Metabolic assessment of the action of targeted cancer therapeutics using magnetic resonance spectroscopy

Developing rational targeted cancer drugs requires the implementation of pharmacodynamic (PD), preferably non-invasive, biomarkers to aid response assessment and patient follow-up. Magnetic resonance spectroscopy (MRS) allows the non-invasive study of tumour metabolism. We describe the MRS-detectable PD biomarkers resulting from the action of targeted therapeutics, and discuss their biological significance and future translation into clinical use

Model free approach to kinetic analysis of real-time hyperpolarized (13)c magnetic resonance spectroscopy data

Real-time detection of the rates of metabolic flux, or exchange rates of endogenous enzymatic reactions, is now feasible in biological systems using Dynamic Nuclear Polarization Magnetic Resonance. Derivation of reaction rate kinetics from this technique typically requires multi-compartmental modeling of dynamic data, and results are therefore model-dependent and prone to misinterpretation. We present a model-free formulism based on the ratio of total areas under the curve (AUC) of the injected and product metabolite, for example pyruvate and lactate. A theoretical framework to support this novel analysis approach is described, and demonstrates that the AUC ratio is proportional to the forward rate constant k. We show that the model-free approach strongly correlates with k for whole cell in vitro experiments across a range of cancer cell lines, and detects response in cells treated with the pan-class I PI3K inhibitor GDC-0941 with comparable or greater sensitivity. The same result is seen in vivo with tumor xenograft-bearing mice, in control tumors and following drug treatment with dichloroacetate. An important finding is that the area under the curve is independent of both the input function and of any other metabolic pathways arising from the injected metabolite. This model-free approach provides a robust and clinically relevant alternative to kinetic model-based rate measurements in the clinical translation of hyperpolarized (13)C metabolic imaging in humans, where measurement of the input function can be problematic

Representative dynamic ¹³C time-courses from an <i>in vivo</i> HT29 colon carcinoma xenograft at 7 T.

<p>The model fits to pyruvate, lactate and alanine are shown with a solid line and residuals between the data and the plots are displayed.</p

Digital holographic imaging as a method for quantitative, live cell imaging of drug response to novel targeted cancer therapies

Digital holographic imaging (DHI) is a noninvasive, live cell imaging technique that enables long-term quantitative visualization of cells in culture. DHI uses phase-shift imaging to monitor and quantify cellular events such as cell division, cell death, cell migration, and drug responses. In recent years, the application of DHI has expanded from its use in the laboratory to the clinical setting, and currently it is being developed for use in theranostics. Here, we describe the use of the DHI platform HoloMonitorM4 to evaluate the effects of novel, targeted cancer therapies on cell viability and proliferation using the HeLa cancer cell line as a model. We present single cell tracking and population-wide analysis of multiple cell morphology parameters

<i>In vitro</i> AUC ratios plotted against forward rate constant (<i>k_PL</i>), derived from the 2-site model.

<p>Data is normalized to initial pyruvate concentration and cell number. An excellent correlation is observed between AUC ratio and <i>k_PL</i> across a range of cell lines. Clustering between cell types can also be seen, and spread between data points of the same cell type tends to be in the direction of the best-fit line.</p

<i>In vivo</i> AUC ratios plotted against forward rate constant (<i>k_PL</i>), derived from kinetic modeling.

<p>Data was acquired from tumor xenografts at 3 T (triangles) or 7 T (circles). A strong correlation is observed between AUC ratio and <i>k_PL</i> at 3 T and 7 T for both HT29 and SW1222 xenografts. Drug treatment with dichloroacetate (DCA) did not appear to affect the relationship between <i>k_PL</i> and AUC ratio.</p

A representation of the fate of hyperpolarized [1-¹³C]pyruvate (P) that is injected into a system with input function <i>P_in</i>(<i>t</i>).

<p>Observable ¹³C signals originating from [1-¹³C]pyruvate are indicated in red. The schematic shows the transport of pyruvate into a cell, facilitated by MCT1 transporters, and its conversion to other metabolites. Solid lines correspond to the cell membrane and dashed lines to the mitochondrial membrane. is the effective relaxation rate of the hyperpolarized signal for metabolite <i>i</i>. Conversion to metabolites [1-¹³C]lactate (L), [1-¹³C]alanine (A), and [1-¹³C]bicarbonate (B) occur with reaction rates (<i>k</i>), and enzymes that catalyze reactions are shown. <i>k_EL</i> and <i>k_LE</i> are the rates of lactate transport into and out of the cell, governed by the MCT4 transporters. Entry of pyruvate into the TCA cycle results in conversion of the 1-¹³C label to CO₂ and then to bicarbonate. Acetyl-CoA is not seen owing to the [1-¹³C] label of pyruvate being utilized in the formation of CO₂. The grey box indicates the terms that need to be considered for the AUC ratio analysis method when the reaction of interest is pyruvate-lactate conversion, whereas kinetic modeling requires fitting of all terms depicted here, except for acetyl-CoA.</p