The Cancer SENESCopedia: A delineation of cancer cell senescence

Abstract

Cellular senescence is characterized as a stable proliferation arrest that can be triggered by multiple stresses. Most knowledge about senescent cells is obtained from studies in primary cells. However, senescence features may be different in cancer cells, since the pathways that are involved in senescence induction are often deregulated in cancer. We report here a comprehensive analysis of the transcriptome and senolytic responses in a panel of 13 cancer cell lines rendered senescent by two distinct compounds. We show that in cancer cells, the response to senolytic agents and the composition of the senescence-associated secretory phenotype are more influenced by the cell of origin than by the senescence trigger. Using machine learning, we establish the SENCAN gene expression classifier for the detection of senescence in cancer cell samples. The expression profiles and senescence classifier are available as an interactive online Cancer SENESCopedia.

Keywords: ABT-263; SASP; SENCAN; SENESCopedia; cancer; cell cycle; gene expression classifier; senescence; senolytics; transcriptome profiling.

Graphical abstract

Figure 1

Senescence induction by alisertib or etoposide in a panel of 13 cancer cell lines (A–C) A549 and HCT116 were cultured for 7 days with the indicated alisertib or etoposide concentrations using (A) colony formation assay and (B) cell proliferation curves (obtained with IncuCyte). Data represent the mean ± SEM of triplicate wells and are representative of two independent experiments. (C) Representative images of SA-β-gal staining from three independent experiments (quantification in D). Scale bar: 100 μm. (D) Quantification of SA-β-gal-positive cells for cells treated for 7 days with DMSO, alisertib, or etoposide (images are shown in C and Figure S1). Bars represent the mean ± SEM. Data are obtained in biological triplicate and analyzed with a two-sided Student’s t test. ^∗p < 0.01. (E) Venn diagram of gene sets from hallmark gene signatures for senescence (Fridman and Casella) and DEGs in senescence (Purcell and Hernandez). Up- and downregulated genes are pooled for Fridman and Casella. (F) RNA-seq was performed on 13 cell lines in parental and treated cells. Data were analyzed with single-sample gene set enrichment analysis (ssGSEA) for four gene sets associated with senescence. Delta ssGSEA scores represent the difference between parental and senescent samples. Values were normalized per gene set. Ali, alisertib; Eto, etoposide.

Figure 2

Senescent cancer cells respond differently to senolytic ABT-263 (A) Schematic outline of the experimental procedures used in (B)–(D). Cells were treated with senescence-inducing concentrations of alisertib or etoposide for 7 days. Senescent cells and parental cells were reseeded in 96 wells and cultured with increasing concentrations of ABT-263. After 5 days, cell viability was measured with Cell Titer Blue (CTB) assay. SEN Ali, alisertib-induced senescent; SEN Eto, etoposide-induced senescent. (B) Dose-response curves and senolytic indexes for ABT-263 of a responsive and unresponsive cell line. Values represent the mean ± SEM of three independent biological experiments. (C) Bar graph of ABT-263 IC₅₀ values extrapolated from dose-response curves presented in (B) and Figure S3. Data were analyzed with two-way ANOVA and post hoc Tukey test. ^∗p adjusted < 0.01. (D) Pearson correlation analysis for the ABT-263 logIC₅₀ values of SEN Eto and SEN Ali. Each dot represents a cell line. r, Pearson correlation coefficient. (E) Representative images of SA-β-gal staining from three technical replicates. Scale bar: 100 μm. (F) Quantification of SA-β-gal-positive and SA-β-gal-negative cells from SA-β-gal staining as depicted in (E). (G) Spearman rank correlation analysis for 13 cancer cell lines. Each dot represents a cell line with its corresponding foldchange of SA-β-gal-positive cells for ABT-263 versus DMSO (x axis; counts are shown in F and Figure S4) and ABT-263 IC₅₀ value (y axis). The names of the most responsive and unresponsive cell lines are presented. r, Spearman rank correlation coefficient; Log2FC, log2 fold change.

Figure 3

Gene expression in senescent cancer cells changes over time and is independent of alisertib or etoposide trigger (A–E) Volcano plots exhibit the results from edgeR paired differential expression analyses. Each dot represents a gene with its corresponding mean Log2FC (x axis) and Benjamini-Hochberg corrected p value (–log10, y axis). Black dots illustrate DEGs, using a cutoff of p < 0.01 and Log2FC > 1 or < −1. (F) Outline of samples that were used in the DGEA. The paired samples of SEN Eto, SEN Ali, and SEN Ali later contained 13, 13, and 9 cancer cell lines, respectively. Most significant DEGs were identified using adjusted p value < 1 × 10⁻⁸ and Log₂FC >1 or <−1). (G and H) Results of GSEAs for senescent versus control samples (presented in F). Lollipop chart shows the normalized enrichment scores (ES), where dot color indicates significance level, and dot size represents the leading edge, a measure for the number of genes that contribute to the enrichment of the gene set. (G) Top 20 enriched GSEA hallmark gene sets ranked based on normalized ES. Direction of ES indicates negative or positive enrichment in senescent samples. EMT, epithelial-to-mesenchymal transition. (H) Top 20 overrepresented GSEA Gene Ontology gene sets, ranked based on normalized ES. ES is non-directional. Sig. trans., signal transduction; Repl.-indep., replication independent; Reg., regulation; Neg., negative; Pos., positive; DDR, DNA damage response.

Figure 4

SASP gene expression is heterogeneous among senescent cancer cells (A) Heatmap representing supervised hierarchical clustering of the gene expression of 62 DEGs encoding secreted protein. (B) Beeswarm plots for the gene expression of IL6 and CXCL8 genes. Each dot represents a cell line.

Figure 5

The senescence phenotype is more influenced by cell type than senescence trigger (A) Quantification of SA-β-gal-positive cells for cells treated for 7 days with DMSO, doxorubicin, or PF-06873600 (PF) (images are shown in Figure S5A). Bars represent the mean ± SEM. Data are obtained in triplicate and analyzed with a two-sided Student’s t test. ^∗p < 0.0001. (B) Western blot for senescence markers for cells treated for 7 days. p, parental; D, doxorubicin. (C) Dose-response curves for ABT-263. Values represent the mean ± SEM of three independent experiments. (D) Bar graph of ABT-263 IC₅₀ values extrapolated from dose-response curves presented in (C). Data were analyzed with two-way ANOVA and post hoc Tukey test. ^∗ p adjusted < 0.01. (E and F) Normalized mRNA expression determined by qPCR. Expression is relative to GAPDH and normalized to A549 parental. (G) Luciferase luminescence measurements derived from cells transduced with NF-κB reporter. (H) Normalized mRNA expression determined by qPCR. Expression is relative to GAPDH and normalized to A549 parental. In (D)–(H), values represent the mean ± SEM of three independent experiments. ^∗p < 0.01. Dots represent the mean of technical duplicates. SEN Ali, alisertib-induced senescent; SEN Eto, etoposide-induced senescent; SEN Dox, doxorubicin-induced senescent; SEN PF, PF-06873600-induced senescent.

Figure 6

Validation of SENCAN elastic net classifier for cancer cell senescence (A) Singscores for Fridman and Casella gene expression signatures. Each dot represents a cell line. CCLE, Cancer Cell Line Encyclopedia. (B) SENCAN scores in cross-validation; all samples of one cell line were left out, and the elastic net was trained on the remaining cell lines. (C) SENCAN scores of external validation samples from senescent cancer cells (GEO:GSE110028; GEO:GSE129182; GEO:GSE121276; GEO:GSE102639; GEO:GSE158743). (D) Senescence scores of 25 normal cells of Casella et al. (2019) (GEO:GSE130727). (E) SENCAN classifier performance in cross-validation (in B) and external validation sets (in C and D).