A nanoparticle formulation of an Aurora B kinase inhibitor uses ion pairing to achieve controlled release and efficacious, nontoxic target inhibition in tumors.

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. ©2015 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. Significance Mass spectrometry imaging (MSI) technology represents a highly promising approach in cancer research. Here, we outline current roadblocks in translational MSI and introduce a comprehensive workflow designed to address current methodological limitations. An integrated bioinformatics platform is presented that allows intuitive histology-directed interrogation of MSI datasets. We show that this strategy permits the analysis of multivariate molecular signatures with direct correlation to morphological regions of interest, which can offer new insights into how different tumor microenvironmental populations interact with one another and generate novel region-of-interest specific biomarkers and therapeutic targets.

All higher plants produce polyphenols, for defence against above-ground herbivory. These polyphenols also influence the soil micro- and macro-fauna that break down plant leaf litter. Polyphenols therefore indirectly affect the fluxes of soil nutrients and, ultimately, carbon turnover and ecosystem functioning in soils. It is unknown how earthworms, the major component of animal biomass in many soils, cope with high-polyphenol diets. Here, we show that earthworms possess a class of unique surface-active metabolites in their gut, which we term ‘drilodefensins’. These compounds counteract the inhibitory effects of polyphenols on earthworm gut enzymes, and high-polyphenol diets increase drilodefensin concentrations in both laboratory and field populations. This shows that drilodefensins protect earthworms from the harmful effects of ingested polyphenols. We have identified the key mechanism for adaptation to a dietary challenge in an animal group that has a major role in organic matter recycling in soils worldwide.