Abstract 2441: Polysome profiling suggests VHL-dependent translational control in clear cell renal cell carcinoma
Bibliographic record
Abstract
Abstract ccRCC is the most common type of renal carcinoma with 80% of incidence among all types of kidney neoplasms. Most cases are localized in the kidney and potentially curable after nephrectomy however about 30% of patients will relapse with distant metastasis. Metastatic patients comprehend one third of all cases and, despite the advances in therapies, they still have low response rates. The identification of molecular mechanisms associated with ccRCC is essential to understand disease progression and treatment resistance. Genes frequently mutated in ccRCC affect the activation of signaling pathways including the mTOR pathway which can cause an unbalance in translational control. Another frequent mutation is in the tumor suppressor gene VHL which regulates response under hypoxia. Hypoxia affects gene expression by both translational and transcriptional controls that contributes to tumor formation and disease progression. Here we aim to understand how translational control can contribute to ccRCC development. We evaluated the activity of mTOR pathway and translational control in cell lines and PDX models with VHL mutation through polysome profiling. We observed lower global translational rates in both VHL mutated models suggesting an important role in translational control. Differentially translated genes identified from polysome associated RNA show a specific translational signature in response to VHL deletion. For human tumors, a cohort of 118 cases was selected between metastatic and non-metastatic patients available at A.C. Camargo Cancer Center Tumor Tissue Biobank. Polysome profiling was performed for all cases and show that increased translational rates are associated with reduced overall and progression-free survival. Citation Format: Julia A. Vassalakis, Stenio C. Zequi, Stephania M. Bezerra, Walter H. da Costa, Ola Larsson, Ivan Topisirovic, Glaucia N. Hajj. Polysome profiling suggests VHL-dependent translational control in clear cell renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2441.
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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 | 0.001 | 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) | 0.003 | 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 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".