Page 160 - 2019_09-HaematologicaMondo-web

J. Lu et al.
The complex network of chronic lymphocytic leukemia energy metabolic predictors
While the analyses presented so far provide insights on pairwise associations between bioenergetic features and other tumor properties, we next aimed to create a sys- tems-level map of the network of gene mutations, DNA methylation, gene expression, ex vivo drug responses, and bioenergetic features. We used multivariate linear regres- sion with lasso regularization to predict each bioenergetic feature by other available biological features and meas- ured prediction performance using cross-validated R2 (Figure 6).
We first assessed to what extent each omics data type alone, or the combination of all the datasets, explained each bioenergetic features. The gene expression data and the drug response data performed best in predicting bioen- ergetic features (Figure 6A). Combining all datasets slight- ly increased the predictive power for each metabolic fea- ture, indicating that each set contains non-redundant information. Notably, the glycolysis-related features were better explained by the multi-omics data than the respira- tion-related features (Figure 6A and Online Supplementary Figure S13).
We visualized predictor profiles for individual bioener- getic features, focusing on the ex vivo drug responses, gene expressions, and genetic variants (Figure 6B and Online Supplementary Figure S13). In accordance with the above univariate analysis, the multivariate model identified IGHV status and response to mitochondria-targeting drugs like venetoclax and rotenone as important predic- tors for glycolysis-related features. In addition, SF3B1 mutation was identified as one of the top predictors for glycolytic capacity and reserve, as its presence is associat- ed with higher values. SF3B1 is an mRNA splicing factor that is frequently mutated in CLL and associated with more aggressive disease and worse survival, but its onco- genic mechanism is still elusive.33 Another genomic aber- ration, deletion of 13q14, was selected as one of the top predictors for basal respiration and ATP production.
Several principal components (PC) from the gene expression datasets were also identified by the multivari- ate modeling. PC8 was the top predictor with positive co- efficient for all respiration-related features. As the genes with high positive loadings on PC8 are enriched in E2F tar- gets, this suggests that higher expression of E2F targets associates with higher respiratory activity in CLL cells. On the other hand, PC10 was the top predictor, with negative coefficient, for maximal respiration and spare respiratory capacity (Online Supplementary Figure S14). Based on enrichment analysis, genes with high negative loadings on PC10 are enriched in the mTOR pathway and therefore this also suggests higher mTOR pathway activity associ- ates with high respiration capability. These findings are in line with previous reports that E2F transcription factors and mTOR pathway are key players in regulating mito- chondrial activity.34,35
PC 2, 4, 6 and 11 were identified as predictors for several glycolysis-related features (Figure 6B and Online Supplementary Figure S13). Gene set enrichment analysis highlighted TNFa-NFκB signaling as the most enriched pathway for genes with high loadings on PC2, 4 and 6 (Online Supplementary Figure S14). This finding is consis- tent with previous reports that NFκB signaling pathway controls energy homeostasis in inflammatory and cancer cells.36 As we also found NFκB activation signatures in the
two published transcriptomic profiling datasets of BCR stimulation (Online Supplementary Figure S3), which is in line with previous reports that BCR stimulation activate NFκB, we suggest that NFκB activation may play a role in increased glycolysis after BCR activation.37,38
Discussion
In this study, we identified molecular features that underlie the heterogeneity of energy metabolism in CLL and linked bioenergetic features with ex vivo drug respons- es and clinical course. We found that, although CLL cells and B cells have a similar basal glycolytic activity, CLL cells had a significantly higher glycolytic capacity and gly- colytic reserve, which are both indicators for the cell’s potential to switch to glycolysis as an energy source when necessary. Interestingly, we also found glycolytic capacity and reserve, but not basal glycolysis, to be novel predic- tors for OS in our cohort; CLL patients with higher gly- colytic capacity and reserve showed worse prognosis. In addition, higher glycolytic capacity and reserve were also found to be correlated with high expression of the CD38 gene, a cell surface marker of B-cell activation and a nega- tive prognostic marker in CLL. These observations can be viewed in the context of a recent report of the increased reliance of CLL cells on aerobic glycolysis to produce ener- gy after a glycolytic switch induced by their contact with stromal cells.39 Although we assayed circulating CLL cells for our study, the glycolytic capacity and reserve in the flux assay may actually measure the ability of CLL cells to adapt to glycolysis in a stimulated state, similar to the stimulation by stromal cells. Our findings thus imply that circulating CLL cells may have previously undergone such metabolic reprogramming and carry the metabolic reper- toire that allows them to quickly switch to glycolysis when a suitable stimulation occurs, e.g. upon stromal con- tact. Our findings also suggest that the magnitude and effi- ciency of this switch can further impact the prognosis of CLLpatients.
We showed that U-CLL has significantly higher gly- colytic rates, which validates the previous hypothesis that U-CLL may have higher reliance on aerobic glycolysis due to higher BCR signaling pathway activity.4,7 In addition, we illustrated that the glycolysis pathway is more active in U-CLL than M-CLL, accompanied by an upregulation of key enzymes regulating cellular glycolysis. This indi- cates that M-CLL and U-CLL have intrinsically different energy metabolisms and that the BCR signaling pathway may have a direct impact on the metabolic reprogram- ming. We had previously attempted to monitor circulat- ing CLL cells in vivo by using fluorodeoxyglucose positron emission tomography (FDG-PET), which pinpoints anatomical locations with high rate of glycolysis.40 This attempt failed due to insufficient sensitivity, and our results suggest that considering the difference between the M-CLL and U-CLL subtypes could increase the sensi- tivity of this diagnostic approach.
We found that the CLL patient samples with gain of 8q24 showed increased respiratory activity. The likely rea- son for this is the oncogenic activity of the extra copy of the MYC proto-oncogene. Previous studies have shown that MYC substantially contributes to mitochondrial bio- genesis, and the overexpression of MYC leads to increased respiratory capability in several cell line models, which is
1838
haematologica | 2019; 104(9)