Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis

Pancreatic cancer has the poorest prognosis among all cancer types. Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically identifiable precursors to pancreatic cancer; hence, early detection and precise risk assessment of IPMN are vital. In this work, we propose a Convolutional Neural Network (CNN) based computer aided diagnosis (CAD) system to perform IPMN diagnosis and risk assessment by utilizing multi-modal MRI. In our proposed approach, we use minimum and maximum intensity projections to ease the annotation variations among different slices and type of MRIs. Then, we present a CNN to obtain deep feature representation corresponding to each MRI modality (T1-weighted and T2-weighted). At the final step, we employ canonical correlation analysis (CCA) to perform a fusion operation at the feature level, leading to discriminative canonical correlation features. Extracted features are used for classification. Our results indicate significant improvements over other potential approaches to solve this important problem. The proposed approach doesn't require explicit sample balancing in cases of imbalance between positive and negative examples. To the best of our knowledge, our study is the first to automatically diagnose IPMN using multi-modal MRI.Comment: Accepted for publication in IEEE International Symposium on Biomedical Imaging (ISBI) 201

Learning Algorithms for Fat Quantification and Tumor Characterization

Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice

Novel Detection and Treatment Strategies for Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with an estimated 5-year survival rate of less than 9%. The high lethality of PDAC is due to two primary reasons: the discovery of PDAC at later stages, with locally invasive or metastatic disease present at the time of initial diagnosis as well as the lack of efficacious therapeutic interventions that significantly impact survival. In this dissertation, we sought to discover and test novel detection and treatment strategies for PDAC. Firstly, serum EVs were investigated as potential non-invasive liquid biopsy biomarkers, to serve as a means of early cancer detection. Secondly, a recently discovered form of cell death, ferroptosis, was investigated as a means of potentiating radiation therapy. The investigation into the potential of extracellular vesicles (EVs) as circulating biomarkers began with a label-free analysis of EVs via surface-enhanced Raman Spectroscopy (SERS) and principal component discriminant function analysis (PC-DFA), to identify tumor-specific spectral signatures. This method differentiated EVs originating from PDAC or normal pancreatic epithelial cell lines with 90% overall accuracy. The proof-of-concept application of this method to EVs purified from patient serum exhibited up to 87% and 90% predictive accuracy for healthy control and early PDAC individual samples, respectively. The specific EV surface proteins that may contribute to the observed SERS differences were investigated via surface shaving LC-MS/MS. This analysis provided protein targets that were selected and validated with a combination of bioinformatics, western blot, and immunogold labeling techniques. The first target protein selected for assessment via ELISA, EPHA2, showed elevated expression in complete cancer patient serum as compared to benign controls. Further, EV specific EPHA2 expression was capable of predicting cancer status in 25% (5/20) of the patient samples with 100% specificity. These data suggest a potential role of EV surface profiling for the early detection of PDAC. However, further work is required to increase the overall accuracy. Additionally, we sought to investigate the involvement of ferroptosis, in radiation-induced cell death. Ferroptosis is a non-apoptotic form of cell death that requires labile ferrous iron (Fe2+) and is caused by the reactive oxygen species (ROS) mediated build-up of lipid hydroperoxides. Further, we tested if the pharmaceutical induction of ferroptosis via the small molecule Erastin can potentiate the lethal effects of radiation in vitro and in vivo. We observed that radiation produces an increase in ROS and free Fe2+ leading to lipid hydroperoxidation, which was enhanced with the addition of Erastin culminating in the likely induction of ferroptosis. The combination of radiation and Erastin synergistically increased cell death in monocultures and patient-derived organoids as well as significantly reduced tumor size in xenograft mouse models. These findings suggest the potential of ferroptosis induction to improve radiation therapy, though specific mechanistic components require further evaluation. Therefore, further studies must be conducted to elucidate the specific role of ferroptosis in radiation-induced cell death. The combination of early detection and novel therapeutic intervention strategies offers a means of improving the survival of those with this dreaded disease

Theoretical and Experimental Tools for Clinical Translation of Quantitative Tissue Optical Sensing.

Quantitative tissue optical spectroscopy has been considered as a promising method for clinical diagnosis, owing to its ability to non-invasively give an objective assessment of biological tissues at cellular and sub-cellular levels. In spite of recent advances in optics and the computational power, not many quantitative tissue optical sensing technologies have been translated into clinical practice. In order to translate this technology in the clinics, we need to further improve the technology. To name a few, we need accurate and rapid quantification method for a real-time diagnostic feedback. Next, we need computational methods for complex tissue-optics problems. Also, we need a novel approach in probe design for the inaccessible organs. This dissertation focuses on the development, verification and validation of theoretical (mathematical and computational) and experimental (instrumental) tool set to promote the translation of quantitative tissue optical spectroscopy into clinical diagnostic applications. For the mathematical tool, a direct-fit photon tissue interaction (DF-PTI) model that could rapidly and accurately extract the parameters associated biophysical features was developed and validated to characterize the precursor lesions of pancreatic cancer. A rapid scattering model on pancreatic tissue reflectance based on principal components analysis (PCA) results was also developed. The diagnostic capability of scattering properties obtained was demonstrated on an 18-patient data set using a rigorous statistical method, which implied the potential of reflectance spectroscopy for real-time detection of pancreatic cancer. For the computational tool, a ray-traced Monte Carlo (RTMC) simulation for the design of fluorescence spectroscopy or imaging system utilizing complex optics to probe turbid biological tissues was devised. This new method was verified computationally with epithelial tissue models and experimentally using tissue-simulating optical phantoms. For the instrumental tool, the design and development of minimally-invasive diagnostic technologies employing optoelectronic components were discussed. In this dissertation, we focused on detection of pancreatic cancer, which has the worst prognosis among other major cancers. Pancreatic tissues were employed as our model system to validate our developed tools. The developed technology and tools can be applied to a variety of other human tissue sites to help in the translation of quantitative tissue optical sensing in a clinical setting.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111401/1/paulslee_1.pd

Evaluation of PD-L1 expression in various formalin-fixed paraffin embedded tumour tissue samples using SP263, SP142 and QR1 antibody clones

Background & objectives: Cancer cells can avoid immune destruction through the inhibitory ligand PD-L1. PD-1 is a surface cell receptor, part of the immunoglobulin family. Its ligand PD-L1 is expressed by tumour cells and stromal tumour infltrating lymphocytes (TIL). Methods: Forty-four cancer cases were included in this study (24 triple-negative breast cancers (TNBC), 10 non-small cell lung cancer (NSCLC) and 10 malignant melanoma cases). Three clones of monoclonal primary antibodies were compared: QR1 (Quartett), SP 142 and SP263 (Ventana). For visualization, ultraView Universal DAB Detection Kit from Ventana was used on an automated platform for immunohistochemical staining Ventana BenchMark GX. Results: Comparing the sensitivity of two different clones on same tissue samples from TNBC, we found that the QR1 clone gave higher percentage of positive cells than clone SP142, but there was no statistically significant difference. Comparing the sensitivity of two different clones on same tissue samples from malignant melanoma, the SP263 clone gave higher percentage of positive cells than the QR1 clone, but again the difference was not statistically significant. Comparing the sensitivity of two different clones on same tissue samples from NSCLC, we found higher percentage of positive cells using the QR1 clone in comparison with the SP142 clone, but once again, the difference was not statistically significant. Conclusion: The three different antibody clones from two manufacturers Ventana and Quartett, gave comparable results with no statistically significant difference in staining intensity/ percentage of positive tumour and/or immune cells. Therefore, different PD-L1 clones from different manufacturers can potentially be used to evaluate the PD- L1 status in different tumour tissues. Due to the serious implications of the PD-L1 analysis in further treatment decisions for cancer patients, every antibody clone, staining protocol and evaluation process should be carefully and meticulously validated

Investigation of 18F-Fluoro-L-Thymidine to monitor treatment response in murine models of pancreatic cancer: development of tools and validation

I characterised performance of the Positron Emission Tomography (PET) and the Computed Tomography (CT) modules of the 2-ring Albira PET/SPECT/CT, a small-animal imaging platform. The evaluation of PET was done in concordance with the National Electrical Manufacturer’s Association (NEMA) NU4-2008 standard. The performance of the Albira CT was assessed using microCT phantom. As a way of verification of the results of the phantom studies, example images from the tri-modal PET/SPECT/CT experiment were analysed. Additionally, gamma counter was evaluated as a tool for measuring biodistribution of the radiolabelled probes ex vivo. 18F-Fluoro-L-Thymidine (18F-FLT) was used in the investigation of the treatment response in the mouse models of pancreatic ductal adenocarcinoma (PDAC). Results from the two studies using mTOR and TGFβ inhibitors are reported. The mTOR inhibitor, rapamycin used 18F-FLT in the PET imaging to study, which aimed to compare the effects of the treatment on proliferation in two mouse models recapitulating the features of human PDAC, namely the KC Pten and KPC. TGFβ inhibitor study characterised the acute impact the administered TGFβ antibody has on proliferation in KPC mice in addition and as opposed to gemcitabine monotherapy, which is currently considered a golden standard in the treatment of pancreatic cancer. This study utilized gamma counting, autoradiography and Ki67 immunohistochemistry (IHC)

Exploring the Mechanobiology of Pancreatic Cancer using Tumour Microenvironment Models.

PhD Thesis.The dismal prognosis and treatment options for pancreatic ductal adenocarcinoma (PDAC) have seen little advances over the past decades, making PDAC one of the most lethal malignancies to date. Hydrogel-based in vitro modelling of this cancer and its tumour microenvironment (TME) is a promising avenue to bridge the gap between laboratory and clinical data. Current collagen gel-based approaches are limited by varying batch composition, little tuneability and limited mechanical properties, which preclude the accurate recapitulation of key PDAC features, such as matrix stiffness, desmoplasia and drug resistance. In this study, gelatin methacryloyl (GelMA)-based hydrogels were used for the three-dimensional (3D) multicellular culture of PDAC and stromal cells (myeloid cells and patient-derived fibroblasts). The hydrogel’s mechanical properties, architecture and matrix protein expression of embedded cells were characterised and benchmarked against collagen gels and native human tissues. Mechanical testing of fresh human tissues was performed to inform the physical properties of the model. PDAC tissues were significantly stiffer (7.4 ± 0.6 kPa, p<0.0001) than normal adjacent tissues (2.2 ± 0.2 kPa), prompting the modelling of these observed biomechanics with the use of GelMA-based hydrogels. Immunofluorescence, flow cytometry, metabolic activity and DNA quantification analyses confirmed the suitability of GelMA hydrogels for 3D PDAC research by showing high viability of embedded cells, spheroid formation ability, expression of cancer-associated markers and proliferation. The simultaneous 3D co-culture of PDAC and stromal cells led to matrix stiffening, increased cell proliferation and increased in vivo tumorigenicity via a stiffness-dependant upregulation of IL-6, IL-8, STAT3 and downregulation of ERK. Transcriptomic analyses revealed that 3D GelMA cultures had signatures that correlated with those of cells grown in collagen gels, as well as primary tumour organoids cultured in Matrigel, while showing an upregulation in mechano-transduction pathways. Treatment with the mechano-modulating inhibitor fasudil led to increased chemotherapy efficiency via relaxation of matrix stiffness, downregulation of pro-survival and matrix gene signatures, and reduced IL-6 and IL-8 secretion. These findings demonstrated that GelMA-based hydrogels are a modular and informative 3D cell culture platform for the investigation of functional, transcriptional and mechanical aspects of the pancreatic TME. The tuneable physical properties of GelMA allowed me to uncover the increased biomechanical functions and to assess treatment responses of PDAC and stromal cells in matrices of physiologically relevant stiffness, which could not be assessed, to this extent, in commonly-used collagen matrices