A Computational Framework to Characterize the Cancer Drug Induced Effect on Aging Using Transcriptomic Data

Abstract

Cancer treatments such as chemotherapies may change or accelerate aging trajectories in cancer patients. Emerging evidence has shown that "omics" data can be used to study molecular changes of the aging process. Here, we integrated the drug-induced and normal aging transcriptomic data to computationally characterize the potential cancer drug-induced aging process in patients. Our analyses demonstrated that the aging-associated gene expression in the GTEx dataset can recapitulate the well-established aging hallmarks. We next characterized the drug-induced transcriptomic changes of 28 FDA approved cancer drugs in brain, kidney, muscle, and adipose tissues. Further drug-aging interaction analysis identified 34 potential drug regulated aging events. Those events include aging accelerating effects of vandetanib (Caprelsa®) and dasatinib (Sprycel®) in brain and muscle, respectively. Our result also demonstrated aging protective effect of vorinostat (Zolinza®), everolimus (Afinitor®), and bosutinib (Bosulif®) in brain.

Keywords: aging; cancer drug; drug-aging interaction; pharmacogenomics; transcriptomics.

FIGURE 1

The computational framework to characterize the cancer drug-aging interaction. (A) Identification of aging-associated expression signatures in different tissue types in GTEx database. (B) Identification of drug-induced expression signatures in different normal cells from LINCS L1000 database. (C) Interaction of drug-induced signature and aging-associated signature characterized by specificity, adjusted concordance ratio and enriched functional pathways.

FIGURE 2

Aging-associated gene expression in the GTEx can recapitulate the well-established aging hallmarks. The Normalized Enrichment Score (NES) and False Discovery Rate (FDR) of GSEA analysis are shown for gene sets grouped by different aging hallmarks in each tissue type.

FIGURE 3

The aging-associated genes in normal brain and muscle tissues are highly overlapped with brain and muscle aging phenotypes. (A) GSEA analysis of aging-associated genes using gene sets derived from the Human Phenotype Ontology. (B) Decreased gene expression in normal brain and muscle tissues of aged donors.

FIGURE 4

Aging-associated genes are enriched with cancer drugs induced gene sets in multiple tissue types. (A) NES and FDR of GSEA analysis of treatment-induced gene sets in each tissue type. (B) Cisplatin induced gene set enriched with genes highly expressed in kidney and brain cortex samples of aged donors. (C) CDKN1A expression in kidney and brain cortex tissues of different donor age groups.

FIGURE 5

Drug-induced transcriptomic alterations recapitulate the mechanism of action of the treatment in L1000 data. (A) The number of expression profiles of normal cells treated by the 28 FDA approved in L1000 data. (NC: Not included, SERM: Selective Estrogen Receptor Modulator). (B–E) GSEA analysis of Reactome database gene signatures. (F) Genes in “metabolism of steroids” showed increased expression in HA1E cells after tamoxifen treatment at different dosages. (G) Genes in “cell cycle checkpoints” showed decreased expression in HA1E cells after mitoxantrone treatment at different dosages. Data are presented as means ± SE.

FIGURE 6

The landscape of drug-aging interaction of 28 FDA approved drugs in brain, kidney, adipose, and muscle. (A) The distribution of the correlation between gene expression and the donor’s age in GTEx data. (B) The specificity (x-axis) and log-transformed p-values of the adjusted concordance ratio (y-axis) for the 326 interactions between drug-induced signatures and aging-associated signatures in multiple normal tissue types. A positive y-value represents CR > 1 and indicates aging-accelerating effect. A negative y-value represents CR < 1 and indicates aging-protective effect. Blue/Red dots are significant interactions with S > 0.45, CR < 0.9/CR > 1.1 and p < 0.01. (C) Network of 34 significant drug-aging interactions. Blue and red edges indicate protective and accelerating effects respectively. (D–G) Pathways enriched with overlapped genes between aging-associated signature and drug-induced signature. A positive/negative Enrichr odds ratio (red/blue bar) represents enrichment with up/downregulated genes.