Abstract 5567: Breast cancer cell alteration of immune cell transcriptome through indirect communication
Bibliographic record
Abstract
Abstract Crosstalk between tumor and non-tumor cells plays an important role in cancer progression and metastasis often leading to a modification of non-malignant cells into pro-oncogenic phenotypes. Understanding this process is important for diagnostic and therapeutic advances. We previously reported clinical validation results for a new way to detect early-stage breast cancer using a panel of RNA biomarkers from whole blood and a machine learning software-powered testing system. Recently, we showed that immune cells undergo transcriptomic and functional changes upon direct contact with breast cancer cells. Here, we report results from investigation of indirect communication between breast cancer and immune cells leading to cancer-driven immune cell transformation and activation of multiple oncogenic pathways. To characterize these modifications, we performed RNAseq and pathway analysis on a human monocytic cell line, THP1, exposed to media from triple negative breast cancer (TNBC) cells (MDA-MB-231) in culture. Our results demonstrate that 24 hours of THP1 cell exposure to soluble factors secreted by tumor cells leads to significant gene modulation, with over 1,100 differentially expressed genes (|Fold Change|>2; p<0.05). KEGG and Reactome enrichment analysis revealed overlap of multiple pathways between THP1 cells exposed to direct and indirect cancer cell contact conditions, such as cytokine-cytokine receptor interaction, JAK-STAT signaling pathways and others. TNF, NMP1, STING1 and TGM2 were identified as key upstream regulators potentially driving this transformation in both direct and indirect contact conditions. The S100 pathway, which we previously found to be activated by fluid shear stress in MDA-MB-231 cells was also activated in THP1 cells upon both direct and indirect contact. The observed gene expression changes in THP1 cells exposed to media from MDA-MB-231 suggests that indirect intercellular communication alone can transmit an oncogenic signal. These results support a role for secreted factors from breast cancer cells in transforming immune cells and modulating the gene expression markers detected by our blood test, expanding the potential for leveraging this type of tumor education response in advancing cancer detection and treatment. Citation Format: Jacob Kennard, Karen Norek, Kenneth Fuh, Robert D. Shepherd, Kristina D. Rinker, Olesya A. Kharenko. Breast cancer cell alteration of immune cell transcriptome through indirect communication [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5567.
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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) | 0.004 | 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".