A Taxonomy for Assessing Real-World Targeted Cancer Therapy Options in the Context of Broad Genomic Profiling

Area of Science:

• Oncology
• Genomics
• Precision Medicine

Background:

• Broad genomic profiling (BGP) is crucial for identifying molecularly targeted cancer therapies.
• A framework is needed to interpret BGP results for clinical decision-making and population-level impact assessment.

Purpose of the Study:

• To develop and apply the Yale Molecular Alteration Taxonomy for Real-World Individualized Treatments (Y-MATRIX) framework.
• To classify BGP results and assess temporal trends in their actionability for advanced non-small cell lung cancer (aNSCLC).

Main Methods:

• Developed Y-MATRIX to categorize BGP results based on FDA approvals and NCCN Guidelines.
• Applied Y-MATRIX to a nationwide database of aNSCLC patients diagnosed between 2017 and 2023.
• Analyzed changes in the distribution of alteration categories over time.

Main Results:

• Y-MATRIX categorizes results into A (first-line), B (later-line), C (off-label, guideline-concordant), D (off-label, guideline-discordant), E (no actionable alterations), and X (no alterations detected).
• The proportion of patients with category A/B alterations in aNSCLC increased from 15.8% in 2017 to 33.6% in 2023.
• Category C/D findings increased, while category E/X decreased, indicating growing BGP actionability. Younger age, nonsmoking status, and nonsquamous histology were linked to category A alterations.

Conclusions:

• Y-MATRIX provides a comprehensive taxonomy for BGP results, demonstrating increased actionability over time in aNSCLC.
• The rise in category C/D findings suggests complex clinical scenarios with potential for both innovative and inappropriate targeted therapy use.
• The Y-MATRIX schema can aid research into molecular profiling's clinical utility across cancers in the era of precision oncology.