Genialis, the RNA biomarker company, announced a new collaboration with Cleveland Clinic, one of the world’s leading academic medical centers, to co-develop AI-driven tools that aim to personalize treatment for patients with pancreatic ductal adenocarcinoma (PDAC)—the most prevalent and deadliest form of pancreatic cancer. The partnership combines Genialis’s advanced computational biology expertise with Cleveland Clinic’s world-class clinical and translational research capabilities to accelerate biomarker discovery and improve outcomes for patients facing one of the most difficult-to-treat malignancies.
A New Frontier in Personalized Cancer Care
At the heart of the collaboration is the Genialis™ Supermodel, a foundation model built upon one of the world’s largest and most diverse RNA-sequencing datasets. This platform applies advanced machine learning to extract insights from transcriptomic data and generate predictive biomarker algorithms. By integrating molecular data with clinical outcomes, the Supermodel is designed to help oncologists identify the most effective therapies for individual PDAC patients.
The goal of this partnership is to develop AI-powered biomarkers that can accurately predict which patients will benefit from specific therapies—including novel targeted drugs and combination regimens—thereby optimizing treatment selection and improving survival rates. These predictive tools will also help accelerate drug discovery and clinical trial design, enabling faster validation of new therapeutic approaches.
Confronting a Deadly Cancer with Limited Options
Pancreatic ductal adenocarcinoma (PDAC) remains one of the most aggressive and lethal cancers worldwide. It accounts for more than 90% of all pancreatic cancer cases, causing over 50,000 deaths annually in the United States. Despite decades of research, the five-year survival rate for PDAC patients remains around 9%, one of the lowest across all cancer types.
Part of the challenge lies in the disease’s biological complexity and late-stage diagnosis. PDAC is often detected only after it has metastasized, leaving few curative options. Moreover, while standard therapies such as chemotherapy and radiation are available, their effectiveness varies widely between patients. Currently, there are no validated biomarkers to predict which patients will respond best to particular treatments, resulting in a trial-and-error approach that consumes precious time.
“For clinicians, time is critical,” said Wen Wee Ma, MD, Director of the Novel Cancer Therapeutics Center and Enterprise Vice Chair for Research at the Cleveland Clinic Cancer Institute. “While standard therapies exist, we lack validated biomarkers to identify which patients are most likely to benefit from various treatment options. Emerging therapies, including KRAS inhibitors, offer new hope but require precise patient selection to achieve meaningful outcomes. Our goal is to bring practical, data-driven tools into the clinic that help us choose the best treatment paths for patients who currently have very few effective options.”
Dr. Ma will serve as an advisor on the collaboration and may receive research funding and royalties for his contributions.
Applying AI to Accelerate Biomarker Discovery
Under the partnership, Genialis and Cleveland Clinic will conduct real-world validation of the Genialis Supermodel’s predictions using Cleveland Clinic’s patient-derived organoid (PDO) center. PDOs—miniaturized, lab-grown tumor models derived directly from patients—enable rapid testing of drug responses under conditions that closely mimic human biology. By comparing AI-generated predictions with actual treatment responses observed in PDOs, the research teams can refine and optimize biomarker algorithms more efficiently than through conventional clinical studies alone.
This iterative process allows for fast, evidence-based improvement of predictive models. The collaboration will explore multiple therapy classes and drug combinations to identify biomarker signatures associated with improved outcomes in PDAC patients.
Targeting KRAS Mutations: A Key Step Forward
More than 90% of PDAC cases harbor mutations in the KRAS gene, which plays a pivotal role in cell growth and division. For decades, KRAS was considered an “undruggable” target, but recent advances in precision oncology have led to the development of KRAS inhibitors (KRASi)—a promising new class of targeted therapies. However, not all patients with KRAS mutations respond equally well to these treatments, highlighting the need for reliable biomarkers to identify likely responders.
Genialis has been at the forefront of KRAS-related biomarker innovation. The company’s Genialis™ krasID, powered by the Genialis Supermodel, is the first biomarker algorithm capable of predicting patient response and clinical benefit to KRAS inhibitors across multiple tissue types and mutation variants.
“Through our work on biomarker algorithms for KRAS inhibitors, Genialis has already made a major investment in understanding the drivers of PDAC,” said Rafael Rosengarten, Ph.D., CEO of Genialis. “Our collaboration with Cleveland Clinic aims to extend these insights to give doctors and patients better tools to combat the disease. This is an important step toward truly data-driven, personalized care in one of the most pressing areas of unmet need.”
From Research to Real-World Clinical Impact
The potential impact of this collaboration extends beyond PDAC. The technologies and methodologies developed through this partnership could serve as a blueprint for applying AI-powered RNA biomarkers to other solid tumors, including lung, colorectal, and ovarian cancers.
The Genialis Supermodel integrates data from preclinical models, clinical trials, and real-world patient samples to generate insights that can guide every stage of the oncology pipeline. In early discovery, it supports compound differentiation and mechanism-of-action studies. In clinical development, it assists with patient stratification, trial design, and combination therapy identification. And in the post-approval phase, it can function as a clinical decision support tool or a companion diagnostic (CDx), helping oncologists match patients to the most effective therapies.
By applying AI to vast, heterogeneous molecular datasets, Genialis is helping bridge the gap between genomic complexity and actionable clinical insights—transforming how precision medicine is practiced.
Toward a Future of Data-Driven Oncology
The collaboration between Genialis and Cleveland Clinic exemplifies the growing convergence of artificial intelligence, molecular biology, and clinical oncology. As cancer treatments become more personalized, the need for predictive biomarkers that connect molecular profiles with therapeutic outcomes is becoming indispensable.
By combining Genialis’s bioinformatics expertise and Cleveland Clinic’s translational research infrastructure, this initiative aims to accelerate the path from discovery to patient benefit—ensuring that precision oncology tools are not only scientifically robust but also clinically actionable.
If successful, the AI-powered biomarkers emerging from this collaboration could revolutionize how pancreatic cancer is diagnosed and treated, turning what has long been a near-uniformly fatal disease into one that can be managed with greater precision, predictability, and hope.
