Predicting and affecting response to cancer therapy based on pathway-level biomarkers

Area of Science:

• Oncology
• Genomics
• Pharmacology

Background:

• Identifying patient-specific biomarkers is crucial for advancing precision oncology.
• Current methods face challenges in predicting therapeutic responses effectively.

Purpose of the Study:

• To develop predictive biomarkers for patient-specific therapeutic strategies in oncology.
• To integrate pathway knowledge with large-scale screening data for biomarker discovery.

Main Methods:

• Coupling pathway knowledge with drug sensitivity, RNAi, and CRISPR-Cas9 screening data from 460 cell lines.
• Analyzing pathway activity levels as predictive biomarkers for protein essentiality and drug sensitivity.

Main Results:

• Pathway activity levels predict the essentiality of 15 proteins, including MAD2L1 in breast cancer.
• Identified predictive biomarkers for sensitivity to 31 compounds, including BCL2 and microtubule inhibitors (MTIs).
• Demonstrated that Bcl-xL inhibition can modulate biomarker pathways and re-sensitize lung cancer to MTI therapy.

Conclusions:

• Pathway-based biomarkers offer a promising approach for precision medicine.
• The findings support the use of pathways to uncover numerous predictive biomarkers for cancer treatment.