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1. A Framework for Interpretability in Machine Learning for Medical Imaging

Author

Alan Q. Wang, Batuhan K. Karaman, Heejong Kim, Jacob Rosenthal, Rachit Saluja, Sean I. Young, and Mert R. Sabuncu

Subjects
Interpretability, explainability, medical imaging, machine learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why does the need for interpretability in MLMI arise? What goals does one actually seek to address when interpretability is needed? To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI. By reasoning about real-world tasks and goals common in both medical image analysis and its intersection with machine learning, we identify five core elements of interpretability: localization, visual recognizability, physical attribution, model transparency, and actionability. From this, we arrive at a framework for interpretability in MLMI, which serves as a step-by-step guide to approaching interpretability in this context. Overall, this paper formalizes interpretability needs in the context of medical imaging, and our applied perspective clarifies concrete MLMI-specific goals and considerations in order to guide method design and improve real-world usage. Our goal is to provide practical and didactic information for model designers and practitioners, inspire developers of models in the medical imaging field to reason more deeply about what interpretability is achieving, and suggest future directions of interpretability research.

Published
2024
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2. Applying Self-Supervised Learning to Medicine: Review of the State of the Art and Medical Implementations

Author

Alexander Chowdhury, Jacob Rosenthal, Jonathan Waring, and Renato Umeton

Subjects
self-supervised learning, healthcare, representation learning, medicine, computer vision, pathology, Information technology, T58.5-58.64
Abstract

Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently, in medicine and healthcare, as well as in most other industries, the two most prevalent machine learning paradigms are supervised learning and transfer learning. Both practices rely on large-scale, manually annotated datasets to train increasingly complex models. However, the requirement of data to be manually labeled leaves an excess of unused, unlabeled data available in both public and private data repositories. Self-supervised learning (SSL) is a growing area of machine learning that can take advantage of unlabeled data. Contrary to other machine learning paradigms, SSL algorithms create artificial supervisory signals from unlabeled data and pretrain algorithms on these signals. The aim of this review is two-fold: firstly, we provide a formal definition of SSL, divide SSL algorithms into their four unique subsets, and review the state of the art published in each of those subsets between the years of 2014 and 2020. Second, this work surveys recent SSL algorithms published in healthcare, in order to provide medical experts with a clearer picture of how they can integrate SSL into their research, with the objective of leveraging unlabeled data.

Published
2021
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3. Federated benchmarking of medical artificial intelligence with MedPerf.

Author

Alexandros Karargyris, Renato Umeton, Micah J. Sheller, Alejandro Aristizabal, Johnu George, Anna Wuest, Sarthak Pati, Hasan Kassem, Maximilian Zenk, Ujjwal Baid, Prakash Narayana Moorthy, Alexander Chowdhury, Junyi Guo, Sahil S. Nalawade, Jacob Rosenthal, David Kanter, Maria Xenochristou, Daniel J. Beutel, Verena Chung, Timothy Bergquist, James A. Eddy, Abubakar Abid, Lewis Tunstall, Omar Sanseviero, Dimitrios Dimitriadis, Yiming Qian, Xinxing Xu, Yong Liu 0026, Rick Siow Mong Goh, Srini Bala, Victor Bittorf, Sreekar Reddy Puchala, Biagio Ricciuti, Soujanya Samineni, Eshna Sengupta, Akshay Chaudhari, Cody Coleman, Bala Desinghu, Gregory F. Diamos, Debo Dutta, Diane Feddema, Grigori Fursin, Xinyuan Huang, Satyananda Kashyap, Nicholas D. Lane, Indranil Mallick, Pietro Mascagni, Virendra Mehta, Cassiano Ferro Moraes, Vivek Natarajan, Nikola Nikolov, Nicolas Padoy, Gennady Pekhimenko, Vijay Janapa Reddi, G. Anthony Reina, Pablo Ribalta, Abhishek Singh, Jayaraman J. Thiagarajan, Jacob Albrecht, Thomas Wolf 0008, Geralyn Miller, Huazhu Fu, Prashant Shah, Daguang Xu, Poonam Yadav, David Talby, Mark M. Awad, Jeremy P. Howard, Michael Rosenthal, Luigi Marchionni, Massimo Loda, Jason M. Johnson, Spyridon Bakas, and Peter Mattson

Published
2023
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6. MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation.

Author

Alexandros Karargyris, Renato Umeton, Micah J. Sheller, Alejandro Aristizabal, Johnu George, Srini Bala, Daniel J. Beutel, Victor Bittorf, Akshay Chaudhari, Alexander Chowdhury, Cody Coleman, Bala Desinghu, Gregory F. Diamos, Debo Dutta, Diane Feddema, Grigori Fursin, Junyi Guo, Xinyuan Huang, David Kanter, Satyananda Kashyap, Nicholas D. Lane, Indranil Mallick, Pietro Mascagni, Virendra Mehta, Vivek Natarajan, Nikola Nikolov, Nicolas Padoy, Gennady Pekhimenko, Vijay Janapa Reddi, G. Anthony Reina, Pablo Ribalta, Jacob Rosenthal, Abhishek Singh, Jayaraman J. Thiagarajan, Anna Wuest, Maria Xenochristou, Daguang Xu, Poonam Yadav, Michael Rosenthal, Massimo Loda, Jason M. Johnson, and Peter Mattson

Published
2021

7. Supplementary Figure 3 from Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum

Author

Massimo Loda, Renato Umeton, Michelangelo Fiorentino, Adam S. Kibel, Rosina Lis, Hannah Coulson, Samuel Peisch, Mark Pomerantz, Francesca Giunchi, Lavinia Stefanizzi, Giuseppe Nicolò Fanelli, Giorgia Zadra, Stephanie Borgstein, Ryan Carelli, Habiba El Fandy, Jacob Rosenthal, Svitlana Tyekucheva, and Kathryn L. Penney

Abstract

Supplementary Figure 3. Scatterplots for metabolites with high correlations between their abundances in tissue and serum.

Published
2023
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8. Supplementary Table 1 from Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum

Author

Massimo Loda, Renato Umeton, Michelangelo Fiorentino, Adam S. Kibel, Rosina Lis, Hannah Coulson, Samuel Peisch, Mark Pomerantz, Francesca Giunchi, Lavinia Stefanizzi, Giuseppe Nicolò Fanelli, Giorgia Zadra, Stephanie Borgstein, Ryan Carelli, Habiba El Fandy, Jacob Rosenthal, Svitlana Tyekucheva, and Kathryn L. Penney

Abstract

Full results of 10-fold cross-validation performance assessment for all ML models and prediction tasks (mean +/- SD).

Published
2023
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9. Supplementary Data from Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum

Author

Massimo Loda, Renato Umeton, Michelangelo Fiorentino, Adam S. Kibel, Rosina Lis, Hannah Coulson, Samuel Peisch, Mark Pomerantz, Francesca Giunchi, Lavinia Stefanizzi, Giuseppe Nicolò Fanelli, Giorgia Zadra, Stephanie Borgstein, Ryan Carelli, Habiba El Fandy, Jacob Rosenthal, Svitlana Tyekucheva, and Kathryn L. Penney

Abstract

Supplementary figure legends Supplementary table 2

Published
2023
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10. Supplementary Figure 4 from Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum

Author

Massimo Loda, Renato Umeton, Michelangelo Fiorentino, Adam S. Kibel, Rosina Lis, Hannah Coulson, Samuel Peisch, Mark Pomerantz, Francesca Giunchi, Lavinia Stefanizzi, Giuseppe Nicolò Fanelli, Giorgia Zadra, Stephanie Borgstein, Ryan Carelli, Habiba El Fandy, Jacob Rosenthal, Svitlana Tyekucheva, and Kathryn L. Penney

Abstract

Supplementary Figure 4. Heatmap of the top twenty metabolites as identified by permutation feature importance analysis of AutoML model.

Published
2023
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11. Supplementary Figure 2 from Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum

Author

Massimo Loda, Renato Umeton, Michelangelo Fiorentino, Adam S. Kibel, Rosina Lis, Hannah Coulson, Samuel Peisch, Mark Pomerantz, Francesca Giunchi, Lavinia Stefanizzi, Giuseppe Nicolò Fanelli, Giorgia Zadra, Stephanie Borgstein, Ryan Carelli, Habiba El Fandy, Jacob Rosenthal, Svitlana Tyekucheva, and Kathryn L. Penney

Abstract

Supplementary Figure 2. Heatmap of top differential (unadjusted p

Published
2023
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12. Supplementary Figure 1 from Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum

Author

Massimo Loda, Renato Umeton, Michelangelo Fiorentino, Adam S. Kibel, Rosina Lis, Hannah Coulson, Samuel Peisch, Mark Pomerantz, Francesca Giunchi, Lavinia Stefanizzi, Giuseppe Nicolò Fanelli, Giorgia Zadra, Stephanie Borgstein, Ryan Carelli, Habiba El Fandy, Jacob Rosenthal, Svitlana Tyekucheva, and Kathryn L. Penney

Abstract

Supplemental Figure 1. Fresh radical prostatectomy specimens were inked and transverse sliced from apex to base.

Published
2023
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17. Multi-omics biomarkers aid prostate cancer prognostication

Author

Zhuoran Xu, Mohamed Omar, Elisa Benedetti, Jacob Rosenthal, Renato Umeton, Jan Krumsiek, Mark Pomerantz, Eddie Imada, Massimo Loda, and Luigi Marchionni

Abstract

Effective biomarkers and diagnostic tools are urgently needed in clinical settings for improved management of prostate cancer patients, especially to reduce over-treatment of indolent tumors and for early identification of aggressive disease. Gene expression signatures are currently the “gold standard” to provide guide clinical decision, however their clinical utility and interpretability is questionable. Multi-modal molecular profiling provides an holistic approach to systematically unravel the biological complexity underlying cancer pathogenesis, hence biomarkers developed using such an integrated approach hold the potential to more accurately capture cancer-driving alterations than signatures based on a single omics modality. Currently, however, robust and reproducible multi-omics biomarkers are still lacking for prostate cancer. In this study, we analyzed transcriptomics and metabolomics profiles jointly in a prostate cancer cohort and identified two prognostic signatures with high statistical powers (signature 1: EGLN3, succinate, trans-4-hydroxyprolin; and signature 2: IL6, SLC22A2, histamine). Our approach leveraged a priori biological knowledge of the cellular metabolism and gene circuitry, enabling the identification of dysregulated network modules. Functional bioinformatics analyses suggest that these signatures can capture relevant molecular alterations in prostate cancer tissues, including dysregulations of cellular signaling, cell cycle progression, and immune system modulation, stratifying patients in distinct risk groups. Next, we trained two gene expression signatures as a proxy for the multi-omics ones, extending our investigation to publicly available data, further confirming their prognostic values in independent patient cohorts. In summary, the analysis of multi-modal molecular grounded in cellular network biology represents a promising approach for the development of robust prognostic biomarkers of detecting and discriminating high grade disease.

Published
2022
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18. Organic farming supports lower pest infestation, but fewer natural enemies than flower strips

Author

Róbert Gallé, Edina Török, Teja Tscharntke, Jacob Rosenthal, Péter Batáry, Sinja Zieger, and Rita Földesi

Subjects
0106 biological sciences, Ecology, biology, Biological pest control, biology.organism_classification, 010603 evolutionary biology, 01 natural sciences, 010602 entomology, Agronomy, Cereal leaf beetle, Organic farming, Field size, Natural enemies, Pest infestation
Published
2021
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19. Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum

Author

Michelangelo Fiorentino, Mark Pomerantz, Giorgia Zadra, Hannah Coulson, Jacob Rosenthal, Giuseppe Nicolò Fanelli, Ryan Carelli, Sam Peisch, Renato Umeton, Habiba El Fandy, Massimo Loda, Rosina T. Lis, Stephanie Borgstein, Svitlana Tyekucheva, Kathryn L. Penney, Adam S. Kibel, Francesca Giunchi, Lavinia Stefanizzi, Penney, Kathryn L, Tyekucheva, Svitlana, Rosenthal, Jacob, El Fandy, Habiba, Carelli, Ryan, Borgstein, Stephanie, Zadra, Giorgia, Fanelli, Giuseppe Nicolo, Stefanizzi, Lavinia, Giunchi, Francesca, Pomerantz, Mark, Peisch, Samuel, Coulson, Hannah, Lis, Rosina T, Kibel, Adam S, Fiorentino, Michelangelo, Umeton, Renato, and Loda, Massimo

Subjects
Male, 0301 basic medicine, Cancer Research, Metabolite, Aggressive disease, Article, Machine Learning, 03 medical and health sciences, Prostate cancer, chemistry.chemical_compound, 0302 clinical medicine, Metabolomics, Text mining, Prostate, Humans, Medicine, Gleason score, Molecular Biology, AutoML, business.industry, Prostatic Neoplasms, Female, Neoplasm Grading, prostate cancer, medicine.disease, Serum samples, Tumor tissue, 030104 developmental biology, medicine.anatomical_structure, Oncology, chemistry, 030220 oncology & carcinogenesis, Cancer research, business, metabolomic
Abstract

Gleason score, a measure of prostate tumor differentiation, is the strongest predictor of lethal prostate cancer at the time of diagnosis. Metabolomic profiling of tumor and of patient serum could identify biomarkers of aggressive disease and lead to the development of a less-invasive assay to perform active surveillance monitoring. Metabolomic profiling of prostate tissue and serum samples was performed. Metabolite levels and metabolite sets were compared across Gleason scores. Machine learning algorithms were trained and tuned to predict transformation or differentiation status from metabolite data. A total of 135 metabolites were significantly different (Padjusted < 0.05) in tumor versus normal tissue, and pathway analysis identified one sugar metabolism pathway (Padjusted = 0.03). Machine learning identified profiles that predicted tumor versus normal tissue (AUC of 0.82 ± 0.08). In tumor tissue, 25 metabolites were associated with Gleason score (unadjusted P < 0.05), 4 increased in high grade while the remainder were enriched in low grade. While pyroglutamine and 1,5-anhydroglucitol were correlated (0.73 and 0.72, respectively) between tissue and serum from the same patient, no metabolites were consistently associated with Gleason score in serum. Previously reported as well as novel metabolites with differing abundance were identified across tumor tissue. However, a “metabolite signature” for Gleason score was not obtained. This may be due to study design and analytic challenges that future studies should consider. Implications: Metabolic profiling can distinguish benign and neoplastic tissues. A novel unsupervised machine learning method can be utilized to achieve this distinction.

Published
2021
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22. Blind Spots in the Formula of Humanity : What Does it Mean not to Treat Someone as an End?

Author

Corinna Mieth and Jacob Rosenthal

Subjects
ddc:100
Abstract

The aim of our paper is to develop a more differentiated understanding of the ‘negative’ part of the formula of humanity.What does it mean to treat others not as ends in themselves? At first glance, one might think that this would mean treating others as mere means, and indeed, the focus within the literature has mainly been on that kind of wrongdoing (recently Kleingeld 2020; Audi 2016, Part I; Kerstein 2013, Part I). But there are more categories. If you do not help someone in need whom you could easily help, but instead simply ignore them, then you do not treat them as an instrument for your purposes, but also not as an end in themselves. One could say that you treat them like a a mere irrellevant thing (cf. Sticker 2021). Furthermore, if someone is in your way and you simply remove them, you treat them neither as a means nor as an irrelevant thing, nor, for that matter, as an end in themselves, but as a mere obstacle. In the first part of our paper, we will explain the relevance of the distinction between (1) treating or regarding someone as a means, (2) as an irrelevant thing, or (3) as an obstacle. These can all be viewed as subcategories of the ‘thing’ part of the person-thing distinction that plays a central role in Kant’s ethics and which is the topic of the second part of our paper. In the third part we will point out that the distinction between ends in themselves or persons, on the one hand, and things, on the other, still does not cover an important kind of moral wrongdoing: we can treat others as negative ends. This is the case when harming others is the ultimate purpose of our actions. published

Published
2022

23. Building tools for machine learning and artificial intelligence in cancer research: best practices and a case study with the PathML toolkit for computational pathology

Author

Luigi Marchionni, Renato Umeton, Mohamed Omar, Massimo Loda, Jacob Rosenthal, Jackson Nyman, Ryan Carelli, Surya Narayanan Hari, Eliezer M. Van Allen, David Brundage, and Ella Halbert

Subjects
Standardization, business.industry, Computer science, Best practice, Usability, Machine learning, computer.software_genre, Information density, Computational pathology, If and only if, Scalability, Cancer research, Leverage (statistics), Artificial intelligence, business, computer
Abstract

Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use-cases. PathML is publicly available at www.pathml.com.

Published
2021
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24. Clinical implementation of a machine learning system to detect deteriorating patients reduces time to response and intervention

Author

Jacob Rosenthal, Kim Gaines, Jill J. Nagel, Jordan M. Kautz, Santiago Romero Brufau, Matthew L. Johnson, Gene C. Dankbar, Julie A. Schmidt, Curtis B. Storlie, Dale Hardin, Jeanne M. Huddleston, Joel Hickman, and Adam VanDeusen

Subjects
Warning system, business.industry, Matched control, Vital signs, Early detection, Machine learning, computer.software_genre, Software deployment, Intervention (counseling), Medicine, Artificial intelligence, business, Rapid response team, Alert system, computer
Abstract

IntroductionAcute physiological deterioration is a major contributor to in-hospital morbidity and mortality. Early detection and intervention of deteriorating patients is key to improving patient outcomes. Prior research has demonstrated the effectiveness of Early Warning Systems and other algorithmic approaches in automatically identifying these patients from passively monitoring vital signs.MethodsIn this work, we conduct a prospective pilot study of clinical deployment of the Mayo Clinic Bedside Patient Rescue (BPR) system using an escalating alerting logic enabled by machine learning. Among four units where the BPR system was deployed, time to response and time to intervention for deteriorating patients were significantly reduced relative to matched control units.ResultsIn pilot units, time to response decreased by 35.4% (from 63.2 minutes to 40.8 minutes) and time to intervention decreased by 48.5% (from 106.3 minutes to 55.9 minutes). No significant differences were observed in counterbalance metrics of mortality, ICU transfer rate, and Rapid Response Team activation rate. Furthermore, the automated alerting system was well-received by clinicians participating in the pilot study, as assessed by survey.DiscussionThese results demonstrate a successful clinical deployment of a practice-changing machine learning alert system with demonstrable impact on improving patient care.

Published
2021
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25. Examining Batch Effect in Histopathology as a Distributionally Robust Optimization Problem

Author

Surya Narayanan Hari, Jackson Nyman, Nicita Mehta, Haitham Elmarakeby, Bowen Jiang, Felix Dietlein, Jacob Rosenthal, Eshna Sengupta, Alexander Chowdhury, Renato Umeton, and Eliezer M. Van Allen

Subjects
Computer science, business.industry, Process (engineering), Confounding, Digital pathology, Robust optimization, Machine learning, computer.software_genre, Clinical decision support system, Empirical risk minimization, Artificial intelligence, Spurious relationship, business, computer, Interpretability
Abstract

Computer vision (CV) approaches applied to digital pathology have informed biological discovery and development of tools to help inform clinical decision-making. However, batch effects in the images have the potential to introduce spurious confounders and represent a major challenge to effective analysis and interpretation of these data. Standard methods to circumvent learning such confounders include (i) application of image augmentation techniques and (ii) examination of the learning process by evaluating through external validation (e.g., unseen data coming from a comparable dataset collected at another hospital). Here, we show that the source site of a histopathology slide can be learned from the image using CV algorithms in spite of image augmentation, and we explore these source site predictions using interpretability tools. A CV model trained using Empirical Risk Minimization (ERM) risks learning this source-site signal as a spurious correlate in the weak-label regime, which we abate by using a training method with abstention. We find that a patch based classifier trained using abstention outperformed a model trained using ERM by 9.9, 10 and 19.4% F1 in the binary classification tasks of identifying tumor versus normal tissue in lung adenocarcinoma, Gleason score in prostate adenocarcinoma, and tumor tissue grade in clear cell renal cell carcinoma, respectively, at the expense of up to 80% coverage (defined as the percent of tiles not abstained on by the model). Further, by examining the areas abstained by the model, we find that the model trained using abstention is more robust to heterogeneity, artifacts and spurious correlates in the tissue. Thus, a method trained with abstention may offer novel insights into relevant areas of the tissue contributing to a particular phenotype. Together, we suggest using data augmentation methods that help mitigate a digital pathology model’s reliance on potentially spurious visual features, as well as selecting models that can identify features truly relevant for translational discovery and clinical decision support.

Published
2021
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26. Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology

Author

Jacob Rosenthal, Ryan Carelli, Mohamed Omar, David Brundage, Ella Halbert, Jackson Nyman, Surya N. Hari, Eliezer M. Van Allen, Luigi Marchionni, Renato Umeton, and Massimo Loda

Subjects
Machine Learning, Cancer Research, Oncology, Artificial Intelligence, Research Design, Neoplasms, Humans, Molecular Biology, Article
Abstract

Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use cases. PathML is publicly available at www.pathml.com.

Published
2021

27. Applied Self-Supervised Learning: Review of the State-of-the-Art and Implementations in Medicine

Author

Alexander Chowdhury, Jacob Rosenthal, Jonathan Waring, and Renato Umeton

Subjects
medicine_pharmacology_other, Self supervised learning, Computer science, business.industry, State (computer science), Artificial intelligence, business, Machine learning, computer.software_genre, Feature learning, Implementation, computer
Abstract

Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently in healthcare, as well as in most other industries, the two most prevalent machine learning paradigms are supervised learning and transfer learning. Both practices rely on large-scale, manually annotated datasets to train increasingly complex models. However, the requirement of data to be manually labeled leaves an excess of unused, unlabeled data available in both public and private data repositories. Self-supervised learning (SSL) is a growing area of machine learning that has the ability to take advantage of unlabeled data. Contrary to other machine learning paradigms, SSL algorithms create artificial supervisory signals from unlabeled data and pretrain algorithms on these signals. The aim of this review is two-fold: firstly, we provide a formal definition of SSL, divide SSL algorithms into their four unique subsets, and review the state-of-the-art published in each of those subsets between the years of 2014-2020. Second, this work surveys recent SSL algorithms published in healthcare, in order to provide medical experts with a clearer picture of how they can integrate SSL into their research, with the objective of leveraging unlabeled data.

Published
2021

28. Abstract 462: Using attention-based deep multiple instance learning to identify key genetic alterations in prostate cancer from whole slide images

Author

Mohamed Omar, Zhuoran Xu, Ryan Carelli, Jacob Rosenthal, David Brundage, Daniela C. Salles, Eddie L. Imada, Renato Umeton, Edward M. Schaeffer, Brian D. Robinson, Tamara L. Lotan, Massimo Loda, and Luigi Marchionni

Subjects
Cancer Research, Oncology
Abstract

Prostate cancer (PCa) is associated with several genetic alterations which play an important role in the disease heterogeneity and clinical outcome. These alterations involve gene fusion between TMPRSS2 and members of the ETS family of transcription factors like ERG, ETV1, and ETV4 together with mutations or deletions in tumor suppressors like TP53 and PTEN. The expanding wealth of digital whole slide images (WSIs) and the increasing adoption of deep learning approaches offer a unique opportunity for pathologists to streamline the detection of these alterations. Here, we used 736 haematoxylin and eosin-stained WSIs from 494 primary PCa patients to identify several key genetic alterations including ERG, ETV1, and ETV4 fusion, PTEN loss, and TP53 and SPOP mutations. Using a custom segmentation pipeline, we identified tissue regions and tiled them into high-resolution (10X magnification) patches (256X256 pixels) which were passed to our deep multiple instance learning framework. Using a pre-trained ResNet50 model, we extracted informative features which were subsequently used for training to predict slide-level labels and to detect slide regions with high diagnostic relevance. Using a 10-folds cross validation approach, we divided the data into training (80%), validation (10%) and testing (10%) sets. The training and validation data were used for training the model and hyperparameters tuning, respectively while the testing data was used to provide an unbiased evaluation of the models’ performance using the mean Area Under the Receiver Operating Characteristic (AUROC) across the ten testing folds as evaluation metric. We managed to accurately detect key molecular alterations including ERG fusion, ETV1 fusion, ETV4 fusion, and PTEN loss. Additionally, we were able to detect mutations in TP53 and SPOP together with the presence of androgen-receptor splice variant 7 (ARv7). In addition to slide-level classification, we also identified subregions with high attention score which can help pathologists identify the distinct morphological features associated with each genetic alteration. Finally, in order to examine the cellular structure associated with each genetic alteration, we used Hover-Net model to segment and classify the nuclei in the high-attention tiles. Our work highlights the utility of using WSIs to accurately identify key molecular alteration in cancer and their associated morphological and cellular features on the slide which would streamline the diagnostic process. To the best of our knowledge, this is the first study that uses routine WSIs to predict and characterize key genetic alterations in PCa. Citation Format: Mohamed Omar, Zhuoran Xu, Ryan Carelli, Jacob Rosenthal, David Brundage, Daniela C. Salles, Eddie L. Imada, Renato Umeton, Edward M. Schaeffer, Brian D. Robinson, Tamara L. Lotan, Massimo Loda, Luigi Marchionni. Using attention-based deep multiple instance learning to identify key genetic alterations in prostate cancer from whole slide images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 462.

Published
2022
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29. Abstract 5858: A multi-omics signature for patients’ risk classification in prostate cancer

Author

Zhuoran Xu, Elisa Benedetti, Ryan Carelli, Jacob Rosenthal, Hubert Pakula, Mohamed Omar, Renato Umeton, David Brundage, Jan Krumsiek, Massimo Loda, and Luigi Marchionni

Subjects
Cancer Research, Oncology
Abstract

Background: Effective biomarkers are urgently needed in the clinical settings. However, most biomarkers are currently developed from single type of omics data. The goal of this study is to identify prognostic prostate cancer signatures using transcriptomics and metabolomics profiles jointly aiming to capture wider spectrum of biological information. Methods: In this study, we included 94 tumor and 48 adjacency normal samples with both transcriptomics and metabolomics profiles from Dana-Farber/Harvard Cancer Center SPORE Prostate Cancer Cohort. There were 85 patients being followed up with median length of 2.02 years including 3 lethal and 8 progression cases. We first constructed a multi-omics covering network that contained minimal set of variable pairs but sufficiently rich to account for observed inter-patient variations. The network was built on known gene-metabolite interaction pairs from Pathway Commons as prior knowledge. Next, we used a diffusion process with each of connected gene-metabolite pairs as seeds to identify candidate signatures in the network. Signature sizes were controlled under 5 by adjusting the tree height during pruning. Hierarchical clustering and survival analyses were then performed to stratify patients into two risk groups for disease-free survival probability. Only the prognostic signatures with power higher than 0.9 from bootstrapping were kept for external validation and functional analyses. Since to our best knowledge, there is no publicly available dataset with both transcriptomics and metabolomics to validate the multi-omics signature we identified. We thus trained a rank-based kTSP classifier using gene expression data in SPORE cohort as a surrogate signature and validated it in TCGA prostate cancer cohort independently. Results: Constructed covering network consisted of 12 metabolites and 54 genes with inter-patient heterogeneities being captured efficiently. We identified one high-powered multi-omics signature (Gene: EGLN3; Metabolites: succinate, trans-4-hydroxyproline) that exhibited good prognostic value where high-risk patients had significant less time of disease-free survival (log rank test: p=0.019, power: 0.974). In addition, genomic variations were observed in different percentages of patients in high and low risk groups including NCOR1(SNV, 70.6% vs 96.3% p=0.025), NKX3.1(CNV, 29.6% vs 64.7% p=0.031) and TNFRSF10C (CNV, 29.6% vs 64.7%, p=0.031). No evidence indicated the patient grouping by the signature depend on Gleason scores (p=0.44). The surrogated gene signature contained 6 pairs of genes that can effectively classify TCGA patients into two prognostic groups (log rank test: p=0.048). Still, no evidence indicated the surrogate gene signature is associated with Gleason score (p=0.23) in TCGA dataset. Conclusions: We identified a prognostic multi-omics signature (EGLN3, succinate, trans-4-hydroxyproline) with high statistical power. Citation Format: Zhuoran Xu, Elisa Benedetti, Ryan Carelli, Jacob Rosenthal, Hubert Pakula, Mohamed Omar, Renato Umeton, David Brundage, Jan Krumsiek, Massimo Loda, Luigi Marchionni. A multi-omics signature for patients’ risk classification in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5858.

Published
2022
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30. Patterns of Population Variation in Two Paleopolyploid Eudicot Lineages Suggest That Dosage-Based Selection on Homeologs Is Long-Lived

Author

Brandon Nielsen, Jacob D. Washburn, Gavin C. Conant, Eric Lyons, Jacob Rosenthal, J. Chris Pires, Yue Hao, and Patrick P. Edger

Subjects
0301 basic medicine, Arabidopsis thaliana, Population, Arabidopsis, Gene Dosage, dosage balance, Brassica, Genes, Plant, Genome, Evolution, Molecular, Polyploidy, 03 medical and health sciences, Negative selection, Phylogenetics, metabolic network, Gene Duplication, Gene duplication, Genetics, Selection, Genetic, education, Phylogeny, Ecology, Evolution, Behavior and Systematics, education.field_of_study, Natural selection, biology, biology.organism_classification, 030104 developmental biology, Evolutionary biology, whole genome duplication, Genome, Plant, Research Article
Abstract

Genes that are inherently subject to strong selective constraints tend to be overretained in duplicate after polyploidy. They also continue to experience similar, but somewhat relaxed, constraints after that polyploidy event. We sought to assess for how long the influence of polyploidy is felt on these genes’ selective pressures. We analyzed two nested polyploidy events in Brassicaceae: the At-α genome duplication that is the most recent polyploidy in the model plant Arabidopsis thaliana and a more recent hexaploidy shared by the genus Brassica and its relatives. By comparing the strength and direction of the natural selection acting at the population and at the species level, we find evidence for continued intensified purifying selection acting on retained duplicates from both polyploidies even down to the present. The constraint observed in preferentially retained genes is not a result of the polyploidy event: the orthologs of such genes experience even stronger constraint in nonpolyploid outgroup genomes. In both the Arabidopsis and Brassica lineages, we further find evidence for segregating mildly deleterious variants, confirming that the population-level data uncover patterns not visible with between-species comparisons. Using the A. thaliana metabolic network, we also explored whether network position was correlated with the measured selective constraint. At both the population and species level, nodes/genes tended to show similar constraints to their neighbors. Our results paint a picture of the long-lived effects of polyploidy on plant genomes, suggesting that even yesterday’s polyploids still have distinct evolutionary trajectories.

Published
2018
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31. Libertarianism and the Problem of Clear Cases

Author

Jacob Rosenthal

Subjects
Libertarianism, Philosophy, libertarianism, clear cases, choice, agency, free will, two-way powers, media_common.quotation_subject, Agency (sociology), ddc:100, Free will, media_common, Law and economics
Abstract

New varieties of libertarianism connect not only free will and moral responsibility to indeterminism, but also agency and choice as such. In this paper, the author highlights what seems to be an embarrassment for all libertarian accounts, but especially for the ones just mentioned. The problem is brought out by clear cases of decisions in which there are strong and rather obvious reasons for one of the options and only relatively weak ones in favour of the alternatives. It is hard to insist that there be indeterminism even in such cases. Either it has no significant role to play, which means that libertarianism is in effect largely abandoned, or it has a purely negative role, being linked to some serious and thoroughgoing defect in the agent’s rationality. Thus, a dilemma for libertarians arises, which the author spells out in the text. Furthermore, he argues that some versions of compatibilism face essentially the same difficulty.

Published
2019

32. Justice as Fairness / Gerechtigkeit als Fairness. Englisch/Deutsch. [Great Papers Philosophie] : Rawls, John – zweisprachige Ausgabe; philosophische Bücher

Author

John Rawls, Corinna Mieth, Jacob Rosenthal, John Rawls, Corinna Mieth, and Jacob Rosenthal

Abstract

John Rawls'Buch ›A Theory of Justice‹ machte den Philosophen 1971 quasi über Nacht weltberühmt: Das Werk gilt seitdem als Klassiker. Grundgedanken seiner Theorie entwickelte Rawls erstmals 1958 in seinem hier zweisprachig und mit ausführlichem Kommentar wiedergegebenen Aufsatz ›Justice as Fairness‹ (›Gerechtigkeit als Fairness‹). Sind Gerechtigkeit und Fairness dasselbe? Nach Rawls Meinung nicht, denn hat man erst einmal akzeptiert, dass es'um das gegenseitige Anerkennen von Prinzipien durch freie Menschen geht, die keine Autorität gegenüber dem anderen haben', wird'das Konzept Fairness für das der Gerechtigkeit fundamental', oder mit anderen Worten: erst die Fairness, dann die Moral. Die Reihe'Great Papers Philosophie'bietet bahnbrechende Aufsätze der Philosophie: - Eine zeichengenaue, zitierfähige Wiedergabe des Textes (links das fremdsprachige Original, rechts eine neue Übersetzung). - Eine philosophiegeschichtliche Einordnung: Wie dachte man früher über das Problem? Welche Veränderung bewirkte der Aufsatz? Wie denkt man heute darüber?- Eine Analyse des Textes bzw. eine Rekonstruktion seiner Argumentationsstruktur, gefolgt von einem Abschnitt über den Autor sowie ein kommentiertes Literaturverzeichnis. E-Book mit Seitenzählung der Originalpaginierung.

Published
2020

33. Genome wide meta-analysis identifies genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders

Author

Phil Lee, Richard Anney, Pieter J. Hoekstra, Barbara Franke, Geschwind Dh, Michael C. Neale, Yen-Chen Anne Feng, Edwin H. Cook, Anttila, E Stahl, Borglum Ad, Kenneth S. Kendler, Santangelo Sl, Wray Nr, Danielle Posthuma, Carol A. Mathews, Sintia Iole Belangero, Amanda B Zheutlin, Cynthia M. Bulik, Jeremiah M. Scharf, Laramie E. Duncan, Luis Augusto Rohde, Jacob Rosenthal, Anna Keski-Rahkonen, Stein Mb, Fanous Ah, Zhaozhong Zhu, Karen S. Mitchell, J Kaprio, Breen G, Michel G. Nivard, G. Kirov, John I. Nurnberger, Patrick F. Sullivan, Daly Mj, Michael Conlon O'Donovan, Andreassen Oa, Hyejung Won, Wang Mm, Faraone Sv, Mattheisen M, Benjamin M. Neale, Gelernter J, Smoller Jw, Henry R. Kranzler, Thornton Lm, Clement C. Zai, Ripke S, Elliot M. Tucker-Drob, Howard J. Edenberg, Lewis Cm, McIntosh Am, Tobias Banaschewski, Dongmei Yu, Andrew D. Grotzinger, Walters Jt, Raymond K. Walters, Giovanni Coppola, and Jurjen J. Luykx

Subjects
0303 health sciences, medicine.medical_specialty, Biology, medicine.disease, Genome, Tourette syndrome, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Pleiotropy, Anorexia nervosa (differential diagnoses), Autism spectrum disorder, Schizophrenia, medicine, Bipolar disorder, Psychiatry, 030217 neurology & neurosurgery, Depression (differential diagnoses), 030304 developmental biology
Abstract

SummaryGenetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed a meta-analysis of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders identifying three groups of inter-related disorders. We detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning in the second trimester prenatally, and play prominent roles in a suite of neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

Published
2019
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34. Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Author

Laura M. Thornton, Paul Lichtenstein, Verneri Anttila, Diego Albani, Josep Antoni Ramos-Quiroga, Roger A.H. Adan, Monika Schlögelhofer, Stephen Sanders, Enrique Castelao, Klaus Berger, Nina Dalkner, Urs Heilbronner, Engilbert Sigurdsson, Pablo Mir, Fuquan Zhang, James T.R. Walters, Patrick F. Sullivan, Fragiskos Gonidakis, F. Kyle Satterstrom, Sara Marsal, Per Hoffmann, Amy Perry, Valentina Ciullo, Beate Herpertz-Dahlmann, Catharina Lavebratt, Kieran C. Murphy, Tammy Hedderly, Hyun Ju Hong, Evald Saemundsen, Sascha B. Fischer, Hailiang Huang, Andrew D. Grotzinger, Nienke Vulink, Murray B. Stein, Mark A. Frye, Laura J. Scott, David Curtis, Todd Lencz, Janiece E. DeSocio, Richard A. Belliveau, Eduard Vieta, Andrea Dietrich, Wade H. Berrettini, Kenneth S. Kendler, Marquis P. Vawter, Paul S. Nestadt, Michael E. Talkowski, Manuel Mattheisen, Ingrid Agartz, Elisa Docampo, Bernhard T. Baune, Stefan Ehrlich, Jolanta Lissowska, Felecia Cerrato, Terje Nærland, Robin M. Murray, Jennifer Reichert, Annette M. Hartmann, Hannelore Ehrenreich, Howard J. Edenberg, Katherine A. Halmi, Qingqin S. Li, Peristera Paschou, Marie Bækvad-Hansen, Esther Walton, Alessio Maria Monteleone, Ted Reichborn-Kjennerud, Frank Bellivier, Jungeun Song, D. Blake Woodside, Young Shin Kim, Jochen Seitz, Jacques Pantel, Palmiero Monteleone, Erika L. Nurmi, Rodney J. Scott, Kang Sim, Ekaterina A. Khramtsova, Udo Dannlowski, Rolf Adolfsson, Danielle Posthuma, Melissa J. Green, Laura Ibanez-Gomez, Jakob Grove, Elvira Bramon, Gregory L. Hanna, Cynthia M. Bulik, Yiran Guo, Stephan Ripke, Mary M. Robertson, Harald N. Aschauer, Adebayo Anjorin, Joanna Martin, Bertram Müller-Myhsok, Deborah Kaminská, Jose Guzman-Parra, Benedetta Nacmias, Erik G. Jönsson, Jonathan R. I. Coleman, Douglas F. Levinson, Hamdi Mbarek, Gun Peggy Knudsen, Karin Egberts, Mette Nyegaard, Patrik K. E. Magnusson, Mark Adams, Douglas Blackwood, Elisabeth B. Binder, Marcus Ising, Anna R. Docherty, Jim van Os, Nese Direk, Lina Martinsson, Maria Arranz, Christel M. Middeldorp, Stefan Kloiber, Sintia Iole Belangero, Eske M. Derks, Ingrid Melle, Erlend Bøen, Jan Haavik, Federica Piras, Unna N. Danner, Anil K. Malhotra, Gerome Breen, Stephen V. Faraone, Amanda B Zheutlin, Timothy Poterba, Stephan Ruhrmann, Inge Joa, Ulrik Fredrik Malt, Sarah E. Bergen, Federica Tozzi, Lauren A. Weiss, Hana Papezova, Dominic Holland, Elliot S. Gershon, Jaakko Kaprio, Merete Nordentoft, Scott D. Gordon, Christopher Pittenger, Keun-Ah Cheon, Jennifer Jordan, Philip Gorwood, Myrna M. Weissman, Preben Bo Mortensen, Melissa A. Munn-Chernoff, Isobel Heyman, Eun-Young Shin, Christie L. Burton, Katherine Gordon-Smith, Sietske G. Helder, Peter Nagy, Till F. M. Andlauer, Yunpeng Wang, Young Key Kim, Kate Langley, Søren Dalsgaard, Richard Delorme, Torbjørn Elvsåshagen, Bennett L. Leventhal, Giovanni Gambaro, Christos Androutsos, Jennifer Tübing, Marion Roberts, Annelie Nordin Adolfsson, Hakon Hakonarson, Dorothy E. Grice, Vaughan J. Carr, Konstantinos Tziouvas, Stephanie Zerwas, Cathy L. Barr, Michael Conlon O'Donovan, Per Qvist, Beate St Pourcain, Samuel Kuperman, Leila Karhunen, Jack Samuels, Markus M. Nöthen, Martien J H Kas, Alfonso Tortorella, Mikael Landén, Jennifer Crosbie, Marco A. Grados, Joanna M. Biernacka, Paul D. Arnold, Irene A. Malaty, Jurjen J. Luykx, Nicholas Bass, Naomi R. Wray, Catharina A. Hartman, Christina M. Hultman, Michael S. Okun, Brandon Wormley, Michael Bauer, Daniel J. Smith, Ian Jones, Kathryn Roeder, Brien P. Riley, Caroline M. Nievergelt, Katrin Gade, Sarah Kittel-Schneider, Roy H. Perlis, James R. Mitchell, Ziarih Hawi, James Lee, Liz Forty, William E. Bunney, Thomas Damm Als, Catherine Schaefer, Digby Quested, Matteo Cassina, Anna C. Koller, Patrick Turley, Agnes A. Steixner, Anu Raevuori, Assen Jablensky, Peter Holmans, Dong-Ho Song, S. Evelyn Stewart, Jan K. Buitelaar, Fernando S. Goes, Alexander Münchau, Ayman H. Fanous, Nicolas Ramoz, James B. Potash, Monica Gratacos Mayora, Tobias Banaschewski, Céline S. Reinbold, Renata Rizzo, Arianna Di Florio, Lenka Foretova, Gianfranco Spalletta, Aarno Palotie, Eleftheria Zeggini, Lawrence W. Brown, Julie K. O'Toole, Lynn E. DeLisi, Ulrich Schall, Mary Roberson, Barbara J. Coffey, Bryan J. Mowry, Murray J. Cairns, Dan J. Stein, Glyn Lewis, Marta Ribasés, C. Robert Cloninger, Bettina Konte, John B. Vincent, Duncan S. Palmer, Radhika Kandaswamy, Christine Ladd-Acosta, Lars Alfredsson, Frank Visscher, Ulrike Schmidt, Aiden Corvin, Susan L. Santangelo, Brenda W.J.H. Penninx, David J. Porteous, Tetsuya Ando, Arne E. Vaaler, Bru Cormand, Laura Carlberg, Claire Churchhouse, Manfred Stuhrmann, Niamh Mullins, Christine Søholm Hansen, Cathy L. Budman, Hartmut Imgart, Dan E. Arking, James J. McGough, Michael Gill, Christel Depienne, Roland Burghardt, Antonio Julià, Anders M. Dale, Sven Sandin, Katharina Domschke, Maria Grigoroiu-Serbanescu, Susana Jiménez-Murcia, Marianne Giørtz Pedersen, Zsanett Tarnok, Gisli Baldursson, Michele T. Pato, David M. Hougaard, Thorgeir E. Thorgeirsson, Katharina Bey, Kerstin J. Plessen, Margaret A. Richter, Ole A. Andreassen, Claudine Laurent-Levinson, Leonid Padyukov, Jacques Mallet, Daniela Degortes, John R. Kelsoe, Robert D. Levitan, Andreas Reif, Chaim Huyser, Derek W. Morris, Sina Wanderer, William Byerley, Edna Grünblatt, E.J.C. de Geus, Hyejung Won, Josephine Elia, Rudolf Uher, Jay A. Tischfield, Andreas Karwautz, Gustavo Turecki, Pieter J. Hoekstra, Dorret I. Boomsma, Jacob Rosenthal, Daniele Cusi, Michael C. Neale, Sara Mostafavi, Gwyneth Zai, F. Anthony O'Neill, Gary Donohoe, Karola Rehnström, Harry Brandt, Helena Gaspar, Francis J. McMahon, H-Erich Wichmann, Andrew W. Bergen, Giovanni Coppola, Lea K. Davis, Lenka Slachtova, Olav B. Smeland, Erin C. Dunn, Nicholas G. Martin, Allan L. Naarden, Jo Knight, Cristina Sánchez-Mora, Masashi Ikeda, Lorraine Southam, Sandro Sorbi, Barbara Franke, Martin Schalling, Russell Schachar, Yen-Chen Anne Feng, Kirsten R. Müller-Vahl, André Scherag, Zhaozhong Zhu, Eric A. Storch, Páll Magnússon, David Cohen, Olafur O Gudmundsson, Harvey S. Singer, Brian Kelly, Jonas Bybjerg-Grauholm, Blanca Garcia-Delgar, Thomas Hansen, Carmel M. Loughland, Christine Lochner, Stacy Steinberg, Martin Woods, Jorge A. Quiroz, Raquel Rabionet, Alden Y. Huang, Janice M. Fullerton, María Soler Artigas, Hans J. Grabe, Philip Asherson, Margit Burmeister, Alicia R. Martin, Martin A. Kennedy, Janet Treasure, Anders D. Børglum, Eva C. Schulte, Andreas Hartmann, Frans Henskens, Youl-Ri Kim, Jens Treutlein, Joanna Hauser, Manfred M. Fichter, Damiaan Denys, Ann E. Pulver, Kelly L. Klump, Paul Sandor, Michael Wagner, Philippe Courtet, Sandra Van der Auwera, Susanne Lucae, Eystein Stordal, Michel G. Nivard, Maurizio Clementi, Astrid Morer, Philip B. Mitchell, Huda Akil, Edwin H. Cook, Jennifer L. Moran, Donald W. Black, Jeremiah M. Scharf, Jana Strohmaier, Colm McDonald, Meg M.-J. Wang, Richard M. Myers, Stephanie Godard, Pablo V. Gejman, Athanasios Maras, Marcella Rietschel, Nancy G. Buccola, Konstantinos Hatzikotoulas, Dalila Pinto, Jouke-Jan Hottenga, Kari Stefansson, James S. Sutcliffe, Andres Metspalu, Amaia Hervás, Joel Gelernter, Wolfgang Herzog, Paula Rovira, Gunnar Morken, Tara Murphy, Mark Weiser, Vincent Millischer, Frank Dudbridge, Dan Rujescu, Vladimir Bencko, Valdo Ricca, Kimberly Chambert, Guy A. Rouleau, James J. Crowley, Thomas G. Schulze, Toni-Kim Clarke, Triinu Peters, Gudrun Wagner, Daniel A. Geller, Henry R. Kranzler, G. Bragi Walters, Vera Golimbet, Clement C. Zai, Nigel Williams, Andreas Birgegård, Joseph D. Buxbaum, Elliot M. Tucker-Drob, Jerome C. Foo, Tracey L. Petryshen, Daniel P. Howrigan, Hunna J. Watson, Franziska Degenhardt, Peter R. Schofield, Jesper Buchhave Poulsen, Stefan Herms, Johannes Hebebrand, Mario Maj, George Kirov, Fabrizio Piras, Sara McDevitt, James T. McCracken, Carol A. Mathews, Michael John Owen, Peter Falkai, Donald L. Gilbert, Enda M. Byrne, Fernando Fernández-Aranda, Csaba Barta, Stéphane Jamain, Jubao Duan, Dongmei Yu, Danielle C. Cath, Ole Mors, Sigrun Hope, Laramie E. Duncan, Alan R. Sanders, Sang-Yun Oh, Carsten Bøcker Pedersen, Henning Tiemeier, Roseann E. Peterson, Raymond K. Walters, Margarita C T Slof-Op 't Landt, Madeline Alexander, Stephanie Le Hellard, Ina Giegling, Annemarie A. van Elburg, Steven P. Hamilton, Vesna Boraska Perica, Thomas V. Fernandez, Danielle M. Dick, Francesco Bettella, Roel A. Ophoff, Grant W. Montgomery, Gerald Nestadt, Nakao Iwata, Jessica H. Baker, Walter H. Kaye, Jeremy M. Silverman, Mark J. Daly, Robert A. King, Sarah E. Medland, Anastasios Konstantinidis, Robert D. Oades, Samuel H. Zinner, Steven Crawford, Daniel H. Geschwind, Patrick W. L. Leung, Martin Alda, Marie Navratilova, Pak C. Sham, Paul A. Tooney, Tian Ge, Veit Roessner, Martin Preisig, Thomas Werge, Eli A. Stahl, David A. Collier, Stephanie H. Witt, Dermot Walsh, Miquel Casas, Anna Keski-Rahkonen, Jane H. Christensen, Silvia De Rubeis, Giorgio Pistis, Sven Cichon, Bruno Etain, Dominique Campion, O. Joseph Bienvenu, Christian Dina, Manolis Kogevinas, Thomas Espeseth, Benjamin M. Neale, Ditte Demontis, Klaus-Peter Lesch, Marina Mitjans, Tiffany A. Greenwood, Marcos Madruga-Garrido, Sibylle G. Schwab, Oedegaard Ketil Joachim, Hreinn Stefansson, Sara A. Paciga, Monica Forzan, Dieter B. Wildenauer, Lena Backlund, A. Jeremy Willsey, Carlos N. Pato, Nicholas John Craddock, Inge A. Meijer, Sandra K. Loo, Filip Rybakowski, Tracey D. Wade, Scott J. Crow, Bernard Lerer, Valsamma Eapen, Esben Agerbo, Andrew M. McIntosh, Luis Augusto Rohde, Susan L. McElroy, Stephan Zipfel, Peter P. Zandi, Cathryn M. Lewis, Lars Klareskog, Martin Begemann, Phil Lee, Richard Anney, Mark A. Bellgrove, Lisa Jones, Andreas J. Forstner, Agnieszka Słopień, Hilary Coon, Dong Li, Alessandro Serretti, Carsten Horn, Christos Pantelis, Ryan L. Collins, David M. Howard, Lucía Colodro-Conde, Pippa A. Thomson, Martin Hautzinger, Alysa E. Doyle, Julie Hagstrøm, Oliver S. P. Davis, Karen S. Mitchell, Jordan W. Smoller, Michael Strober, John I. Nurnberger, Andrea G. Ludolph, Monika Budde, Anna Maaser, Lambertus Klei, Aribert Rothenberger, Yulia Worbe, Fabian Streit, James L. Kennedy, Barbara E. Stranger, Ashley Dumont, Jianxin Shi, Dale R. Nyholt, Craig Johnson, Jonna Kuntsi, Yun-Joo Koh, Loes M. Olde Loohuis, Robert B. Freedman, Anke Hinney, Susanne Walitza, Enrico Domenici, Margarita Rivera, Sodahm Kook, Erica Greenberg, Tetyana Zayats, Josef Frank, Gary A. Heiman, Andrew McQuillin, Abraham Reichenberg, Piotr M. Czerski, Humberto Nicolini, Lee P.H., Anttila V., Won H., Feng Y.-C.A., Rosenthal J., Zhu Z., Tucker-Drob E.M., Nivard M.G., Grotzinger A.D., Posthuma D., Wang M.M.-J., Yu D., Stahl E.A., Walters R.K., Anney R.J.L., Duncan L.E., Ge T., Adolfsson R., Banaschewski T., Belangero S., Cook E.H., Coppola G., Derks E.M., Hoekstra P.J., Kaprio J., Keski-Rahkonen A., Kirov G., Kranzler H.R., Luykx J.J., Rohde L.A., Zai C.C., Agerbo E., Arranz M.J., Asherson P., Baekvad-Hansen M., Baldursson G., Bellgrove M., Belliveau R.A., Buitelaar J., Burton C.L., Bybjerg-Grauholm J., Casas M., Cerrato F., Chambert K., Churchhouse C., Cormand B., Crosbie J., Dalsgaard S., Demontis D., Doyle A.E., Dumont A., Elia J., Grove J., Gudmundsson O.O., Haavik J., Hakonarson H., Hansen C.S., Hartman C.A., Hawi Z., Hervas A., Hougaard D.M., Howrigan D.P., Huang H., Kuntsi J., Langley K., Lesch K.-P., Leung P.W.L., Loo S.K., Martin J., Martin A.R., McGough J.J., Medland S.E., Moran J.L., Mors O., Mortensen P.B., Oades R.D., Palmer D.S., Pedersen C.B., Pedersen M.G., Peters T., Poterba T., Poulsen J.B., Ramos-Quiroga J.A., Reif A., Ribases M., Rothenberger A., Rovira P., Sanchez-Mora C., Satterstrom F.K., Schachar R., Artigas M.S., Steinberg S., Stefansson H., Turley P., Walters G.B., Werge T., Zayats T., Arking D.E., Bettella F., Buxbaum J.D., Christensen J.H., Collins R.L., Coon H., De Rubeis S., Delorme R., Grice D.E., Hansen T.F., Holmans P.A., Hope S., Hultman C.M., Klei L., Ladd-Acosta C., Magnusson P., Naerland T., Nyegaard M., Pinto D., Qvist P., Rehnstrom K., Reichenberg A., Reichert J., Roeder K., Rouleau G.A., Saemundsen E., Sanders S.J., Sandin S., St Pourcain B., Stefansson K., Sutcliffe J.S., Talkowski M.E., Weiss L.A., Willsey A.J., Agartz I., Akil H., Albani D., Alda M., Als T.D., Anjorin A., Backlund L., Bass N., Bauer M., Baune B.T., Bellivier F., Bergen S.E., Berrettini W.H., Biernacka J.M., Blackwood D.H.R., Boen E., Budde M., Bunney W., Burmeister M., Byerley W., Byrne E.M., Cichon S., Clarke T.-K., Coleman J.R.I., Craddock N., Curtis D., Czerski P.M., Dale A.M., Dalkner N., Dannlowski U., Degenhardt F., Di Florio A., Elvsashagen T., Etain B., Fischer S.B., Forstner A.J., Forty L., Frank J., Frye M., Fullerton J.M., Gade K., Gaspar H.A., Gershon E.S., Gill M., Goes F.S., Gordon S.D., Gordon-Smith K., Green M.J., Greenwood T.A., Grigoroiu-Serbanescu M., Guzman-Parra J., Hauser J., Hautzinger M., Heilbronner U., Herms S., Hoffmann P., Holland D., Jamain S., Jones I., Jones L.A., Kandaswamy R., Kelsoe J.R., Kennedy J.L., Joachim O.K., Kittel-Schneider S., Kogevinas M., Koller A.C., Lavebratt C., Lewis C.M., Li Q.S., Lissowska J., Loohuis L.M.O., Lucae S., Maaser A., Malt U.F., Martin N.G., Martinsson L., McElroy S.L., McMahon F.J., McQuillin A., Melle I., Metspalu A., Millischer V., Mitchell P.B., Montgomery G.W., Morken G., Morris D.W., Muller-Myhsok B., Mullins N., Myers R.M., Nievergelt C.M., Nordentoft M., Adolfsson A.N., Nothen M.M., Ophoff R.A., Owen M.J., Paciga S.A., Pato C.N., Pato M.T., Perlis R.H., Perry A., Potash J.B., Reinbold C.S., Rietschel M., Rivera M., Roberson M., Schalling M., Schofield P.R., Schulze T.G., Scott L.J., Serretti A., Sigurdsson E., Smeland O.B., Stordal E., Streit F., Strohmaier J., Thorgeirsson T.E., Treutlein J., Turecki G., Vaaler A.E., Vieta E., Vincent J.B., Wang Y., Witt S.H., Zandi P., Adan R.A.H., Alfredsson L., Ando T., Aschauer H., Baker J.H., Bencko V., Bergen A.W., Birgegard A., Perica V.B., Brandt H., Burghardt R., Carlberg L., Cassina M., Clementi M., Courtet P., Crawford S., Crow S., Crowley J.J., Danner U.N., Davis O.S.P., Degortes D., DeSocio J.E., Dick D.M., Dina C., Docampo E., Egberts K., Ehrlich S., Espeseth T., Fernandez-Aranda F., Fichter M.M., Foretova L., Forzan M., Gambaro G., Giegling I., Gonidakis F., Gorwood P., Mayora M.G., Guo Y., Halmi K.A., Hatzikotoulas K., Hebebrand J., Helder S.G., Herpertz-Dahlmann B., Herzog W., Hinney A., Imgart H., Jimenez-Murcia S., Johnson C., Jordan J., Julia A., Kaminska D., Karhunen L., Karwautz A., Kas M.J.H., Kaye W.H., Kennedy M.A., Kim Y.-R., Klareskog L., Klump K.L., Knudsen G.P.S., Landen M., Le Hellard S., Levitan R.D., Li D., Lichtenstein P., Maj M., Marsal S., McDevitt S., Mitchell J., Monteleone P., Monteleone A.M., Munn-Chernoff M.A., Nacmias B., Navratilova M., O'Toole J.K., Padyukov L., Pantel J., Papezova H., Rabionet R., Raevuori A., Ramoz N., Reichborn-Kjennerud T., Ricca V., Roberts M., Rujescu D., Rybakowski F., Scherag A., Schmidt U., Seitz J., Slachtova L., Slof-Op't Landt M.C.T., Slopien A., Sorbi S., Southam L., Strober M., Tortorella A., Tozzi F., Treasure J., Tziouvas K., van Elburg A.A., Wade T.D., Wagner G., Walton E., Watson H.J., Wichmann H.-E., Woodside D.B., Zeggini E., Zerwas S., Zipfel S., Adams M.J., Andlauer T.F.M., Berger K., Binder E.B., Boomsma D.I., Castelao E., Colodro-Conde L., Direk N., Docherty A.R., Domenici E., Domschke K., Dunn E.C., Foo J.C., de. 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H., Anttila, V., Won, H., Feng, Y. -C. A., Rosenthal, J., Zhu, Z., Tucker-Drob, E. M., Nivard, M. G., Grotzinger, A. D., Posthuma, D., Wang, M. M. -J., Yu, D., Stahl, E. A., Walters, R. K., Anney, R. J. L., Duncan, L. E., Ge, T., Adolfsson, R., Banaschewski, T., Belangero, S., Cook, E. H., Coppola, G., Derks, E. M., Hoekstra, P. J., Kaprio, J., Keski-Rahkonen, A., Kirov, G., Kranzler, H. R., Luykx, J. J., Rohde, L. A., Zai, C. C., Agerbo, E., Arranz, M. J., Asherson, P., Baekvad-Hansen, M., Baldursson, G., Bellgrove, M., Belliveau, R. A., Buitelaar, J., Burton, C. L., Bybjerg-Grauholm, J., Casas, M., Cerrato, F., Chambert, K., Churchhouse, C., Cormand, B., Crosbie, J., Dalsgaard, S., Demontis, D., Doyle, A. E., Dumont, A., Elia, J., Grove, J., Gudmundsson, O. O., Haavik, J., Hakonarson, H., Hansen, C. S., Hartman, C. A., Hawi, Z., Hervas, A., Hougaard, D. M., Howrigan, D. P., Huang, H., Kuntsi, J., Langley, K., Lesch, K. -P., Leung, P. W. L., Loo, S. K., Martin, J., Martin, A. R., Mcgough, J. J., Medland, S. E., Moran, J. L., Mors, O., Mortensen, P. B., Oades, R. D., Palmer, D. S., Pedersen, C. B., Pedersen, M. G., Peters, T., Poterba, T., Poulsen, J. B., Ramos-Quiroga, J. A., Reif, A., Ribases, M., Rothenberger, A., Rovira, P., Sanchez-Mora, C., Satterstrom, F. K., Schachar, R., Artigas, M. S., Steinberg, S., Stefansson, H., Turley, P., Walters, G. B., Werge, T., Zayats, T., Arking, D. E., Bettella, F., Buxbaum, J. D., Christensen, J. H., Collins, R. L., Coon, H., De Rubeis, S., Delorme, R., Grice, D. E., Hansen, T. F., Holmans, P. A., Hope, S., Hultman, C. M., Klei, L., Ladd-Acosta, C., Magnusson, P., Naerland, T., Nyegaard, M., Pinto, D., Qvist, P., Rehnstrom, K., Reichenberg, A., Reichert, J., Roeder, K., Rouleau, G. A., Saemundsen, E., Sanders, S. J., Sandin, S., St Pourcain, B., Stefansson, K., Sutcliffe, J. S., Talkowski, M. E., Weiss, L. A., Willsey, A. J., Agartz, I., Akil, H., Albani, D., Alda, M., Als, T. 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B., Brandt, H., Burghardt, R., Carlberg, L., Cassina, M., Clementi, M., Courtet, P., Crawford, S., Crow, S., Crowley, J. J., Danner, U. N., Davis, O. S. P., Degortes, D., Desocio, J. E., Dick, D. M., Dina, C., Docampo, E., Egberts, K., Ehrlich, S., Espeseth, T., Fernandez-Aranda, F., Fichter, M. M., Foretova, L., Forzan, M., Gambaro, G., Giegling, I., Gonidakis, F., Gorwood, P., Mayora, M. G., Guo, Y., Halmi, K. A., Hatzikotoulas, K., Hebebrand, J., Helder, S. G., Herpertz-Dahlmann, B., Herzog, W., Hinney, A., Imgart, H., Jimenez-Murcia, S., Johnson, C., Jordan, J., Julia, A., Kaminska, D., Karhunen, L., Karwautz, A., Kas, M. J. H., Kaye, W. H., Kennedy, M. A., Kim, Y. -R., Klareskog, L., Klump, K. L., Knudsen, G. P. S., Landen, M., Le Hellard, S., Levitan, R. D., Li, D., Lichtenstein, P., Maj, M., Marsal, S., Mcdevitt, S., Mitchell, J., Monteleone, P., Monteleone, A. M., Munn-Chernoff, M. A., Nacmias, B., Navratilova, M., O'Toole, J. 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A., Schaefer, C., Schulte, E. C., Shi, J., Smith, D. J., Thomson, P. A., Tiemeier, H., Uher, R., van der Auwera, S., Weissman, M. M., Alexander, M., Begemann, M., Bramon, E., Buccola, N. G., Cairns, M. J., Campion, D., Carr, V. J., Cloninger, C. R., Cohen, D., Collier, D. A., Corvin, A., Delisi, L. E., Donohoe, G., Dudbridge, F., Duan, J., Freedman, R., Gejman, P. V., Golimbet, V., Godard, S., Ehrenreich, H., Hartmann, A. M., Henskens, F. A., Ikeda, M., Iwata, N., Jablensky, A. V., Joa, I., Jonsson, E. G., Kelly, B. J., Knight, J., Konte, B., Laurent-Levinson, C., Lee, J., Lencz, T., Lerer, B., Loughland, C. M., Malhotra, A. K., Mallet, J., Mcdonald, C., Mitjans, M., Mowry, B. J., Murphy, K. C., Murray, R. M., O'Neill, F. A., Oh, S. -Y., Palotie, A., Pantelis, C., Pulver, A. E., Petryshen, T. L., Quested, D. J., Riley, B., Sanders, A. R., Schall, U., Schwab, S. G., Scott, R. J., Sham, P. C., Silverman, J. M., Sim, K., Steixner, A. A., Tooney, P. A., van Os, J., Vawter, M. P., Walsh, D., Weiser, M., Wildenauer, D. B., Williams, N. M., Wormley, B. K., Zhang, F., Androutsos, C., Arnold, P. D., Barr, C. L., Barta, C., Bey, K., Bienvenu, O. J., Black, D. W., Brown, L. W., Budman, C., Cath, D., Cheon, K. -A., Ciullo, V., Coffey, B. J., Cusi, D., Davis, L. K., Denys, D., Depienne, C., Dietrich, A., Eapen, V., Falkai, P., Fernandez, T. V., Garcia-Delgar, B., Geller, D. A., Gilbert, D. L., Grados, M. A., Greenberg, E., Grunblatt, E., Hagstrom, J., Hanna, G. L., Hartmann, A., Hedderly, T., Heiman, G. A., Heyman, I., Hong, H. J., Huang, A., Huyser, C., Ibanez-Gomez, L., Khramtsova, E. A., Kim, Y. K., Kim, Y. -S., King, R. A., Koh, Y. -J., Konstantinidis, A., Kook, S., Kuperman, S., Leventhal, B. L., Lochner, C., Ludolph, A. G., Madruga-Garrido, M., Malaty, I., Maras, A., Mccracken, J. T., Meijer, I. A., Mir, P., Morer, A., Muller-Vahl, K. R., Munchau, A., Murphy, T. L., Naarden, A., Nagy, P., Nestadt, G., Nestadt, P. S., Nicolini, H., Nurmi, E. L., Okun, M. S., Paschou, P., Piras, F., Pittenger, C., Plessen, K. J., Richter, M. A., Rizzo, R., Robertson, M., Roessner, V., Ruhrmann, S., Samuels, J. F., Sandor, P., Schlogelhofer, M., Shin, E. -Y., Singer, H., Song, D. -H., Song, J., Spalletta, G., Stein, D. J., Stewart, S. E., Storch, E. A., Stranger, B., Stuhrmann, M., Tarnok, Z., Tischfield, J. A., Tubing, J., Visscher, F., Vulink, N., Wagner, M., Walitza, S., Wanderer, S., Woods, M., Worbe, Y., Zai, G., Zinner, S. H., Sullivan, P. F., Franke, B., Daly, M. J., Bulik, C. M., Mcintosh, A. M., O'Donovan, M. C., Zheutlin, A., Andreassen, O. A., Borglum, A. D., Breen, G., Edenberg, H. J., Fanous, A. H., Faraone, S. V., Gelernter, J., Mathews, C. A., Mattheisen, M., Mitchell, K. S., Neale, M. C., Nurnberger, J. I., Ripke, S., Santangelo, S. L., Scharf, J. M., Stein, M. B., Thornton, L. M., Walters, J. T. R., Wray, N. R., Geschwind, D. H., Neale, B. M., Kendler, K. S., and Smoller, J. W.

Subjects
Netherlands Twin Register (NTR), cross-disorder genetics, Medizin, Genome-wide association study, Tourette syndrome, functional genomics, gene expression, genetic architecture, genetic correlation, GWAS, neurodevelopment, pleiotropy, psychiatric disorders, Psychiatric genetics, 0302 clinical medicine, Pleiotropy, functional genomic, WIDE ASSOCIATION, cross-disorder genetic, 0303 health sciences, Mental Disorders, Genetic Pleiotropy, HUMAN BRAIN, INSIGHTS, Autism spectrum disorder, Schizophrenia, DISEASES, GENETIC CORRELATIONS, medicine.medical_specialty, Neurogenesis, Quantitative Trait Loci, BF, Biology, GENOTYPE IMPUTATION, Psychiatric geneticscross-disorder geneticspsychiatric disorderspleiotropyneurodevelopmentGWASgenetic correlationgene expressiongenetic architecturefunctional genomics, Article, General Biochemistry, Genetics and Molecular Biology, psychiatric disorder, 03 medical and health sciences, SDG 3 - Good Health and Well-being, medicine, Humans, Genetic Predisposition to Disease, Bipolar disorder, TRANSCRIPTOME, Psychiatry, 030304 developmental biology, Gwas, Psychiatric Genetics, Cross-disorder Genetics, Functional Genomics, Gene Expression, Genetic Architecture, Genetic Correlation, Neurodevelopment, Psychiatric Disorders, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], IDENTIFICATION, MUTATIONS, medicine.disease, Genetic architecture, DEMETHYLASE, RC0321, 1182 Biochemistry, cell and molecular biology, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

Published
2019
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36. T cell epitope mimicry between Sjögren's syndrome Antigen A (SSA)/Ro60 and oral, gut, skin and vaginal bacteria

Author

Harini Bagavant, Agnieszka Szymula, Umesh S. Deshmukh, Barbara Szczerba, Jacob Rosenthal, and Shu Man Fu

Subjects
T-Lymphocytes, T cell, Immunology, Epitopes, T-Lymphocyte, Autoimmunity, Cross Reactions, Biology, Lymphocyte Activation, medicine.disease_cause, Article, Epitope, Microbiology, Mice, HLA-DR3 Antigen, Immune system, Antigen, von Willebrand Factor, medicine, Animals, Humans, Lupus Erythematosus, Systemic, Immunology and Allergy, Amino Acid Sequence, Escherichia coli, Skin, Mouth, Hybridomas, Molecular Mimicry, Human microbiome, Recombinant Proteins, Intestines, stomatognathic diseases, Molecular mimicry, Sjogren's Syndrome, medicine.anatomical_structure, Ribonucleoproteins, Vagina, Female, Peptides, Capnocytophaga
Abstract

This study was undertaken to test the hypothesis that Sjogren's syndrome Antigen A (SSA)/Ro60-reactive T cells are activated by peptides originating from oral and gut bacteria. T cell hybridomas generated from HLA-DR3 transgenic mice recognized 3 regions on Ro60, with core epitopes mapped to amino acids 228-238, 246-256 and 371-381. BLAST analysis identified several mimicry peptides, originating from human oral, intestinal, skin and vaginal bacteria, as well as environmental bacteria. Amongst these, a peptide from the von Willebrand factor type A domain protein (vWFA) from the oral microbe Capnocytophaga ochracea was the most potent activator. Further, Ro60-reactive T cells were activated by recombinant vWFA protein and whole Escherichia coli expressing this protein. These results demonstrate that peptides derived from normal human microbiota can activate Ro60-reactive T cells. Thus, immune responses to commensal microbiota and opportunistic pathogens should be explored as potential triggers for initiating autoimmunity in SLE and Sjögren's syndrome.

Published
2014
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