Mots-Clés
Cancer
Skin
Spatial
Immunology
Trajectory
Description
SHAKE: Profiling Spatial Heterogeneity in Actinic Keratosis Evolution with Multiplexed Imaging and Deep Learning
Project Overview
This project is part of the SHAKE initiative, aiming to understand how actinic keratosis (AK) progresses toward cutaneous squamous cell carcinoma (cSCC) by exploring spatial and metabolic tissue organization using highly-multiplexed spatial proteomics. This effort aims to identify disease trajectories taken by lesions over the course of the disease, and find targetable metabolic and multicellular vulnerabilities to prevent carcinogenesis. You will play a central role in a pilot study by analyzing state-of-the-art multiplexed ion beam imaging (MIBI) and applying in-house deep learning (DL) tools to quantify and interpret lesion heterogeneity in patients.
You will work with 40-plexed spatial proteomics data acquired from clinical biopsies and apply our DL-based analytical pipeline to assess cell types, metabolic states, and lesion organization. This work will lay the foundation for discovering patient-specific malignant trajectories and potential targets for intervention.
Student Tasks
- Optionally, participate in the sample preparation and image acquisition
- Preprocess and curate high-dimensional MIBI datasets from fixed skin lesions
- Quantify cell types, metabolic marker profiles and spatial organization in single cells
- Run models to derive comprehensive tissue profiles and map malignant trajectories
- Assist in integrating and visualizing data across patients and lesion stages
Learning Opportunities
- Unique opportunity to work together with clinicians, immunologists and computational biologists
- Gain independence, with close guidance from experienced researchers in computational biology and cancer immunology, and space to follow your own research questions.
- Develop a valuable skill set by getting familiar with spatial omics technologies and DL-based computational analyses
- Contribute to an ambitious and innovative project in translational cancer research, with the perspective of shining light into disease mechanisms
Candidates should be curious, motivated, rigorous, and have working knowledge of Python. Prior experience with spatial data or deep learning is a plus, but not required. We welcome students eager to learn. Theses and internships of 4-6 months are preferred.
Hosts: Systems Immunology and Single Cell Biology group at the German Cancer Research Center (DKFZ). Please send your application (including CV and cover letter) to felix.hartmann@dkfz-heidelberg.de and loan.vulliard@dkfz-heidelberg.de