The Science of Predictive Proteomics in Oncology

A patented proprietary approach to personalized cancer care

We’ve developed a proprietary, AI-ready database built from unique tumor signatures — enabling a new level of precision in oncology.

Unlike conventional approaches that rely on generalized reference profiles, our method compares each tumor to the biological baseline of the patient’s own organ. This individualized comparison captures subtle, patient-specific variations that standard models miss.

As a result, we’re uniquely positioned to predict treatment response with greater accuracy — unlocking more effective, personalized therapies for every patient.

Step 1

Analysis

Proteomic analysis of the patient’s tumor and micro-environment profile defines the tumor's specific signature.

Step 2

Proprietary database

Specific tumor signatures from patient cohorts are collected and paired with their observed treatment response, building a foundational dataset that correlates hundreds of profiles to therapy effectiveness.

Step 3

AI-Powered prediction

An icon representing the BeLiver's AI engine.

BeLiver's AI model is trained on this database to understand the complex patterns of treatment response. For a new patient, the model analyzes their signature to predict a treatment response likelihood.

Why focusing on the proteome ?

Proteins, not genes, drive disease biology. Unlike DNA or RNA readouts, proteomics captures the active molecules that reflect real‑time cellular function after genetic and regulatory layers have taken effect.

Because proteins execute cell programs and are the primary drug targets, the proteome is the closest layer to pathology and therapeutic action.

By measuring protein expression and signaling pathway activity, we reveal mechanisms that shape treatment response, enabling deeper insight and more precise interventions.

An illustration of a human with his liver and a therapy targeting the liver.

A validated proof-of-concept in liver cancer

Our initial proof-of-concept studies, conducted on over 80 liver cancer patients, have successfully demonstrated our ability to distinguish responders from non-responders to major systemic therapies including both first- and second-line treatments.

Building on these promising results, we are now expanding our clinical validation through partnerships with seven leading French hospitals, with the goal of confirming our test’s ability to optimize first-line treatment selection and improve patient outcomes.

A platform proven to be scalable across multiple cancers

Our platform has already demonstrated promising results beyond liver cancer, successfully addressing other cancer types where reliable biomarkers are lacking and prognosis remains poor.

By leveraging deep biological profiling and AI-driven insights, the platform adapts to diverse tumor complexities, enabling personalized treatment decisions across a broad spectrum of cancers.

This proven scalability positions us to transform oncology care on a larger scale meeting urgent unmet needs and improving outcomes for many more patients worldwide.