Breast cancer is a heterogeneous disease characterized by varying responses to therapeutic agents and significant differences in long-term survival. Thus, there remains an unmet need for early diagnostic and prognostic tools and improved histologic characterization for more accurate disease stratification and personalized therapeutic intervention. This study evaluated a comprehensive metabolic phenotyping method in breast cancer tissue that uses desorption electrospray ionization mass spectrometry imaging (DESI MSI), both as a novel diagnostic tool and as a method to further characterize metabolic changes in breast cancer tissue and the tumor microenvironment. In this prospective single-center study, 126 intraoperative tissue biopsies from tumor and tumor bed from 50 patients undergoing surgical resections were subject to DESI MSI. Global DESI MSI models were able to distinguish adipose, stromal, and glandular tissue based on their metabolomic fingerprint. Tumor tissue and tumor-associated stroma showed evident changes in their fatty acid and phospholipid composition compared with normal glandular and stromal tissue. Diagnosis of breast cancer was achieved with an accuracy of 98.2% based on DESI MSI data (PPV 0.96, NVP 1, specificity 0.96, sensitivity 1). In the tumor group, correlation between metabolomic profile and tumor grade/hormone receptor status was found. Overall classification accuracy was 87.7% (PPV 0.92, NPV 0.9, specificity 0.9, sensitivity 0.92). These results demonstrate that DESI MSI may be a valuable tool in the improved diagnosis of breast cancer in the future. The identified tumor-associated metabolic changes support theories of de novo lipogenesis in tumor tissue and the role of stroma tissue in tumor growth and development and overall disease prognosis. Cancer Res; 75(9); 1828-37. Ó2015 AACR.

Mass spectrometry imaging (MSI) provides the opportunity to investigate tumor biology from an entirely novel biochemical perspective and could lead to the identification of a new pool of cancer biomarkers. Effective clinical translation of histology-driven MSI in systems oncology requires precise colocalization of morphological and biochemical features as well as advanced methods for data treatment and interrogation. Currently proposed MSI workflows are subject to several limitations, including nonoptimized raw data preprocessing, imprecise image coregistration, and limited pattern recognition capabilities. Here we outline a comprehensive strategy for histology-driven MSI, using desorption electrospray ionization that covers (i) optimized data preprocessing for improved information recovery; (ii ) precise image coregistration; and (iii) efficient extraction of tissue-specific molecular ion signatures for enhanced biochemical distinction of different tissue types. The proposed workflow has been used to investigate region-specific lipid signatures in colorectal cancer tissue. Unique lipid patterns were observed using this approach according to tissue type, and a tissue recognition system using multivariate molecular ion patterns allowed highly accurate (>98%) identification of pixels according to morphology (cancer, healthy mucosa, smooth muscle, and microvasculature). This strategy offers unique insights into tumor microenvironmental biochemistry and should facilitate compilation of a large-scale tissue morphology-specific MSI spectral database with which to pursue next-generation, fully automated histological approaches. M ass spectrometry imaging (MSI) of biological tissue sections can provide topographically localized biochemical information to supplement conventional histopathological classification systems (1-3). Together with emerging metabolomicsbased profiling approaches, MSI represents a highly promising approach in molecular systems oncology (4, 5) and is increasingly being used for the discovery of next-generation cancer biomarker panels (6, 7). Among the MSI techniques currently available, the three most commonly used are matrix-assisted laser desorption ionization (MALDI) (2, 6), secondary ion mass spectrometry (SIMS) (8, 9), and desorption electrospray ionization (DESI) (10, 11). With each of these described approaches, operating characteristics and experimental parameters can be modulated to suit specific analytical objectives and can be customized for the identification of particular biomolecular species. Here, we have opted to use the DESI technique as there are several practical advantages with this method for metabolome-wide imaging studies, primarily attributable to lack of requirement for matrix deposition and ambient ionization, which requires minimal sample preparation (11,12).Currently MSI is likely to exert greatest influence at the prognostic and therapeutic stages of the disease continuum (Fig. 1), with three fundamental areas of application in cancer phenotyping. First, it offers a mea...

Efforts to apply nanotechnology in cancer have focused almost exclusively on the delivery of cytotoxic drugs to improve therapeutic index. There has been little consideration of molecularly targeted agents, in particular kinase inhibitors, which can also present considerable therapeutic index limitations. We describe the development of Accurin polymeric nanoparticles that encapsulate the clinical candidate AZD2811, an Aurora B kinase inhibitor, using an ion pairing approach. Accurins increase biodistribution to tumor sites and provide extended release of encapsulated drug payloads. AZD2811 nanoparticles containing pharmaceutically acceptable organic acids as ion pairing agents displayed continuous drug release for more than 1 week in vitro and a corresponding extended pharmacodynamic reduction of tumor phosphorylated histone H3 levels in vivo for up to 96 hours after a single administration. A specific AZD2811 nanoparticle formulation profile showed accumulation and retention in tumors with minimal impact on bone marrow pathology, and resulted in lower toxicity and increased efficacy in multiple tumor models at half the dose intensity of AZD1152, a water-soluble prodrug of AZD2811. These studies demonstrate that AZD2811 can be formulated in nanoparticles using ion pairing agents to give improved efficacy and tolerability in preclinical models with less frequent dosing. Accurins specifically, and nanotechnology in general, can increase the therapeutic index of molecularly targeted agents, including kinase inhibitors targeting cell cycle and oncogenic signal transduction pathways, which have to date proved toxic in humans.

Rapid evaporative ionization mass spectrometry (REIMS) was investigated for its suitability as a general identification system for bacteria and fungi. Strains of 28 clinically relevant bacterial species were analyzed in negative ion mode, and corresponding data was subjected to unsupervised and supervised multivariate statistical analyses. The created supervised model yielded correct cross-validation results of 95.9%, 97.8%, and 100% on species, genus, and Gram-stain level, respectively. These results were not affected by the resolution of the mass spectral data. Blind identification tests were performed for strains cultured on different culture media and analyzed using different instrumental platforms which led to 97.8-100% correct identification. Seven different Escherichia coli strains were subjected to different culture conditions and were distinguishable with 88% accuracy. In addition, the technique proved suitable to distinguish five pathogenic Candida species with 98.8% accuracy without any further modification to the experimental workflow. These results prove that REIMS is sufficiently specific to serve as a culture condition-independent tool for the identification and characterization of microorganisms.

Purpose: Osimertinib is a potent and selective EGFR tyrosine kinase inhibitor (EGFR-TKI) of both sensitizing and T790M resistance mutations. To treat metastatic brain disease, blood–brain barrier (BBB) permeability is considered desirable for increasing clinical efficacy. Experimental Design: We examined the level of brain penetration for 16 irreversible and reversible EGFR-TKIs using multiple in vitro and in vivo BBB preclinical models. Results: In vitro osimertinib was the weakest substrate for human BBB efflux transporters (efflux ratio 3.2). In vivo rat free brain to free plasma ratios (Kpuu) show osimertinib has the most BBB penetrance (0.21), compared with the other TKIs (Kpuu ≤ 0.12). PET imaging in Cynomolgus macaques demonstrated osimertinib was the only TKI among those tested to achieve significant brain penetrance (Cmax %ID 1.5, brain/blood Kp 2.6). Desorption electrospray ionization mass spectroscopy images of brains from mouse PC9 macrometastases models showed osimertinib readily distributes across both healthy brain and tumor tissue. Comparison of osimertinib with the poorly BBB penetrant afatinib in a mouse PC9 model of subclinical brain metastases showed only osimertinib has a significant effect on rate of brain tumor growth. Conclusions: These preclinical studies indicate that osimertinib can achieve significant exposure in the brain compared with the other EGFR-TKIs tested and supports the ongoing clinical evaluation of osimertinib for the treatment of EGFR-mutant brain metastasis. This work also demonstrates the link between low in vitro transporter efflux ratios and increased brain penetrance in vivo supporting the use of in vitro transporter assays as an early screen in drug discovery.