Abstract B34: Signaling in the tumor microenvironment: Proteomics analyses of stromal-tumor interaction in oral cancers
Bibliographic record
Abstract
Abstract Paracrine signaling between cancer-associated fibroblasts (CAFs) and cancer cells creates a bidirectional and cooperative network that drives cancer growth and progression. During the development of oral squamous cell carcinoma (OSCC), alterations in the tumor microenvironment and secretion of soluble proteins from subpopulations of CAFs generate a niche that has functional implications on tumor progression. To investigate stromal heterogeneity in OSCC and identify fibroblast-associated proteins that actively contribute to oral cancer carcinogenesis, we employed a proteomics approach to uncover the secretome of patient-derived oral CAFs. Communication in the tumor microenvironment can be mediated by classically secreted molecules, as well as by the release of extracellular vesicles, which are involved in the transfer of oncogenic factors to recipient cells. Hence, we comprehensively characterized the protein content of fibroblasts-derived conditioned media (CM) and exosomes (Exo), to identify secreted, cytoplasmic or membrane-associated molecules that could function as regulators of OSCC progression. Furthermore, to investigate how secreted signals from the surrounding stroma interact with target surface receptors, we also generated a comprehensive proteomic database of highly purified plasma membrane proteins isolated from two established tongue cancer cell lines (SCC4 and SCC25). The aim of the current study is to investigate the molecular crosstalk between CAF-derived factors and epithelial oral cancer cells to improve our understanding of the complex tumor-stroma interactions. Matched pairs of human primary fibroblasts were isolated from resected tongue cancers (CAFs) and tumor adjacent tissue (AFs), characterized according to morphology, expression of myofibroblast markers (α-SMA, tropomyosin), and the ability to degrade collagen. CM and Exo were collected after 48 hours of serum deprivation and Exo were purified by differential ultracentrifugation. Plasma membrane molecules from SCCs were isolated using colloidal silica-beads followed by density gradient ultracentrifugation. Each sample was analyzed by nano-flow ultra-performance liquid chromatography (UHPLC) coupled to a Q-Exactive tandem mass spectrometer. Our proteomic analyses quantified a total of 6638 proteins, 2855 in the CAFs/NAFs secretome (CM and Exo) and 5754 in the SCCs lines (membrane depleted (MD) and plasma membrane (PM) fractions) using the MaxQuant pipeline. CM was highly enriched in fibroblast-secreted proteins such as MMPs, VIM, IGFBPs, SPARC and Gene Ontology (GO) terms related to cytoplasmic and extracellular components. The quality of the Exo purification was confirmed by the presence of known markers such as CD81, CD63, TSG101 and FLOT1 and GO enrichment for endosomal and cytoplasmic vesicle-related terms. We used a subtractive, quantitative proteomics approach to highlight a CAF-enriched exosomal cluster consisting of 255 proteins groups differentially expressed, compared to patient matched AFs. Comparative Reactome pathway analysis revealed that this cluster is significantly enriched in metabolic enzymes involved in glycolysis and catabolic processes, as well as membrane-bound signaling receptors and proteins involved in transport or vesicles trafficking. Our proteomic analyses provide a detailed overview of signaling factors, receptors and intracellular proteins, associated with the induction of a pro-invasive stroma. These findings highlight differential expression of key signal transduction molecules that have been previously associated with cancer, albeit their precise roles in cancer progression need to be further validated. Our CAF-enriched secretome signature, complemented with the plasma membrane proteomics of tongue cancer cells represent a comprehensive data resource to investigate molecular signaling mechanisms within the tumor microenvironment. Citation Format: Simona Principe, Vladimir Ignatchenko, Alexander Ignatchenko, Ankit Sinha, Keira Pereira, Laurie Ailles, Thomas Kislinger. Signaling in the tumor microenvironment: Proteomics analyses of stromal-tumor interaction in oral cancers. [abstract]. In: Abstracts: AACR Special Conference on Cellular Heterogeneity in the Tumor Microenvironment; 2014 Feb 26-Mar 1; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(1 Suppl):Abstract nr B34. doi:10.1158/1538-7445.CHTME14-B34
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 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".