Citations

Oncolytic viruses would ideally be of use for systemic therapy to treat disseminated cancer. To do this safely, this may require multiple layers of cancer specificity. The pharmacology and specificity of oncolytic adenoviruses can be modified by (1) physical retargeting, (2) physical detargeting, (3) chemical shielding, or (4) by modifying the ability of viral early gene products to selectively activate in cancer versus normal cells. We explored the utility of these approaches with oncolytic adenovirus serotype 5 (Ad5) in immunocompetent Syrian hamsters bearing subcutaneous HaK tumors. After a single intravenous injection to reach the distant tumors, the physically hepatocyte-detargeted virus Ad5-hexon-BAP was more effective than conditionally replicating Ad5-dl1101/07 with mutations in its E1A protein. When these control or Ad5 treated animals were treated a second time by intratumoral injection, prior exposure to Ad5 did not affect tumor growth, suggesting that anti-Ad immunity neither prevented treatment nor amplified anti-tumor immune responses. Ad5-dl1101/07 was next chemically shielded with polyethylene glycol (PEG). While 5 kDa of PEG blunted pro-inflammatory IL-6 production induced by Ad5-dl1101/07, this shielding reduced Ad oncolytic activity.

We describe a family with multiple endocrine neoplasia type 2A (MEN2A) caused by the D631Y RET mutation resulting in an atypical phenotype. The index case was a 24-year-old man with history of recurrent anaplastic ependymoma incidentally found to have the D631Y RET mutation. At first assessment, four family members had evidence of large pheochromocytomas. One patient was found to have micromedullary thyroid cancer at 79 years of age. None of the patients had primary hyperparathyroidism. Patients with MEN2A caused by a D631Y RET mutation most commonly present with pheochromocytomas. Medullary thyroid cancer is a less common part of the syndrome when compared with other RET mutations.

Abstract Introduction Identifying at what point atrophy rates first change in Alzheimer's disease is important for informing design of presymptomatic trials. Methods Serial T1‐weighted magnetic resonance imaging scans of 94 participants (28 noncarriers, 66 carriers) from the Dominantly Inherited Alzheimer Network were used to measure brain, ventricular, and hippocampal atrophy rates. For each structure, nonlinear mixed‐effects models estimated the change‐points when atrophy rates deviate from normal and the rates of change before and after this point. Results Atrophy increased after the change‐point, which occurred 1–1.5 years (assuming a single step change in atrophy rate) or 3–8 years (assuming gradual acceleration of atrophy) before expected symptom onset. At expected symptom onset, estimated atrophy rates were at least 3.6 times than those before the change‐point. Discussion Atrophy rates are pathologically increased up to seven years before “expected onset”. During this period, atrophy rates may be useful for inclusion and tracking of disease progression.

6087 Background: NTRK1-3 fusions, identified in pediatric radiation-associated thyroid cancers, are low frequency oncogenic drivers that can trigger constitutive activation of TrK1-3 tyrosine kinase domains with responsively heightened downstream signaling through RAS-MAPK, PI3K-AKT, and/or PLCγ-PKC. NTRK point mutations are rare and of largely uninvestigated oncogenic potential, but some appear oncogenic. Methods: Advanced thyroid cancer (TC) patients (pts) requiring systemic therapy at Mayo Clinic sites underwent tumor genetic interrogation using Foundation One analyses with the goal of informing salvage therapeutic strategies. In this IRB-approved study, data from pts with TCs bearing NTRK point mutations were retrospectively analyzed, as this occurrence was unexpected but potentially of prognostic and/or therapeutic relevance. Results: Five of 55 pts (9%) with advanced TCs subject to Foundation One tumor interrogation had NTRK1-3 point mutations (allele frequency, AF, 15-49%); none had fusions. All 5 tumors had microsatellite stability; 4/5 had low tumor mutation burden (2-5 mutations/megabase). NTRK mutations in 3 anaplastic TC (ATC) pts (15% prevalence) were: NTRK3 I749M (AF 15%; overall survival, OS, 15 mos), NTRK1 R744H (AF not available; OS 9.5 mos), and NTRK1 R583H (AF 20.1%; OS > 4.9 mos); NTRK2 S167Y (AF 49%; OS > 51 mos) or NTRK1 R6W (AF 42%; OS > 60 mos) were found in one papillary (PTC) and one Hürthle cell (HCC) TC pt respectively (summary table below). Conclusions: NTRK point mutations, but not fusions, were unexpectedly identified in 9% of assessed advanced TCs - but remarkably in 15% of ATCs. Mutated NTRKs, along with oncogenic p53 and TERT, may contribute to TC progression, especially in ATC, consequent to downstream activation of MAPK and PI3K-AKT pathways. We posit that NTRK point mutations may have therapeutic implications for treatment of refractory disease. [Table: see text]

AbstractIntroductionThe objective of this study was to evaluate the relationship between self‐reported exercise levels and Alzheimer's disease (AD) biomarkers, in a cohort of autosomal dominant AD mutation carriers.MethodsIn 139 presymptomatic mutation carriers from the Dominantly Inherited Alzheimer Network, the relationship between self‐reported exercise levels and brain amyloid load, cerebrospinal fluid (CSF) Aβ42, and CSF tau levels was evaluated using linear regression.ResultsNo differences in brain amyloid load, CSF Aβ42, or CSF tau were observed between low and high exercise groups. Nevertheless, when examining only those already accumulating AD pathology (i.e., amyloid positive), low exercisers had higher mean levels of brain amyloid than high exercisers. Furthermore, the interaction between exercise and estimated years from expected symptom onset was a significant predictor of brain amyloid levels.DiscussionOur findings indicate a relationship exists between self‐reported exercise levels and brain amyloid in autosomal dominant AD mutation carriers.

Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. Methods We used standard searches to find publications using ADNI data. Results (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β‐amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white‐matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion‐like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by “classic” AD pathology but also by normal biomarkers, accelerated decline, and suspected non‐Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. Discussion Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.