BeLiver is a French biotech startup 🇫🇷 based in Bordeaux, spun out from leading research institutions (CHU Bordeaux, INSERM/CNRS unit BRIC).
To revolutionize cancer care through personalized medicine. We are initially focused on Hepatocellular Carcinoma (HCC), aiming to significantly improve treatment outcomes by addressing the challenge of predicting patient-specific responses to therapies. Our ultimate goal is to empower oncologists with insightful tools 💡 for more effective and personalized therapeutic decisions.
We develop innovative diagnostic solutions by integrating:
- Deep Analysis of Patient Biology: Analyzing comprehensive biological data derived directly from patient samples to understand the unique characteristics of their disease.
- Advanced Artificial Intelligence: Utilizing machine learning and deep learning models 🤖 to interpret this complex biological data.
Our integrated approach aims to translate deep biological insights into clear, actionable predictions of treatment efficacy for individual patients.
BeLiver is committed to scientific rigor and transparency 🧑🔬.
While our core technology development happens internally, we strongly support reproducible research. Therefore, you may find public repositories here in the future, specifically containing code or supplementary materials associated with our scientific publications 📄.
The science powering the BeLiver technology:
When treating advanced liver cancer (HCC), it's difficult to know beforehand which drug will work best for a specific patient. This study analyzed proteins in tiny tumor samples taken before treatment began. The researchers discovered unique protein patterns ("signatures") that could successfully predict whether patients were likely to respond well to two common treatments (atezolizumab/bevacizumab or sorafenib), suggesting this protein analysis approach could help doctors choose the most effective therapy for individual patients right from the start.
Sometimes, even benign (non-cancerous) liver tumors with the same genetic mutation can look and act differently in different parts, like the edge versus the center. This research used detailed protein analysis to show that the cells at the very edge (rim) of certain benign liver tumors behave much more like normal liver cells than those in the tumor's core, likely due to differences in the local environment like blood flow. This highlights that a tumor's location and surroundings, not just its genes, play a big role in its characteristics.
Accurately identifying the specific subtype of benign liver tumors (HCAs) is important because different types have different risks (like bleeding or turning into cancer). This pioneering work analyzed proteins from patient biopsies to create unique "fingerprints" for each HCA subtype and built a computer-based (machine learning) tool to match new cases against this library. This proteomic approach proved effective for diagnosing HCA subtypes, even in complex cases, and for identifying tumors with a higher risk of becoming malignant, offering a potential new tool to improve patient care.
- Visit our official website: https://www.beliver.fr
- 💼 Follow us on LinkedIn
- 📧 Contact Us: For inquiries, please use the contact form on our website or reach out on LinkedIn.
- 🔵 @beliver-bio.bsky.social on BlueSky
- 🟠 @beliver-bio on HuggingFace
Thank you for your interest in BeLiver! ✨