Abstract 638: Clinical characteristics of breast cancer xenograft models
Bibliographic record
Abstract
Abstract Purpose: We have expertise in breast cancer xenograft models and have previously published data on clonal dynamics. Our aim was to explore clinical characteristics of those patients (pts) whose breast cancer tumours engrafted versus those that did not. Methods: Tissue from pts enrolled in a locally advanced/metastatic (MBC) study and a breast tumour tissue repository between Sept. 2008 and July 2014 underwent xenografting using NodScid/IL2rgKO (NSG) mice. Xenografts were passaged when tumour volume reached 1 cm3. Mice were sacrificed if no engraftment by 12 months (mos). Pt charts were reviewed to determine biomarker status (hormone receptor [HR], HER2), grade, LVI, time free from disease/progression, and pathologic complete response (pCR) for pts receiving neoadjuvant therapy. Results: A total of 64 pts had known xenograft status: 32 engrafters, 32 non-engrafters. Biomarker status did not predict likelihood of engraftment (p = .0695) and is shown in Table 1 along with site/timing of biopsy for tissue engraftment within biomarker groups. When HER2+ cases are excluded from analysis, the HR-/HER2- phenotype yields a 72% probability of engraftment compared to 39% for the HR+/HER2- group (p = .0418). For engrafters, 22/32 (68%) of pts had or went on to have relapsed or de novo MBC. For non-engrafters, 7/32 (22%) had or went on to have relapsed disease (p = .0004). Grade and LVI did not predict engraftment (p = .1806 and p = .8657 respectively). No grade 1 tumours engrafted (n = 3). There were 25 pts who received neoadjuvant chemotherapy: 14 engrafters, 11 non-engrafters. None of the engrafters achieved a pCR; 3 non-engrafters achieved a pCR. Median time to engraftment was 5 mos for pts with relapsed/advanced disease vs 9.8 mos for pts who did not relapse however, treatment was variable. Conclusion: This preliminary study highlights potential differences in clinical characteristics of engrafters vs non-engrafters in breast cancer xenograft models and warrants further exploration. (Funded by CBCRA, BCCF) TABLE 1.Biomarker status, tissue site/type in attempted xenografts.Engrafter (N = 32) N, (%)Non-engrafter (N = 32) N, (%)HR+/HER2-14 (44)22 (69)Tissue from:Primary tumour419Recurrence23Advanced dz on therapy80HR-/HER2-13 (40)5 (16)Tissue from:Primary tumour114Recurrence21Advanced dz on therapy00HER2+/HR+0 (0)2 (9)Tissue from:Primary tumour02Recurrence00Advanced dz on therapy00HER2+/HR-5 (16)3 (9)Tissue from:Primary tumour42Recurrence01Advanced dz on therapy10 Citation Format: Wendie D. den Brok, Stephen Chia, Cherie Bates, Steve Kalloger, Samuel Aparicio, Mar Mar, Karen Gelmon, Peter Eirew. Clinical characteristics of breast cancer xenograft 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 638.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".