Acute Myeloid Leukemia (AML) presents a significant challenge in hematologic oncology. It is a heterogeneous malignancy characterized by high relapse rates and poor prognosis, especially after multiple lines of therapy. This AML case report highlights the successful application of AI-driven drug sensitivity testing in a heavily pretreated patient. The case demonstrates how functional precision medicine can guide salvage therapy when standard protocols fail.
The Challenge of Multiple Relapsed AML
Patients with multiple relapsed AML often face cumulative chemoresistance. Salvage regimens frequently yield only short-lived responses in these individuals. Consequently, there is an urgent need for individualized strategies to bridge patients to curative treatments like allogeneic hematopoietic stem cell transplantation (allo-HSCT).
A recent case involved a 28-year-old female diagnosed with de novo AML. She initially achieved complete remission (CR) following standard 7+3 induction and consolidation. However, she experienced a relapse in November 2023. Although she achieved a second CR with a mitoxantrone, etoposide, and cytarabine (MEC) regimen, loss to follow-up delayed her transplant.
Implementing Personalized Medicine via Optim.AI™
In July 2024, the patient relapsed again. Clinicians administered a FLAVIDA regimen, achieving remission, but pulmonary tuberculosis further delayed her transplant. By January 2025, she faced a third relapse. At this critical juncture, the medical team employed a functional personalized medicine approach.
They utilized Optim.AI™, a platform that tests live cancer cells ex vivo. This method predicts a patient-specific drug sensitivity profile against a panel of 12 drugs. The process involves several steps:
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Sample Collection: Obtaining bone marrow aspirate.
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Isolation: Separating cancer cells for testing.
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Sensitivity Test: Exposing cells to drugs in varying combinations.
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Analysis: Using Optim.AI™ software to rank treatments based on cell viability.
Study Findings and Clinical Outcome
The AI-driven analysis provided crucial data. It identified a high predicted sensitivity to a combination of venetoclax, etoposide, and cytarabine (VEC). The platform assigns a Normalized Cell Viability (NCV) score, where a lower score indicates higher sensitivity.
The VEC combination achieved an NCV of 0.2402, ranking it as the top 3-drug combination. Other findings included:
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Venetoclax (single): NCV 0.6930
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Etoposide (single): NCV 0.3800
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Etoposide + Fludarabine: NCV 0.3379
Studies suggest that treatments with an NCV of 0.64 or lower predict a partial or complete response. Based on this report, clinicians treated the patient with the Optim.AI™-guided VEC regimen. Subsequently, the patient achieved an MRD-negative remission.
Conclusion
This AML case report illustrates the potential of Optim.AI™-guided drug sensitivity testing in complex clinical scenarios. By identifying effective regimens despite prior failures, personalized medicine platforms can optimize salvage therapy. This approach may successfully bridge patients with multiple relapsed AML to transplantation when conventional algorithms are exhausted. Further studies will validate its role in routine practice.