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126 results on '"Sahinalp, Cenk"'

1. SubGraph2Vec: Highly-Vectorized Tree-likeSubgraph Counting

Author

Chen, Langshi, Li, Jiayu, Azad, Ariful, Sahinalp, Cenk, Marathe, Madhav, Vullikanti, Anil, Nikolaev, Andrey, Smirnov, Egor, Israfilov, Ruslan, and Qiu, Judy

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Subgraph counting aims to count occurrences of a template T in a given network G(V, E). It is a powerful graph analysis tool and has found real-world applications in diverse domains. Scaling subgraph counting problems is known to be memory bounded and computationally challenging with exponential complexity. Although scalable parallel algorithms are known for several graph problems such as Triangle Counting and PageRank, this is not common for counting complex subgraphs. Here we address this challenge and study connected acyclic graphs or trees. We propose a novel vectorized subgraph counting algorithm, named Subgraph2Vec, as well as both shared memory and distributed implementations: 1) reducing algorithmic complexity by minimizing neighbor traversal; 2) achieving a highly-vectorized implementation upon linear algebra kernels to significantly improve performance and hardware utilization. 3) Subgraph2Vec improves the overall performance over the state-of-the-art work by orders of magnitude and up to 660x on a single node. 4) Subgraph2Vec in distributed mode can scale up the template size to 20 and maintain good strong scalability. 5) enabling portability to both CPU and GPU., Comment: arXiv admin note: text overlap with arXiv:1903.04395

Published
2020
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3. The Proteogenomic Landscape of Curable Prostate Cancer

Author

Sinha, Ankit, Huang, Vincent, Livingstone, Julie, Wang, Jenny, Fox, Natalie S, Kurganovs, Natalie, Ignatchenko, Vladimir, Fritsch, Katharina, Donmez, Nilgun, Heisler, Lawrence E, Shiah, Yu-Jia, Yao, Cindy Q, Alfaro, Javier A, Volik, Stas, Lapuk, Anna, Fraser, Michael, Kron, Ken, Murison, Alex, Lupien, Mathieu, Sahinalp, Cenk, Collins, Colin C, Tetu, Bernard, Masoomian, Mehdi, Berman, David M, van der Kwast, Theodorus, Bristow, Robert G, Kislinger, Thomas, and Boutros, Paul C

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Bioinformatics and Computational Biology, Genetics, Oncology and Carcinogenesis, Aging, Biotechnology, Prostate Cancer, Cancer, Urologic Diseases, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Adult, Aged, Aged, 80 and over, Biomarkers, Tumor, Cell Line, Tumor, Epigenomics, Gene Expression Profiling, Humans, Male, Middle Aged, Prognosis, Prostatic Neoplasms, Proteogenomics, Proto-Oncogene Proteins c-ets, Translocation, Genetic, Whole Genome Sequencing, biomarker, epigenome, genome, multi-omic features, prostate cancer, proteome, transcriptome, Neurosciences, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

DNA sequencing has identified recurrent mutations that drive the aggressiveness of prostate cancers. Surprisingly, the influence of genomic, epigenomic, and transcriptomic dysregulation on the tumor proteome remains poorly understood. We profiled the genomes, epigenomes, transcriptomes, and proteomes of 76 localized, intermediate-risk prostate cancers. We discovered that the genomic subtypes of prostate cancer converge on five proteomic subtypes, with distinct clinical trajectories. ETS fusions, the most common alteration in prostate tumors, affect different genes and pathways in the proteome and transcriptome. Globally, mRNA abundance changes explain only ∼10% of protein abundance variability. As a result, prognostic biomarkers combining genomic or epigenomic features with proteomic ones significantly outperform biomarkers comprised of a single data type.

Published
2019

4. Analysis of the androgen receptor–regulated lncRNA landscape identifies a role for ARLNC1 in prostate cancer progression

Author

Zhang, Yajia, Pitchiaya, Sethuramasundaram, Cieślik, Marcin, Niknafs, Yashar S, Tien, Jean C-Y, Hosono, Yasuyuki, Iyer, Matthew K, Yazdani, Sahr, Subramaniam, Shruthi, Shukla, Sudhanshu K, Jiang, Xia, Wang, Lisha, Liu, Tzu-Ying, Uhl, Michael, Gawronski, Alexander R, Qiao, Yuanyuan, Xiao, Lanbo, Dhanasekaran, Saravana M, Juckette, Kristin M, Kunju, Lakshmi P, Cao, Xuhong, Patel, Utsav, Batish, Mona, Shukla, Girish C, Paulsen, Michelle T, Ljungman, Mats, Jiang, Hui, Mehra, Rohit, Backofen, Rolf, Sahinalp, Cenk S, Freier, Susan M, Watt, Andrew T, Guo, Shuling, Wei, John T, Feng, Felix Y, Malik, Rohit, and Chinnaiyan, Arul M

Subjects
Biochemistry and Cell Biology, Biological Sciences, Cancer, Prostate Cancer, Genetics, Aging, Urologic Diseases, Aetiology, 2.1 Biological and endogenous factors, Androgens, Cell Line, Tumor, Disease Progression, Gene Expression Regulation, Neoplastic, Humans, Male, Prostate, Prostatic Neoplasms, RNA, Long Noncoding, Receptors, Androgen, Signal Transduction, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

The androgen receptor (AR) plays a critical role in the development of the normal prostate as well as prostate cancer. Using an integrative transcriptomic analysis of prostate cancer cell lines and tissues, we identified ARLNC1 (AR-regulated long noncoding RNA 1) as an important long noncoding RNA that is strongly associated with AR signaling in prostate cancer progression. Not only was ARLNC1 induced by the AR protein, but ARLNC1 stabilized the AR transcript via RNA-RNA interaction. ARLNC1 knockdown suppressed AR expression, global AR signaling and prostate cancer growth in vitro and in vivo. Taken together, these data support a role for ARLNC1 in maintaining a positive feedback loop that potentiates AR signaling during prostate cancer progression and identify ARLNC1 as a novel therapeutic target.

Published
2018

5. Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads

Author

Keskus, Ayse, primary, Bryant, Asher, additional, Ahmad, Tanveer, additional, Yoo, Byunggil, additional, Aganezov, Sergey, additional, Goretsky, Anton, additional, Donmez, Ataberk, additional, Lansdon, Lisa A., additional, Rodriguez, Isabel, additional, Park, Jimin, additional, Liu, Yuelin, additional, Cui, Xiwen, additional, Gardner, Joshua, additional, McNulty, Brandy, additional, Sacco, Samuel, additional, Shetty, Jyoti, additional, Zhao, Yongmei, additional, Tran, Bao, additional, Narzisi, Giuseppe, additional, Helland, Adrienne, additional, Cook, Daniel E., additional, Chang, Pi-Chuan, additional, Kolesnikov, Alexey, additional, Carroll, Andrew, additional, Molloy, Erin K., additional, Pushel, Irina, additional, Guest, Erin, additional, Pastinen, Tomi, additional, Shafin, Kishwar, additional, Miga, Karen H., additional, Malikic, Salem, additional, Day, Chi-Ping, additional, Robine, Nicolas, additional, Sahinalp, Cenk, additional, Dean, Michael, additional, Farooqi, Midhat S., additional, Paten, Benedict, additional, and Kolmogorov, Mikhail, additional

Published
2024
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7. Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data

Author

Yamada, Makoto, Tang, Jiliang, Lugo-Martinez, Jose, Hodzic, Ermin, Shrestha, Raunak, Saha, Avishek, Ouyang, Hua, Yin, Dawei, Mamitsuka, Hiroshi, Sahinalp, Cenk, Radivojac, Predrag, Menczer, Filippo, and Chang, Yi

Subjects
Statistics - Machine Learning
Abstract

Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible for data with millions of features. Here we introduce the first feature selection method for nonlinear learning problems that can scale up to large, ultra-high dimensional biological data. More specifically, we scale up the novel Hilbert-Schmidt Independence Criterion Lasso (HSIC Lasso) to handle millions of features with tens of thousand samples. The proposed method is guaranteed to find an optimal subset of maximally predictive features with minimal redundancy, yielding higher predictive power and improved interpretability. Its effectiveness is demonstrated through applications to classify phenotypes based on module expression in human prostate cancer patients and to detect enzymes among protein structures. We achieve high accuracy with as few as 20 out of one million features --- a dimensionality reduction of 99.998%. Our algorithm can be implemented on commodity cloud computing platforms. The dramatic reduction of features may lead to the ubiquitous deployment of sophisticated prediction models in mobile health care applications., Comment: Substantially improved version of arXiv:1411.2331

Published
2016

8. Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations

Author

Simmons, Sean, Sahinalp, Cenk, and Berger, Bonnie

Subjects
Quantitative Biology - Quantitative Methods, Statistics - Applications
Abstract

The projected increase of genotyping in the clinic and the rise of large genomic databases has led to the possibility of using patient medical data to perform genomewide association studies (GWAS) on a larger scale and at a lower cost than ever before. Due to privacy concerns, however, access to this data is limited to a few trusted individuals, greatly reducing its impact on biomedical research. Privacy preserving methods have been suggested as a way of allowing more people access to this precious data while protecting patients. In particular, there has been growing interest in applying the concept of differential privacy to GWAS results. Unfortunately, previous approaches for performing differentially private GWAS are based on rather simple statistics that have some major limitations. In particular, they do not correct for population stratification, a major issue when dealing with the genetically diverse populations present in modern GWAS. To address this concern we introduce a novel computational framework for performing GWAS that tailors ideas from differential privacy to protect private phenotype information, while at the same time correcting for population stratification. This framework allows us to produce privacy preserving GWAS results based on two of the most commonly used GWAS statistics: EIGENSTRAT and linear mixed model (LMM) based statistics. We test our differentially private statistics, PrivSTRAT and PrivLMM, on both simulated and real GWAS datasets and find that they are able to protect privacy while returning meaningful GWAS results., Comment: To be presented at RECOMB 2016

Published
2016

9. Clonal Evolution and Heterogeneity of Osimertinib Acquired Resistance Mechanisms in EGFR Mutant Lung Cancer

Author

Roper, Nitin, Brown, Anna-Leigh, Wei, Jun S., Pack, Svetlana, Trindade, Christopher, Kim, Chul, Restifo, Olivia, Gao, Shaojian, Sindiri, Sivasish, Mehrabadi, Farid, El Meskini, Rajaa, Ohler, Zoe Weaver, Maity, Tapan K., Venugopalan, Abhilash, Cultraro, Constance M., Akoth, Elizabeth, Padiernos, Emerson, Chen, Haobin, Kesarwala, Aparna, Smart, DeeDee K., Nilubol, Naris, Rajan, Arun, Piotrowska, Zofia, Xi, Liqiang, Raffeld, Mark, Panchenko, Anna R., Sahinalp, Cenk, Hewitt, Stephen, Hoang, Chuong D., Khan, Javed, and Guha, Udayan

Published
2020
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10. The lncRNA landscape of breast cancer reveals a role for DSCAM-AS1 in breast cancer progression.

Author

Niknafs, Yashar S, Han, Sumin, Ma, Teng, Speers, Corey, Zhang, Chao, Wilder-Romans, Kari, Iyer, Matthew K, Pitchiaya, Sethuramasundaram, Malik, Rohit, Hosono, Yasuyuki, Prensner, John R, Poliakov, Anton, Singhal, Udit, Xiao, Lanbo, Kregel, Steven, Siebenaler, Ronald F, Zhao, Shuang G, Uhl, Michael, Gawronski, Alexander, Hayes, Daniel F, Pierce, Lori J, Cao, Xuhong, Collins, Colin, Backofen, Rolf, Sahinalp, Cenk S, Rae, James M, Chinnaiyan, Arul M, and Feng, Felix Y

Subjects
Cell Line, Tumor, Humans, Breast Neoplasms, Neoplasm Invasiveness, Tamoxifen, Receptors, Estrogen, Antineoplastic Agents, Hormonal, Gene Expression Regulation, Neoplastic, Drug Resistance, Neoplasm, Female, Gene Knockdown Techniques, RNA, Long Noncoding, Antineoplastic Agents, Hormonal, Cell Line, Tumor, Drug Resistance, Neoplasm, Gene Expression Regulation, Neoplastic, RNA, Long Noncoding, Receptors, Estrogen
Abstract

Molecular classification of cancers into subtypes has resulted in an advance in our understanding of tumour biology and treatment response across multiple tumour types. However, to date, cancer profiling has largely focused on protein-coding genes, which comprise

Published
2016

11. How has the AI boom impacted algorithmic biology?

Author

Singh, Mona, Sahinalp, Cenk, Zeng, Jianyang, Li, Wei Vivian, Kingsford, Carl, Zhang, Qiangfeng, Przytycka, Teresa, Welch, Joshua, Ma, Jian, and Berger, Bonnie

Abstract

This Voices piece will highlight the impact of artificial intelligence on algorithm development among computational biologists. How has worldwide focus on AI changed the path of research in computational biology? What is the impact on the algorithmic biology research community?

Published
2024
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12. Melanoma clonal subline analysis uncovers heterogeneity-driven immunotherapy resistance mechanisms

Author

Gruen, Charli, primary, Yang, Howard H., additional, Sassano, Antonella, additional, Wu, Emily, additional, Gopalan, Vishaka, additional, Marie, Kerrie L., additional, Castro, Andrea, additional, Mehrabadi, Farid Rashidi, additional, Wu, Chih Hao, additional, Church, Isabella, additional, Needle, Gabriel A., additional, Smith, Cari, additional, Chin, Sung, additional, Ebersole, Jessica, additional, Marcelus, Christina, additional, Fon, Anyen, additional, Liu, Huaitian, additional, Malikic, Salem, additional, Sahinalp, Cenk, additional, Carter, Hanna, additional, Hannenhalli, Sridhar, additional, Day, Chi-Ping, additional, Lee, Maxwell P., additional, Merlino, Glenn, additional, and Perez-Guijarro, Eva, additional

Published
2023
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14. CYP2C8, CYP2C9, and CYP2C19 Characterization Using Next-Generation Sequencing and Haplotype Analysis: A GeT-RM Collaborative Project

Author

Gaedigk, Andrea, Boone, Erin C., Scherer, Steven E., Lee, Seung-been, Numanagić, Ibrahim, Sahinalp, Cenk, Smith, Joshua D., McGee, Sean, Radhakrishnan, Aparna, Qin, Xiang, Wang, Wendy Y., Farrow, Emily G., Gonzaludo, Nina, Halpern, Aaron L., Nickerson, Deborah A., Miller, Neil A., Pratt, Victoria M., and Kalman, Lisa V.

Subjects
Cytochrome P-450 CYP2C19, Cytochrome P-450 CYP2C8, Genotype, Haplotypes, High-Throughput Nucleotide Sequencing, Humans, Regular Article, Genetic Testing, Alleles, Cytochrome P-450 CYP2C9
Abstract

Pharmacogenetic tests typically target selected sequence variants to identify haplotypes that are often defined by star (∗) allele nomenclature. Due to their design, these targeted genotyping assays are unable to detect novel variants that may change the function of the gene product and thereby affect phenotype prediction and patient care. In the current study, 137 DNA samples that were previously characterized by the Genetic Testing Reference Material (GeT-RM) program using a variety of targeted genotyping methods were recharacterized using targeted and whole genome sequencing analysis. Sequence data were analyzed using three genotype calling tools to identify star allele diplotypes for CYP2C8, CYP2C9, and CYP2C19. The genotype calls from next-generation sequencing (NGS) correlated well to those previously reported, except when novel alleles were present in a sample. Six novel alleles and 38 novel suballeles were identified in the three genes due to identification of variants not covered by targeted genotyping assays. In addition, several ambiguous genotype calls from a previous study were resolved using the NGS and/or long-read NGS data. Diplotype calls were mostly consistent between the calling algorithms, although several discrepancies were noted. This study highlights the utility of NGS for pharmacogenetic testing and demonstrates that there are many novel alleles that are yet to be discovered, even in highly characterized genes such as CYP2C9 and CYP2C19.

Published
2022

15. Genomic hallmarks of localized, non-indolent prostate cancer

Author

Fraser, Michael, Sabelnykova, Veronica Y., Yamaguchi, Takafumi N., Heisler, Lawrence E., Livingstone, Julie, Huang, Vincent, Shiah, Yu-Jia, Yousif, Fouad, Lin, Xihui, Masella, Andre P., Fox, Natalie S., Xie, Michael, Prokopec, Stephenie D., Berlin, Alejandro, Lalonde, Emilie, Ahmed, Musaddeque, Trudel, Dominique, Luo, Xuemei, Beck, Timothy A., Meng, Alice, Zhang, Junyan, DʼCosta, Alister, Denroche, Robert E., Kong, Haiying, Espiritu, Shadrielle Melijah G., Chua, Melvin L. K., Wong, Ada, Chong, Taryne, Sam, Michelle, Johns, Jeremy, Timms, Lee, Buchner, Nicholas B., Orain, Michèle, Picard, Valérie, Hovington, Helène, Murison, Alexander, Kron, Ken, Harding, Nicholas J., Pʼng, Christine, Houlahan, Kathleen E., Chu, Kenneth C., Lo, Bryan, Nguyen, Francis, Li, Constance H., Sun, Ren X., de Borja, Richard, Cooper, Christopher I., Hopkins, Julia F., Govind, Shaylan K., Fung, Clement, Waggott, Daryl, Green, Jeffrey, Haider, Syed, Chan-Seng-Yue, Michelle A., Jung, Esther, Wang, Zhiyuan, Bergeron, Alain, Pra, Alan Dal, Lacombe, Louis, Collins, Colin C., Sahinalp, Cenk, Lupien, Mathieu, Fleshner, Neil E., He, Housheng H., Fradet, Yves, Tetu, Bernard, van der Kwast, Theodorus, McPherson, John D., Bristow, Robert G., and Boutros, Paul C.

Published
2017
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16. CYP2C8, CYP2C9, and CYP2C19 Characterization Using Next-Generation Sequencing and Haplotype Analysis

Author

Gaedigk, Andrea, primary, Boone, Erin C., additional, Scherer, Steven E., additional, Lee, Seung-been, additional, Numanagić, Ibrahim, additional, Sahinalp, Cenk, additional, Smith, Joshua D., additional, McGee, Sean, additional, Radhakrishnan, Aparna, additional, Qin, Xiang, additional, Wang, Wendy Y., additional, Farrow, Emily G., additional, Gonzaludo, Nina, additional, Halpern, Aaron L., additional, Nickerson, Deborah A., additional, Miller, Neil A., additional, Pratt, Victoria M., additional, and Kalman, Lisa V., additional

Published
2022
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17. ImmunoTyper-SR: A Novel Computational Approach for Genotyping Immunoglobulin Heavy Chain Variable Genes using Short Read Data

Author

Ford, Michael, primary, Hari, Ananth, additional, Rodriguez, Oscar, additional, Xu, Junyan, additional, Lack, Justin, additional, Oguz, Cihan, additional, Zhang, Yu, additional, Weber, Sarah, additional, Magliocco, Mary, additional, Barnett, Jason, additional, Xirasagar, Sandhya, additional, Samuel, Smilee, additional, Imberti, Luisa, additional, Bonfanti, Paolo, additional, Biondi, Andrea, additional, Dalgard, Clifton, additional, Chanock, Stephen, additional, Rosen, Lindsey, additional, Holland, Steven, additional, Su, Helen, additional, Notarangelo, Luigi, additional, Vishkin, Uzi, additional, Watson, Corey, additional, and Sahinalp, Cenk, additional

Published
2022
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18. Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study

Author

Städler, Nicolas, Dondelinger, Frank, Hill, Steven M., Akbani, Rehan, Lu, Yiling, Mills, Gordon B., Mukherjee, Sach, and Sahinalp, Cenk

Abstract

Motivation Molecular pathways and networks play a key role in basic and disease biology. An emerging notion is that networks encoding patterns of molecular interplay may themselves differ between contexts, such as cell type, tissue or disease (sub)type. However, while statistical testing of differences in mean expression levels has been extensively studied, testing of network differences remains challenging. Furthermore, since network differences could provide important and biologically interpretable information to identify molecular subgroups, there is a need to consider the unsupervised task of learning subgroups and networks that define them. This is a nontrivial clustering problem, with neither subgroups nor subgroup-specific networks known at the outset. Results We leverage recent ideas from high-dimensional statistics for testing and clustering in the network biology setting. The methods we describe can be applied directly to most continuous molecular measurements and networks do not need to be specified beforehand. We illustrate the ideas and methods in a case study using protein data from The Cancer Genome Atlas (TCGA). This provides evidence that patterns of interplay between signalling proteins differ significantly between cancer types. Furthermore, we show how the proposed approaches can be used to learn subtypes and the molecular networks that define them. Availability and implementation As the Bioconductor package nethet. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2021

20. Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes.

Author

Dentro, Stefan C, Leshchiner, Ignaty, Haase, Kerstin, Tarabichi, Maxime, Wintersinger, Jeff J.A., Deshwar, Amit G, Yu, Kaixian, Rubanova, Yulia, Macintyre, Geoff, Demeulemeester, Jonas, Vázquez-García, Ignacio, Kleinheinz, Kortine, Livitz, Dimitri G, Malikic, Salem, Donmez, Nilgun, Sengupta, Subhajit, Anur, Pavana, Jolly, Clemency, Cmero, Marek, Rosebrock, Daniel, Schumacher, Steven, Fan, Yu, Fittall, Matthew, Drews, Ruben M, Yao, Xiaotong, Watkins, Thomas B K, Lee, Juhee, Schlesner, Matthias, Zhu, Hongtu, Adams, David J, McGranahan, Nicholas, Swanton, Charles, Getz, Gad, Boutros, Paul C, Imielinski, Marcin, Beroukhim, Rameen, Sahinalp, Cenk, Ji, Yuan, Peifer, Martin, Martincorena, Inigo, Markowetz, Florian, Mustonen, Ville, Yuan, Ke, Gerstung, Moritz, Spellman, Paul T, Wang, Wenyi, Morris, Quaid D., Wedge, David C, Van Loo, Peter, Dentro, Stefan C, Leshchiner, Ignaty, Haase, Kerstin, Tarabichi, Maxime, Wintersinger, Jeff J.A., Deshwar, Amit G, Yu, Kaixian, Rubanova, Yulia, Macintyre, Geoff, Demeulemeester, Jonas, Vázquez-García, Ignacio, Kleinheinz, Kortine, Livitz, Dimitri G, Malikic, Salem, Donmez, Nilgun, Sengupta, Subhajit, Anur, Pavana, Jolly, Clemency, Cmero, Marek, Rosebrock, Daniel, Schumacher, Steven, Fan, Yu, Fittall, Matthew, Drews, Ruben M, Yao, Xiaotong, Watkins, Thomas B K, Lee, Juhee, Schlesner, Matthias, Zhu, Hongtu, Adams, David J, McGranahan, Nicholas, Swanton, Charles, Getz, Gad, Boutros, Paul C, Imielinski, Marcin, Beroukhim, Rameen, Sahinalp, Cenk, Ji, Yuan, Peifer, Martin, Martincorena, Inigo, Markowetz, Florian, Mustonen, Ville, Yuan, Ke, Gerstung, Moritz, Spellman, Paul T, Wang, Wenyi, Morris, Quaid D., Wedge, David C, and Van Loo, Peter

Abstract

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types and identify cancer type-specific subclonal patterns of driver gene mutations, fusions, structural variants, and copy number alterations as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution and provide a pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data., info:eu-repo/semantics/published

Published
2021

23. A community effort to create standards for evaluating tumor subclonal reconstruction

Author

Salcedo, Adriana, Tarabichi, Maxime, Espiritu, Shadrielle Melijah G., Deshwar, Amit G., David, Matei, Wilson, Nathan M., Dentro, Stefan, Wintersinger, Jeff A., Liu, Lydia Y., Ko, Minjeong, Sivanandan, Srinivasan, Zhang, Hongjiu, Zhu, Kaiyi, Ou Yang, Tai-Hsien, Chilton, John M., Buchanan, Alex, Lalansingh, Christopher M., P'ng, Christine, Anghel, Catalina V., Umar, Imaad, Lo, Bryan, Zou, William, Simpson, Jared T., Stuart, Joshua M., Anastassiou, Dimitris, Guan, Yuanfang, Ewing, Adam D., Ellrott, Kyle, Wedge, David C., Morris, Quaid, Van Loo, Peter, Boutros, Paul C., Jha, Alokkumar, Huang, Tanxiao, Yang, Tsun-Po, Peifer, Martin, Sahinalp, Cenk, Malikic, Salem, Vazquez-Garcia, Ignacio, Mustonen, Ville, Yang, Hsih-Te, Lee, Ken-Ray, Ji, Yuan, Sengupta, Subhajit, Rudewicz, Justine, Nikolski, Macha, Schaeverbeke, Quentin, Yuan, Ke, Markowetz, Florian, Macintyre, Geoff, Cmero, Marek, Chaudhary, Belal, Leshchiner, Ignaty, Livitz, Dimitri, Getz, Gad, Loher, Phillipe, Yu, Kaixian, Wang, Wenyi, Zhu, Hongtu, Salcedo, Adriana, Tarabichi, Maxime, Espiritu, Shadrielle Melijah G., Deshwar, Amit G., David, Matei, Wilson, Nathan M., Dentro, Stefan, Wintersinger, Jeff A., Liu, Lydia Y., Ko, Minjeong, Sivanandan, Srinivasan, Zhang, Hongjiu, Zhu, Kaiyi, Ou Yang, Tai-Hsien, Chilton, John M., Buchanan, Alex, Lalansingh, Christopher M., P'ng, Christine, Anghel, Catalina V., Umar, Imaad, Lo, Bryan, Zou, William, Simpson, Jared T., Stuart, Joshua M., Anastassiou, Dimitris, Guan, Yuanfang, Ewing, Adam D., Ellrott, Kyle, Wedge, David C., Morris, Quaid, Van Loo, Peter, Boutros, Paul C., Jha, Alokkumar, Huang, Tanxiao, Yang, Tsun-Po, Peifer, Martin, Sahinalp, Cenk, Malikic, Salem, Vazquez-Garcia, Ignacio, Mustonen, Ville, Yang, Hsih-Te, Lee, Ken-Ray, Ji, Yuan, Sengupta, Subhajit, Rudewicz, Justine, Nikolski, Macha, Schaeverbeke, Quentin, Yuan, Ke, Markowetz, Florian, Macintyre, Geoff, Cmero, Marek, Chaudhary, Belal, Leshchiner, Ignaty, Livitz, Dimitri, Getz, Gad, Loher, Phillipe, Yu, Kaixian, Wang, Wenyi, and Zhu, Hongtu

Abstract

Methods for reconstructing tumor evolution are benchmarked in the DREAM Somatic Mutation Calling Tumour Heterogeneity Challenge. Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.

Published
2020

24. The evolutionary history of 2,658 cancers

Author

Gerstung, Moritz, Jolly, Clemency, Leshchiner, Ignaty, Dentro, Stefan, Gonzalez, Santiago, Rosebrock, Daniel, Mitchell, Thomas J., Rubanova, Yulia, Anur, Pavana, Yu, Kaixian, Tarabichi, Maxime, Deshwar, Amit, Wintersinger, Jeff J.A., Kleinheinz, Kortine, Vázquez-García, Ignacio, Haase, Kerstin, Jerman, Lara, Sengupta, Subhajit, Macintyre, Geoff, Malikic, Salem, Donmez, Nilgun, Livitz, Dimitri, Cmero, Marek, Demeulemeester, Jonas, Schumacher, Steven, Fan, Yu, Yao, Xiaotong, Lee, Juhee, Schlesner, Matthias, Boutros, Paul, Bowtell, David, Zhu, Hongtu, Getz, Gad, Imielinski, Marcin, Beroukhim, Rameen, Sahinalp, Cenk, Ji, Yuan, Peifer, Martin, Markowetz, Florian, Mustonen, Ville, Yuan, Ke, Wang, Wenyi, Morris, Quaid D., Spellman, Paul, Wedge, David C, Van Loo, Peter, Gerstung, Moritz, Jolly, Clemency, Leshchiner, Ignaty, Dentro, Stefan, Gonzalez, Santiago, Rosebrock, Daniel, Mitchell, Thomas J., Rubanova, Yulia, Anur, Pavana, Yu, Kaixian, Tarabichi, Maxime, Deshwar, Amit, Wintersinger, Jeff J.A., Kleinheinz, Kortine, Vázquez-García, Ignacio, Haase, Kerstin, Jerman, Lara, Sengupta, Subhajit, Macintyre, Geoff, Malikic, Salem, Donmez, Nilgun, Livitz, Dimitri, Cmero, Marek, Demeulemeester, Jonas, Schumacher, Steven, Fan, Yu, Yao, Xiaotong, Lee, Juhee, Schlesner, Matthias, Boutros, Paul, Bowtell, David, Zhu, Hongtu, Getz, Gad, Imielinski, Marcin, Beroukhim, Rameen, Sahinalp, Cenk, Ji, Yuan, Peifer, Martin, Markowetz, Florian, Mustonen, Ville, Yuan, Ke, Wang, Wenyi, Morris, Quaid D., Spellman, Paul, Wedge, David C, and Van Loo, Peter

Abstract

Cancer develops through a process of somatic evolution 1,2 .Sequencing data from a single biopsy represent a snapshot of this process that can reveal the timing of specific genomic aberrations and the changing influence of mutational processes 3 .Here, by whole-genome sequencing analysis of 2,658 cancers as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) 4 ,we reconstruct the life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer. Early oncogenesis is characterized by mutations in a constrained set of driver genes, and specific copy number gains, such as trisomy 7 in glioblastoma and isochromosome 17q in medulloblastoma. The mutational spectrum changes significantly throughout tumour evolution in 40% of samples. A nearly fourfold diversification of driver genes and increased genomic instability are features of later stages. Copy number alterations often occur in mitotic crises, and lead to simultaneous gains of chromosomal segments. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. Together, these results determine the evolutionary trajectories of cancer, and highlight opportunities for early cancer detection., info:eu-repo/semantics/published

Published
2020

25. Proteogenomic Analysis Unveils the HLA Class I-Presented Immunopeptidome in Melanoma and EGFR-Mutant Lung Adenocarcinoma

Author

Qi, Yue A., primary, Maity, Tapan K., additional, Cultraro, Constance M., additional, Misra, Vikram, additional, Zhang, Xu, additional, Ade, Catherine, additional, Gao, Shaojian, additional, Milewski, David, additional, Nguyen, Khoa D., additional, Ebrahimabadi, Mohammad H., additional, Hanada, Ken-ichi, additional, Khan, Javed, additional, Sahinalp, Cenk, additional, Yang, James C., additional, and Guha, Udayan, additional

Published
2021
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26. The structure and evolution of centromeric transition regions within the human genome

Author

She, Xinwei, Horvath, Julie E., Jiang, Zhaoshi, Liu, Ge, Furey, Terrence S., Christ, Laurie, Clark, Royden, Graves, Tina, Gulden, Cassy L., Alkan, Can, Bailey, Jeff A., Sahinalp, Cenk, Rocchi, Mariano, Haussler, David, Wilson, Richard K., Miller, Webb, Schwartz, Stuart, and Eichler, Evan E.

Subjects
Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Xinwei She [1, 2, 8]; Julie E. Horvath [1, 8]; Zhaoshi Jiang [1, 2]; Ge Liu [1]; Terrence S. Furey [3]; Laurie Christ [1]; Royden Clark [1]; Tina Graves [...]

Published
2004
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27. Proteogenomic analysis unveils the HLA Class I presented immunopeptidome in melanoma and EGFR mutant lung adenocarcinoma

Author

Qi, Yue A., primary, Maity, Tapan K., additional, Cultraro, Constance M., additional, Misra, Vikram, additional, Zhang, Xu, additional, Ade, Catherine, additional, Gao, Shaojian, additional, Milewski, David, additional, Nguyen, Khoa D., additional, Ebrahimabadi, Mohammad H., additional, Hanada, Ken-ichi, additional, Khan, Javed, additional, Sahinalp, Cenk, additional, Yang, James C., additional, and Guha, Udayan, additional

Published
2020
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28. Proteogenomic Analysis Unveils the HLA Class I Presented Immunopeptidome in Melanoma and EGFR Mutant Lung Adenocarcinoma

Author

Qi, Yue, primary, Maity, Tapan K., additional, Cultraro, Constance M., additional, Misra, Vikram, additional, Zhang, Xu, additional, Ade, Catherine, additional, Gao, Shaojian, additional, Milewski, David, additional, Nguyen, Khoa D., additional, Ebrahimabadi, Mohammad H., additional, Hanada, Ken-ichi, additional, Khan, Javed, additional, Sahinalp, Cenk, additional, Yang, James C., additional, and Guha, Udayan, additional

Published
2020
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29. A map of human genome variation from population-scale sequencing

Author

Durbin, Richard M., Altshuler, David L., Durbin, Richard M., Abecasis, Gonçalo R., Bentley, David R., Chakravarti, Aravinda, Clark, Andrew G., Collins, Francis S., De La Vega, Francisco M., Donnelly, Peter, Egholm, Michael, Flicek, Paul, Gabriel, Stacey B., Gibbs, Richard A., Knoppers, Bartha M., Lander, Eric S., Lehrach, Hans, Mardis, Elaine R., McVean, Gil A., Nickerson, Debbie A., Peltonen, Leena, Schafer, Alan J., Sherry, Stephen T., Wang, Jun, Wilson, Richard K., Gibbs, Richard A., Deiros, David, Metzker, Mike, Muzny, Donna, Reid, Jeff, Wheeler, David, Wang, Jun, Li, Jingxiang, Jian, Min, Li, Guoqing, Li, Ruiqiang, Liang, Huiqing, Tian, Geng, Wang, Bo, Wang, Jian, Wang, Wei, Yang, Huanming, Zhang, Xiuqing, Zheng, Huisong, Lander, Eric S., Altshuler, David L., Ambrogio, Lauren, Bloom, Toby, Cibulskis, Kristian, Fennell, Tim J., Gabriel, Stacey B., Jaffe, David B., Shefler, Erica, Sougnez, Carrie L., Bentley, David R., Gormley, Niall, Humphray, Sean, Kingsbury, Zoya, Koko-Gonzales, Paula, Stone, Jennifer, McKernan, Kevin J., Costa, Gina L., Ichikawa, Jeffry K., Lee, Clarence C., Sudbrak, Ralf, Lehrach, Hans, Borodina, Tatiana A., Dahl, Andreas, Davydov, Alexey N., Marquardt, Peter, Mertes, Florian, Nietfeld, Wilfiried, Rosenstiel, Philip, Schreiber, Stefan, Soldatov, Aleksey V., Timmermann, Bernd, Tolzmann, Marius, Egholm, Michael, Affourtit, Jason, Ashworth, Dana, Attiya, Said, Bachorski, Melissa, Buglione, Eli, Burke, Adam, Caprio, Amanda, Celone, Christopher, Clark, Shauna, Conners, David, Desany, Brian, Gu, Lisa, Guccione, Lorri, Kao, Kalvin, Kebbel, Andrew, Knowlton, Jennifer, Labrecque, Matthew, McDade, Louise, Mealmaker, Craig, Minderman, Melissa, Nawrocki, Anne, Niazi, Faheem, Pareja, Kristen, Ramenani, Ravi, Riches, David, Song, Wanmin, Turcotte, Cynthia, Wang, Shally, Mardis, Elaine R., Wilson, Richard K., Dooling, David, Fulton, Lucinda, Fulton, Robert, Weinstock, George, Durbin, Richard M., Burton, John, Carter, David M., Churcher, Carol, Coffey, Alison, Cox, Anthony, Palotie, Aarno, Quail, Michael, Skelly, Tom, Stalker, James, Swerdlow, Harold P., Turner, Daniel, De Witte, Anniek, Giles, Shane, Bainbridge, Matthew, Challis, Danny, Sabo, Aniko, Yu, Fuli, Yu, Jin, Fang, Xiaodong, Guo, Xiaosen, Li, Yingrui, Luo, Ruibang, Tai, Shuaishuai, Wu, Honglong, Zheng, Hancheng, Zheng, Xiaole, Zhou, Yan, Marth, Gabor T., Garrison, Erik P., Huang, Weichun, Indap, Amit, Kural, Deniz, Lee, Wan-Ping, Fung Leong, Wen, Quinlan, Aaron R., Stewart, Chip, Stromberg, Michael P., Ward, Alistair N., Wu, Jiantao, Lee, Charles, Mills, Ryan E., Shi, Xinghua, Daly, Mark J., DePristo, Mark A., Ball, Aaron D., Banks, Eric, Browning, Brian L., Garimella, Kiran V., Grossman, Sharon R., Handsaker, Robert E., Hanna, Matt, Hartl, Chris, Kernytsky, Andrew M., Korn, Joshua M., Li, Heng, Maguire, Jared R., McCarroll, Steven A., McKenna, Aaron, Nemesh, James C., Philippakis, Anthony A., Poplin, Ryan E., Price, Alkes, Rivas, Manuel A., Sabeti, Pardis C., Schaffner, Stephen F., Shlyakhter, Ilya A., Cooper, David N., Ball, Edward V., Mort, Matthew, Phillips, Andrew D., Stenson, Peter D., Sebat, Jonathan, Makarov, Vladimir, Ye, Kenny, Yoon, Seungtai C., Bustamante, Carlos D., Clark, Andrew G., Boyko, Adam, Degenhardt, Jeremiah, Gravel, Simon, Gutenkunst, Ryan N., Kaganovich, Mark, Keinan, Alon, Lacroute, Phil, Ma, Xin, Reynolds, Andy, Clarke, Laura, Flicek, Paul, Cunningham, Fiona, Herrero, Javier, Keenen, Stephen, Kulesha, Eugene, Leinonen, Rasko, McLaren, William M., Radhakrishnan, Rajesh, Smith, Richard E., Zalunin, Vadim, Zheng-Bradley, Xiangqun, Korbel, Jan O., Stütz, Adrian M., Humphray, Sean, Bauer, Markus, Cheetham, Keira R., Cox, Tony, Eberle, Michael, James, Terena, Kahn, Scott, Murray, Lisa, Ye, Kai, De La Vega, Francisco M., Fu, Yutao, Hyland, Fiona C. L., Manning, Jonathan M., McLaughlin, Stephen F., Peckham, Heather E., Sakarya, Onur, Sun, Yongming A., Tsung, Eric F., Batzer, Mark A., Konkel, Miriam K., Walker, Jerilyn A., Albrecht, Marcus W., Amstislavskiy, Vyacheslav S., Herwig, Ralf, Parkhomchuk, Dimitri V., Sherry, Stephen T., Agarwala, Richa, Khouri, Hoda M., Morgulis, Aleksandr O., Paschall, Justin E., Phan, Lon D., Rotmistrovsky, Kirill E., Sanders, Robert D., Shumway, Martin F., Xiao, Chunlin, McVean, Gil A., Auton, Adam, Iqbal, Zamin, Lunter, Gerton, Marchini, Jonathan L., Moutsianas, Loukas, Myers, Simon, Tumian, Afidalina, Desany, Brian, Knight, James, Winer, Roger, Craig, David W., Beckstrom-Sternberg, Steve M., Christoforides, Alexis, Kurdoglu, Ahmet A., Pearson, John V., Sinari, Shripad A., Tembe, Waibhav D., Haussler, David, Hinrichs, Angie S., Katzman, Sol J., Kern, Andrew, Kuhn, Robert M., Przeworski, Molly, Hernandez, Ryan D., Howie, Bryan, Kelley, Joanna L., Cord Melton, S., Abecasis, Gonçalo R., Li, Yun, Anderson, Paul, Blackwell, Tom, Chen, Wei, Cookson, William O., Ding, Jun, Min Kang, Hyun, Lathrop, Mark, Liang, Liming, Moffatt, Miriam F., Scheet, Paul, Sidore, Carlo, Snyder, Matthew, Zhan, Xiaowei, Zöllner, Sebastian, Awadalla, Philip, Casals, Ferran, Idaghdour, Youssef, Keebler, John, Stone, Eric A., Zilversmit, Martine, Jorde, Lynn, Xing, Jinchuan, Eichler, Evan E., Aksay, Gozde, Alkan, Can, Hajirasouliha, Iman, Hormozdiari, Fereydoun, Sahinalp, Cenk S., Sudmant, Peter H., Mardis, Elaine R., Chen, Ken, Chinwalla, Asif, Ding, Li, Koboldt, Daniel C., McLellan, Mike D., Wallis, John W., Wendl, Michael C., Zhang, Qunyuan, Albers, Cornelis A., Ayub, Qasim, Balasubramaniam, Senduran, Barrett, Jeffrey C., Chen, Yuan, Conrad, Donald F., Danecek, Petr, Dermitzakis, Emmanouil T., Hu, Min, Huang, Ni, Hurles, Matt E., Jin, Hanjun, Jostins, Luke, Keane, Thomas M., Quang Le, Si, Lindsay, Sarah, Long, Quan, MacArthur, Daniel G., Montgomery, Stephen B., Parts, Leopold, Tyler-Smith, Chris, Walter, Klaudia, Zhang, Yujun, Gerstein, Mark B., Snyder, Michael, Abyzov, Alexej, Balasubramanian, Suganthi, Bjornson, Robert, Du, Jiang, Grubert, Fabian, Habegger, Lukas, Haraksingh, Rajini, Jee, Justin, Khurana, Ekta, Lam, Hugo Y. K., Leng, Jing, Jasmine Mu, Xinmeng, Urban, Alexander E., Zhang, Zhengdong, Lee, Charles, McCarroll, Steven A., DePristo, Mark A., Korbel, Jan O., De La Vega, Francisco M., Blackwell, Tom, Eichler, Evan E., Kidd, Jeffrey M., Hurles, Matt E., Gibbs, Richard A., Coafra, Cristian, Dinh, Huyen, Kovar, Christie, Lee, Sandy, Nazareth, Lynne, Marth, Gabor T., Wilkinson, Jane, Flicek, Paul, Sherry, Stephen T., Abecasis, Gonçalo R., Mardis, Elaine R., Coffey, Allison, Scott, Carol, Gerstein, Mark B., Chakravarti, Aravinda, Knoppers, Bartha M., Bustamante, Carlos D., Gharani, Neda, Jorde, Lynn, Kaye, Jane S., Kent, Alastair, Li, Taosha, McGuire, Amy L., Ossorio, Pilar N., Rotimi, Charles N., Su, Yeyang, Toji, Lorraine H., Brooks, Lisa D., Felsenfeld, Adam L., McEwen, Jean E., Abdallah, Assya, Juenger, Christopher R., Clemm, Nicholas C., Duncanson, Audrey, Green, Eric D., Guyer, Mark S., and Peterson, Jane L.

Published
2010
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31. Protecting Genomic Data Privacy with Probabilistic Modeling

Author

Massachusetts Institute of Technology. Department of Mathematics, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Simmons, Sean, Berger Leighton, Bonnie, Sahinalp, Cenk, Massachusetts Institute of Technology. Department of Mathematics, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Simmons, Sean, Berger Leighton, Bonnie, and Sahinalp, Cenk

Abstract

As genetic sequencing becomes less expensive and data sets linking genetic data and medical records (e.g., Biobanks) become larger and more common, issues of data privacy and computational challenges become more necessary to address in order to realize the benefits of these datasets. One possibility for alleviating these issues is through the use of already-computed summary statistics (e.g., slopes and standard errors from a regression model of a phenotype on a genotype). If groups share summary statistics from their analyses of biobanks, many of the privacy issues and computational challenges concerning the access of these data could be bypassed. In this paper we explore the possibility of using summary statistics from simple linear models of phenotype on genotype in order to make inferences about more complex phenotypes (those that are derived from two or more simple phenotypes). We provide exact formulas for the slope, intercept, and standard error of the slope for linear regressions when combining phenotypes. Derived equations are validated via simulation and tested on a real data set exploring the genetics of fatty acids. Keywords: privacy; biobank; genetics; genome-wide association study; single nucleotide variant; computational challenges; data security; phenotypes

Published
2019

32. Widespread and Functional RNA Circularization in Localized Prostate Cancer

Author

Chen, Sujun, primary, Huang, Vincent, additional, Xu, Xin, additional, Livingstone, Julie, additional, Soares, Fraser, additional, Jeon, Jouhyun, additional, Zeng, Yong, additional, Hua, Junjie Tony, additional, Petricca, Jessica, additional, Guo, Haiyang, additional, Wang, Miranda, additional, Yousif, Fouad, additional, Zhang, Yuzhe, additional, Donmez, Nilgun, additional, Ahmed, Musaddeque, additional, Volik, Stas, additional, Lapuk, Anna, additional, Chua, Melvin L.K., additional, Heisler, Lawrence E., additional, Foucal, Adrien, additional, Fox, Natalie S., additional, Fraser, Michael, additional, Bhandari, Vinayak, additional, Shiah, Yu-Jia, additional, Guan, Jiansheng, additional, Li, Jixi, additional, Orain, Michèle, additional, Picard, Valérie, additional, Hovington, Hélène, additional, Bergeron, Alain, additional, Lacombe, Louis, additional, Fradet, Yves, additional, Têtu, Bernard, additional, Liu, Stanley, additional, Feng, Felix, additional, Wu, Xue, additional, Shao, Yang W., additional, Komor, Malgorzata A., additional, Sahinalp, Cenk, additional, Collins, Colin, additional, Hoogstrate, Youri, additional, de Jong, Mark, additional, Fijneman, Remond J.A., additional, Fei, Teng, additional, Jenster, Guido, additional, van der Kwast, Theodorus, additional, Bristow, Robert G., additional, Boutros, Paul C., additional, and He, Housheng Hansen, additional

Published
2019
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33. Pervasive intra-tumour heterogeneity and subclonal selection across cancer types

Author

Dentro, Stefan, Leshchiner, Ignaty, Haase, Kerstin, Wintersinger, Jeff, Deshwar, Amit, Tarabichi, Maxime, Rubanova, Yulia, Yu, Kaixian, Garcia, Ignacio Vazquez, Macintyre, Geoff, Kleinheinz, Kortine, Livitz, Dimitri, Malikic, Salem, Donmez, Nilgun, Sengupta, Subhajit, Ji, Yuan, Demeulemeester, Jonas, Anur, Pavana, Jolly, Clemency, Cmero, Marek, Rosebrock, Daniel, Schumacher, Steve, Fan, Yu, Fittall, Matthew, Yao, Xiaotong, Lee, Juhee, Schlesner, Matthias, Zhu, Hongtu, Adams, David, Getz, Gad, Boutros, Paul, Imielinski, Marcin, Beroukhim, Rameen, Sahinalp, Cenk, Peifer, Martin, Martincorena, Inigo, Markowetz, Florian, Mustonen, Ville, Yuan, Ke, Gerstung, Moritz, Wang, Wenyi, Spellman, Paul, Morris, Quaid, Wedge, David, Van Loo, Peter, Dentro, Stefan, Leshchiner, Ignaty, Haase, Kerstin, Wintersinger, Jeff, Deshwar, Amit, Tarabichi, Maxime, Rubanova, Yulia, Yu, Kaixian, Garcia, Ignacio Vazquez, Macintyre, Geoff, Kleinheinz, Kortine, Livitz, Dimitri, Malikic, Salem, Donmez, Nilgun, Sengupta, Subhajit, Ji, Yuan, Demeulemeester, Jonas, Anur, Pavana, Jolly, Clemency, Cmero, Marek, Rosebrock, Daniel, Schumacher, Steve, Fan, Yu, Fittall, Matthew, Yao, Xiaotong, Lee, Juhee, Schlesner, Matthias, Zhu, Hongtu, Adams, David, Getz, Gad, Boutros, Paul, Imielinski, Marcin, Beroukhim, Rameen, Sahinalp, Cenk, Peifer, Martin, Martincorena, Inigo, Markowetz, Florian, Mustonen, Ville, Yuan, Ke, Gerstung, Moritz, Wang, Wenyi, Spellman, Paul, Morris, Quaid, Wedge, David, and Van Loo, Peter

Published
2018

34. Enabling Privacy-Preserving GWASs in Heterogeneous Human Populations

Author

Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Mathematics, Simmons, Sean Kenneth, Berger Leighton, Bonnie, Sahinalp, Cenk, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Mathematics, Simmons, Sean Kenneth, Berger Leighton, Bonnie, and Sahinalp, Cenk

Abstract

The proliferation of large genomic databases offers the potential to perform increasingly larger-scale genome-wide association studies (GWASs). Due to privacy concerns, however, access to these data is limited, greatly reducing their usefulness for research. Here, we introduce a computational framework for performing GWASs that adapts principles of differential privacy-a cryptographic theory that facilitates secure analysis of sensitive data-to both protect private phenotype information (e.g., disease status) and correct for population stratification. This framework enables us to produce privacy-preserving GWAS results based on EIGENSTRAT and linear mixed model (LMM)-based statistics, both of which correct for population stratification. We test our differentially private statistics, PrivSTRAT and PrivLMM, on simulated and real GWAS datasets and find they are able to protect privacy while returning meaningful results. Our framework can be used to securely query private genomic datasets to discover which specific genomic alterations may be associated with a disease, thus increasing the availability of these valuable datasets., National Institutes of Health (U.S.) (Grant GM108348)

Published
2018

38. Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data

Author

Yamada, Makoto, primary, Tang, Jiliang, additional, Lugo-Martinez, Jose, additional, Hodzic, Ermin, additional, Shrestha, Raunak, additional, Saha, Avishek, additional, Ouyang, Hua, additional, Yin, Dawei, additional, Mamitsuka, Hiroshi, additional, Sahinalp, Cenk, additional, Radivojac, Predrag, additional, Menczer, Filippo, additional, and Chang, Yi, additional

Published
2018
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39. Abstract 3000: Pervasive intra-tumour heterogeneity and subclonal selection across cancer types

Author

Dentro, Stefan, primary, Leshchiner, Ignaty, additional, Haase, Kerstin, additional, Wintersinger, Jeff, additional, Deshwar, Amit, additional, Tarabichi, Maxime, additional, Rubanova, Yulia, additional, Yu, Kaixian, additional, Vázquez García, Ignacio, additional, Macintyre, Geoff, additional, Kleinheinz, Kortine, additional, Livitz, Dimitri, additional, Malikic, Salem, additional, Donmez, Nilgun, additional, Sengupta, Subhajit, additional, Ji, Yuan, additional, Demeulemeester, Jonas, additional, Anur, Pavana, additional, Jolly, Clemency, additional, Cmero, Marek, additional, Rosebrock, Daniel, additional, Schumacher, Steve, additional, Fan, Yu, additional, Fittall, Matthew, additional, Yao, Xiaotong, additional, Lee, Juhee, additional, Schlesner, Matthias, additional, Zhu, Hongtu, additional, Adams, David, additional, Getz, Gad, additional, Boutros, Paul, additional, Imielinski, Marcin, additional, Beroukhim, Rameen, additional, Sahinalp, Cenk, additional, Peifer, Martin, additional, Martincorena, Inigo, additional, Markowetz, Florian, additional, Mustonen, Ville, additional, Yuan, Ke, additional, Gerstung, Moritz, additional, Wang, Wenyi, additional, Spellman, Paul, additional, Morris, Quaid, additional, Wedge, David, additional, and Loo, Peter Van, additional

Published
2018
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40. The long noncoding RNA landscape of neuroendocrine prostate cancer and its clinical implications

Author

Ramnarine, Varune Rohan, primary, Alshalalfa, Mohammed, additional, Mo, Fan, additional, Nabavi, Noushin, additional, Erho, Nicholas, additional, Takhar, Mandeep, additional, Shukin, Robert, additional, Brahmbhatt, Sonal, additional, Gawronski, Alexander, additional, Kobelev, Maxim, additional, Nouri, Mannan, additional, Lin, Dong, additional, Tsai, Harrison, additional, Lotan, Tamara L, additional, Karnes, R Jefferey, additional, Rubin, Mark A, additional, Zoubeidi, Amina, additional, Gleave, Martin E, additional, Sahinalp, Cenk, additional, Wyatt, Alexander W, additional, Volik, Stanislav V, additional, Beltran, Himisha, additional, Davicioni, Elai, additional, Wang, Yuzhuo, additional, and Collins, Colin C, additional

Published
2018
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41. Abstract 378: The Cancer Genome Collaboratory

Author

Yung, Christina K., primary, Mihaiescu, George L., additional, Tiernay, Bob, additional, Zhang, Junjun, additional, Gerthoffert, Francois, additional, Yang, Andy, additional, Baker, Jared, additional, Bourque, Guillaume, additional, Boutros, Paul C., additional, Knoppers, Bartha M., additional, Ouellette, BF Francis, additional, Sahinalp, Cenk, additional, Shah, Sohrab P., additional, Ferretti, Vincent, additional, and Stein, Lincoln D., additional

Published
2017
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45. Spatial genomic heterogeneity within localized, multifocal prostate cancer

Author

Boutros, Paul C., Boutros, Paul C., Fraser, Michael, Harding, Nicholas J., de Borja, Richard, Trudel, Dominique, Lalonde, Emilie, Meng, Alice, Hennings-Yeomans, Pablo H., McPherson, Andrew, Sabelnykova, Veronica Y., Zia, Amin, Fox, Natalie S., Livingstone, Julie, Shiah, Yu-Jia, Wang, Jianxin, Beck, Timothy A., Have, Cherry L., Chong, Taryne, Sam, Michelle, Johns, Jeremy, Timms, Lee, Buchner, Nicholas, Wong, Ada, Watson, John D., Simmons, Trent T., P'ng, Christine, Zafarana, Gaetano, Nguyen, Francis, Luo, Xuemei, Chu, Kenneth C., Prokopec, Stephenie D., Sykes, Jenna, Dal Pra, Alan, Berlin, Alejandro, Brown, Andrew, Chan-Seng-Yue, Michelle A., Yousif, Fouad, Denroche, Robert E., Chong, Lauren C., Chen, Gregory M., Jung, Esther, Fung, Clement, Starmans, Maud H. W., Chen, Hanbo, Govind, Shaylan K., Hawley, James, D'Costa, Alister, Pintilie, Melania, Waggott, Daryl, Hach, Faraz, Lambin, Philippe, Muthuswamy, Lakshmi B., Cooper, Colin, Eeles, Rosalind, Neal, David, Tetu, Bernard, Sahinalp, Cenk, Stein, Lincoln D., Fleshner, Neil, Shah, Sohrab P., Collins, Colin C., Hudson, Thomas J., McPherson, John D., van der Kwast, Theodorus, Bristow, Robert G., Boutros, Paul C., Boutros, Paul C., Fraser, Michael, Harding, Nicholas J., de Borja, Richard, Trudel, Dominique, Lalonde, Emilie, Meng, Alice, Hennings-Yeomans, Pablo H., McPherson, Andrew, Sabelnykova, Veronica Y., Zia, Amin, Fox, Natalie S., Livingstone, Julie, Shiah, Yu-Jia, Wang, Jianxin, Beck, Timothy A., Have, Cherry L., Chong, Taryne, Sam, Michelle, Johns, Jeremy, Timms, Lee, Buchner, Nicholas, Wong, Ada, Watson, John D., Simmons, Trent T., P'ng, Christine, Zafarana, Gaetano, Nguyen, Francis, Luo, Xuemei, Chu, Kenneth C., Prokopec, Stephenie D., Sykes, Jenna, Dal Pra, Alan, Berlin, Alejandro, Brown, Andrew, Chan-Seng-Yue, Michelle A., Yousif, Fouad, Denroche, Robert E., Chong, Lauren C., Chen, Gregory M., Jung, Esther, Fung, Clement, Starmans, Maud H. W., Chen, Hanbo, Govind, Shaylan K., Hawley, James, D'Costa, Alister, Pintilie, Melania, Waggott, Daryl, Hach, Faraz, Lambin, Philippe, Muthuswamy, Lakshmi B., Cooper, Colin, Eeles, Rosalind, Neal, David, Tetu, Bernard, Sahinalp, Cenk, Stein, Lincoln D., Fleshner, Neil, Shah, Sohrab P., Collins, Colin C., Hudson, Thomas J., McPherson, John D., van der Kwast, Theodorus, and Bristow, Robert G.

Abstract

Herein we provide a detailed molecular analysis of the spatial heterogeneity of clinically localized, multifocal prostate cancer to delineate new oncogenes or tumor suppressors. We initially determined the copy number aberration (CNA) profiles of 74 patients with index tumors of Gleason score 7. Of these, 5 patients were subjected to whole-genome sequencing using DNA quantities achievable in diagnostic biopsies, with detailed spatial sampling of 23 distinct tumor regions to assess intraprostatic heterogeneity in focal genomics. Multifocal tumors are highly heterogeneous for single-nucleotide variants (SNVs), CNAs and genomic rearrangements. We identified and validated a new recurrent amplification of MYCL, which is associated with TP53 deletion and unique profiles of DNA damage and transcriptional dysregulation. Moreover, we demonstrate divergent tumor evolution in multifocal cancer and, in some cases, tumors of independent clonal origin. These data represent the first systematic relation of intraprostatic genomic heterogeneity to predicted clinical outcome and inform the development of novel biomarkers that reflect individual prognosis.

Published
2015

46. deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data

Author

McPherson, Andrew, Hormozdiari, Fereydoun, Sahinalp, Cenk, Zayed, Abdalnasser, Giuliany, Ryan, Ha, Gavin, and Sun, Mark

Abstract

Gene fusions created by somatic genomic rearrangements are known to play an important role in the onset and development of some cancers, such as lymphomas and sarcomas. RNA-Seq (whole transcriptome shotgun sequencing) is proving to be a useful tool for the discovery of novel gene fusions in cancer transcriptomes. However, algorithmic methods for the discovery of gene fusions using RNA-Seq data remain underdeveloped. We have developed deFuse, a novel computational method for fusion discovery in tumor RNA-Seq data. Unlike existing methods that use only unique best-hit alignments and consider only fusion boundaries at the ends of known exons, deFuse considers all alignments and all possible locations for fusion boundaries. As a result, deFuse is able to identify fusion sequences with demonstrably better sensitivity than previous approaches. To increase the specificity of our approach, we curated a list of 60 true positive and 61 true negative fusion sequences (as confirmed by RT-PCR), and have trained an adaboost classifier on 11 novel features of the sequence data. The resulting classifier has an estimated value of 0.91 for the area under the ROC curve. We have used deFuse to discover gene fusions in 40 ovarian tumor samples, one ovarian cancer cell line, and three sarcoma samples. We report herein the first gene fusions discovered in ovarian cancer. We conclude that gene fusions are not infrequent events in ovarian cancer and that these events have the potential to substantially alter the expression patterns of the genes involved; gene fusions should therefore be considered in efforts to comprehensively characterize the mutational profiles of ovarian cancer transcriptomes.

Published
2011

47. Spatial genomic heterogeneity within localized, multifocal prostate cancer

Author

Boutros, Paul C, primary, Fraser, Michael, additional, Harding, Nicholas J, additional, de Borja, Richard, additional, Trudel, Dominique, additional, Lalonde, Emilie, additional, Meng, Alice, additional, Hennings-Yeomans, Pablo H, additional, McPherson, Andrew, additional, Sabelnykova, Veronica Y, additional, Zia, Amin, additional, Fox, Natalie S, additional, Livingstone, Julie, additional, Shiah, Yu-Jia, additional, Wang, Jianxin, additional, Beck, Timothy A, additional, Have, Cherry L, additional, Chong, Taryne, additional, Sam, Michelle, additional, Johns, Jeremy, additional, Timms, Lee, additional, Buchner, Nicholas, additional, Wong, Ada, additional, Watson, John D, additional, Simmons, Trent T, additional, P'ng, Christine, additional, Zafarana, Gaetano, additional, Nguyen, Francis, additional, Luo, Xuemei, additional, Chu, Kenneth C, additional, Prokopec, Stephenie D, additional, Sykes, Jenna, additional, Dal Pra, Alan, additional, Berlin, Alejandro, additional, Brown, Andrew, additional, Chan-Seng-Yue, Michelle A, additional, Yousif, Fouad, additional, Denroche, Robert E, additional, Chong, Lauren C, additional, Chen, Gregory M, additional, Jung, Esther, additional, Fung, Clement, additional, Starmans, Maud H W, additional, Chen, Hanbo, additional, Govind, Shaylan K, additional, Hawley, James, additional, D'Costa, Alister, additional, Pintilie, Melania, additional, Waggott, Daryl, additional, Hach, Faraz, additional, Lambin, Philippe, additional, Muthuswamy, Lakshmi B, additional, Cooper, Colin, additional, Eeles, Rosalind, additional, Neal, David, additional, Tetu, Bernard, additional, Sahinalp, Cenk, additional, Stein, Lincoln D, additional, Fleshner, Neil, additional, Shah, Sohrab P, additional, Collins, Colin C, additional, Hudson, Thomas J, additional, McPherson, John D, additional, van der Kwast, Theodorus, additional, and Bristow, Robert G, additional

Published
2015
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49. PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension.

Author

Feng Chen, Chenghong Wang, Wenrui Dai, Xiaoqian Jiang, Mohammed, Noman, Al Aziz, Md Momin, Nazmus Sadat, Md, Sahinalp, Cenk, Lauter, Kristin, and Shuang Wang

Subjects
NUCLEOTIDE sequencing, GENETIC engineering, DATA encryption, CLOUD computing, CLOUD storage
Abstract

Background: Advances in DNA sequencing technologies have prompted a wide range of genomic applications to improve healthcare and facilitate biomedical research. However, privacy and security concerns have emerged as a challenge for utilizing cloud computing to handle sensitive genomic data. Methods: We present one of the first implementations of Software Guard Extension (SGX) based securely outsourced genetic testing framework, which leverages multiple cryptographic protocols and minimal perfect hash scheme to enable efficient and secure data storage and computation outsourcing. Results: We compared the performance of the proposed PRESAGE framework with the state-of-the-art homomorphic encryption scheme, as well as the plaintext implementation. The experimental results demonstrated significant performance over the homomorphic encryption methods and a small computational overhead in comparison to plaintext implementation. Conclusions: The proposed PRESAGE provides an alternative solution for secure and efficient genomic data outsourcing in an untrusted cloud by using a hybrid framework that combines secure hardware and multiple crypto protocols. [ABSTRACT FROM AUTHOR]

Published
2017
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