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103. An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets.

Author

Kirsch, Adam, Mitzenmacher, Michael, Pietracaprina, Andrea, Pucci, Geppino, Upfal, Eli, and Vandin, Fabio

Subjects
ALGORITHMS, STATISTICAL significance, PATTERN recognition systems, DATA mining, FALSE discovery rate, DATABASE searching
Abstract

As advances in technology allow for the collection, storage, and analysis of vast amounts of data, the task of screening and assessing the significance of discovered patterns is becoming a major challenge in data mining applications. In this work, we address significance in the context of frequent itemset mining. Specifically, we develop a novel methodology to identify a meaningful support threshold s* for a dataset, such that the number of itemsets with support at least s* represents a substantial deviation from what would be expected in a random dataset with the same number of transactions and the same individual item frequencies. These itemsets can then be flagged as statistically significant with a small false discovery rate. We present extensive experimental results to substantiate the effectiveness of our methodology. [ABSTRACT FROM AUTHOR]

Published
2012
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104. Differentially Mutated Subnetworks Discovery

Author

Hajkarim, Morteza Chalabi, Upfal, Eli, Vandin, Fabio, Hajkarim, Morteza Chalabi, Upfal, Eli, and Vandin, Fabio

Abstract

We study the problem of identifying differentially mutated subnetworks of a large gene-gene interaction network, that is, subnetworks that display a significant difference in mutation frequency in two sets of cancer samples. We formally define the associated computational problem and show that the problem is NP-hard. We propose a novel and efficient algorithm, called DAMOKLE to identify differentially mutated subnetworks given genome-wide mutation data for two sets of cancer samples. We prove that DAMOKLE identifies subnetworks with a statistically significant difference in mutation frequency when the data comes from a reasonable generative model, provided enough samples are available. We test DAMOKLE on simulated and real data, showing that DAMOKLE does indeed find subnetworks with significant differences in mutation frequency and that it provides novel insights not obtained by standard methods.

Published
2018
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105. Differentially Mutated Subnetworks Discovery

Author

Morteza Chalabi Hajkarim and Eli Upfal and Fabio Vandin, Hajkarim, Morteza Chalabi, Upfal, Eli, Vandin, Fabio, Morteza Chalabi Hajkarim and Eli Upfal and Fabio Vandin, Hajkarim, Morteza Chalabi, Upfal, Eli, and Vandin, Fabio

Abstract

We study the problem of identifying differentially mutated subnetworks of a large gene-gene interaction network, that is, subnetworks that display a significant difference in mutation frequency in two sets of cancer samples. We formally define the associated computational problem and show that the problem is NP-hard. We propose a novel and efficient algorithm, called DAMOKLE to identify differentially mutated subnetworks given genome-wide mutation data for two sets of cancer samples. We prove that DAMOKLE identifies subnetworks with a statistically significant difference in mutation frequency when the data comes from a reasonable generative model, provided enough samples are available. We test DAMOKLE on simulated and real data, showing that DAMOKLE does indeed find subnetworks with significant differences in mutation frequency and that it provides novel insights not obtained by standard methods.

Published
2018
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106. Erratum to:CoMEt: A statistical approach to identify combinations of mutually exclusive alterations in cancer [Genome Biol., 16, (2015), (160)]

Author

Leiserson, Mark D.M., Wu, Hsin Ta, Vandin, Fabio, and Raphael, Benjamin J.

Abstract

After the publication of this work [1] it has been brought to our attention that the descriptions of the generation of the two simulated datasets were confusing. In the section 'Benchmarking of methods for individual gene sets', the first sentence of the second paragraph should specify the number of genes included in the gene set as 100, and it should read: "We compared CoMEt to the other methods on datasets with m= 100 genes and n = 500 samples and with implanted pathways with coverages ? ranging from 0.1 to 1.0." In the section 'Benchmarking identification of collections of gene sets', the first paragraph should specify that genes mutated in fewer than 1% of total samples (that is in fewer than 5 out of 500 samples) were removed from the simulation, and the sixth sentence in this paragraph should read: "Third, we include m= 20,000 genes and remove those genes that are mutated in fewer than 1% of total samples (that is in fewer than 5 out of 500 samples) (Additional file 1: Figure S2)." Additionally, these descriptions are also included in the Additional file 1, section S3, and should read: "We generated two different versions of the simulated datasets, depending on whether we implanted a single or multiple gene sets. We describe the method for generating datasets with multiple implanted gene sets in the main text. For all simulated datasets, we used n = 500, |C| = 5, ?C = (0.67, 0.49, 0.29, 0.29, 0.2), and q = 0.0027538462.1 We used μP = (0.5, 0.35, 0.15) for the single pathway simulations." We also updated the description of our procedure for assessing the convergence of the MCMC algorithm in Additional file 1, section S2, which should read: "To assess the convergence of the MCMC algorithm, we ran multiple chains with different initializations. For one of these initializations, we used the collection output by Multi-Dendrix [21] (using the same values of the parameters t and k as in CoMEt). The remaining initializations were random collections. We ran the MCMC algorithm with these initializations, running each chain for a given number of iterations. We consider the chains converged if the mean total variation distance between the chains is smaller than 0.005. Otherwise, we increase the number of iterations by a factor of 1.5. We repeat this process until the chains converge or the total number of iterations per chain reaches a maximum number of iterations, which we set as 1 billion. The output of the MCMC algorithm is the union of the sampling distributions from the different initializations." The corrected Additional file 1 is included in this Erratum.

Published
2016
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107. Clustering uncertain graphs

Author

Ceccarello, Matteo, primary, Fantozzi, Carlo, additional, Pietracaprina, Andrea, additional, Pucci, Geppino, additional, and Vandin, Fabio, additional

Published
2017
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111. Differentially Methylated Genomic Regions in Birth-Weight Discordant Twin Pairs

Author

Chen, Mubo, Baumbach, Jan, Vandin, Fabio, Röttger, Richard, Barbosa, Eudes, Dong, Mingchui, Frost, Morten, Christiansen, Lene, and Tan, Qihua

Subjects
Adult, Male, Twins, Birth-weight discordance, Monozygotic, Epigenesis, Genetic, Genetic, Genetics, Birth Weight, Humans, Identical twins, Genetics (clinical), Aged, Genome, DMR, Genome, Human, Medicine (all), Genomics, Twins, Monozygotic, DNA Methylation, Middle Aged, Linear Models, DNA methylation, Female, Human, Epigenesis
Abstract

Poor nutrition during critical growth phases may alter the structural and physiologic development of vital organs thus “programming” the susceptibility to adult-onset diseases and disease-related health conditions. Epigenome-wide association studies have been performed in birth-weight discordant twin pairs to find evidence for such “programming” effects, but no significant results emerged. We further investigated this issue using a new computational approach: Instead of probing single genomic sites for significant alterations in epigenetic marks, we scan for differentially methylated genomic regions. Whole genome DNA methylation levels were measured in whole blood from 150 pairs of adult identical twins discordant for birth-weight. Intrapair differential DNA methylation was associated with qualitative (large or small) and quantitative (percentage) birth-weight discordance at each genomic site using regression models adjusting for age and sex. Based on the regression results, genomic regions with consistent alteration patterns of DNA methylation were located and tested for significant robustness using computational permutation tests. This yielded an interesting genomic region on chromosome 1, which is significantly differentially methylated for quantitative birth-weight discordance. The region covers two genes (TYW3 and CRYZ) both reportedly associated with metabolism. We conclude that prenatal conditions for birth-weight discordance may result in persistent epigenetic modifications potentially affecting even adult health.

Published
2015
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112. De novo pathway-based biomarker identification

Author

Alcaraz, Nicolas, List, Markus, Batra, Richa, Vandin, Fabio, Ditzel, Henrik J., Baumbach, Jan, Alcaraz, Nicolas, List, Markus, Batra, Richa, Vandin, Fabio, Ditzel, Henrik J., and Baumbach, Jan

Abstract

Gene expression profiles have been extensively discussed as an aid to guide the therapy by predicting disease outcome for the patients suffering from complex diseases, such as cancer. However, prediction models built upon single-gene (SG) features show poor stability and performance on independent datasets. Attempts to mitigate these drawbacks have led to the development of network-based approaches that integrate pathway information to produce meta-gene (MG) features. Also, MG approaches have only dealt with the two-class problem of good versus poor outcome prediction. Stratifying patients based on their molecular subtypes can provide a detailed view of the disease and lead to more personalized therapies. We propose and discuss a novel MG approach based on de novo pathways, which for the first time have been used as features in a multi-class setting to predict cancer subtypes. Comprehensive evaluation in a large cohort of breast cancer samples from The Cancer Genome Atlas (TCGA) revealed that MGs are considerably more stable than SG models, while also providing valuable insight into the cancer hallmarks that drive them. In addition, when tested on an independent benchmark non-TCGA dataset, MG features consistently outperformed SG models. We provide an easy-to-use web service at http://pathclass.compbio.sdu.dk where users can upload their own gene expression datasets from breast cancer studies and obtain the subtype predictions from all the classifiers.

Published
2017

114. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin

Author

Hoadley, Katherine A., Yau, Christina, Wolf, Denise M., Cherniack, Andrew D., Tamborero, David, Sam, Ng, Leiserson, Max D. M., Niu, Beifang, Mclellan, Michael D., Uzunangelov, Vladislav, Zhang, Jiashan, Kandoth, Cyriac, Akbani, Rehan, Shen, Hui, Omberg, Larsson, Chu, Andy, Margolin, Adam A., Van'T Veer, Laura J., Lopez Bigas, Nuria, Laird, Peter W., Raphael, Benjamin J., Ding, Li, Robertson, A. Gordon, Byers, Lauren A., Mills, Gordon B., Weinstein, John N., Van Waes, Carter, Chen, Zhong, Collisson, Eric A., Benz, Christopher C, Perou, Charles M., Stuart, Joshua M., Rachel, Abbott, Scott, Abbott, Arman Aksoy, B., Kenneth, Aldape, Adrian, Ally, Samirku mar Amin, Dimitris, Anastassiou, Todd Auman, J., Baggerly, Keith A., Miruna, Balasundaram, Saianand, Balu, Baylin, Stephen B., Benz, Stephen C., Berman, Benjamin P., Brady, Bernard, Bhatt, Ami S., Inanc, Birol, Black, Aaron D., Tom, Bodenheimer, Bootwalla, Moiz S., Jay, Bowen, Ryan, Bressler, Bristow, Christopher A., Brooks, Angela N., Bradley, Broom, Elizabeth, Buda, Robert, Burton, Butterfield, Yaron S. N., Daniel, Carlin, Carter, Scott L., Casasent, Tod D., Kyle, Chang, Stephen, Chanock, Lynda, Chin, Dong Yeon Cho, Juok, Cho, Eric, Chuah, Chun, Hye Jung E., Kristian, Cibulskis, Giovanni, Ciriello, James Cle land, Melisssa, Cline, Brian, Craft, Creighton, Chad J., Ludmila, Danilova, Tanja, Davidsen, Caleb, Davis, Dees, Nathan D., Kim, Delehaunty, Demchok, John A., Noreen, Dhalla, Daniel, Dicara, Huyen, Dinh, Dobson, Jason R., Deepti, Dodda, Harshavardhan, Doddapaneni, Lawrence, Donehower, Dooling, David J., Gideon, Dresdner, Jennifer, Drummond, Andrea, Eakin, Mary, Edgerton, Eldred, Jim M., Greg, Eley, Kyle, Ellrott, Cheng, Fan, Suzanne, Fei, Ina, Felau, Scott, Frazer, Freeman, Samuel S., Jessica, Frick, Fronick, Catrina C., Ful ton, Lucinda L., Robert, Fulton, Gabriel, Stacey B., Jianjiong, Gao, Gastier Foster, Julie M., Nils, Gehlenborg, Myra, George, Gad, Getz, Richard, Gibbs, Mary, Goldman, Abel Gonzalez Perez, Benjamin, Gross, Ranabir, Guin, Preethi, Gunaratne, Angela, Hadjipanayis, Hamilton, Mark P., Hamilton, Stanley R., Leng, Han, Han, Yi, Harper, Hollie A., Psalm, Haseley, David, Haussler, Neil Hayes, D., Heiman, David I., Elena, Helman, Carmen, Helsel, Herbrich, Shelley M., Her man, James G., Toshinori, Hinoue, Carrie, Hirst, Martin, Hirst, Holt, Robert A., Hoyle, Alan P., Lisa, Iype, Anders, Jacobsen, Jeffreys, Stuart R., Jensen, Mark A., Jones, Corbin D., Jones, Steven J. M., Zhenlin, Ju, Joonil, Jung, Andre, Kahles, Ari, Kahn, Joelle Kalicki Veizer, Divya, Kalra, Krishna Latha Kanchi, Kane, David W., Hoon, Kim, Jaegil, Kim, Theo, Knijnenburg, Koboldt, Daniel C., Christie, Kovar, Roger, Kramer, Richard, Kreisberg, Raju, Kucherlapati, Marc, Ladanyi, Lander, Eric S., Larson, David E., Lawrence, Michael S., Darlene, Lee, Eunjung, Lee, Semin, Lee, William, Lee, Kjong Van Lehmann, Kalle, Leinonen, Ler aas, Kristen M., Seth, Lerner, Levine, Douglas A., Lora, Lewis, Ley, Timothy J., Haiyan I., Li, Jun, Li, Wei, Li, Han, Liang, Lichtenberg, Tara M., Jake, Lin, Ling, Lin, Pei, Lin, Wen bin Liu, Yingchun, Liu, Yuexin, Liu, Lorenzi, Philip L., Charles, Lu, Yiling, Lu, Luquette, Love lace J., Singer, Ma, Magrini, Vincent J., Mahadeshwar, Harshad S., Mardis, Elaine R., Adam, Margolin, Marra, Marco A., Michael, Mayo, Cynthia, Mcallister, Mcguire, Sean E., Mcmichael, Joshua F., James, Melott, Shaowu, Meng, Matthew, Meyerson, Mieczkowski, Piotr A., Miller, Christopher A., Miller, Martin L., Michael, Miller, Moore, Richard A., Margaret, Morgan, Donna, Morton, Mose, Lisle E., Mungall, Andrew J., Donna, Muzny, Lam, Nguyen, Noble, Michael S., Houtan, Noushmehr, Michelle, O’Laughlin, Ojesina, Akinyemi I., Tai Hsien Ou Yang, Brad, Ozenberger, Angeliki, Pantazi, Michael, Parfenov, Park, Peter J., Parker, Joel S., Evan, Paull, Chandra Sekhar Pedamallu, Todd, Pihl, Craig, Pohl, David, Pot, Alexei, Protopopov, Teresa, Przytycka, Amie Raden baugh, Ramirez, Nilsa C., Ricardo, Ramirez, Gunnar Ra, ̈ tsch, Jeffrey, Reid, Xiao jia Ren, Boris, Reva, Reynolds, Sheila M., Rhie, Suhn K., Jeffrey, Roach, Hector, Rovira, Michael, Ryan, Gordon, Saksena, Sofie, Salama, Chris, Sander, Netty, Santoso, Schein, Jacqueline E., Heather, Schmidt, Nikolaus, Schultz, Schumacher, Steven E., Jonathan, Seidman, Yasin, Senbabaoglu, Sahil, Seth, Saman tha Sharpe, Ronglai, Shen, Margi, Sheth, Yan, Shi, Ilya, Shmulevich, Silva, Grace O., Simons, Janae V., Rileen, Sinha, Payal, Sipahimalani, Smith, Scott M., Sofia, Heidi J., Artem, Sokolov, Soloway, Mathew G., Xingzhi, Song, Carrie Soug nez, Paul, Spellman, Louis, Staudt, Chip, Stewart, Petar, Stojanov, Xiaoping, Su, Onur Sumer, S., Yichao, Sun, Teresa, Swatloski, Barbara, Tabak, Angela, Tam, Donghui, Tan, Jiabin, Tang, Roy, Tarnuzzer, Taylor, Barry S., Nina, Thiessen, Ves teinn Thorsson, Timothy Triche, J. r., Van Den Berg, David J., Vandin, Fabio, Varhol, Richard J., Vaske, Charles J., Umadevi, Veluvolu, Roeland, Verhaak, Doug, Voet, Jason, Walker, Wallis, John W., Peter, Waltman, Yunhu, Wan, Min, Wang, Wenyi, Wang, Zhining, Wang, Scot, Waring, Nils, Weinhold, Weisenberger, Daniel J., Wendl, Michael C., David, Wheeler, Wilkerson, Matthew D., Wilson, Richard K., Lisa, Wise, Andrew, Wong, Chang Jiun Wu, Chia Chin Wu, Hsin Ta Wu, Junyuan, Wu, Todd, Wylie, Liu, Xi, Ruibin, Xi, Zheng, Xia, Andrew W., Xu, Yang, Da, Liming, Yang, Lixing, Yang, Yang, Yang, Jun, Yao, Rong, Yao, Kai, Ye, Ko suke Yoshihara, Yuan, Yuan, Yung, Alfred K., Travis, Zack, Dong, Zeng, Jean Claude Zenklusen, Hailei, Zhang, Jianhua, Zhang, Nianxiang, Zhang, Qunyuan, Zhang, Wei, Zhang, Wei, Zhao, Siyuan, Zheng, Jing, Zhu, Erik, Zmuda, and Lihua, Zou

Subjects
Genetics and Molecular Biology (all), Cluster Analysis, Humans, Neoplasms, Transcriptome, Biochemistry, Genetics and Molecular Biology (all), Extramural, Biochemistry, Genetics and Molecular Biology(all), Cancer, Computational biology, Disease, Biology, medicine.disease, Bioinformatics, Biochemistry, General Biochemistry, Genetics and Molecular Biology, Article, 3. Good health, Molecular classification, TP63, CLUSTERS (ANÁLISE), medicine, Head and neck, Gene
Abstract

Summary Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies.

Published
2014
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120. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia

Author

Ley, Timothy, Miller, Christopher, Ding, Li, Raphael, Benjamin J., Mungall, Andrew J., Robertson, A. Gordon, Hoadley, Katherine, Triche, Timothy J., Laird, Peter W., Baty, Jack D., Fulton, Lucinda L., Fulton, Robert, Heath, Sharon E., Kalicki Veizer, Joelle, Kandoth, Cyriac, Klco, Jeffery M., Koboldt, Daniel C., Kanchi, Krishna Latha, Shashikant, Kulkarni, M. S., P. h. D., F. A. C. M. G., Lamprecht, Tamara L., B. S., Washington, University, Louis, S. t., Larson, David E., P. h. D., Ling, Lin, M. S., Charles, Lu, Mclellan, Michael D., Mcmichael, Joshua F., the Genome Institute at Washington University, Jacqueline, Payton, M. D., P. h. D., Heather, Schmidt, Spencer, David H., Tomasson, Michael H., M. D., Siteman Cancer Center, S. t. Louis, Wallis, John W., Wartman, Lukas D., Watson, Mark A., John, Welch, Wendl, Michael C., Adrian, Ally, B. S. c., Miruna, Balasundaram, B. A. S. c., Inanc, Birol, Yaron, Butterfield, Readman, Chiu, M. S. c., Andy, Chu, Eric, Chuah, Hye Jung Chun, Richard, Corbett, Noreen, Dhalla, Ranabir, Guin, An, He, Carrie, Hirst, Martin, Hirst, Holt, Robert A., Steven, Jones, Aly, Karsan, Darlene, Lee, Haiyan I., Li, Marra, Marco A., Michael, Mayo, Moore, Richard A., Karen, Mungall, Jeremy, Parker, Erin, Pleasance, Patrick, Plettner, Jacquie, Schein, Dominik, Stoll, Lucas, Swanson, Angela, Tam, Nina, Thiessen, Richard, Varhol, Natasja, Wye, Yongjun, Zhao, M. S. c., D. V. M., British Columbia Cancer Agency's Genome Sciences Centre, Vancouver, Canada, Stacey, Gabriel, Gad, Getz, Carrie, Sougnez, Lihua, Zou, Broad Institute of Harvard, Massachusetts Institute of Technology, Cambridge, Ma, Mark D. M. Leiserson, B. A., Vandin, Fabio, Hsin Ta Wu, Brown, University, Center for Computational Molecular Biology, Providence, Ri, Frederick, Applebaum, Fred Hutchinson Cancer Research Center, Division of Medical Oncology, Seattle Cancer Care Alliance, Seattle, Baylin, Stephen B., Johns Hopkins University, Baltimore, Rehan, Akbani, Broom, Bradley M., Ken, Chen, Motter, Thomas C., B. A., Khanh, Nguyen, Weinstein, John N., Nianziang, Zhang, Anderson Cancer Center, University of Texas M. D., Houston, Ferguson, Martin L., Mlf, Consulting, Biotechnology Consultant, Boston, Christopher, Adams, Aaron, Black, Jay, Bowen, Julie Gastier Foster, Thomas, Grossman, Tara, Lichtenberg, Lisa, Wise, the Research Institute at Nationwide Children's Hospital, Columbus, Oh, Tanja, Davidsen, Demchok, John A., Mills Shaw, Kenna R., Margi, Sheth, National Cancer Institute, Bethesda, Md, Sofia, Heidi J., P. h. D., M. P. H., National Human Genome Research Institute, Liming, Yang, Downing, James R., Jude Children's Research Hospital, S. t., Memphis, Greg, Eley, Sciementis, Llc, Statham, Ga, Shelley, Alonso, Brenda, Ayala, Julien, Baboud, Mark, Backus, Barletta, Sean P., Berton, Dominique L., M. S. C. S., Chu, Anna L., Stanley, Girshik, Jensen, Mark A., Ari, Kahn, Prachi, Kothiyal, Nicholls, Matthew C., Pihl, Todd D., Pot, David A., Rohini, Raman, B. E., Sanbhadti, Rashmi N., Snyder, Eric E., Deepak, Srinivasan, Jessica, Walton, Yunhu, Wan, Zhining, Wang, Sra, International, Fairfax, Va, Issa, Jean Pierre J., Temple, University, Philadelphia, Michelle Le Beau, University of Chicago, Chicago, Martin, Carroll, University of Pennsylvania, Hagop Kantarjian, M. D., Steven, Kornblau, Bootwalla, Moiz S., B. S. c., M. S., Lai, Phillip H., Hui, Shen, Van Den Berg, David J., Weisenberger, Daniel J., University of Southern California, Epigenome, Center, Los, Angeles, Daniel C. Link, M. D., Walter, Matthew J., Ozenberger, Bradley A., Mardis, Elaine R., Peter, Westervelt, Graubert, Timothy A., Dipersio, John F., and Wilson, Richard K.

Subjects
Myeloid, Adult, Epigenomics, Male, NPM1, Gene Expression, CpG Islands, DNA Methylation, Female, Gene Fusion, Genome, Human, Humans, Leukemia, Myeloid, Acute, MicroRNAs, Middle Aged, Sequence Analysis, DNA, Mutation, Acute, Enasidenib, Biology, CEBPA, Genetics, Genome, Leukemia, Massive parallel sequencing, MicroRNA sequencing, Myeloid leukemia, DNA, General Medicine, KMT2A, biology.protein, Sequence Analysis, Nucleophosmin, Human, Comparative genomic hybridization
Abstract

BACKGROUND—Many mutations that contribute to the pathogenesis of acute myeloid leukemia (AML) are undefined. The relationships between patterns of mutations and epigenetic phenotypes are not yet clear. METHODS—We analyzed the genomes of 200 clinically annotated adult cases of de novo AML, using either whole-genome sequencing (50 cases) or whole-exome sequencing (150 cases), along with RNA and microRNA sequencing and DNA-methylation analysis. RESULTS—AML genomes have fewer mutations than most other adult cancers, with an average of only 13 mutations found in genes. Of these, an average of 5 are in genes that are recurrently mutated in AML. A total of 23 genes were significantly mutated, and another 237 were mutated in two or more samples. Nearly all samples had at least 1 nonsynonymous mutation in one of nine categories of genes that are almost certainly relevant for pathogenesis, including transcriptionfactor fusions (18% of cases), the gene encoding nucleophosmin (NPM1) (27%), tumorsuppressor genes (16%), DNA-methylation–related genes (44%), signaling genes (59%), chromatin-modifying genes (30%), myeloid transcription-factor genes (22%), cohesin-complex genes (13%), and spliceosome-complex genes (14%). Patterns of cooperation and mutual exclusivity suggested strong biologic relationships among several of the genes and categories. CONCLUSIONS—We identified at least one potential driver mutation in nearly all AML samples and found that a complex interplay of genetic events contributes to AML pathogenesis in individual patients. The databases from this study are widely available to serve as a foundation for further investigations of AML pathogenesis, classification, and risk stratification. (Funded by the National Institutes of Health.) The molecular pathogenesis of acute myeloid leukemia (AML) has been studied with the use of cytogenetic analysis for more than three decades. Recurrent chromosomal structural variations are well established as diagnostic and prognostic markers, suggesting that acquired genetic abnormalities (i.e., somatic mutations) have an essential role in pathogenesis. 1,2 However, nearly 50% of AML samples have a normal karyotype, and many of these genomes lack structural abnormalities, even when assessed with high-density comparative genomic hybridization or single-nucleotide polymorphism (SNP) arrays 3-5 (see Glossary). Targeted sequencing has identified recurrent mutations in FLT3, NPM1, KIT, CEBPA, and TET2. 6-8 Massively parallel sequencing enabled the discovery of recurrent mutations in DNMT3A 9,10 and IDH1. 11 Recent studies have shown that many patients with

Published
2013

128. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes

Author

Leiserson, Mark D M, primary, Vandin, Fabio, additional, Wu, Hsin-Ta, additional, Dobson, Jason R, additional, Eldridge, Jonathan V, additional, Thomas, Jacob L, additional, Papoutsaki, Alexandra, additional, Kim, Younhun, additional, Niu, Beifang, additional, McLellan, Michael, additional, Lawrence, Michael S, additional, Gonzalez-Perez, Abel, additional, Tamborero, David, additional, Cheng, Yuwei, additional, Ryslik, Gregory A, additional, Lopez-Bigas, Nuria, additional, Getz, Gad, additional, Ding, Li, additional, and Raphael, Benjamin J, additional

Published
2014
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142. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes.

Author

Leiserson, Mark D M, Raphael, Benjamin J, Thomas, Jacob L, Vandin, Fabio, Ding, Li, Eldridge, Jonathan V, Kim, Younhun, McLellan, Michael, Gonzalez-Perez, Abel, Cheng, Yuwei, Dobson, Jason R, Niu, Beifang, Ryslik, Gregory A, Lopez-Bigas, Nuria, Wu, Hsin-Ta, Getz, Gad, Papoutsaki, Alexandra, Lawrence, Michael S, and Tamborero, David

Subjects
CANCER, DNA mutational analysis, HETEROGENEITY, SOMATIC hybrids, PROTEINS
Abstract

Cancers exhibit extensive mutational heterogeneity, and the resulting long-tail phenomenon complicates the discovery of genes and pathways that are significantly mutated in cancer. We perform a pan-cancer analysis of mutated networks in 3,281 samples from 12 cancer types from The Cancer Genome Atlas (TCGA) using HotNet2, a new algorithm to find mutated subnetworks that overcomes the limitations of existing single-gene, pathway and network approaches. We identify 16 significantly mutated subnetworks that comprise well-known cancer signaling pathways as well as subnetworks with less characterized roles in cancer, including cohesin, condensin and others. Many of these subnetworks exhibit co-occurring mutations across samples. These subnetworks contain dozens of genes with rare somatic mutations across multiple cancers; many of these genes have additional evidence supporting a role in cancer. By illuminating these rare combinations of mutations, pan-cancer network analyses provide a roadmap to investigate new diagnostic and therapeutic opportunities across cancer types. [ABSTRACT FROM AUTHOR]

Published
2015
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143. Efficient Incremental Mining of Top-K Frequent Closed Itemsets.

Author

Carbonell, Jaime G., Siekmann, Jörg, Corruble, Vincent, Takeda, Masayuki, Suzuki, Einoshin, Pietracaprina, Andrea, and Vandin, Fabio

Abstract

In this work we study the mining of top-K frequent closed itemsets, a recently proposed variant of the classical problem of mining frequent closed itemsets where the support threshold is chosen as the maximum value sufficient to guarantee that the itemsets returned in output be at least K. We discuss the effectiveness of parameter K in controlling the output size and develop an efficient algorithm for mining top-K frequent closed itemsets in order of decreasing support, which exhibits consistently better performance than the best previously known one, attaining substantial improvements in some cases. A distinctive feature of our algorithm is that it allows the user to dynamically raise the value K with no need to restart the computation from scratch. [ABSTRACT FROM AUTHOR]

Published
2007
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144. Efficient detection of differentially methylated regions using DiMmeR.

Author

Almeida, Diogo, Skov, Ida, Silva, Artur, Vandin, Fabio, Qihua Tan, Röttger, Richard, and Baumbach, Jan

Subjects
CHEMICALS, DNA methylation, EPIDEMIOLOGY, METABOLISM, GRAPHICAL user interfaces
Abstract

Motivation: Epigenome-wide association studies (EWAS) generate big epidemiological datasets. They aim for detecting differentially methylated DNA regions that are likely to influence transcriptional gene activity and, thus, the regulation of metabolic processes. The by far most widely used technology is the Illumina Methylation BeadChip, which measures the methylation levels of 450 (850) thousand cytosines, in the CpG dinucleotide context in a set of patients compared to a control group. Many bioinformatics tools exist for raw data analysis. However, most of them require some knowledge in the programming language R, have no user interface, and do not offer all necessary steps to guide users from raw data all the way down to statistically significant differentially methylated regions (DMRs) and the associated genes. Results: Here, we present DiMmeR (Discovery of Multiple Differentially Methylated Regions), the first free standalone software that interactively guides with a user-friendly graphical user interface (GUI) scientists the whole way through EWAS data analysis. It offers parallelized statistical methods for efficiently identifying DMRs in both Illumina 450K and 850K EPIC chip data. DiMmeR computes empirical P-values through randomization tests, even for big datasets of hundreds of patients and thousands of permutations within a few minutes on a standard desktop PC. It is independent of any third-party libraries, computes regression coefficients, P-values and empirical P-values, and it corrects for multiple testing. [ABSTRACT FROM AUTHOR]

Published
2017
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145. HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology

Author

Shrestha, Raunak, Hodzic, Ermin, Sauerwald, Thomas, Dao, Phuong, Wang, Kendric, Yeung, Jake, Anderson, Shawn, Vandin, Fabio, Haffari, Gholamreza, Collins, Colin C, and Sahinalp, S Cenk

Subjects
DNA Copy Number Variations, Mutation, Computational Biology, Humans, Breast Neoplasms, Female, Genomics, Protein Interaction Maps, Transcriptome, Software, 3. Good health
Abstract

Prioritizing molecular alterations that act as drivers of cancer remains a crucial bottleneck in therapeutic development. Here we introduce HIT'nDRIVE, a computational method that integrates genomic and transcriptomic data to identify a set of patient-specific, sequence-altered genes, with sufficient collective influence over dysregulated transcripts. HIT'nDRIVE aims to solve the "random walk facility location" (RWFL) problem in a gene (or protein) interaction network, which differs from the standard facility location problem by its use of an alternative distance measure: "multihitting time," the expected length of the shortest random walk from any one of the set of sequence-altered genes to an expression-altered target gene. When applied to 2200 tumors from four major cancer types, HIT'nDRIVE revealed many potentially clinically actionable driver genes. We also demonstrated that it is possible to perform accurate phenotype prediction for tumor samples by only using HIT'nDRIVE-seeded driver gene modules from gene interaction networks. In addition, we identified a number of breast cancer subtype-specific driver modules that are associated with patients' survival outcome. Furthermore, HIT'nDRIVE, when applied to a large panel of pan-cancer cell lines, accurately predicted drug efficacy using the driver genes and their seeded gene modules. Overall, HIT'nDRIVE may help clinicians contextualize massive multiomics data in therapeutic decision making, enabling widespread implementation of precision oncology.

146. De novo discovery of mutated driver pathways in cancer.

Author

Vandin, Fabio, Upfal, Eli, and Raphael, Benjamin J.

Subjects
*NUCLEOTIDE sequence, *GENOMES, *SOMATIC mutation, *CANCER patients, *GENES, *ALGORITHMS
Abstract

Next-generation DNA sequencing technologies are enabling genome-wide measurements of somatic mutations in large numbers of cancer patients. A major challenge in the interpretation of these data is to distinguish functional "driver mutations" important for cancer development from random "passenger mutations." A common approach for identifying driver mutations is to find genes that are mutated at significant frequency in a large cohort of cancer genomes. This approach is confounded by the observation that driver mutations target multiple cellular signaling and regulatory pathways. Thus, each cancer patient may exhibit a different combination of mutations that are sufficient to perturb these pathways. This mutational heterogeneity presents a problem for predicting driver mutations solely from their frequency of occurrence. We introduce two combinatorial properties, coverage and exclusivity, that distinguish driver pathways, or groups of genes containing driver mutations, from groups of genes with passenger mutations. We derive two algorithms, called Dendrix, to find driver pathways de novo from somatic mutation data. We apply Dendrix to analyze somatic mutation data from 623 genes in 188 lung adenocarcinoma patients, 601 genes in 84 glioblastoma patients, and 238 known mutations in 1000 patients with various cancers. In all data sets, we find groups of genes that are mutated in large subsets of patients and whose mutations are approximately exclusive. Our Dendrix algorithms scale to whole-genome analysis of thousands of patients and thus will prove useful for larger data sets to come from The Cancer Genome Atlas (TCGA) and other large-scale cancer genome sequencing projects. [ABSTRACT FROM AUTHOR]

Published
2012
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147. Jllumina - A comprehensive Java-based API for statistical Illumina Infinium HumanMethylation450 and MethylationEPIC data processing.

Author

Almeida D, Skov I, Lund J, Mohammadnejad A, Silva A, Vandin F, Tan Q, Baumbach J, and Röttger R

Subjects
Cohort Studies, Genome-Wide Association Study, Humans, Computational Biology, DNA Methylation, Programming Languages
Abstract

Measuring differential methylation of the DNA is the nowadays most common approach to linking epigenetic modifications to diseases (called epigenome-wide association studies, EWAS). For its low cost, its efficiency and easy handling, the Illumina HumanMethylation450 BeadChip and its successor, the Infinium MethylationEPIC BeadChip, is the by far most popular techniques for conduction EWAS in large patient cohorts. Despite the popularity of this chip technology, raw data processing and statistical analysis of the array data remains far from trivial and still lacks dedicated software libraries enabling high quality and statistically sound downstream analyses. As of yet, only R-based solutions are freely available for low-level processing of the Illumina chip data. However, the lack of alternative libraries poses a hurdle for the development of new bioinformatic tools, in particular when it comes to web services or applications where run time and memory consumption matter, or EWAS data analysis is an integrative part of a bigger framework or data analysis pipeline. We have therefore developed and implemented Jllumina, an open-source Java library for raw data manipulation of Illumina Infinium HumanMethylation450 and Infinium MethylationEPIC BeadChip data, supporting the developer with Java functions covering reading and preprocessing the raw data, down to statistical assessment, permutation tests, and identification of differentially methylated loci. Jllumina is fully parallelizable and publicly available at http://dimmer.compbio.sdu.dk/download.html.

Published
2016
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148. Discovery of mutated subnetworks associated with clinical data in cancer.

Author

Vandin F, Clay P, Upfal E, and Raphael BJ

Subjects
Algorithms, Computational Biology, Databases, Genetic statistics & numerical data, Female, Humans, Models, Genetic, Ovarian Neoplasms genetics, Phenotype, Gene Regulatory Networks, Mutation, Neoplasms genetics
Abstract

A major goal of cancer sequencing projects is to identify genetic alterations that determine clinical phenotypes, such as survival time or drug response. Somatic mutations in cancer are typically very diverse, and are found in different sets of genes in different patients. This mutational heterogeneity complicates the discovery of associations between individual mutations and a clinical phenotype. This mutational heterogeneity is explained in part by the fact that driver mutations, the somatic mutations that drive cancer development, target genes in cellular pathways, and only a subset of pathway genes is mutated in a given patient. Thus, pathway-based analysis of associations between mutations and phenotype are warranted. Here, we introduce an algorithm to find groups of genes, or pathways, whose mutational status is associated to a clinical phenotype without prior definition of the pathways. Rather, we find subnetworks of genes in an gene interaction network with the property that the mutational status of the genes in the subnetwork are significantly associated with a clinical phenotype. This new algorithm is built upon HotNet, an algorithm that finds groups of mutated genes using a heat diffusion model and a two-stage statistical test. We focus here on discovery of statistically significant correlations between mutated subnetworks and patient survival data. A similar approach can be used for correlations with other types of clinical data, through use of an appropriate statistical test. We apply our method to simulated data as well as to mutation and survival data from ovarian cancer samples from The Cancer Genome Atlas. In the TCGA data, we discover nine subnetworks containing genes whose mutational status is correlated with survival. Genes in four of these subnetworks overlap known pathways, including the focal adhesion and cell adhesion pathways, while other subnetworks are novel.

Published
2012

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