Ottolenghi, A. | bioRxiv Science

bioRxiv2021

IL-1α secreted by subcapsular sinus macrophages promotes melanoma metastasis in the sentinel lymph node by upregulating STAT3 signaling in the tumor.

During melanoma metastasization, tumor cells originated in the skin migrate via lymphatic vessels to the sentinel lymph node (sLN) in a process that facilitates their spread across the body. Here, we characterized the innate inflammatory responses to melanoma metastasis in the sLN. For this purpose, we confirmed the migration of fluorescent metastatic melanoma cells to the sLN and we characterized the inflammatory response in the metastatic microenvironment. We found that macrophages located in the subcapsular sinus (SSM), produce pro-tumoral IL-1 after recognition of tumor antigens. Moreover, we confirmed that the administration of an anti-IL-1 depleting antibody reduced metastasis. Conversely, the administration of recombinant IL-1 accelerated the lymphatic spreading of the tumor. Additionally, the elimination of the macrophages significantly reduced the progression of the metastatic spread. To understand the mechanism of action of IL-1 in the context of the lymph node microenvironment, we applied single-cell RNA sequencing to dissected metastases obtained from animals treated with an anti-IL-1 blocking antibody. Amongst the different pathways affected, we identified STAT3 as one of the main targets of IL-1 signaling in metastatic cells. Moreover, we found that the anti-IL-1 anti-tumoral effect was not mediated by lymphocytes, as IL-1R1 KO mice did not show any improvement in metastasis growth. Finally, we found a synergistic anti-metastatic effect of the combination of IL-1 blocking and the STAT3 inhibitor (STAT3i) stattic. In summary, we described a new mechanism by which SSM support melanoma metastasis, highlighting a new target for immunotherapy.

immunology↗

bioRxiv2020

Radiation-induced cell cycle perturbations: acomputational tool validated with flow-cytometrydata

Cell cycle progression can be studied with computational models that allow to describe and predict its perturbation by agents as ionizing radiation or drugs. Such models can then be integrated in tools for pre-clinical/clinical use, e.g. to optimize kinetically-based administration protocols of radiation therapy and chemotherapy. We present a deterministic compartmental model, specifically reproducing how cells that survive radiation exposure are distributed in the cell cycle as a function of dose and time after exposure. Model compartments represent the four cell-cycle phases, as a fuction of DNA content and time. A system of differential equations, whose parameters represent transition rates, division rate and DNA synthesis rate, describes the temporal evolution. Initial model inputs are data from unexposed cells in exponential growth. Perturbation is implemented as an alteration of model parameters that allows to best reproduce cell-cycle profiles post-irradiation. The model is validated with dedicated in vitro measurements on human lung fibroblasts (IMR90). Cells were irradiated with 2 and 5 Gy with a Varian 6 MV Clinac at IRCCS Maugeri. Flow cytometry analysis was performed at the RadBioPhys Laboratory (University of Pavia), obtaining cell percentages in each of the four phases in all studied conditions up to 72 hours post-irradiation. Cells show early G2-phase block (increasing in duration as dose increases) and later G1-phase accumulation. For each condition, we identified the best sets of model parameters that lead to a good agreement between model and experimental data, varying transition rates from G1- to S- and from G2- to M-phase. This work offers a proof-of-concept validation of the new computational tool, opening to its future development and, in perspective, to its integration in a wider framework for clinical use. Author summaryWe implemented a computational model able to describe how the progression in the cell cycle is perturbed when cells are exposed to ionizing radiation. It is known that radiation causes delays or arrest in cell cycle progression, and also that cells that are in different phases of the cycle at the time of exposure show different sensitivity to radiation. Chemotherapeutic drugs also affect cell cycle, and their action can be phase-specific. These findings can be exploited to find the optimal protocol of a combined radiotherapy/chemotherapy cancer treatment: to this aim, we need to know not only the effectiveness of an agent (dose/drug) in terms of cell killing, but also how surviving cells are distributed in the cell cycle. With the model we present, this information can be reproduced as a function of dose and time after radiation exposure. To test the model performance we measured distributions of cells in different phases of the cycle (using flow-cytometry) for human healthy fibroblast cells exposed to X-rays. The results of this work constitute a first step for further development of our model and its future integration in a tool for pre-clinical/clinical use.

biophysics↗