Distinct brain network features predict internalizing and externalizing traits in children and adults.

Internalizing and externalizing traits are two distinct classes of behaviors in psychiatry. However, whether shared or unique brain network features predict internalizing and externalizing behaviors in children and adults remain poorly understood. Using a sample of 2262 children from the Adolescent Brain Cognitive Development (ABCD) study and 752 adults from the Human Connectome Project (HCP), we show that network features predicting internalizing and externalizing behavior are, at least in part, dissociable in children, but not in adults. In ABCD children, traits within internalizing and externalizing behavioral categories are predicted by more similar network features concatenated across task and resting states than those between different categories. We did not observe this pattern in HCP adults. Distinct network features predict internalizing and externalizing behaviors in ABCD children and HCP adults. These data reveal shared and unique brain network features accounting for individual variation within broad internalizing and externalizing categories across developmental stages.

A landscape of response to drug combinations in non-small cell lung cancer.

Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.

Functional connectivity subtypes associate robustly with ASD diagnosis.

Our understanding of the changes in functional brain organization in autism is hampered by the extensive heterogeneity that characterizes this neurodevelopmental disorder. Data driven clustering offers a straightforward way to decompose autism heterogeneity into subtypes of connectivity and promises an unbiased framework to investigate behavioral symptoms and causative genetic factors. Yet, the robustness and generalizability of functional connectivity subtypes is unknown. Here, we show that a simple hierarchical cluster analysis can robustly relate a given individual and brain network to a connectivity subtype, but that continuous assignments are more robust than discrete ones. We also found that functional connectivity subtypes are moderately associated with the clinical diagnosis of autism, and these associations generalize to independent replication data. We explored systematically 18 different brain networks as we expected them to associate with different behavioral profiles as well as different key regions. Contrary to this prediction, autism functional connectivity subtypes converged on a common topography across different networks, consistent with a compression of the primary gradient of functional brain organization, as previously reported in the literature. Our results support the use of data driven clustering as a reliable data dimensionality reduction technique, where any given dimension only associates moderately with clinical manifestations.

Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI.

A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC.

Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study.

How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.

Reproducible brain-wide association studies require thousands of individuals.

Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1-3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain-behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available-with a total sample size of around 50,000 individuals-to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain-phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.

Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity.

Algorithmic biases that favor majority populations pose a key challenge to the application of machine learning for precision medicine. Here, we assessed such bias in prediction models of behavioral phenotypes from brain functional magnetic resonance imaging. We examined the prediction bias using two independent datasets (preadolescent versus adult) of mixed ethnic/racial composition. When predictive models were trained on data dominated by white Americans (WA), out-of-sample prediction errors were generally higher for African Americans (AA) than for WA. This bias toward WA corresponds to more WA-like brain-behavior association patterns learned by the models. When models were trained on AA only, compared to training only on WA or an equal number of AA and WA participants, AA prediction accuracy improved but stayed below that for WA. Overall, the results point to the need for caution and further research regarding the application of current brain-behavior prediction models in minority populations.

PCA062, a P-cadherin Targeting Antibody-Drug Conjugate, Displays Potent Antitumor Activity Against P-cadherin-expressing Malignancies.

The cell surface glycoprotein P-cadherin is highly expressed in a number of malignancies, including those arising in the epithelium of the bladder, breast, esophagus, lung, and upper aerodigestive system. PCA062 is a P-cadherin specific antibody-drug conjugate that utilizes the clinically validated SMCC-DM1 linker payload to mediate potent cytotoxicity in cell lines expressing high levels of P-cadherin in vitro, while displaying no specific activity in P-cadherin-negative cell lines. High cell surface P-cadherin is necessary, but not sufficient, to mediate PCA062 cytotoxicity. In vivo, PCA062 demonstrated high serum stability and a potent ability to induce mitotic arrest. In addition, PCA062 was efficacious in clinically relevant models of P-cadherin-expressing cancers, including breast, esophageal, and head and neck. Preclinical non-human primate toxicology studies demonstrated a favorable safety profile that supports clinical development. Genome-wide CRISPR screens reveal that expression of the multidrug-resistant gene ABCC1 and the lysosomal transporter SLC46A3 differentially impact tumor cell sensitivity to PCA062. The preclinical data presented here suggest that PCA062 may have clinical value for treating patients with multiple cancer types including basal-like breast cancer.

Selective deletion of connective tissue growth factor attenuates experimentally-induced pulmonary fibrosis and pulmonary arterial hypertension.

Connective tissue growth factor (CTGF, CCN2) is a matricellular protein which plays key roles in normal mammalian development and in tissue homeostasis and repair. In pathological conditions, dysregulated CCN2 has been associated with cancer, cardiovascular disease, and tissue fibrosis. In this study, genetic manipulation of the CCN2 gene was employed to investigate the role of CCN2 expression in vitro and in experimentally-induced models of pulmonary fibrosis and pulmonary arterial hypertension (PAH). Knocking down CCN2 using siRNA reduced expression of pro-fibrotic markers (fibronectin p < 0.01, collagen type I p < 0.05, α-SMA p < 0.0001, TIMP-1 p < 0.05 and IL-6 p < 0.05) in TGF-ß-treated lung fibroblasts derived from systemic sclerosis patients. In vivo studies were performed in mice using a conditional gene deletion strategy targeting CCN2 in a fibroblast-specific and time-dependent manner in two models of lung disease. CCN2 deletion significantly reduced pulmonary interstitial scarring and fibrosis following bleomycin-instillation, as assessed by fibrotic scores (wildtype bleomycin 3.733 ± 0.2667 vs CCN2 knockout (KO) bleomycin 4.917 ± 0.3436, p < 0.05) and micro-CT. In the well-established chronic hypoxia/Sugen model of pulmonary hypertension, CCN2 gene deletion resulted in a significant decrease in pulmonary vessel remodelling, less right ventricular hypertrophy and a reduction in the haemodynamic measurements characteristic of PAH (RVSP and RV/LV + S were significantly reduced (p < 0.05) in CCN2 KO compared to WT mice in hypoxic/SU5416 conditions). These results support a prominent role for CCN2 in pulmonary fibrosis and in vessel remodelling associated with PAH. Therefore, therapeutics aimed at blocking CCN2 function are likely to benefit several forms of severe lung disease.

Epigenomic programming in early fetal brain development.

Aim: To provide a comprehensive understanding of gene regulatory networks in the developing human brain and a foundation for interpreting pathogenic deregulation. Materials & methods: We generated reference epigenomes and transcriptomes of dissected brain regions and primary neural progenitor cells (NPCs) derived from cortical and ganglionic eminence tissues of four normal human fetuses. Results: Integration of these data across developmental stages revealed a directional increase in active regulatory states, transcription factor activities and gene transcription with developmental stage. Consistent with differences in their biology, NPCs derived from cortical and ganglionic eminence regions contained common, region specific, and gestational week specific regulatory states. Conclusion: We provide a high-resolution regulatory network for NPCs from different brain regions as a comprehensive reference for future studies.

A highly predictive signature of cognition and brain atrophy for progression to Alzheimer's dementia.

BACKGROUND: Clinical trials in Alzheimer's disease need to enroll patients whose cognition will decline over time, if left untreated, in order to demonstrate the efficacy of an intervention. Machine learning models used to screen for patients at risk of progression to dementia should therefore favor specificity (detecting only progressors) over sensitivity (detecting all progressors), especially when the prevalence of progressors is low. Here, we explore whether such high-risk patients can be identified using cognitive assessments and structural neuroimaging by training machine learning tools in a high-specificity regime. RESULTS: A multimodal signature of Alzheimer's dementia was first extracted from the ADNI1 dataset. We then validated the predictive value of this signature on ADNI1 patients with mild cognitive impairment (N = 235). The signature was optimized to predict progression to dementia over 3 years with low sensitivity (55.1%) but high specificity (95.6%), resulting in only moderate accuracy (69.3%) but high positive predictive value (80.4%, adjusted for a "typical" 33% prevalence rate of true progressors). These results were replicated in ADNI2 (N = 235), with 87.8% adjusted positive predictive value (96.7% specificity, 47.3% sensitivity, 85.1% accuracy). CONCLUSIONS: We found that cognitive measures alone could identify high-risk individuals, with structural measurements providing a slight improvement. The signature had comparable receiver operating characteristics to standard machine learning tools, yet a marked improvement in positive predictive value was achieved over the literature by selecting a high-specificity operating point. The multimodal signature can be readily applied for the enrichment of clinical trials.

Capmatinib (INC280) Is Active Against Models of Non-Small Cell Lung Cancer and Other Cancer Types with Defined Mechanisms of MET Activation.

PURPOSE: The selective MET inhibitor capmatinib is being investigated in multiple clinical trials, both as a single agent and in combination. Here, we describe the preclinical data of capmatinib, which supported the clinical biomarker strategy for rational patient selection. EXPERIMENTAL DESIGN: The selectivity and cellular activity of capmatinib were assessed in large cellular screening panels. Antitumor efficacy was quantified in a large set of cell line- or patient-derived xenograft models, testing single-agent or combination treatment depending on the genomic profile of the respective models. RESULTS: Capmatinib was found to be highly selective for MET over other kinases. It was active against cancer models that are characterized by MET amplification, marked MET overexpression, MET exon 14 skipping mutations, or MET activation via expression of the ligand hepatocyte growth factor (HGF). In cancer models where MET is the dominant oncogenic driver, anticancer activity could be further enhanced by combination treatments, for example, by the addition of apoptosis-inducing BH3 mimetics. The combinations of capmatinib and other kinase inhibitors resulted in enhanced anticancer activity against models where MET activation co-occurred with other oncogenic drivers, for example EGFR activating mutations. CONCLUSIONS: Activity of capmatinib in preclinical models is associated with a small number of plausible genomic features. The low fraction of cancer models that respond to capmatinib as a single agent suggests that the implementation of patient selection strategies based on these biomarkers is critical for clinical development. Capmatinib is also a rational combination partner for other kinase inhibitors to combat MET-driven resistance.

Differential responses of MET activations to MET kinase inhibitor and neutralizing antibody.

BACKGROUND: Aberrant MET tyrosine kinase signaling is known to cause cancer initiation and progression. While MET inhibitors are in clinical trials against several cancer types, the clinical efficacies are controversial and the molecular mechanisms toward sensitivity remain elusive. METHODS: With the goal to investigate the molecular basis of MET amplification (METamp) and hepatocyte growth factor (HGF) autocrine-driven tumors in response to MET tyrosine kinase inhibitors (TKI) and neutralizing antibodies, we compared cancer cells harboring METamp (MKN45 and MHCCH97H) or HGF-autocrine (JHH5 and U87) for their sensitivity and downstream biological responses to a MET-TKI (INC280) and an anti-MET monoclonal antibody (MetMab) in vitro, and for tumor inhibition in vivo. RESULTS: We find that cancer cells driven by METamp are more sensitive to INC280 than are those driven by HGF-autocrine activation. In METamp cells, INC280 induced a DNA damage response with activation of repair through the p53BP1/ATM signaling pathway. Although MetMab failed to inhibit METamp cell proliferation and tumor growth, both INC280 and MetMab reduced HGF-autocrine tumor growth. In addition, we also show that HGF stimulation promoted human HUVEC cell tube formation via the Src pathway, which was inhibited by either INC280 or MetMab. These observations suggest that in HGF-autocrine tumors, the endothelial cells are the secondary targets MET inhibitors. CONCLUSIONS: Our results demonstrate that METamp and HGF-autocrine activation favor different molecular mechanisms. While combining MET TKIs and ATM inhibitors may enhance the efficacy for treating tumors harboring METamp, a combined inhibition of MET and angiogenesis pathways may improve the therapeutic efficacy against HGF-autocrine tumors.

Brain properties predict proximity to symptom onset in sporadic Alzheimer's disease.

See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article.Alzheimer's disease is preceded by a lengthy 'preclinical' stage spanning many years, during which subtle brain changes occur in the absence of overt cognitive symptoms. Predicting when the onset of disease symptoms will occur is an unsolved challenge in individuals with sporadic Alzheimer's disease. In individuals with autosomal dominant genetic Alzheimer's disease, the age of symptom onset is similar across generations, allowing the prediction of individual onset times with some accuracy. We extend this concept to persons with a parental history of sporadic Alzheimer's disease to test whether an individual's symptom onset age can be informed by the onset age of their affected parent, and whether this estimated onset age can be predicted using only MRI. Structural and functional MRIs were acquired from 255 ageing cognitively healthy subjects with a parental history of sporadic Alzheimer's disease from the PREVENT-AD cohort. Years to estimated symptom onset was calculated as participant age minus age of parental symptom onset. Grey matter volume was extracted from T1-weighted images and whole-brain resting state functional connectivity was evaluated using degree count. Both modalities were summarized using a 444-region cortical-subcortical atlas. The entire sample was divided into training (n = 138) and testing (n = 68) sets. Within the training set, individuals closer to or beyond their parent's symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer's disease. Machine learning was used to identify a weighted set of imaging features trained to predict years to estimated symptom onset. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. We next tested if these brain properties could be generalized to predict time to clinical progression in a subgroup of 26 individuals from the Alzheimer's Disease Neuroimaging Initiative, who eventually converted either to mild cognitive impairment or to Alzheimer's dementia. The feature set trained on years to estimated symptom onset in the PREVENT-AD predicted variance in time to clinical conversion in this separate longitudinal dataset. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals.

The Genome of the Northern Sea Otter (Enhydra lutris kenyoni).

The northern sea otter inhabits coastal waters of the northern Pacific Ocean and is the largest member of the Mustelidae family. DNA sequencing methods that utilize microfluidic partitioned and non-partitioned library construction were used to establish the sea otter genome. The final assembly provided 2.426 Gbp of highly contiguous assembled genomic sequences with a scaffold N50 length of over 38 Mbp. We generated transcriptome data derived from a lymphoma to aid in the determination of functional elements. The assembled genome sequence and underlying sequence data are available at the National Center for Biotechnology Information (NCBI) under the BioProject accession number PRJNA388419.

The Genome of the Beluga Whale (Delphinapterus leucas).

The beluga whale is a cetacean that inhabits arctic and subarctic regions, and is the only living member of the genus Delphinapterus. The genome of the beluga whale was determined using DNA sequencing approaches that employed both microfluidic partitioning library and non-partitioned library construction. The former allowed for the construction of a highly contiguous assembly with a scaffold N50 length of over 19 Mbp and total reconstruction of 2.32 Gbp. To aid our understanding of the functional elements, transcriptome data was also derived from brain, duodenum, heart, lung, spleen, and liver tissue. Assembled sequence and all of the underlying sequence data are available at the National Center for Biotechnology Information (NCBI) under the Bioproject accession number PRJNA360851A.

Characterization of the human thyroid epigenome.

The thyroid gland, necessary for normal human growth and development, functions as an essential regulator of metabolism by the production and secretion of appropriate levels of thyroid hormone. However, assessment of abnormal thyroid function may be challenging suggesting a more fundamental understanding of normal function is needed. One way to characterize normal gland function is to study the epigenome and resulting transcriptome within its constituent cells. This study generates the first published reference epigenomes for human thyroid from four individuals using ChIP-seq and RNA-seq. We profiled six histone modifications (H3K4me1, H3K4me3, H3K27ac, H3K36me3, H3K9me3, H3K27me3), identified chromatin states using a hidden Markov model, produced a novel quantitative metric for model selection and established epigenomic maps of 19 chromatin states. We found that epigenetic features characterizing promoters and transcription elongation tend to be more consistent than regions characterizing enhancers or Polycomb-repressed regions and that epigenetically active genes consistent across all epigenomes tend to have higher expression than those not marked as epigenetically active in all epigenomes. We also identified a set of 18 genes epigenetically active and consistently expressed in the thyroid that are likely highly relevant to thyroid function. Altogether, these epigenomes represent a powerful resource to develop a deeper understanding of the underlying molecular biology of thyroid function and provide contextual information of thyroid and human epigenomic data for comparison and integration into future studies.

Use of Patterned Collagen Coated Slides to Study Normal and Scleroderma Lung Fibroblast Migration.

Systemic sclerosis (SSc) is a spreading fibrotic disease affecting the skin and internal organs. We aimed to model pathogenic fibroblast migration in SSc in order to identify enhancing factors, measure the effect of migrating cells on underlying extracellular matrix (ECM) and test possible therapeutic inhibitors. Novel patterned collagen substrates were used to investigate alignment and migration of skin and lung fibroblasts from SSc patients and healthy controls. Normal lung but not skin fibroblasts consistently elongated and aligned with underlying collagen and migrated dependent on PDGF or serum. SSc lung fibroblasts remained growth factor dependent, did not migrate more rapidly and were less restricted to alignment of the collagen. Multiple collagen proline and lysine-modifying enzymes were identified in SSc but not control fibroblast extracellular matrix preparations, indicating differential levels of ECM modification by the diseased cells. Profiling of migrating cells revealed a possible SCF/c-Kit paracrine mechanism contributing to migration via a subpopulation of cells. Heparin, which binds ligands including PDGF and SCF, and imatininib which blocks downstream tyrosine kinase receptors, both inhibited lung fibroblast migration individually but showed synergy in SSc cells. Pathologic lung fibroblasts from SSc patients modify ECM during migration but remain growth factor dependent and sensitive to inhibitors.

Resting-state network dysfunction in Alzheimer's disease: A systematic review and meta-analysis.

INTRODUCTION: We performed a systematic review and meta-analysis of the Alzheimer's disease (AD) literature to examine consistency of functional connectivity alterations in AD dementia and mild cognitive impairment, using resting-state functional magnetic resonance imaging. METHODS: Studies were screened using a standardized procedure. Multiresolution statistics were performed to assess the spatial consistency of findings across studies. RESULTS: Thirty-four studies were included (1363 participants, average 40 per study). Consistent alterations in connectivity were found in the default mode, salience, and limbic networks in patients with AD dementia, mild cognitive impairment, or in both groups. We also identified a strong tendency in the literature toward specific examination of the default mode network. DISCUSSION: Convergent evidence across the literature supports the use of resting-state connectivity as a biomarker of AD. The locations of consistent alterations suggest that highly connected hub regions in the brain might be an early target of AD.