Abstract 3269: Recapitulating the BETH adjuvant breast cancer trial (NCT00625898) using clinically accurate orthotopic surgical resection models
Bibliographic record
Abstract
Abstract Background: An overwhelming majority of phase III clinical trials in the oncology setting fail as promising results observed in the ‘pre-clinic’ are not successfully translated to patients. As such, improved models which more accurately predict outcomes are now required. Herein, we have established orthotopic surgical resection models of Her2+ breast cancer, which replicate the phenotype of clinical post-resection metastasis [1,2]. Using these models we have recapitulated the BETH adjuvant breast cancer trial (NCT00625898) where the addition of bevacizumab (BVZ) to chemotherapy plus trastuzumab (TRAST) failed to provide additional benefit in the adjuvant setting. Methods: SCID mice were orthotopically implanted with bioluminescent Her2+ MDA-MB-231or HCC1954 cells and palpable tumors resected approximately 5 weeks later. 3 weeks after resection, mice were treated with 10 mg/kg TRAST + 5mg/kg paclitaxel IP once weekly for 6 cycles with or without weekly BVZ (5mg/kg IP). Metastasis was monitored by weekly bioluminescence imaging. Results: Tumor growth and imaging data confirmed that the addition of BVZ to adjuvant TRAST + chemotherapy provided no additional benefit compared with TRAST + chemotherapy alone. Previous pre-clinical studies using inappropriate non-resection models failed to predict this response. Furthermore, Reverse Phase Protein Array analysis of treated tumors implicated HER2, VEGF, mTOR and the intrinsic mitochondrial cell death apoptotic pathways in treatment resistance. Conclusion: In summary, our data provides evidence for pre-clinical models which better predict clinical outcome in the breast cancer adjuvant setting, and which represent an important resource for interrogating resistance pathways and identifying novel biomarker signatures. [1]. Breast. 2013 Aug;22 Suppl 2:S57-65; [2]. Cancer Res. 2013 May 1;73(9):2743-8.This work was performed under Irish HPRA Authorization AE18982 and was supported by the Clinical Cancer Research Trust. ATB receives funding from the Irish Cancer Society Collaborative Cancer Research Centre BREAST-PREDICT Grant (CCRC13GAL). Citation Format: Liam S. Shiels, Ian S. Miller, Clare Morgan, Mattia Cremona, Robert Kerbel, Bryan Hennessey, Norma O’Donovan, John Crown, Annette T. Byrne. Recapitulating the BETH adjuvant breast cancer trial (NCT00625898) using clinically accurate orthotopic surgical resection models. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3269.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".