Katja Pinker-Domenig

Multiparametric MRI of the breast: A review

During their development, cancers acquire several functional capabilities, which are defined as the hallmarks of cancer. For a deeper understanding of the hallmarks of cancer, and, consequently, improved personalized patient care, diagnostic tests must be multilayered and complex to identify the relevant underlying processes of cancer development and progression. In this context, magnetic resonance imaging (MRI) has emerged as an exceptionally powerful, versatile, and precise imaging technique. MRI of the breast is an essential tool in breast imaging, with multiple indications. Dynamic contrast‐enhanced MRI (CE‐MRI) is the most sensitive test for breast cancer detection, with a good specificity. CE‐MRI provides mainly morphological, and, to some extent, functional information about tumor perfusion and vascularity. Recently, several functional imaging techniques in MRI, such as diffusion‐weighted imaging and spectroscopy, have been assessed for breast imaging and this combined application is defined as multiparametric imaging. Furthermore, the application of higher field strengths (≥3T) has demonstrated improved sensitivity and specificity of breast cancer detection. Multiparametric imaging with different functional MRI parameters (mpMRI) visualizes and quantifies the functional processes of cancer development and progression at multiple levels, and provides specific information about the hallmarks of cancer. MpMRI of the breast improves diagnostic accuracy in breast cancer, obviates unnecessary breast biopsies, and enables an improved assessment and prediction of response to neoadjuvant therapy. This review will provide a comprehensive overview of the current possibilities and emerging techniques for mpMRI of the breast.

Dynamical complex network theory applied to the therapeutics of brain malignancies

An important problem in modern therapeutics at the metabolomic, transcriptomic or phosphoproteomic level remains to identify therapeutic targets in a plentitude of high-throughput data from experiments relevant to a variety of diseases. This paper presents the application of novel graph algorithms and modern control solutions applied to the graph networks resulting from specific experiments to discover disease-related pathways and drug targets in glioma cancer stem cells (GSCs). The theoretical frameworks provides us with the minimal number of ”driver nodes” necessary to determine the full control over the obtained graph network in order to provide a change in the network’s dynamics from an initial state (disease) to a desired state (non-disease). The achieved results will provide biochemists with techniques to identify more metabolic regions and biological pathways for complex diseases, and design and test novel therapeutic solutions.

Technical Note: Scintillation well counters and particle counting digital autoradiography devices can be used to detect activities associated with genomic profiling adequacy of biopsy specimens obtained after a low activity 18F‐FDG injection

PURPOSE Genomic profiling of biopsied tissue is the basis for precision cancer therapy. However, biopsied materials may not contain sufficient amounts of tumor deoxyribonucleonic acid needed for the analysis. We propose a method to determine the adequacy of specimens for performing genomic profiling by quantifying their metabolic activity. METHODS We estimated the average density of tumor cells in biopsy specimens needed to successfully perform genomic analysis following the Memorial Sloan Kettering Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) protocol from the minimum amount of deoxyribonucleonic acid needed and the volume of tissue typically used for analysis. The average 18 F-FDG uptake per cell was assessed by incubating HT-29 adenocarcinoma tumor cells in 18 F-FDG containing solution and then measuring their activity with a scintillation well counter. Consequently, we evaluated the response of two devices around the minimum expected activities which would indicate genomic profiling adequacy of biopsy specimens obtained under 18 F-FDG PET/CT guidance. Surrogate samples obtained using 18G core needle biopsies of gels containing either 18 F-FDG-loaded cells in the expected concentrations or the corresponding activity were measured using autoradiography and a scintillation well counter. Autoradiography was performed using a CCD-based device with real-time image display as well as with digital autoradiography imaging plates following a 30-min off-line protocol for specimen activity determination against previously established calibration. RESULTS Cell incubation experiments and estimates obtained from quantitative autoradiography of biopsy specimens (QABS) indicate that specimens acquired under 18 F-FDG PET/CT guidance that contained the minimum amount of cells needed for genomic profiling would have an average activity concentration in the range of about 3 to about 9 kBq/mL. When exposed to specimens with similar activity concentration, both a CCD-based autoradiography device and a scintillation well counter produced signals with sufficient signal-to-background ratio for specimen genomic adequacy identification in less than 10 min, which is short enough to allow procedure guidance. CONCLUSION Scintillation well counter measurements and CCD-based autoradiography have adequate sensitivity to detect the tumor burden needed for genomic profiling during 18 F-FDG PET/CT-guided 18G core needle biopsies of liver adenocarcinoma metastases.

Multiparametric Imaging: Cutting-Edge Sequences and Techniques Including Diffusion-Weighted Imaging, Magnetic Resonance Spectroscopy, and PET/CT or PET/MRI

Magnetic resonance imaging (MRI) of the breast is an indispensible tool in breast imaging, with several indications. Dynamic contrast-enhanced MRI (DCE-MRI) provides mainly morphological, and, to some extent, functional information about perfusion and vascularity, resulting in excellent sensitivity and good specificity for breast cancer diagnosis. Multiparametric imaging of the breast aims to quantify and visualize biological, physiological, and pathological processes at the cellular and molecular levels. Multiparametric imaging of the breast can be performed at different field-strengths (1.5–7 T) and comprises established and emerging MRI parameters, such as diffusion-weighted imaging (DWI), MR spectroscopy (MRS), sodium imaging (23Na MRI), chemical exchange saturation transfer (CEST) imaging, blood oxygen level–dependent (BOLD) MRI, nuclear imaging, such as positron emission tomography (PET) with different radiotracers, and combinations of techniques (e.g., PET/CT and PET/MRI).

Personalized Medicine, Biomarkers of Risk and Breast MRI

Breast cancer is a heterogeneous disease with inter- and intra-tumor genetic variation impacting predictive and prognostic risk. This chapter discusses the use of breast MRI, the most sensitive imaging modality for high-risk screening and pre-operative assessment, to predict breast cancer risk, to define extent of disease and to monitor neoadjuvant chemotherapeutic response at the level of the individual patient. In the current clinical landscape, immunohistochemical surrogates are used to define molecular subtypes and personalized cancer treatment and care. Radiogenomics involves the correlation of genomic information with imaging features. Feature extraction from breast MRI is being pursued on a large scale as a potential non-invasive means of defining molecular subtypes and/or developing phenotypic biomarkers that can be clinically analogous to commercially available genomic assays. Neoadjuvant chemotherapy, treatment administered in operable cancers before surgery, is increasingly used, allowing for breast conservation in women who would traditionally require mastectomy. As breast cancer genetic molecular subtypes are predictive of recurrence free and overall survival, treatment based on breast cancer molecular subtype and breast MRI is critical in evaluating response though improvement in its sensitivity for pathologic complete response. Breast MRI in the neoadjuvant cohort has provided biomarkers of response and insight into the biologic basis of disease. MRI is at the forefront of technology providing prognostic indicators as well as a crucial tool in personalizing medicine.

SU-F-J-07: Evaluating the Adequacy of Biopsy Specimens for Genetic Signature Assessment by Measuring the Metabolic Activity in Specimens Obtained Under 18F-FDG PET/CT Guidance

PURPOSE Genetic profiling of biopsied tissue is the basis for personalized cancer therapy. However biopsied materials may not contain sufficient amounts of DNA needed for analysis. We propose a method to determine the adequacy of specimens for performing genetic profiling by quantifying metabolic activity. METHODS We measured the response of two radiation detectors to the activity contained in the minimum amount of tumor cells needed for genetic profiling in biopsy specimens obtained under 2-deoxy-2-(18 F)fluoro-D-glucose (18 F-FDG) PET/CT guidance. The expected tumor cell concentration in biopsy specimens was evaluated from the amount of DNA needed (∼100 µg) and the number of pathology sections typically used for the analysis. The average 18 F-FDG uptake per cell was measured by incubating KPC-4662 pancreatic tumor cells and HT-29 colorectal adenocarcinoma tumor cells in 18 F-FDG containing solution (activity concentrations between 0.0122 and 1.51 MBq/mL and glucose concentrations of 3.1 and 1 g/L) for 1 to 1.75 hours and then measuring the activity of a known number of cells. Measurements of surrogate specimens obtained using 18G needle biopsies of gels containing these cells in expected concentrations (∼104 µL-1 ) were performed using an autoradiography CCD based device (up to 20 min exposure) and a scintillation well counter (∼1 min measurements) about 3 and 5 hours after the end of incubation respectively. RESULTS At start of autoradiography there were between 0.16 and 1.5 18 F-FDG molecules/cell and between 1.14 and 5.43×10718 F-FDG molecules/mL. For the scintillation well counter, sample to minimum-detectable-count rate ratios were greater than 7 and the counting error was less than 25% for ≤80 s measurement times. Images of the samples were identifiable on the autoradiograph for ∼10 min and longer exposure times. CONCLUSION Scintillation well counter measurements and CCD based autoradiography have adequate sensitivity to detect the tumor burden needed for genetic profiling in 18G core needle biopsies. Supported in part through the NIH/NCI Cancer Center Support Grant P30 CA008748 and by a sponsored research agreement with Biospace Lab S.A.

Visual exploratory analysis of integrated chromosome 19 proteomic data derived from glioma cancer stem-cell lines based on novel nonlinear dimensional data reduction techniques

Chromosome 19 is known to be linked to neurodegeneration and many cancers. Glioma-derived cancer stem cells (GSCs) are tumor-initiating cells and may be refractory to radiation and chemotherapy and thus have important implications for tumor biology and therapeutics. The analysis and interpretation of large proteomic data sets requires the development of new data mining and visualization approaches. Traditional techniques are insufficient to interpret and visualize these resulting experimental data. The emphasis of this paper lies in the presentation of novel approaches for the visualization, clustering and projection representation to unveil hidden data structures relevant for the accurate interpretation of biological experiments. These qualitative and quantitative methods are applied to the proteomic analysis of data sets derived from the GSCs. The achieved clustering and visualization results provide a more detailed insight into the expression patterns for chromosome 19 proteins.