Clinical and molecular characterization of virus-positive and virus-negative Merkel cell carcinoma

Background: Merkel cell carcinoma (MCC) is a highly aggressive neuroendocrine carcinoma of the skin caused by either the integration of Merkel cell polyomavirus (MCPyV) and expression of viral T antigens or by ultraviolet-induced damage to the tumor genome from excessive sunlight exposure. An increasing number of deep sequencing studies of MCC have identified significant differences between the number and types of point mutations, copy number alterations, and structural variants between virus-positive and virus-negative tumors. However, it has been challenging to reliably distinguish between virus positive and UV damaged MCC. Methods: In this study, we assembled a cohort of 71 MCC patients and performed deep sequencing with OncoPanel, a clinically implemented, next-generation sequencing assay targeting over 400 cancer-associated genes. To improve the accuracy and sensitivity for virus detection compared to traditional PCR and IHC methods, we developed a hybrid capture baitset against the entire MCPyV genome and software to detect integration sites and structure. Results: Sequencing from this approach revealed distinct integration junctions in the tumor genome and generated assemblies that strongly support a model of microhomology-initiated hybrid, virus-host, circular DNA intermediate that promotes focal amplification of host and viral DNA. Using the clear delineation between virus-positive and virus-negative tumors from this method, we identified recurrent somatic alterations common across MCC and alterations specific to each class of tumor, associated with differences in overall survival. Finally, comparing the molecular and clinical data from these patients revealed a surprising association of immunosuppression with virus-negative MCC and significantly shortened overall survival. Conclusions: These results demonstrate the value of high-confidence virus detection for identifying molecular mechanisms of UV and viral oncogenesis in MCC. Furthermore, integrating these data with clinical data revealed features that could impact patient outcome and improve our understanding of MCC risk factors

Bio-Inspired Multi-Spectral and Polarization Imaging Sensors for Image-Guided Surgery

Image-guided surgery (IGS) can enhance cancer treatment by decreasing, and ideally eliminating, positive tumor margins and iatrogenic damage to healthy tissue. Current state-of-the-art near-infrared fluorescence imaging systems are bulky, costly, lack sensitivity under surgical illumination, and lack co-registration accuracy between multimodal images. As a result, an overwhelming majority of physicians still rely on their unaided eyes and palpation as the primary sensing modalities to distinguish cancerous from healthy tissue. In my thesis, I have addressed these challenges in IGC by mimicking the visual systems of several animals to construct low power, compact and highly sensitive multi-spectral and color-polarization sensors. I have realized single-chip multi-spectral imagers with 1000-fold higher sensitivity and 7-fold better spatial co-registration accuracy compared to clinical imaging systems in current use by monolithically integrating spectral tapetal and polarization filters with an array of vertically stacked photodetectors. These imaging sensors yield the unique capabilities of imaging simultaneously color, polarization, and multiple fluorophores for near-infrared fluorescence imaging. Preclinical and clinical data demonstrate seamless integration of this technologies in the surgical work flow while providing surgeons with real-time information on the location of cancerous tissue and sentinel lymph nodes, respectively. Due to its low cost, the bio-inspired sensors will provide resource-limited hospitals with much-needed technology to enable more accurate value-based health care

Riverine flooding using GIS and remote sensing

Floods are caused by extreme meteorological and hydrological changes that are influenced directly or indirectly by human activities within the environment. The flood trends show that floods will reoccur and shall continue to affect the livelihoods, property, agriculture and the surrounding environment. This research has analyzed the riverine flood by integrating remote sensing, Geographical Information Systems (GIS), and hydraulic and/or hydrological modeling, to develop informed flood mapping for flood risk management. The application of Hydrological Engineering Center River Analysis System (HEC RAS) and HEC HMS models, developed by the USA Hydrologic Engineering Center of the Army Corps of Engineers in a data-poor environment of a developing country were successful, as a flood modeling tools in early warning systems and land use planning. The methodology involved data collection, preparation, and model simulation using 30m Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) as a critical data input of HEC RAS model. The findings showed that modeling using HEC-RAS and HEC HMS models in a data-poor environment requires intensive data enhancements and adjustments; multiple utilization of open sources data; carrying out multiple model computation iterations and calibration; multiple field observation, which may be constrained with time and resources to get reasonable output

The real-time molecular characterisation of human brain tumours during surgery using Rapid Evaporative Ionization Mass Spectrometry [REIMS] and Raman spectroscopy: a platform for precision medicine in neurosurgery

Aim: To investigate new methods for the chemical detection of tumour tissue during neurosurgery. Rationale: Surgeons operating on brain tumours currently lack the ability to directly and immediately assess the presence of tumour tissue to help guide resection. Through developing a first in human application of new technology we hope to demonstrate the proof of concept that chemical detection of tumour tissue is possible. It will be further demonstrated that information can be obtained to potentially aid treatment decisions. This new technology could, therefore, become a platform for more effective surgery and introducing precision medicine to Neurosurgery. Methods: Molecular analysis was performed using Raman spectroscopy and Rapid Evaporative Ionization Mass Spectrometry (REIMS). These systems were first developed for use in brain surgery. A single centre prospective observational study of both modalities was designed involving a total of 75 patients undergoing craniotomy and resection of a range of brain tumours. A neuronavigation system was used to register spectral readings in 3D space. Precise intraoperative readings from different tumour zones were taken and compared to matched core biopsy samples verified by routine histopathology. Results: Multivariate statistics including PCA/LDA analysis was used to analyse the spectra obtained and compare these to the histological data. The systems identified normal versus tumour tissue, tumour grade, tumour type, tumour density and tissue status of key markers of gliomagenesis. Conclusions: The work in this thesis provides proof of concept that useful real time intraoperative spectroscopy is possible. It can integrate well with the current operating room setup to provide key information which could potentially enhance surgical safety and effectiveness in increasing extent of resection. The ability to group tissue samples with respect to genomic data opens up the possibility of using this information during surgery to speed up treatment, escalate/deescalate surgery in specific phenotypic groups to introduce precision medicine to Neurosurgery.Open Acces