The primacy of NF1 loss as the driver of tumorigenesis in neurofibromatosis type 1-associated plexiform neurofibromas.

Neurofibromatosis type 1 (NF1) is a common tumor-predisposition disorder due to germline mutations in the tumor suppressor gene NF1. A virtually pathognomonic finding of NF1 is the plexiform neurofibroma (PN), a benign, likely congenital tumor that arises from bi-allelic inactivation of NF1. PN can undergo transformation to a malignant peripheral nerve sheath tumor, an aggressive soft-tissue sarcoma. To better understand the non-NF1 genetic contributions to PN pathogenesis, we performed whole-exome sequencing, RNASeq profiling and genome-wide copy-number determination for 23 low-passage Schwann cell cultures established from surgical PN material with matching germline DNA. All resected tumors were derived from routine debulking surgeries. None of the tumors were considered at risk for malignant transformation at the time; for example, there was no pain or rapid growth. Deep (~500X) NF1 exon sequencing was also conducted on tumor DNA. Non-NF1 somatic mutation verification was performed using the Ampliseq/IonTorrent platform. We identified 100% of the germline NF1 mutations and found somatic NF1 inactivation in 74% of the PN. One individual with three PNs had different NF1 somatic mutations in each tumor. The median number of somatic mutations per sample, including NF1, was one (range 0-8). NF1 was the only gene that was recurrently somatically inactivated in multiple tumors. Gene Set Enrichment Analysis of transcriptome-wide tumor RNA sequencing identified five significant (FDR<0.01) and seven trending (0.01⩽FDR<0.02) gene sets related to DNA replication, telomere maintenance and elongation, cell cycle progression, signal transduction and cell proliferation. We found no recurrent non-NF1 locus copy-number variation in PN. This is the first multi-sample whole-exome and whole-transcriptome sequencing study of NF1-associated PN. Taken together with concurrent copy-number data, our comprehensive genetic analysis reveals the primacy of NF1 loss as the driver of PN tumorigenesis.

Hippocampal shape analysis in Alzheimer's disease: a population-based study.

BACKGROUND: Hippocampal atrophy--particularly of the CA1 region--may be useful as a biomarker for Alzheimer's disease (AD) or the risk for AD. The extent to which the AD hippocampus can be distinguished in vivo from changes due to normal aging or other processes that affect the hippocampus is of clinical importance and is an area of active research. In this study, we use structural imaging techniques to model hippocampal size and regional shape differences between elderly men with incident AD and a non-demented comparison group of elderly men. METHODS: Participants are Japanese-American men from the Honolulu Asia Aging Study (HAAS). The HAAS cohort has been followed since 1965. The following analysis is based on a sub-group of men who underwent MRI examination in 1994-1996. Participants were diagnosed with incident AD (n=24: age=82.5+/-4.6) or were not demented (n=102: age=83.0+/-5.9). One reader, blinded to dementia diagnosis, manually outlined the left and right hippocampal formation using published criteria. We used 3D structural shape analysis methods developed at the Laboratory of Neuro Imaging (LONI) to compare regional variation in hippocampal diameter between the AD cases and the non-demented comparison group. RESULTS: Mean total hippocampal volume was 11.5% smaller in the AD cases than the non-demented controls (4903+/-857 mm(3) vs. 5540+/-805 mm(3)), with a similar size difference for the median left (12.0%) and median right (11.6%) hippocampus. Shape analysis showed a regional pattern of shape difference between the AD and non-demented hippocampus, more evident for the hippocampal body than the head, and the appearance of more consistent differences in the left hippocampus than the right. While assignment to a specific sub-region is not possible with this method, the surface changes primarily intersect the area of the hippocampus body containing the CA1 region (and adjacent CA2 and distal CA3), subiculum, and the dentate gyrus-hilar region.

Analysis and validation of automated skull stripping tools: a validation study based on 296 MR images from the Honolulu Asia aging study.

As population-based epidemiologic studies may acquire images from thousands of subjects, automated image post-processing is needed. However, error in these methods may be biased and related to subject characteristics relevant to the research question. Here, we compare two automated methods of brain extraction against manually segmented images and evaluate whether method accuracy is associated with subject demographic and health characteristics. MRI data (n = 296) are from the Honolulu Asia Aging Study, a population-based study of elderly Japanese-American men. The intracranial space was manually outlined on the axial proton density sequence by a single operator. The brain was extracted automatically using BET (Brain Extraction Tool) and BSE (Brain Surface Extractor) on axial proton density images. Total intracranial volume was calculated for the manually segmented images (ticvM), the BET segmented images (ticvBET) and the BSE segmented images (ticvBSE). Mean ticvBSE was closer to that of ticvM, but ticvBET was more highly correlated with ticvM than ticvBSE. BSE had significant over (positive error) and underestimated (negative error) ticv, but net error was relatively low. BET had large positive and very low negative error. Method accuracy, measured in percent positive and negative error, varied slightly with age, head circumference, presence of the apolipoprotein eepsilon4 polymorphism, subcortical and cortical infracts and enlarged ventricles. This epidemiologic approach to the assessment of potential bias in image post-processing tasks shows both skull-stripping programs performed well in this large image dataset when compared to manually segmented images. Although method accuracy was statistically associated with some subject characteristics, the extent of the misclassification (in terms of percent of brain volume) was small.