Radiotherapy Treatment Assessment using DCE-MRI

Abstract

The goal of modern radiotherapy is to precisely deliver a prescribed radiation dose to delineated target volumes that contain a significant amount of tumor cells while sparing the surrounding healthy tissues/organs. Precise delineation of treatment and avoidance volumes is the key for the precision radiation therapy. In recent years, considerable clinical and research efforts have been devoted to integrate MRI into radiotherapy workflow motivated by the superior soft tissue contrast and functional imaging possibility. Dynamic contrast-enhanced MRI (DCE-MRI) is a noninvasive technique that measures properties of tissue microvasculature. Its sensitivity to radiation-induced vascular pharmacokinetic (PK) changes has been preliminary demonstrated. In spite of its great potential, two major challenges have limited DCE-MRI’s clinical application in radiotherapy assessment: the technical limitations of accurate DCE-MRI imaging implementation and the need of novel DCE-MRI data analysis methods for richer functional heterogeneity information.

This study aims at improving current DCE-MRI techniques and developing new DCE-MRI analysis methods for particular radiotherapy assessment. Thus, the study is naturally divided into two parts. The first part focuses on DCE-MRI temporal resolution as one of the key DCE-MRI technical factors, and some improvements regarding DCE-MRI temporal resolution are proposed; the second part explores the potential value of image heterogeneity analysis and multiple PK model combination for therapeutic response assessment, and several novel DCE-MRI data analysis methods are developed.

I. Improvement of DCE-MRI temporal resolution. First, the feasibility of improving DCE-MRI temporal resolution via image undersampling was studied. Specifically, a novel MR image iterative reconstruction algorithm was studied for DCE-MRI reconstruction. This algorithm was built on the recently developed compress sensing (CS) theory. By utilizing a limited k-space acquisition with shorter imaging time, images can be reconstructed in an iterative fashion under the regularization of a newly proposed total generalized variation (TGV) penalty term. In the retrospective study of brain radiosurgery patient DCE-MRI scans under IRB-approval, the clinically obtained image data was selected as reference data, and the simulated accelerated k-space acquisition was generated via undersampling the reference image full k-space with designed sampling grids. Two undersampling strategies were proposed: 1) a radial multi-ray grid with a special angular distribution was adopted to sample each slice of the full k-space; 2) a Cartesian random sampling grid series with spatiotemporal constraints from adjacent frames was adopted to sample the dynamic k-space series at a slice location. Two sets of PK parameters’ maps were generated from the undersampled data and from the fully-sampled data, respectively. Multiple quantitative measurements and statistical studies were performed to evaluate the accuracy of PK maps generated from the undersampled data in reference to the PK maps generated from the fully-sampled data. Results showed that at a simulated acceleration factor of four, PK maps could be faithfully calculated from the DCE images that were reconstructed using undersampled data, and no statistically significant differences were found between the regional PK mean values from undersampled and fully-sampled data sets. DCE-MRI acceleration using the investigated image reconstruction method has been suggested as feasible and promising.

Second, for high temporal resolution DCE-MRI, a new PK model fitting method was developed to solve PK parameters for better calculation accuracy and efficiency. This method is based on a derivative-based deformation of the commonly used Tofts PK model, which is presented as an integrative expression. This method also includes an advanced Kolmogorov-Zurbenko (KZ) filter to remove the potential noise effect in data and solve the PK parameter as a linear problem in matrix format. In the computer simulation study, PK parameters representing typical intracranial values were selected as references to simulated DCE-MRI data for different temporal resolution and different data noise level. Results showed that at both high temporal resolutions (<1s) and clinically feasible temporal resolution (~5s), this new method was able to calculate PK parameters more accurate than the current calculation methods at clinically relevant noise levels; at high temporal resolutions, the calculation efficiency of this new method was superior to current methods in an order of 102. In a retrospective of clinical brain DCE-MRI scans, the PK maps derived from the proposed method were comparable with the results from current methods. Based on these results, it can be concluded that this new method can be used for accurate and efficient PK model fitting for high temporal resolution DCE-MRI.

II. Development of DCE-MRI analysis methods for therapeutic response assessment. This part aims at methodology developments in two approaches. The first one is to develop model-free analysis method for DCE-MRI functional heterogeneity evaluation. This approach is inspired by the rationale that radiotherapy-induced functional change could be heterogeneous across the treatment area. The first effort was spent on a translational investigation of classic fractal dimension theory for DCE-MRI therapeutic response assessment. In a small-animal anti-angiogenesis drug therapy experiment, the randomly assigned treatment/control groups received multiple fraction treatments with one pre-treatment and multiple post-treatment high spatiotemporal DCE-MRI scans. In the post-treatment scan two weeks after the start, the investigated Rényi dimensions of the classic PK rate constant map demonstrated significant differences between the treatment and the control groups; when Rényi dimensions were adopted for treatment/control group classification, the achieved accuracy was higher than the accuracy from using conventional PK parameter statistics. Following this pilot work, two novel texture analysis methods were proposed. First, a new technique called Gray Level Local Power Matrix (GLLPM) was developed. It intends to solve the lack of temporal information and poor calculation efficiency of the commonly used Gray Level Co-Occurrence Matrix (GLCOM) techniques. In the same small animal experiment, the dynamic curves of Haralick texture features derived from the GLLPM had an overall better performance than the corresponding curves derived from current GLCOM techniques in treatment/control separation and classification. The second developed method is dynamic Fractal Signature Dissimilarity (FSD) analysis. Inspired by the classic fractal dimension theory, this method measures the dynamics of tumor heterogeneity during the contrast agent uptake in a quantitative fashion on DCE images. In the small animal experiment mentioned before, the selected parameters from dynamic FSD analysis showed significant differences between treatment/control groups as early as after 1 treatment fraction; in contrast, metrics from conventional PK analysis showed significant differences only after 3 treatment fractions. When using dynamic FSD parameters, the treatment/control group classification after 1st treatment fraction was improved than using conventional PK statistics. These results suggest the promising application of this novel method for capturing early therapeutic response.

The second approach of developing novel DCE-MRI methods is to combine PK information from multiple PK models. Currently, the classic Tofts model or its alternative version has been widely adopted for DCE-MRI analysis as a gold-standard approach for therapeutic response assessment. Previously, a shutter-speed (SS) model was proposed to incorporate transcytolemmal water exchange effect into contrast agent concentration quantification. In spite of richer biological assumption, its application in therapeutic response assessment is limited. It might be intriguing to combine the information from the SS model and from the classic Tofts model to explore potential new biological information for treatment assessment. The feasibility of this idea was investigated in the same small animal experiment. The SS model was compared against the Tofts model for therapeutic response assessment using PK parameter regional mean value comparison. Based on the modeled transcytolemmal water exchange rate, a biological subvolume was proposed and was automatically identified using histogram analysis. Within the biological subvolume, the PK rate constant derived from the SS model were proved to be superior to the one from Tofts model in treatment/control separation and classification. Furthermore, novel biomarkers were designed to integrate PK rate constants from these two models. When being evaluated in the biological subvolume, this biomarker was able to reflect significant treatment/control difference in both post-treatment evaluation. These results confirm the potential value of SS model as well as its combination with Tofts model for therapeutic response assessment.

In summary, this study addressed two problems of DCE-MRI application in radiotherapy assessment. In the first part, a method of accelerating DCE-MRI acquisition for better temporal resolution was investigated, and a novel PK model fitting algorithm was proposed for high temporal resolution DCE-MRI. In the second part, two model-free texture analysis methods and a multiple-model analysis method were developed for DCE-MRI therapeutic response assessment. The presented works could benefit the future DCE-MRI routine clinical application in radiotherapy assessment.

Effective Dose Estimation in Fast-kV Switch Dual Energy Computed Tomography

Purpose

The objective of our study was to test a new approach to approximating organ dose by using the effective energy of the combined 80kV/140kV beam used in fast kV switch dual-energy (DE) computed tomography (CT). The two primary focuses of the study were to first validate experimentally the dose equivalency between MOSFET and ion chamber (as a gold standard) in a fast kV switch DE environment, and secondly to estimate effective dose (ED) of DECT scans using MOSFET detectors and an anthropomorphic phantom.

Materials and Methods

A GE Discovery 750 CT scanner was employed using a fast-kV switch abdomen/pelvis protocol alternating between 80 kV and 140 kV. The specific aims of our study were to (1) Characterize the effective energy of the dual energy environment; (2) Estimate the f-factor for soft tissue; (3) Calibrate the MOSFET detectors using a beam with effective energy equal to the combined DE environment; (4) Validate our calibration by using MOSFET detectors and ion chamber to measure dose at the center of a CTDI body phantom; (5) Measure ED for an abdomen/pelvis scan using an anthropomorphic phantom and applying ICRP 103 tissue weighting factors; and (6) Estimate ED using AAPM Dose Length Product (DLP) method. The effective energy of the combined beam was calculated by measuring dose with an ion chamber under varying thicknesses of aluminum to determine half-value layer (HVL).

Results

The effective energy of the combined dual-energy beams was found to be 42.8 kV. After calibration, tissue dose in the center of the CTDI body phantom was measured at 1.71 ± 0.01 cGy using an ion chamber, and 1.73±0.04 and 1.69±0.09 using two separate MOSFET detectors. This result showed a -0.93% and 1.40 % difference, respectively, between ion chamber and MOSFET. ED from the dual-energy scan was calculated as 16.49 ± 0.04 mSv by the MOSFET method and 14.62 mSv by the DLP method.

Developing and Validating a Perinatal Depression Screening Tool in Bungoma County, Kenya

Background: Depression-screening tools exist and are widely used in Western settings. There have been few studies done to explore whether or not existing tools are valid and effective to use in sub-Saharan Africa. Our study aimed to develop and validate a perinatal depression-screening tool in rural Kenya.

Methods: We utilized conducted free listing and card sorting exercises with a purposive sample of 12 women and 38 CHVs living in a rural community to explore the manifestations of perinatal depression in that setting. We used the information obtained to produce a locally relevant depression-screening tool that comprised of existing Western psychiatric concepts and locally derived items. Subsequently, we administered the novel depression-screening tool and two existing screening tools (the Edinburgh Postnatal Depression Scale and the Patient Health Questionnaire-9) to 193 women and compared the results of the screening tool with that of a gold standard structured clinical interview to determine validity.

Results: The free listing and card sorting exercise produced a set of 60 screening items. Of the items in this set, we identified the 10 items that most accurately classified cases and non-cases. This 10-item scale had a sensitivity of 100.0 and specificity of 81.2. This compared to 90.0, 31.5 and 90.0, 49.7 for the EPDS and the PHQ-9, respectively. Overall, we found a prevalence of depression of 5.2 percent.

Conclusions: The new scale does very well in terms of diagnostic validity, having the highest scores in this domain compared to the EPDS, EPDS-R and PHQ-9. The adapted scale does very well with regards to convergent validity-illustrating clear distinction between mean scores across the different categories. It does well with regards to discriminant validity, internal consistency reliability, and test-retest reliability- not securing top scores in those domains but still yielding satisfactory results.

Combining Information from Multiple Sources in Bayesian Modeling

Surveys can collect important data that inform policy decisions and drive social science research. Large government surveys collect information from the U.S. population on a wide range of topics, including demographics, education, employment, and lifestyle. Analysis of survey data presents unique challenges. In particular, one needs to account for missing data, for complex sampling designs, and for measurement error. Conceptually, a survey organization could spend lots of resources getting high-quality responses from a simple random sample, resulting in survey data that are easy to analyze. However, this scenario often is not realistic. To address these practical issues, survey organizations can leverage the information available from other sources of data. For example, in longitudinal studies that suffer from attrition, they can use the information from refreshment samples to correct for potential attrition bias. They can use information from known marginal distributions or survey design to improve inferences. They can use information from gold standard sources to correct for measurement error.

This thesis presents novel approaches to combining information from multiple sources that address the three problems described above.

The first method addresses nonignorable unit nonresponse and attrition in a panel survey with a refreshment sample. Panel surveys typically suffer from attrition, which can lead to biased inference when basing analysis only on cases that complete all waves of the panel. Unfortunately, the panel data alone cannot inform the extent of the bias due to attrition, so analysts must make strong and untestable assumptions about the missing data mechanism. Many panel studies also include refreshment samples, which are data collected from a random sample of new

individuals during some later wave of the panel. Refreshment samples offer information that can be utilized to correct for biases induced by nonignorable attrition while reducing reliance on strong assumptions about the attrition process. To date, these bias correction methods have not dealt with two key practical issues in panel studies: unit nonresponse in the initial wave of the panel and in the

refreshment sample itself. As we illustrate, nonignorable unit nonresponse

can significantly compromise the analyst's ability to use the refreshment samples for attrition bias correction. Thus, it is crucial for analysts to assess how sensitive their inferences---corrected for panel attrition---are to different assumptions about the nature of the unit nonresponse. We present an approach that facilitates such sensitivity analyses, both for suspected nonignorable unit nonresponse

in the initial wave and in the refreshment sample. We illustrate the approach using simulation studies and an analysis of data from the 2007-2008 Associated Press/Yahoo News election panel study.

The second method incorporates informative prior beliefs about

marginal probabilities into Bayesian latent class models for categorical data.

The basic idea is to append synthetic observations to the original data such that

(i) the empirical distributions of the desired margins match those of the prior beliefs, and (ii) the values of the remaining variables are left missing. The degree of prior uncertainty is controlled by the number of augmented records. Posterior inferences can be obtained via typical MCMC algorithms for latent class models, tailored to deal efficiently with the missing values in the concatenated data.

We illustrate the approach using a variety of simulations based on data from the American Community Survey, including an example of how augmented records can be used to fit latent class models to data from stratified samples.

The third method leverages the information from a gold standard survey to model reporting error. Survey data are subject to reporting error when respondents misunderstand the question or accidentally select the wrong response. Sometimes survey respondents knowingly select the wrong response, for example, by reporting a higher level of education than they actually have attained. We present an approach that allows an analyst to model reporting error by incorporating information from a gold standard survey. The analyst can specify various reporting error models and assess how sensitive their conclusions are to different assumptions about the reporting error process. We illustrate the approach using simulations based on data from the 1993 National Survey of College Graduates. We use the method to impute error-corrected educational attainments in the 2010 American Community Survey using the 2010 National Survey of College Graduates as the gold standard survey.

Development of tumour imaging and therapy strategies utilising unengineered bacteria

The ability of systemically administered bacteria to target and replicate to high numbers within solid tumours is well established. Tumour localising bacteria can be exploited as biological vehicles for the delivery of nucleic acid, protein or therapeutic payloads to tumour sites and present researchers with a highly targeted and safe vehicle for tumour imaging and cancer therapy. This work aimed to utilise bacteria to activate imaging probes or prodrugs specifically within target tissue in order to facilitate the development of novel imaging and therapeutic strategies. The vast majority of existing bacterial-mediated cancer therapy strategies rely on the use of bacteria that have been genetically modified (GM) to express genes of interest. While these approaches have been shown to be effective in a preclinical setting, GM presents extra regulatory hurdles in a clinical context. Also, many strains of bacteria are not genetically tractably and hence cannot currently be engineered to express genes of interest. For this reason, the development of imaging and therapeutic systems that utilise unengineered bacteria for the activation of probes or drugs represents a significant improvement on the current gold standard. Endogenously expressed bacterial enzymes that are not found in mammalian cells can be used for the targeted activation of imaging probes or prodrugs whose activation is only achieved in the presence of these enzymes. Exploitation of the intrinsic enzymatic activity of bacteria allows the use of a wider range of bacteria and presents a more clinically relevant system than those that are currently in use. The nitroreductase (NTR) enzymes, found only in bacteria, represent one such option. Chapter 2 introduces the novel concept of utilising native bacterial NTRs for the targeted activation of the fluorophore CytoCy5S. Bacterial-mediated probe activation allowed for non-invasive fluorescence imaging of in vivo bacteria in models of infection and cancer. Chapter 3 extends the concept of using native bacterial enzymes to activate a novel luminescent, NTR activated probe. The use of luminescence based imaging improved the sensitivity of the system and provides researchers with a more accessible modality for preclinical imaging. It also represents an improvement over existing caged luciferin probe systems described to date. Chapter 4 focuses on the employment of endogenous bacterial enzymes for use in a therapeutic setting. Native bacterial enzymatic activity (including NTR enzymes) was shown to be capable of activating multiple prodrugs, in isolation and in combination, and eliciting therapeutic responses in murine models of cancer. Overall, the data presented in this thesis advance the fields of bacterial therapy and imaging and introduce novel strategies for disease diagnosis and treatment. These preclinical studies demonstrate potential for clinical translation in multiple fields of research and medicine.

Development of a novel probe integrated with a micro-structured impedance sensor for the detection of breast cancer

The work described in this thesis focuses on the development of an innovative bioimpedance device for the detection of breast cancer using electrical impedance as the detection method. The ability for clinicians to detect and treat cancerous lesions as early as possible results in improved patient outcomes and can reduce the severity of the treatment the patient has to undergo. Therefore, new technology and devices are continually required to improve the specificity and sensitivity of the accepted detection methods. The gold standard for breast cancer detection is digital x-ray mammography but it has some significant downsides associated with it. The development of an adjunct technology to aid in the detection of breast cancers could represent a significant patient and economic benefit. In this project silicon substrates were pattern with two gold microelectrodes that allowed electrical impedance measurements to be recorded from intact tissue structures. These probes were tested and characterised using a range of in vitro and ex vivo experiments. The end application of this novel sensor device was in a first-in-human clinical trial. The initial results of this study showed that the silicon impedance device was capable of differentiating between normal and abnormal (benign and cancerous) breast tissue. The mean separation between the two tissue types 4,340 Ω with p < 0.001. The cancer type and grade at the site of the probe recordings was confirmed histologically and correlated with the electrical impedance measurements to determine if the different subtypes of cancer could each be differentiated. The results presented in this thesis showed that the novel impedance device demonstrated excellent electrochemical recording potential; was biocompatible with the growth of cultured cell lines and was capable of differentiating between intact biological tissues. The results outlined in this thesis demonstrate the potential feasibility of using electrical impedance for the differentiation of biological tissue samples. The novelty of this thesis is in the development of a new method of tissue determination with an application in breast cancer detection.

MicroRNA expression and function in neonatal hypoxic ischaemic encephalopathy

Hypoxic ischaemic encephalopathy (HIE) is a devastating neonatal condition which affects 2-3 per 1000 infants annually. The current gold standard of treatment - induced hypothermia, has the ability to reduce neonatal mortality and improve neonatal morbidity. However, to be effective it needs to be initiated within the therapeutic window which exists following initial insult until approximately 6 hours after birth. Current methods of assessment which are relied upon to identify infants with HIE are subjective and unreliable. To overcome this issue, an early and reliable biomarker of HIE severity must be identified. MicroRNA (miRNA) are a class of small non-coding RNA molecules which have potential as biomarkers of disease state and potential therapeutic targets. These tiny molecules can modulate gene expression by inhibiting translation of messenger RNA (mRNA) and as a result, can regulate protein synthesis. These miRNA are understood to be released into the circulation during cellular stress, where they are highly stable and relatively easy to quantify. Therefore, these miRNAs may be ideal candidates for biomarkers of HIE severity and may aid in directing the clinical management of these infants. By using both transcriptomic and proteomic approaches to analyse the expression of miRNAs and their potential targets in the umbilical cord blood, I have confirmed that infants with perinatal asphyxia and HIE have a significantly different UCB miRNA signature compared to UCB samples from healthy controls. Finally, I have identified and investigated 2 individual miRNAs; both of which show some potential as classifiers of HIE severity and predictors of long term outcome, particularly when coupled with their downstream targets. While this work will need to be validated and expanded in a new and larger cohort of infants, it suggests the potential of miRNA as biomarkers of neonatal pathological conditions such as HIE.

A new method for resolving uncertainty of energy requirements in large water breathers: the ‘mega-flume’ seagoing swim-tunnel respirometer

Body size is a key determinant of metabolic rate, but logistical constraints have led to a paucity of energetics measurements from large water-breathing animals. As a result, estimating energy requirements of large fish generally relies on extrapolation of metabolic rate from individuals of lower body mass using allometric relationships that are notoriously variable. Swim-tunnel respirometry is the ‘gold standard’ for measuring active metabolic rates in water-breathing animals, yet previous data are entirely derived from body masses <10 kg – at least one order of magnitude lower than the body masses of many top-order marine predators. Here, we describe the design and testing of a new method for measuring metabolic rates of large water-breathing animals: a c. 26 000 L seagoing ‘mega-flume’ swim-tunnel respirometer. We measured the swimming metabolic rate of a 2·1-m, 36-kg zebra shark Stegostoma fasciatum within this new mega-flume and compared the results to data we collected from other S. fasciatum (3·8–47·7 kg body mass) swimming in static respirometers and previously published measurements of active metabolic rate measurements from other shark species. The mega-flume performed well during initial tests, with intra- and interspecific comparisons suggesting accurate metabolic rate measurements can be obtained with this new tool. Inclusion of our data showed that the scaling exponent of active metabolic rate with mass for sharks ranging from 0·13 to 47·7 kg was 0·79; a similar value to previous estimates for resting metabolic rates in smaller fishes. We describe the operation and usefulness of this new method in the context of our current uncertainties surrounding energy requirements of large water-breathing animals. We also highlight the sensitivity of mass-extrapolated energetic estimates in large aquatic animals and discuss the consequences for predicting ecosystem impacts such as trophic cascades.

A new method for resolving uncertainty of energy requirements in large water breathers: the ‘mega-flume’ seagoing swim-tunnel respirometer

Body size is a key determinant of metabolic rate, but logistical constraints have led to a paucity of energetics measurements from large water-breathing animals. As a result, estimating energy requirements of large fish generally relies on extrapolation of metabolic rate from individuals of lower body mass using allometric relationships that are notoriously variable. Swim-tunnel respirometry is the ‘gold standard’ for measuring active metabolic rates in water-breathing animals, yet previous data are entirely derived from body masses <10 kg – at least one order of magnitude lower than the body masses of many top-order marine predators. Here, we describe the design and testing of a new method for measuring metabolic rates of large water-breathing animals: a c. 26 000 L seagoing ‘mega-flume’ swim-tunnel respirometer. We measured the swimming metabolic rate of a 2·1-m, 36-kg zebra shark Stegostoma fasciatum within this new mega-flume and compared the results to data we collected from other S. fasciatum (3·8–47·7 kg body mass) swimming in static respirometers and previously published measurements of active metabolic rate measurements from other shark species. The mega-flume performed well during initial tests, with intra- and interspecific comparisons suggesting accurate metabolic rate measurements can be obtained with this new tool. Inclusion of our data showed that the scaling exponent of active metabolic rate with mass for sharks ranging from 0·13 to 47·7 kg was 0·79; a similar value to previous estimates for resting metabolic rates in smaller fishes. We describe the operation and usefulness of this new method in the context of our current uncertainties surrounding energy requirements of large water-breathing animals. We also highlight the sensitivity of mass-extrapolated energetic estimates in large aquatic animals and discuss the consequences for predicting ecosystem impacts such as trophic cascades.

Comparing long-term placements for young children in care: Does type of placement really matter?

This paper presents findings from the third phase of a longitudinal study, entitled Care Pathways and Outcomes, which has been tracking the placements and measuring outcomes for a population of children (n = 374) who were under the age of five and in care in Northern Ireland on the 31st March 2000. It explores how a sub-sample of these children at age nine to 14 years old were getting on in the placements provided for them, in comparative terms across five placement types: adoption; foster care; kinship foster care (with relatives); on Residence Order; and living with birth parents. This specifically focused on the development of attachment and self-concept from the perspective of the children, and behavioural and emotional function, and parenting stress, from the perspective of parents and carers. Findings showed no significant placement effect from the perspective of children, and a statistically weak, but descriptively compelling, effect from the perspective of parents. The findings challenge the notion of adoption as the gold standard in long-term placements, specifically from the perspective of children in terms of their parent/carer attachments and self-concept, and highlight what appears to be the central importance of placement longevity for delivering positive longer-term outcomes for these children, irrespective of placement type.

ALK translocation is associated with ALK immunoreactivity and extensive signet-ring morphology in primary lung adenocarcinoma.

BACKGROUND: ALK rearrangement is particularly observed in signet-ring sub-type adenocarcinoma. Since fluorescence in situ hybridization (FISH) is not suitable for mass screening, we aimed to characterize the predictive utility of tumour morphology and ALK immunoreactivity to identify ALK rearrangement, in a primary lung adenocarcinoma dataset enriched for signet-ring morphology, compared with that of other morphology. METHODS: 7 adenocarcinomas from diagnostic archives reported with signet-ring morphology were assessed and compared with 11 adenocarcinomas without signet-ring features over the same time period. Growth patterns were reviewed, ALK expression was assessed by standard immunohistochemistry using ALK1 clone and Envision detection (Dako), and ALK rearrangement was assessed by FISH (Abbott Molecular). Associations between groups and predictive utility of tumour morphology and ALK expression using FISH as gold standard were calculated. RESULTS: 2 excision lung biopsy cases with pure (100%) signet-ring morphology and solid patterns demonstrated diffuse moderate cytoplasmic ALK immunoreactivity (2+) and harboured ALK rearrangements (p=0.007), unlike 5 mixed-signet-ring and 11 non-signet-ring adenocarcinomas, which showed negative or 1+ immunoreactivity; and did not harbour ALK rearrangements (p>0.1). ALK expression was not associated with ALK copy number. 6 of 7 cases with signet ring morphology stained for TTF-1. Pure signet-ring morphology and moderate ALK expression were both associated with ALK rearranged tumours. CONCLUSION: ALK rearrangement is strongly associated with ALK immunoreactivity, and was seen only in tumours with pure signet-ring morphology and solid growth pattern. Tumour morphology, growth pattern and ALK immunoreactivity appear to be good indicators of ALK rearrangement, with TTF-1 positivity aiding in proving primary pulmonary origin.

Targeting treatment resistant breast cancer stem cells with FKBPL and its peptide derivative, AD-01

FKBPL and its peptide derivative, AD-01, have already demonstrated well-established inhibitory effects on breast cancer growth and CD44 dependent anti-angiogenic activity1, 2, 3. Since breast cancer stem cells (BCSCs) are CD44 positive, we wanted to explore if AD-01 could specifically target BCSCs. FKBPL stable overexpression or AD-01 treatment were highly effective at reducing the BCSC population measured by inhibiting mammosphere forming efficiency (MFE) in cell lines and primary breast cancer samples from both solid breast tumours and pleural effusions. Flow cytometry, to assess the ESA+/CD44+/CD24- subpopulation, validated these results. The ability of AD-01 to inhibit the self-renewal capacity of BCSCs was confirmed across three generations of mammospheres, where mammospheres were completely eradicated by the third generation (p<0.001). Clonogenic assays suggested that AD-01 mediated BCSC differentiation, with a significant decrease in the number of holoclones and an associated increase in meroclones/paraclones. In support of this, the stem cell markers, Nanog and Oct4 were significantly reduced following AD-01 treatment, whilst transfection of FKBPL-targeted siRNAs led to an increase in these markers and in mammosphere forming potential, highlighting the endogenous role of FKBPL in stem cell signalling. The clinical relevance of this was confirmed using a publically available microarray data set (GSE7390), where, high FKBPL and low Nanog expression were independently associated with improved overall survival in breast cancer patients (log rank test p=0.03; hazard ratio=3.01). When AD-01 was combined with other agents, we observed synergistic activity with the Notch inhibitor, DAPT and AD-01 was also able to abrogate a chemo- and radiotherapy induced enrichment in BCSCs. Importantly, using ‘gold standard’ in vivo limiting dilution assays we demonstrated a delay in tumour initiation and reoccurrence in AD-01 treated xenografts. In summary, AD-01 appears to have dual anti-angiogenic and anti-BCSC activity which will be advantageous as this agent enters clinical trial.

Targeting treatment resistant cancer stem cells with FKBPL and its peptide therapeutics

FKBPL and its peptide derivatives have already demonstrated well-established inhibitory effects on cancer growth and CD44-dependent anti-angiogenic activity. Since cancer stem cells (CSCs) are CD44 positive, we wanted to explore if these therapeutics could specifically target CSCs in breast and ovarian cancer. In a tumoursphere assay, FKBPL stable overexpression or FKBPL-based peptide (AD-01, preclinical peptide or ALM201, clinical peptide candidate) treatment were highly effective at reducing the CSC population measured by inhibiting tumoursphere forming efficiency in breast and ovarian cancer cell lines and primary breast cancer samples from both solid breast tumours and pleural effusions. Flow cytometry, to assess the ESA+/CD44+/CD24- and ALDH+ cell subpopulations representative of CSCs, validated these results. The ability of AD-01 and ALM201 to inhibit the self-renewal capacity of CSCs was confirmed across three generations, eradicating CSC completely by the third generation (p<0.001). Furthermore, clonogenic assay demonstrated that FKBPL-based peptides mediated CSC differentiation, with a significant decrease in the number of CSCs or holoclones and an associated increase in differentiated cancer cells or meroclones/paraclones. In addition, AD-01 treatment in vitro and in vivo led to a significant reduction in the stem cell markers, Nanog, Sox2 and Oct4 protein and mRNA levels; whilst transfection of FKBPL-targeted siRNAs led to an increase in these markers and in tumoursphere forming potential, highlighting the endogenous role of FKBPL in stem cell signalling. The clinical relevance of this was confirmed using a publically available microarray data set (GSE7390), where, high FKBPL and low Nanog expression were independently associated with improved overall survival in breast cancer patients (log rank test p=0.03; hazard ratio=3.01). Additionally, when AD-01 was combined with other agents, we observed additive activity with the Notch inhibitor, DAPT and AD-01 was also able to abrogate a chemo- and radiotherapy induced enrichment in CSCs. Importantly, using gold standard in vivo limiting dilution assays we demonstrated a delay in tumour initiation and reoccurrence in AD-01 treated xenografts. In summary, FKBPL-based peptides appear to have dual anti-angiogenic and anti-CSC activity which will be advantageous as this agent enters clinical trial.

Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules

Background
It is generally acknowledged that a functional understanding of a biological system can only be obtained by an understanding of the collective of molecular interactions in form of biological networks. Protein networks are one particular network type of special importance, because proteins form the functional base units of every biological cell. On a mesoscopic level of protein networks, modules are of significant importance because these building blocks may be the next elementary functional level above individual proteins allowing to gain insight into fundamental organizational principles of biological cells.
Results
In this paper, we provide a comparative analysis of five popular and four novel module detection algorithms. We study these module prediction methods for simulated benchmark networks as well as 10 biological protein interaction networks (PINs). A particular focus of our analysis is placed on the biological meaning of the predicted modules by utilizing the Gene Ontology (GO) database as gold standard for the definition of biological processes. Furthermore, we investigate the robustness of the results by perturbing the PINs simulating in this way our incomplete knowledge of protein networks.
Conclusions
Overall, our study reveals that there is a large heterogeneity among the different module prediction algorithms if one zooms-in the biological level of biological processes in the form of GO terms and all methods are severely affected by a slight perturbation of the networks. However, we also find pathways that are enriched in multiple modules, which could provide important information about the hierarchical organization of the system

Suivi par élastographie ultrasonore après réparation endovasculaire d’anévrisme aorto-iliaque : étude de faisabilité in vivo

Les maladies cardiovasculaires sont la première cause de mortalité dans le monde et les anévrismes de l’aorte abdominale (AAAs) font partie de ce lot déplorable. Un anévrisme est la dilatation d’une artère pouvant conduire à la mort. Une rupture d’AAA s’avère fatale près de 80% du temps. Un moyen de traiter les AAAs est l’insertion d’une endoprothèse (SG) dans l’aorte, communément appelée la réparation endovasculaire (EVAR), afin de réduire la pression exercée par le flux sanguin sur la paroi. L’efficacité de ce traitement est compromise par la survenue d’endofuites (flux sanguins entre la prothèse et le sac anévrismal) pouvant conduire à la rupture de l’anévrisme. Ces flux sanguins peuvent survenir à n’importe quel moment après le traitement EVAR. Une surveillance par tomodensitométrie (CT-scan) annuelle est donc requise, augmentant ainsi le coût du suivi post-EVAR et exposant le patient à la radiation ionisante et aux complications des contrastes iodés. L’endotension est le concept de dilatation de l’anévrisme sans la présence d’une endofuite apparente au CT-scan. Après le traitement EVAR, le sang dans le sac anévrismal coagule pour former un thrombus frais, qui deviendra progressivement un thrombus plus fibreux et plus organisé, donnant lieu à un rétrécissement de l’anévrisme. Il y a très peu de données dans la littérature pour étudier ce processus temporel et la relation entre le thrombus frais et l’endotension. L’étalon d’or du suivi post-EVAR, le CT-scan, ne peut pas détecter la présence de thrombus frais. Il y a donc un besoin d’investir dans une technique sécuritaire et moins coûteuse pour le suivi d’AAAs après EVAR. Une méthode récente, l’élastographie dynamique, mesure l’élasticité des tissus en temps réel. Le principe de cette technique repose sur la génération d’ondes de cisaillement et l’étude de leur propagation afin de remonter aux propriétés mécaniques du milieu étudié. Cette thèse vise l’application de l’élastographie dynamique pour la détection des endofuites ainsi que de la caractérisation mécanique des tissus du sac anévrismal après le traitement EVAR. Ce projet dévoile le potentiel de l’élastographie afin de réduire les dangers de la radiation, de l’utilisation d’agent de contraste ainsi que des coûts du post-EVAR des AAAs. L’élastographie dynamique utilisant le « Shear Wave Imaging » (SWI) est prometteuse. Cette modalité pourrait complémenter l’échographie-Doppler (DUS) déjà utilisée pour le suivi d’examen post-EVAR. Le SWI a le potentiel de fournir des informations sur l’organisation fibreuse du thrombus ainsi que sur la détection d’endofuites. Tout d’abord, le premier objectif de cette thèse consistait à tester le SWI sur des AAAs dans des modèles canins pour la détection d’endofuites et la caractérisation du thrombus. Des SGs furent implantées dans un groupe de 18 chiens avec un anévrisme créé au moyen de la veine jugulaire. 4 anévrismes avaient une endofuite de type I, 13 avaient une endofuite de type II et un anévrisme n’avait pas d’endofuite. Des examens échographiques, DUS et SWI ont été réalisés à l’implantation, puis 1 semaine, 1 mois, 3 mois et 6 mois après le traitement EVAR. Une angiographie, un CT-scan et des coupes macroscopiques ont été produits au sacrifice. Les régions d’endofuites, de thrombus frais et de thrombus organisé furent identifiées et segmentées. Les valeurs de rigidité données par le SWI des différentes régions furent comparées. Celles-ci furent différentes de façon significative (P < 0.001). Également, le SWI a pu détecter la présence d’endofuites où le CT-scan (1) et le DUS (3) ont échoué. Dans la continuité de ces travaux, le deuxième objectif de ce projet fut de caractériser l’évolution du thrombus dans le temps, de même que l’évolution des endofuites après embolisation dans des modèles canins. Dix-huit anévrismes furent créés dans les artères iliaques de neuf modèles canins, suivis d’une endofuite de type I après EVAR. Deux gels embolisants (Chitosan (Chi) ou Chitosan-Sodium-Tetradecyl-Sulfate (Chi-STS)) furent injectés dans le sac anévrismal pour promouvoir la guérison. Des examens échographiques, DUS et SWI ont été effectués à l’implantation et après 1 semaine, 1 mois, 3 mois et 6 mois. Une angiographie, un CT-scan et un examen histologique ont été réalisés au sacrifice afin d’évaluer la présence, le type et la grosseur de l’endofuite. Les valeurs du module d’élasticité des régions d’intérêts ont été identifiées et segmentées sur les données pathologiques. Les régions d’endofuites et de thrombus frais furent différentes de façon significative comparativement aux autres régions (P < 0.001). Les valeurs d’élasticité du thrombus frais à 1 semaine et à 3 mois indiquent que le SWI peut évaluer la maturation du thrombus, de même que caractériser l’évolution et la dégradation des gels embolisants dans le temps. Le SWI a pu détecter des endofuites où le DUS a échoué (2) et, contrairement au CT-scan, détecter la présence de thrombus frais. Finalement, la dernière étape du projet doctoral consistait à appliquer le SWI dans une phase clinique, avec des patients humains ayant déjà un AAA, pour la détection d’endofuite et la caractérisation de l’élasticité des tissus. 25 patients furent sélectionnés pour participer à l’étude. Une comparaison d’imagerie a été produite entre le SWI, le CT-scan et le DUS. Les valeurs de rigidité données par le SWI des différentes régions (endofuite, thrombus) furent identifiées et segmentées. Celles-ci étaient distinctes de façon significative (P < 0.001). Le SWI a détecté 5 endofuites sur 6 (sensibilité de 83.3%) et a eu 6 faux positifs (spécificité de 76%). Le SWI a pu détecter la présence d’endofuites où le CT-scan (2) ainsi que le DUS (2) ont échoué. Il n’y avait pas de différence statistique notable entre la rigidité du thrombus pour un AAA avec endofuite et un AAA sans endofuite. Aucune corrélation n’a pu être établie de façon significative entre les diamètres des AAAs ainsi que leurs variations et l’élasticité du thrombus. Le SWI a le potentiel de détecter les endofuites et caractériser le thrombus selon leurs propriétés mécaniques. Cette technique pourrait être combinée au suivi des AAAs post-EVAR, complémentant ainsi l’imagerie DUS et réduisant le coût et l’exposition à la radiation ionisante et aux agents de contrastes néphrotoxiques.