Case study
May 16, 2023

Case study: Stratification biomarker

Authors
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Case study
May 16, 2023

Case study: Stratification biomarker

Authors
No items found.
About Owkin

Owkin is an AI biotechnology company that uses AI to find the right treatment for every patient. We combine the best of human and artificial intelligence to answer the research questions shared by biopharma and academic researchers. By closing the translational gap between complex biology and new treatments, we bring new diagnostics and drugs to patients sooner.

We use AI to identify new treatments, de-risk and accelerate clinical trials and build diagnostic tools. Using federated learning, a pioneering collaborative AI framework, Owkin enables partners to unlock valuable insights from siloed datasets while protecting patient privacy and securing proprietary data.

Owkin was co-founded by Thomas Clozel MD, a former assistant professor in clinical onco-hematology, and Gilles Wainrib, a pioneer in the field of machine learning in biology, in 2016. Owkin has raised over $300 million and became a unicorn through investments from leading biopharma companies (Sanofi and BMS) and venture funds (Fidelity, GV and BPI, among others).

Case study: Stratification biomarker

Biomarker of response to immunotheraphy in NSCLC

Stratification biomarker
Histology Data
Clinical Data
NSCLC
Stratification biomarker
Histology Data
Clinical Data
NSCLC
+15
Our multimodal prognostic model significantly outperforms current FDA-approved biomarkers (PD-L1 expression) in predicting both PFS and OS with an increase of 15 points on the C-index.
Context

In non-small cell lung cancer (NSCLC), immunce checkpoint inhibitors (ICIs) that block PD-1 or PD-L1 checkpoint proteins obtain durable responses, but only 20% to 30% of patients respond to this treatment. Today, PD-L1 expression detected by IHC is a widely used, but imperfect, biomarker.

Methods

Owkin has developed AI-driven biomarker models to predict treatment response to ICIs and prognosis in NSCLC patients from clinical and histological data.

Results

Our multimodal prognostic model significantly outperforms current FDA-approved biomarkers (PD-L1 expression) in predicting both progression-free (PFS) and overall survival (OS) with an increase of 15 points on the C-index.

Impact

Pharma companies can use this model to select high-value subgroups of patients that are most likely to respond to the ICI being tested, therefore improving the statistical power of the trial.

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