Citations

Abstract Background: Chronic lymphocytic leukemia (CLL) patients exhibit variable times from diagnosis to the need to initiate therapy. Previous studies have demonstrated that the rates of CLL cell proliferation (birth) and elimination (death plus recirculation to solid tissues) could be estimated using the incorporation of deuterium (2H), taken in the form of heavy water (2H2O), into the DNA of dividing cells. In a cross-sectional study of patients with early and late stage CLL, birth rates above 0.35% per day were associated with more aggressive disease. The NIH-sponsored CLL Research Consortium (CRC) conducted a prospective study on patients with early stage disease to assess the relationship between CLL-cell birth rate and the length of time to initiation of initial therapy. Methods: Eligible patients had untreated early stage (Rai 0, 1 or 2) disease, and were no more than 3 years from diagnosis. Additional clinical parameters included age at diagnosis, gender, mutational status of the immunoglobulin heavy chain variable region (IGHV), 70-kD zeta-associated protein (ZAP-70) (binary split at 20%) and CD38 (binary split at 30%) expression, and FISH cytogenetics by the Dohner hierarchy. Heavy water was administered daily for 6 weeks, and peripheral blood was obtained for kinetic measurements at the end of weeks 3, 6, 9, 12, and 16. CLL birth rates for blood samples were measured at the same facility (KineMed, Inc, Emeryville, CA) by quantifying deuterium incorporation into the deoxyribose moiety of DNA of tumor cells, using isotope ratio mass spectrometry (IRMS). Subjects were followed for time to administration of therapy; subjects still without treatment at time of analysis were censored, as were subjects who exited the study to receive treatment outside of the established criteria. To assess the impact of birth rate and other factors on time to first therapy, Kaplan-Meier curves were compared using the logrank test for univariable analyses; stepwise Cox proportional hazards regression models were used to build multivariable models. Results: Between July 2005 and February 2009, 119 subjects were enrolled through the CRC. 107 subjects were eligible. For 10 eligible patients, exit from the study prior to completion of protocol-mandated heavy water consumption (n=5) or technical issues (n=5) made it impossible to obtain estimates of birth rate. The 97 eligible subjects for whom birth rates were available form the basis of this report. 60% of subjects were male; median age 57 years (range 40-85). Median birth rate was 0.32%/day (range 0.07-1.31%/day). 43 subjects (44%) had birth rates higher than the previously reported threshold of 0.35%/day. With a median follow up of 4 years, 33 eligible subjects (34%) required treatment. IGHV status (31 subjects unmutated), elevated ZAP-70 (31 subjects), elevated CD38 (21 subjects), and hierarchical categorization of FISH anomalies (4 del(17p), 5 del(11q), 8 tri(12), 50 del(13q)) were examined for association with differential time to first therapy in univariable analyses. All were associated with earlier need for therapy (p <0.001) except for genomic aberrations defined by FISH (p = 0.96). Birth rate > 0.35%/day was also significantly associated with shorter time to first therapy (p < 0.001). In multivariable modeling, only birth rate and IGHV status met the criteria for inclusion (Wald p-value < 0.05). Hazard ratios were 3.51 (p = 0.002) for birth rate above 0.35%/day and 5.0 for unmutated IGHV status (p < 0.001). The association between elevated birth rate and shorter time to first treatment was observed within subjects with both unmutated and mutated IGHV (see Figure 1). Conclusion: An increased birth rate of CLL cells early in the disease course is a strong predictor of an earlier need for treatment in univariable analysis, as are unmutated IGHV, elevated ZAP-70 expression and elevated CD38 expression. However, only unmutated IGHV and an increased birth rate contribute significantly to the multivariable model. This adds a direct measure of B-cell kinetics to the array of biomarkers available to subjects with CLL. Figure 1 Time to treatment by birth rate and IGHV status Figure 1. Time to treatment by birth rate and IGHV status Disclosures Murphy: KineMed, Inc: Equity Ownership. Emson:KineMed, Inc.: Employment, Equity Ownership. Li:KineMed, Inc.: Employment, Equity Ownership. McConnel:KineMed, Inc.: Equity Ownership. Turner:KineMed, Inc.: Employment, Equity Ownership. Busch:KineMed, Inc.: Equity Ownership. Hellerstein:KineMed Inc: Consultancy, Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Chiorazzi:KineMed, Inc.: Stock options Other.

AbstractAlzheimer's disease (AD) is the most common type of dementia in the elderly with no effective treatment currently. Recent studies of noninvasive neuroimaging, resting‐state functional magnetic resonance imaging (rs‐fMRI) with graph theoretical analysis have shown that patients with AD and mild cognitive impairment (MCI) exhibit disrupted topological organization in large‐scale brain networks. In previous work, it is a common practice to threshold such networks. However, it is not only difficult to make a principled choice of threshold values, but also worse is the discard of potential important information. To address this issue, we propose a threshold‐free feature by integrating a prior persistent homology‐based topological feature (the zeroth Betti number) and a newly defined connected component aggregation cost feature to model brain networks over all possible scales. We show that the induced topological feature (Integrated Persistent Feature) follows a monotonically decreasing convergence function and further propose to use its slope as a concise and persistent brain network topological measure. We apply this measure to study rs‐fMRI data from the Alzheimer's Disease Neuroimaging Initiative and compare our approach with five other widely used graph measures across five parcellation schemes ranging from 90 to 1,024 region‐of‐interests. The experimental results demonstrate that the proposed network measure shows more statistical power and stronger robustness in group difference studies in that the absolute values of the proposed measure of AD are lower than MCI and much lower than normal controls, providing empirical evidence for decreased functional integration in AD dementia and MCI.

Abstract Introduction The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI is a multisite, longitudinal, observational study that has collected many biomarkers since 2004. Recent publications highlight the multifactorial nature of late‐onset AD. We discuss selected topics that provide insights into AD progression and outline how this knowledge may improve clinical trials. Methods We used standard methods to identify nearly 600 publications using ADNI data from 2016 and 2017 (listed in Supplementary Material and searchable at http://adni.loni.usc.edu/news‐publications/publications/ ). Results (1) Data‐driven AD progression models supported multifactorial interactions rather than a linear cascade of events. (2) β‐Amyloid (Aβ) deposition occurred concurrently with functional connectivity changes within the default mode network in preclinical subjects and was followed by specific and progressive disconnection of functional and anatomical networks. (3) Changes in functional connectivity, volumetric measures, regional hypometabolism, and cognition were detectable at subthreshold levels of Aβ deposition. 4. Tau positron emission tomography imaging studies detailed a specific temporal and spatial pattern of tau pathology dependent on prior Aβ deposition, and related to subsequent cognitive decline. 5. Clustering studies using a wide range of modalities consistently identified a “typical AD” subgroup and a second subgroup characterized by executive impairment and widespread cortical atrophy in preclinical and prodromal subjects. 6. Vascular pathology burden may act through both Aβ dependent and independent mechanisms to exacerbate AD progression. 7. The APOE ε4 allele interacted with cerebrovascular disease to impede Aβ clearance mechanisms. 8. Genetic approaches identified novel genetic risk factors involving a wide range of processes, and demonstrated shared genetic risk for AD and vascular disorders, as well as the temporal and regional pathological associations of established AD risk alleles. 9. Knowledge of early pathological changes guided the development of novel prognostic biomarkers for preclinical subjects. 10. Placebo populations of randomized controlled clinical trials had highly variable trajectories of cognitive change, underscoring the importance of subject selection and monitoring. 11. Selection criteria based on Aβ positivity, hippocampal volume, baseline cognitive/functional measures, and APOE ε4 status in combination with improved cognitive outcome measures were projected to decrease clinical trial duration and cost. 12. Multiple concurrent therapies targeting vascular health and other AD pathology in addition to Aβ may be more effective than single therapies. Discussion ADNI publications from 2016 and 2017 supported the idea of AD as a multifactorial disease and provided insights into the complexities of AD disease progression. These findings guided the development of novel biomarkers and suggested that subject selection on the basis of multiple factors may lower AD clinical trial costs and duration. The use of multiple concurrent therapies in these trials may prove more effective in reversing AD disease progression.

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer’s disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, high-angular resolution diffusion MRI. We adapted GWASs to screen the brain’s connectivity pattern, allowing us to discover genetic variants that affect the human brain’s wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer’s disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism ( MACROD2 ), development ( NEDD4 ), and mental retardation ( UBE2A ) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.