Local concentrations of mutations are well-known in human cancers. However, their 3-dimensional (3D) spatial relationships have yet to be systematically explored. We developed a computational tool, HotSpot3D, to identify such spatial hotspots (clusters) and to interpret the potential function of variants within them. We applied HotSpot3D to >4,400 TCGA tumors across 19 cancer types, discovering >6,000 intra- and inter-molecular clusters, some of which showed tumor/tissue specificity. In addition, we identified 369 rare mutations from genes including TP53, PTEN, VHL, EGFR, and FBXW7 and 99 medium recurrence mutations from genes such as RUNX1, MTOR, CA3, PI3, and PTPN11, all residing within clusters having potential functional implications. As a proof of concept, we validated our predictions in EGFR using high throughput phosphorylation data and cell-line based experimental evaluation. Finally, drug-mutation cluster/network analysis predicted over 800 promising candidates of druggable mutations, raising new possibilities for designing personalized treatments for patients carrying specific mutations.

Objective The main objective of this study was to investigate, in a population of normal postmenopausal women, the association between menopause and severity of lumbar disc degeneration from the fi rst lumbar to the fi rst sacral vertebra on magnetic resonance imaging.Methods Between January 2010 and May 2013, 846 normal women and 4230 intervertebral discs were retrospectively analyzed. Age, height, weight and years since menopause (YSM) were recorded. Disc degeneration was evaluated using the modifi ed Pfi rrmann grading system. ResultsCompared to premenopausal and perimenopausal women, postmenopausal women had more severe disc degeneration after removal of age, height and weight effects ( p Ͻ 0.0001). Postmenopausal women were divided into six subgroups for every 5 YSM. When YSM was below 15 years, there was a signifi cant difference between every two groups, i.e. groups 1 -5 YSM, 6 -10 YSM and 11 -15 YSM ( p Ͻ 0.01). A positive trend was observed between YSM and severity of disc degeneration, respectively, i.e. L1/L2 ( r ϭ 0.235), L2/ L3 ( r ϭ 0.161), L3/L4 ( r ϭ 0.173), L4/L5 ( r ϭ 0.146), L5/S1 ( r ϭ 0.137) and all lumbar discs ( r ϭ 0.259) ( p Ͻ 0.05 or 0.01). However, when YSM was above 15, there was no difference, i.e. groups 16 -20 YSM, 21 -25 YSM and 26 -30 YSM ( p Ͼ 0.05), and the signifi cance correlation also disappeared ( p Ͼ 0.05).Conclusion Menopause is associated with disc degeneration in the lumbar spine. The association almost entirely occurred in the fi rst 15 years since menopause, suggesting estrogen decrease may be a risk factor for lumbar disc degeneration. Source of fundingNil.Climacteric Downloaded from informahealthcare.com by Nyu Medical Center on 06/15/15For personal use only.

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Background and PurposeThe presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis (MS) lesion. Increased prevalence of these paramagnetic rim lesions (PRLs) is associated with a more severe disease course in MS. The identification of these lesions is time-consuming to perform manually. We present a method to automatically detect PRLs on 3T T2*-phase images.MethodsT1-weighted, T2-FLAIR, and T2*-phase MRI of the brain were collected at 3T for 19 subjects with MS. The images were then processed with lesion segmentation, lesion center detection, lesion labelling, and lesion-level radiomic feature extraction. A total of 877 lesions were identified, 118 (13%) of which contained a paramagnetic rim. We divided our data into a training set (15 patients, 673 lesions) and a testing set (4 patients, 204 lesions). We fit a random forest classification model on the training set and assessed our ability to classify lesions as PRL on the test set.ResultsThe number of PRLs per subject identified via our automated lesion labelling method was highly correlated with the gold standard count of PRLs per subject, r = 0.91 (95% CI [0.79, 0.97]). The classification algorithm using radiomic features can classify a lesion as PRL or not with an area under the curve of 0.80 (95% CI [0.67, 0.86]).ConclusionThis study develops a fully automated technique for the detection of paramagnetic rim lesions using standard T1 and FLAIR sequences and a T2*phase sequence obtained on 3T MR images.HighlightsA fully automated method for both the identification and classification of paramagnetic rim lesions is proposed.Radiomic features in conjunction with machine learning algorithms can accurately classify paramagnetic rim lesions.Challenges for classification are largely driven by heterogeneity between lesions, including equivocal rim signatures and lesion location.