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18 Publications visible to you, out of a total of 18
Digital-Tier Strategy Improves Newborn Screening for Glutaric Aciduria Type 1

Abstract (Expand)

Glutaric aciduria type 1 (GA1) is a rare inherited metabolic disease increasingly included in newborn screening (NBS) programs worldwide. Because of the broad biochemical spectrum of individuals with …dividuals with GA1 and the lack of reliable second-tier strategies, NBS for GA1 is still confronted with a high rate of false positives. In this study, we aim to increase the specificity of NBS for GA1 and, hence, to reduce the rate of false positives through machine learning methods. Therefore, we studied NBS profiles from 1,025,953 newborns screened between 2014 and 2023 at the Heidelberg NBS Laboratory, Germany. We identified a significant sex difference, resulting in twice as many false-positives male than female newborns. Moreover, the proposed digital-tier strategy based on logistic regression analysis, ridge regression, and support vector machine reduced the false-positive rate by over 90% compared to regular NBS while identifying all confirmed individuals with GA1 correctly. An in-depth analysis of the profiles revealed that in particular false-positive results with high associated follow-up costs could be reduced significantly. In conclusion, understanding the origin of false-positive NBS and implementing a digital-tier strategy to enhance the specificity of GA1 testing may significantly reduce the burden on newborns and their families from false-positive NBS results.

Authors: Elaine Zaunseder, Julian Teinert, Nikolas Boy, Sven F. Garbade, Saskia Haupt, Patrik Feyh, Georg F. Hoffmann, Stefan Kölker, Ulrike Mütze, Vincent Heuveline

Date Published: 1st Dec 2024

Publication Type: Journal

Deep Learning and Explainable Artificial Intelligence for Improving Specificity and Detecting Metabolic Patterns in Newborn Screening

Abstract

Not specified

Authors: Elaine Zaunseder, Ulrike Mütze, Sven F. Garbade, Saskia Haupt, Stefan Kölker, Vincent Heuveline

Date Published: 5th Dec 2023

Publication Type: Journal

Dominantly inherited micro-satellite instable cancer – the four Lynch syndromes - an EHTG, PLSD position statement

Abstract (Expand)

Abstract The recognition of dominantly inherited micro-satellite instable (MSI) cancers caused by pathogenic variants in one of the four mismatch repair ( MMR ) genes MSH2, MLH1, MSH6 and PMS2 has … MMR ) genes MSH2, MLH1, MSH6 and PMS2 has modified our understanding of carcinogenesis. Inherited loss of function variants in each of these MMR genes cause four dominantly inherited cancer syndromes with different penetrance and expressivities: the four Lynch syndromes. No person has an “average sex “or a pathogenic variant in an “average Lynch syndrome gene” and results that are not stratified by gene and sex will be valid for no one. Carcinogenesis may be a linear process from increased cellular division to localized cancer to metastasis. In addition, in the Lynch syndromes (LS) we now recognize a dynamic balance between two stochastic processes: MSI producing abnormal cells, and the host’s adaptive immune system’s ability to remove them. The latter may explain why colonoscopy surveillance does not reduce the incidence of colorectal cancer in LS, while it may improve the prognosis. Most early onset colon, endometrial and ovarian cancers in LS are now cured and most cancer related deaths are after subsequent cancers in other organs. Aspirin reduces the incidence of colorectal and other cancers in LS. Immunotherapy increases the host immune system’s capability to destroy MSI cancers. Colonoscopy surveillance, aspirin prevention and immunotherapy represent major steps forward in personalized precision medicine to prevent and cure inherited MSI cancer.

Authors: Pal Møller, Toni T. Seppälä, Aysel Ahadova, Emma J. Crosbie, Elke Holinski-Feder, Rodney Scott, Saskia Haupt, Gabriela Möslein, Ingrid Winship, Sanne W. Bajwa-ten Broeke, Kelly E. Kohut, Neil Ryan, Peter Bauerfeind, Laura E. Thomas, D. Gareth Evans, Stefan Aretz, Rolf H. Sijmons, Elizabeth Half, Karl Heinimann, Karoline Horisberger, Kevin Monahan, Christoph Engel, Giulia Martina Cavestro, Robert Fruscio, Naim Abu-Freha, Levi Zohar, Luigi Laghi, Lucio Bertario, Bernardo Bonanni, Maria Grazia Tibiletti, Leonardo S. Lino-Silva, Carlos Vaccaro, Adriana Della Valle, Benedito Mauro Rossi, Leandro Apolinário da Silva, Ivana Lucia de Oliveira Nascimento, Norma Teresa Rossi, Tadeusz Dębniak, Jukka-Pekka Mecklin, Inge Bernstein, Annika Lindblom, Lone Sunde, Sigve Nakken, Vincent Heuveline, John Burn, Eivind Hovig, Matthias Kloor, Julian R. Sampson, Mev Dominguez-Valentin

Date Published: 1st Dec 2023

Publication Type: Journal

Mathematical modeling of Lynch syndrome carcinogenesis

Abstract (Expand)

Cancer is one of the leading causes of disease-related death worldwide. In recent years, large amounts of data on cancer genetics and molecular characteristics have become available and accumulated … with increasing speed. However, the current understanding of cancer as a disease is still limited by the lack of suitable models that allow interpreting these data in proper ways. Thus, the highly interdisciplinary research field of mathematical oncology has evolved to use mathematics, modeling, and simulations to study cancer with the overall goal to improve clinical patient care. This dissertation aims at developing mathematical models and tools for different spatial scales of cancer development at the example of colorectal cancer in Lynch syndrome, the most common inherited colorectal cancer predisposition syndrome. We derive model-driven approaches for carcinogenesis at the DNA, cell, and crypt level, as well as data-driven methods for cancer-immune interactions at the DNA level and for the evaluation of diagnostic procedures at the Lynch syndrome population level. The developed models present an important step toward an improved understanding of hereditary cancer as a disease aiming at rapid implementation into clinical management guidelines and into the development of novel, innovative approaches for prevention and treatment.

Author: Saskia Haupt

Date Published: 28th Apr 2023

Publication Type: Doctoral Thesis

A “two-in-one hit” model of shortcut carcinogenesis in MLH1 Lynch syndrome carriers

Abstract

Not specified

Authors: Aysel Ahadova, Albrecht Stenzinger, Toni Seppälä, Robert Hüneburg, Matthias Kloor, Hendrik Bläker, Jan-Niklas Wittemann, Volker Endris, Leonie Gerling, Veit Bertram, Marie Theres Neumuth, Johannes Witt, Sebastian Graf, Glen Kristiansen, Oliver Hommerding, Saskia Haupt, Alexander Zeilmann, Vincent Heuveline, Daniel Kazdal, Johannes Gebert, Magnus von Knebel Doeberitz, Jukka-Pekka Mecklin, Jacob Nattermann

Date Published: 11th Mar 2023

Publication Type: Journal

Mortality by age, gene and gender in carriers of pathogenic mismatch repair gene variants receiving surveillance for early cancer diagnosis and treatment: a report from the prospective Lynch syndrome database

Abstract

Not specified

Authors: Mev Dominguez-Valentin, Saskia Haupt, Toni T. Seppälä, Julian R. Sampson, Lone Sunde, Inge Bernstein, Mark A. Jenkins, Christoph Engel, Stefan Aretz, Maartje Nielsen, Gabriel Capella, Francesc Balaguer, Dafydd Gareth Evans, John Burn, Elke Holinski-Feder, Lucio Bertario, Bernardo Bonanni, Annika Lindblom, Zohar Levi, Finlay Macrae, Ingrid Winship, John-Paul Plazzer, Rolf Sijmons, Luigi Laghi, Adriana Della Valle, Karl Heinimann, Tadeusz Dębniak, Robert Fruscio, Francisco Lopez-Koestner, Karin Alvarez-Valenzuela, Lior H. Katz, Ido Laish, Elez Vainer, Carlos Vaccaro, Dirce Maria Carraro, Kevin Monahan, Elizabeth Half, Aine Stakelum, Des Winter, Rory Kennelly, Nathan Gluck, Harsh Sheth, Naim Abu-Freha, Marc Greenblatt, Benedito Mauro Rossi, Mabel Bohorquez, Giulia Martina Cavestro, Leonardo S. Lino-Silva, Karoline Horisberger, Maria Grazia Tibiletti, Ivana do Nascimento, Huw Thomas, Norma Teresa Rossi, Leandro Apolinário da Silva, Attila Zaránd, Juan Ruiz-Bañobre, Vincent Heuveline, Jukka-Pekka Mecklin, Kirsi Pylvänäinen, Laura Renkonen-Sinisalo, Anna Lepistö, Päivi Peltomäki, Christina Therkildsen, Mia Gebauer Madsen, Stefan Kobbelgaard Burgdorf, John L. Hopper, Aung Ko Win, Robert W. Haile, Noralane Lindor, Steven Gallinger, Loïc Le Marchand, Polly A. Newcomb, Jane Figueiredo, Daniel D. Buchanan, Stephen N. Thibodeau, Magnus von Knebel Doeberitz, Markus Loeffler, Nils Rahner, Evelin Schröck, Verena Steinke-Lange, Wolff Schmiegel, Deepak Vangala, Claudia Perne, Robert Hüneburg, Silke Redler, Reinhard Büttner, Jürgen Weitz, Marta Pineda, Nuria Duenas, Joan Brunet Vidal, Leticia Moreira, Ariadna Sánchez, Eivind Hovig, Sigve Nakken, Kate Green, Fiona Lalloo, James Hill, Emma Crosbie, Miriam Mints, Yael Goldberg, Douglas Tjandra, Sanne W. ten Broeke, Revital Kariv, Guy Rosner, Suresh H. Advani, Lidiya Thomas, Pankaj Shah, Mithun Shah, Florencia Neffa, Patricia Esperon, Walter Pavicic, Giovana Tardin Torrezan, Thiago Bassaneze, Claudia Alejandra Martin, Gabriela Moslein, Pål Moller

Date Published: 1st Mar 2023

Publication Type: Journal

Machine Learning Methods Improve Specificity in Newborn Screening for Isovaleric Aciduria

Abstract (Expand)

Isovaleric aciduria (IVA) is a rare disorder of leucine metabolism and part of newborn screening (NBS) programs worldwide. However, NBS for IVA is hampered by, first, the increased birth prevalence due …prevalence due to the identification of individuals with an attenuated disease variant (so-called “mild” IVA) and, second, an increasing number of false positive screening results due to the use of pivmecillinam contained in the medication. Recently, machine learning (ML) methods have been analyzed, analogous to new biomarkers or second-tier methods, in the context of NBS. In this study, we investigated the application of machine learning classification methods to improve IVA classification using an NBS data set containing 2,106,090 newborns screened in Heidelberg, Germany. Therefore, we propose to combine two methods, linear discriminant analysis, and ridge logistic regression as an additional step, a digital-tier, to traditional NBS. Our results show that this reduces the false positive rate by 69.9% from 103 to 31 while maintaining 100% sensitivity in cross-validation. The ML methods were able to classify mild and classic IVA from normal newborns solely based on the NBS data and revealed that besides isovalerylcarnitine (C5), the metabolite concentration of tryptophan (Trp) is important for improved classification. Overall, applying ML methods to improve the specificity of IVA could have a major impact on newborns, as it could reduce the newborns’ and families’ burden of false positives or over-treatment.

Authors: Elaine Zaunseder, Ulrike Mütze, Sven F. Garbade, Saskia Haupt, Patrik Feyh, Georg F. Hoffmann, Vincent Heuveline, Stefan Kölker

Date Published: 1st Feb 2023

Publication Type: Journal

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