Identification of prostate cancer modifier pathways using parental strain expression mapping

Abstract

Inherited genetic risk factors play an important role in cancer. However, other than the Mendelian fashion cancer susceptibility genes found in familial cancer syndromes, little is known about risk modifiers that control individual susceptibility. Here we developed a strategy, parental strain expression mapping, that utilizes the homogeneity of inbred mice and genome-wide mRNA expression analyses to directly identify candidate germ-line modifier genes and pathways underlying phenotypic differences among murine strains exposed to transgenic activation of AKT1. We identified multiple candidate modifier pathways and, specifically, the glycolysis pathway as a candidate negative modulator of AKT1-induced proliferation. In keeping with the findings in the murine models, in multiple human prostate expression data set, we found that enrichment of glycolysis pathways in normal tissues was associated with decreased rates of cancer recurrence after prostatectomy. Together, these data suggest that parental strain expression mapping can directly identify germ-line modifier pathways of relevance to human disease.

Fig. 1.

Genetic differences of six inbred mice are reflected by expression differences of VP. (A) The genes most highly expressed for each strain were selected by one-versus-all. The expression of each probe set (rows) in each sample (columns) is represented by the number of standard deviations above (red) or below (blue) the mean for that probe set across all samples. (B) A 4 × 3 self-organizing map separates all four samples from each strain into distinct strain-specific clusters. Shown in each cell is the mean (black diamonds) and expression range (red lines) of each of 2,212 genes used for sample clustering. For each cluster, the majority of average gene expressions are not discernable and formed a thick black line at the base of each cell, whereas the distinguishable black diamonds form different patterns for each cell (cluster).

Fig. 2.

Introduction of the AKT1 transgene into F₁ mice with FVBxB6 background results in increased BrdU incorporation in the VP. (A–D) Representative images of BrdU incorporation in the VPs of FVB, FVB-AKT1, FB6-WT, and FB6-AKT1 mice, respectively. FB6, F₁(FVB-AKT1 × C57BL/6). (Scale bar, 50 μm.) (E) Quantitation of BrdU levels; the data are expressed as means ± SE. The number of mice analyzed for each group is indicated. Δ2 = μ_FB-AKT1 − μ_FVB-AKT1; Δ1 = μ_FB-wt − μ_FVB-wt. By using a two-way complete ANOVA model to compare the six group means, the following statistics were found: *, P = 0.0014 (FB6 vs. FVB); **, P < 0.0001 (FB6-AKT1 vs. FVB-AKT1); ***, P = 0.0153 (Δ2 vs. Δ1); ****, P < 0.002 (FVB-AKT1 vs. FVB); *****, P < 0.00001 (FB6-AKT1 vs. FB6).

Fig. 3.

Variation in BrdU incorporation among FVB-AKT1 and five F₁ transgenic mice. (A–F) Representative images of BrdU incorporation in the transgenic VPs of FAK [F₁(FVB-AKT1 × AKR)] (A); FSW [F₁(FVB-AKT1 × SWR)] (B); F129 [F₁(FVB-AKT1 × 129×1)] (C); FVB (FVB-AKT1) (D); FBc [F₁(FVB-AKT1 × BALB/c)] (E); and FB6 (F) mice. (Scale bar, 50 μm.) (G) Quantitation of BrdU levels per ×40 field; the data are expressed as means ± SE. The number of mice in each group is indicated.

Fig. 4.

Identification of candidate genes with significant positive or negative correlation to the BrdU profile. See Fig. 1A for the heat map method. The ideal proliferation index is shown in the first row. The genes are ranked by the descending Pearson coefficient. Shown in the upper half is the top 20 probe sets positively correlated, whereas the lower half shows the top 20 probe sets negatively correlated with the BrdU index. Probe sets that passed Bonferroni P ≤ 0.05 are in bold characters.

Fig. 5.

The differential expression of the glycolysis pathway is most prominently associated with AKT1-induced proliferation. (A) The significant gene sets enriched for the BrdU profile ordered by increasing normalized enrichment score (NES). The glycolytic gene sets and Hif-1 target gene set are highlighted in blue, and the Egfr gene set is highlighted in red. (B) Leading-edge analysis of 14 negatively enriched pathways passing the false discovery rate threshold of 0.1. The presence of a probe set in a given predefined gene set is indicated in red, and the absence of a probe set is indicated in white. Seven glycolysis gene sets, indicated in blue, are grouped into one clustered by the core leading-edge genes they share. The blue square points out the shared core genes (SI Fig. 10). (C) Validation of differential expression of core glycolysis genes in the VP by using quantitative RT-PCR. Expression values are shown relative to that of FVB mice. Ldhb, lactate dehydrogenase b; Pgk1, phosphoglycerate kinase 1; Eno1, enolase 1. The Pearson coefficient to the ideal BrdU index is shown for each gene.

Fig. 6.

High germ-line glycolysis gene expressions associate with nonrecurrent outcome. (A) Higher expression of glycolysis genes in the normal human prostate associates with nonrecurrent patient outcome. See Fig. 1A for the heat map method. The red and blue circles below the heat map indicate high and low expression, respectively, of all glycolysis genes in the corresponding sample. The contingency table below summarizes the result. Fisher's exact test: P = 0.04. (B) The significant (nominal P value, <0.05) gene sets enriched for nonrecurrence in human normal prostate expression profiles. The gene sets are shown along the y axis ordered by NES, and NES is shown along the x axis. (C) MAP00010 is significantly enriched for the nonrecurrent phenotype in the second data set (17). The enrichment score running curve is shown in red, and positions of each gene in this set among all genes are shown as blue vertical bars. (D) Differential activation of glycolytic gene expression in FVB-AKT1 mice when compared with inbred (eighth generation) B6-AKT1 mice. All probe sets for glycolysis genes were compared by t test. Shown are those significantly different between FVB-AKT1 and B6-AKT1 (P < 0.05) (data are depicted as in Fig. 1A).