Abstract SP112: Spatial variance signatures/Intra-tumor zonation in TNBC
Bibliographic record
Abstract
Abstract It is increasingly clear that triple negative breast cancer (TNBC) is a heterogeneous disease with variable clinical presentation, histological features and response to therapy. While these differences have been partially explained by inter- and intra-tumoral heterogeneity, spatial heterogeneity represented by the tumor architecture plays a critical role in clonal evolution and displays a landscape for different compartment-specific processes and cell-to-cell specific interactions. To better understand the impact of spatial heterogeneity on gene-expression-defined cell populations, we have coupled single-cell RNA sequencing with multiplex immunofluorescence (IF). Multiplex immunofluorescence using specific markers for each cell cluster, integrated with computational image analyses and neighborhood maps, has revealed spatial zonation of single cell subpopulations. Zonation of single cell populations was coupled to hypoxia and overlayed distinct metabolic tumor zones and defined areas of differential stress and cell plasticity that give rise to tumor cells with enhanced fitness and aggressivity. Citation Format: M Park, C Martínez Ramirez, Y Yang, A Blanchet-Cohen, H Kuasne, A Fortier, J Ragoussis, P Savage, A Omeroglou, S Meterissian, S Costantino, C Kleinman. Spatial variance signatures/Intra-tumor zonation in TNBC [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr SP112.
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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 | 0.000 |
| 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".