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2. Optimizing agent behavior over long time scales by transporting value

Author

Chia-Chun Hung, Timothy Lillicrap, Josh Abramson, Yan Wu, Mehdi Mirza, Federico Carnevale, Arun Ahuja, and Greg Wayne

Subjects
Science
Abstract

People are able to mentally time travel to distant memories and reflect on the consequences of those past events. Here, the authors show how a mechanism that connects learning from delayed rewards with memory retrieval can enable AI agents to discover links between past events to help decide better courses of action in the future.

Published
2019
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3. Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer

Author

Timothy O’Donnell, Elizabeth L. Christie, Arun Ahuja, Jacqueline Buros, B. Arman Aksoy, David D. L. Bowtell, Alexandra Snyder, and Jeff Hammerbacher

Subjects
Neoantigen, Mutational signature, Chemotherapy, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Patients with highly mutated tumors, such as melanoma or smoking-related lung cancer, have higher rates of response to immune checkpoint blockade therapy, perhaps due to increased neoantigen expression. Many chemotherapies including platinum compounds are known to be mutagenic, but the impact of standard treatment protocols on mutational burden and resulting neoantigen expression in most human cancers is unknown. Methods We sought to quantify the effect of chemotherapy treatment on computationally predicted neoantigen expression for high grade serous ovarian carcinoma patients enrolled in the Australian Ovarian Cancer Study. In this series, 35 of 114 samples were collected after exposure to chemotherapy; 14 are matched with an untreated sample from the same patient. Our approach integrates whole genome and RNA sequencing of bulk tumor samples with class I MHC binding prediction and mutational signatures extracted from studies of chemotherapy-exposed Caenorhabditis elegans and Gallus gallus cells. We additionally investigated the relationship between neoantigens, tumor infiltrating immune cells estimated from RNA-seq with CIBERSORT, and patient survival. Results Greater neoantigen burden and CD8+ T cell infiltration in primary, pre-treatment samples were independently associated with improved survival. Relapse samples collected after chemotherapy harbored a median of 78% more expressed neoantigens than untreated primary samples, a figure that combines the effects of chemotherapy and other processes operative during relapse. The contribution from chemotherapy-associated signatures was small, accounting for a mean of 5% (range 0–16) of the expressed neoantigen burden in relapse samples. In both treated and untreated samples, most neoantigens were attributed to COSMIC Signature (3), associated with BRCA disruption, Signature (1), associated with a slow mutagenic process active in healthy tissue, and Signature (8), of unknown etiology. Conclusion Relapsed ovarian cancers harbor more predicted neoantigens than primary tumors, but the increase is due to pre-existing mutational processes, not mutagenesis from chemotherapy.

Published
2018
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4. Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: An exploratory multi-omic analysis.

Author

Alexandra Snyder, Tavi Nathanson, Samuel A Funt, Arun Ahuja, Jacqueline Buros Novik, Matthew D Hellmann, Eliza Chang, Bulent Arman Aksoy, Hikmat Al-Ahmadie, Erik Yusko, Marissa Vignali, Sharon Benzeno, Mariel Boyd, Meredith Moran, Gopa Iyer, Harlan S Robins, Elaine R Mardis, Taha Merghoub, Jeff Hammerbacher, Jonathan E Rosenberg, and Dean F Bajorin

Subjects
Medicine
Abstract

BACKGROUND:Inhibition of programmed death-ligand 1 (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens, and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance. METHODS AND FINDINGS:The goals of this study were to (1) evaluate the association of mutation load and predicted neoantigen load with therapeutic benefit and (2) determine whether intratumoral and peripheral blood T cell receptor (TCR) clonality inform clinical outcomes in urothelial carcinoma treated with atezolizumab. We hypothesized that an elevated mutation load in combination with T cell clonal dominance among intratumoral lymphocytes prior to treatment or among peripheral T cells after treatment would be associated with effective tumor control upon treatment with anti-PD-L1 therapy. We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pretreatment tumor samples as well as TCR-seq of matched, serially collected peripheral blood, collected before and after treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression-free survival [PFS] >6 months), PFS, and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state. Patients with DCB displayed a higher proportion of tumor-infiltrating T lymphocytes (TIL) (n = 24, Mann-Whitney p = 0.047). Pretreatment peripheral blood TCR clonality below the median was associated with improved PFS (n = 29, log-rank p = 0.048) and OS (n = 29, log-rank p = 0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n = 22, Mann-Whitney p = 0.022). The combination of high pretreatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n = 10, hazard ratio (HR) (mean) = 89.88, HR (median) = 23.41, 95% CI [2.43, 506.94], p(HR > 1) = 0.0014). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which, in turn, impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load, and expressed neoantigen load did not demonstrate significant association with DCB (n = 25, Mann-Whitney p = 0.22, n = 25, Mann-Whitney p = 0.55, and n = 25, Mann-Whitney p = 0.29, respectively). Instead, we found evidence of time-varying effects of somatic mutation load on PFS in this cohort (n = 25, p = 0.044). A limitation of our study is its small sample size (n = 29), a subset of the patients treated on IMvigor 210 (NCT02108652). Given the number of exploratory analyses performed, we intend for these results to be hypothesis-generating. CONCLUSIONS:These results demonstrate the complex nature of immune response to checkpoint blockade and the compelling need for greater interrogation and data integration of both host and tumor factors. Incorporating these variables in prospective studies will facilitate identification and treatment of resistant patients.

Published
2017
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6. Imitating Language via Scalable Inverse Reinforcement Learning.

Author

Markus Wulfmeier, Michael Bloesch, Nino Vieillard, Arun Ahuja, Jorg Bornschein, Sandy H. Huang, Artem Sokolov, Matt Barnes 0001, Guillaume Desjardins, Alex Bewley, Sarah Maria Elisabeth Bechtle, Jost Tobias Springenberg, Nikola Momchev, Olivier Bachem, Matthieu Geist, and Martin A. Riedmiller

Published
2024
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7. Scaling Instructable Agents Across Many Simulated Worlds.

Author

SIMA Team, Maria Abi Raad, Arun Ahuja, Catarina Barros, Frederic Besse, Andrew Bolt, Adrian Bolton, Bethanie Brownfield, Gavin Buttimore, Max Cant, Sarah Chakera, Stephanie C. Y. Chan, Jeff Clune, Adrian Collister, Vikki Copeman, Alex Cullum, Ishita Dasgupta 0001, Dario de Cesare, Julia Di Trapani, Yani Donchev, Emma Dunleavy, Martin Engelcke, Ryan Faulkner 0001, Frankie Garcia, Charles Gbadamosi, Zhitao Gong, Lucy Gonzalez, Kshitij Gupta, Karol Gregor, Arne Olav Hallingstad, Tim Harley, Sam Haves, Felix Hill, Ed Hirst, Drew A. Hudson, Jony Hudson, Steph Hughes-Fitt, Danilo J. Rezende, Mimi Jasarevic, Laura Kampis, Nan Rosemary Ke, Thomas Keck, Junkyung Kim, Oscar Knagg, Kavya Kopparapu, Andrew K. Lampinen, Shane Legg, Alexander Lerchner, Marjorie Limont, Yulan Liu, Maria Loks-Thompson, Joseph Marino, Kathryn Martin Cussons, Loic Matthey, Siobhan Mcloughlin, Piermaria Mendolicchio, Hamza Merzic, Anna Mitenkova, Alexandre Moufarek, Valéria Oliveira, Yanko Gitahy Oliveira, Hannah Openshaw, Renke Pan, Aneesh Pappu, Alex Platonov, Ollie Purkiss, David P. Reichert, John Reid, Pierre Harvey Richemond, Tyson Roberts, Giles Ruscoe, Jaume Sanchez Elias, Tasha Sandars, Daniel P. Sawyer, Tim Scholtes, Guy Simmons, Daniel Slater, Hubert Soyer, Heiko Strathmann, Peter Stys, Allison C. Tam, Denis Teplyashin, Tayfun Terzi, Davide Vercelli, Bojan Vujatovic, Marcus Wainwright, Jane X. Wang, Zhengdong Wang, Daan Wierstra, Duncan Williams, Nathaniel Wong, Sarah York, and Nick Young

Published
2024
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8. Behavior Priors for Efficient Reinforcement Learning.

Author

Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, and Nicolas Heess

Published
2022

14. Evaluating Multimodal Interactive Agents.

Author

Josh Abramson, Arun Ahuja, Federico Carnevale, Petko Georgiev, Alex Goldin, Alden Hung, Jessica Landon, Timothy P. Lillicrap, Alistair Muldal, Blake A. Richards, Adam Santoro, Tamara von Glehn, Greg Wayne, Nathaniel Wong, and Chen Yan

Published
2022
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18. Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning.

Author

Josh Abramson, Arun Ahuja, Arthur Brussee, Federico Carnevale, Mary Cassin, Felix Fischer 0004, Petko Georgiev, Alex Goldin, Tim Harley, Felix Hill, Peter Conway Humphreys, Alden Hung, Jessica Landon, Timothy P. Lillicrap, Hamza Merzic, Alistair Muldal, Adam Santoro, Guy Scully, Tamara von Glehn, Greg Wayne, Nathaniel Wong, Chen Yan, and Rui Zhu

Published
2021

21. Behavior Priors for Efficient Reinforcement Learning.

Author

Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, and Nicolas Heess

Published
2020

22. Imitating Interactive Intelligence.

Author

Josh Abramson, Arun Ahuja, Arthur Brussee, Federico Carnevale, Mary Cassin, Stephen Clark, Andrew Dudzik, Petko Georgiev, Aurelia Guy, Tim Harley, Felix Hill, Alden Hung, Zachary Kenton, Jessica Landon, Timothy P. Lillicrap, Kory W. Mathewson, Alistair Muldal, Adam Santoro, Nikolay Savinov, Vikrant Varma, Greg Wayne, Nathaniel Wong, Chen Yan, and Rui Zhu

Published
2020

27. Data from Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade

Author

Jeff Hammerbacher, Alexandra Snyder, Jedd D. Wolchok, Taha Merghoub, Eliezer Van Allen, Diana Miao, Matthew D. Hellmann, Bulent Arman Aksoy, Alexander Rubinsteyn, Arun Ahuja, and Tavi Nathanson

Abstract

Immune checkpoint inhibitors are promising treatments for patients with a variety of malignancies. Toward understanding the determinants of response to immune checkpoint inhibitors, it was previously demonstrated that the presence of somatic mutations is associated with benefit from checkpoint inhibition. A hypothesis was posited that neoantigen homology to pathogens may in part explain the link between somatic mutations and response. To further examine this hypothesis, we reanalyzed cancer exome data obtained from our previously published study of 64 melanoma patients treated with CTLA-4 blockade and a new dataset of RNA-Seq data from 24 of these patients. We found that the ability to accurately predict patient benefit did not increase as the analysis narrowed from somatic mutation burden, to inclusion of only those mutations predicted to be MHC class I neoantigens, to only including those neoantigens that were expressed or that had homology to pathogens. The only association between somatic mutation burden and response was found when examining samples obtained prior to treatment. Neoantigen and expressed neoantigen burden were also associated with response, but neither was more predictive than somatic mutation burden. Neither the previously described tetrapeptide signature nor an updated method to evaluate neoepitope homology to pathogens was more predictive than mutation burden. Cancer Immunol Res; 5(1); 84–91. ©2016 AACR.

Published
2023
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28. Supplementary Figures 1 through 4 from Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade

Author

Jeff Hammerbacher, Alexandra Snyder, Jedd D. Wolchok, Taha Merghoub, Eliezer Van Allen, Diana Miao, Matthew D. Hellmann, Bulent Arman Aksoy, Alexander Rubinsteyn, Arun Ahuja, and Tavi Nathanson

Abstract

1. Mutation burden and survival using updated mutation calling system. 2. Similarity scores between samples and viral or non-viral epitopes. 3. Deconvolution of immune infiltrates. 4. Gene set enrichment analysis.

Published
2023
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30. Unsupervised Predictive Memory in a Goal-Directed Agent.

Author

Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack W. Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley, Josh Abramson, Shakir Mohamed, Danilo Jimenez Rezende, David Saxton, Adam Cain, Chloe Hillier, David Silver, Koray Kavukcuoglu, Matthew M. Botvinick, Demis Hassabis, and Timothy P. Lillicrap

Published
2018

40. Optimizing agent behavior over long time scales by transporting value

Author

Josh Abramson, Timothy P. Lillicrap, Chia-Chun Hung, Federico Carnevale, Yan Wu, Greg Wayne, Mehdi Mirza, and Arun Ahuja

Subjects
0301 basic medicine, Value (ethics), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Computer Science - Artificial Intelligence, Chronesthesia, Transfer, Psychology, Science, General Physics and Astronomy, Information technology, Time travel, Models, Psychological, Learning algorithms, Behavioral economics, General Biochemistry, Genetics and Molecular Biology, Article, Machine Learning (cs.LG), 03 medical and health sciences, 0302 clinical medicine, Mental Processes, Artificial Intelligence, Humans, Learning, lcsh:Science, Problem Solving, Multidisciplinary, Recall, Regret, General Chemistry, 030104 developmental biology, Artificial Intelligence (cs.AI), Action (philosophy), lcsh:Q, Reinforcement, Psychology, 030217 neurology & neurosurgery, Algorithms, Cognitive psychology, Storytelling
Abstract

Humans prolifically engage in mental time travel. We dwell on past actions and experience satisfaction or regret. More than storytelling, these recollections change how we act in the future and endow us with a computationally important ability to link actions and consequences across spans of time, which helps address the problem of long-term credit assignment: the question of how to evaluate the utility of actions within a long-duration behavioral sequence. Existing approaches to credit assignment in AI cannot solve tasks with long delays between actions and consequences. Here, we introduce a paradigm where agents use recall of specific memories to credit past actions, allowing them to solve problems that are intractable for existing algorithms. This paradigm broadens the scope of problems that can be investigated in AI and offers a mechanistic account of behaviors that may inspire models in neuroscience, psychology, and behavioral economics., People are able to mentally time travel to distant memories and reflect on the consequences of those past events. Here, the authors show how a mechanism that connects learning from delayed rewards with memory retrieval can enable AI agents to discover links between past events to help decide better courses of action in the future.

Published
2019

41. Correction to 'Proton-driven plasma wakefield acceleration in AWAKE'

Author

Brennan Goddard, S. Doebert, Spencer Gessner, Anthony Hartin, John Molendijk, Patrick Muggli, M. Krupa, Erik Adli, Graeme Burt, S. Rey, F. Friebel, James Henderson, B. Williamson, Ans Pardons, Eric Chevallay, M. Hüther, V. A. Minakov, John P. Farmer, Konstantin Lotov, Moses Chung, F. Batsch, Olaf Grulke, A. P. Sosedkin, L. Garolfi, Carsten Welsch, Chiara Bracco, V. A. Verzilov, Florian Kraus, Stefano Mazzoni, P. V. Tuev, Allen Caldwell, M. Turner, J. Chappell, S. Liu, M. Moreira, R. Apsimon, Ricardo Fonseca, V. N. Fedosseev, M. D. Kelisani, A. A. Gorn, Alexander Pukhov, Matthew Wing, Eduardo Granados, F. Keeble, H. Damerau, Sung Youb Kim, B. Buttenschön, Peter Sherwood, S. Burger, O. Apsimon, F. Peña Asmus, L. Verra, Simon Jolly, A. Perera, Arun Ahuja, H. Panuganti, Guoxing Xia, M. Wendt, A.-M. Bachmann, B. Woolley, A. Helm, Jorge Vieira, F. Braunmüller, Yang Li, Thibaut Lefèvre, Alexey Petrenko, J. T. Moody, I. Gorgisyan, L. H. Deubner, Ralph Fiorito, Nelson Lopes, M. Ibison, Amos Dexter, Edda Gschwendtner, Francesco Velotti, L. O. Silva, M. Martyanov, and David R. Cooke

Subjects
Physics, Nuclear physics, Proton, Physics::Plasma Physics, General Mathematics, General Engineering, General Physics and Astronomy, Physics::Accelerator Physics, Articles, Plasma acceleration, Corrections
Abstract

In this article, we briefly summarize the experiments performed during the first run of the Advanced Wakefield Experiment, AWAKE, at CERN (European Organization for Nuclear Research). The final goal of AWAKE Run 1 (2013-2018) was to demonstrate that 10-20 MeV electrons can be accelerated to GeV energies in a plasma wakefield driven by a highly relativistic self-modulated proton bunch. We describe the experiment, outline the measurement concept and present first results. Last, we outline our plans for the future. This article is part of the Theo Murphy meeting issue 'Directions in particle beam-driven plasma wakefield acceleration'.

Published
2019

42. Experimental observation of plasma wakefield growth driven by the seeded self-modulation of a proton bunch

Author

M. Martyanov, H. Vincke, John Molendijk, A. Perera, L. Garolfi, R. Fiorito, R. Mompo, Luis O. Silva, David R. Cooke, E. Ozturk, J. T. Moody, Erik Adli, J. Batkiewicz, S. Bustamante, A. Guerrero, J. Bauche, L. Soby, Diego Barrientos, S. Rey, Amos Dexter, G. LeGodec, Michele Cascella, Moses Chung, Edda Gschwendtner, E. Öz, Florian Kraus, K. Rieger, Jorge Vieira, Janet Schmidt, K. Pepitone, D. Medina Godoy, M. Moreira, Chiara Bracco, Alexander Pukhov, Andrea Boccardi, Nelson Lopes, F. Braunmüller, Allen Caldwell, Yang Li, Thierry Bogey, A. Helm, Francesco Velotti, Ans Pardons, T. Lefevre, S. Doebert, B. Biskup, F. Keeble, Benjamin Woolley, Gennady Plyushchev, C. Hessler, Peter Sherwood, F. Peña Asmus, H. Damerau, F. Friebel, V. A. Verzilov, L. Deacon, T. Bohl, Alexey Petrenko, F. Batsch, Konstantin Lotov, M. Hüther, Spencer Gessner, A.-M. Bachmann, S. Liu, M. Bernardini, A. P. Sosedkin, Olaf Grulke, M. Ibison, V. N. Fedosseev, L. Jensen, G. Fior, V. K. Berglyd Olsen, C. Pasquino, L. Verra, Robert Apsimon, P. V. Tuev, R. Speroni, J. Chappell, L. Maricalva Brun, J. R. Henderson, J. D. Hansen, I. Gorgisyan, L. H. Deubner, A. A. Gorn, John P. Farmer, S. Burger, Sung Youb Kim, B. Buttenschön, B. Williamson, M. Barros Marin, Matthew Wing, Eduardo Granados, O. Apsimon, Wolfgang Höfle, Eric Chevallay, V. A. Minakov, Graeme Burt, Hartmut Ruhl, E. Shaposhnikova, Patric Muggli, Simon Jolly, Stefano Mazzoni, C. Mutin, I. A. Shalimova, M. Turner, Arun Ahuja, Guoxing Xia, Sam Pitman, Carsten Welsch, R. I. Spitsyn, Ricardo Fonseca, and James Mitchell

Subjects
Accelerator Physics (physics.acc-ph), Proton, FOS: Physical sciences, General Physics and Astronomy, Electron, 7. Clean energy, 01 natural sciences, Ciências Naturais::Ciências Físicas [Domínio/Área Científica], Acceleration, Physics::Plasma Physics, 0103 physical sciences, 010306 general physics, Nuclear Experiment, physics.acc-ph, awake, Physics, electrons, acceleration, Plasma, Accelerators and Storage Rings, ddc, instability, Transverse plane, Amplitude, beam, Physics::Accelerator Physics, Physics - Accelerator Physics, Seeding, Atomic physics, Energy (signal processing)
Abstract

We measure the effects of transverse wakefields driven by a relativistic proton bunch in plasma with densities of 2.1×1014 and 7.7×1014 electrons/cm3. We show that these wakefields periodically defocus the proton bunch itself, consistently with the development of the seeded self-modulation process. We show that the defocusing increases both along the bunch and along the plasma by using time resolved and time-integrated measurements of the proton bunch transverse distribution. We evaluate the transverse wakefield amplitudes and show that they exceed their seed value (

Published
2019
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43. Catch & Carry: Reusable Neural Controllers for Vision-Guided Whole-Body Tasks

Author

Yuval Tassa, Vu Pham, Arun Ahuja, Leonard Hasenclever, Saran Tunyasuvunakool, Nicolas Heess, Josh Merel, Tom Erez, and Greg Wayne

Subjects
Physics, FOS: Computer and information sciences, business.industry, Motor control, 020207 software engineering, Robotics, 02 engineering and technology, Animation, Computer Graphics and Computer-Aided Design, Task (computing), Artificial Intelligence (cs.AI), Robustness (computer science), Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, Reinforcement learning, Robot, Artificial intelligence, Graphics, business, Robotics (cs.RO)
Abstract

We address the longstanding challenge of producing flexible, realistic humanoid character controllers that can perform diverse whole-body tasks involving object interactions. This challenge is central to a variety of fields, from graphics and animation to robotics and motor neuroscience. Our physics-based environment uses realistic actuation and first-person perception - including touch sensors and egocentric vision - with a view to producing active-sensing behaviors (e.g. gaze direction), transferability to real robots, and comparisons to the biology. We develop an integrated neural-network based approach consisting of a motor primitive module, human demonstrations, and an instructed reinforcement learning regime with curricula and task variations. We demonstrate the utility of our approach for several tasks, including goal-conditioned box carrying and ball catching, and we characterize its behavioral robustness. The resulting controllers can be deployed in real-time on a standard PC. 1

Published
2019
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44. Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer

Author

Bulent Arman Aksoy, Alexandra Snyder, Jeff Hammerbacher, Arun Ahuja, Timothy O'Donnell, David D.L. Bowtell, Elizabeth L. Christie, and Jacqueline L. Buros

Subjects
0301 basic medicine, Cancer Research, medicine.medical_treatment, CD8-Positive T-Lymphocytes, chemistry.chemical_compound, 0302 clinical medicine, Surgical oncology, Ovarian carcinoma, Ovarian Neoplasms, 0303 health sciences, Genome, integumentary system, Melanoma, High-Throughput Nucleotide Sequencing, Middle Aged, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, 3. Good health, Gene Expression Regulation, Neoplastic, Mutational signature, Oncology, 030220 oncology & carcinogenesis, Neoplastic Stem Cells, Female, medicine.drug, Research Article, Cyclophosphamide, Biology, lcsh:RC254-282, 03 medical and health sciences, Antigen, Antigens, Neoplasm, Genetics, medicine, Chemotherapy, Animals, Humans, Lung cancer, Caenorhabditis elegans, 030304 developmental biology, Aged, Platinum, Cisplatin, business.industry, Macrophages, medicine.disease, Carboplatin, Immune checkpoint, 030104 developmental biology, chemistry, Immunology, Mutation, Cancer research, Neoplasm Recurrence, Local, business, Ovarian cancer, Neoantigen, Chickens
Abstract

BackgroundPatients with highly mutated tumors, such as melanoma or smoking-related lung cancer, have higher rates of response to immune checkpoint blockade therapy, perhaps due to increased neoantigen expression. Many chemotherapies including platinum compounds are known to be mutagenic, but the impact of standard treatment protocols on mutational burden and resulting neoantigen expression in most human cancers is unknown.MethodsWe sought to quantify the effect of chemotherapy treatment on computationally predicted neoantigen expression for 12 high grade serous ovarian carcinoma (HGSC) patients with pre- and post-chemotherapy samples collected in the Australian Ovarian Cancer Study. We additionally analyzed 16 patients from the cohort with post-treatment samples only, including five primary surgical samples exposed to neoadjuvant chemotherapy. Our approach integrates tumor whole genome and RNA sequencing with class I MHC binding prediction and mutational signatures of chemotherapy exposure extracted from two preclinical studies.ResultsThe mutational signatures for cisplatin and cyclophosphamide identified in a preclinical model had significant but inexact associations with the relevant exposure in the clinical samples. In an analysis stratified by tissue type (solid tumor or ascites), relapse samples collected after chemotherapy harbored a median of 90% more expressed neoantigens than untreated primary samples, a figure that combines the effects of chemotherapy and other mutagenic processes operative during relapse. Neoadjuvant-treated primary samples showed no detectable increase over untreated samples. The contribution from chemotherapy-associated signatures was small, accounting for a mean of 5% (range 0–16) of the expressed neoantigen burden in relapse samples. In both treated and untreated samples, most neoantigens were attributed to COSMIC Signature (3), associated with BRCA disruption, Signature (1), associated with a slow mutagenic process active in healthy tissue, and Signature (8), of unknown etiology.ConclusionRelapsed HGSC tumors harbor nearly double the predicted expressed neoantigen burden of primary samples, but mutations associated with chemotherapy signatures account for only a small part of this increase. The mutagenic processes responsible for most neoantigens are similar between primary and relapse samples. Our analyses are based on mutations detectable from whole genome sequencing of bulk samples and do not account for neoantigens present in small populations of cells.

Published
2018

45. Learning Representations for Weakly Supervised Natural Language Processing Tasks

Author

Alexander Yates, Arun Ahuja, Yuhong Guo, Fei Huang, Doug Downey, and Yi Yang

Subjects
Linguistics and Language, Markov random field, Computer science, business.industry, computer.software_genre, Machine learning, Language and Linguistics, Computer Science Applications, Information extraction, Task (computing), Artificial Intelligence, Labeled data, Language model, Artificial intelligence, Graphical model, Hidden Markov model, business, computer, Natural language processing
Abstract

Finding the right representations for words is critical for building accurate NLP systems when domain-specific labeled data for the task is scarce. This article investigates novel techniques for extracting features from n-gram models, Hidden Markov Models, and other statistical language models, including a novel Partial Lattice Markov Random Field model. Experiments on part-of-speech tagging and information extraction, among other tasks, indicate that features taken from statistical language models, in combination with more traditional features, outperform traditional representations alone, and that graphical model representations outperform n-gram models, especially on sparse and polysemous words.

Published
2014
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46. Contribution of systemic and somatic factors to clinical response and resistance in urothelial cancer: an exploratory multi-omic analysis

Author

Bulent Arman Aksoy, Tavi Nathanson, Meredith Maisie Moran, Eliza Chang, Matthew D. Hellmann, Harlan Robins, Arun Ahuja, Taha Merghoub, Alexandra Snyder, Samuel Funt, Marissa Vignali, Elaine R. Mardis, Mariel Elena Boyd, Erik Yusko, Jacqueline Buros Novik, Hikmat Al-Ahmadie, Gopa Iyer, Jonathan E. Rosenberg, Dean F. Bajorin, Sharon Benzeno, and Jeff Hammerbacher

Subjects
Oncology, 0303 health sciences, medicine.medical_specialty, biology, T-cell receptor, Context (language use), 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Germline mutation, Immune system, Atezolizumab, 030220 oncology & carcinogenesis, PD-L1, Internal medicine, medicine, biology.protein, Progression-free survival, Exome sequencing, 030304 developmental biology
Abstract

Background:Inhibition of programmed death-ligand one (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance.Methods and Findings:The goals of this study were to (1) evaluate the association of mutation load and predicted neoantigen load with therapeutic benefit, and (2) determine whether intratumoral and peripheral blood T cell receptor (TCR) clonality inform clinical outcomes in urothelial carcinoma treated with atezolizumab. We hypothesized that an elevated mutation load in combination with T cell clonal dominance among intratumoral lymphocytes prior to treatment or among peripheral T cells after treatment would be associated with effective tumor control upon treatment with anti-PD-L1 therapy. We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pre-treatment tumor samples as well as TCR sequencing of matched, serially collected peripheral blood collected before and after treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression free survival (PFS) > 6 months), PFS, and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state.Patients with DCB displayed a higher proportion of tumor infiltrating T lymphocytes (TIL) (n=24, Mann-Whitney p=0.047). Pre-treatment peripheral blood TCR clonality below the median was associated with improved PFS (n=29, log-rank p=0.048) and OS (n=29, log-rank p=0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n=22, Mann-Whitney p=0.022). The combination of high pre-treatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n=10, HR (mean)=89.88, HR (median)=23.41, 95% CI (2.43, 506.94), p(HR>1)=0.0014). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which in turn impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load and expressed neoantigen load did not demonstrate significant association with DCB (n=25, Mann-Whitney p=0.22, n=25, Mann-Whitney p=0.55, and n=25, Mann-Whitney p=0.29 respectively). Instead, we found evidence of time-varying effects of somatic mutation load on progression-free survival in this cohort (n=25, p=0.044). A limitation of our study is its small sample size (n=29), a subset of the patients treated on IMvigor 210 (NCT02108652). Given the number of exploratory analyses performed, we intend for these results to be hypothesis-generating.Conclusions:These results demonstrate the complex nature of immune response to checkpoint blockade and the compelling need for greater interrogation and data integration of both host and tumor factors. Incorporating these variables in prospective studies will facilitate identification and treatment of resistant patients.

Published
2016
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47. Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: An exploratory multi-omic analysis

Author

Harlan Robins, Bulent Arman Aksoy, Tavi Nathanson, Hikmat Al-Ahmadie, Elaine R. Mardis, Meredith Maisie Moran, Sharon Benzeno, Taha Merghoub, Jacqueline Buros Novik, Eliza Chang, Matthew D. Hellmann, Arun Ahuja, Mariel Elena Boyd, Gopa Iyer, Marissa Vignali, Samuel Funt, Jonathan E. Rosenberg, Alexandra Snyder, Dean F. Bajorin, Erik Yusko, and Jeff Hammerbacher

Subjects
0301 basic medicine, Oncology, Male, Physiology, Cancer Treatment, lcsh:Medicine, Immune Receptors, Biochemistry, B7-H1 Antigen, White Blood Cells, 0302 clinical medicine, Animal Cells, Medicine and Health Sciences, Exome, Immune Response, Aged, 80 and over, Immune System Proteins, biology, T Cells, Antibodies, Monoclonal, General Medicine, Middle Aged, 3. Good health, Body Fluids, medicine.anatomical_structure, Blood, 030220 oncology & carcinogenesis, Monoclonal, Female, Immunotherapy, Antibody, Anatomy, Cellular Types, Research Article, Signal Transduction, medicine.medical_specialty, Urologic Neoplasms, T cell, Immune Cells, Immunology, Receptors, Antigen, T-Cell, Antineoplastic Agents, Antibodies, Monoclonal, Humanized, Cancer Immunotherapy, 03 medical and health sciences, Immune system, Germline mutation, Antigen, Atezolizumab, PD-L1, Internal medicine, medicine, Genetics, Humans, Aged, Blood Cells, Sequence Analysis, RNA, lcsh:R, Carcinoma, Biology and Life Sciences, Proteins, Cell Biology, T Cell Receptors, 030104 developmental biology, Mutation, biology.protein, Somatic Mutation, Clinical Immunology, Urothelium, Clinical Medicine, Biomarkers
Abstract

Background Inhibition of programmed death-ligand 1 (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens, and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance. Methods and findings The goals of this study were to (1) evaluate the association of mutation load and predicted neoantigen load with therapeutic benefit and (2) determine whether intratumoral and peripheral blood T cell receptor (TCR) clonality inform clinical outcomes in urothelial carcinoma treated with atezolizumab. We hypothesized that an elevated mutation load in combination with T cell clonal dominance among intratumoral lymphocytes prior to treatment or among peripheral T cells after treatment would be associated with effective tumor control upon treatment with anti-PD-L1 therapy. We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pretreatment tumor samples as well as TCR-seq of matched, serially collected peripheral blood, collected before and after treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression-free survival [PFS] >6 months), PFS, and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state. Patients with DCB displayed a higher proportion of tumor-infiltrating T lymphocytes (TIL) (n = 24, Mann-Whitney p = 0.047). Pretreatment peripheral blood TCR clonality below the median was associated with improved PFS (n = 29, log-rank p = 0.048) and OS (n = 29, log-rank p = 0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n = 22, Mann-Whitney p = 0.022). The combination of high pretreatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n = 10, hazard ratio (HR) (mean) = 89.88, HR (median) = 23.41, 95% CI [2.43, 506.94], p(HR > 1) = 0.0014). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which, in turn, impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load, and expressed neoantigen load did not demonstrate significant association with DCB (n = 25, Mann-Whitney p = 0.22, n = 25, Mann-Whitney p = 0.55, and n = 25, Mann-Whitney p = 0.29, respectively). Instead, we found evidence of time-varying effects of somatic mutation load on PFS in this cohort (n = 25, p = 0.044). A limitation of our study is its small sample size (n = 29), a subset of the patients treated on IMvigor 210 (NCT02108652). Given the number of exploratory analyses performed, we intend for these results to be hypothesis-generating. Conclusions These results demonstrate the complex nature of immune response to checkpoint blockade and the compelling need for greater interrogation and data integration of both host and tumor factors. Incorporating these variables in prospective studies will facilitate identification and treatment of resistant patients., Alexandra Snyder and colleagues reveal the complex nature of the immune response to checkpoint blockade in metastatic urothelial cancer patients. Time-varying effects of somatic mutation load on survival are reported., Author summary Why was this study done? A new type of cancer treatment called checkpoint blockade therapy activates the immune system to fight cancer. When these therapies work, patients with advanced disease can experience long-lasting disease control or even cures. However, most patients will not experience these benefits, and it is crucial to identify these patients in advance so that we can develop better treatments for them. What did the researchers do and find? In this study, we studied 29 patients with advanced bladder cancers treated with a checkpoint blockade drug called atezolizumab. We examined features of the tumor and the immune system, as well as clinical features. We found that these features were related to each other, and to the success of therapy, in various ways. Patients who had a diverse repertoire of T cells in their blood tended to survive longer. Patients who had poor clinical prognostic factors, like having cancer that had traveled to their liver, tended to have worse survival. What did the research findings mean? This study demonstrates that we need to take the tumor, immune system, and clinical picture into account if we are to improve the efficacy of immune-mobilizing therapies in cancer. Some patients may be too sick to benefit from checkpoint blockade therapy, despite, in some cases, having biomarkers in their tumors that would predict benefit.

Published
2016

48. Genomic Features of Response to Combination Immunotherapy in Patients with Advanced Non-Small-Cell Lung Cancer

Author

Matthew D. Hellmann, Hossein Borghaei, Megan Tenet, Francisco Sanchez-Vega, Jeff Hammerbacher, Patrik Vitazka, Margaret K. Callahan, Jamie E. Chaft, Cailian Liu, Jennifer L. Sauter, William J. Geese, Arun Ahuja, Levi Mangarin, Taha Merghoub, Tavi Nathanson, Jacqueline Buros Novik, Nicholas McGranahan, Kelly L. Covello, Scott J. Antonia, Charles Swanton, Natasha Rekhtman, Andrea Renninger, Ai Ni, Mohsen Abu-Akeel, Alexandra Snyder, Martin H. Voss, Eliza Chang, Xuemei Li, Hira Rizvi, Jedd D. Wolchok, Benjamin C. Creelan, and Charles M. Rudin

Subjects
0301 basic medicine, Oncology, Cancer Research, medicine.medical_specialty, Combination therapy, medicine.medical_treatment, Ipilimumab, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, medicine, Progression-free survival, Lung cancer, business.industry, Immunotherapy, medicine.disease, Immune checkpoint, respiratory tract diseases, 3. Good health, Blockade, 030104 developmental biology, 030220 oncology & carcinogenesis, Nivolumab, business, medicine.drug
Abstract

Combination immune checkpoint blockade has demonstrated promising benefit in lung cancer, but predictors of response to combination therapy are unknown. Using whole-exome sequencing to examine non-small-cell lung cancer (NSCLC) treated with PD-1 plus CTLA-4 blockade, we found that high tumor mutation burden (TMB) predicted improved objective response, durable benefit, and progression-free survival. TMB was independent of PD-L1 expression and the strongest feature associated with efficacy in multivariable analysis. The low response rate in TMB low NSCLCs demonstrates that combination immunotherapy does not overcome the negative predictive impact of low TMB. This study demonstrates the association between TMB and benefit to combination immunotherapy in NSCLC. TMB should be incorporated in future trials examining PD-(L)1 with CTLA-4 blockade in NSCLC.

Published
2018
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49. Rethinking Data-Intensive Science Using Scalable Analytics Systems

Author

Uri Laserson, Zhao Zhang, Frank Austin Nothaft, Anthony D. Joseph, Michael D. Linderman, Michael J. Franklin, Carl Yeksigian, Arun Ahuja, David A. Patterson, Jey Kottalam, Timothy Danford, Jeff Hammerbacher, and Matt Massie

Subjects
Data acquisition, Analytics, business.industry, Computer science, Spark (mathematics), Scalability, Big data, business, Data science, Pipeline (software)
Abstract

"Next generation" data acquisition technologies are allowing scientists to collect exponentially more data at a lower cost. These trends are broadly impacting many scientific fields, including genomics, astronomy, and neuroscience. We can attack the problem caused by exponential data growth by applying horizontally scalable techniques from current analytics systems to accelerate scientific processing pipelines. In this paper, we describe ADAM, an example genomics pipeline that leverages the open-source Apache Spark and Parquet systems to achieve a 28x speedup over current genomics pipelines, while reducing cost by 63%. From building this system, we were able to distill a set of techniques for implementing scientific analyses efficiently using commodity "big data" systems. To demonstrate the generality of our architecture, we then implement a scalable astronomy image processing system which achieves a 2.8--8.9x improvement over the state-of-the-art MPI-based system.

Published
2015
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50. Measurement of jet multiplicity distributions in t(t)over-bar production in pp collisions at root s = 7 TeV (vol 74, 3014, 2014)

Author

Chatrchyan, S. Khachatryan, V. Sirunyan, A. M. Tumasyan, A. and Adam, W. Bergauer, T. Dragicevic, M. Eroe, J. and Fabjan, C. Friedl, M. Fruehwirth, R. Ghete, V. M. Hartl, C. Hoermann, N. Hrubec, J. Jeitler, M. Kiesenhofer, W. and Knuenz, V. Krammer, M. Kraetschmer, I. Liko, D. and Mikulec, I. Rabady, D. Rahbaran, B. Rohringer, H. and Schoefbeck, R. Strauss, J. Taurok, A. Treberer-Treberspurg, W. Waltenberger, W. Wulz, C. -E. Mossolov, V. Shumeiko, N. Gonzalez, J. Suarez Alderweireldt, S. Bansal, M. and Bansal, S. Cornelis, T. De Wolf, E. A. Janssen, X. and Knutsson, A. Luyckx, S. Ochesanu, S. Roland, B. Rougny, R. Van Haevermaet, H. Van Mechelen, P. Van Remortel, N. and Van Spilbeeck, A. Blekman, F. Blyweert, S. D'Hondt, J. and Heracleous, N. Kalogeropoulos, A. Keaveney, J. Kim, T. J. and Lowette, S. Maes, M. Olbrechts, A. Strom, D. and Tavernier, S. Van Doninck, W. Van Mulders, P. Van Onsem, G. P. Villella, I. Caillol, C. Clerbaux, B. De Lentdecker, G. Favart, L. Gay, A. P. R. Leonard, A. Marage, P. E. and Mohammadi, A. Pernie, L. Reis, T. Seva, T. Thomas, L. Vander Velde, C. Vanlaer, P. Wang, J. Adler, V. and Beernaert, K. Benucci, L. Cimmino, A. Costantini, S. and Crucy, S. Dildick, S. Garcia, G. Klein, B. Lellouch, J. and Mccartin, J. Rios, A. A. Ocampo Ryckbosch, D. Diblen, S. Salva Sigamani, M. Strobbe, N. Thyssen, F. Tytgat, M. and Walsh, S. Yazgan, E. Zaganidis, N. Basegmez, S. and Beluffi, C. Bruno, G. Castello, R. Caudron, A. Ceard, L. and Da Silveira, G. G. Delaere, C. du Pree, T. Favart, D. and Forthomme, L. Giammanco, A. Hollar, J. Jez, P. Komm, M. Lemaitre, V. Liao, J. Militaru, O. Nuttens, C. and Pagano, D. Pin, A. Piotrzkowski, K. Popov, A. and Quertenmont, L. Selvaggi, M. Marono, M. Vidal Garcia, J. M. Vizan Beliy, N. Caebergs, T. Daubie, E. Hammad, G. H. and Alves, G. A. Correa Martins Junior, M. Martins, T. Pol, M. E. Souza, M. H. G. Alda Junior, W. L. Carvalho, W. and Chinellato, J. Custodio, A. Da Costa, E. M. De Jesus Damiao, D. De Oliveira Martins, C. Fonseca De Souza, S. Malbouisson, H. Malek, M. Matos Figueiredo, D. Mundim, L. Nogima, H. and Prado Da Silva, W. L. Santaolalla, J. Santoro, A. and Sznajder, A. Tonelli Manganote, E. J. Vilela Pereira, A. and Bernardes, C. A. Dias, F. A. Fernandez Perez Tomei, T. R. and Gregores, E. M. Mercadante, P. G. Novaes, S. F. Padula, Sandra S. Genchev, V. Iaydjiev, P. Marinov, A. Piperov, S. Rodozov, M. Sultanov, G. Vutova, M. Dimitrov, A. and Glushkov, I. Hadjiiska, R. Kozhuharov, V. Litov, L. and Pavlov, B. Petkov, P. Bian, J. G. Chen, G. M. Chen, H. S. Chen, M. Du, R. Jiang, C. H. Liang, D. Liang, S. and Meng, X. Plestina, R. Tao, J. Wang, X. Wang, Z. and Asawatangtrakuldee, C. Ban, Y. Guo, Y. Li, Q. Li, W. and Liu, S. Mao, Y. Qian, S. J. Wang, D. Zhang, L. Zou, W. Avila, C. Chaparro Sierra, L. F. Florez, C. Gomez, J. P. Gomez Moreno, B. Sanabria, J. C. Godinovic, N. Lelas, D. Polic, D. Puljak, I. Antunovic, Z. Kovac, M. and Brigljevic, V. Kadija, K. Luetic, J. Mekterovic, D. and Morovic, S. Tikvica, L. Attikis, A. Mavromanolakis, G. and Mousa, J. Nicolaou, C. Ptochos, F. Razis, P. A. Finger, M. Finger, Jr., M. Assran, Y. Elgammal, S. Kamel, A. Ellithi Mahmoud, M. A. Mahrous, A. Radi, A. Kadastik, M. and Muentel, M. Murumaa, M. Raidal, M. Tiko, A. Eerola, P. Fedi, G. Voutilainen, M. Harkonen, J. Karimaki, V. and Kinnunen, R. Kortelainen, M. J. Lampen, T. and Lassila-Perini, K. Lehti, S. Linden, T. Luukka, P. and Maenpaa, T. Peltola, T. Tuominen, E. Tuominiemi, J. and Tuovinen, E. Wendland, L. Tuuva, T. Besancon, M. and Couderc, F. Dejardin, M. Denegri, D. Fabbro, B. Faure, J. L. Ferri, F. Ganjour, S. Givernaud, A. Gras, P. and de Monchenault, G. Hamel Jarry, P. Locci, E. Malcles, J. and Nayak, A. Rander, J. Rosowsky, A. Titov, M. Baffioni, S. and Beaudette, F. Busson, P. Charlot, C. Daci, N. Dahms, T. Dalchenko, M. Dobrzynski, L. Filipovic, N. Florent, A. de Cassagnac, R. Granier Mastrolorenzo, L. Mine, P. and Mironov, C. Naranjo, I. N. Nguyen, M. Ochando, C. and Paganini, P. Sabes, D. Salerno, R. Sauvan, J. B. Sirois, Y. Veelken, C. Yilmaz, Y. Zabi, A. Agram, J. -L. and Andrea, J. Bloch, D. Brom, J. -M. Chabert, E. C. and Collard, C. Conte, E. Drouhin, F. Fontaine, J. -C. Gele, D. Goerlach, U. Goetzmann, C. Juillot, P. Le Bihan, A. -C. Van Hove, P. Gadrat, S. Beauceron, S. Beaupere, N. and Boudoul, G. Brochet, S. Montoya, C. A. Carrillo and Chasserat, J. Chierici, R. Contardo, D. Depasse, P. El Mamouni, H. Fan, J. Fay, J. Gascon, S. Gouzevitch, M. and Ille, B. Kurca, T. Lethuillier, M. Mirabito, L. and Perries, S. Alvarez, J. D. Ruiz Sgandurra, L. Sordini, V. and Vander Donckt, M. Verdier, P. Viret, S. Xiao, H. and Tsamalaidze, Z. Autermann, C. Beranek, S. Bontenackels, M. and Calpas, B. Edelhoff, M. Feld, L. Hindrichs, O. and Klein, K. Ostapchuk, A. Perieanu, A. Raupach, F. Sammet, J. Schael, S. Sprenger, D. Weber, H. Wittmer, B. and Zhukov, V. Ata, M. Caudron, J. Dietz-Laursonn, E. and Duchardt, D. Erdmann, M. Fischer, R. Gueth, A. Hebbeker, T. Heidemann, C. Hoepfner, K. Klingebiel, D. Knutzen, S. and Kreuzer, P. Merschmeyer, M. Meyer, A. Olschewski, M. and Padeken, K. Papacz, P. Reithler, H. Schmitz, S. A. and Sonnenschein, L. Teyssier, D. Thueer, S. Weber, M. and Cherepanov, V. Erdogan, Y. Fluegge, G. Geenen, H. and Geisler, M. Ahmad, W. Haj Hoehle, F. Kargoll, B. Kress, T. Kuessel, Y. Lingemann, J. Nowack, A. Nugent, I. M. and Perchalla, L. Pooth, O. Stahl, A. Asin, I. Bartosik, N. Behr, J. Behrenhoff, W. Behrens, U. Bell, A. J. and Bergholz, M. Bethani, A. Borras, K. Burgmeier, A. Cakir, A. Calligaris, L. Campbell, A. Choudhury, S. Costanza, F. Pardos, C. Diez Dooling, S. Dorland, T. Eckerlin, G. and Eckstein, D. Eichhorn, T. Flucke, G. Geiser, A. and Grebenyuk, A. Gunnellini, P. Habib, S. Hauk, J. Hellwig, G. Hempel, M. Horton, D. Jung, H. Kasemann, M. and Katsas, P. Kieseler, J. Kleinwort, C. Kraemer, M. and Kruecker, D. Lange, W. Leonard, J. Lipka, K. Lohmann, W. and Lutz, B. Mankel, R. Marfin, I. Melzer-Pellmann, I-A. and Meyer, A. B. Mnich, J. Mussgiller, A. Naumann-Emme, S. and Novgorodova, O. Nowak, F. Ntomari, E. Perrey, H. and Petrukhin, A. Pitzl, D. Placakyte, R. Raspereza, A. and Cipriano, P. M. Ribeiro Riedl, C. Ron, E. Sahin, M. Oe and Salfeld-Nebgen, J. Saxena, P. Schmidt, R. and Schoerner-Sadenius, T. Schroeder, M. Stein, M. Trevino, A. D. R. Vargas Walsh, R. Wissing, C. Martin, M. Aldaya and Blobel, V. Enderle, H. Erfle, J. Garutti, E. Goebel, K. and Goerner, M. Gosselink, M. Haller, J. Hoeing, R. S. and Kirschenmann, H. Klanner, R. Kogler, R. Lange, J. and Lapsien, T. Lenz, T. Marchesini, I. Ott, J. Peiffer, T. and Pietsch, N. Rathjens, D. Sander, C. Schettler, H. and Schleper, P. Schlieckau, E. Schmidt, A. Seidel, M. and Sibille, J. Sola, V. Stadie, H. Steinbrueck, G. and Troendle, D. Usai, E. Vanelderen, L. Barth, C. Baus, C. and Berger, J. Boeser, C. Butz, E. Chwalek, T. De Boer, W. Descroix, A. Dierlamm, A. Feindt, M. Guthoff, M. and Hartmann, F. Hauth, T. Held, H. Hoffmann, K. H. and Husemann, U. Katkov, I. Kornmayer, A. Kuznetsova, E. and Pardo, P. Lobelle Martschei, D. Mozer, M. U. Mueller, Th. and Niegel, M. Nuernberg, A. Oberst, O. Quast, G. and Rabbertz, K. Ratnikov, F. Roecker, S. Schilling, F. -P. and Schott, G. Simonis, H. J. Stober, F. M. Ulrich, R. and Wagner-Kuhr, J. Wayand, S. Weiler, T. Wolf, R. Zeise, M. and Anagnostou, G. Daskalakis, G. Geralis, T. and Giakoumopoulou, V. A. Kesisoglou, S. Kyriakis, A. Loukas, D. and Markou, A. Markou, C. Psallidas, A. Topsis-Giotis, I. and Gouskos, L. Panagiotou, A. Saoulidou, N. Stiliaris, E. and Aslanoglou, X. Evangelou, I. Flouris, G. Foudas, C. and Jones, J. Kokkas, P. Manthos, N. Papadopoulos, I. and Paradas, E. Bencze, G. Hajdu, C. Hidas, P. Horvath, D. and Sikler, F. Veszpremi, V. Vesztergombi, G. Zsigmond, A. J. Beni, N. Czellar, S. Molnar, J. Palinkas, J. and Szillasi, Z. Karancsi, J. Raics, P. Trocsanyi, Z. L. and Ujvari, B. Swain, S. K. 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Published
2015

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