SAN FRANCISCO — Blank Bio has raised $7.2 million in seed financing and entered a strategic collaboration with Pacific Biosciences to advance RNA foundation models for precision oncology.
The applied AI research lab said it is developing models that learn from tumor transcriptomes, with the goal of improving patient-level prediction in cancer. Proceeds from the financing will support model development, new long-read RNA sequencing datasets and expanded collaborations with pharmaceutical and diagnostics companies.
“Bulk RNA-seq is one of the most clinically accessible and information-rich assays in oncology, but much of its signal is still reduced to simplified gene-level summaries,” said Jonathan Hsu, CEO and Co-Founder of Blank Bio. “Blank Bio was founded to apply foundation models to the full molecular detail contained in each patient’s tumor transcriptome and turn that information into more precise, clinically useful predictions. This financing will support continued model development and partnership expansion, while our collaboration with PacBio will generate the high-resolution long-read RNA data needed to further train and evaluate these models in patient tumor samples.”
Under the collaboration, Blank Bio will generate PacBio HiFi long-read bulk RNA sequencing data from up to 100 fresh frozen patient tumor samples across multiple cancer types. Sequencing will be performed at Seattle Children’s Research Institute, where Kinnex RNA libraries are automated on the SPTLabtech firefly+ platform.
Blank Bio said it will use the data to further train and evaluate its models for oncology applications, including patient stratification, biomarker discovery and clinical interpretation.
“PacBio HiFi long-read sequencing was built to resolve biology that other technologies miss, and nowhere is that more consequential than in the complex transcriptomes of patient tumors,” said David Miller, Global Vice President of Marketing at PacBio. “Blank Bio’s foundation models demonstrate how high-resolution RNA data and machine learning can advance the next generation of precision oncology applications, from biomarkers and diagnostics to clinical trial design.”
Blank Bio said bulk RNA sequencing is increasingly used in oncology research, drug development and clinical care because it can capture the molecular state of tumor samples at a scale and cost suited to clinical use. But the company said standard analysis often reduces RNA sequencing data to gene-level summaries, limiting the ability to capture isoforms, mutational complexity and other patient-specific tumor features.
The seed round included Define Ventures, Leonis Capital, Nova Threshold, Ripple Ventures, SignalFire, Y Combinator and other investors.
“Blank Bio is building at the intersection of two major shifts in biology: the expanding clinical use of RNA-seq and the emergence of foundation models capable of learning complex biological patterns at scale,” said Sahir Raoof, TechBio advisor to SignalFire. “The company brings together deep scientific and technical expertise in RNA biology, machine learning, and oncology, with a platform that has the potential to turn transcriptomic data into a more powerful layer of patient-level insight for drug development and diagnostics.”
The company’s team includes AI scientists and engineers from Recursion, Deep Genomics, DeepMind and Amazon, as well as researchers from Memorial Sloan Kettering Cancer Centre, Stanford and the Vector Institute. Blank Bio said team members have published work in Nature Methods, Nature Genetics, Nature Biotechnology, ICML and NeurIPS, including Orthrus, an academic project on RNA foundation models recently published in Nature Methods.
