hit patients have a dire prognosis despite modern therapies and should be considered for novel therapeutic approaches.

The association of genetic lesions detected by FISH with survival was analyzed in 1069 patients with newly presenting myeloma treated in the Medical Research Council (MRC) Myeloma IX trial, with the aim of identifying patients associated with the worst prognosis. A comprehensive FISH panel was performed, and the lesions associated with short PFS and OS in multivariate analysis were +1q21, del(17p13) and an adverse IGH translocation group incorporating t(4;14), t(14;16) and t(14;20). These lesions frequently co-segregated, and there was an association between the accumulation of these adverse FISH lesions and a progressive impairment of survival. This observation was used to define a series of risk groups based on number of adverse lesions. Taking this approach we defined a favorable risk group by the absence of adverse genetic lesions, an intermediate group with 1 adverse lesion and a high risk group defined by the co-segregation of >1 adverse lesion. This genetic grouping was independent of the ISS and so was integrated with the ISS to identify an ultra-high risk group defined by ISS II or III and >1 adverse lesion. This group constituted 13.8 % of patients and was associated with a median OS of 19.4 months.