Istvan Petak, MD #ASCO20 Oncompass Medicine SHIVA01 Trial Update

Istvan Petak, MD #ASCO20 Oncompass Medicine SHIVA01 Trial Update

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Istvan Petak, MD #ASCO20 Oncompass Medicine SHIVA01 Trial Update

Context:
Precision oncology demands that individual molecular pathomechanisms be identified in order to find appropriate tailored treatment strategies for each cancer patient. Owing to the lack of a reproducible, systematic method of clinical decision-making, the integration of complex molecular knowledge into routine clinical practice remains a major challenge. 

Approaches:
We are designing a precision oncology decision support system, the Realtime Oncology Molecular Treatment Calculator (MTC), to provide a structured method for molecular analysis. MTC is a medical information engine based on rules that aggregates and ranks applicable science and clinical data dynamically using currently 26,000 evidence-based associations and reproducible driver scoring algorithms, molecular targets to adapt molecular changes to successful therapies. We used data from the SHIVA01 trial of molecularly focused therapy (Lancet Oncol 2015 16:1324-34) to validate this novel approach and framework. Participant molecular profiles were submitted to MTC, and aggregated evidence level (AEL) values were determined for related targeted therapies, including those used in the SHIVA01 study. 

Reviews:
The MTC production provided a prioritized list of drugs in the patient molecular profile associated with the driver changes, where ranking is based on AEL values. Disease control was experienced in 63 cases (PR: 5, SD: 58) of 113 patients who received targeted therapy with available clinical best-response results, while disease progression occurred in 50 cases. In the sensitive group , the mean AEL score for the therapies used was significantly higher than in the non-responsive group (1512 and 614, respectively (p=0.049)). In 94 cases MTC rated medications higher than those used for therapy. The average difference in AEL between the top-ranked and the used drugs was in inverse association with clinical response, i.e. smaller differences correlated with a better result. 

Findings:
Results show that the aggregation of evidence-based tumor-driver-target-drug associations using this computational tool's structured mathematical algorithms is a promising novel approach to enhancing precision oncology clinical decisions. To explore the full therapeutic potential of this innovative medical approach, further confirmation based on findings from other targeted clinical trials and real-life evidence using more accurate molecular profiles is warranted.

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