Abstract 3142: Derivation and analysis of preclinical models of human her-2 positive breast cancer
Bibliographic record
Abstract
Abstract Although a number of Her-2 targeting drugs are now available for the treatment of Her-2 positive breast cancers, resistance to these therapies rapidly emerges. There is therefore a need to study how Her-2 targeting strategies work in vivo, and the mechanisms by which tumors eventually become resistant to anti-Her-2 therapies. We report the in vivo selection of variants of the human Her-2 positive breast cancer cell lines BT474 and MDA-MB361. Both cell lines were initially implanted orthotopically into Severely Compromised Immunodeficient mice, and the resulting tumors were serially passaged into new hosts over a 4 year period. This selection process produced the BT474V3 and MDA361V3 variants, which grow rapidly in vivo and which retain their Her-2 over-expression, and which respond (p<0.05, relative to controls) to trastuzumab (20mg/kg i.p. every 3 days). Tumor variants were also derived from selected tumors that relapsed after 3 months of continuous trastuzumab monotherapy. MDA361V3 tumors showed a significant (p<0.05) response to the combination therapy of trastuzumab plus metronomic cyclophosphamide (20mg/kg/day, p.o.). Tumors eventually progressed under this combination therapy, and cell lines were derived from the drug resistant tumors. Non-invasive monitoring of the Her-2 positive models in vivo was achieved by luciferase transfection of the tumor variants, or by transfection of chorionic gonadotropin (hCG) cDNA which allows for relative tumor growth to be evaluated via the resulting hCG levels in the mouse urine. Luciferase transfected BT474V3 were implanted intracranially (coordinates for implantation from Bregma were AP, +1.1; LM, -2.0; and DV, -3), and bioluminescence was detectable 20 days later. The implanted tumors in the brain eventually showed a doubling of bioluminescence every 2 weeks, which provides ample time for therapeutic intervention in this model of Her-2 positive breast cancer brain metastasis. To further model the response of Her-2 positive metastatic breast cancer, we employed our met2.hCG model, derived from the human MDA-MB-231 human breast cancer cell line. Thus, MDA-MB-231 were transduced to overexpress Her-2, then tagged with hCG, and then selected for high spontaneous metastatic capacity. Orthotopically implanted met2.hCG tumors responded to trastuzumab as a monotherapy, and when given in combination with metronomic cyclophosphamide. Following surgical removal of the orthotopically implanted met2.hCG tumors, urine hCG levels indicated that the metastases also responded (p<0.05, compared to saline treated controls) to the combination of metronomic cyclophosphamide plus trastuzumab, but they did not respond to the trastuzumab monotherapy. Collectively, our data shows that we have derived models of Her-2 positive breast cancer that can be used to evaluate anti-Her-2 therapeutic strategies, and to study the emergence of resistance to anti-Her-2 based therapies. Citation Format: Paloma A. Valenzuela, Sarah N. Jallad, Karla Parra, Natzidielly Lerma, Irving Miramontes, Alejandra Gallegos, Ping Xu, William Cruz-Munoz, Shan Man, Robert S. Kerbel, Giulio Francia. Derivation and analysis of preclinical models of human her-2 positive breast cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3142. doi:10.1158/1538-7445.AM2014-3142
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".