ACCELERATE

ACCELERATE is a TRANSCAN-3 funded project which will develop an innovative precision medicine platform designed to transform the treatment of pancreatic cancer through personalized therapy. By integrating patient-derived tumor organoids, molecular profiling, clinical data, and advanced artificial intelligence, the project identifies the most effective drug combinations for each individual patient. This hybrid approach combines laboratory-based functional testing with deep learning models to deliver real-time, clinically actionable treatment recommendations. ACCELERATE aims to overcome current limitations in pancreatic cancer care by enabling faster, more accurate, and patient-specific therapeutic decisions.

Why ACCELERATE

Pancreatic cancer remains one of the most lethal malignancies worldwide, ranking among the leading causes of cancer-related death. Despite therapeutic advances, prognosis remains extremely poor, with a five-year survival rate of approximately 13%. Most patients are diagnosed at an advanced stage and have limited access to targeted therapies due to the scarcity of actionable genetic alterations. Current treatment options rely mainly on cytotoxic chemotherapy. Beyond first-line regimens, few effective alternatives are available, and surgery remains the only curative option for a minority of patients. As a result, many patients experience limited benefit and significant treatment-related toxicity. There is an urgent need for more precise, individualized therapeutic strategies.

The ACCELERATE approach

Precision oncology has traditionally focused on genomic profiling to guide treatment decisions. However, genomic data alone often fail to predict therapy response, as many clinically relevant cancer behaviors are driven by non-genetic mechanisms, including epigenetic regulation and tumor microenvironment interactions.

Functional Precision Medicine (FPM) has emerged as a promising complementary approach. By directly testing drugs on patient-derived tumor cells, FPM provides dynamic and actionable information on drug sensitivity. Advances in three-dimensional culture systems, particularly patient-derived organoids (PDOs), have strengthened this approach by faithfully reproducing tumor biology and treatment responses.

At the same time, combination therapies have demonstrated superior clinical outcomes compared to monotherapies. However, the vast number of possible drug combinations makes experimental testing impractical. High-throughput screening is costly and limited in scale, while many mechanisms underlying successful combinations remain poorly understood.

Computational and artificial intelligence-based methods have been developed to address this challenge. Existing models can predict drug synergy in cell lines but lack personalization and limited clinical relevance. Few approaches can accurately predict dose-specific and patient-specific combination efficacy.

ACCELERATE impacts

• Improve treatment effectiveness and patient survival.
• Reduce unnecessary toxicity by avoiding ineffective therapies.
• Enable more efficient and personalized clinical trial design.
• Accelerate biomarker discovery and understanding of resistance mechanisms.
• Establish a scalable model for precision oncology applicable beyond pancreatic cancer.
• Support clinicians with evidence-based decision tools that improve treatment effectiveness and reduce unnecessary toxicity.

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