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Influence on [18F]FDG uptake by cancer cells after anti-PD-1 therapy in an enforced-immune activated mouse tumor.

ABSTRACT: BACKGROUND:Anti-programmed cell death 1 (PD-1) antibody is an immune checkpoint inhibitor, and anti-PD-1 therapy improves the anti-tumor functions of T cells and affects tumor microenvironment. We previously reported that anti-PD-1 treatment affected tumor glycolysis by using 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography (PET). That study showed that anti-PD-1 therapy in a mouse B16F10 melanoma model increased glucose metabolism in cancer cells at the point where anti-PD-1 therapy did not cause a significant inhibition of tumor growth. However, the B16F10 melanoma model is poorly immunogenic, so it is not clear how anti-PD-1 treatment affects glucose metabolism in highly immunogenic cancer models. In this study, we used a cyclic dinucleotide GMP-AMP (cGAMP)-injected B16F10 melanoma model to investigate the effect of anti-PD-1 therapy on [18F]FDG uptake in a highly immune activated tumor in mice. RESULTS:To compare the cGAMP-injected B16F10 model with the B16F10 model, experiments were performed as described in our previous manuscript. [18F]FDG-PET was measured before treatment and 7 days after the start of treatment. In this study, [18F]FDG uptake in tumors in the cGAMP/anti-PD-1 combination group was lower than that in the anti-PD-1 treatment group tumors on day 7, as shown by PET and ex vivo validation. Flow-cytometry was performed to assess immune cell populations and glucose metabolism. Anti-PD-1 and/or cGAMP treatment increased the infiltration level of immune cells into tumors. The cGAMP/anti-PD-1 combination group had significantly lower levels of GLUT1high cells/hexokinase IIhigh cells in CD45- cancer cells compared with tumors in the anti-PD-1 treated group. These results suggested that if immune responses in tumors are higher than a certain level, glucose uptake in cancer cells is reduced depending on that level. Such a change of glucose uptake might be caused by the difference in infiltration or activation level of immune cells between the anti-PD-1 treated group and the cGAMP/anti-PD-1 combination group. CONCLUSIONS:[18F]FDG uptake in cancer cells after anti-PD-1 treatment might be affected by the tumor immune microenvironment including immune cell infiltration, composition, and activation status.

SUBMITTER: Tomita M

PROVIDER: S-EPMC7080890 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Influence on [<sup>18</sup>F]FDG uptake by cancer cells after anti-PD-1 therapy in an enforced-immune activated mouse tumor.

Tomita Mayu M Suzuki Motofumi M Kono Yusuke Y Nakajima Kohei K Matsuda Takuma T Kuge Yuji Y Ogawa Mikako M

EJNMMI research 20200319 1

<h4>Background</h4>Anti-programmed cell death 1 (PD-1) antibody is an immune checkpoint inhibitor, and anti-PD-1 therapy improves the anti-tumor functions of T cells and affects tumor microenvironment. We previously reported that anti-PD-1 treatment affected tumor glycolysis by using 2-deoxy-2-[<sup>18</sup>F]fluoro-D-glucose ([<sup>18</sup>F]FDG) positron emission tomography (PET). That study showed that anti-PD-1 therapy in a mouse B16F10 melanoma model increased glucose metabolism in cancer c ...[more]

PMID: 32189078

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Project description:BackgroundTo quantify the relationship between [18F]FDG uptake of the primary tumour measured by PET-imaging with immunohistochemical (IHC) expression of ER, PR, HER2, Ki-67, and clinical subtypes based on these markers in breast cancer patients.MethodsPubMed and Embase were searched for studies that compared SUVmax between breast cancer patients negative and positive for IHC expression of ER, PR, HER2, Ki-67, and clinical subtypes based on these markers. Two reviewers independently screened the studies and extracted the data. Standardized mean differences (SMD) and 95% confidence intervals (CIs) were estimated by using DerSimonian-Laird random-effects models. P values less than or equal to 5% indicated statistically significant results.ResultsFifty studies were included in the final analysis. SUVmax is significantly higher in ER-negative (31 studies, SMD 0.66, 0.56-0.77, P < 0.0001), PR-negative (30 studies, SMD 0.56; 0.40-0.71, P < 0.0001), HER2-positive (32 studies, SMD - 0.29, - 0.49 to - 0.10, P = 0.0043) or Ki-67-positive (19 studies, SMD - 0.77; - 0.93 to - 0.61, P < 0.0001) primary tumours compared to their counterparts. The majority of clinical subtypes were either luminal A (LA), luminal B (LB), HER2-positive or triple negative breast cancer (TNBC). LA is associated with significantly lower SUVmax compared to LB (11 studies, SMD - 0.49, - 0.68 to - 0.31, P = 0.0001), HER2-positive (15 studies, SMD - 0.91, - 1.21 to - 0.61, P < 0.0001) and TNBC (17 studies, SMD - 1.21, - 1.57 to - 0.85, P < 0.0001); and LB showed significantly lower uptake compared to TNBC (10 studies, SMD - 0.77, - 1.05 to - 0.49, P = 0.0002). Differences in SUVmax between LB and HER2-positive (9 studies, SMD - 0.32, - 0.88 to 0.24, P = 0.2244), and HER2-positive and TNBC (17 studies, SMD - 0.29, - 0.61 to 0.02, P = 0.0667) are not significant.ConclusionPrimary tumour SUVmax is significantly higher in ER-negative, PR-negative, HER2-positive and Ki-67-positive breast cancer patients. Luminal tumours have the lowest and TNBC tumours the highest SUVmax. HER2 overexpression has an intermediate effect.

Project description:BackgroundAssessment of dual time point (DTP) positron emission tomography was carried out with the aim of a quantitative determination of Km, the metabolic uptake rate of [18F]fluorodeoxyglucose as a measure of glucose consumption.MethodsStarting from the Patlak equation, it is shown that Km?mt/ca0+V?r/?a, where mt is the secant slope of the tissue response function between the dual time point measurements centered at t?=?t0. ca0=ca(t0) denotes arterial tracer concentration, V?r is an estimate of the Patlak intercept, and ?a is the time constant of the ca(t) decrease. We compared the theoretical predictions with the observed relation between Ks=mt/ca0 and Km in a group of nine patients with liver metastases of colorectal cancer for which dynamic scans were available, and Km was derived from conventional Patlak analysis. Twenty-two lesion regions of interest (ROIs) were evaluated. ca(t) was determined from a three-dimensional ROI in the aorta. Furthermore, the correlation between Km and late standard uptake value (SUV) as well as retention index was investigated. Additionally, feasibility of the approach was demonstrated in a whole-body investigation.ResultsPatlak analysis yielded a mean Vr of V?r=0.53±0.08 ml/ml. The patient averaged ?a was 99?±?23 min. Linear regression between Patlak-derived Km and DTP-derived Ks according to Ks?=?b?·?Km?+?a yielded b?=?0.98?±?0.05 and a?=?-0.0054?±?0.0013 ml/min/ml (r?=?0.98) in full accordance with the theoretical predictions b?=?1 and a?-V?r/?a. Ks exhibits better correlation with Km than late SUV and retention index, respectively. Ks(c)=Ks+V?r/?a is proposed as a quantitative estimator of Km which is independent of patient weight, scan time, and scanner calibration.ConclusionQuantification of Km from dual time point measurements compatible with clinical routine is feasible. The proposed approach eliminates the issues of static SUV and conventional DTP imaging regarding influence of chosen scanning times and inter-study variability of the input function. Ks and Ks(c) exhibit improved stability and better correlation with the true Km. These properties might prove especially relevant in the context of radiation treatment planning and therapy response control.

Project description:PurposeTo quantify the post-radiotherapy 2-[(18)F]-fluoro-2-deoxyglucose (FDG) pulmonary uptake dose-response in lung cancer patients and determine its relationship with radiation pneumonitis symptoms.Methods and materialsThe data from 24 patients treated for lung cancer with thoracic radiotherapy who received restaging PET/CT imaging between 4 and 12 weeks after radiotherapy completion were evaluated. Their radiation dose distribution was registered with the post-treatment restaging PET/CT. Using histogram analysis, the voxel average FDG-PET uptake vs. radiation dose was obtained for each case and linear regression was performed. The resulting slope, the pulmonary metabolic radiation response (PMRR), was used to characterize the dose-response. The Common Toxicity Criteria version 3 was used to score clinical pulmonary toxicity symptoms. Receiver operating characteristic (ROC) curves were used to determine the level of FDG uptake vs. dose, MLD, V(5), V(10), V(20), and V(30) that can best predict symptomatic and asymptomatic patients.ResultsThe median time between radiotherapy completion and FDG-PET imaging was 59 days (range, 26-70 days). The median of the mean SUV from lung that received 0-5 Gy was 1.00 (range, 0.37-1.48), 5-10 Gy was 1.01 (range, 0.37-1.77), 10-20 Gy was 1.04 (0.42-1.53), and >20 Gy was 1.29 (range, 0.41-8.01). Using the dose range of 0 Gy to the maximum dose minus 10 Gy, hierarchical linear regression model of the radiation dose and normalized FDG uptake per case found an adequate fit with the linear model. Pneumonitis scores were: Grade 0 for 13, Grade 1 for 5, Grade 2 for 6, and Grade 3, 4 or 5 for none. Using a PMRR threshold of 0.017 yields an associated true positive rate of 0.67 and false positive rate of 0.15 with average error of 30%. A V(5) threshold of 57.6 gives an associated true positive rate of 0.67 and false positive rate of 0.05 with a 20% average error.ConclusionThe metabolic radiation pneumonitis dose-response was evaluated from post-treatment FDG-PET/CT imaging. Statistical modeling found a linear relationship. The FDG uptake dose-response and V(5) correlated with symptomatic radiation pneumonitis.

Project description:We introduce a novel computational framework to enable automated identification of texture and shape features of lesions on (18)F-FDG-PET images through a graph-based image segmentation method. The proposed framework predicts future morphological changes of lesions with high accuracy. The presented methodology has several benefits over conventional qualitative and semi-quantitative methods, due to its fully quantitative nature and high accuracy in each step of (i) detection, (ii) segmentation, and (iii) feature extraction. To evaluate our proposed computational framework, thirty patients received 2 (18)F-FDG-PET scans (60 scans total), at two different time points. Metastatic papillary renal cell carcinoma, cerebellar hemongioblastoma, non-small cell lung cancer, neurofibroma, lymphomatoid granulomatosis, lung neoplasm, neuroendocrine tumor, soft tissue thoracic mass, nonnecrotizing granulomatous inflammation, renal cell carcinoma with papillary and cystic features, diffuse large B-cell lymphoma, metastatic alveolar soft part sarcoma, and small cell lung cancer were included in this analysis. The radiotracer accumulation in patients' scans was automatically detected and segmented by the proposed segmentation algorithm. Delineated regions were used to extract shape and textural features, with the proposed adaptive feature extraction framework, as well as standardized uptake values (SUV) of uptake regions, to conduct a broad quantitative analysis. Evaluation of segmentation results indicates that our proposed segmentation algorithm has a mean dice similarity coefficient of 85.75 ± 1.75%. We found that 28 of 68 extracted imaging features were correlated well with SUV(max) (p<0.05), and some of the textural features (such as entropy and maximum probability) were superior in predicting morphological changes of radiotracer uptake regions longitudinally, compared to single intensity feature such as SUV(max). We also found that integrating textural features with SUV measurements significantly improves the prediction accuracy of morphological changes (Spearman correlation coefficient = 0.8715, p<2e-16).

Dataset Information

Influence on [18F]FDG uptake by cancer cells after anti-PD-1 therapy in an enforced-immune activated mouse tumor.

Publications

Influence on [<sup>18</sup>F]FDG uptake by cancer cells after anti-PD-1 therapy in an enforced-immune activated mouse tumor.

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