Lipidomic Analysis Demonstrates that 36:1 Phosphatidylcholine is Enriched in Cell Models of Drug Resistance Prostate Cancer
Authors: Finnerty Morgan C. , Ingram Lishann M. , Mansoura Maryam , Cummings Brian S. 
Published: April 2020

A lack of understanding exists between the association between circulating lipids and clinical outcomes of drug‐resistant castration‐resistant prostate cancer (CRPC). Although an increase in select circulating lipids correlates to decreased patient survival, neither the mechanisms mediating alterations in these lipids, nor the correlation to drug resistance are well characterized. This gap‐in‐knowledge was addressed using in vitro models of non‐cancerous, hormone‐sensitive, CRPC and drug‐resistant cell lines combined with quantitative HPLC‐ESI‐Orbitrap‐MS lipidomic analysis.

Lipid extracts were obtained using Bligh‐Dyer (methanol/chloroform/water) extraction from non‐cancerous, hormone‐sensitive, CRPC and drug‐resistant prostate cell lines. Lipids present in both cells and media were initially analyzed using an untargeted shotgun approach (ESI‐MS), followed by a targeted‐based quantitative HPLC‐ESI‐Orbitrap‐MS approach. Shotgun experiments were performed using commercially available standards. For target‐based lipidomics, LC separation yielded specific lipid class separation prior to ESI/MS/MS enabling enhanced detection of lipids suppressed in the shotgun approach. Post data acquisition for both shotgun and HPLC‐ESI/MS/MS lipidomics were based on multiple methods including isotope, carbon number (to internal standards) and ionization efficiency‐based corrections. Pathway analysis was also performed on the various prostate cell lines. Online resources and software’s utilized included MetaboAnalyst, LIPIDMATCH LIPID MAPS, XCMS, and MZmine.

Principle component analysis (PCA) generated scores plots of non‐cancerous, hormone‐sensitive, CRPC parent cell lines and drug‐resistant CRPC cell lines in the positive mode (ESI+) had a total variance of 66%. Additionally, a distinct separation between drug‐resistant and parent control cell lines was shown. Several discriminatory ion features were identified between prostate cell lines, especially between drug‐resistant and parent control neuroendocrine cell lines. Of these discriminatory features, 83 showed significant alterations. Two of the identified lipids had alterations matching those measured in plasma from prostate cancer patients.

Distinct phospholipid classes were observed to have increased levels in prostate cancer cells, when compared to non‐cancer cells. Lipidomic analysis identified phosphatidylcholines (PC 36:1), whose levels were increased in Docetaxel resistant CRPC. These data also show that the lipidomic profiles of prostate cancer cell lines mirror that seen in the plasma of prostate cancer patients. These data may lead to new early detection strategies and more personalized treatment options for patients.

Support or Funding Information

Department of Defense Prostate Cancer Research Program Idea Development Award (PC150431 GRANT11996600)

Phospholipids in Prostate Cancer

(A. Scores Plot) PCA scores plot comparing drug‐resistant CRPC cells to DU145 control parent cells in the positive mode (ESI+). ~80% of the total variance is explained by Component 1 and Component 2. (B. MTT Assay) MTT assay shows decrease in MTT staining in parent cell lines as compared to docetaxel‐resistant cell lines. This suggests DU145‐DR (DR) maintain resistance to Docetaxel as compared to DU145 parent cell line. (C. Heat Map) Each row represents a metabolite feature and each column represents a sample. Metabolite features whose levels vary significantly (p< 0.01) are projected on the heat map and used for sample clustering displayed above. (D. Cloud Plot) Differential feature plot: Only features that are dysregulated in both DU145‐DR cells and DU145‐DR media (p‐value<= 0.05 threshold, fold change >= 1.5 threshold) are displayed above. Up‐regulated features (features that have a positive fold change) are graphed above the x‐axis while down‐regulated features (features that have a negative fold change) are graphed below the x‐axis.

Figure 1

PC 36‐1 in Prostate Cancer

(A) Cloud plot showing 7460 discriminatory ion features (positive ion data) in human cell lines. Only dysregulated features are displayed above (p‐value <= 0.05 threshold, fold change >= 1.5 threshold). The radius of each bubble corresponds to the log fold change of that feature. Features with higher fold change (or higher intensity) will have larger radii. The shade of bubbles corresponds to the magnitude of the p‐value (the darker the color, the smaller the p‐value). The statistical significance of the fold change, as calculated by a Welch t‐test with unequal variances.

Figure
6. (B) Box‐plot identified 788.6 feature in human cell lines. Of the lipids identified by the cloud plot, PC(36:1) was one of the few lipid species enriched in all drug resistant prostate cancer cell lines as compared to control cells. This was previously observed in castration‐resistant prostate cancer patient‐derived plasma samples.

Figure 2