Cucurbit[7]uril Macrocyclic Sensors for Optical Fingerprinting: Predicting Protein Structural Changes to Identifying Disease-Specific Amyloid Assemblies.

In a three-dimensional (3D) representation, each protein molecule displays a specific pattern of chemical and topological features, which are altered during its misfolding and aggregation pathway. Generating a recognizable fingerprint from such features could provide an enticing approach not only to identify these biomolecules but also to gain clues regarding their folding state and the occurrence of pathologically lethal misfolded aggregates. We report here a universal strategy to generate a fluorescent fingerprint from biomolecules by employing the pan-selective molecular recognition feature of a cucurbit[7]uril (CB[7]) macrocyclic receptor. We implemented a direct sensing strategy by covalently tethering CB[7] with a library of fluorescent reporters. When CB[7] recognizes the chemical and geometrical features of a biomolecule, it brings the tethered fluorophore into the vicinity, concomitantly reporting the nature of its binding microenvironment through a change in their optical signature. The photophysical properties of the fluorophores allow a multitude of probing modes, while their structural features provide additional binding diversity, generating a distinct fluorescence fingerprint from the biomolecule. We first used this strategy to rapidly discriminate a diverse range of protein analytes. The macrocyclic sensor was then applied to probe conformational changes in the protein structure and identify the formation of oligomeric and fibrillar species from misfolded proteins. Notably, the sensor system allowed us to differentiate between different self-assembled forms of the disease-specific amyloid-ß (Aß) aggregates and segregated them from other generic amyloid structures with a 100% identification accuracy. Ultimately, this sensor system predicted clinically relevant changes by fingerprinting serum samples from a cohort of pregnant women.

Base-Position Error Rate Analysis of Next-Generation Sequencing Applied to Circulating Tumor DNA in Non-Small Cell Lung Cancer: A Prospective Study.

BACKGROUND: Circulating tumor DNA (ctDNA) is an approved noninvasive biomarker to test for the presence of EGFR mutations at diagnosis or recurrence of lung cancer. However, studies evaluating ctDNA as a noninvasive "real-time" biomarker to provide prognostic and predictive information in treatment monitoring have given inconsistent results, mainly due to methodological differences. We have recently validated a next-generation sequencing (NGS) approach to detect ctDNA. Using this new approach, we evaluated the clinical usefulness of ctDNA monitoring in a prospective observational series of patients with non-small cell lung cancer (NSCLC). METHODS AND FINDINGS: We recruited 124 patients with newly diagnosed advanced NSCLC for ctDNA monitoring. The primary objective was to analyze the prognostic value of baseline ctDNA on overall survival. ctDNA was assessed by ultra-deep targeted NGS using our dedicated variant caller algorithm. Common mutations were validated by digital PCR. Out of the 109 patients with at least one follow-up marker mutation, plasma samples were contributive at baseline (n = 105), at first evaluation (n = 85), and at tumor progression (n = 66). We found that the presence of ctDNA at baseline was an independent marker of poor prognosis, with a median overall survival of 13.6 versus 21.5 mo (adjusted hazard ratio [HR] 1.82, 95% CI 1.01-3.55, p = 0.045) and a median progression-free survival of 4.9 versus 10.4 mo (adjusted HR 2.14, 95% CI 1.30-3.67, p = 0.002). It was also related to the presence of bone and liver metastasis. At first evaluation (E1) after treatment initiation, residual ctDNA was an early predictor of treatment benefit as judged by best radiological response and progression-free survival. Finally, negative ctDNA at E1 was associated with overall survival independently of Response Evaluation Criteria in Solid Tumors (RECIST) (HR 3.27, 95% CI 1.66-6.40, p < 0.001). Study population heterogeneity, over-representation of EGFR-mutated patients, and heterogeneous treatment types might limit the conclusions of this study, which require future validation in independent populations. CONCLUSIONS: In this study of patients with newly diagnosed NSCLC, we found that ctDNA detection using targeted NGS was associated with poor prognosis. The heterogeneity of lung cancer molecular alterations, particularly at time of progression, impairs the ability of individual gene testing to accurately detect ctDNA in unselected patients. Further investigations are needed to evaluate the clinical impact of earlier evaluation times at 1 or 2 wk. Supporting clinical decisions, such as early treatment switching based on ctDNA positivity at first evaluation, will require dedicated interventional studies.

Multi-atlas and label fusion approach for patient-specific MRI based skull estimation.

PURPOSE: MRI-based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1-weighted volume. METHODS: The skull is estimated using a multi-atlas segmentation approach. Using a whole head computed tomography (CT) scan database, the skull in a new MRI volume is detected by nonrigid image registration of the volume to every CT, and combination of the individual segmentations by label-fusion. We have compared Majority Voting, Simultaneous Truth and Performance Level Estimation (STAPLE), Shape Based Averaging (SBA), and the Selective and Iterative Method for Performance Level Estimation (SIMPLE) algorithms. RESULTS: The pipeline has been evaluated quantitatively using images from the Retrospective Image Registration Evaluation database (reaching an overlap of 72.46 ± 6.99%), a clinical CT-MR dataset (maximum overlap of 78.31 ± 6.97%), and a whole head CT-MRI pair (maximum overlap 78.68%). A qualitative evaluation has also been performed on MRI acquisition of volunteers. CONCLUSION: It is possible to automatically segment the complete skull from MRI data using a multi-atlas and label fusion approach. This will allow the creation of complete MRI-based tissue models that can be used in electromagnetic dosimetry applications and attenuation correction in PET/MR.

Analysis of Base-Position Error Rate of Next-Generation Sequencing to Detect Tumor Mutations in Circulating DNA.

BACKGROUND: Detecting single-nucleotide variations and insertions/deletions in circulating tumor DNA is challenging because of their low allele frequency. The clinical use of circulating tumor DNA to characterize tumor genetic alterations requires new methods based on next-generation sequencing. METHODS: We developed a method based on quantification of error rate of each base position [position error rate (PER)]. To identify mutations, a binomial test was used to compare the minor-allele frequency to the measured PER at each base position. This process was validated in control samples and in 373 plasma samples from patients with lung or pancreatic cancer. RESULTS: Minimal mutated allele frequencies were 0.003 for single-nucleotide variations and 0.001 for insertions/deletions. Independent testing performed by droplet digital PCR (n = 231 plasma samples) showed strong agreement with the base-PER method (κ = 0.90). CONCLUSIONS: Targeted next-generation sequencing analyzed with the base-PER method represents a robust and low cost method to detect circulating tumor DNA in patients with cancer.

Comprehensive ctDNA Measurements Improve Prediction of Clinical Outcomes and Enable Dynamic Tracking of Disease Progression in Advanced Pancreatic Cancer.

PURPOSE: Circulating tumor DNA (ctDNA) has emerged as a promising tumor-specific biomarker in pancreatic cancer, but current evidence of the clinical potential of ctDNA is limited. In this study, we used comprehensive detection methodology to explore the utility of longitudinal ctDNA measurements in patients with advanced pancreatic cancer. EXPERIMENTAL DESIGN: A targeted eight-gene next-generation sequencing panel was used to detect point mutations and copy-number aberrations (CNA) in ctDNA from 324 pre-treatment and longitudinal plasma samples obtained from 56 patients with advanced pancreatic cancer. The benefit of ctDNA measurements to predict clinical outcome and track disease progression was assessed. RESULTS: We detected ctDNA in 35/56 (63%) patients at baseline and found that it was an independent predictor of shorter progression-free survival (PFS) and overall survival (OS). After initiation of treatment, ctDNA levels decreased significantly before significantly increasing by the time of progression. In some patients, ctDNA persistence was observed after the first chemotherapy cycles, and it was associated with rapid disease progression and shorter OS. Longitudinal monitoring of ctDNA levels in 27 patients for whom multiple samples were available detected progression in 19 (70%) patients. The median lead time of ctDNA measurements on radiologically determined progression/time of death was 19 days (P = 0.002), compared with 6 days (P = 0.007) using carbohydrate antigen 19-9. CONCLUSIONS: ctDNA is an independent prognostic marker that can be used to detect treatment failure and disease progression in patients with advanced pancreatic cancer.

Generalization of the normal-exponential model: exploration of a more accurate parametrisation for the signal distribution on Illumina BeadArrays.

BACKGROUND: Illumina BeadArray technology includes non specific negative control features that allow a precise estimation of the background noise. As an alternative to the background subtraction proposed in BeadStudio which leads to an important loss of information by generating negative values, a background correction method modeling the observed intensities as the sum of the exponentially distributed signal and normally distributed noise has been developed. Nevertheless, Wang and Ye (2012) display a kernel-based estimator of the signal distribution on Illumina BeadArrays and suggest that a gamma distribution would represent a better modeling of the signal density. Hence, the normal-exponential modeling may not be appropriate for Illumina data and background corrections derived from this model may lead to wrong estimation. RESULTS: We propose a more flexible modeling based on a gamma distributed signal and a normal distributed background noise and develop the associated background correction, implemented in the R-package NormalGamma. Our model proves to be markedly more accurate to model Illumina BeadArrays: on the one hand, it is shown on two types of Illumina BeadChips that this model offers a more correct fit of the observed intensities. On the other hand, the comparison of the operating characteristics of several background correction procedures on spike-in and on normal-gamma simulated data shows high similarities, reinforcing the validation of the normal-gamma modeling. The performance of the background corrections based on the normal-gamma and normal-exponential models are compared on two dilution data sets, through testing procedures which represent various experimental designs. Surprisingly, we observe that the implementation of a more accurate parametrisation in the model-based background correction does not increase the sensitivity. These results may be explained by the operating characteristics of the estimators: the normal-gamma background correction offers an improvement in terms of bias, but at the cost of a loss in precision. CONCLUSIONS: This paper addresses the lack of fit of the usual normal-exponential model by proposing a more flexible parametrisation of the signal distribution as well as the associated background correction. This new model proves to be considerably more accurate for Illumina microarrays, but the improvement in terms of modeling does not lead to a higher sensitivity in differential analysis. Nevertheless, this realistic modeling makes way for future investigations, in particular to examine the characteristics of pre-processing strategies.

Novel hybridization- and tag-based error-corrected method for sensitive ctDNA mutation detection using ion semiconductor sequencing.

Circulating tumor DNA (ctDNA) analysis has emerged as a clinically useful tool for cancer diagnostics and treatment monitoring. However, ctDNA detection is complicated by low DNA concentrations and technical challenges. Here we describe our newly developed sensitive method for ctDNA detection on the Ion Torrent sequencing platform, which we call HYbridization- and Tag-based Error-Corrected sequencing (HYTEC-seq). This method combines hybridization-based capture with molecular tags, and the novel variant caller PlasmaMutationDetector2 to eliminate background errors. We describe the validation of HYTEC-seq using control samples with known mutations, demonstrating an analytical sensitivity down to 0.1% at > 99.99% specificity. Furthermore, to demonstrate the utility of this method in a clinical setting, we analyzed plasma samples from 44 patients with advanced pancreatic cancer, revealing mutations in 57% of the patients at allele frequencies as low as 0.23%.

Hierarchical segmentation using equivalence test (HiSET): Application to DCE image sequences.

Dynamical contrast enhanced (DCE) imaging allows non invasive access to tissue micro-vascularization. It appears as a promising tool to build imaging biomarkers for diagnostic, prognosis or anti-angiogenesis treatment monitoring of cancer. However, quantitative analysis of DCE image sequences suffers from low signal to noise ratio (SNR). SNR may be improved by averaging functional information in a large region of interest when it is functionally homogeneous. We propose a novel method for automatic segmentation of DCE image sequences into functionally homogeneous regions, called DCE-HiSET. Using an observation model which depends on one parameter a and is justified a posteriori, DCE-HiSET is a hierarchical clustering algorithm. It uses the p-value of a multiple equivalence test as dissimilarity measure and consists of two steps. The first exploits the spatial neighborhood structure to reduce complexity and takes advantage of the regularity of anatomical features, while the second recovers (spatially) disconnected homogeneous structures at a larger (global) scale. Given a minimal expected homogeneity discrepancy for the multiple equivalence test, both steps stop automatically by controlling the Type I error. This provides an adaptive choice for the number of clusters. Assuming that the DCE image sequence is functionally piecewise constant with signals on each piece sufficiently separated, we prove that DCE-HiSET will retrieve the exact partition with high probability as soon as the number of images in the sequence is large enough. The minimal expected homogeneity discrepancy appears as the tuning parameter controlling the size of the segmentation. DCE-HiSET has been implemented in C++ for 2D and 3D image sequences with competitive speed.

Fast Patch-Based Pseudo-CT Synthesis from T1-Weighted MR Images for PET/MR Attenuation Correction in Brain Studies.

UNLABELLED: Attenuation correction in hybrid PET/MR scanners is still a challenging task. This paper describes a methodology for synthesizing a pseudo-CT volume from a single T1-weighted volume, thus allowing us to create accurate attenuation correction maps. METHODS: We propose a fast pseudo-CT volume generation from a patient-specific MR T1-weighted image using a groupwise patch-based approach and an MRI-CT atlas dictionary. For every voxel in the input MR image, we compute the similarity of the patch containing that voxel to the patches of all MR images in the database that lie in a certain anatomic neighborhood. The pseudo-CT volume is obtained as a local weighted linear combination of the CT values of the corresponding patches. The algorithm was implemented in a graphical processing unit (GPU). RESULTS: We evaluated our method both qualitatively and quantitatively for PET/MR correction. The approach performed successfully in all cases considered. We compared the SUVs of the PET image obtained after attenuation correction using the patient-specific CT volume and using the corresponding computed pseudo-CT volume. The patient-specific correlation between SUV obtained with both methods was high (R(2) = 0.9980, P < 0.0001), and the Bland-Altman test showed that the average of the differences was low (0.0006 ± 0.0594). A region-of-interest analysis was also performed. The correlation between SUVmean and SUVmax for every region was high (R(2) = 0.9989, P < 0.0001, and R(2) = 0.9904, P < 0.0001, respectively). CONCLUSION: The results indicate that our method can accurately approximate the patient-specific CT volume and serves as a potential solution for accurate attenuation correction in hybrid PET/MR systems. The quality of the corrected PET scan using our pseudo-CT volume is comparable to having acquired a patient-specific CT scan, thus improving the results obtained with the ultrashort-echo-time-based attenuation correction maps currently used in the scanner. The GPU implementation substantially decreases computational time, making the approach suitable for real applications.

Automatic sex determination of skulls based on a statistical shape model.

Sex determination from skeletons is an important research subject in forensic medicine. Previous skeletal sex assessments are through subjective visual analysis by anthropologists or metric analysis of sexually dimorphic features. In this work, we present an automatic sex determination method for 3D digital skulls, in which a statistical shape model for skulls is constructed, which projects the high-dimensional skull data into a low-dimensional shape space, and Fisher discriminant analysis is used to classify skulls in the shape space. This method combines the advantages of metrical and morphological methods. It is easy to use without professional qualification and tedious manual measurement. With a group of Chinese skulls including 127 males and 81 females, we choose 92 males and 58 females to establish the discriminant model and validate the model with the other skulls. The correct rate is 95.7% and 91.4% for females and males, respectively. Leave-one-out test also shows that the method has a high accuracy.

Parameter estimation and change-point detection from Dynamic Contrast Enhanced MRI data using stochastic differential equations.

Dynamic Contrast Enhanced imaging (DCE-imaging) following a contrast agent bolus allows the extraction of information on tissue micro-vascularization. The dynamic signals obtained from DCE-imaging are modeled by pharmacokinetic compartmental models which integrate the Arterial Input Function. These models use ordinary differential equations (ODEs) to describe the exchanges between the arterial and capillary plasma and the extravascular-extracellular space. Their least squares fitting takes into account measurement noises but fails to deal with unpredictable fluctuations due to external/internal sources of variations (patients' anxiety, time-varying parameters, measurement errors in the input function, etc.). Adding Brownian components to the ODEs leads to stochastic differential equations (SDEs). In DCE-imaging, SDEs are discretely observed with an additional measurement noise. We propose to estimate the parameters of these noisy SDEs by maximum likelihood, using the Kalman filter. In DCE-imaging, the contrast agent injected in vein arrives in plasma with an unknown time delay. The delay parameter induces a change-point in the drift of the SDE and ODE models, which is estimated also. Estimations based on the SDE and ODE pharmacokinetic models are compared to real DCE-MRI data. They show that the use of SDE provides robustness in the estimation results. A simulation study confirms these results.

Craniofacial reconstruction as a prediction problem using a Latent Root Regression model.

In this paper, we present a computer-assisted method for facial reconstruction. This method provides an estimation of the facial shape associated with unidentified skeletal remains. Current computer-assisted methods using a statistical framework rely on a common set of extracted points located on the bone and soft-tissue surfaces. Most of the facial reconstruction methods then consist of predicting the position of the soft-tissue surface points, when the positions of the bone surface points are known. We propose to use Latent Root Regression for prediction. The results obtained are then compared to those given by Principal Components Analysis linear models. In conjunction, we have evaluated the influence of the number of skull landmarks used. Anatomical skull landmarks are completed iteratively by points located upon geodesics which link these anatomical landmarks, thus enabling us to artificially increase the number of skull points. Facial points are obtained using a mesh-matching algorithm between a common reference mesh and individual soft-tissue surface meshes. The proposed method is validated in term of accuracy, based on a leave-one-out cross-validation test applied to a homogeneous database. Accuracy measures are obtained by computing the distance between the original face surface and its reconstruction. Finally, these results are discussed referring to current computer-assisted reconstruction facial techniques.

A local technique based on vectorized surfaces for craniofacial reconstruction.

In this paper, we focus on the automation of facial reconstruction. Since they consider the whole head as the object of interest, usual reconstruction techniques are global and involve a large number of parameters to be estimated. We present a local technique which aims at reaching a good trade-off between bias and variance following the paradigm of non-parametric statistics. The estimation is localized on patches delimited by surface geodesics between anatomical points of the skull. The technique relies on a continuous representation of the individual surfaces embedded in the vectorial space of extended normal vector fields. This allows to compute deformations and averages of surfaces. It consists in estimating the soft-tissue surface over patches. Using a homogeneous database described in [31], we obtain results on the chin and nasal regions with an average error below 1mm, outperforming the global reconstruction techniques.

Construction and analysis of a head CT-scan database for craniofacial reconstruction.

This paper is devoted to the construction of a complete database which is intended to improve the implementation and the evaluation of automated facial reconstruction. This growing database is currently composed of 85 head CT-scans of healthy European subjects aged 20-65 years old. It also includes the triangulated surfaces of the face and the skull of each subject. These surfaces are extracted from CT-scans using an original combination of image-processing techniques which are presented in the paper. Besides, a set of 39 referenced anatomical skull landmarks were located manually on each scan. Using the geometrical information provided by triangulated surfaces, we compute facial soft-tissue depths at each known landmark positions. We report the average thickness values at each landmark and compare our measures to those of the traditional charts of [J. Rhine, C.E. Moore, Facial Tissue Thickness of American Caucasoïds, Maxwell Museum of Anthropology, Albuquerque, New Mexico, 1982] and of several recent in vivo studies [M.H. Manhein, G.A. Listi, R.E. Barsley, et al., In vivo facial tissue depth measurements for children and adults, Journal of Forensic Sciences 45 (1) (2000) 48-60; S. De Greef, P. Claes, D. Vandermeulen, et al., Large-scale in vivo Caucasian facial soft tissue thickness database for craniofacial reconstruction, Forensic Science International 159S (2006) S126-S146; R. Helmer, Schödelidentifizierung durch elektronische bildmischung, Kriminalistik Verlag GmbH, Heidelberg, 1984].