Keywords
Mendelian randomization, breast cancer, sphingomyelins, lipids, metabolism
Mendelian randomization, breast cancer, sphingomyelins, lipids, metabolism
Changes in cellular metabolism are a hallmark of cancer1. Sphingolipids can control the rate of cell proliferation during malignant transformation and affect chemoresistance2. Sphingomyelin is a type of sphingolipid, a class of lipids containing sphingoid bases (Figure 1). As a response to cellular stress, sphingolipids mediate apoptosis and autophagy, through the synthesis and/or accumulation of ceramide. Ceramide can be hydrolyzed from sphingomyelin3. Due to immense interest in how sphingolipids influence chemoresistance, much is known about the impact of sphingolipids on cancer treatment and little is known about role sphingolipids in the induction of tumors in humans.
Estrogen triggers sphingolipid signaling cascades2. Due to this, it was hypothesized that circulating sphingomyelins might be involved in acquisition of breast cancer. The causal impact of circulating levels of sphingomyelins on risk for breast cancer was appraised with Mendelian randomization (MR).
MR is an instrumental variables technique; i.e., genetic variants (typically single-nucleotide polymorphisms, SNPs) strongly associated with traits are used in statistical models instead of the traits themselves. This avoids most environmental sources of confounding and averts reverse causation, which preclude causal inference in observational studies. Two-sample MR is an adaptation of the procedure that uses summary statistics from two genome-wide association (GWA) studies4.
MR depends on the validity of three assumptions: (i) the SNPs acting as the instrumental variables must be strongly associated with the exposure; (ii) the instrumental variables must be independent of confounders of the exposure and the outcome; and (iii) the instrumental variables must be associated with the outcome only through the exposure.
Step 1. Kettunen et al. (2016) performed a genome-wide association (GWA) study of 123 circulating metabolites—including sphingomyelins—in European participants (n=13,476 for sphingomyelins)5. From this, independent (those not in linkage disequilibrium; R2 < 0.01) SNPs associated at genome-wide significance (P < 5 × 10-8) with a standard-deviation (SD) increase in circulating sphingomyelins were identified. The Kettunen GWA is available through MR-Base5.
Step 2. A publicly available GWA study of breast cancer performed by the Breast Cancer Association Consortium (BCAC) on 122,977 breast cancer cases and 105,974 controls of European ancestry was chosen as the outcome GWA for breast cancer7.
A seven-SNP instrument for circulating sphingomyelins was constructed from the SNPs strongly associated with circulating sphingomyelin levels. Estimates of the proportion of variance in circulating sphingomyelins explained by the genetic instrument (R2) and the strength of the association between the genetic instrument and sphingomyelins (F-statistic) were generated (conventionally F-statistics <10 are weak). The instrument for sphingomyelins has an R2 = 0.032 and the F-statistic = 1089. The study was powered using the mRnd MR power calculator (available at http://cnsgenomics.com/shiny/mRnd/). It had >90% power to detect an OR of 0.90.
The log-odds for breast cancer per SD increase in circulating sphingomyelins was calculated, using the inverse-variance weighted (IVW) MR method. The “TwoSampleMR” package4 was used for the MR analysis.
All described analyses were performed in R version 3.5.2.
Several sensitivity estimators can be used appraise pleiotropic bias. Three were chosen to complement the primary IVW causal tests: MR Egger regression, weighted median, and weighted mode estimations. In addition to these sensitivity estimators, a test for heterogeneity was performed, since variability in the causal estimates between SNPs can indicate pleiotropy. The test for heterogeneity was performed using Cochrane’s Q-statistic.
There was a null effect for circulating sphingomyelins on breast cancer (OR = 0.94; 95% CI = 0.85, 1.05; P = 0.30). The sensitivity estimators had effect estimates in the same direction and were of comparable magnitude to the IVW estimate, indicating no evidence for substantial bias due to unwanted pleiotropy. There was no evidence for heterogeneity in the estimates (Table 1). The MR-Egger intercept test, which provides an assessment of potential directional pleiotropy in the IVW was null. A null effect indicates a lack of evidence for pleiotropy (Estimate = 1.01; 95% CI = 0.97, 1.04; P =0.55).
This is the first causal report in humans that sphingomyelins on breast cancer initiation is null. The null effect might reflect the complex interplay of pro-apoptotic and pro-growth ceramides8, perhaps with greater upregulation of the pro-apoptotic pathways, which may be different for different tissues. Future investigations of risk in other cancer types are needed to further explore the potential role of sphingolipid biology in cancer etiology.
One potential limitation of this analysis is that unwanted pleiotropy cannot be entirely ruled out in MR studies. However, the sensitivity estimators provide evidence against this. Given the many ways in which a finding could be a false-positive, null findings from well-powered MR studies are, in some ways, more believable than reports of causal associations9. A major strength of this two-sample MR analysis is that it capitalized on the power of very large GWA studies. If sphingomyelins were causal for breast cancer initiation, it is highly unlikely that the effect would go undetected with more than 100,000 cases and 100,000 controls in BCAC.
The sphingomyelin data are publicly available through the MR-Base repository (http://www.mrbase.org/) under a GNU General Public License v3.
The breast cancer outcome data are freely available on the BCAC website (http://bcac.ccge.medschl.cam.ac.uk/).
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Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
No
If applicable, is the statistical analysis and its interpretation appropriate?
I cannot comment. A qualified statistician is required.
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Partly
References
1. D Adams C: Circulating sphingomyelins on estrogen receptor-positive and estrogen receptor-negative breast cancer-specific survival. Breast Cancer Management. 2020. Publisher Full TextCompeting Interests: No competing interests were disclosed.
Reviewer Expertise: cancer, biomarkers
Is the work clearly and accurately presented and does it cite the current literature?
Yes
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
I cannot comment. A qualified statistician is required.
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Hepatology, metabolomics, lipidomics, sphingolipids
Alongside their report, reviewers assign a status to the article:
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Version 1 18 Dec 19 |
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