Preclinical Lymph Node Model for Intraoperative Molecular Imaging of Cancer.

cancer imaging intraoperative molecular imaging lymph node model

Journal

Research square
Titre abrégé: Res Sq
Pays: United States
ID NLM: 101768035

Informations de publication

Date de publication:
12 Jun 2023
Historique:
pubmed: 3 7 2023
medline: 3 7 2023
entrez: 3 7 2023
Statut: epublish

Résumé

Lymph node(LN) dissection is part of most oncologic resections. Intraoperatively identifying a positive LN(+ LN), that harbors malignant cells, can be challenging. We hypothesized that intraoperative molecular imaging(IMI) using a cancer-targeted fluorescent prober can identify + LNs. This study aimed to develop a preclinical model of a + LN and test it using an activatable cathepsin-based enzymatic probe, VGT-309. In the first model, we used peripheral blood mononuclear cells (PBMC), representing the lymphocytic composition of the LN, mixed with different concentrations of human lung adenocarcinoma cell line A549. Then, they were embedded in a Matrigel A significant difference in MFI from our PBMC control was noted when A549 cells were 25% of the LN (p = 0.046) in both 3D cell aggregate models-where the LNs native parenchyma is replaced and the one where the tumor grows over the native parenchyma. For the anthracitic equivalents of these models, the first significant MFI compared to the control was when A549 cells were 9% of the LN (p = 0.002) in the former model, and 16.7% of the LN (p = 0.033) in the latter. In our spleen model, we first noted significance in MFI when A549 cells were 16.67% of the cellular composition.(p = 0.02). A + LN model allows for a granular evaluation of different cellular burdens in + LN that can be assessed using IMI. This first exvivo + LN model can be used in preclinical testing of several existing dyes and in creating more sensitive cameras for IMI-guided LN detection.

Identifiants

pubmed: 37398120
doi: 10.21203/rs.3.rs-2953015/v1
pmc: PMC10312951
pii:
doi:

Types de publication

Preprint

Langues

eng

Subventions

Organisme : NCI NIH HHS
ID : P01 CA254859
Pays : United States

Déclaration de conflit d'intérêts

Conflict of Interest The author(s) declare(s) that there is no conflict of interest regarding the publication of this article.

Auteurs

Patrick Bou-Samra (P)

University of Pennsylvania Perelman School of Medicine.

Austin Chang (A)

University of Pennsylvania Perelman School of Medicine.

Sachinthani Arambepola (S)

University of Pennsylvania Perelman School of Medicine.

Emily Guo (E)

University of Pennsylvania Perelman School of Medicine.

Feredun Azari (F)

University of Pennsylvania Perelman School of Medicine.

Gregory Kennedy (G)

University of Pennsylvania Perelman School of Medicine.

Alix Segil (A)

University of Pennsylvania Perelman School of Medicine.

Sunil Singhal (S)

University of Pennsylvania Perelman School of Medicine.

Classifications MeSH