Abstract PO-084: Protein marker heterogeneity in breast cancer subtypes measured using immunofluorescence protein multiplexing and quantitative, single cell image analysis
Bibliographic record
Abstract
Abstract The extent of intra-tumoral heterogeneity - the variation in the composition of cells in a given tumor and those in the local tumor microenvironment - could have potential impact on diagnosis, treatment planning and subsequent response to treatment. To evaluate the extent of cancer cellular heterogeneity, we conducted quantitative protein marker multiplex imaging to study the variations in protein marker expression patterns on individual cells and spatial localizations. A multiplexed immunofluorescence imaging platform (MxIF, Cell DIVE) was used to measure the cellular expression of Estrogen Receptor (ER), Progesterone Receptor (PR), Epidermal Growth Factor Receptor 2 (HER2), Ki67, p53, p21WAF1 and p16INK4A in cancer epithelium. Analysis was conducted on a tissue microarray (TMA) representing subtypes classified as Luminal A-like, Luminal B-like (HER2-negative), Luminal B-like (HER2-positive), HER2-positive (non-luminal) or Triple-negative based on tumor grade and immune activity according to the St. Gallen surrogate classification. Of the 101 cores from 59 cases studied, high levels of heterogeneity were observed in ER and PR expression among the hormonal receptor-positive tumors. As expected Luminal A-like cancers exhibited higher proportions of individual cells co-expressing ER and PR, while cells in Luminal B-like, HER2-negative cancers showed ER expression only. Luminal B-like, HER2-positive cores were composed of cells with strong HER2 staining, and some cells co-expressing PR and HER2. Single cells with strong ER and HER2 labelling were rarely observed. Spatial visualizations illustrated that cells with similar expression signatures tend to be clustered together. Among cases which showed p53 overexpression with immunohistochemistry, the overall MxIF-measured p53 level was highest in TNBC compared to HER2+ and Luminal B-like cases. TNBC exhibited the highest proliferative fraction and most incidence of abnormal p53 and p16. We did not observe an association of p21 expression to P53 or P16 patterns, yet a slightly higher proportion of Luminal B-like cancers showed increased P21 levels compared to the other subtypes. Our study demonstrated the application of protein marker multiplexing and quantitative image analysis in measuring heterogeneity of protein co-expression signatures within breast cancer subtypes. Our next step is to apply the methods developed here to study a cohort where molecular profiling and radiomics were conducted (Bayani et al.) to reveal the extent of heterogeneity of breast cancer with a multi-omics approach. Citation Format: Alison M. Cheung, Dan Wang, Kela Liu, Fiona Ginty, Sharon Nofech-Mozes, Jane Bayani, John M.S. Bartlett, Anne Martel, Martin J. Yaffe. Protein marker heterogeneity in breast cancer subtypes measured using immunofluorescence protein multiplexing and quantitative, single cell image analysis [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-084.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".