Abstract 3012: Characterizing the functional significance of "variant of uncertain significance" of the tumour suppressor <i>CDH1</i>
Bibliographic record
Abstract
Abstract We aim to develop a new approach to systematically characterize the functional significance of “variant of uncertain significance (VUS),” focusing on the tumor suppressor gene CDH1. Genetic testing is a powerful clinical tool for identifying individuals at risk of developing inherited or familial cancers. If a potential cancer-causing genetic variant (mutation) is found, clinicians can begin managing the patient’s risk early by initiating surveillance or prophylactic treatment. For an aggressive form of inherited stomach cancer called hereditary diffuse gastric cancer (HDGC), the most commonly mutated gene is CDH1, which encodes the tumor suppressor E-Cadherin. However, due to insufficient clinical evidence, over 50% of all publicly reported CDH1 variants either have conflicting interpretations or cannot be classified, thus are not actionable. Consequently, almost 1 in 2 patients who undergo genetic testing for HDGC will not have a definitive test result and cannot benefit from early screening, causing strain on the healthcare system and psychological burden for the patients. To overcome this challenge, we developed a single-cell screening system to accurately classify CDH1 variants using a combination of functional assays, machine learning, and deep learning. Our hypothesis, now supported by preliminary data, is that pathogenic variants will disrupt normal E-Cadherin’s ability to control its signaling partner beta-catenin. Thus, loss-of-function (LoF) CDH1 variants would cause protein mislocalization and altered signaling which lead to abnormal proliferation, an early indicator for carcinogenesis. We express CDH1 variants in human cell lines and use multiplexed high-content imaging to visualize E-Cadherin and beta-catenin. Next, our deep learning pipeline extracts single-cell phenotypic profiles, and our classifier then classifies variants as functional or LoF. Initial analysis of prioritized variants show that our pipeline can accurately separate clinically pathogenic and benign variants based on their functional status as predicted by our pipeline. The outcomes of this project will contribute to a systematic approach to reclassify VUS. Citation Format: Jasmine Wen, Ajay Singh, David Nguyen, Kaitlynn Meier-Ross, Ben Martin, Vedanta Khan, Kiran Dhami, Jesse Chao. Characterizing the functional significance of "variant of uncertain significance" of the tumour suppressor CDH1 [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 3012.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".