Passenger Mutations Research Articles

An emerging place for lung cancer genomics in 2013.

Lung cancer is a disease with a dismal prognosis and is the biggest cause of cancer deaths in many countries. Nonetheless, rapid technological developments in genome science promise more effective prevention and treatment strategies. Since the Human Genome Project, scientific advances have revolutionized the diagnosis and treatment of human cancers, including thoracic cancers. The latest, massively parallel, next generation sequencing (NGS) technologies offer much greater sequencing capacity than traditional, capillary-based Sanger sequencing. These modern but costly technologies have been applied to whole genome-, and whole exome sequencing (WGS and WES) for the discovery of mutations and polymorphisms, transcriptome sequencing for quantification of gene expression, small ribonucleic acid (RNA) sequencing for microRNA profiling, large scale analysis of deoxyribonucleic acid (DNA) methylation and chromatin immunoprecipitation mapping of DNA-protein interaction. With the rise of personalized cancer care, based on the premise of precision medicine, sequencing technologies are constantly changing. To date, the genomic landscape of lung cancer has been captured in several WGS projects. Such work has not only contributed to our understanding of cancer biology, but has also provided impetus for technical advances that may improve our ability to accurately capture the cancer genome. Issues such as short read lengths contribute to sequenced libraries that contain challenging gaps in the aligned genome. Emerging platforms promise longer reads as well as the ability to capture a range of epigenomic signals. In addition, ongoing optimization of bioinformatics strategies for data analysis and interpretation are critical, especially for the differentiation between driver and passenger mutations. Moreover, broader deployment of these and future generations of platforms, coupled with an increasing bioinformatics workforce with access to highly sophisticated technologies, could see many of these discoveries translated to the clinic at a rapid pace. We look forward to these advances making a difference for the many patients we treat in the Asia-Pacific region and around the world.

Abstract PR07: Integrative functional genomics of breast cancer

Abstract Cancer is a disease of the genome. Using genomic approaches alone, it is difficult to ascertain which variants drive pathogenesis because most of these are functionally irrelevant passenger mutations. Highly recurrent events point to drivers; however, the majority of genomic alterations in tumors occur at low frequency. In addition, the products of many oncogenes and tumor suppressor genes are not druggable. However, such abnormalities can cause unanticipated gene/pathway dependencies (synthetic lethality), providing alternate avenues for drug development. Lentiviral-based shRNA libraries enables genome-wide screening of cultured cancer cells in a pooled format, facilitating the identification of genes necessary for cancer cell proliferation and survival in cultured cells. We screened a panel of > 75 breast cancer cell lines using an 80,000 lentiviral shRNA library targeting 16,000 genes and integrated these screen results with gene expression, copy-number variation (CNV), and somatic mutations derived from the same lines. We identified several classes of gene dropouts, which are required for survival or growth of all (or some) cell lines, irrespective of subtype and several subtype-specific genes, whose essentiality is restricted to a defined subtype. These include well-known HER2 subtype-specific genes, ERBB2, ERBB3, and TFAP2C and luminal subtype-specific gene FOXA1, SPDEF, GATA3, and ESR1. In addition, the unprecedented number of lines allows the identification of synthetic lethal interaction with common breast cancer somatic mutations or CNV such as PIK3CA and PTEN or 9p21 deletion, respectively, which encodes the CDKN2A, CDKN2B, and MTAP genes. Finally, integration of gene expression, copy number variation, and functional screening results identified potential biomarkers with common genetic changes and functional drivers. Overall, our study represents an extensive functional genetic survey of breast cancer, reveals complexities between genomic and functional genomic results, and uncovers several unexpected gene dependencies and potential novel therapeutic target for each subtype. This abstract is also presented as Poster A033. Citation Format: Richard Marcotte, Azin Sayad, Cathy Iorio, Maliha Haider, Jason Moffat, Benjamin G. Neel. Integrative functional genomics of breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research: Genetics, Biology, and Clinical Applications; Oct 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2013;11(10 Suppl):Abstract nr PR07.

Guanine holes are prominent targets for mutation in cancer and inherited disease.

Single base substitutions constitute the most frequent type of human gene mutation and are a leading cause of cancer and inherited disease. These alterations occur non-randomly in DNA, being strongly influenced by the local nucleotide sequence context. However, the molecular mechanisms underlying such sequence context-dependent mutagenesis are not fully understood. Using bioinformatics, computational and molecular modeling analyses, we have determined the frequencies of mutation at G•C bp in the context of all 64 5′-NGNN-3′ motifs that contain the mutation at the second position. Twenty-four datasets were employed, comprising >530,000 somatic single base substitutions from 21 cancer genomes, >77,000 germline single-base substitutions causing or associated with human inherited disease and 16.7 million benign germline single-nucleotide variants. In several cancer types, the number of mutated motifs correlated both with the free energies of base stacking and the energies required for abstracting an electron from the target guanines (ionization potentials). Similar correlations were also evident for the pathological missense and nonsense germline mutations, but only when the target guanines were located on the non-transcribed DNA strand. Likewise, pathogenic splicing mutations predominantly affected positions in which a purine was located on the non-transcribed DNA strand. Novel candidate driver mutations and tissue-specific mutational patterns were also identified in the cancer datasets. We conclude that electron transfer reactions within the DNA molecule contribute to sequence context-dependent mutagenesis, involving both somatic driver and passenger mutations in cancer, as well as germline alterations causing or associated with inherited disease.

Low-copy piggyBac transposon mutagenesis in mice identifies genes driving melanoma

Despite considerable efforts to sequence hypermutated cancers such as melanoma, distinguishing cancer-driving genes from thousands of recurrently mutated genes remains a significant challenge. To circumvent the problematic background mutation rates and identify new melanoma driver genes, we carried out a low-copy piggyBac transposon mutagenesis screen in mice. We induced eleven melanomas with mutation burdens that were 100-fold lower relative to human melanomas. Thirty-eight implicated genes, including two known drivers of human melanoma, were classified into three groups based on high, low, or background-level mutation frequencies in human melanomas, and we further explored the functional significance of genes in each group. For two genes overlooked by prevailing discovery methods, we found that loss of membrane associated guanylate kinase, WW and PDZ domain containing 2 and protein tyrosine phosphatase, receptor type, O cooperated with the v-raf murine sarcoma viral oncogene homolog B (BRAF) recurrent V600E mutation to promote cellular transformation. Moreover, for infrequently mutated genes often disregarded by current methods, we discovered recurrent mitogen-activated protein kinase kinase kinase 1 (Map3k1)-activating insertions in our screen, mirroring recurrent MAP3K1 up-regulation in human melanomas. Aberrant expression of Map3k1 enabled growth factor-autonomous proliferation and drove BRAF-independent ERK signaling, thus shedding light on alternative means of activating this prominent signaling pathway in melanoma. In summary, our study contributes several previously undescribed genes involved in melanoma and establishes an important proof-of-principle for the utility of the low-copy transposon mutagenesis approach for identifying cancer-driving genes, especially those masked by hypermutation.

Comparative Oncogenomic Analysis of Copy Number Alterations in Human and Zebrafish Tumors Enables Cancer Driver Discovery

The identification of cancer drivers is a major goal of current cancer research. Finding driver genes within large chromosomal events is especially challenging because such alterations encompass many genes. Previously, we demonstrated that zebrafish malignant peripheral nerve sheath tumors (MPNSTs) are highly aneuploid, much like human tumors. In this study, we examined 147 zebrafish MPNSTs by massively parallel sequencing and identified both large and focal copy number alterations (CNAs). Given the low degree of conserved synteny between fish and mammals, we reasoned that comparative analyses of CNAs from fish versus human MPNSTs would enable elimination of a large proportion of passenger mutations, especially on large CNAs. We established a list of orthologous genes between human and zebrafish, which includes approximately two-thirds of human protein-coding genes. For the subset of these genes found in human MPNST CNAs, only one quarter of their orthologues were co-gained or co-lost in zebrafish, dramatically narrowing the list of candidate cancer drivers for both focal and large CNAs. We conclude that zebrafish-human comparative analysis represents a powerful, and broadly applicable, tool to enrich for evolutionarily conserved cancer drivers.

DrGaP: A Powerful Tool for Identifying Driver Genes and Pathways in Cancer Sequencing Studies

Cancers are caused by the accumulation of genomic alterations. Driver mutations are required for the cancer phenotype, whereas passenger mutations are irrelevant to tumor development and accumulate through DNA replication. A major challenge facing the field of cancer genome sequencing is to identify cancer-associated genes with mutations that drive the cancer phenotype. Here, we describe a powerful and flexible statistical framework for identifying driver genes and driver signaling pathways in cancer genome-sequencing studies. Biological knowledge of the mutational process in tumors is fully integrated into our statistical models and includes such variables as the length of protein-coding regions, transcript isoforms, variation in mutation types, differences in background mutation rates, the redundancy of genetic code, and multiple mutations in one gene. This framework provides several significant features that are not addressed or naively obtained by previous methods. In particular, on the observation of low prevalence of somatic mutations in individual tumors, we propose a heuristic strategy to estimate the mixture proportion of chi-square distribution of likelihood ratio test (LRT) statistics. This provides significantly increased statistical power compared to regular LRT. Through a combination of simulation and analysis of TCGA cancer sequencing study data, we demonstrate high accuracy and sensitivity in our methods. Our statistical methods and several auxiliary bioinformatics tools have been incorporated into a computational tool, DrGaP. The newly developed tool is immediately applicable to cancer genome-sequencing studies and will lead to a more complete identification of altered driver genes and driver signaling pathways in cancer.

Another Option in Our KIT of Effective Therapies for Advanced Melanoma

Another Option in Our KIT of Effective Therapies for Advanced Melanoma

Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP.

We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: http://www.cbs.dtu.dk/services/NetDiseaseSNP

Abstract PR05: Reversing the oncogenic roles of misdirected chromatin remodeling: Disruption of mSWI/SNF (BAF) complexes by the SS18-SSX fusion in human synovial sarcoma

Abstract Recent exon- and genome-wide sequencing studies of human cancers have revealed frequent mutations in genes encoding subunits of mSWI/SNF or BAF complexes and implicated them as tumor suppressors. Indeed, we estimate that they are mutated in >20% of human cancers (Kadoch et al, Nature Genetics 2013). Interestingly, the subunits most commonly mutated are not required for chromatin remodeling in vitro and thus may contribute to chromatin regulation in a novel way. One difficultly in understanding the oncogenic role(s) of these complexes is distinguishing primary driving mutations from passenger mutations. To investigate the mechanism underlying oncogenesis, we studied human synovial sarcoma (SS), in which transformation results from a remarkably precise and uniform translocation of exactly 78 amino acids of SSX (SSX1, 2 or 4) to the SS18 protein. In a proteomic study we discovered that SS18 is a dedicated, non-exchangeable subunit of BAF complexes. We demonstrate that the hallmark SS18-SSX fusion incorporates into BAF complexes, evicting both the wild-type SS18 protein and the tumor suppressor, hSNF5 (BAF47), which is known to be biallelically inactivated in malignant rhabdoid tumors. The altered complex binds the Sox2 locus, reversing polycomb-mediated H3K27me3 repression and activating Sox2. Sox2 is uniformly expressed in SS tumors and is essential for proliferation. Remarkably, increasing the concentration of wild-type SS18 leads to reassembly of wild-type complexes, retargeting of BAF complexes, returned polycomb-mediated repression at the Sox2 locus and cessation of SS cell proliferation. This mechanism of transformation depends on a two amino acid hydrophilic region of SSX and hence provides a potential foundation for therapeutic intervention. Citation Format: Cigall Kadoch, Gerald R. Crabtree. Reversing the oncogenic roles of misdirected chromatin remodeling: Disruption of mSWI/SNF (BAF) complexes by the SS18-SSX fusion in human synovial sarcoma. [abstract]. In: Proceedings of the AACR Special Conference on Chromatin and Epigenetics in Cancer; Jun 19-22, 2013; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2013;73(13 Suppl):Abstract nr PR05.

Identifying driver mutations from sequencing data of heterogeneous tumors in the era of personalized genome sequencing

Distinguishing driver mutations from passenger mutations is critical to the understanding of the molecular mechanisms of carcinogenesis and for identifying prognostic and diagnostic markers as well as therapeutic targets. We reviewed the current approaches and software for identifying driver mutations from passenger mutations including both biology-based approaches and machine-learning-based approaches. We also reviewed approaches to identify driver mutations in the context of pathways or gene sets. Finally, we discussed the challenges of predicting driver mutations considering the complexities of inter- and intra-tumor heterogeneity as well as the evolution and progression of tumors.

PDGFRA alterations in cancer: characterization of a gain-of-function V536E transmembrane mutant as well as loss-of-function and passenger mutations

Activating mutations in the platelet-derived growth factor (PDGF) receptor alpha (PDGFRA) have been described in patients with gastrointestinal stromal tumors or myeloid malignancies associated with hypereosinophilia. These patients respond well to imatinib mesylate, raising the question as to whether patients with a PDGF receptor mutation in other tumor types should receive a tyrosine kinase inhibitor treatment. We characterized 10 novel somatic point mutations in PDGFRA that have been reported in isolated cases of glioblastoma, melanoma, acute myeloid leukemia, peripheral nerve sheath tumors and neuroendocrine carcinoma. The PDGFRA transmembrane domain mutation V536E stimulated Ba/F3 cell growth and signaling via ERK and STAT5 in the absence of ligand. This mutant, identified in glioblastoma, was strongly inhibited by imatinib. Modeling suggested that the mutation modulates the packing of the transmembrane domain helices in the receptor dimer. By contrast, two mutations in highly conserved residues affected the receptor traffic to the cell surface or kinase activity, thereby preventing the response to PDGF. The other mutations had no significant impact on the receptor activity. This functional analysis matched the predictions of SIFT and PolyPhen for only five mutations and these algorithms do not discriminate gain from loss of function. Finally, an E996K variant that had been identified in a melanoma cell line was not expressed in these cells. Altogether, several newly identified PDGFRA mutations do not activate the receptor and may therefore represent passenger mutations. Our results also underline the importance of characterizing novel kinase alterations in cancer patients.

Simultaneous Identification of Multiple Driver Pathways in Cancer

Distinguishing the somatic mutations responsible for cancer (driver mutations) from random, passenger mutations is a key challenge in cancer genomics. Driver mutations generally target cellular signaling and regulatory pathways consisting of multiple genes. This heterogeneity complicates the identification of driver mutations by their recurrence across samples, as different combinations of mutations in driver pathways are observed in different samples. We introduce the Multi-Dendrix algorithm for the simultaneous identification of multiple driver pathways de novo in somatic mutation data from a cohort of cancer samples. The algorithm relies on two combinatorial properties of mutations in a driver pathway: high coverage and mutual exclusivity. We derive an integer linear program that finds set of mutations exhibiting these properties. We apply Multi-Dendrix to somatic mutations from glioblastoma, breast cancer, and lung cancer samples. Multi-Dendrix identifies sets of mutations in genes that overlap with known pathways – including Rb, p53, PI(3)K, and cell cycle pathways – and also novel sets of mutually exclusive mutations, including mutations in several transcription factors or other genes involved in transcriptional regulation. These sets are discovered directly from mutation data with no prior knowledge of pathways or gene interactions. We show that Multi-Dendrix outperforms other algorithms for identifying combinations of mutations and is also orders of magnitude faster on genome-scale data. Software available at: http://compbio.cs.brown.edu/software.

Predicting the functional consequences of cancer-associated amino acid substitutions

Motivation: The number of missense mutations being identified in cancer genomes has greatly increased as a consequence of technological advances and the reduced cost of whole-genome/whole-exome sequencing methods. However, a high proportion of the amino acid substitutions detected in cancer genomes have little or no effect on tumour progression (passenger mutations). Therefore, accurate automated methods capable of discriminating between driver (cancer-promoting) and passenger mutations are becoming increasingly important. In our previous work, we developed the Functional Analysis through Hidden Markov Models (FATHMM) software and, using a model weighted for inherited disease mutations, observed improved performances over alternative computational prediction algorithms. Here, we describe an adaptation of our original algorithm that incorporates a cancer-specific model to potentiate the functional analysis of driver mutations.Results: The performance of our algorithm was evaluated using two separate benchmarks. In our analysis, we observed improved performances when distinguishing between driver mutations and other germ line variants (both disease-causing and putatively neutral mutations). In addition, when discriminating between somatic driver and passenger mutations, we observed performances comparable with the leading computational prediction algorithms: SPF-Cancer and TransFIC.Availability and implementation: A web-based implementation of our cancer-specific model, including a downloadable stand-alone package, is available at http://fathmm.biocompute.org.uk.Contact: fathmm@biocompute.org.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Into the eye of the storm: breast cancer's somatic mutation landscape points to DNA damage and repair.

Distinguishing the handful of somatic mutations expected to initiate and maintain cancer growth, so-called driver mutations, from mutations that play no role in cancer development, passenger mutations, remains a major hurdle for understanding the mechanisms of cancer and the design of more effective treatments. Recognizing this, National Cancer Institute’s “Provocative Questions” Project (1) specifically highlights the urgent need to better discriminate between driver and passenger events as a key research priority.

Assessment of computational methods for predicting the effects of missense mutations in human cancers

BackgroundRecent advances in sequencing technologies have greatly increased the identification of mutations in cancer genomes. However, it remains a significant challenge to identify cancer-driving mutations, since most observed missense changes are neutral passenger mutations. Various computational methods have been developed to predict the effects of amino acid substitutions on protein function and classify mutations as deleterious or benign. These include approaches that rely on evolutionary conservation, structural constraints, or physicochemical attributes of amino acid substitutions. Here we review existing methods and further examine eight tools: SIFT, PolyPhen2, Condel, CHASM, mCluster, logRE, SNAP, and MutationAssessor, with respect to their coverage, accuracy, availability and dependence on other tools.ResultsSingle nucleotide polymorphisms with high minor allele frequencies were used as a negative (neutral) set for testing, and recurrent mutations from the COSMIC database as well as novel recurrent somatic mutations identified in very recent cancer studies were used as positive (non-neutral) sets. Conservation-based methods generally had moderately high accuracy in distinguishing neutral from deleterious mutations, whereas the performance of machine learning based predictors with comprehensive feature spaces varied between assessments using different positive sets. MutationAssessor consistently provided the highest accuracies. For certain combinations metapredictors slightly improved the performance of included individual methods, but did not outperform MutationAssessor as stand-alone tool.ConclusionsOur independent assessment of existing tools reveals various performance disparities. Cancer-trained methods did not improve upon more general predictors. No method or combination of methods exceeds 81% accuracy, indicating there is still significant room for improvement for driver mutation prediction, and perhaps more sophisticated feature integration is needed to develop a more robust tool.

Are Results of Targeted Gene Sequencing Ready to Be Used for Clinical Decision Making for Patients with Acute Myelogenous Leukemia?

Acute myeloid leukemia (AML) is the most common acute leukemia in the USA, which despite recent advances, continues to have a high mortality rate. It is a biologically active disease characterized by numerous cytogenetic abnormalities and multiple genetic mutations. Next-generation sequencing (NGS) will perhaps not reveal all the factors that make AML a complex disease, but does have the potential to affect the diagnosis and risk stratification of AML patients and allow more personalized therapy. AML cells are easy to obtain from the patient and samples are only minimally contaminated with normal cells, which makes it an attractive cancer to study. Several studies have now demonstrated that the majority of AML patients are cytogenetically normal and the genome of these patients may contain fewer mutations than cancer genomes that are highly aneuploidy, suggesting that mutations in diploid genomes are more likely to be pathogenetically relevant. Whole-genome, exome, transcriptome, and targeted gene sequencing studies have been conducted successfully in AML and have provided with valuable information. The challenges for the future include: reducing the cost of sequencing, understanding epigenetic changes, managing data across various platforms, separating the driver mutations from the sea of passenger mutations, and finally, educating future generations to allow a better understanding and easy availability of these complex methodologies.

Abstract 4407: Structural and biochemical analysis of ERBB4 mutations in cancer.

Abstract Genes encoding the ErbB receptor tyrosine kinases (EGFR/ERBB1, ERBB2, ERBB3, and ERBB4) are key regulators of cellular proliferation, survival, and differentiation, and thus represent potent proto-oncogenes. In particular, mutations or copy number variations of EGFR or ERBB2 are present in human malignancies and serve as biomarkers for targeted therapies. Recently, somatic mutations of the ERBB4 gene have been identified in various cancer types, such as non-small cell lung cancer and melanoma, indicating a role for mutated ERBB4 as a potential drug target. However, the structural and functional consequences of the ERBB4 mutations remain to be fully elucidated. We used both a structural as well as a biochemical approach to address the functional effects the ERBB4 mutations for ErbB4 signaling. The structural effects of the mutations were assessed in both active and inactive ErbB4 conformation in silico. The biochemical effects of the mutations on ErbB4 signaling were analyzed in vitro using assays measuring the kinase activity of ErbB4 as well as activation of downstream signaling pathways. Although all structural subdomains of ERBB4 have been reported to be affected by somatic mutations, our analyses indicated that the mutations cluster to regions important for activation of the receptor, such as the dimerization interface and the kinase domain active site. Some of the mutations were structurally non-hazardous and probably represent passenger mutations. Consistently, the biochemical analyses indicated for the presence of both activating and functionally non-significant mutations. These observations suggest for the presence of both driver as well as passenger mutations of ERBB4 in human cancer. Citation Format: Kari Kurppa, Konstantin Denessiouk, Mark S. Johnson, Klaus Elenius. Structural and biochemical analysis of ERBB4 mutations in cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4407. doi:10.1158/1538-7445.AM2013-4407

Abstract 2315: Reversible disruption of mSWI/SNF-like BAF complexes by the SS18-SSX oncogenic fusion in synovial sarcoma.

Abstract Recent exon sequencing studies have revealed frequent mutations in subunits of SWI/SNF or BAF complexes and implicated them as tumor suppressors. Indeed, we estimate that they are mutated in 15.8 percent of sequenced human cancers. Interestingly, the subunits most commonly mutated are not required for chromatin remodeling in vitro and thus may contribute to chromatin regulation in a novel way. One difficultly in understanding the role of these complexes in cancer is distinguishing primary driving mutations from passenger mutations. To investigate the underlying mechanism we studied human synovial sarcoma (SS), in which transformation results from a remarkably precise and uniform translocation of exactly 78 amino acids of SSX to the SS18 protein. In a proteomic study we discovered that SS18 is a dedicated, non-exchangeable subunit of BAF complexes. We demonstrate that the hallmark SS18-SSX fusion incorporates into BAF complexes, evicting both the wild-type SS18 protein and the tumor suppressor, hSNF5 (BAF47). The altered complex binds the Sox2 locus, reversing polycomb-mediated repression and results in Sox2 activation. Sox2 is uniformly expressed in SS tumors and is essential for their proliferation. Increasing the concentration of wild-type SS18 leads to reassembly of the wild-type complex, retargeting of BAF complexes away from the Sox2 locus, polycomb-mediated repression of Sox2 and cessation of proliferation. This mechanism of transformation depends on only two amino acids of SSX and hence provides a potential foundation for therapeutic intervention. Citation Format: Cigall Kadoch, Gerald R. Crabtree. Reversible disruption of mSWI/SNF-like BAF complexes by the SS18-SSX oncogenic fusion in synovial sarcoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2315. doi:10.1158/1538-7445.AM2013-2315

Abstract 5148: CanDrA: Cancer-specific driver missense mutation annotation with optimized features.

Abstract Driver mutations are somatic mutations that provide growth advantage to tumor cells, while passenger mutations are those that are not functionally associated with oncogenesis. Distinguishing drivers from passengers is challenging because drivers occur much less frequently than passengers, they turn to have low prevalence, and their functions are multifactorial. Missense mutations are excellent candidates for driver identification, as they occur more frequently and are potentially easier to target than other types of mutations. Although several methods have been developed for predicting the functional impact of missense mutations, only a few are specifically designed for identifying driver mutations. As more mutations are being discovered, more accurate predictive models can be developed using machine learning approaches that systematically characterize the commonality and peculiarity of somatic mutations under specific cancer background. Here, we present such a Cancer Driver Annotation (CanDrA) tool that assesses the driver potential of missense somatic mutations based on a set of 95 structural and evolutionary features computed by over 10 functional prediction algorithms from SNVBOX, VEP, ANNOVAR and Mutation Assessor. Through feature optimization and supervised training, CanDrA outperforms existing tools in analysing the glioblastoma multiforme and the ovarian carcinoma data in The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia project. Citation Format: Yong Mao, Han Chen, Han Liang, Funda Meric-Bernstam, Gordon Mills, Ken Chen. CanDrA: Cancer-specific driver missense mutation annotation with optimized features. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5148. doi:10.1158/1538-7445.AM2013-5148

Abstract 3803: Genome-wide approach to identify gene targets of pleuropulmonary blastoma.

Abstract Pleuropulmonary blastoma (PPB) is an extremely rare and highly aggressive pediatric pulmonary malignancy that is often part of inherited cancer syndrome. Recently, germline mutations of DICER1, a gene encoding an endoribonuclease type III critical to the generation of small noncoding regulatory RNAs were reported in the majority of familial PPB. However, little is known about other genetic changes than DICER1 mutations that are responsible for the development and/or progression of PPB. Therefore, to assess the genetic alterations underlying the pathogenesis of PPB, genome-wide analyses using whole-exome sequencing and 250K SNP array were performed in total of ten cases with non-familial PPB. Whole-exome sequencing was performed in two cases which primary tumor-relapse tumor pairs were available. Although most mutations were commonly detected in both the primary and relapse tumors, several alterations were private to either of both samples, suggesting that clonal evolution derives genetic divergence in relapsed/metastatic tumors of PPB. In accordance with this, SNP array analysis also revealed several shared or private copy change variations, the most common finding was gain of the long arm of chromosome 8 in 7 cases, which was previously reported common in PPB. Whole-exome sequencing disclosed that DICER1 mutation, major cause of familial PPB, was recurrently found in the primary and relapse tumors of one case, but not detected in the another case. In the validation cohort of 8 cases of non-familial PPB, all of them had DICER1 mutations, and 6 cases had compound heterozygous mutations of DICER1. Notably, homozygous DICER1 mutation was observed in one case, and subsequent SNP array analysis revealed uniparental disomy of 14q lesion harboring DICER1 in this case. Somatic origins of missense changes were confirmed in 2 of 10 cases, for which DNA was obtained from peripheral blood. Meanwhile, whole-exome sequencing identified 13 candidate genes including the cadherin-related gene and a negative regulator of cell cycle in one case without DICER1 mutation. These gene mutations shared in primary and relapse samples, suggests that these are driver mutations rather than passenger mutations or non-functional SNPs. In conclusion, DICER1 mutation might be a driver mutation not only in familial PPB, but also in non-familial PPB. While DICER1 mutation was reported only germline heterozygous mutation in familial PPB, we notably found compound heterozygous or homozygous DICER1 mutations in non-familial PPB. Furthermore, there were different clones between primary and relapse samples, suggests that clonal evolution contributes to relapse tumor progression. Citation Format: Masafumi Seki, Riki Nisimura, Yusuke Okuno, Kenichi Yoshida, Yuichi Shiraishi, Masashi Sanada, Keisuke Kato, Katsuyoshi Koh, Ryoji Hanada, Yasuhide Hayashi, Satoru Miyano, Seishi Ogawa, Junko Takita. Genome-wide approach to identify gene targets of pleuropulmonary blastoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3803. doi:10.1158/1538-7445.AM2013-3803