Personalized analysis of human cancer multi-omics for precision oncology

Abstract

Multi-omics technologies, encompassing genomics, proteomics, and transcriptomics, provide profound insights into cancer biology. A fundamental computational approach for analyzing multi-omics data is differential analysis, which identifies molecular distinctions between cancerous and normal tissues. Traditional methods, however, often fail to address the distinct heterogeneity of individual tumors, thereby neglecting crucial patient-specific molecular traits. This shortcoming underscores the necessity for tailored differential analysis algorithms, which focus on particular patient variations. Such approaches offer a more nuanced understanding of cancer biology and are instrumental in pinpointing personalized therapeutic strategies. In this review, we summarize the principles of current individualized techniques. We also review their efficacy in analyzing cancer multi-omics data and discuss their potential applications in clinical practice.

Keywords: Individualized analysis; Multi-omics; Precision oncology.

Fig. 1

Overview of individualized analysis methods for human cancer multi-omics.

Fig. 2

RankComp and Penda methods for individualized analysis. (A) Detection of stable and reversed pairs in RankComp. (B, C) Defining gene lists using the Penda method.

Fig. 3

Assessing the performance of individualized differential analysis methods. (A) Reproducibility of stable pairs in two independent datasets: POP and concordance analysis. (B) Establishing a gold standard for precision evaluation. (C) Step-by-step overview of performance evaluation.