Elucidating functional interactions of the RET receptor tyrosine kinase in cancer using a synthetic dosage lethal screen
Bibliographic record
Abstract
RET (REarranged during Transfection) is a receptor tyrosine kinase that plays essential roles in the early development of the kidneys and neural crest-derived lineages (e.g., enteric nervous system). Its ability to activate pro-growth signaling pathways has implicated RET in several malignancies. While a clear oncogenic role for RET has been established, the precise molecular mechanisms that contribute to RET-mediated oncogenic growth are less well known. To address this gap, we performed an unbiased, genome-wide synthetic dosage lethal (SDL) screen in collaboration with Dr. Franco Vizeacoumar (University of Saskatchewan) to uncover genes that are functionally linked to RET-mediated growth of SH-SY5Y neuroblastoma cells. This project used an integrative bioinformatics approach that combined statistical analysis with machine learning to identify and prioritize candidate genes from the screen results. Two genes, ASCL1 and NCAM1, were selected for experimental validation of our approach. The work presented in this thesis provides the first known evidence of a role for ASCL1 in transcription of RET in a neuroblastoma cell line. Furthermore, we show that NCAM1 may play a role in transdifferentiation of SH-SY5Y cells which may modulate cell growth. Together, this project serves as an important proof-of-principle study for assessing the utility of the SDL screen in uncovering genes that contribute to RET-mediated processes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".