Abstract A022: Synthetic lethality of <i>ERBB2</i> and <i>CCND1</i> in breast cancer at scale
Bibliographic record
Abstract
Abstract Introduction: Synthetically lethal mutations offer unique molecular targets for oncologic therapy. At scale, synthetic lethality (SL) is observed when alterations to one gene alone do not correspond with worse overall survival (OS), but simultaneous expression with another gene does correspond with worse OS. Mutually exclusive gene expression is not a requirement for SL. Existing breast cancer (BC) literature suggests synthetic lethality between CKS1B and PLK1, as well as BRCA1/BRCA2 and PARP1. Unaltered RB1 and altered CCND1 are also known to exhibit SL in the HER2-deficient environment of triple-negative BC. CCND1 and ERBB2 are typically amplified in invasive ductal carcinoma, with ERBB2 amplifications correlating to larger tumor size. This relationship is not observed in invasive lobular carcinoma, although expression of E2F1, a downstream transcription factor of CCND1, is inversely correlated with tumor grade. Methods: Our in silico approach investigated SL between ERBB2 and CCND1 in breast invasive carcinoma without distinction to lobular or ductal origin (TCGA, PanCancer Atlas 2018). We identified 17 genes of interest based on molecular alterations present in >10% of the sample (n=1084) and common mentions in BC literature. SL was determined if alterations to one gene alone did not significantly worsen OS, while alterations to two genes corresponded with worse OS. Results: Twenty-three SL interactions were observed. CCND1 and ERBB2 were selected because neither gene significantly worsened OS alone (p=0.915 & p=0.0951 respectively). However, patients with alterations to both genes had significantly worse OS compared to those with alterations to one gene alone. CCND1 and ERBB2 alterations were present in 14.9% and 13.7% of the sample, respectively. HR=2.568 (p=0.0138) for SL patients compared to those with CCND1 alterations alone; HR=2.244 (p=0.0497) for SL patients compared to those with ERBB2 alterations alone. Mutual exclusivity was not significantly observed (p=0.084). Multivariate analysis was performed using Cox regression models; neither age (older or younger than 55 years) nor race (White, Black, Asian) confounded our results due to p>0.05. Conclusion: Our findings support potential therapeutic use of CCND1 targets in HER2+ BC, specifically for patients with SL between CCND1 and ERBB2. These include PLK1 inhibitors, commonly used in estrogen receptor-positive patients with CDK4/6 inhibitor resistance, along with HER2 inhibitors. A combination of MAP2K and PIK3CA inhibitors should also be investigated in HER2+ BC patients with CCND1 amplification, since HER2+ colorectal cancer patients have been demonstrated to fare better with this treatment. Citation Format: Rishi Nair, Evan P. White, Roy Khalife, Anthony M. Magliocco. Synthetic lethality of ERBB2 and CCND1 in breast cancer at scale [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A022.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".