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

Istvan Petak1,2,3, Maud Kamal4, Anna Dirner5

  • 1Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary. istvan.petak.dr@gmail.com.

NPJ Precision Oncology
|June 24, 2021
PubMed

Insights

Artificial intelligence (AI) enhances precision oncology by using a digital drug-assignment (DDA) system to match molecularly targeted agents (MTAs) to individual tumor profiles. This AI approach improves patient outcomes and disease control in cancer treatment.

Area of Science:

  • Oncology
  • Computational Biology
  • Artificial Intelligence

Background:

  • Precision oncology currently relies on matching molecularly targeted agents (MTAs) to single genetic drivers, which is limited by tumor complexity.
  • Tumors possess numerous genetic alterations, complicating the efficacy of traditional precision oncology approaches.

Purpose of the Study:

  • To introduce and evaluate an AI-assisted computational method, the digital drug-assignment (DDA) system.
  • To assess the clinical benefit of the DDA system in prioritizing MTAs based on individual tumor molecular profiles.

Main Methods:

  • Development of the AI-assisted digital drug-assignment (DDA) system.
  • Analysis of clinical outcome data from the SHIVA01 precision oncology trial.
  • Correlation of DDA scores with patient disease control and progression-free survival (PFS).

Main Results:

  • The DDA system assigned significantly higher scores to MTAs in patients with disease control compared to those with progressive disease (1523 vs. 580, P=0.037).
  • Patients receiving MTAs with high DDA scores demonstrated a significantly longer median PFS (3.95 months) than those with low DDA scores (1.95 months, P=0.044).

Conclusions:

  • AI-based systems like DDA show promise for improving precision oncology outcomes.
  • The DDA system offers a novel computational approach to optimize MTA selection for individual cancer patients.

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