Role of New Functional MRI Techniques in the Diagnosis, Staging, and Followup of Gynecological Cancer: Comparison with PET-CT

Optoacoustic imaging identifies ovarian cancer using a microenvironment targeted theranostic wormhole mesoporous silica nanoparticle

At the intersection of the newly emerging fields of optoacoustic imaging and theranostic nanomedicine, promising clinical progress can be made in dismal prognosis of ovarian cancer. An acidic pH targeted wormhole mesoporous silica nanoparticle (V7-RUBY) was developed to serve as a novel tumor specific theranostic nanoparticle detectable using multispectral optoacoustic tomographic (MSOT) imaging. We report the synthesis of a small, < 40 nm, biocompatible asymmetric wormhole pore mesoporous silica core particle that has both large loading capacity and favorable release kinetics combined with tumor-specific targeting and gatekeeping. V7-RUBY exploits the acidic tumor microenvironment for tumor-specific targeting and tumor-specific release. In vitro, treatment with V7-RUBY containing either paclitaxel or carboplatin resulted in increased cell death at pH 6.6 in comparison to drug alone (p < 0.0001). In orthotopic ovarian xenograft mouse models, V7-RUBY containing IR780 was specifically detected within the tumor 7X and 4X higher than the liver and >10X higher than in the kidney using both multispectral optoacoustic tomography (MSOT) imaging with secondary confirmation using near infrared fluorescence imaging (p < 0.0004). The V7-RUBY system carrying a cargo of either contrast agent or an anti-neoplastic drug has the potential to become a theranostic nanoparticle which can improve both diagnosis and treatment of ovarian cancer.

Revealing cancer subtypes with higher-order correlations applied to imaging and omics data

Background

Patient stratification to identify subtypes with different disease manifestations, severity, and expected survival time is a critical task in cancer diagnosis and treatment. While stratification approaches using various biomarkers (including high-throughput gene expression measurements) for patient-to-patient comparisons have been successful in elucidating previously unseen subtypes, there remains an untapped potential of incorporating various genotypic and phenotypic data to discover novel or improved groupings.

Methods

Here, we present HOCUS, a unified analytical framework for patient stratification that uses a community detection technique to extract subtypes out of sparse patient measurements. HOCUS constructs a patient-to-patient network from similarities in the data and iteratively groups and reconstructs the network into higher order clusters. We investigate the merits of using higher-order correlations to cluster samples of cancer patients in terms of their associations with survival outcomes.

Results

In an initial test of the method, the approach identifies cancer subtypes in mutation data of glioblastoma, ovarian, breast, prostate, and bladder cancers. In several cases, HOCUS provides an improvement over using the molecular features directly to compare samples. Application of HOCUS to glioblastoma images reveals a size and location classification of tumors that improves over human expert-based stratification.

Conclusions

Subtypes based on higher order features can reveal comparable or distinct groupings. The distinct solutions can provide biologically- and treatment-relevant solutions that are just as significant as solutions based on the original data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-017-0256-3) contains supplementary material, which is available to authorized users.