Circulating levels of PCSK9, ANGPTL3 and Lp(a) in stage III breast cancers
Bibliographic record
Abstract
Abstract Background/ Synopsis: Cholesterol plays an important role in sustaining tumor growth and metastasis in a large variety of cancers. New and powerful cholesterol-lowering drugs are being used in combination to treat cardiovascular diseases. Thus, it becomes intuitive to verify whether these drugs could be combined to induce a cholesterol shortage sufficient to impede tumor progression. Circulating levels of the targets of a new generation of lipid-lowering drugs have not been fully investigated in cancers. Given that drugs directed against PCSK9 (evolocumab, alirocumab, inclisiran), ANGPTL3 (evinacumab) and Lp(a) (pelacarsen) are available, it becomes important to assess circulating levels of these drug targets and their role in cancers. Objective/Purpose: To compare circulating levels of PCSK9, ANGPTL3, and Lp(a) in women with stage III breast cancer versus women with premalignant or benign breast lesions. Methods: Twenty-three plasma samples from women diagnosed with a stage III breast cancer (ductal, lobular or mixed) were matched for age with twenty-three plasma samples from women bearing premalignant (stage 0, n=9) or benign (n= 14) breast lesions. The lipid profile (Apo B, total cholesterol, HDL cholesterol and triglycerides levels) and Lp(a) were measured on a Roche Modular analytical platform, whereas LDL levels were calculated with the Friedewald formula. ANGPTL3 and PCSK9 plasma levels were quantitated by ELISA. All statistical analyses were performed using SAS software version 9.4. Results: PCSK9 levels were significantly higher in women with stage III breast cancer compared to age-matched counterparts presenting a benign lesion (95.9 +/- 27.1 ng/mL vs. 78.5 +/- 19.3 ng/mL, p<0.05, n=14). Moreover, PCSK9 levels positively correlated with breast disease severity (benign, stage 0, stage III) (Rho=0.34, p<0.05, n=46). In contrast, ANGPTL3 and Lp(a) plasma levels did not display any association with breast disease status and lipids did not correlate with disease severity. Conclusion: In this cohort of 46 women, PCSK9 levels increased along with the severity of breast disease. Given that PCSK9 plays an important role in maintaining cholesterolemia and has been shown to degrade MHC-I on tumor cells, impeding immune T-cells response to tumors, the association between PCSK9 levels and breast disease severity needs to be further investigated in larger studies.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 0.000 |
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.
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".