Abstract 6233: Characterizing variants of uncertain significance in the tumor suppressor gene known as phosphatase and tensin homolog (PTEN)
Bibliographic record
Abstract
Abstract PTEN is an important tumor suppressor that plays an essential role in regulating cell proliferation. Loss of PTEN function is implicated in approximately 50% of metastatic cancers. Individuals with pathogenic PTEN variants are at significantly elevated risk of developing hereditary cancer syndromes such as PTEN hamartoma tumor syndrome. While genetic testing for PTEN is valuable for identifying individuals at risk, 47% of 3065 publicly reported PTEN variants are not conclusively classified; they either have conflicting interpretations of pathogenicity or are labeled as “Variants of Uncertain Significance (VUS)” and are not clinically actionable. The lack of a clear interpretation for these PTEN variants hinders the targeted care and surveillance of patients and places a strain on the healthcare system. To overcome this issue, our aim is to develop a clinically relevant tool for assessing the pathogenic effects of PTEN variants.We hypothesize that loss of function (LoF) PTEN variants will lead to changes in cellular phenotypic profiles that can be measured through single-cell phenotypic profiling. Thus, our goal is to investigate the functional status of exogenously expressed PTEN variants in PTEN-/- HEK 293 cells. We developed a multiplexed high content imaging assay to characterize PTEN and its key signaling partners such as phospho-Akt. Additionally, we developed a deep learning based single-cell phenotypic profiling pipeline named Paracell that uses subcellular segmentation to extract 540 phenotypic features. Cells that exhibit abnormal phenotypic profiles are indicative of loss of PTEN function. Preliminary results from testing 73 missense variants of PTEN showed that functional and LoF PTEN variants form distinctive clusters in a two-dimensional UMAP plot based on Paracell outputs, suggesting that our approach can identify potentially pathogenic variants of PTEN.Through characterizing novel VUS, we can elucidate structural domains involved in PTEN’s tumor suppressing activity. The result of these classifications will enable more optimized detection, diagnosis, and surveillance strategies for PTEN-related cancers. Ultimately, we aim to contribute to improving the genetic testing of PTEN and the development of targeted therapies. Citation Format: Ajay P. Singh, Jasmine Wen, David Nguyen, Vedanta Khan, Kiran Dhami, Kaitlynn Meier-Ross, Benjamin Martin, Jesse T. Chao. Characterizing variants of uncertain significance in the tumor suppressor gene known as phosphatase and tensin homolog (PTEN) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6233.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".