GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in...

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Lung cancer is a leading cause of cancer mortality globally.
• While targeted therapies and immunotherapies show promise, patient responses vary significantly.
• Current driver gene mutation status has limited prognostic and predictive value.

Purpose of the Study:

• To develop a statistical framework, Genomic Aberration-Derived Signature for Patient Stratification (GASPS), for stratifying lung cancer patients.
• To characterize transcriptomic deregulation associated with driver genomic aberrations.
• To improve prognostic risk assessment and treatment efficacy through personalized therapies.

Main Methods:

• Developed GASPS framework to analyze transcriptomic deregulation of driver genomic aberrations.
• Applied GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) dataset to create gene signatures for 38 driver aberrations.
• Validated signatures on independent lung cancer datasets comprising 2,226 patients.

Main Results:

• Driver gene signatures derived by GASPS demonstrated superior prognostic value compared to individual genomic mutations.
• EGFR mutation and amplification signatures showed distinct associations with prognosis, treatment response, and tumor microenvironment immune infiltration.
• STK11 mutation signature, not mutation status alone, predicted response and long-term benefit to immune checkpoint blockade therapy.

Conclusions:

• GASPS provides a robust framework for stratifying lung cancer patients based on transcriptomic deregulation.
• GASPS-derived signatures offer improved prognostic and predictive capabilities over traditional mutation status.
• The GASPS framework is adaptable for various cancer types, enhancing personalized treatment strategies.