Personalized regression enables sample-specific pan-cancer analysis.

Area of Science:

• Genomics
• Statistical modeling
• Bioinformatics

Background:

• Inter-sample heterogeneity is crucial for understanding complex biological processes, particularly in cancer genomics.
• Traditional genomic analysis methods often average data, masking individual patient variations and hindering the identification of causal mutations.
• Developing personalized statistical models is essential for accurately analyzing patient heterogeneity.

Purpose of the Study:

• To propose a novel regularizer for achieving patient-specific personalized estimation.
• To address the limitations of population-level analysis in uncovering inter-sample heterogeneity.
• To develop interpretable, patient-specific models for complex diseases.

Main Methods:

• A novel regularizer is proposed that learns two latent distance metrics (between personalized parameters and clinical covariates).
• The method allows data to dictate the structure of these latent distance metrics, avoiding prior assumptions.
• Applied to a pan-cancer gene expression dataset from over 30 cancer types.

Main Results:

• The method successfully learned patient-specific, interpretable models for a pan-cancer gene expression dataset.
• Strong evidence of personalization effects was found both between cancer types and between individual patients.
• Sample-specific aberrations, overlooked by population-level methods, were uncovered.

Conclusions:

• The proposed regularizer offers a promising new path for the precision analysis of complex diseases like cancer.
• Personalized statistical models can effectively capture inter-sample heterogeneity, leading to more accurate biological insights.
• The findings highlight the importance of moving beyond averaged views in genomic analysis.