Abstract P4-06-10: Rates of successful engraftment in breast cancer xenograft models based on tissue type: Primary vs relapsed disease
Bibliographic record
Abstract
Abstract Purpose: As we have published expertise in breast cancer xenograft models and clonal dynamics, our aim was to explore rates of engraftment based on type of tissue for attempted xenografting (primary vs relapsed/metastatic disease) and clinical breast biomarker subtype. Methods: Tissue from patients (pts) enrolled in a locally advanced/metastatic study and a breast tumour tissue repository (ie. resectable primaries) between Sept. 2008 and July 2015 underwent xenografting using NodScid/IL2rgKO (NSG) mice. Xenografts were passaged when tumour volume reached 1 cm3. Mice with no engraftment after 12 months (mos) were sacrificed. Pt charts were reviewed to determine biomarker status (hormone receptor [HR], HER2), date and type of tissue collection for xenografting. Prediction of successful engraftment based on tissue type and biomarker status was performed using nominal logistic regression. Results: A total of 70 tissue samples with known engraftment status were included in the analysis: 51 from primary breast tumour, 10 from relapsed disease (dz) with ≤ 1 line of therapy in the advanced setting and 9 from relapsed dz with > 1 line of therapy in the advanced setting. Tumours from pts treated with > 1 line of therapy were more likely to engraft compared to primary or recurrent dz with ≤ 1 line of therapy (89%, 35%, and 40% respectively; p=.008). HR- primary tumours were more likely to engraft compared to HR+ primary tumours: 71% of HR-/HER2- (triple negative) and 67% of HR-/HER2+ tumours versus 4% of HR+/HER2- and 38% of HR+/HER2+ tumours; p<.0001. Combining all tissue types, HR- tumours were more likely to engraft compared to HR+ tumours: 76% of HR-/HER2- and 67% of HR-/HER2+ tumours versus 37% of HR+/HER2+ and 22% of HR+/HER2- tumours; p=.0007. Table 1 shows the rate of engraftment for each tissue type and biomarker status. Combining these 2 variables predicts engraftment in 80% of cases. Conclusion: This preliminary study highlights potential differences in successful xenoengraftment based on biomarker status at diagnosis and type of tissue, primary vs relapsed tumour, the latter suggesting that the underlying biology of primary or first relapsed recurrent disease is distinct from more refractory disease, and warrants further exploration. This work is ongoing. (Funded by CBCRA, BCCF) Engraftment of primary tumour vs relapsed disease Primary tumour (N=52) N, (%)Recurrent disease and ≤ 1 line of Rx in advanced setting (N=10) N, (%)Recurrent disease and > 1 line of Rx in advanced setting (N=9) N, (%)Engraftment Yes18 (35)4 (40)8 (89)HR-/HER2-10 (55)1 (25)2 (25)HR-/HER2+4 (22)1 (25)1 (13)HR+/HER2+3 (17)00HR+/HER2-1 (6)2 (50)5 (62)Engraftment No33 (65)6 (60)1 (11)HR-/HER2-4 (12)00HR-/HER2+2 (6)1 (17)0HR+/HER2+5 (15)00HR+/HER2-22 (67)5 (83)1 (100) Citation Format: den Brok W-l, Chia S, Kalloger S, Bates C, Aparicio S, Mar C, Gelmon K, Eirew P. Rates of successful engraftment in breast cancer xenograft models based on tissue type: Primary vs relapsed disease [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-06-10.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".