Abstract 2698: Spatial gene expression profiling in breast cancer
Bibliographic record
Abstract
Abstract Over the last decade breast cancer survival has improved, largely due to the therapies offered to patients with the disease. However, despite the advances in diagnosis and treatment of breast cancer, it is still remains the second leading cause of death from cancer in women. Breast cancer is a heterogeneous disease, this in part, explains why the majority of current therapeutic approaches for cancer work best when multiple agents are combined. The interaction between immune and tumor cells is critical in the development and progression of breast cancer. Here we present in situ transcriptomic profiling, using the NanoString Digital Spatial Profiler (DSP) cancer transcriptomic atlas (CTA) assay, of a cohort of breast cancer lumpectomies to reveal the extent of heterogeneity in pathologically defined unifocal and multifocal cancers. In this study, lumpectomies were processed as whole mounts with serial blocks reviewed. Tissue cores were taken from at least three different regions through the lumpectomy for tissue microarray (TMA) construction, focusing on morphologic/histological differences in addition to the spatial orientation of the sampled region within the lumpectomy. In situ quantification of 1800 tumor and immune genes across 60 patients revealed heterogeneity of tumor and immune genes in most patients. Expression of genes such as HER2, ER and AKT were enriched in the tumor compartment. Whereas, genes such as COLA1, CD68 and CD3 were enriched in the immune compartment. Using a SpatialDecon algorithm for mixed cell deconvolution on the immune areas heterogeneity of the tumor infiltrate at a cell type levels was observed. Fibroblasts and macrophages were prevalent in all samples while immune dense areas also contained B-cells and T-cells. While there are a number of clinically validated transcriptional assays available for breast cancer, we have demonstrated that the immune microenvironment needs to be considered to develop rational stratification of patients to currently available targeted therapies. Citation Format: Melanie Spears, Vida Talebian, Linda Liao, Megan Hopkins, Drashti Jain, Mary Anne Quintayo, Jane Bayani, Alison Cheung, Martin Yaffe, John M. Bartlett. Spatial gene expression profiling in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2698.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".