ESTRO 2022 - Abstract Book

S315

Abstract book

ESTRO 2022

MO-0380 Radiosensitivity Index and M2 macrophage in tumor microenvironment of glioblastoma

B. Jang 1 , I.A. Kim 1

1 Seoul National University Bundang Hospital, Radiation Oncology, Seongnam, Korea Republic of

Purpose or Objective Tumor-associated macrophages (TAMs) Macrophage are predominant in glioblastoma tumor microenvironment (TME), supporting for neoplastic cell expansion and invasion. We investigated the relationship between radiosensitivity of glioblastoma and M1/M2 macrophage profiles in bulk and single cell RNA sequencing datasets. Materials and Methods We used radiosensitivity index (RSI) gene signature and estimated RSI score based on the ranking of genes by expression level. Two large glioma datasets ─ The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) were employed to identify whether RSI is clinically predictive of overall survival following radiation therapy. To analyze the association between M1/M2 macrophages and RSI within spatial context, the Ivy Glioblastoma Atlas Project dataset was investigated and single cell RNA sequencing dataset (GSE84465) was analyzed as well. Macrophages were profiled using a deconvolution algorithm, CIBERSORTx. Results The RSI-high group having radioresistant tumors showed worse overall survival than the RSI-low group in both the TCGA (HR=1.87, 95% CI=1.06-3.29, P=0.031) and the CGGA (HR=1.61, 95% CI=1.04-2.50, P=0.031) glioblastoma population. In the Ivy Glioblastoma Atlas Project dataset, radiosensitive tumor having lower RSI was significantly more found in more vascular region including hyperplastic and microvascular region (coefficient=-0.07, P=0.001), meanwhile, radioresistant tumor was significantly clustered in necrotic region including perinecrotic and pseudopalisading regions (coefficient=0.07, P<0.001). The proportion of M1/M2 macrophage and RSI score showed an inverse relationship (coefficient=-0.23, P=0.015), indicating that radioresistant glioblastomas are related with TME having more M2 than M1 macrophage. In single cell RNA sequencing dataset composed of immune and tumor cells collected from four patients, mean RSI of neoplastic cells was positively correlated with high proportion of M2 macrophages. Conclusion RSI can predict radiation response in terms of overall survival in glioblastoma patients. High proportion of M2 macrophage may play an important role in TME of radioresistant glioblastoma. N.A. Iacovelli 1 , T. Rancati 2 , R. Ingargiola 1 , S. Alfieri 3 , L. De Cecco 4 , F. Badenchini 2 , A. Cavallo 5 , A. Cicchetti 2 , N. Zaffaroni 6 , V. Doldi 6 , E. Mancinelli 4 , M.S. Serafini 4 , A. Devecchi 4 , R. Valdagni 7 , E. Orlandi 1 1 Fondazione IRCCS Istituto Nazionale Tumori , Division of Radiation Oncology 2, Milan, Italy; 2 Fondazione IRCCS Istituto Nazionale Tumori , Prostate Cancer Program, Milan, Italy; 3 Fondazione IRCCS Istituto Nazionale Tumori , Division of Medical Oncology 3, Milan, Italy; 4 Fondazione IRCCS Istituto Nazionale Tumori , Department of Applied Research and Technology Development, Milan, Italy; 5 Fondazione IRCCS Istituto Nazionale Tumori , Division of Medical Physics, Milan, Italy; 6 Fondazione IRCCS Istituto Nazionale Tumori , Division of Molecular Pharmacology, Milan, Italy; 7 Fondazione IRCCS Istituto Nazionale Tumori , Division of Radiation Oncology 1, Milan, Italy Purpose or Objective We hypothesised that the saliva microbiota (MB) and cytokine levels before radiotherapy (RT) differ between patients with/wout acute toxicity (tox) after RT for and head&neck (HNC) cancer Materials and Methods We enrolled 114 consecutive HNC pts treated with conventional (54-70Gy @2Gy/fr) or moderately hypofractionated (46.6- 69.9Gy @2.1-2.2Gy/fr) VMAT+IGRT. A detailed evaluation was done pre-, during & at RT end, including saliva MB measures (16S sequencing and pooling in Operational Taxonomic Units -OTUs- with Uclust software) and saliva assessment of cytokines (TN α , IL1b according to previous literature, Bossi2016). Tox was scored weakly using CTCAE; we chose a longitudinal definition of tox, taking both severity & duration into account. Average grade>1.5 for oral mucositis during RT (aOM) was the endpoint for this analysis We used logistic regression (LR) to derive inflammation signatures (based on cytokine levels at baseline) and unsupervised clustering (fuzzy c-means) to partition pts into MB clusters based on the relative abundance of OTUs before RT start. Information on inflammation & MB clustering was introduced in a sigmoid-shaped dosimetric a Normal Tissue Complication Probability (NTCP) model to test their added value. MO-0381 Saliva microbiota and inflammation markers predict acute toxicity after RT for head-and-neck cancer

Results