A tailored histology-driven molecular profiling algorithm proposal for salivary gland cancers

Area of Science:

• Oncology
• Genomics
• Translational Research

Background:

• Salivary gland cancers (SGCs) are rare and diverse malignancies with limited treatment options.
• Optimizing molecular profiling (MP) strategies is crucial for effective therapeutic decision-making in SGCs.

Purpose of the Study:

• To define a histology-driven molecular profiling (MP) algorithm for SGCs.
• To optimize testing strategies and support therapeutic decisions for SGC patients.

Main Methods:

• Retrospective analysis of 253 SGC patients undergoing MP (2016-2023).
• Utilized DNA/RNA next-generation sequencing (NGS), immunohistochemistry (IHC), and fluorescence in situ hybridization (FISH) for gene rearrangements and variants.
• Classified SGCs into adenoid cystic carcinomas (AdCCs) and non-AdCCs (low or high aggression).

Main Results:

• MYB/MYBL1 fusions identified in 47% of AdCCs; pathogenic/likely pathogenic variants (P-LPV) were rare.
• P-LPV found in 55% of high-aggression non-AdCCs (e.g., TP53, PIK3CA) and 50% of low-aggression non-AdCCs.
• HER2 positivity detected in 23% of high-aggression non-AdCCs; P-LPV rates varied by HER2 status and aggression level.

Conclusions:

• A tailored, histology-driven MP algorithm for SGCs is proposed.
• This algorithm can optimize genomic testing resources and guide clinical management.
• Supports the clinical implementation of MP for improved SGC patient care.