Abstract 2450: A systemic approach to decipher the interactome of RET receptor isoforms
Bibliographic record
Abstract
Abstract The REarranged during Transfection (RET) receptor tyrosine kinase is pivotal for normal tissue development, but is also an oncogene driver involved in several human cancers. Alternative splicing at the 3' end of the RET gene leads to expression of two conserved protein isoforms, RET9 and RET51, that differ in their subcellular localization and protein trafficking, as well as their functional roles in tumorigenesis and metastatic processes. Importantly, RET9 and RET51 have unique C-terminal phospho-tyrosine binding sites, suggesting that they may also differ in their interactomes. We used a combination of cell-based screening and in silico approaches to identify novel potential interaction partners of RET isoforms. We performed a Mammalian Membrane Two-Hybrid (MaMTH) screen using a library of SH2 domain-containing adaptor and signaling proteins, to identify interactions with each RET isoform. We complemented these studies by using sequence homology detection models (HMM, PSSM), based on SH2 domain sequences known to interact with RET, to rank the SH2 library members and predict novel interactions. Independently, we compared published consensus binding sequences for each SH2 domain library member with predicted RET phosphotyrosine motifs to identify potential interactors. Predicted interactions were validated in co-immunoprecipitation assays. We confirmed previously known interactions of RET with SH2 domain proteins including SHC1, GRB2 and GRB10, and identified additional novel RET-binding proteins, a subset of which showed differential interactions that were mediated through RET isoform-specific docking sites. Our results suggest that combinations of distinct interaction partners may contribute to RET isoform-specific functions. Together, our research has developed a systematic approach to map and characterize RET isoform interactions. Our data suggest that no single method identified all confirmed RET interactions, and that a combination of multiple approaches improves characterization of growth factor receptor interactomes. Citation Format: Samira Kheitan, Annika E. Pedersen, Brandy D. Hyndman, Luka Drecun, Punit Saraon, Igor Stagliar, Lois M. Mulligan. A systemic approach to decipher the interactome of RET receptor isoforms [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 2450.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".