Abstract 4765: Impact of TP53 status and functional classification on molecular profiles in breast cancer subtypes
Bibliographic record
Abstract
Abstract Breast cancer is a multifaceted disease with several clinical, pathological and molecular attributes contributing to disease prognosis or treatment outcome. Treatment measures in breast cancer are based on hormone/growth factor receptor -estrogen/progesterone receptor (ER/PR) or human epidermal growth factor receptor 2 (Her2) status. TP53 pathway inactivation in breast cancer is well-established. Although TP53's therapeutic relevance is well-recognized, it remains under-utilized in patient-management, since all TP53 mutants are treated equally in the diagnostic context. In reality, enormous heterogeneity exists in nature, type and functional impact of TP53 variants. Therefore, understanding the diversity of TP53 variants in breast cancer subtypes may enhance its diagnostic utility in this cancer. We utilized clinical NGS data, obtained using commercially available targeted panels, TruSeq Amplicon Cancer Panel (Illumina) and Ion AmpliSeq Cancer Hotspot Panel v2 (Thermofisher) to analyze tumor DNAs from cancer patients at the Advanced Molecular Diagnostic Laboratory (Princess Margaret Cancer Centre, Toronto, Canada). We focused on data from 105 advanced breast cancer patients. We consolidated several schemes proposed in the literature to classify TP53 variants, and evaluated patient molecular profiling and pathology data based on: (1) presence of TP53 variants; (b) coding effect; and (c) transcriptional activity. We further investigated whether TP53 variants were associated with reportable variant load, co-occurrence with other molecular changes and hormone/growth-factor receptor status. In our study group, 70.4% cases carried one or more variants. TP53 alterations were prevalent (40.9%) in our cohort, followed by PIK3CA variants (36.2%). 15/105 cases (14.3%) carried variants in both genes. Unlike in other cancer types, where missense TP53 variants predominate (e.g., colorectal, 72.6%), missense (49%) and nonsense/frameshift (42%) variants were similarly distributed in breast cancers. Gain-of-Function (GOF) and Loss-of-Function (LOF) TP53 variants were also equally distributed (32% vs. 33%). However, TP53mut PIK3CAmut breast cancer cases were more likely to carry missense and/or LOF variants (10/15 cases). TP53 variants were also associated with hormone/growth-factor receptor status. A greater proportion of ER- vs ER+, PR- vs PR+, and ER-PR-Her2- vs ER+PR+Her2- breast cancer cases carried missense GOF TP53 variants respectively when compared to missense LOF and variants of unknown significance taken together(80-85% vs 50-55%; p<0.0001). Finally, TP53mut cases were more likely to carry multiple variants in contrast to TP53wt cases (37.2% vs. 12.9%). Taken together, we define a stratification strategy for TP53 that takes into account the diversity of TP53 variants, and demonstrate its application to molecular profiling and clinico-pathological data in breast cancer. Citation Format: Swati Garg, Mahadeo A. Sukhai, Maksym Misyura, Mariam Thomas, Tong Zhang, Lillian L. Siu, Philippe L. Bedard, Tracy L. Stockley, Suzanne Kamel-Reid. Impact of TP53 status and functional classification on molecular profiles in breast cancer subtypes. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4765.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".