Behind the Screens: A Literature Review for High Throughput Proteomic Screens Related to RET
Bibliographic record
Abstract
Rearranged during transfection (RET) is a proto-oncogene that encodes a transmembrane receptor tyrosine kinase critical for kidney, nervous system, and male germ cell development. Due to its implication in various cancers, targeting RET for therapeutic purposes has garnered significant interest. While multi-kinase inhibitors have shown efficacy, selective RET inhibitors like selpercatinib and pralsetinib offer promising alternatives. Nonetheless, challenges such as acquired resistance and long-term safety concerns persist with RET inhibitors, thus emphasizing the necessity of identifying alternative inhibitory methods that block RET’s signaling pathways. In this regard, high-throughput proteomic screens (HTPSs), such as mass spectrometry, can offer valuable insights into RET’s signaling pathways and potential therapeutic targets. Given the significance of HTPSs in RET oncogenic research and the absence of comprehensive reviews, this literature review aims to outline strategies for identifying RET-related high-throughput proteomic screening studies and provide an overview of the methodologies employed, thereby aiding research and therapeutic development efforts. In total, the literature review strategy comprised two phases: an initial search on Google Scholar to compile relevant keywords and MeSH terms, followed by a formal review using a systematic workflow for OVID and Google Scholar. This systematic workflow incorporated MeSH terms and keywords to identify relevant articles from 2013 to 2023, which were then filtered based on predetermined criteria. Of the 734 articles screened, only 11 studies met the inclusion and exclusion criteria. Notable keywords like proto-oncogene proteins c-ret (n = 6) and proteomics (n = 5) yielded the most relevant articles, whereas others like mass spectrometry (n = 0) and liquid chromatography (n = 0) were less productive. Furthermore, OVID (n = 6) and Google Scholar (n = 5) contributed almost equally to the findings, with some overlap in retrieved articles (n = 1). While most studies employed label-free quantification with mass spectrometry (n = 7), some utilized traditional labeling approaches or alternative proteomic screens like reverse phase protein arrays (n = 3) and mammalian-membrane two-hybrid assays (n = 1). Overall, the search strategy effectively addressed the study’s objectives, but there is room for improvement, such as incorporating additional keywords to capture the latest advancements in HTPS, expanding the databases used, and incorporating a "double-check" step to enhance reliability in article selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".