Abstract 2102: Assessing glioma-protein inhibition in sunitinib-resistance using a 3-dimentional tissue-engineered renal cell carcinoma patient derived model
Bibliographic record
Abstract
Objective: To identify the role of glioma-proteins (Gli) in sunitinib-resistant metastatic renal cell carcinoma and the effect of Gli-inhibition using a 3D tissue-engineered patient-derived model.Background: Renal cell carcinoma (RCC) is the 6th most common malignancy with 2.3% annual increase in Canada. Metastatic RCC (mRCC) patients are treated with anti-angiogenic tyrosine kinase inhibitors (TKIs, sunitinib) as the first-line of treatment because mRCC tumor is heavily depend on VEGF pathway. Therefore, identifying the factor(s) responsible for sunitinib-resistance development and disease advancement in mRCC is imperative. Cancer reflect aspects of developmental patterning and the Hedgehog family proteins are known to play a central role. Among other proteins in the family, the inappropriate activation and/or maintenance of Sonic hedgehog (Shh)-Gli pathway results in various tumors. However, the potential of Shh-Gli pathway inhibitors in sunitinib-resistant mRCC have never been tested. To screen for effective Gli-inhibitors, we will use a 3D tissue-engineered patient-derived tumor model utilizing decellularized kidneys as matrices. Organ decellularization is the process of complete cell removal from the organ and maximum preservation of the extracellular matrix (ECM) microarchitecture, leaving some reported growth factors in which tumor cells from patients can be reseeded, regenerating the organ. This 3D-model is an economic and reliable platform to screen the Gli-inhibitors, and identify tumour-specific optimal therapies.Results: We established sunitinib-conditioned cell-line (Caki-1DC) from the parental cell-line (Caki-1WT) and a mouse model of acquired sunitinib-resistance. Proteomics and microarray results from these two models showed transcriptional and translational changes between the sensitive and resistant phenotypes, particularly in the Hedgehog signalling. In Caki-1DC, an upregulation of Gli-2 was observed, which could be abrogated with Shh pathway inhibitor, cyclopamine. Furthermore, in our decellularization and reseeding techniques of kidney, we have assessed cell viability for up to a month exhibiting adequate cell viability. This 3D platform will be used to assess the effectiveness of Gli-inhibitors.Conclusion: Our data from in vitro and in vivo models established Shh-Gli pathway activation in sunitinib-resistant mRCC. Testing the effectiveness of Gli-inhibitors will help tailor drugs for the patients to improve progression free survival. The 3D patient-derived mRCC platform will enable us to generate many in vitro avatars to accurately recreate the tumor of a patient, and simultaneously screen for suitable drugs and dose for the patient. The identification of the Shh-Gli driver pathway and the effective therapy will translate into improving patient’s quality of life and survival.Citation Format: Ninadh M. D'Costa, Zheng Tan, Claudia Chavez-Munoz, Alan So. Assessing glioma-protein inhibition in sunitinib-resistance using a 3-dimentional tissue-engineered renal cell carcinoma patient derived model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2102.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.000 |
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 teacher head, 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".