When the first whole human genome was sequenced in 2001 it led to high hopes for better outcome for cancer patients if treated with drugs targeted towards the genetic deviations in the tumour. However, interpretation of the deviations impact on cancer development is challenging. Still 50-97% of patient may not respond to such targeted drugs.
Seald develops a unique first in class cancer drug selection tool for each individual patient by including a primary factor: the actual live tumour response to selected drug, assessed by bio-imaging. Our aim is better outcome for cancer patient, improved selection of patients to clinical studies and sustainable use of health expenditure.
Through DIGI-B-CUBE support we aim to establish a scalable secure data platform that allows for collaboration with partners and treatment facilities while leveraging machine learning capabilities enabled by public cloud environment. Innovation challenges include strategies for collecting, storing and processing disperse real-life datasets and using them to develop ML-based algorithms to perform diagnosis. The prototype scopes an end-to-end environment for securely storing and sharing of clinical data. Simple analytics is performed in a public cloud environment. Basic use cases will be implemented consuming available cloud services to ensure little infrastructure setup, limited coding and leverage requirements for storing and processing healthcare information according to the Norwegian Code of Conduct.