Human Tumor-Associated Macrophages and Neutrophils Regulate Antitumor Antibody Efficacy through Lethal and Sublethal Trogocytosis.

The clinical benefits of tumor-targeting antibodies (tAb) are modest in solid human tumors. The efficacy of many tAbs is dependent on Fc receptor (FcR)-expressing leukocytes that bind Fc fragments of tAb. Tumor-associated macrophages (TAM) and neutrophils (TAN) represent the majority of FcR+ effectors in solid tumors. A better understanding of the mechanisms by which TAMs and TANs regulate tAb response could help improve the efficacy of cancer treatments. Here, we found that myeloid effectors interacting with tAb-opsonized lung cancer cells used antibody-dependent trogocytosis (ADT) but not antibody-dependent phagocytosis. During this process, myeloid cells "nibbled off" tumor cell fragments containing tAb/targeted antigen (tAg) complexes. ADT was only tumoricidal when the tumor cells expressed high levels of tAg and the effectors were present at high effector-to-tumor ratios. If either of these conditions were not met, which is typical for solid tumors, ADT was sublethal. Sublethal ADT, mainly mediated by CD32hiCD64hi TAM, led to two outcomes: (i) removal of surface tAg/tAb complexes from the tumor that facilitated tumor cell escape from the tumoricidal effects of tAb; and (ii) acquisition of bystander tAgs by TAM with subsequent cross-presentation and stimulation of tumor-specific T-cell responses. CD89hiCD32loCD64lo peripheral blood neutrophils (PBN) and TAN stimulated tumor cell growth in the presence of the IgG1 anti-EGFR Ab cetuximab; however, IgA anti-EGFR Abs triggered the tumoricidal activity of PBN and negated the stimulatory effect of TAN. Overall, this study provides insights into the mechanisms by which myeloid effectors mediate tumor cell killing or resistance during tAb therapy. SIGNIFICANCE: The elucidation of the conditions and mechanisms by which human FcR+ myeloid effectors mediate cancer cell resistance and killing during antibody treatment could help develop improved strategies for treating solid tumors.

Preclinical Lymph Node Model for Intraoperative Molecular Imaging of Cancer.

Purpose: 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. Procedures: 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® matrix. A black dye was added to mimic LN anthracosis. Model two was created using a murine spleen, the largest lymphoid organ, injected with various concentrations of A549. To test these models, we co-cultured A549 cells with VGT-309. Mean fluorescence intensity(MFI) was. An independent sample t-test was used to compare the average MFI of each A549:negative control ratio. Results: 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). Conclusions: 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.