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Record W2558211192 · doi:10.1016/j.ebiom.2016.12.001

Lipids and Their Effects in Chronic Lymphocytic Leukemia

2016· letter· en· W2558211192 on OpenAlexaboutno aff
Daphne R. Friedman

Bibliographic record

VenueEBioMedicine · 2016
Typeletter
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsnot available
Fundersnot available
KeywordsChronic lymphocytic leukemiaLeukemiaMedicineImmunology

Abstract

fetched live from OpenAlex

Chronic lymphocytic leukemia (CLL) is an incurable common B-cell malignancy with a spectrum of clinical outcomes. Over the past decade, our increasing understanding of the drivers of CLL progression has led to the development and use of novel therapeutics. For example, B-cell receptor (BCR) signaling was shown to be overactive in CLL, and subsequently the kinase inhibitors ibrutinib (BTK inhibitor) and idelalisib (PI3K delta inhibitor) were found to have clinical efficacy in this malignancy (Byrd et al., 2013Byrd J.C. Furman R.R. Coutre S.E. et al.Targeting BTK with ibrutinib in relapsed chronic lymphocytic leukemia.N. Engl. J. Med. 2013; 369: 32-42Crossref PubMed Scopus (1749) Google Scholar, Furman et al., 2014Furman R.R. Sharman J.P. Coutre S.E. et al.Idelalisib and rituximab in relapsed chronic lymphocytic leukemia.N. Engl. J. Med. 2014; 370: 997-1007Crossref PubMed Scopus (1376) Google Scholar). Despite these new treatments, CLL remains incurable and there remains a need to identify new therapeutic targets. The therapeutic target of interest for McCaw et al., 2017McCaw L. Shi Y. Wang G. Li Y.J. Spaner D.E. Low density lipoproteins amplify cytokine-signaling in chronic lymphocytic leukemia cells.EBioMedicine. 2017; 15: 24-35Summary Full Text Full Text PDF PubMed Scopus (24) Google Scholar in their EBioMedicine article is lipid metabolism. It has been appreciated for many years that lipids have importance in CLL progression and outcomes. Most notably, lipoprotein lipase is a well-known (although not routinely measured clinically) prognostic factor in CLL, with higher levels associated with inferior clinical outcomes. LPL is not expressed in normal lymphocytes, but its expression is increased in CLL cells, particularly in the IGHV unmutated subset (Heintel et al., 2005Heintel D. Kienle D. Shehata M. et al.High expression of lipoprotein lipase in poor risk B-cell chronic lymphocytic leukemia.Leukemia. 2005; 19: 1216-1223Crossref PubMed Scopus (132) Google Scholar). LPL catalyzes hydrolysis of VLDL and chylomicrons, releasing fatty acids. LPL also has non-catalytic functions, for example co-localizing with lipoproteins at the cell surface. In CLL cells, the exact function of LPL and the reason for its overexpression compared to normal B-cells is not fully understood. However, recent work has demonstrated that inhibition of LPL with orlistat induces CLL apoptosis, and that LPL expression is increased by BCR cross-linking, by binding of STAT3 to the LPL promoter, and by certain CLL stimulants that induce demethylation of the LPL gene (Moreno et al., 2013Moreno P. Abreu C. Borge M. et al.Lipoprotein lipase expression in unmutated CLL patients is the consequence of a demethylation process induced by the microenvironment.Leukemia. 2013; 27: 721-725Crossref PubMed Scopus (14) Google Scholar, Pallasch et al., 2008Pallasch C.P. Schwamb J. Konigs S. et al.Targeting lipid metabolism by the lipoprotein lipase inhibitor orlistat results in apoptosis of B-cell chronic lymphocytic leukemia cells.Leukemia. 2008; 22: 585-592Crossref PubMed Scopus (82) Google Scholar, Rozovski et al., 2015Rozovski U. Grgurevic S. Bueso-Ramos C. et al.Aberrant LPL expression, driven by STAT3, mediates free fatty acid metabolism in CLL cells.Mol. Cancer Res. 2015; 13: 944-953Crossref PubMed Scopus (55) Google Scholar). Together, this previous work has suggested that free fatty acids, liberated by LPL, may be a protective factor for CLL lymphocytes. Within this context, McCaw et al., 2017McCaw L. Shi Y. Wang G. Li Y.J. Spaner D.E. Low density lipoproteins amplify cytokine-signaling in chronic lymphocytic leukemia cells.EBioMedicine. 2017; 15: 24-35Summary Full Text Full Text PDF PubMed Scopus (24) Google Scholar provide a compelling argument for the role of lipids in inducing second messenger signaling in CLL. The authors were intrigued by a recent case-control study in Canada that demonstrated that CLL patients have more dyslipidemia than age-matched controls, and that CLL patients who took HMG-CoA reductase inhibitors (“statins”) had improved survival compared to CLL patients who did not take these medications, which confirmed similar results in smaller CLL cohorts (Chae et al., 2014Chae Y.K. Trinh L. Jain P. et al.Statin and aspirin use is associated with improved outcome of FCR therapy in relapsed/refractory chronic lymphocytic leukemia.Blood. 2014; 123: 1424-1426Crossref PubMed Scopus (19) Google Scholar, Friedman et al., 2010Friedman D.R. Magura L.A. Warren H.A. Harrison J.D. Diehl L.F. Weinberg J.B. Statin use and need for therapy in chronic lymphocytic leukemia.Leuk. Lymphoma. 2010; 51: 2295-2298Crossref PubMed Scopus (15) Google Scholar, Mozessohn et al., 2017Mozessohn L. Earle C. Spaner D. Cheng S.Y. Kumar M. Buckstein R. The association of dyslipidemia with chronic lymphocytic leukemia: a population-based study.J. Natl. Cancer Inst. 2017; 109Crossref PubMed Scopus (11) Google Scholar). Together with the story regarding lipoprotein lipase, these clinical data beg the question of if and how LDLs affect CLL cells. In their paper, McCaw et al., 2017McCaw L. Shi Y. Wang G. Li Y.J. Spaner D.E. Low density lipoproteins amplify cytokine-signaling in chronic lymphocytic leukemia cells.EBioMedicine. 2017; 15: 24-35Summary Full Text Full Text PDF PubMed Scopus (24) Google Scholar focus on LDL potentiation of cytokine-induced STAT3 phosphorylation. The authors demonstrate that LDLs are able to increase STAT3 phosphorylation within the context of cytokine stimulation, not BCR cross-linking. The induced STAT3 phosphorylation was suppressed by anti-IL10 antibodies and by small molecule JAK inhibition, suggesting overlapping pathways with IL10 and JAK mediated signaling. The authors evaluated which of the different components of LDL contributed to the effect on STAT3 phosphorylation, and they found that long-chain fatty acids and free cholesterol were the main actors. Lastly, the authors found a negative correlation between the extent of LDL-potentiated STAT3 phosphorylation and HMGCoA reductase expression. Since HMGCoA reductase is the rate limiting step in cholesterol synthesis, this suggests that the subset of CLL cells with lower intracellular cholesterol synthesis are affected more by LDL incubation, and that this mechanism may be important for disease progression amongst these patients. McCaw et al.'s work (McCaw et al., 2017McCaw L. Shi Y. Wang G. Li Y.J. Spaner D.E. Low density lipoproteins amplify cytokine-signaling in chronic lymphocytic leukemia cells.EBioMedicine. 2017; 15: 24-35Summary Full Text Full Text PDF PubMed Scopus (24) Google Scholar) adds important information to the growing knowledge regarding the effect of lipids on CLL cell biology, however numerous unknowns remain. For example, molecular prognostic markers in the CLL patients in these experiments are not fully detailed, LPL levels are unknown, and serum lipid levels are unknown. These could affect the in vitro findings observed. Second, the relevance of the results in this manuscript within the context of research related to LPL is not explored. Third, it would be helpful to investigate LDL-induced effects on a broader representation of relevant CLL signaling pathways including other chemokines, TNF family members (BAFF, APRIL), and TLR agonists. This would provide insight into the relative importance of lipoprotein metabolism in different aspects of CLL cell biology. Fourth, as more attention is paid to the CLL microenvironment, it would be interesting to learn if and how lipids and lipoproteins modulate the interaction between CLL cells and nurse-like cells. Lastly, from a therapeutic perspective, do lipid-lowering medications, such as statins, synergize with BTK or PI3K inhibitors in CLL? The key messages to take away from the work performed by McCaw et al., 2017McCaw L. Shi Y. Wang G. Li Y.J. Spaner D.E. Low density lipoproteins amplify cytokine-signaling in chronic lymphocytic leukemia cells.EBioMedicine. 2017; 15: 24-35Summary Full Text Full Text PDF PubMed Scopus (24) Google Scholar is that lipids and lipoproteins appear to contribute to intracellular second messenger signaling in CLL cells. As these findings occur in the context of stimulated CLL cells in vitro, it is not clear whether these results are important for CLL patients themselves. These concerns are addressed in part by the findings that CLL patients with dyslipidemia have inferior outcomes, but additional confirmatory studies are needed. The next logical areas to investigate are (1) the effect of lipids and lipoproteins on CLL cell viability, particularly in the context of supportive nurse-like cells, (2) the effect of lipids and lipoproteins in the Eμ-TCL1 CLL mouse model, and (3) the extent to which lipid lowering therapies can add to existing kinase inhibitors in their anti-CLL effect (either in vitro or in vivo). McCaw and colleagues' studies will be a key stepping stone in the future understanding of this important pathway in CLL. The author declared no conflicts of interest. Low Density Lipoproteins Amplify Cytokine-signaling in Chronic Lymphocytic Leukemia CellsRecent studies suggest there is a high incidence of elevated low-density lipoprotein (LDL) levels in Chronic Lymphocytic Leukemia (CLL) patients and a survival benefit from cholesterol-lowering statin drugs. The mechanisms of these observations and the kinds of patients they apply to are unclear. Using an in vitro model of the pseudofollicles where CLL cells originate, LDLs were found to increase plasma membrane cholesterol, signaling molecules such as tyrosine-phosphorylated STAT3, and activated CLL cell numbers. Full-Text PDF Open Access

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.193
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.275
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations10
Published2016
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