Docosahexaenoic acid enrichment of tumor phospholipid membranes increases tumor necroptosis in mice bearing triple negative breast cancer patient-derived xenografts
Bibliographic record
Abstract
Docosahexaenoic acid (DHA) reduces breast cancer tumor growth in preclinical models. To better understand how DHA amplifies the actions of docetaxel (TXT) chemotherapy, we examined the effects of two doses of dietary DHA on tumor size, membrane DHA content and necroptosis using a drug resistant triple negative breast cancer (TNBC) patient derived xenograft (PDX) model. Female NOD.Cb-Prkdc scid Il2rg mice bearing TNBC PDXs were randomized to one of three nutritionally complete diets (20% w/w fat): control (0% DHA), high DHA (3.8% HDHA), or low DHA (1.6% LDHA) with or without intraperitoneal injections of 5 mg/kg TXT, twice weekly for 6 weeks ( n =8 per group). Tumors from mice fed either HDHA+TXT or LDHA+TXT were similar in size to each other, but were 36% and 32% smaller than tumors from mice fed control+TXT, respectively ( P <.05). A dose effect of DHA incorporation was observed in plasma total phospholipids and in phosphatidylethanolamine and phosphatidylinositol. Both doses of DHA resulted in similarly increased necrotic tissue and decreased NFκB protein expression compared to control tumors, however only the HDHA+TXT had increased expression of necroptosis related proteins: RIPK1, RIPK3 and MLKL ( P <.05). Increased MLKL was observed in the lipid raft portion of HDHA+TXT tumor extracts. This work confirms the efficacy of a combination therapy consisting of DHA supplementation and TXT chemotherapy using two doses of DHA as indicated by reduced tumor growth in a TNBC PDX model. Moreover, the results suggest that decreased growth may occur through increased DHA incorporation into tumor phospholipid membranes and necroptosis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".