Imaging Colon Cancer Response Following Treatment with AZD1152: A Preclinical Analysis of [18F]Fluoro-2-deoxyglucose and 3′-deoxy-3′-[18F]Fluorothymidine Imaging

Moroz, Maxim A.; Kochetkov, Tatiana; Cai, Sanjun; Wu, Jiyuan; Shamis, Mikhail; Nair, Jayasree S.; Stanchina, Elisa de; Serganova, Inna; Schwartz, Gary K.; Banerjee, Debabrata; Bertino, Joseph R.; Blasberg, Ronald G.

doi:10.1158/1078-0432.ccr-10-1430

Cited by 28 publications

(35 citation statements)

References 47 publications

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“…The wide range of pretreatment FLT uptake values may be explained by the wide variation in proliferation (13,26,27). Other mechanisms including relative use of salvage versus de novo precursors for DNA synthesis could also influence FLT uptake (28,29). We used the SUV at 60 minutes as a semiquantitative measure of this biochemical activity.…”

Section: Discussionmentioning

confidence: 99%

[18F]-3′Deoxy-3′-Fluorothymidine Positron Emission Tomography and Breast Cancer Response to Docetaxel

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Kenny

Stebbing

et al. 2011

Clinical Cancer Research

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Purpose: To establish biomarkers indicating clinical response to taxanes, we determined whether early changes in [18 F]-3 0 deoxy-3 0 -fluorothymidine positron emission tomography (FLT-PET) can predict benefit from docetaxel therapy in breast cancer. Experimental Design: This was a prospective unblinded study in 20 patients with American Joint Committee on Cancer (AJCC) stage II-IV breast cancer unresponsive to first-line chemotherapy or progressing on previous therapy. Individuals underwent a baseline dynamic FLT-PET scan followed by a scan 2 weeks after initiating the first or second cycle of docetaxel. PET variables were compared with anatomic response midtherapy (after 3 cycles).Results: Average and maximum tumor standardized uptake values at 60 minutes (SUV 60,av and SUV 60, max ) normalized to body surface area ranged between 1.7 and 17.0 and 5.6 and 26.9 Â 10 À5 m 2 /mL, respectively. Docetaxel treatment resulted in a significant decrease in FLT uptake (P ¼ 0.0003 for SUV 60,av and P ¼ 0.0002 for SUV 60,max ). Reduction in tumor SUV 60,av was associated with target lesion size changes midtherapy (Pearson R for SUV 60,av ¼ 0.64; P ¼ 0.004) and predicted midtherapy target lesion response (0.85 sensitivity and 0.80 specificity). Decreases in SUV 60,av in responders were due, at least in part, to reduced net intracellular trapping of FLT (rate constant, K i ). Docetaxel significantly reduced K i by 51.1% (AE28.4%, P ¼ 0.0009).Conclusion: Changes in tumor proliferation assessed by FLT-PET early after initiating docetaxel chemotherapy can predict lesion response midtherapy with good sensitivity warranting prospective trials to assess the ability to stop therapy in the event of non-FLT-PET response. Clin Cancer Res; 17(24); 7664-72. Ó2011 AACR.

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Section: Discussionmentioning

confidence: 99%

[18F]-3′Deoxy-3′-Fluorothymidine Positron Emission Tomography and Breast Cancer Response to Docetaxel

Contractor

Kenny

Stebbing

et al. 2011

Clinical Cancer Research

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“…The ADC dispersion rate averaged in the range 50-250 Hz was shown sensitive to treatment of colorectal tumor SW620 with barasertib (AZD1152) [51], known to induce the formation of new chromosomic structures at subcellular level, increased cell size and eventually apoptosis [52].…”

Section: Adc Dispersion Ratementioning

confidence: 99%

Time-Dependent Diffusion MRI in Cancer: Tissue Modeling and Applications

Reynaud

2017

Front. Phys.

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In diffusion weighted imaging (DWI), the apparent diffusion coefficient (ADC) has been recognized as a useful and sensitive surrogate for cell density, paving the way for non-invasive tumor staging, and characterization of treatment efficacy in cancer. However, microstructural parameters, such as cell size, density and/or compartmental diffusivities affect diffusion in various fashions, making of conventional DWI a sensitive but non-specific probe into changes happening at cellular level. Alternatively, tissue complexity can be probed and quantified using the time dependence of diffusion metrics, sometimes also referred to as temporal diffusion spectroscopy when only using oscillating diffusion gradients. Time-dependent diffusion (TDD) is emerging as a strong candidate for specific and non-invasive tumor characterization. Despite the lack of a general analytical solution for all diffusion times/frequencies, TDD can be probed in various regimes where systems simplify in order to extract relevant information about tissue microstructure. The fundamentals of TDD are first reviewed (a) in the short time regime, disentangling structural and diffusive tissue properties, and (b) near the tortuosity limit, assuming weakly heterogeneous media near infinitely long diffusion times. Focusing on cell bodies (as opposed to neuronal tracts), a simple but realistic model for intracellular diffusion can offer precious insight on diffusion inside biological systems, at all times. Based on this approach, the main three geometrical models implemented so far (IMPULSED, POMACE, VERDICT) are reviewed. Their suitability to quantify cell size, intra-and extracellular spaces (ICS and ECS) and diffusivities are assessed. The proper modeling of tissue membrane permeability-hardly a newcomer in the field, but lacking applications-and its impact on microstructural estimates are also considered. After discussing general issues with tissue modeling and microstructural parameter estimation (i.e., fitting), potential solutions are detailed. The in vivo applications of this new, non-invasive, specific approach in cancer are reviewed, ranging from the characterization of gliomas in rodent brains and observation of time-dependence in breast tissue lesions and prostate cancer, to the recent preclinical evaluation of new treatments efficacy. It is expected that clinical applications of TDD will strongly benefit the community in terms of non-invasive cancer screening.

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“…Using a cell-based assay, FLT retention in cells was found to be strongly associated with TK1 expression (5). In some cases, tumor cells predominately synthesize the nucleosides that are needed for cell growth de novo (6), which results in low [ 18 F]FLT tracer avidity in the proliferating tumor, leading to false negative results (7,8). Clearly, in order for [ 18 F]FLT-PET imaging to "light up" proliferating cells, a number of factors must come into play.…”

Section: Introductionmentioning

confidence: 99%

“…In this respect, translational imaging research has been considerably emphasized for correlating [ 18 F]FLT-PET imaging readouts with target biomarker changes that are measured by other tools in the preclinical and clinical arenas. A number of preclinical [ 18 F]FLT-PET translational imaging studies have reported using early investigative anticancer agents, including the MAP/ERK kinase inhibitor PD-0325901 (11), the epidermal growth factor receptor (EGFR) inhibitor erlotinib (12), the mTOR inhibitor everolimus (13), the P-cadherin antibody PF-03732010 (14), and the aurora B kinase inhibitor AZD1152 (7). Using the tumor models that are responding to therapy, these reports showed that treatment-induced modulation of the targets is closely associated with a decline in [ 18 F]FLT retention.…”

Section: Introductionmentioning

confidence: 99%

[18F]FLT–PET Imaging Does Not Always “Light Up” Proliferating Tumor Cells

Zhang

Yan

et al. 2012

Clinical Cancer Research

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Imaging Colon Cancer Response Following Treatment with AZD1152: A Preclinical Analysis of [18F]Fluoro-2-deoxyglucose and 3′-deoxy-3′-[18F]Fluorothymidine Imaging

Abstract: Purpose: To determine whether treatment response to the Aurora B kinase inhibitor, AZD1152, could be monitored early in the course of therapy by noninvasive

Cited by 28 publications

References 47 publications

[18F]-3′Deoxy-3′-Fluorothymidine Positron Emission Tomography and Breast Cancer Response to Docetaxel

[18F]-3′Deoxy-3′-Fluorothymidine Positron Emission Tomography and Breast Cancer Response to Docetaxel

Time-Dependent Diffusion MRI in Cancer: Tissue Modeling and Applications

[18F]FLT–PET Imaging Does Not Always “Light Up” Proliferating Tumor Cells

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