A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial

Abstract

Precision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology.

The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence

“…AI-based virtual screening supports the identification of candidate compounds with highly specific selectivity for targeted oncogenic driver aberrations and low toxicity. For example, Istvan et al reported an AI-assisted computational method, which is a proprietary technology of Oncompass Medicine Inc., to prioritize potential molecular targeted therapies based on the complex individual molecular profile of the tumor in each patient [ 100 ]. They analyzed the clinical benefits of the digital drug-assignment system using the data from the SHIVA01 precision oncology clinical trial, and showed that the system identified substantial molecular targets with the fitting inhibitors, including in lung cancer patients, such as FMS Related Receptor Tyrosine Kinase 3 mutation with sorafenib and Androgen receptor expression with abiraterone.…”

“…Therefore, establishing new framework for analyzing huge size of omics data, such as academia–industry collaboration and academia–government technological collaboration, will be important as well as the AI development. With regard to radiology and molecular targeted therapies, some academia–industry collaborations have successfully complemented each other [ 45 , 46 , 100 ], and AI-based screening has been accelerated toward clinical applications. Prospectively, these frameworks, which can lead to further progression of inter-industry activities, and medical AI systems could be a detector of microchanges in patients that can go unnoticed by human eyes, and be a selector of suitable treatments for individual patients to support clinicians, resulting in more early intervention and in improving the quality of life of patients.…”

Personalized First-Line Treatment of Metastatic Pancreatic Neuroendocrine Carcinoma Facilitated by Liquid Biopsy and Computational Decision Support

“…Tumor mutational burden (TMB) was low (2 mutations/megabase). * AEL: Aggregated Evidence Level, a computed driver evidence score according to the digital drug assignment system [24].…”

“…All exonic variants were analyzed by our digital drug assignment (DDA) system (RealTime Oncology Treatment Calculator™ v1.64) [24]. Based on the scientific evidence in relation to the molecular profile, the system ranked the phosphatidylinositol-3-kinase (PI3K) inhibitors, alpelisib and copanlisib, and the mTOR inhibitor everolimus as of the highest relevance (Table 2).…”

“…Digital drug-assignment (DDA) is a computational method for precision oncology [24]. DDA helps rapid clinical decisions by assigning a DDA score, the "aggregated evidence level" (AEL) to each genetic alteration and the associated targeted drugs, based on a network of evidence-based associations between all genetic alterations present in the tumor and the associated druggable targets, and targeted therapies.…”