Deep learning approach for automated segmentation of myocardium using bone scintigraphy single-photon emission computed tomography/computed tomography in patients with suspected cardiac amyloidosis

BACKGROUND

We employed deep learning to automatically detect myocardial bone-seeking uptake as a marker of transthyretin cardiac amyloid cardiomyopathy (ATTR-CM) in patients undergoing 99mTc-pyrophosphate (PYP) or hydroxydiphosphonate (HDP) single-photon emission computed tomography (SPECT)/computed tomography (CT).

METHODS

We identified a primary cohort of 77 subjects at Brigham and Women's Hospital and a validation cohort of 93 consecutive patients imaged at the University of Pennsylvania who underwent SPECT/CT with PYP and HDP, respectively, for evaluation of ATTR-CM. Global heart regions of interest (ROIs) were traced on CT axial slices from the apex of the ventricle to the carina. Myocardial images were visually scored as grade 0 (no uptake), 1 (uptakeribs). A 2D U-net architecture was used to develop whole-heart segmentations for CT scans. Uptake was determined by calculating a heart-to-blood pool (HBP) ratio between the maximal counts value of the total heart region and the maximal counts value of the most superior ROI.

RESULTS

Deep learning and ground truth segmentations were comparable (p=0.63). A total of 42 (55%) patients had abnormal myocardial uptake on visual assessment. Automated quantification of the mean HBP ratio in the primary cohort was 3.1±1.4 versus 1.4±0.2 (p<0.01) for patients with positive and negative cardiac uptake, respectively. The model had 100% accuracy in the primary cohort and 98% in the validation cohort.

CONCLUSION

We have developed a highly accurate diagnostic tool for automatically segmenting and identifying myocardial uptake suggestive of ATTR-CM.

Tau maturation in the clinicopathological spectrum of Lewy body and Alzheimer's disease

OBJECTIVE

Alzheimer's disease neuropathologic change and alpha-synucleinopathy commonly co-exist and contribute to the clinical heterogeneity of dementia. Here, we examined tau epitopes marking various stages of tangle maturation to test the hypotheses that tau maturation is more strongly associated with beta-amyloid compared to alpha-synuclein, and within the context of mixed pathology, mature tau is linked to Alzheimer's disease clinical phenotype and negatively associated with Lewy body dementia.

METHODS

We used digital histology to measure percent area-occupied by pathology in cortical regions among individuals with pure Alzheimer's disease neuropathologic change, pure alpha-synucleinopathy, and a co-pathology group with both Alzheimer's and alpha-synuclein pathologic diagnoses. Multiple tau monoclonal antibodies were used to detect early (AT8, MC1) and mature (TauC3) epitopes of tangle progression. We used linear/logistic regression to compare groups and test the association between pathologies and clinical features.

RESULTS

There were lower levels of tau pathology (β = 1.86-2.96, p < 0.001) across all tau antibodies in the co-pathology group compared to the pure Alzheimer's pathology group. Among individuals with alpha-synucleinopathy, higher alpha-synuclein was associated with greater early tau (AT8 β = 1.37, p < 0.001; MC1 β = 1.2, p < 0.001) but not mature tau (TauC3 p = 0.18), whereas mature tau was associated with beta-amyloid (β = 0.21, p = 0.01). Finally, lower tau, particularly TauC3 pathology, was associated with lower frequency of both core clinical features and categorical clinical diagnosis of dementia with Lewy bodies.

INTERPRETATION

Mature tau may be more closely related to beta-amyloidosis than alpha-synucleinopathy, and pathophysiological processes of tangle maturation may influence the clinical features of dementia in mixed Lewy-Alzheimer's pathology.

Novel data-driven subtypes and stages of brain atrophy in the ALS-FTD spectrum

BACKGROUND

TDP-43 proteinopathies represent a spectrum of neurological disorders, anchored clinically on either end by amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes, highlighting its heterogeneity. This study was aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD spectrum.

METHODS

We used a data-driven procedure to identify 13 anatomic clusters of brain volume for 57 behavioral variant FTD (bvFTD; with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants), 103 ALS, and 47 ALS-FTD patients with likely TDP-43. A Subtype and Stage Inference (SuStaIn) model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns, and we related subtypes and stages to clinical, genetic, and neuropathological features of disease.

RESULTS

SuStaIn identified three novel subtypes: two disease subtypes with predominant brain atrophy in either prefrontal/somatomotor regions or limbic-related regions, and a normal-appearing group without obvious brain atrophy. The limbic-predominant subtype tended to present with more impaired cognition, higher frequencies of pathogenic variants in TBK1 and TARDBP genes, and a higher proportion of TDP-43 types B, E and C. In contrast, the prefrontal/somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type A. The normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients, higher cognitive capacity, higher proportion of lower motor neuron onset, milder motor symptoms, and lower frequencies of genetic pathogenic variants. The overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration, higher King's stage, and cognitive decline. Additionally, SuStaIn stages differed across clinical phenotypes, genotypes and types of TDP-43 pathology.

CONCLUSIONS

Our findings suggest distinct neurodegenerative subtypes of disease along the ALS-FTD spectrum that can be identified in vivo, each with distinct brain atrophy, clinical, genetic and pathological patterns.

A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain

The mammalian brain consists of millions to billions of cells that are organized into many cell types with specific spatial distribution patterns and structural and functional properties1-3. Here we report a comprehensive and high-resolution transcriptomic and spatial cell-type atlas for the whole adult mouse brain. The cell-type atlas was created by combining a single-cell RNA-sequencing (scRNA-seq) dataset of around 7 million cells profiled (approximately 4.0 million cells passing quality control), and a spatial transcriptomic dataset of approximately 4.3 million cells using multiplexed error-robust fluorescence in situ hybridization (MERFISH). The atlas is hierarchically organized into 4 nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We present an online platform, Allen Brain Cell Atlas, to visualize the mouse whole-brain cell-type atlas along with the single-cell RNA-sequencing and MERFISH datasets. We systematically analysed the neuronal and non-neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell-type organization in different brain regions-in particular, a dichotomy between the dorsal and ventral parts of the brain. The dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. Our study also uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types. Finally, we found that transcription factors are major determinants of cell-type classification and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole mouse brain transcriptomic and spatial cell-type atlas establishes a benchmark reference atlas and a foundational resource for integrative investigations of cellular and circuit function, development and evolution of the mammalian brain.

Discrepancies in Patient and Caregiver Ratings of Personality Change in Alzheimer's Disease and Related Dementias

OBJECTIVE

Personality change in Alzheimer's disease and related dementias (ADRD) is complicated by the patient and informant factors that confound accurate reporting of personality traits. We assessed the impact of caregiver burden on informant report of Big Five personality traits (extraversion, agreeableness, conscientiousness, neuroticism, and openness) and investigated the regional cortical volumes associated with larger discrepancies in the patient and informant report of the Big Five personality traits.

METHOD

Sixty-four ADRD participants with heterogeneous neurodegenerative clinical phenotypes and their informants completed the Big Five Inventory (BFI). Caregiver burden was measured using the Zarit Burden Interview. Discrepancy scores were computed as the difference between patient and informant ratings for the BFI. Regional gray matter volumes from T1-weighted 3T MRI were normalized to intracranial volume and related to global Big Five discrepancy scores using linear regression.

RESULTS

Higher levels of caregiver burden were associated with higher informant ratings of patient neuroticism (ß = 0.08, p = .012) and with lower informant ratings of patient agreeableness (ß = 0.11, p = .021) and conscientiousness (ß = 0.04, p = .034) independent of disease severity. Patients with greater Big Five discrepancy scores showed smaller cortical volumes in the right medial prefrontal cortex (β = -5.24, p = .045) and right superior temporal gyrus (β = -7.91, p = .028).

CONCLUSIONS

Informant ratings of personality traits in ADRD can be confounded by the caregiver burden, highlighting the need for more objective measures of personality and behavior in dementia samples. Discrepancies between informant and patient ratings of personality may additionally reflect loss of insight secondary to cortical atrophy in the frontal and temporal structures.

Neuroanatomical and cellular degeneration associated with a social disorder characterized by new ritualistic belief systems in a TDP-C patient vs. a Pick patient

Frontotemporal dementia (FTD) is a spectrum of clinically and pathologically heterogenous neurodegenerative dementias. Clinical and anatomical variants of FTD have been described and associated with underlying frontotemporal lobar degeneration (FTLD) pathology, including tauopathies (FTLD-tau) or TDP-43 proteinopathies (FTLD-TDP). FTD patients with predominant degeneration of anterior temporal cortices often develop a language disorder of semantic knowledge loss and/or a social disorder often characterized by compulsive rituals and belief systems corresponding to predominant left or right hemisphere involvement, respectively. The neural substrates of these complex social disorders remain unclear. Here, we present a comparative imaging and postmortem study of two patients, one with FTLD-TDP (subtype C) and one with FTLD-tau (subtype Pick disease), who both developed new rigid belief systems. The FTLD-TDP patient developed a complex set of values centered on positivity and associated with specific physical and behavioral features of pigs, while the FTLD-tau patient developed compulsive, goal-directed behaviors related to general themes of positivity and spirituality. Neuroimaging showed left-predominant temporal atrophy in the FTLD-TDP patient and right-predominant frontotemporal atrophy in the FTLD-tau patient. Consistent with antemortem cortical atrophy, histopathologic examinations revealed severe loss of neurons and myelin predominantly in the anterior temporal lobes of both patients, but the FTLD-tau patient showed more bilateral, dorsolateral involvement featuring greater pathology and loss of projection neurons and deep white matter. These findings highlight that the regions within and connected to anterior temporal lobes may have differential vulnerability to distinct FTLD proteinopathies and serve important roles in human belief systems.

Novel data-driven subtypes and stages of brain atrophy in the ALS-FTD spectrum

Background

TDP-43 proteinopathies represents a spectrum of neurological disorders, anchored clinically on either end by amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes, highlighting its heterogeneity. This study aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD spectrum.

Methods

We used a data-driven procedure to identify 13 anatomic clusters of brain volumes for 57 behavioral variant FTD (bvFTD; with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants), 103 ALS, and 47 ALS-FTD patients with likely TDP-43. A Subtype and Stage Inference (SuStaIn) model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns, and we related subtypes and stages to clinical, genetic, and neuropathological features of disease.

Results

SuStaIn identified three novel subtypes: two disease subtypes with predominant brain atrophy either in prefrontal/somatomotor regions or limbic-related regions, and a normal-appearing group without obvious brain atrophy. The Limbic-predominant subtype tended to present with more impaired cognition, higher frequencies of pathogenic variants in TBK1 and TARDBP genes, and a higher proportion of TDP-43 type B, E and C. In contrast, the Prefrontal/Somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type A. The normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients, higher cognitive capacity, higher proportion of lower motor neuron onset, milder motor symptoms, and lower frequencies of genetic pathogenic variants. Overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration, higher King's stage, and cognitive decline. Additionally, SuStaIn stages differed across clinical phenotypes, genotypes and types of TDP-43 pathology.

Conclusions

Our findings suggest distinct neurodegenerative subtypes of disease along the ALS-FTD spectrum that can be identified in vivo, each with distinct brain atrophy, clinical, genetic and pathological patterns.

Dynamic structural remodeling of the human visual system prompted by bilateral retinal gene therapy

The impact of changes in visual input on neuronal circuitry is complex and much of our knowledge on human brain plasticity of the visual systems comes from animal studies. Reinstating vision in a group of patients with low vision through retinal gene therapy creates a unique opportunity to dynamically study the underlying process responsible for brain plasticity. Historically, increases in the axonal myelination of the visual pathway has been the biomarker for brain plasticity. Here, we demonstrate that to reach the long-term effects of myelination increase, the human brain may undergo demyelination as part of a plasticity process. The maximum change in dendritic arborization of the primary visual cortex and the neurite density along the geniculostriate tracks occurred at three months (3MO) post intervention, in line with timing for the peak changes in postnatal synaptogenesis within the visual cortex reported in animal studies. The maximum change at 3MO for both the gray and white matter significantly correlated with patients' clinical responses to light stimulations called full field sensitivity threshold (FST). Our results shed a new light on the underlying process of brain plasticity by challenging the concept of increase myelination being the hallmark of brain plasticity and instead reinforcing the idea of signal speed optimization as a dynamic process for brain plasticity.

A guide to the BRAIN Initiative Cell Census Network data ecosystem

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Discrepancies in patient and caregiver ratings of personality change in Alzheimer's disease and related dementias

Background

Assessment of personality change in Alzheimer's disease and related dementias (ADRD) is clinically meaningful but complicated by patient (i.e., reduced insight) and informant (i.e., caregiver burden) factors that confound accurate reporting of personality traits. This study assessed the impact of caregiver burden on informant report of Big Five personality traits (Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness) and investigated regional cortical volumes associated with larger discrepancies in patient and informant report of Big Five personality traits.

Methods

Sixty-four ADRD participants with heterogeneous neurodegenerative clinical phenotypes and their informants completed the Big Five Inventory (BFI). Caregiver burden was measured using the Zarit Burden Interview (ZBI). Discrepancy scores were computed as the absolute value of the difference between patient and informant ratings for all BFI trait scores and summed to create a global score. Regional grey matter volumes from T1-weighted 3T MRI were normalized to intracranial volume and related to global Big Five discrepancy scores using linear regression.

Results

Higher levels of caregiver burden were associated with higher informant ratings of patient Neuroticism (ß =0.27, p =.016) and lower informant ratings of patient Agreeableness (ß =-0.32, p =.002), Conscientiousness (ß =-0.3, p =.002), and Openness (ß =-0.34, p =.003) independent of disease severity. Patients with greater Big Five discrepancy scores showed smaller cortical volumes in right medial PFC (β = -0.00015, p = .002), right superior temporal gyrus (β = -0.00028, p = .025), and left inferior frontal gyrus (β = -0.00006 p = .013).

Conclusions

Informant ratings of personality traits in ADRD can be confounded by caregiver burden, highlighting the need for more objective measures of personality and behavior in dementia samples. Discrepancies between informant and patient ratings of personality may additionally reflect loss of insight secondary to cortical atrophy in frontal and temporal structures.

A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain

The mammalian brain is composed of millions to billions of cells that are organized into numerous cell types with specific spatial distribution patterns and structural and functional properties. An essential step towards understanding brain function is to obtain a parts list, i.e., a catalog of cell types, of the brain. Here, we report a comprehensive and high-resolution transcriptomic and spatial cell type atlas for the whole adult mouse brain. The cell type atlas was created based on the combination of two single-cell-level, whole-brain-scale datasets: a single-cell RNA-sequencing (scRNA-seq) dataset of ~7 million cells profiled, and a spatially resolved transcriptomic dataset of ~4.3 million cells using MERFISH. The atlas is hierarchically organized into five nested levels of classification: 7 divisions, 32 classes, 306 subclasses, 1,045 supertypes and 5,200 clusters. We systematically analyzed the neuronal, non-neuronal, and immature neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell type organization in different brain regions, in particular, a dichotomy between the dorsal and ventral parts of the brain: the dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. We also systematically characterized cell-type specific expression of neurotransmitters, neuropeptides, and transcription factors. The study uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types across the brain, suggesting they mediate a myriad of modes of intercellular communications. Finally, we found that transcription factors are major determinants of cell type classification in the adult mouse brain and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole-mouse-brain transcriptomic and spatial cell type atlas establishes a benchmark reference atlas and a foundational resource for deep and integrative investigations of cell type and circuit function, development, and evolution of the mammalian brain.

Information bottleneck-based interpretable multitask network for breast cancer classification and segmentation

Breast cancer is one of the most common causes of death among women worldwide. Early signs of breast cancer can be an abnormality depicted on breast images (e.g., mammography or breast ultrasonography). However, reliable interpretation of breast images requires intensive labor and physicians with extensive experience. Deep learning is evolving breast imaging diagnosis by introducing a second opinion to physicians. However, most deep learning-based breast cancer analysis algorithms lack interpretability because of their black box nature, which means that domain experts cannot understand why the algorithms predict a label. In addition, most deep learning algorithms are formulated as a single-task-based model that ignores correlations between different tasks (e.g., tumor classification and segmentation). In this paper, we propose an interpretable multitask information bottleneck network (MIB-Net) to accomplish simultaneous breast tumor classification and segmentation. MIB-Net maximizes the mutual information between the latent representations and class labels while minimizing information shared by the latent representations and inputs. In contrast from existing models, our MIB-Net generates a contribution score map that offers an interpretable aid for physicians to understand the model's decision-making process. In addition, MIB-Net implements multitask learning and further proposes a dual prior knowledge guidance strategy to enhance deep task correlation. Our evaluations are carried out on three breast image datasets in different modalities. Our results show that the proposed framework is not only able to help physicians better understand the model's decisions but also improve breast tumor classification and segmentation accuracy over representative state-of-the-art models. Our code is available at https://github.com/jxw0810/MIB-Net.

Fully Automated Placental Volume Quantification From 3D Ultrasound for Prediction of Small-for-Gestational-Age Infants

OBJECTIVES

Early placental volume (PV) has been associated with small-for-gestational-age infants born under the 10th/5th centiles (SGA10/SGA5). Manual or semiautomated PV quantification from 3D ultrasound (3DUS) is time intensive, limiting its incorporation into clinical care. We devised a novel convolutional neural network (CNN) pipeline for fully automated placenta segmentation from 3DUS images, exploring the association between the calculated PV and SGA.

METHODS

Volumes of 3DUS obtained from singleton pregnancies at 11-14 weeks' gestation were automatically segmented by our CNN pipeline trained and tested on 99/25 images, combining two 2D and one 3D models with downsampling/upsampling architecture. The PVs derived from the automated segmentations (PV_CNN ) were used to train multivariable logistic-regression classifiers for SGA10/SGA5. The test performance for predicting SGA was compared to PVs obtained via the semiautomated VOCAL (GE-Healthcare) method (PV_VOCAL ).

RESULTS

We included 442 subjects with 37 (8.4%) and 18 (4.1%) SGA10/SGA5 infants, respectively. Our segmentation pipeline achieved a mean Dice score of 0.88 on an independent test-set. Adjusted models including PV_CNN or PV_VOCAL were similarly predictive of SGA10 (area under curve [AUC]: PV_CNN  = 0.780, PV_VOCAL  = 0.768). The addition of PV_CNN to a clinical model without any PV included (AUC = 0.725) yielded statistically significant improvement in AUC (P < .05); whereas PV_VOCAL did not (P = .105). Moreover, when predicting SGA5, including the PV_CNN (0.897) brought statistically significant improvement over both the clinical model (0.839, P = .015) and the PV_VOCAL model (0.870, P = .039).

CONCLUSIONS

First trimester PV measurements derived from our CNN segmentation pipeline are significantly associated with future SGA. This fully automated tool enables the incorporation of including placental volumetric biometry into the bedside clinical evaluation as part of a multivariable prediction model for risk stratification and patient counseling.

Divergent Histopathological Networks of Frontotemporal Degeneration Proteinopathy Subytpes

Network analyses inform complex systems such as human brain connectivity, but this approach is seldom applied to gold-standard histopathology. Here, we use two complimentary computational approaches to model microscopic progression of the main subtypes of tauopathy versus TDP-43 proteinopathy in the human brain. Digital histopathology measures were obtained in up to 13 gray matter (GM) and adjacent white matter (WM) cortical brain regions sampled from 53 tauopathy and 66 TDP-43 proteinopathy autopsy patients. First, we constructed a weighted non-directed graph for each group, where nodes are defined as GM and WM regions sampled and edges in the graph are weighted using the group-level Pearson's correlation coefficient for each pairwise node comparison. Additionally, we performed mediation analyses to test mediation effects of WM pathology between anterior frontotemporal and posterior parietal GM nodes. We find greater correlation (i.e., edges) between GM and WM node pairs in tauopathies compared with TDP-43 proteinopathies. Moreover, WM pathology strongly correlated with a graph metric of pathology spread (i.e., node-strength) in tauopathies (r = 0.60, p < 0.03) but not in TDP-43 proteinopathies (r = 0.03, p = 0.9). Finally, we found mediation effects for WM pathology on the association between anterior and posterior GM pathology in FTLD-Tau but not in FTLD-TDP. These data suggest distinct tau and TDP-43 proteinopathies may have divergent patterns of cellular propagation in GM and WM. More specifically, axonal spread may be more influential in FTLD-Tau progression. Network analyses of digital histopathological measurements can inform models of disease progression of cellular degeneration in the human brain.SIGNIFICANCE STATEMENT In this study, we uniquely perform two complimentary computational approaches to model and contrast microscopic disease progression between common frontotemporal lobar degeneration (FTLD) proteinopathy subtypes with similar clinical syndromes during life. Our models suggest white matter (WM) pathology influences cortical spread of disease in tauopathies that is less evident in TDP-43 proteinopathies. These data support the hypothesis that there are neuropathologic signatures of cellular degeneration within neurocognitive networks for specific protienopathies. These distinctive patterns of cellular pathology can guide future efforts to develop tissue-sensitive imaging and biological markers with diagnostic and prognostic utility for FTLD. Moreover, our novel computational approach can be used in future work to model various neurodegenerative disorders with mixed proteinopathy within the human brain connectome.

Ex vivo MRI and histopathology detect novel iron-rich cortical inflammation in frontotemporal lobar degeneration with tau versus TDP-43 pathology

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Comparative study of whole-hemisphere ex vivo T2*-weighted MRI and histopathology.

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Sample of FTLD-Tau and FTLD-TDP subtypes with reference to healthy and AD brain.

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Novel focal upper cortical-layer iron-rich pathology distinguishes FTLD-TDP from clinically-similar FTLD-Tau and AD.

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Distinct novel iron-rich FTLD-Tau pathology in mid-to-deep cortical-layers and WM.

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T2*-weighted MRI signatures offer in vivo biomarker targets for FTLD proteinopathy.

Frontotemporal lobar degeneration (FTLD) is a heterogeneous spectrum of age-associated neurodegenerative diseases that include two main pathologic categories of tau (FTLD-Tau) and TDP-43 (FTLD-TDP) proteinopathies. These distinct proteinopathies are often clinically indistinguishable during life, posing a major obstacle for diagnosis and emerging therapeutic trials tailored to disease-specific mechanisms. Moreover, MRI-derived measures have had limited success to date discriminating between FTLD-Tau or FTLD-TDP. T₂*-weighted (T2*w) ex vivo MRI has previously been shown to be sensitive to non-heme iron in healthy intracortical lamination and myelin, and to pathological iron deposits in amyloid-beta plaques and activated microglia in Alzheimer’s disease neuropathologic change (ADNC). However, an integrated, ex vivo MRI and histopathology approach is understudied in FTLD. We apply joint, whole-hemisphere ex vivo MRI at 7 T and histopathology to the study autopsy-confirmed FTLD-Tau (n = 4) and FTLD-TDP (n = 3), relative to ADNC disease-control brains with antemortem clinical symptoms of frontotemporal dementia (n = 2), and an age-matched healthy control. We detect distinct laminar patterns of novel iron-laden glial pathology in both FTLD-Tau and FTLD-TDP brains. We find iron-positive ameboid and hypertrophic microglia and astrocytes largely in deeper GM and adjacent WM in FTLD-Tau. In contrast, FTLD-TDP presents prominent superficial cortical layer iron reactivity in astrocytic processes enveloping small blood vessels with limited involvement of adjacent WM, as well as more diffuse distribution of punctate iron-rich dystrophic microglial processes across all GM lamina. This integrated MRI/histopathology approach reveals ex vivo MRI features that are consistent with these pathological observations distinguishing FTLD-Tau and FTLD-TDP subtypes, including prominent irregular hypointense signal in deeper cortex in FTLD-Tau whereas FTLD-TDP showed upper cortical layer hypointense bands and diffuse cortical speckling. Moreover, differences in adjacent WM degeneration and iron-rich gliosis on histology between FTLD-Tau and FTLD-TDP were also readily apparent on MRI as hyperintense signal and irregular areas of hypointensity, respectively that were more prominent in FTLD-Tau compared to FTLD-TDP. These unique histopathological and radiographic features were distinct from healthy control and ADNC brains, suggesting that iron-sensitive T2*w MRI, adapted to in vivo application at sufficient resolution, may eventually offer an opportunity to improve antemortem diagnosis of FTLD proteinopathies using tissue-validated methods.

Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a multi-system disease characterized primarily by progressive muscle weakness. Cognitive dysfunction is commonly observed in patients; however, factors influencing risk for cognitive dysfunction remain elusive. Using sparse canonical correlation analysis (sCCA), an unsupervised machine-learning technique, we observed that single nucleotide polymorphisms collectively associate with baseline cognitive performance in a large ALS patient cohort (N = 327) from the multicenter Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) Consortium. We demonstrate that a polygenic risk score derived using sCCA relates to longitudinal cognitive decline in the same cohort and also to in vivo cortical thinning in the orbital frontal cortex, anterior cingulate cortex, lateral temporal cortex, premotor cortex, and hippocampus (N = 90) as well as post-mortem motor cortical neuronal loss (N = 87) in independent ALS cohorts from the University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Our findings suggest that common genetic polymorphisms may exert a polygenic contribution to the risk of cortical disease vulnerability and cognitive dysfunction in ALS.

Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a multi-system disease characterized primarily by progressive muscle weakness. Cognitive dysfunction is commonly observed in patients; however, factors influencing risk for cognitive dysfunction remain elusive. Using sparse canonical correlation analysis (sCCA), an unsupervised machine-learning technique, we observed that single nucleotide polymorphisms collectively associate with baseline cognitive performance in a large ALS patient cohort (N = 327) from the multicenter Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) Consortium. We demonstrate that a polygenic risk score derived using sCCA relates to longitudinal cognitive decline in the same cohort and also to in vivo cortical thinning in the orbital frontal cortex, anterior cingulate cortex, lateral temporal cortex, premotor cortex, and hippocampus (N = 90) as well as post-mortem motor cortical neuronal loss (N = 87) in independent ALS cohorts from the University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Our findings suggest that common genetic polymorphisms may exert a polygenic contribution to the risk of cortical disease vulnerability and cognitive dysfunction in ALS.

Minimally interactive placenta segmentation from three-dimensional ultrasound images

Purpose: Placental size in early pregnancy has been associated with important clinical outcomes, including fetal growth. However, extraction of placental size from three-dimensional ultrasound (3DUS) requires time-consuming interactive segmentation methods and is prone to user variability. We propose a semiautomated segmentation technique that requires minimal user input to robustly measure placental volume from 3DUS images. Approach: For semiautomated segmentation, a single, central 2D slice was manually annotated to initialize an automated multi-atlas label fusion (MALF) algorithm. The dataset consisted of 47 3DUS volumes obtained at 11 to 14 weeks in singleton pregnancies (28 anterior and 19 posterior). Twenty-six of these subjects were imaged twice within the same session. Dice overlap and surface distance were used to quantify the automated segmentation accuracy compared to expert manual segmentations. The mean placental volume measurements obtained by our method and VOCAL (virtual organ computer-aided analysis), a leading commercial semiautomated method, were compared to the manual reference set. The test-retest reliability was also assessed. Results: The overlap between our automated segmentation and manual (mean Dice: 0.824 ± 0.061 , median: 0.831) was within the range reported by other methods requiring extensive manual input. The average surface distance was 1.66 ± 0.96    mm . The correlation coefficient between test-retest volumes was r = 0.88 , and the intraclass correlation was ICC ( 1 ) = 0.86 . Conclusions: MALF is a promising method that can allow accurate and reliable segmentation of the placenta with minimal user interaction. Further refinement of this technique may allow for placental biometry to be incorporated into clinical pregnancy surveillance.

Abstract P2-12-01: Dose- and exposure-response relationship and biomarker correlation analysis in breast tumors from patients treated with capivasertib, an AKT inhibitor, in the STAKT randomized, placebo controlled pre-surgical study

Background: Capivasertib (AZD5363), an AKT1,2,3 inhibitor, significantly improved progression-free and overall survival when added to paclitaxel in triple negative breast cancer (BC) patients (Schmid et al. ASCO 2018). We have previously reported in STAKT, robust target inhibition at 480mg BD versus placebo, including significant decreases in the primary biomarkers (PBs) - Ki67, pPRAS40 & pGSK3β - in primary BCs (Robertson et al. SABCS 2017). We now report the dose- and exposure-response relationship of capivasertib and the correlation between primary and secondary (pAKT, pS6, nuclear FOXO3a) tumor biomarkers.

Design: STAKT was a two-stage, double blind, randomized, placebo controlled 'window-of-opportunity' trial in newly diagnosed ER+ BC patients. Stage 1 assessed capivasertib at a dose of 480mg BD p.o. versus placebo. Stage 2 assessed capivasertib at two lower doses 360mg and 240mg BD. Tumor biopsies were taken prior to 1st dose and after 4.5 days of dosing. Evaluable patients (who required pre-defined minimum baseline PD values for PBs) included placebo (n=11), capivasertib at 480mg (n=17), 360mg (n=5) and 240mg (n=6). Blood samples for pharmacokinetic (PK) studies were scheduled at pre-dose; 2, 4, optional 6 & 8 hrs post first dose on Day 1; ˜2-4 h post last dose on Day 5 (before biopsy). The % change from baseline for PBs were evaluated against the following exposure variables (placebo=0): i) Dose, ii) Observed Cmax Day 1 (˜2h post-dose), iii) Observed plasma concentration on Day 5, iv) Model-predicted plasma concentration Day 5 at time of biopsy, and v) Model-predicted AUC on Day 5. Spearman correlation coefficient measured the strength and direction of association between biomarkers.

Results:

· Significant mean reductions in % change from baseline were observed for the PBs pGSK3β (-39%; p&lt;0.006), pPRAS40 (-50%; p&lt;0.0001) and Ki67 (-23%; p=0.052) at 480mg versus placebo. At 360mg and 240mg, mean % changes from baseline in pGSK3β were -27% and -9%, respectively; in pPRAS40 -45% and -28%, respectively; and in Ki67 0% and +22%, respectively.

· Dose-response relationships for individual % change from baseline could be described by an Emax model for all PBs. Overall, the correlation to PK exposure (observed or predicted) was similar to the correlation to dose.

· Correlation coefficient analyses between biomarkers at capivasertib 480mg BD identified- i) Positive correlations for pGSK3β with Ki67 (ρ = 0.52, p-value &lt; 0.05) & with pS6 (ρ = 0.54, p-value&lt;0.05); ii) Negative correlations between FOXO3a and Ki67 (ρ = -0.75, p-value&lt;0.001) pGSK3β (ρ = -0.71, p-value&lt;0.001) & also pS6 (ρ = -0.61, p-value&lt;0.001).Correlation coefficients for lower doses are not robust due to small sample size in these groups.

Conclusions

· Capivasertib caused dose- and concentration- dependent effects on biomarkers after only 4.5 days.

· Significant changes in the PBs were demonstrated at 480 mg BD. Biomarker changes was observed at 360mg and 240mg BD, but statistical analysis was limited by the small sample size at lower doses.

· Correlation between a number of tumor biomarkers (relative changes) were identified for capivasertib 480mg BD.

Citation Format: Gee J, Coleman RE, Cheung KL, Evans A, Holcombe C, Skene A, Rea D, Ahmed S, Jahan A, Horgan K, Rauchhaus P, Littleford R, Finlay P, Cheung A, Cullberg M, de Bruin E, Foxley A, Koulai L, Pass M, Schiavon G, Rugman P, Deb R, Robertson JFR. Dose- and exposure-response relationship and biomarker correlation analysis in breast tumors from patients treated with capivasertib, an AKT inhibitor, in the STAKT randomized, placebo controlled pre-surgical study [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P2-12-01.

A new scale for the assessment of conjunctival bulbar redness

PURPOSE

Current scales for assessment of bulbar conjunctival redness have limitations for evaluating digital images. We developed a scale suited for evaluating digital images and compared it to the Validated Bulbar Redness (VBR) scale.

METHODS

From a digital image database of 4889 color corrected bulbar conjunctival images, we identified 20 images with varied degrees of redness. These images, ten each of nasal and temporal views, constitute the Digital Bulbar Redness (DBR) scale. The chromaticity of these images was assessed with an established image processing algorithm. Using 100 unique, randomly selected images from the database, three trained, non-physician graders applied the DBR scale and printed VBR scale. Agreement was assessed with weighted Kappa statistics (K_w).

RESULTS

The DBR scale scores provide linear increments of 10 from 10-100 when redness is measured objectively with an established image processing algorithm. Exact agreement of all graders was 38% and agreement with no more than a difference of ten units between graders was 91%. K_w for agreement between any two graders ranged from 0.57 to 0.73 for the DBR scale and from 0.38 to 0.66 for the VBR scale. The DBR scale allowed direct comparison of digital to digital images, could be used in dim lighting, had both temporal and nasal conjunctival reference images, and permitted viewing reference and test images at the same magnification.

CONCLUSION

The novel DBR scale, with its objective linear chromatic steps, demonstrated improved reproducibility, fewer visualization artifacts and improved ease of use over the VBR scale for assessing conjunctival redness.

Development and Evaluation of Semiautomated Quantification of Lissamine Green Staining of the Bulbar Conjunctiva From Digital Images

Importance

Lissamine green (LG) staining of the conjunctiva is a key biomarker in evaluating ocular surface disease. The disease currently is assessed using relatively coarse subjective scales. Objective assessment would standardize comparisons over time and between clinicians.

Objective

To develop a semiautomated, quantitative system to assess lissamine green staining of the bulbar conjunctiva on digital images.

Design, Setting, and Participants

Using a standard photography protocol, 35 digital images of the conjunctiva of 11 patients with a diagnosis of dry eye disease based on characteristic signs and symptoms were obtained after topical administration of preservative-free LG, 1%, solution. Images were scored independently by 2 masked ophthalmologists in an academic medical center using the van Bijsterveld and National Eye Institute (NEI) scales. The region of interest was identified by manually marking 7 anatomic landmarks on the images. An objective measure was developed by segmenting the images, forming a vector of key attributes, and then performing a random forest regression. Subjective scores were correlated with the output from a computer algorithm using a cross-validation technique. The ranking of images from least to most staining was compared between the algorithm and the ophthalmologists. The study was conducted from April 26, 2012, through June 2, 2016.

Main Outcomes and Measures

Correlation and level of agreement among computerized algorithm scores, van Bijsterveld scale clinical scores, and NEI scale clinical scores.

Results

The scores from the automated algorithm correlated well with the mean scores obtained from the gradings of 2 ophthalmologists for the 35 images using the van Bijsterveld scale (Spearman correlation coefficient, rs = 0.79), and moderately with the NEI scale (rs = 0.61) scores. For qualitative ranking of staining, the correlation between the automated algorithm and the 2 ophthalmologists was rs = 0.78 and rs = 0.83.

Conclusions and Relevance

The algorithm performed well when evaluating LG staining of the conjunctiva, as evidenced by good correlation with subjective gradings using 2 different grading scales. Future longitudinal studies are needed to assess the responsiveness of the algorithm to change of conjunctival staining over time.

Brain network efficiency is influenced by the pathologic source of corticobasal syndrome

Objective:

To apply network-based statistics to diffusion-weighted imaging tractography data and detect Alzheimer disease vs non-Alzheimer degeneration in the context of corticobasal syndrome.

Methods:

In a cross-sectional design, pathology was confirmed by autopsy or a pathologically validated CSF total tau-to-β-amyloid ratio (T-tau/Aβ). Using structural MRI data, we identify association areas in fronto-temporo-parietal cortex with reduced gray matter density in corticobasal syndrome (n = 40) relative to age-matched controls (n = 40). Using these fronto-temporo-parietal regions of interest, we construct structural brain networks in clinically similar subgroups of individuals with Alzheimer disease (n = 21) or non-Alzheimer pathology (n = 19) by linking these regions by the number of white matter streamlines identified in a deterministic tractography analysis of diffusion tensor imaging data. We characterize these structural networks using 5 graph-based statistics, and assess their relative utility in classifying underlying pathology with leave-one-out cross-validation using a supervised support vector machine.

Results:

Gray matter density poorly discriminates between Alzheimer disease and non-Alzheimer pathology subgroups with low sensitivity (57%) and specificity (52%). In contrast, a statistic of local network efficiency demonstrates very good discriminatory power, with 85% sensitivity and 84% specificity.

Conclusions:

Our results indicate that the underlying pathologic sources of corticobasal syndrome can be classified more accurately using graph theoretical statistics derived from patterns of white matter network organization in association cortex than by regional gray matter density alone. These results highlight the importance of a multimodal neuroimaging approach to diagnostic analyses of corticobasal syndrome.

Use of Crossed Polarizers to Enhance Images of the Eyelids

PURPOSE

To describe imaging of the external eye with Crossed Polarizers to enhance clinically important features in digital photographs of the eyelids.

METHODS

External photographs with and without crossed polarizing filters were taken of patients with blepharitis and controls with no clinical eye pathology.

RESULTS

Photographing eyelid skin through Crossed Polarizers decreased reflections on the skin surface and improved visualization of eyelid telangiectasias and blood vessels in patients with a broad range of skin pigmentation and ethnicities.

CONCLUSIONS

The use of Crossed Polarizers in imaging the external eye reduces reflections and glare from the eyelid skin and margins, thereby allowing for a more detailed evaluation of underlying structures and analysis of images. These findings suggest that including Crossed Polarizers in clinical photography has informative applications for assessing eyelid disease.

Joint alignment of multispectral images via semidefinite programming

In this paper, we introduce a novel feature-point-matching based framework for achieving an optimized joint-alignment of sequential images from multispectral imaging (MSI). It solves a low-rank and semidefinite matrix that stores all pairwise-image feature-mappings by minimizing the total amount of point-to-point matching cost via a convex optimization of a semidefinite programming formulation. This unique strategy takes a complete consideration of the information aggregated by all point-matching costs and enables the entire set of pairwise-image feature-mappings to be solved simultaneously and near-optimally. Our framework is capable of running in an automatic or interactive fashion, offering an effective tool for eliminating spatial misalignments introduced into sequential MSI images during the imaging process. Our experimental results obtained from a database of 28 sequences of MSI images of human eye demonstrate the superior performances of our approach to the state-of-the-art techniques. Our framework is potentially invaluable in a large variety of practical applications of MSI images.

Multi-modal automatic montaging of adaptive optics retinal images

We present a fully automated adaptive optics (AO) retinal image montaging algorithm using classic scale invariant feature transform with random sample consensus for outlier removal. Our approach is capable of using information from multiple AO modalities (confocal, split detection, and dark field) and can accurately detect discontinuities in the montage. The algorithm output is compared to manual montaging by evaluating the similarity of the overlapping regions after montaging, and calculating the detection rate of discontinuities in the montage. Our results show that the proposed algorithm has high alignment accuracy and a discontinuity detection rate that is comparable (and often superior) to manual montaging. In addition, we analyze and show the benefits of using multiple modalities in the montaging process. We provide the algorithm presented in this paper as open-source and freely available to download.

Groupwise registration of sequential images from multispectral imaging (MSI) of the retina and choroid

Multispectral Imaging (MSI) produces a sequence of discrete spectral slices that penetrate different light-absorbing species or chromophores and is a noninvasive technology useful for the early detection of various retinal, optic nerve and choroidal diseases. However, eye movement during the image acquisition process may introduce spatial misalignment between MSI images. This potentially causes trouble in the manual/automatic interpretation of MSI, but still remains an unresolved problem to this date. To deal with this MSI misalignment problem, we present a method on the groupwise registration of sequential images from MSI of the retina and choroid. The advantage of our algorithm is at least threefold: 1) simultaneous estimation of landmark correspondences and a parametric motion model via quadratic programming, 2) enforcement of temporal smoothness on the estimated motion, and 3) inclusion of a robust matching cost function. As validated in our experiments with a database of 22 MSI sequences, our algorithm outperforms two state-of-the-art registration techniques proposed originally in other domains. Our algorithm is potentially invaluable in ophthalmologists' clinical practice regarding various eye diseases.

The Power of Connecting Dots: Advanced Techniques to Evaluate Brain Functional Connectivity in Humans

Brain functional connectivity estimation allows us to depict patterns of cerebral activity not understandable otherwise with the standard brain imaging techniques such as functional magnetic resonance imaging (fMRI) as well as electro or magnetoencephalography (hr-EEG, MEG). This special issue of the IEEE Transactions on Biomedical Engineering reports a range of methodological innovations toward the estimation of functional connectivity from brain activity data, with emphasis on neuroelectric and hemodynamic imaging modalities. Functional connectivity methodologies enable "connecting of the dots" derived from brain activity observations over multiple distributed sites, as depicted by such fMRI and hr-EEG/MEG devices.

Quantitative assessment of anatomical change using a virtual proton depth radiograph for adaptive head and neck proton therapy

The aim of this work is to demonstrate the feasibility of using water-equivalent thickness (WET) and virtual proton depth radiographs (PDRs) of intensity corrected cone-beam computed tomography (CBCT) to detect anatomical change and patient setup error to trigger adaptive head and neck proton therapy. The planning CT (pCT) and linear accelerator (linac) equipped CBCTs acquired weekly during treatment of a head and neck patient were used in this study. Deformable image registration (DIR) was used to register each CBCT with the pCT and map Hounsfield units (HUs) from the planning CT (pCT) onto the daily CBCT. The deformed pCT is referred as the corrected CBCT (cCBCT). Two dimensional virtual lateral PDRs were generated using a ray-tracing technique to project the cumulative WET from a virtual source through the cCBCT and the pCT onto a virtual plane. The PDRs were used to identify anatomic regions with large variations in the proton range between the cCBCT and pCT using a threshold of 3 mm relative difference of WET and 3 mm search radius criteria. The relationship between PDR differences and dose distribution is established. Due to weight change and tumor response during treatment, large variations in WETs were observed in the relative PDRs which corresponded spatially with an increase in the number of failing points within the GTV, especially in the pharynx area. Failing points were also evident near the posterior neck due to setup variations. Differences in PDRs correlated spatially to differences in the distal dose distribution in the beam's eye view. Virtual PDRs generated from volumetric data, such as pCTs or CBCTs, are potentially a useful quantitative tool in proton therapy. PDRs and WET analysis may be used to detect anatomical change from baseline during treatment and trigger further analysis in adaptive proton therapy.

The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery

BACKGROUND AND PURPOSE

Diffusion tensor imaging (DTI) tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography-derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop.

METHODS

Eight international teams from leading institutions reconstructed the pyramidal tract in four neurosurgical cases presenting with a glioma near the motor cortex. Tractography methods included deterministic, probabilistic, filtered, and global approaches. Standardized evaluation of the tracts consisted in the qualitative review of the pyramidal pathways by a panel of neurosurgeons and DTI experts and the quantitative evaluation of the degree of agreement among methods.

RESULTS

The evaluation of tractography reconstructions showed a great interalgorithm variability. Although most methods found projections of the pyramidal tract from the medial portion of the motor strip, only a few algorithms could trace the lateral projections from the hand, face, and tongue area. In addition, the structure of disagreement among methods was similar across hemispheres despite the anatomical distortions caused by pathological tissues.

CONCLUSIONS

The DTI Challenge provides a benchmark for the standardized evaluation of tractography methods on neurosurgical data. This study suggests that there are still limitations to the clinical use of tractography for neurosurgical decision making.

Abstract P1-06-12: The therapeutic sensitivity of ER+/Her2+ breast cancer cells is attenuated in 3D matrix culture and involves switching from AKT to MAPK pathways

Increasing evidence points to interplay between a tumour and components of its microenvironment as a significant determinant of therapeutic sensitivity and response. Thus, a better understanding of this phenomenon is crucial for the development of more effective treatment strategies. Around half of HER2+ breast cancers express the ER. Despite the effectiveness of endocrine and HER2-targeted therapies for such tumours in pre-clinical, two-dimensional models, response can be variable clinically. Here we have investigated the impact of 3D culture within an extracellular matrix on the signaling pathway activity and therapeutic response to trastuzumab and endocrine (tamoxifen and fulvestrant) treatment of ER+/Her2+ breast cancer cell models.

ER+/Her2+ BT474 and MDAMB361 cells were grown as 2D monolayers or as 3D cultures in Matrigel and their proliferative response to trastuzumab (0-100nM) and endocrine (tamoxifen or fulvestrant, 0-100nM) mono- and combination therapy assessed using coulter counting and immunocytochemical staining of the proliferation antigen, Ki67. A comparison of signaling pathway activation in 2D versus 3D culture, and in response to treatment in these contexts, was investigated using Western blotting.

Whilst culture of both cell lines in 3D matrices did not significantly affect their endogenous growth rate, 3D culture resulted in a significant loss of PI3K/AKT pathway activity in both cell lines and augmented (BT474) MAPK signaling. The growth inhibitory effects of both trastuzumab and endocrine treatment, either as single agents or in combination, were significantly attenuated in 3D cultures when compared with 2D cultures (% growth inhibition ± sem: 65%±3.8 (3D) vs. 82%±1.3 (2D), p&lt;0.01 [BT474+trastuzumab], 34%±6.4 (3D) vs. 56%±2.2 (2D), p&lt;0.02 [BT474+tamoxifen], and 51%±0.88 (3D) vs. 70%±0.48 (2D), p&lt;0.0001 [MDAMB361+tamoxifen], 53%±7.2 (3D) vs. 77%±0.60 (2D), p&lt;0.02 [MDAMB361+fulvestrant]). Similar effects were observed in Ki67 levels, with a greater suppression of Ki67 in 2D versus 3D culture in response to treatments. Trastuzumab and endocrine treatments, either as monotherapies or in combination, suppressed MAPK signaling in 2D monolayers in contrast to 3D culture, where MAPK activity was maintained (BT474) or augmented (MDAMB361). Consequently, inhibition of MAPK signaling using U0126 in 3D cultures significantly improved trastuzumab and endocrine response in these cells.

These data demonstrate that ER+/Her2+ breast cancer cells significantly alter their signaling pathway activity when cultured in a 3D, matrix-enriched environment, which may in turn act to limit response to a range of endocrine and targeted therapies. Targeting of such adaptive pathways that maintain growth in 3D culture may represent an effective strategy to improve therapeutic response clinically.

Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P1-06-12.

Transcriptome tomography for brain analysis in the web-accessible anatomical space

Increased information on the encoded mammalian genome is expected to facilitate an integrated understanding of complex anatomical structure and function based on the knowledge of gene products. Determination of gene expression-anatomy associations is crucial for this understanding. To elicit the association in the three-dimensional (3D) space, we introduce a novel technique for comprehensive mapping of endogenous gene expression into a web-accessible standard space: Transcriptome Tomography. The technique is based on conjugation of sequential tissue-block sectioning, all fractions of which are used for molecular measurements of gene expression densities, and the block- face imaging, which are used for 3D reconstruction of the fractions. To generate a 3D map, tissues are serially sectioned in each of three orthogonal planes and the expression density data are mapped using a tomographic technique. This rapid and unbiased mapping technique using a relatively small number of original data points allows researchers to create their own expression maps in the broad anatomical context of the space. In the first instance we generated a dataset of 36,000 maps, reconstructed from data of 61 fractions measured with microarray, covering the whole mouse brain (ViBrism: http://vibrism.riken.jp/3dviewer/ex/index.html) in one month. After computational estimation of the mapping accuracy we validated the dataset against existing data with respect to the expression location and density. To demonstrate the relevance of the framework, we showed disease related expression of Huntington's disease gene and Bdnf. Our tomographic approach is applicable to analysis of any biological molecules derived from frozen tissues, organs and whole embryos, and the maps are spatially isotropic and well suited to the analysis in the standard space (e.g. Waxholm Space for brain-atlas databases). This will facilitate research creating and using open-standards for a molecular-based understanding of complex structures; and will contribute to new insights into a broad range of biological and medical questions.

White matter imaging contributes to the multimodal diagnosis of frontotemporal lobar degeneration

OBJECTIVE

To evaluate the distribution of white matter (WM) disease in frontotemporal lobar degeneration (FTLD) and Alzheimer disease (AD) and to evaluate the relative usefulness of WM and gray matter (GM) for distinguishing these conditions in vivo.

METHODS

Patients were classified as having FTLD (n = 50) or AD (n = 42) using autopsy-validated CSF values of total-tau:β-amyloid (t-tau:Aβ(1-42)) ratios. Patients underwent WM diffusion tensor imaging (DTI) and volumetric MRI of GM. We employed tract-specific analyses of WM fractional anisotropy (FA) and whole-brain GM density analyses. Individual patient classification was performed using receiver operator characteristic (ROC) curves with FA, GM, and a combination of the 2 modalities.

RESULTS

Regional FA and GM were significantly reduced in FTLD and AD relative to healthy seniors. Direct comparisons revealed significantly reduced FA in the corpus callosum in FTLD relative to AD. GM analyses revealed reductions in anterior temporal cortex for FTLD relative to AD, and in posterior cingulate and precuneus for AD relative to FTLD. ROC curves revealed that a multimodal combination of WM and GM provide optimal classification (area under the curve = 0.938), with 87% sensitivity and 83% specificity.

CONCLUSIONS

FTLD and AD have significant WM and GM defects. A combination of DTI and volumetric MRI modalities provides a quantitative method for distinguishing FTLD and AD in vivo.

P1-07-21: Analysis of Molecular Markers by Immunohistochemistry (IHC) Method on Formalin Fixed Paraffin Embedded (FFPE) Tissues Could Predict Shorter Recurrence Free Survival (RFS) and Overall Survival (OS) among Patients Who Have Received Adjuvant Chemotherapy for Early Breast Cancer

Background: Various molecular markers assessed by IHC (ER, PR, HER2) and gene expression profiling (e.g. Oncotype Dx) have been developed as prognostic and predictive tools for breast cancer. Gene profiling is said to be superior to IHC but at a considerable cost with limited availability. IHC is relatively inexpensive and more readily available. If early breast cancer patients who are going to relapse within 5 years of curative surgery despite adjuvant chemotherapy could be identified by IHC on FFPE tissue alternative adjuvant therapies could be explored. In this context, here we evaluate IHC for expression of a panel of molecular markers implicated in: growth signalling pathways (ER, PR, HER2, EGFR, CD71, Ki67, MCM2), cell survival (Bcl-2, Bag 1), angiogenesis (PDGFRa) and cell cycle progression (Aurora A, MCM2). Of note, this study includes markers of breast cancer molecular subtype (ER, PR, HER2, Ki67, EGFR, also CK5/6) and several proteins encoded by genes in the Oncotype Dx test (ER, PR, HER2, Ki 67, Bcl2, Bag1 and CD68).

Materials and Method: 72 cases (R) relapsing within 5 years of curative surgery, 72 controls (C), relapse free &gt; 5 years were identified from the hospital records. All patients had adjuvant chemotherapy. Controls were matched to cases by Adjuvant! recurrence risk (ARR). Optimised IHC was performed on FFPE TMA slides using a Ventana autostainer. Protein expression was evaluated on digitalised images (Mirax scanner). Survival analysis by molecular markers expression and also 5 molecular subtypes, Luminal A (LA = ER/PR+, HER2−, Ki67-), Luminal B (LB = ER/PR+, HER2/Ki67+), HER2 enriched (H = ER-, PR-, HER2+), Core Basal (CB = ER-, PR-, HER2−, CK5/6/EGFR+) and 5-negative (5N = negative for ER,PR,HER2,EGFR,CK5/6)], were performed. SPSS 16v. was used for statistical analysis.

Findings: All but four cases had died at the time of analysis. Four controls developed relapse at 83.8, 90.6, 107.7, 127.6 months respectively. Two controls died from non-breast cancer causes. Median (m) follow-up for the controls group (ie. mOS)was 104.9 mo (72.8 - 164.4). For cases, mRFS and mOS were 23.2 (4.5 - 59.9) and 39.7(8.1 - 129). mRFS and mOS for IHC molecular subtypes were: Subtype LA = not yet & not yet; LB = 58.1 & 86.1; CB = 15.4 & 30.4; H = 28 & 55.9; 5N = 19.9 & 26 (p &lt; 0.0001 & &lt;0.0001 by Log rank test). Better RFS and OS were found for positive Bcl2 (p = 0.036 & 0.058) and MCM2 (p = 0.022 & 0.048), negative Aurora A (p = 0.01 & 0.001) and PDGFRa (p = 0.07 & 0.086) expressions. For this study cohort there was no correlation between ARR and survival outcome or molecular subtypes. Result of ongoing multivariate analysis and correlation between survival and CD68, CD71 and Bag 1 expressions will be presented in the conference.

Discussion: Subtypes CB & 5N, negative Bcl-2 & MCM2, positive Aurora A & PDGFRa expression as measured by IHC were predictive of poor RFS and OS. While these findings need to be verified in an independent cohort, IHC profiles nevertheless have potential to stratify different risk groups for clinical trials and effective adjuvant treatments.

Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P1-07-21.

P2-05-01: Acquired Tamoxifen Resistance Promotes Angiogenic Responses in ER+ Breast Cancer Cells

Background: In the treatment of pre-menopausal women with oestrogen positive (ER+) breast cancer, tamoxifen represents a first line of adjuvant treatment with demonstrable benefits. Despite this, resistance is frequently acquired to tamoxifen with an associated poor prognosis. Breast cancer cell models have revealed the importance of growth factor signalling networks in sustaining growth of endocrine-resistant cancers and, more recently, their ability to promote a highly migratory and invasive phenotype, together with the expression of genes with pro-angiogenic ontology. The potential of endocrine resistant cells to elicit angiogenic responses, however, remains unknown.

Materials and Methods: Real-time PCR was used to validate results from preliminary Affymetrix-based gene profiling of pro-angiogenic gene expression in endocrine-sensitive MCF7 cells and their tamoxifen-resistant (TamR) counterparts. The expression of pro-angiogenic factors in conditioned media (CM) from these cells was assessed by ELISA. The proliferative and migratory effects of MCF7 and TamR CM on vascular endothelial cells (HUVEC and HECV cells), was determined by MTS cell proliferation assay, wound closure assays and Matrigel tubule formation assays. Changes in endothelial cell migration following co-culture with endocrine-resistant cells were examined using Boyden-chamber chemotaxis assays. Growth factor signalling and migration pathway activation in endothelial cells in response to CM was determined by Western blotting. Results: TamR cells were found to express high levels of HIF-1α, IL-8 and VEGF-A at an mRNA level compared with expression in MCF7 cells. High levels of VEGF-A protein were also confirmed in the conditioned media from TamR cells versus their endocrine-sensitive counterparts. TamR conditioned media promoted endothelial cell proliferation, migration and the formation of tubules to a greater extent than that seen in MCF7 CM treated cells. TamR conditioned media was found to stimulate VEGFR2 phosphorylation and downstream activation of MAPK in endothelial cells compared to MCF7 CM. Pharmacological inhibition of VEGFR2 activity in endothelial cells suppressed TamR-induced endothelial cell proliferation and VEGFR phosphorylation. Further pharmacological manipulation of erbB receptors and intracellular kinases in TamR cells revealed an ERGF/Her2-Src kinase dependent mechanism of VEGF-A production in these cells.

Discussion: These data suggest acquired tamoxifen resistance is accompanied by development of an erbB receptor/Src kinase-dependant pro-angiogenic phenotype which, if recapitulated in vivo, may promote tumour progression. Therapeutic targeting of erbB/Src axis may prove beneficial in such cases.

Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P2-05-01.

Accuracy of the cylinder approximation for susceptometric measurement of intravascular oxygen saturation

Susceptometry-based MR oximetry has previously been shown suitable for quantifying hemoglobin oxygen saturation in large vessels for studying vascular reactivity and quantification of global cerebral metabolic rate of oxygen utilization. A key assumption underlying this method is that large vessels can be modeled as long paramagnetic cylinders. However, bifurcations, tapering, noncircular cross-section, and curvature of these vessels produce substantial deviations from cylindrical geometry, which may lead to errors in hemoglobin oxygen saturation quantification. Here, the accuracy of the "long cylinder" approximation is evaluated via numerical computation of the induced magnetic field from 3D segmented renditions of three veins of interest (superior sagittal sinus, femoral and jugular vein). At a typical venous oxygen saturation of 65%, the absolute error in hemoglobin oxygen saturation estimated via a closed-form cylinder approximation was 2.6% hemoglobin oxygen saturation averaged over three locations in the three veins studied and did not exceed 5% for vessel tilt angles <30° at any one location. In conclusion, the simulation results provide a significant level of confidence for the validity of the cylinder approximation underlying MR susceptometry-based oximetry of large vessels.

Medial temporal structures and memory functions in adolescents with heavy cannabis use

Converging lines of evidence suggest an adverse effect of heavy cannabis use on adolescent brain development, particularly on the hippocampus. In this preliminary study, we compared hippocampal morphology in 14 "treatment-seeking" adolescents (aged 18-20) with a history of prior heavy cannabis use (5.8 joints/day) after an average of 6.7 months of drug abstinence, and 14 demographically matched normal controls. Participants underwent a high-resolution 3D MRI as well as cognitive testing including the California Verbal Learning Test (CVLT). Heavy-cannabis users showed significantly smaller volumes of the right (p < 0.04) and left (p < 0.02) hippocampus, but no significant differences in the amygdala region compared to controls. In controls, larger hippocampus volumes were observed to be significantly correlated with higher CVLT verbal learning and memory scores, but these relationships were not observed in cannabis users. In cannabis users, a smaller right hippocampus volume was correlated with a higher amount of cannabis use (r = -0.57, p < 0.03). These data support a hypothesis that heavy cannabis use may have an adverse effect on hippocampus development. These findings, after an average 6.7 month of supervised abstinence, lend support to a theory that cannabis use may impart long-term structural and functional damage. Alternatively, the observed hippocampal volumetric abnormalities may represent a risk factor for cannabis dependence. These data have potential significance for understanding the observed relationship between early cannabis exposure during adolescence and subsequent development of adult psychopathology reported in the literature for schizophrenia and related psychotic disorders.

Hierarchical organization of scripts: converging evidence from FMRI and frontotemporal degeneration

The present study examined the organization of complex familiar activities, known as "scripts" (e.g., "going fishing"). We assessed whether events in a script are processed in a linear-sequential manner or clustered-hierarchical manner, and we evaluated the neural basis for this processing capacity. Converging evidence was obtained from functional neuroimaging in healthy young adults and from behavioral and structural magnetic resonance imaging (MRI) data in patients with focal neurodegenerative disease. In both studies, participants judged the order of consecutive event pairs taken from a script. Event pairs either were clustered together within a script or were from different clusters within the script. Controls judged events more accurately and quickly if taken from the same cluster within a script compared with different clusters, even though all event pairs were consecutive, consistent with the hierarchical organization of a script. Functional magnetic resonance imaging associated this with bilateral inferior frontal activation. Patients with progressive nonfluent aphasia or behavior-variant frontotemporal dementia did not distinguish between event pairs from the same cluster or from different clusters within a script. Structural MRI associated this deficit with significant frontal cortical atrophy. Our findings suggest that frontal cortex contributes to clustering events during script comprehension, underlining the role of frontal cortex in the hierarchical organization of a script.

Why are patients with progressive nonfluent aphasia nonfluent?

OBJECTIVE

To investigate the cognitive and neural basis for nonfluent speech in progressive nonfluent aphasia (PNFA).

BACKGROUND

Nonfluent speech is the hallmark feature of PNFA, and this has been attributed to impairments in syntactic processing, motor-speech planning, and executive functioning that also occur in these patients. Patients with PNFA have left inferior frontal atrophy.

METHODS

A large semi-structured speech sample and neuropsychological measures of language and executive functioning were examined in 16 patients with PNFA, 12 patients with behavioral-variant frontotemporal dementia (bvFTD), and 13 age-matched controls. Speech fluency was quantified as words per minute (WPM) in the semi-structured speech sample. Stepwise linear regression analyses were used to relate WPM to grammatic, motor-speech planning, and executive aspects of patient functioning. These measures were then related to cortical thickness in 8 patients with PNFA and 7 patients with bvFTD using structural MRI.

RESULTS

WPM was significantly reduced in patients with PNFA relative to controls and patients with bvFTD. Regression analyses revealed that only grammatic measures predicted WPM in PNFA, whereas executive measures were the only significant predictor of WPM in bvFTD. Cortical thinning was significant in PNFA relative to controls in left inferior frontal and anterior-superior temporal regions, and a regression analysis related this area to reduced WPM in PNFA. Significant cortical thinning associated with limited grammatic processing also was seen in the left inferior frontal-superior temporal region in PNFA, and this overlapped with the area of frontal-temporal thinning related to reduced WPM.

CONCLUSION

Nonfluent speech in PNFA may be due in part to difficulty with grammatic processing associated with left inferior frontal and anterior-superior temporal disease.

Speech errors in progressive non-fluent aphasia

The nature and frequency of speech production errors in neurodegenerative disease have not previously been precisely quantified. In the present study, 16 patients with a progressive form of non-fluent aphasia (PNFA) were asked to tell a story from a wordless children's picture book. Errors in production were classified as either phonemic, involving language-based deformations that nevertheless result in possible sequences of English speech segments; or phonetic, involving a motor planning deficit and resulting in non-English speech segments. The distribution of cortical atrophy as revealed by structural MRI scans was examined quantitatively in a subset of PNFA patients (N=7). The few errors made by healthy seniors were only phonemic in type. PNFA patients made more than four times as many errors as controls. This included both phonemic and phonetic errors, with a preponderance of errors (82%) classified as phonemic. The majority of phonemic errors were substitutions that shared most distinctive features with the target phoneme. The systematic nature of these substitutions is not consistent with a motor planning deficit. Cortical atrophy was found in prefrontal regions bilaterally and peri-Sylvian regions of the left hemisphere. We conclude that the speech errors produced by PNFA patients are mainly errors at the phonemic level of language processing and are not caused by a motor planning impairment.

ONCOPOOL - a European database for 16,944 cases of breast cancer

ONCOPOOL is a retrospectively compiled database of primary operable invasive breast cancers treated in the 1990s in 10 European breast cancer Units. Sixteen thousand and nine hundred and forty four cases were entered, with tumours less than 5 cm diameter in women aged 70 or less (mean age 55).

DATA

Data were date of birth, mode of diagnosis, pathology (size, lymph node status, grade, type, lympho-vascular invasion and hormone receptor) and therapies and outcome measures: first local, regional or distant recurrences, contralateral primary, date and cause of death.

TUMOUR CHARACTERISTICS

Mean diameter 1.8 cm, 66% lymph node negative, 24% 1-3 lymph nodes involved and 10% had 4 or more involved. Grade 1, 29%; Grade 2, 41%; and Grade 3, 30%. Polynomial relationships were established between grade, stage and size. Seventy-five percent were oestrogen receptor (ER) positive. ER closely related to grade.

OUTCOMES

Overall Survival was 89% at 5 years from diagnosis, 80% 10 years and 73% 15 years; Breast Cancer-Specific survivals were 91%, 84% and 79%. Survival strongly related to the Nottingham Prognostic Index (NPI). Cases detected at screening had 84% 10-year survival, those presenting symptomatically 76%. ER positive cases treated with adjuvant hormone therapy had a reduction in risk of death of 13% over those not receiving adjuvant therapy (p=0.000). ER negative cases treated with chemotherapy showed a risk reduction of 23% over those not receiving chemotherapy (p=0.000).