Abstract 3119: Predicting drug resistance in metastatic renal cell carcinoma: Personalized medicine by xenografting patient tumors into chicken embryos
Bibliographic record
Abstract
Abstract Introduction: Approximately 30% of patients with metastatic renal cell carcinoma (mRCC) will be primarily resistant to targeted agents. The remainder will develop resistance over a longer time course. Improved therapeutic acumen prior to the initiation of treatment may improve patient outcomes. Aim: To develop a patient-derived xenograft model to determine the sensitivity of each RCC patient's tumor cells to a defined targeted therapy prior to the start of systemic treatment. Methods: We developed a patient−derived xenograft model using chicken embryos that enables the investigator to evaluate the interplay of angiogenesis and tumor growth. Drugs can also be applied directly to tumors to evaluate drug sensitivities. A variety of patient-derived xenograft primary RCC cell lines (XP127, XP121, XP22, XP206, PF22, XP258, XP158, XP185) were implanted into the chorioallantoic membrane (CAM) of chicken embryos for drug evaluation and their effect on tumor take ratios. Each cell line was prepared for xenografting by mixing cell pellets with Matrigel (BD Biosciences Inc.) in a 1:1 ratio and 10 uL of this mix was implanted into the CAM of D9 chicken embryos. To evaluate drug sensitivities in vivo, cell lines were pre−treated overnight with 5 uM sunitinib. Intravital imaging was performed to assess tumor size and tumor microvessel density was determined by histology of resected tumors. Tumor take rates were determined 6−8 days post implantation. Results: Using the 786-O cell line that is sensitive to sunitinib, tumor take rates were 82% in vehicle treated tumors compared to 46% in sunitinib treated tumors. Tumor xenografts underwent extensive angiogenesis as observed by IV injection of Dextran-Alexa555 (10 kDa size). We implanted seven different patient derived RCC cell lines into the CAM (N>15 each cell line and treatment), four of which are resistant to sunitinib (XP127, XP121, XP258, PF22) and three were sensitive to sunitinib (XP206, XP158, XP185) as determined previously in murine heterotopic models. Tumor take rates varied amongst cell lines with decreased tumor take rates observed in sunitinib-sensitive cell lines when compared to their vehicle-treated control counterparts. Conclusion: This patient derived xenograft model is a potentially useful model for drug sensitivity evaluation, enabling the investigator to potentially individualize targeted treatments to each patient with metastatic RCC upfront from the start of systemic therapy. Citation Format: Clarisse Mazzola, Chantalle Willie, Connor D. MacMillan, Ann F. Chambers, James B. Brugarolas, Nicholas Power, Hon S. Leong. Predicting drug resistance in metastatic renal cell carcinoma: Personalized medicine by xenografting patient tumors into chicken embryos. [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 3119. doi:10.1158/1538-7445.AM2014-3119
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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) | 0.002 | 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".