Human Breast Cancer Xenograft Model Implicates Peroxisome Proliferator-activated Receptor Signaling as Driver of Cancer-induced Muscle Fatigue

Abstract

Purpose: This study tested the hypothesis that a patient-derived orthotopic xenograft (PDOX) model would recapitulate the common clinical phenomenon of breast cancer-induced skeletal muscle (SkM) fatigue in the absence of muscle wasting. This study additionally sought to identify drivers of this condition to facilitate the development of therapeutic agents for patients with breast cancer experiencing muscle fatigue.

Experimental design: Eight female BC-PDOX-bearing mice were produced via transplantation of tumor tissue from 8 female patients with breast cancer. Individual hind limb muscles from BC-PDOX mice were isolated at euthanasia for RNA-sequencing, gene and protein analyses, and an ex vivo muscle contraction protocol to quantify tumor-induced aberrations in SkM function. Differentially expressed genes (DEG) in the BC-PDOX mice relative to control mice were identified using DESeq2, and multiple bioinformatics platforms were employed to contextualize the DEGs.

Results: We found that SkM from BC-PDOX-bearing mice showed greater fatigability than control mice, despite no differences in absolute muscle mass. PPAR, mTOR, IL6, IL1, and several other signaling pathways were implicated in the transcriptional changes observed in the BC-PDOX SkM. Moreover, 3 independent in silico analyses identified PPAR signaling as highly dysregulated in the SkM of both BC-PDOX-bearing mice and human patients with early-stage nonmetastatic breast cancer.

Conclusions: Collectively, these data demonstrate that the BC-PDOX model recapitulates the expected breast cancer-induced SkM fatigue and further identify aberrant PPAR signaling as an integral factor in the pathology of this condition.

Figure 1:. Establishment of BC-PDOX model.

Analysis of SkM from BC-PDOX mice compared to NSG controls indicates widespread transcriptional reprogramming of SkM transcriptome in response to tumor growth. (A) Schematic diagram outlining establishment of BC-PDOX models (P0) and passaging of tumor to generate study animals (P0-P2). (B) Normalized gene expression heat map showing differential expression patterns of the 50 most differentially expressed genes between BC-PDOX (n = 4) and NSG control mice (n = 4) organized according to unsupervised clustering analysis. (C) Principle component analysis showing that transcriptional profiles of NSG and BC-PDOX SkM cluster separately, indicating that tumor growth induces transcriptional alterations in SkM.

Figure 2:. In Silico Pathway and Transcription Factor Analysis of RNA-Seq Data.

(A) Selected canonical pathways identified by IPA in the transcriptome of BC-PDOX mice. For each pathway listed on the x-axis, the y-axis reports the raw number of differentially expressed genes in the BC-PDOX SkM relative to NSG controls, separated into up- and downregulated transcripts. The y-axis additionally reports the –log(p-value) reported by IPA; –log(p-value) > 1.33 is equivalent to p < 0.05. The figure reports pathways identified by IPA as significantly dysregulated in our model that have been previously implicated in cancer cachexia (left), significantly dysregulated in our model but not previously implicated in cancer cachexia (center), or previously implicated in cancer cachexia but not identified by IPA as significantly dysregulated in our model (right). (B) IPA predicts decreased release of calcium from the sarcoplasmic reticulum in the SkM of BC-PDOX mice resulting in decreased muscle contraction. (C) Complex IV and V of the electron transport chain are predicted by IPA to have decreased activity in the BC-PDOX mice relative to NSG controls, resulting in decreased ATP production. (D) Top ten most highly ranked transcriptional regulators identified by Enrichr as regulating the transcriptional alterations observed in SkM of BC-PDOX and human BC patients where the input gene list was exclusively downregulated transcripts. Transcription factors are ranked by overlap% (calculated as [(transcripts regulated by transcription factor x) ∩ (transcripts differentially expressed in SkM of tumor-bearing subject)] / (transcripts regulated by transcription factor x)). The y-axis reports overlap%, –log(adjusted p-value), and combined scores for each transcription factor as reported by Enrichr. Arrows identify PPARG and NFE2L2/NRF2. (E) IPA Upstream Regulator analytic predicts weak inhibition of STAT3. Key for (B, C, E): Green shading indicates decreased expression of transcripts coding for the shaded entity, red shading indicates increased expression of transcripts coding for the shaded entity, blue shading indicates decreased predicted activity/production of the shaded entity, and orange shading indicates increased predicted activity/production of the shaded entity.

Figure 3:. Genes with Altered Expression in BC Patients and BC-PDOX Mice Identifies PPAR Signaling as Potential Driver of BC-Induced SkM Fatigue.

(A) Venn diagram identifying 40 genes with commonly altered expression (e.g. downregulated in both sets) in both human BC patients and BC-PDOX mice, with experimentally verified targets of PPARs underlined. (B) STRING in silico protein-protein interaction analysis identifies significant functional interactions between the 40 commonly altered transcripts (p-value = 4.87e-14), with PPARγ and related signaling molecules central to the generated network of protein-protein interactions present in the set of 40 commonly altered transcripts (red, KEGG 03320, FDR = 0.003). Insulin signaling molecules are also significantly enriched in this gene set (blue, KEGG 04910, FDR = 0.018). (C) PPAR signaling is identified by GeneAnalytics twice in the eight pathways identified as high scoring matches (corrected p-value < 0.0001). (D) Enrichr in silico transcription factor analysis (ENCODE and ChEA Consensus from ChIP-X) identified PPARγ as the single significantly enriched transcriptional regulator in the 40 commonly altered transcripts. The y-axis reports overlap%, –log(adjusted p-value), and combined scores for each transcription factor as reported by Enrichr; -log(adjusted p-value) > 1.33 is equivalent to adjusted p-value < 0.05.

Figure 4:. qRT-PCR and protein verification and validation of RNA-Seq.

qRT-PCR was performed for Pparg and a subset of known and predicted Pparg target genes to both verify and validate the RNA-Seq completed in muscles from BC-PDOX mice and human BC patients. (A) Verification and validation of RNA-Seq in muscle samples from control and BC-PDOX mice. (B) Verification and validation of RNA-Seq in muscle samples from control and BC patients. Each graph includes the log₂(FC) and associated p-value for the gene analyzed. (C) Protein quantification of PPARγ in muscle samples from control (n=5) and BC patients (n=15). One representative blot is presented. *, p<0.05.

Figure 5:. BC-PDOX Induces SkM Fatigue.

(A) Average ex vivo SkM fatigue curves generated for NSG control animals (NSG, n = 6), surgical control animals (PDOX-CON, n = 5), and BC-PDOX mice (n = 6), using the EDL muscle. The leftward shift of the BC-PDOX fatigue curve indicates greater fatigability. The chart presents mean fatigue index +/− SEM for every 10^th contraction in the fatigue protocol; PDOX-CON vs PDOX groups compared via two-way ANOVA. (B) AUC for the fatigue curves presented in A. (C) First-derivative curves generated for the fatigue curves presented in A, representing the rate of change in force output of the EDL muscle. (D) Absolute mass comparisons of EDL, soleus, gastrocnemius, and tibialis anterior muscles in the BC-PDOX mice, NSG mice, and PDOX-CON, showing no significant differences; * p < 0.05; ** p < 0.01, *** p < 0.001; means compared via one-way ANOVA unless otherwise stated.