Emerging landscape of oncogenic signatures across human cancers.

Cancer therapy is challenged by the diversity of molecular implementations of oncogenic processes and by the resulting variation in therapeutic responses. Projects such as The Cancer Genome Atlas (TCGA) provide molecular tumor maps in unprecedented detail. The interpretation of these maps remains a major challenge. Here we distilled thousands of genetic and epigenetic features altered in cancers to ∼500 selected functional events (SFEs). Using this simplified description, we derived a hierarchical classification of 3,299 TCGA tumors from 12 cancer types. The top classes are dominated by either mutations (M class) or copy number changes (C class). This distinction is clearest at the extremes of genomic instability, indicating the presence of different oncogenic processes. The full hierarchy shows functional event patterns characteristic of multiple cross-tissue groups of tumors, termed oncogenic signature classes. Targetable functional events in a tumor class are suggestive of class-specific combination therapy. These results may assist in the definition of clinical trials to match actionable oncogenic signatures with personalized therapies

Validating the concept of mutational signatures with isogenic cell models.

The diversity of somatic mutations in human cancers can be decomposed into individual mutational signatures, patterns of mutagenesis that arise because of DNA damage and DNA repair processes that have occurred in cells as they evolved towards malignancy. Correlations between mutational signatures and environmental exposures, enzymatic activities and genetic defects have been described, but human cancers are not ideal experimental systems-the exposures to different mutational processes in a patient's lifetime are uncontrolled and any relationships observed can only be described as an association. Here, we demonstrate the proof-of-principle that it is possible to recreate cancer mutational signatures in vitro using CRISPR-Cas9-based gene-editing experiments in an isogenic human-cell system. We provide experimental and algorithmic methods to discover mutational signatures generated under highly experimentally-controlled conditions. Our in vitro findings strikingly recapitulate in vivo observations of cancer data, fundamentally validating the concept of (particularly) endogenously-arising mutational signatures

Whole-genome sequencing identifies genetic alterations in pediatric low-grade gliomas

The most common pediatric brain tumors are low-grade gliomas (LGGs). We used whole-genome sequencing to identify multiple new genetic alterations involving BRAF, RAF1, FGFR1, MYB, MYBL1 and genes with histone-related functions, including H3F3A and ATRX, in 39 LGGs and low-grade glioneuronal tumors (LGGNTs). Only a single non-silent somatic alteration was detected in 24 of 39 (62%) tumors. Intragenic duplications of the portion of FGFR1 encoding the tyrosine kinase domain (TKD) and rearrangements of MYB were recurrent and mutually exclusive in 53% of grade II diffuse LGGs. Transplantation of Trp53-null neonatal astrocytes expressing FGFR1 with the duplication involving the TKD into the brains of nude mice generated high-grade astrocytomas with short latency and 100% penetrance. FGFR1 with the duplication induced FGFR1 autophosphorylation and upregulation of the MAPK/ERK and PI3K pathways, which could be blocked by specific inhibitors. Focusing on the therapeutically challenging diffuse LGGs, our study of 151 tumors has discovered genetic alterations and potential therapeutic targets across the entire range of pediatric LGGs and LGGNTs.Jinghui Zhang, Gang Wu, Claudia P Miller, Ruth G Tatevossian, James D Dalton, Bo Tang, Wilda Orisme, Chandanamali Punchihewa, Matthew Parker, Ibrahim Qaddoumi, Fredrick A Boop, Charles Lu, Cyriac Kandoth, Li Ding, Ryan Lee, Robert Huether, Xiang Chen, Erin Hedlund, Panduka Nagahawatte, Michael Rusch, Kristy Boggs, Jinjun Cheng, Jared Becksfort, Jing Ma, Guangchun Song, Yongjin Li, Lei Wei, Jianmin Wang, Sheila Shurtleff, John Easton, David Zhao, Robert S Fulton, Lucinda L Fulton, David J Dooling, Bhavin Vadodaria, Heather L Mulder, Chunlao Tang, Kerri Ochoa, Charles G Mullighan, Amar Gajjar, Richard Kriwacki, Denise Sheer, Richard J Gilbertson, Elaine R Mardis, Richard K Wilson, James R Downing, Suzanne J Baker and David W Elliso

Mapping genetic variations to three- dimensional protein structures to enhance variant interpretation: a proposed framework

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods

The molecular diversity of Luminal A breast tumors

Breast cancer is a collection of diseases with distinct molecular traits, prognosis, and therapeutic options. Luminal A breast cancer is the most heterogeneous, both molecularly and clinically. Using genomic data from over 1,000 Luminal A tumors from multiple studies, we analyzed the copy number and mutational landscape of this tumor subtype. This integrated analysis revealed four major subtypes defined by distinct copy-number and mutation profiles. We identified an atypical Luminal A subtype characterized by high genomic instability, TP53 mutations, and increased Aurora kinase signaling; these genomic alterations lead to a worse clinical prognosis. Aberrations of chromosomes 1, 8, and 16, together with PIK3CA, GATA3, AKT1, and MAP3K1 mutations drive the other subtypes. Finally, an unbiased pathway analysis revealed multiple rare, but mutually exclusive, alterations linked to loss of activity of co-repressor complexes N-Cor and SMRT. These rare alterations were the most prevalent in Luminal A tumors and may predict resistance to endocrine therapy. Our work provides for a further molecular stratification of Luminal A breast tumors, with potential direct clinical implications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10549-013-2699-3) contains supplementary material, which is available to authorized users