Abstract 5311: Lipidomic profiling of extracellular vesicles derived from cancer cell lines: Lipid species as potential biomarkers and cellular uptake enhancers
Bibliographic record
Abstract
Abstract Introduction: Extracellular vesicles (EVs) are lipid bilayer-made particles shed by cells to the extracellular space. They carry different cargo proteins, nucleic acids, and other metabolites. EVs play a role in disseminating cancer to distant organs by communicating with the tumor microenvironment to prepare the metastatic niche and also through horizontal transfer of oncogenic traits to recipient cells. The EV surface, which includes proteins and lipids, plays a role in organotropism and cellular uptake. While proteins have been extensively characterized, lipids have not been explored sufficiently. This work aims to evaluate EV lipids as potential biomarkers and their role in enhancing cellular uptake. Methods: To detect which lipid species (LS) were differentially expressed, we used two cell models of liver metastatic cells: colorectal cancer (HT29) and uveal melanoma (MP41, MP46, MEL 270, OMM 2.5) cell lines. Colon (CCD18-Co) and fibroblast (BJ) immortalized non-cancerous cells were used as controls. EVs were isolated from culture media by ultrafiltration using 100 kDa units filters. Lipids were extracted by methyl-tert-butyl ether for high-throughput lipidomics. High-resolution ‘shotgun’ mass spectrometry was performed. Data was analyzed using LipidView software (SCIEX), and the lipid % normalized was reported. MarkerView (SCIEX) was used to perform Principal Component Analysis. The LS segregating cancerous vs. non-cancerous cells were identified. To evaluate the influence on cellular uptake, we used liposomes as EV models with lipid formulations containing the segregating LS to compare them with naturally occurring lipids and EVs using Incucyte live cell imaging. Results: We identified four LS that segregated EV subpopulations. PE 34:1 and PS 36:1 divided cancerous vs non-cancerous cells, uveal melanoma cells were segregated by PE 36:2, and normal colon cells were segregated by LPC 18:0. We validated the effect of PS 36:1 in cellular uptake by producing liposomes with a lipid formulation resembling the lipid profile of naturally occurring EVs lipid profile with an artificially high DOPS concentration (17% of the total molar ratio). We determined that human hepatocytes preferentially internalized liposomes made of naturally occurring EVs, followed by the ones with a high concentration of DOPS and lastly by a control EV lipid formulation. Conclusion: This study identified EV LS that segregated cancerous, normal, and melanoma cell lines. We showed that LS could be used to distinguish cell populations. Moreover, we demonstrated that LS alone influences cellular uptake and that adding the segregating LS to lipid formulations in excess effects cellular uptake. These results pave the way to identify EV lipid biomarkers and better understand EV based cancer dissemination. Citation Format: Ruben R. Lopez Salazar, Prisca Bustamante, Chaymaa Zouggari, Yunxi Chen, Thupten Tsering, Ion Stiharu, Catherine Mounier, Vahe Nerguizian, Julia Burnier. Lipidomic profiling of extracellular vesicles derived from cancer cell lines: Lipid species as potential biomarkers and cellular uptake enhancers. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5311.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".