Assessing the clinical utility of cancer genomic and proteomic data across tumor types

Yuan, Yuan; Allen, Eliezer M. Van; Omberg, Larsson; Wagle, Nikhil; Amin‐Mansour, Ali; Sokolov, Artem; Byers, Lauren A.; Xu, Yanxun; Hess, Kenneth R.; Diao, Lixia; Han, Leng; Huang, Xuelin; Lawrence, Michael S.; Weinstein, John N.; Stuart, Joshua M.; Mills, Gordon B.; Garraway, Levi A.; Margolin, Adam A.; Getz, Gad; Liang, Han

doi:10.1038/nbt.2940

Cited by 264 publications

(274 citation statements)

References 49 publications

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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

1,108

1,156

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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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mentioning

confidence: 99%

The landscape of genomic alterations across childhood cancers

Gröbner¹,

Worst²,

Weischenfeldt³

et al. 2018

Nature

1,108

1,156

View full text Add to dashboard Cite

show abstract

“…We next assessed the prognostic utility of a methylation signature for each type of cancer. Clinical and demographic characteristics, including age, sex, race, and American Joint Committee on Cancer stage, were included in the analysis as well, because the prognostic power can be greatly improved by combining this information with informative molecular data (17). For each cancer category, we used two different statistical learning algorithms, lasso and boosting, to reduce the dimensionality of markers and construct a predictive model.…”

Section: Resultsmentioning

confidence: 99%

DNA methylation markers for diagnosis and prognosis of common cancers

Hao

Luo

Krawczyk

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

368

278

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The ability to identify a specific cancer using minimally invasive biopsy holds great promise for improving the diagnosis, treatment selection, and prediction of prognosis in cancer. Using whole-genome methylation data from The Cancer Genome Atlas (TCGA) and machine learning methods, we evaluated the utility of DNA methylation for differentiating tumor tissue and normal tissue for four common cancers (breast, colon, liver, and lung). We identified cancer markers in a training cohort of 1,619 tumor samples and 173 matched adjacent normal tissue samples. We replicated our findings in a separate TCGA cohort of 791 tumor samples and 93 matched adjacent normal tissue samples, as well as an independent Chinese cohort of 394 tumor samples and 324 matched adjacent normal tissue samples. The DNA methylation analysis could predict cancer versus normal tissue with more than 95% accuracy in these three cohorts, demonstrating accuracy comparable to typical diagnostic methods. This analysis also correctly identified 29 of 30 colorectal cancer metastases to the liver and 32 of 34 colorectal cancer metastases to the lung. We also found that methylation patterns can predict prognosis and survival. We correlated differential methylation of CpG sites predictive of cancer with expression of associated genes known to be important in cancer biology, showing decreased expression with increased methylation, as expected. We verified gene expression profiles in a mouse model of hepatocellular carcinoma. Taken together, these findings demonstrate the utility of methylation biomarkers for the molecular characterization of cancer, with implications for diagnosis and prognosis.

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“…However, some other information should also be considered. For example, clinical features, such as ages, genders, pathological stages and blood test records, can better predict the overall survivals than omics data [78]. Unlike the omics data which only contain molecular level information of cancer tissues, clinical features provide more macro-scale perspectives of patients.…”

Section: Discussionmentioning

confidence: 99%

Integrative clustering methods of multi‐omics data for molecule‐based cancer classifications

Wang

2016

Quant. Biol.

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One goal of precise oncology is to re-classify cancer based on molecular features rather than its tissue origin. Integrative clustering of large-scale multi-omics data is an important way for molecule-based cancer classification. The data heterogeneity and the complexity of inter-omics variations are two major challenges for the integrative clustering analysis. According to the different strategies to deal with these difficulties, we summarized the clustering methods as three major categories: direct integrative clustering, clustering of clusters and regulatory integrative clustering. A few practical considerations on data pre-processing, post-clustering analysis and pathway-based analysis are also discussed.

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Assessing the clinical utility of cancer genomic and proteomic data across tumor types

Cited by 264 publications

References 49 publications

The landscape of genomic alterations across childhood cancers

The landscape of genomic alterations across childhood cancers

DNA methylation markers for diagnosis and prognosis of common cancers

Integrative clustering methods of multi‐omics data for molecule‐based cancer classifications

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