Transforming Cancer Detection

RenovaroCube Introduces Flamingo, a novel AI model based on Fragmentomics

LOS ANGELES and AMSTERDAM, April 30, 2024 (GLOBE NEWSWIRE) -- RenovaroCube, wholly owned subsidiary of Renovaro, Inc. (Nasdaq: RENB), a company at the forefront of AI-driven healthcare innovation, proudly unveils Flamingo, a potentially groundbreaking multi-cancer detection model contributing to its mission to transform early cancer diagnostics. Leveraging ultra-low pass whole genome sequencing (ULP-WGS) of cell-free DNA (cfDNA), Flamingo represents a promising leap forward in the fight against cancer.

Traditional cancer detection methods often fall short in identifying cancers at an early stage when treatment is the most effective. However, Flamingo has the potential to overcome these limitations by harnessing the power of AI to analyze minute amounts of cfDNA data that is highly accurate.

"At RenovaroCube, we believe in pushing the boundaries of possibility," states Daan Vessies, senior scientist at RenovaroCube. "Flamingo undescores our commitment to change cancer diagnostics in a transformative way, ultimately offering clinicians a powerful tool to detect cancer across diverse omic layers."

The Company believes that no single model or molecular modality will reach the requisite sensitivity and specificity throughout the entire patient journey for personalized, precision medicine, from early detection, to predicting the effectiveness of various treatment options, to monitoring the response to therapy within days of starting it, to detecting recurrence at the earliest possible moment. Therefore, our AI/machine learning platform, The Cube, integrates multi-omic data, offering a uniquely comprehensive approach to cancer detection by leveraging a library of trained models for multiple omic layers. One such model Flamingo focuses on is the detection of cancer from ultra-low pass whole genome sequencing (ULP-WGS) cfDNA data using fragmentomics.

Flamingo's development marks a significant milestone in the quest for early cancer detection with RenovaroCube’s engine. By utilizing as few as only 200,000 cell-free DNA fragments per sample, integrating fragment lengths, sequence motifs and employing a meticulously designed neural network, Flamingo achieves remarkable performance in distinguishing cancer from healthy samples.

By augmenting The Cube's arsenal of models operating across various omic layers, Flamingo contributes to the development of non-invasive diagnostics to detect cancer early, enabling timely interventions and improving patient outcomes.