A handheld and biocompatible mass spectrometry system provides nondestructive molecular diagnosis of cancer tissue samples.

Objective: The goal of this research project was to develop a volumetric strategy for real-time monitoring and characterization of tumor blood flow using microbubble contrast agents and ultrasound (US) imaging. Materials and Methods: Volumetric contrast-enhanced US (VCEUS) imaging was implemented on a SONIX RP US system (Ultrasonix Medical Corp, Richmond, BC) equipped with a broadband 4DL14–5/38 probe. Using a microbubble-sensitive harmonic imaging mode (transducer transmits at 5 MHz and receives at 10 MHz), acquisition of postscan-converted VCEUS data was achieved at a volume rate of 1 Hz. After microbubble infusion, custom data processing software was used to derive microbubble time-intensity curve-specific parameters, namely, blood volume (I_PK), transit time (T_1/2PK), flow rate (S_PK), and tumor perfusion (AUC). Results: Using a preclinical breast cancer animal model, it is shown that millimeter-sized deviations in transducer positioning can have profound implications on US-based blood flow estimators, with errors ranging from 6.4% to 40.3% and dependent on both degree of misalignment (offset) and particular blood flow estimator. These errors indicate that VCEUS imaging should be considered in tumor analyses, because they incorporate the entire mass and not just a representative planar cross-section. After administration of an antiangiogenic therapeutic drug (bevacizumab), tumor growth was significantly retarded compared with control tumors (P > 0.03) and reflects observed changes in VCEUS-based blood flow measurements. Analysis of immunohistologic data revealed no differences in intratumoral necrosis levels (P = 0.70), but a significant difference was found when comparing microvessel density counts in control with therapy group tumors (P = 0.05). Conclusions: VCEUS imaging was shown to be a promising modality for monitoring changes in tumor blood flow. Preliminary experimental results are encouraging, and this imaging modality may prove clinically feasible for detecting and monitoring the early antitumor effects in response to cancer drug therapy.

This study identifies physiological habitats using quantitative magnetic resonance imaging (MRI) to elucidate intertumoral differences and characterize microenvironmental response to targeted and cytotoxic therapy. BT-474 human epidermal growth factor receptor 2 (HER2+) breast tumors were imaged before and during treatment (trastuzumab, paclitaxel) with diffusion-weighted MRI and dynamic contrast-enhanced MRI to measure tumor cellularity and vascularity, respectively. Tumors were stained for anti-CD31, anti-ɑSMA, anti-CD45, anti-F4/80, anti-pimonidazole, and H&E. MRI data was clustered to identify and label each habitat in terms of vascularity and cellularity. Pre-treatment habitat composition was used stratify tumors into two “tumor imaging phenotypes” (Type 1, Type 2). Type 1 tumors showed significantly higher percent tumor volume of the high-vascularity high-cellularity (HV-HC) habitat compared to Type 2 tumors, and significantly lower volume of low-vascularity high-cellularity (LV-HC) and low-vascularity low-cellularity (LV-LC) habitats. Tumor phenotypes showed significant differences in treatment response, in both changes in tumor volume and physiological composition. Significant positive correlations were found between histological stains and tumor habitats. These findings suggest that the differential baseline imaging phenotypes can predict response to therapy. Specifically, the Type 1 phenotype indicates increased sensitivity to targeted or cytotoxic therapy compared to Type 2 tumors.

This study identifies physiological tumor habitats from quantitative magnetic resonance imaging (MRI) data and evaluates their alterations in response to therapy. Two models of breast cancer (BT-474 and MDA-MB-231) were imaged longitudinally with diffusion-weighted MRI and dynamic contrast-enhanced MRI to quantify tumor cellularity and vascularity, respectively, during treatment with trastuzumab or albumin-bound paclitaxel. Tumors were stained for anti-CD31, anti-Ki-67, and H&E. Imaging and histology data were clustered to identify tumor habitats and percent tumor volume (MRI) or area (histology) of each habitat was quantified. Histological habitats were correlated with MRI habitats. Clustering of both the MRI and histology data yielded three clusters: high-vascularity high-cellularity (HV-HC), low-vascularity high-cellularity (LV-HC), and low-vascularity low-cellularity (LV-LC). At day 4, BT-474 tumors treated with trastuzumab showed a decrease in LV-HC (p = 0.03) and increase in HV-HC (p = 0.03) percent tumor volume compared to control. MDA-MB-231 tumors treated with low-dose albumin-bound paclitaxel showed a longitudinal decrease in LV-HC percent tumor volume at day 3 (p = 0.01). Positive correlations were found between histological and imaging-derived habitats: HV-HC (BT-474: p = 0.03), LV-HC (MDA-MB-231: p = 0.04), LV-LC (BT-474: p = 0.04; MDA-MB-231: p < 0.01). Physiologically distinct tumor habitats associated with therapeutic response were identified with MRI and histology data in preclinical models of breast cancer.