Comprehensive Analysis of Hypermutation in Human Cancer

Campbell, Brittany; Light, Nicholas; Fabrizio, David; Zatzman, Matthew; Fuligni, Fabio; Borja, Richard de; Davidson, Scott; Edwards, Melissa; Elvin, Julia A.; Hodel, Karl P.; Zahurancik, Walter J.; Suo, Zucai; Lipman, Tatiana; Wimmer, Katharina; Kratz, Christian P.; Bowers, Daniel C.; Laetsch, Theodore W.; Dunn, Gavin P.; Johanns, Tanner M.; Grimmer, Matthew; Смирнов, Иван; Larouche, Valérie; Samuel, David; Bronsema, Annika; Osborn, Michael; Stearns, Duncan; Raman, Pichai; Cole, Kristina A.; Storm, Phillip B.; Yalon, Michal; Opocher, Enrico; Mason, Gary; Thomas, Gregory A.; Sabel, Magnus; George, Ben; Ziegler, David S.; Lindhorst, Scott; Issai, Vanan Magimairajan; Constantini, Shlomi; Toledano, Helen; Elhasid, Ronit; Farah, Roula; Dvir, Rina; Dirks, Peter B.; Huang, Annie; Galati, Melissa A.; Chung, Jiil; Ramaswamy, Vijay; Irwin, Meredith S.; Aronson, Melyssa; Durno, Carol; Taylor, Michael D.; Rechavi, Gideon; Maris, John M.; Bouffet, Éric; Hawkins, Cynthia; Costello, J; Meyn, Stephen; Pursell, Zachary F.; Malkin, David; Tabori, Uri; Shlien, Adam

doi:10.1016/j.cell.2017.09.048

Cited by 616 publications

(661 citation statements)

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“…1d). Tumours with more than 10 mutations per Mb have been referred to as 'hypermutators' , and are often related to deficiencies in mismatch repair (MMR) 10,11 . In this cohort, hypermutation occurred exclusively in H3.3 or H3.1 K27-wildtype (K27wt) high-grade gliomas with biallelic germline mutations in MSH6 or PMS2, with an extremely high mutational burden similar to the highest among adult tumours (in POLE-or POLQ-mutated carcinomas) 7,12 (Fig.…”

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confidence: 99%

The landscape of genomic alterations across childhood cancers

Gröbner¹,

Worst²,

Weischenfeldt³

et al. 2018

Nature

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The landscape of genomic alterations across childhood cancers a list of authors and affiliations appears at the end of the paper. OPENPan-cancer analyses that examine commonalities and differences among various cancer types have emerged as a powerful way to obtain novel insights into cancer biology. Here we present a comprehensive analysis of genetic alterations in a pan-cancer cohort including 961 tumours from children, adolescents, and young adults, comprising 24 distinct molecular types of cancer. Using a standardized workflow, we identified marked differences in terms of mutation frequency and significantly mutated genes in comparison to previously analysed adult cancers. Genetic alterations in 149 putative cancer driver genes separate the tumours into two classes: small mutation and structural/copy-number variant (correlating with germline variants). Structural variants, hyperdiploidy, and chromothripsis are linked to TP53 mutation status and mutational signatures. Our data suggest that 7-8% of the children in this cohort carry an unambiguous predisposing germline variant and that nearly 50% of paediatric neoplasms harbour a potentially druggable event, which is highly relevant for the design of future clinical trials.Cure rates for childhood cancers have increased to about 80% in recent decades, but cancer is still the leading cause of death by disease in the developed world among children over one year of age 1,2 . Furthermore, many children who survive cancer suffer from long-term sequelae of surgery, cytotoxic chemotherapy, and radiotherapy, including mental disabilities, organ toxicities, and secondary cancers 3 . A crucial step in developing more specific and less damaging therapies is the unravelling of the complete genetic repertoire of paediatric malignancies, which differ from adult malignancies in terms of their histopathological entities and molecular subtypes 4 . Over the past few years, many entityspecific sequencing efforts have been launched, but the few paediatric pan-cancer studies thus far have focused only on mutation frequencies, germline predisposition, and alterations in epigenetic regulators [4][5][6] .We have carried out a broad exploration of cancers in children, adolescents, and young adults, by incorporating small mutations and copy-number or structural variants on somatic and germline levels, and by identifying putative cancer genes and comparing them to those previously reported in adult cancers by The Cancer Genome Atlas (TCGA) 7 . We have also examined mutational signatures and potential drug targets. The compendium of genetic alterations presented here is available to the scientific community at http://www.pedpancan.com.This integrative analysis includes 24 types of cancer and covers all major childhood cancer entities, many of which occur exclusively in children 8 (Fig. 1, Supplementary Table 1). Ninety-five per cent of the patients in this study were diagnosed during childhood or adolescence (aged 18 years or younger) and 5% as young adults (up to 25 years) (Extended Data ...

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confidence: 99%

The landscape of genomic alterations across childhood cancers

Gröbner¹,

Worst²,

Weischenfeldt³

et al. 2018

Nature

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1,049

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“…The upper‐SCC has the highest number of significantly dependent pairs (see , and http://sdmn.leloir.org.ar, bottom link), while other type of cancers with high number of tested pairs and high mutational burden (Campbell et al., ; Chalmers et al., ) have considerable less pairs of dependent mutations ( and data in ).…”

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confidence: 99%

Comutation and exclusion analysis in human tumors: A tool for cancer biology studies and for rational selection of multitargeted therapeutic approaches

et al. 2019

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Malignant tumors originate from somatic mutations and other genomic and epigenomic alterations, which lead to loss of control of the cellular circuitry. These alterations present patterns of co‐occurrence and mutual exclusivity that can influence prognosis and modify response to drugs, highlighting the need for multitargeted therapies. Studies in this area have generally focused in particular malignancies and considered whole genes instead of specific mutations, ignoring the fact that different alterations in the same gene can have widely different effects. Here, we present a comprehensive analysis of co‐dependencies of individual somatic mutations in the whole spectrum of human tumors. Combining multitesting with conditional and expected mutational probabilities, we have discovered rules governing the codependencies of driver and nondriver mutations. We also uncovered pairs and networks of comutations and exclusions, some of them restricted to certain cancer types and others widespread. These pairs and networks are not only of basic but also of clinical interest, and can be of help in the selection of multitargeted antitumor therapies. In this respect, recurrent driver comutations suggest combinations of drugs that might be effective in the clinical setting, while recurrent exclusions indicate combinations unlikely to be useful.

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“…For example, it has been asserted that many false drivers have been wrongly reported due to the difficulty to construct a proper background model that accounts for highly mutated samples, cancer types and genomic loci 9 . In fact, in many pipelines it is common practice to filter out hyper-mutated samples prior to the searching of driver genes 40,41 , resulting the loss of much of the data. Different choices of normalization techniques and thresholds for hyper-mutated samples may bring very different results, leading to lingering uncertainty.…”

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confidence: 99%

Modeling Functional Genetic Alteration in Cancer Reveals New Candidate Driver Genes

Brandes

Linial

2018

Preprint

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Compiling the catalogue of genes actively involved in tumorigenesis (known as cancer drivers) is an ongoing endeavor, with profound implications to the understanding of tumorigenesis and treatment of the disease. An abundance of computational methods have been developed to screening the genome for candidate driver genes based on genomic data of somatic mutations in tumors. Most methods rely on detecting genes displaying excessive mutation rates compared to some background model. This approach is susceptible to false discoveries, due to its sensitivity to the assumptions of the background model, such as the need to account for hyper-mutated samples, cancer types and genomic loci. We present a fundamentally different approach. Instead of focusing on the number of mutations, we examine their content, and their expected effects on the functions of genes. We use a machine-learning model to predict functional effect scores of somatic mutations. For each gene, we compare the distribution of observed effect scores with the distribution expected at random, and report genes showing significant bias. By applying our framework on the ~20k protein-coding human genes, we detected 593 genes showing significant bias towards harmful mutations in the context of cancer. In contrast, we found only 6 significant genes biased in the opposite direction. The list of 593 genes, constructed without any prior knowledge of their role in cancer, shows an overwhelming overlap with known cancer driver genes, but also highlights many overlooked genes. These overlooked genes are promising candidates for novel cancer drivers. Our model is generic and is not restricted to the context of cancer. Applying the same framework to data of human-population genetic variation reveals the opposite trend. Unlike cancer, which is dominated by a bias towards harmful mutations, long-term evolution in healthy individuals results a bias towards less harmful mutations. The underlying assumptions of our framework are minimal, making it ideal for analyzing genetic data in search of genes subjected to positive or negative selection. It is fully open sourced and available for installation and use. Our framework presents a substantial development towards the application of state-of-the-art machine-learning algorithms in genetic studies.

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Comprehensive Analysis of Hypermutation in Human Cancer

Cited by 616 publications

References 48 publications

The landscape of genomic alterations across childhood cancers

The landscape of genomic alterations across childhood cancers

Comutation and exclusion analysis in human tumors: A tool for cancer biology studies and for rational selection of multitargeted therapeutic approaches

Modeling Functional Genetic Alteration in Cancer Reveals New Candidate Driver Genes

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