The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution

Crucial transitions in cancer—including tumor initiation, local expansion, metastasis, and therapeutic resistance—involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer

P67-phox (NCF2) Lacking Exons 11 and 12 Is Functionally Active and Leads to an Extremely Late Diagnosis of Chronic Granulomatous Disease (CGD)

Two brothers in their fifties presented with a medical history of suspected fungal allergy, allergic bronchopulmonary aspergillosis, alveolitis, and invasive aspergillosis and pulmonary fistula, respectively. Eventually, after a delay of 50 years, chronic granulomatous disease (CGD) was diagnosed in the index patient. We found a new splice mutation in the NCF2 (p67-phox) gene, c.1000+2T→G, that led to several splice products one of which lacked exons 11 and 12. This deletion was in frame and allowed for remarkable residual NADPH oxidase activity as determined by transduction experiments using a retroviral vector. We conclude that p67-phox which lacks the 34 amino acids encoded by the two exons can still exert considerable functional activity. This activity can partially explain the long-term survival of the patients without adequate diagnosis and treatment, but could not prevent progressing lung damage

An Iterative Jackknife Approach for Assessing Reliability and Power of fMRI Group Analyses

For functional magnetic resonance imaging (fMRI) group activation maps, so-called second-level random effect approaches are commonly used, which are intended to be generalizable to the population as a whole. However, reliability of a certain activation focus as a function of group composition or group size cannot directly be deduced from such maps. This question is of particular relevance when examining smaller groups (<20–27 subjects). The approach presented here tries to address this issue by iteratively excluding each subject from a group study and presenting the overlap of the resulting (reduced) second-level maps in a group percent overlap map. This allows to judge where activation is reliable even upon excluding one, two, or three (or more) subjects, thereby also demonstrating the inherent variability that is still present in second-level analyses. Moreover, when progressively decreasing group size, foci of activation will become smaller and/or disappear; hence, the group size at which a given activation disappears can be considered to reflect the power necessary to detect this particular activation. Systematically exploiting this effect allows to rank clusters according to their observable effect size. The approach is tested using different scenarios from a recent fMRI study (children performing a “dual-use” fMRI task, n = 39), and the implications of this approach are discussed

Whole Exome Sequencing of Patients with Steroid-Resistant Nephrotic Syndrome

BACKGROUND AND OBJECTIVES: Steroid-resistant nephrotic syndrome overwhelmingly progresses to ESRD. More than 30 monogenic genes have been identified to cause steroid-resistant nephrotic syndrome. We previously detected causative mutations using targeted panel sequencing in 30% of patients with steroid-resistant nephrotic syndrome. Panel sequencing has a number of limitations when compared with whole exome sequencing. We employed whole exome sequencing to detect monogenic causes of steroid-resistant nephrotic syndrome in an international cohort of 300 families. DESIGN, SETTING, PARTIIPANTS AND MEASUREMENTS: Three hundred thirty-five individuals with steroid-resistant nephrotic syndrome from 300 families were recruited from April of 1998 to June of 2016. Age of onset was restricted to <25 years of age. Exome data were evaluated for 33 known monogenic steroid-resistant nephrotic syndrome genes. RESULTS: In 74 of 300 families (25%), we identified a causative mutation in one of 20 genes known to cause steroid-resistant nephrotic syndrome. In 11 families (3.7%), we detected a mutation in a gene that causes a phenocopy of steroid-resistant nephrotic syndrome. This is consistent with our previously published identification of mutations using a panel approach. We detected a causative mutation in a known steroid-resistant nephrotic syndrome gene in 38% of consanguineous families and in 13% of nonconsanguineous families, and 48% of children with congenital nephrotic syndrome. A total of 68 different mutations were detected in 20 of 33 steroid-resistant nephrotic syndrome genes. Fifteen of these mutations were novel. NPHS1, PLCE1, NPHS2, and SMARCAL1 were the most common genes in which we detected a mutation. In another 28% of families, we detected mutations in one or more candidate genes for steroid-resistant nephrotic syndrome. CONCLUSIONS: Whole exome sequencing is a sensitive approach toward diagnosis of monogenic causes of steroid-resistant nephrotic syndrome. A molecular genetic diagnosis of steroid-resistant nephrotic syndrome may have important consequences for the management of treatment and kidney transplantation in steroid-resistant nephrotic syndrome

The Base Excision Repair System of Salmonella enterica serovar Typhimurium Counteracts DNA Damage by Host Nitric Oxide

Intracellular pathogens must withstand nitric oxide (NO·) generated by host phagocytes. Salmonella enterica serovar Typhimurium interferes with intracellular trafficking of inducible nitric oxide synthase (iNOS) and possesses multiple systems to detoxify NO·. Consequently, the level of NO· stress encountered by S. Typhimurium during infection in vivo has been unknown. The Base Excision Repair (BER) system recognizes and repairs damaged DNA bases including cytosine and guanine residues modified by reactive nitrogen species. Apurinic/apyrimidinic (AP) sites generated by BER glycosylases require subsequent processing by AP endonucleases. S. Typhimurium xth nfo mutants lacking AP endonuclease activity exhibit increased NO· sensitivity resulting from chromosomal fragmentation at unprocessed AP sites. BER mutant strains were thus used to probe the nature and extent of nitrosative damage sustained by intracellular bacteria during infection. Here we show that an xth nfo S. Typhimurium mutant is attenuated for virulence in C3H/HeN mice, and virulence can be completely restored by the iNOS inhibitor L-NIL. Inactivation of the ung or fpg glycosylase genes partially restores virulence to xth nfo mutant S. Typhimurium, demonstrating that NO· fluxes in vivo are sufficient to modify cytosine and guanine bases, respectively. Mutants lacking ung or fpg exhibit NO·–dependent hypermutability during infection, underscoring the importance of BER in protecting Salmonella from the genotoxic effects of host NO·. These observations demonstrate that host-derived NO· damages Salmonella DNA in vivo, and the BER system is required to maintain bacterial genomic integrity

Baseline Levels of Influenza-Specific CD4 Memory T-Cells Affect T-Cell Responses to Influenza Vaccines

BACKGROUND: Factors affecting immune responses to influenza vaccines have not been studied systematically. We hypothesized that T-cell and antibody responses to the vaccines are functions of pre-existing host immunity against influenza antigens. METHODOLOGY/PRINCIPAL FINDINGS: During the 2004 and 2005 influenza seasons, we have collected data on cellular and humoral immune reactivity to influenza virus in blood samples collected before and after immunization with inactivated or live attenuated influenza vaccines in healthy children and adults. We first used cross-validated lasso regression on the 2004 dataset to identify a group of candidate baseline correlates with T-cell and antibody responses to vaccines, defined as fold-increase in influenza-specific T-cells and serum HAI titer after vaccination. The following baseline parameters were examined: percentages of influenza-reactive IFN-gamma(+) cells in T and NK cell subsets, percentages of influenza-specific memory B-cells, HAI titer, age, and type of vaccine. The candidate baseline correlates were then tested with the independent 2005 dataset. Baseline percentage of influenza-specific IFN-gamma(+) CD4 T-cells was identified as a significant correlate of CD4 and CD8 T-cell responses, with lower baseline levels associated with larger T-cell responses. Baseline HAI titer and vaccine type were identified as significant correlates for HAI response, with lower baseline levels and the inactivated vaccine associated with larger HAI responses. Previously we reported that baseline levels of CD56(dim) NK reactivity against influenza virus inversely correlated with the immediate T-cell response to vaccination, and that NK reactivity induced by influenza virus depended on IL-2 produced by influenza-specific memory T-cells. Taken together these results suggest a novel mechanism for the homeostasis of virus-specific T-cells, which involves interaction between memory helper T-cells, CD56(dim) NK and DC. SIGNIFICANCE: These results demonstrate that assessment of baseline biomarkers may predict immunologic outcome of influenza vaccination and may reveal some of the mechanisms responsible for variable immune responses following vaccination and natural infection

Multivariate Protein Signatures of Pre-Clinical Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Plasma Proteome Dataset

Background: Recent Alzheimer's disease (AD) research has focused on finding biomarkers to identify disease at the pre-clinical stage of mild cognitive impairment (MCI), allowing treatment to be initiated before irreversible damage occurs. Many studies have examined brain imaging or cerebrospinal fluid but there is also growing interest in blood biomarkers. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has generated data on 190 plasma analytes in 566 individuals with MCI, AD or normal cognition. We conducted independent analyses of this dataset to identify plasma protein signatures predicting pre-clinical AD. Methods and Findings: We focused on identifying signatures that discriminate cognitively normal controls (n = 54) from individuals with MCI who subsequently progress to AD (n = 163). Based on p value, apolipoprotein E (APOE) showed the strongest difference between these groups (p = 2.3×10−13). We applied a multivariate approach based on combinatorial optimization ((α,β)-k Feature Set Selection), which retains information about individual participants and maintains the context of interrelationships between different analytes, to identify the optimal set of analytes (signature) to discriminate these two groups. We identified 11-analyte signatures achieving values of sensitivity and specificity between 65% and 86% for both MCI and AD groups, depending on whether APOE was included and other factors. Classification accuracy was improved by considering “meta-features,” representing the difference in relative abundance of two analytes, with an 8-meta-feature signature consistently achieving sensitivity and specificity both over 85%. Generating signatures based on longitudinal rather than cross-sectional data further improved classification accuracy, returning sensitivities and specificities of approximately 90%. Conclusions: Applying these novel analysis approaches to the powerful and well-characterized ADNI dataset has identified sets of plasma biomarkers for pre-clinical AD. While studies of independent test sets are required to validate the signatures, these analyses provide a starting point for developing a cost-effective and minimally invasive test capable of diagnosing AD in its pre-clinical stages

Identification and Validation of Novel Cerebrospinal Fluid Biomarkers for Staging Early Alzheimer's Disease

Ideally, disease modifying therapies for Alzheimer disease (AD) will be applied during the 'preclinical' stage (pathology present with cognition intact) before severe neuronal damage occurs, or upon recognizing very mild cognitive impairment. Developing and judiciously administering such therapies will require biomarker panels to identify early AD pathology, classify disease stage, monitor pathological progression, and predict cognitive decline. To discover such biomarkers, we measured AD-associated changes in the cerebrospinal fluid (CSF) proteome.CSF samples from individuals with mild AD (Clinical Dementia Rating [CDR] 1) (n = 24) and cognitively normal controls (CDR 0) (n = 24) were subjected to two-dimensional difference-in-gel electrophoresis. Within 119 differentially-abundant gel features, mass spectrometry (LC-MS/MS) identified 47 proteins. For validation, eleven proteins were re-evaluated by enzyme-linked immunosorbent assays (ELISA). Six of these assays (NrCAM, YKL-40, chromogranin A, carnosinase I, transthyretin, cystatin C) distinguished CDR 1 and CDR 0 groups and were subsequently applied (with tau, p-tau181 and Aβ42 ELISAs) to a larger independent cohort (n = 292) that included individuals with very mild dementia (CDR 0.5). Receiver-operating characteristic curve analyses using stepwise logistic regression yielded optimal biomarker combinations to distinguish CDR 0 from CDR>0 (tau, YKL-40, NrCAM) and CDR 1 from CDR<1 (tau, chromogranin A, carnosinase I) with areas under the curve of 0.90 (0.85-0.94 95% confidence interval [CI]) and 0.88 (0.81-0.94 CI), respectively.Four novel CSF biomarkers for AD (NrCAM, YKL-40, chromogranin A, carnosinase I) can improve the diagnostic accuracy of Aβ42 and tau. Together, these six markers describe six clinicopathological stages from cognitive normalcy to mild dementia, including stages defined by increased risk of cognitive decline. Such a panel might improve clinical trial efficiency by guiding subject enrollment and monitoring disease progression. Further studies will be required to validate this panel and evaluate its potential for distinguishing AD from other dementing conditions