Abstract 5775: A unique 3D tissue-engineered human melanoma model combining lymphatic and blood microvasculature to study cancer cell dissemination
Bibliographic record
Abstract
Abstract INTRODUCTION: Melanoma is amongst malignancies with constantly increasing incidence in developed countries. One of the underlying causes of death for patients diagnosed with melanoma is metastasis, which can spread through lymphatic or blood vessels. Mechanisms controlling the dissemination paths are poorly understood and relevant models for studying metastasis physiopathology are often inadequate. To address this, we hypothesized that the human tumor microenvironment can be mimicked in vitro by combining tissue-engineered microvascularized skin and melanoma microtissues. METHODS: Tissue-engineered skin was produced using primary dermal, epidermal and microvascular endothelial cells by the self-assembly technique without any exogenous biomaterial. Tumor microtissues were produced using the hanging drop method. Six melanoma cell lines were used originating from primary tumor sites (A375, SK-MEL 28 and WM983a) and from metastatic sites (RPMI 7951, Malme 3M and WM983b). Tumor development and growth were assessed by histology, immunofluorescence and confocal microscopy, while cytokine secretion profiles were determined by ELISA. WM983a and WM983b models were treated for 11 days with vemurafenib. Response to treatment was assessed by counting the ratio of tumor cells positive for Ki67, representative of the tumor proliferation. RESULTS: We obtained a tissue-engineered skin displaying two distinct microvascular networks: a VE-cadherin+ CD31+ blood network, and a PDPN+ LYVE-1+ CD31+ lymphatic network. Blood capillaries were thin and highly connected whereas lymphatic capillaries were larger and presented a distinct morphology. Histological analyses revealed tumor microtissue integration at the dermoepidermal junction within the reconstructed skin. The pro-lymphangiogenic factor and tumor-secreted VEGF-C was detected in conditioned media from the melanoma microtissues (662 pg/ml). Furthermore, CCL21, a chemoattractant known to be secreted by lymphatic endothelial cells, displayed secretion levels that were 10-fold higher in microvascularized tissues compared to the non-microvascularized skin (P ≤ 0.001). Both of these cytokines are involved in the cross-talk between tumor cells and capillaries, and thus in tumor dissemination. The 3D melanoma model responded to vemurafenib with up to a 5-fold decrease of tumor cell proliferation and a partial pigmentation of the tumor. CONCLUSION: This unique 3D in vitro melanoma model mimics tumor microenvironment by combining blood and lymphatic capillaries with melanoma microtissues in a reconstructed skin. Being responsive to treatment such as vemurafenib, it represents a valuable tool for studying mechanisms of metastasis and drug response in a fully human cell and matrix microenvironment, and thus testing anti-metastatic compounds could better predict their safety and efficacy. Citation Format: Jennifer Bourland, Julie Fradette, François A. Auger. A unique 3D tissue-engineered human melanoma model combining lymphatic and blood microvasculature to study cancer cell dissemination [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5775. doi:10.1158/1538-7445.AM2017-5775
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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) | 0.001 | 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".