Abstract 2554: Development/validation of a molecular and clinical evidence-based algorithm for selecting optimal precision therapeutic strategy for cancer patients - continuous study
Authors: Yuliang Sun, Tobias Meissner, Rachel Elsey, Crystal Hattum, Casey Williams
Journal: Cancer Research (2024)

Abstract Background: The integration of genotyping and genomics in clinical practice has brought about significant advancements in precision oncology. However, a persistent challenge for treating oncologists lies in selecting the most effective patient-specific therapeutic strategy due to the molecular rationale, disease relevance, and patient-specific issues. To address this challenge, we have developed an algorithm that incorporates both molecular and clinical evidence-based criteria to rank therapeutic strategies in order to deliver optimal care and improve outcomes in patients with malignancy. The purpose of this part of our studies was to evaluate the effectiveness and accuracy of our novel algorithm. Methods: History of present illness and comprehensive genomic profiling results of 306 patients with malignant tumors were reviewed by Avera Molecular Tumor Board (MTB) from June 2021 to February 2023. Therapeutic recommendations were provided with the Matching Score that we used in the I-PREDICT study based on molecular matching only as well as with the Ranking Score calculated by our novel algorithm with the criteria not only focusing on molecular matching, but also including disease relevance, patient-specific clinical considerations and treatment availability as weighting factors (Cohort 2). The other 50 patients with previously treated solid tumors reviewed by MTB (before 2018) and treatment recommendations provided with Matching Score only, were used as a control group (Cohort 1). The matching rates from recommendations and treatment outcomes of the patients were then assessed. Results: In Cohort 1, of the 50 patients, 33 patients (66%) received matched therapeutic plans recommended by our MTB. The other 17 patients were not on a matched plan from the MTB. At 12 weeks, 12 patients (36.4% of 33 patients) remained progression free, including 8 patients (24.2%) who were progression free at 6 months. The other 21 patients (63.6% of 33 patients) could not be assessed due to treatment termination per drug toxicity or death before the follow-up time point. The median PFS/overall survival (OS) for patients with a Matching Score &amp;gt; 50% (N = 17) were 7.5/10.5 months, whereas with Matching Score ≤ 50% (N = 16) were 3/7.35 months. In Cohort2, of the 306 patients, 130 (42.5 %) patients have initiated matched therapeutic plans and follow-up is ongoing. Updated results will be presented at the meeting. Conclusion: Our novel molecular and clinical evidence-based algorithm may be used to support oncologists’ decision-making to utilize the most clinically appropriate and effective therapeutic options to benefit patients. Further validation studies and development of a user-friendly computational ranking platform based on the algorithm are planned in order. Citation Format: Yuliang Sun, Tobias Meissner, Rachel Elsey, Crystal Hattum, Casey Williams. Development/validation of a molecular and clinical evidence-based algorithm for selecting optimal precision therapeutic strategy for cancer patients - continuous study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2554.