Abstract PO-053: Investigating intra-tumour metabolic heterogeneity in triple-negative breast cancer
Bibliographic record
Abstract
Abstract Intra-tumour heterogeneity is a great challenge in cancer treatment. Cancer cells presenting different metabolic profiles can mutually support each other through metabolic symbiosis mechanism, which contributes to tumour proliferation, metastasis, and therapeutic resistance. Triple-negative breast cancer (TNBC) shows poor clinical outcomes due to a high level of heterogeneity and a lack of targeted therapies. Through multiplex immunofluorescence tissue section staining of TNBC patient-derived xenografts (PDX), we identified two mutually exclusive and metabolically distinct cell populations within a TNBC tumour. One cancer cell population expresses glucose transporter 1 (GLUT1) and is located in hypoxic areas, whereas another specifically expresses glutamine synthetase (GS) and is located near vasculatures. Single-cell RNA sequencing data from a TNBC PDX with mutually exclusive GLUT1- and GS-positive cells shows that GLUT1-positive cells are enriched for hypoxic and glycolytic gene signatures, while GS-positive cells are associated with glutathione, pyrimidine and purine metabolism. Importantly, our data suggest that GLUT1- and GS-positive cells exchange glutamate and lactate metabolites as part of metabolic symbiosis and targeting this mechanism could successfully impair the growth of TNBC. Citation Format: Marina Fukano, Geneviève Deblois, Dongmei Zuo, Constanza Martinez, Yang Yang, Ioannis Ragoussis, Claudia Kleinman, Morag Park. Investigating intra-tumour metabolic heterogeneity in triple-negative breast cancer [abstract]. In: Abstracts: AACR Special Virtual Conference on Epigenetics and Metabolism; October 15-16, 2020; 2020 Oct 15-16. Philadelphia (PA): AACR; Cancer Res 2020;80(23 Suppl):Abstract nr PO-053.
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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 | 0.000 | 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) | 0.003 | 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".