Glioblastoma research
Join our AI moonshot to accelerate open science
GBM is one of the most challenging diseases to cure
With new datasets and AI approaches we are on the cusp of successful breakthroughs to solve:
- Highly heterogenous, poorly understood biology
- Standard of Care: Maximally safe surgery + concurrent chemoradiation with adjuvant chemotherapy
- Little improvement and 400+ clinical trials failed since 2005
- New therapies are needed to overcome a cold immune-environment, brain invasion, and major resistance to treatment
Contribute to our key scientific goals
Goal 1
- To identify those most likely to benefit from treatment / most at risk
- To discover new biology of long survivors
AI capabilities & datasets
Develop histology-based survival predictive models, using Owkin’s multiple-instance learning algorithm1
Use interpretable AI models to identify associated histological biomarker(s)
Large dataset (>500 patients) from APHP2 that includes long survivors, with clinical, H&E, and molecular data
Goal 2
- Target identification using novel data modalities (e.g. spatial omics)
- Associate the targets to novel, spatially-defined glioblastoma subtypes
AI capabilities & datasets
Co-optimization and prioritization of targets and subgroups using end-to-end machine learning methods
In-house team of biologists and pharmacologists, and wet lab capabilities to validate ML findings
Multimodal and spatial dataset (100+ patients, 6 modalities) from Owkin’s MOSAIC project
[2] Assistance Publique–Hôpitaux de Paris (AP-HP) is the university hospital trust operating in Paris and its surroundings. With 38 hospitals, it is the largest hospital system in Europe and one of the largest in the world.
Mission milestones
Become a GBM explorer with us
Join our cutting-edge Glioblastoma research: Seeking innovative lab facilities for wet-lab validation of our GBM targets and computing partners to revolutionize Glioblastoma patient care.