Abstract 1254: Mechanistic interrogation of pre-treatment low dose aspirin effects in HER 2 positive breast cancer
Bibliographic record
Abstract
Abstract Background: Prior data (Barron et al. Cancer Res. 2014 74:4065-77) suggests that pre-diagnostic exposure to aspirin can have significant effects on breast tumor biology and patient outcome. It has been proposed that aspirin inhibition of COX-2 may suppress lymphangiogenesis and metastasis (Karnezis et al Cancer Cell. 2014. 21:181-95). Here, we sought to recapitulate pre-diagnostic aspirin exposure in rodent models of Her2+ breast cancer and elucidate mechanisms of action. We also determined the effect of aspirin on tumor stroma, using a co-culture system of human tumor and mesenchymal stem cells (MSC). Methods: NOD/SCID mice were orthotopically implanted with Her2+ MDA-MB-231 or HCC1954 cells. 48hr later, animals began a daily low dose [30mg/kg or 120mg/kg] of aspirin, until tumors reached 250mm3. They were then resected. 3 weeks later, HCC1954 implanted animals were treated with trastuzumab (15mg/kg) and paclitaxel (5mg/kg) for 6 weeks. Primary tissues were analysed by immunohistochemistry to assess VEGF-C, -D, COX-2, LYVE1 and CD31. RNAseq was performed on tumours to identify aspirin perturbed molecular pathways. To determine the stromal response to aspirin, patient derived MSCs were cultured either alone or with HCC1954 cells and exposed to aspirin (2.5 or 7.5mM). Secreted VEGF-C was quantified. A tubule formation assay was performed to determine the impact of aspirin on angiogenesis. Pro-angiogenic protein expression was investigated using a human angiogenesis array platform. Results: A significant delay in tumor growth was observed in both tumor models following aspirin treatment (p<.01). Assessment of metastatic progression revealed that 120 mg/kg aspirin significantly (p<.05) increased time to metastasis and reduced primary regrowth in the MDA MB 231 model (p<.01). Immunohistochemical analysis of VEGF C, D and LYVE1 showed a significant dose dependant reduction (p<.01) in both models. RNAseq pathway analysis revealed a significant over-representation of mitochondrial electron transport chain genes. Downstream factors of AMPK showed significant (p<.01) upregulation suggesting alterations in metabolism. Aspirin (7.5mM) exposure resulted in loss of VEGF-C secretion from co-cultured tumor / MSC cell populations. Conditioned media harvested following aspirin treatment limited support tubule formation. Expression of pro-angiogenic factors in HCC1954 cells showed alterations following treatment, with the greatest decrease seen in Urokinase Plasminogen Activator (-42%) and its inhibitor Serpine1 (+55%). Conclusion: We have successfully recapitulated pre-treatment aspirin response in surgical resection models of Her2+ breast cancer, with IHC analysis confirming the impact of treatment on angiogenic and lymphangiogenic factors. RNAseq analysis implicates aspirin mediated alterations in cellular metabolism. Our data further reveals increased response to aspirin in stromal cell populations. Citation Format: Ian S. Miller, Sonja Khan, Liam P. Shiels, Sudipto Das, Bruce Moran, Finbarr P. Leacy, Paul M. Loadman, Robert S. Kerbel, Darran O' Connor, Kathleen Bennett, Róisín M. Dwyer, Annette T. Byrne. Mechanistic interrogation of pre-treatment low dose aspirin effects in HER 2 positive breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1254. doi:10.1158/1538-7445.AM2017-1254
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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) | 0.004 | 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".