Logic programming reveals alteration of key transcription factors in multiple myeloma

Area of Science:

• Computational Biology
• Genomics
• Systems Biology

Background:

• Understanding complex diseases like cancer requires integrating diverse biological data.
• Regulatory networks (RN) and gene expression profiles (GEP) offer complementary insights into cellular mechanisms.
• Identifying key molecular players and pathways is crucial for developing targeted therapies.

Purpose of the Study:

• To develop a computational framework combining RN and GEP for improved understanding of multiple myeloma (MM).
• To identify key molecular nodes and potential therapeutic targets by analyzing causal relationships within RNs.
• To investigate the impact of genetic alterations on RNs and their correlation with patient survival.

Main Methods:

• Automatically generated regulatory networks were integrated with gene expression profiles from MM patients.
• A global reasoning approach was applied to the RN causality to identify key nodes.
• Patient-specific models were created using GEP and RNs, incorporating 'repairs' to explain GEP variability, potentially representing mutations.
• Inferred unmeasured protein states and simulated protein perturbations to identify therapeutic targets.

Main Results:

• JUN/FOS and FOXM1 activities were found to be altered in nearly all MM patients analyzed.
• Two significant survival markers for MM patients were identified.
• JUN/FOS activation demonstrated a substantial impact on the overall RN in the context of GEP.
• FOXM1 activation emerged as a potential therapeutic strategy for a specific MM subgroup.

Conclusions:

• The integration of RNs and GEP provides a powerful approach for dissecting disease mechanisms and identifying therapeutic targets.
• Altered JUN/FOS and FOXM1 activities are critical in multiple myeloma pathogenesis.
• Targeting FOXM1 may offer a personalized therapeutic avenue for a subset of MM patients.