Cell Fate Engineering and Disease Modeling

  • Cell and Tumor Biology
  • Junior Research Group
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Dr. Moritz Mall

One of the most exciting concepts in biology is the plasticity of cell fate that allows cellular identity to be reset. Strikingly, this plasticity is essential for normal development, but several human diseases are also associated with unwanted changes in cell identity.

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Our Research

Dedifferentiation and adoption of stem cell-like properties for example are hallmarks of cancer and aberrant gene expression is linked to neuropsychiatric diseases. Investigating the mechanisms that safeguard cell identity will therefore provide new opportunities to understand and treat these devastating diseases.

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Description

Cell fate engineering enables researchers to change the identity of cells from one type to another. It allows for example to take blood or skin cells from a patient suffering autism and to reprogram them into functional human neurons in order to study the disease in the culture dish. This revolutionizing technology offers a novel platform to analyze cell identity and disease in cells that are normally not available, such as patient brain cells (Mall and Wernig, 2017; Yang, 2017). Our work showed that active repression of unwanted genetic programs is important to safeguard the identity of neurons (Treutlein, Nature 2016; Mall, Nature 2017). Since the factors involved are also linked to mental disorders and brain malignancies we will investigate the exciting possibility how loss of neuronal identity can contribute to these devastating diseases.

Projects

Universitäre KI-Forschung zur Entwicklung einer Software als Medizinprodukt für die klinische Patientenversorgung am Beispiel eines Assistenzsystems für die Hautkrebsdiagnostik 

Weltweit wurden im Jahr 2020 rund 325.000 Fälle des Melanoms diagnostiziert, ca. 60.000 Menschen sind letztlich daran gestorben. Während einige Melanome bereits im Frühstadium ein aggressives Verhalten zeigen, steigt die Wahrscheinlichkeit einer Metastasierung mit zunehmender Tumordicke an. Folglich ist eine schnelle und präzise Identifizierung des Melanoms von immenser Bedeutung. 

Die Frühdiagnose ist jedoch selbst für erfahrene DermatologInnen nicht einfach, da sich Melanome und atypische Nävi häufig morphologisch überschneiden. Die diagnostische Herausforderung, Melanome frühzeitig zu erkennen und dabei gleichzeitig die Überdiagnose (Falsch-Positiv-Rate) zu minimieren, erfordert daher die Entwicklung fortschrittlicher Diagnosesysteme. In diesem Kontext zeigten insbesondere sogenannte Deep Neural Networks bei der Klassifizierung verdächtiger dermatoskopischer Bildaufnahmen vergleichbare oder sogar bessere Ergebnisse als erfahrene DermatologInnen. 

Was bislang jedoch fehlt, ist die Translation dieser vielversprechenden Forschungsergebnisse in die klinische Routine, um einen realen Nutzen für PatientInnen, ÄrztInnen und das Gesundheitssystem zu schaffen. Vor diesem Hintergrund wird im Rahmen des sKIn-Projekts modellhaft ein auf künstlicher Intelligenz (KI) basierendes Assistenzsystem für die Melanomdiagnostik weiterentwickelt une unter Berücksichtigung der europäischen Medizinprodukteverordnung (MDR) aus der universitären Forschung zur Marktfähigkeit gebracht. Diese erklärbare KI wird weiterhin gemeinsam mit einem renommierten Dermatoskophersteller in digitale Dermatoskope integriert, um so flächendeckend eine Integration in die Hautkrebsscreening-Untersuchungen zu ermöglichen. Auf diese Weise wird eine Verbesserung der Melanomdiagnostik und somit realer Mehrwert für PatientInnen, ÄrztInnen sowie das Gesundheitssystem geschaffen. 

Gleichzeitig dient das sKIn-Projekt als Blaupause für andere Forschungseinrichtungen und soll perspektivisch die Translation KI-basierter Software erleichtern. Hierfür werden konkrete Handlungsempfehlungen erarbeitet und Anregungen für die weitere Ausgestaltung regulatorischer und gesundheitspolitischer Rahmenbedingungen aufbereitet.

Team

A team of 20 people from the fields of medicine, molecular biology and computer science / data science focuses on the identification of relevant patterns in patient data as well as increased explainability and security of artificial intelligence decisions.

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    Dr. Moritz Mall

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Future Outlook

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Beschreibung (Bildunterschrift)

Selected Publications

2022 - Neuro-Oncology

MEOX2 homeobox gene promotes growth of malignant gliomas

2020 - Nature Cell Biology

Pro-neuronal activity of Myod1 due to promiscuous binding to neuronal genes

Get in touch with us

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Dr. Moritz Mall

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