Implications of all the available evidence It is possible to utilise deep learning to develop biomarkers for automatic prediction of patient outcome directly from conventional histopathology images. In colorectal cancer, the marker was found to be a clinically useful prognostic marker in analysis of a large series of patients who received consistent, modern cancer treatment.

We present here a new technique with which to visualize nuclei in living muscle fibres in the intact animal, involving injection of labelled DNA into single cells. This approach allowed us to determine the position of all of nuclei within a sarcolemma without labelling satellite cells. In contrast to what has been reported in tissue culture, we found that the nuclei were immobile, even when observed over several days. Nucleic density was uniform along the fibre except for the endplate and some myotendinous junctions, where the density was higher. The perijunctional region had the same number of nuclei as the rest of the fibre. In the extensor digitorum longus (EDL) muscle, the extrajunctional nuclei were elongated and precisely aligned to the long axis of the fibre. In the soleus, the nuclei were rounder and not well aligned. When comparing small and large fibres in the soleus, the number of nuclei varied approximately in proportion to cytoplasmic volume, while in the EDL the number was proportional to surface area. Statistical analysis revealed that the nuclei were not randomly distributed in either the EDL or the soleus. For each fibre, actual distributions were compared with computer simulations in which nuclei were assumed to repel each other, which optimizes the distribution of nuclei with respect to minimizing transport distances. The simulated patterns were regular, with clear row-like structures when the density of nuclei was low. The nonrandom and often row-like distribution of nuclei observed in muscle fibres may thus reflect regulatory mechanisms whereby nuclei repel each other in order to minimize transport distances.

BackgroundCancer progression is associated with genomic instability and an accumulation of gains and losses of DNA. The growing variety of tools for measuring genomic copy numbers, including various types of array-CGH, SNP arrays and high-throughput sequencing, calls for a coherent framework offering unified and consistent handling of single- and multi-track segmentation problems. In addition, there is a demand for highly computationally efficient segmentation algorithms, due to the emergence of very high density scans of copy number.ResultsA comprehensive Bioconductor package for copy number analysis is presented. The package offers a unified framework for single sample, multi-sample and multi-track segmentation and is based on statistically sound penalized least squares principles. Conditional on the number of breakpoints, the estimates are optimal in the least squares sense. A novel and computationally highly efficient algorithm is proposed that utilizes vector-based operations in R. Three case studies are presented.ConclusionsThe R package is a software suite for segmentation of single- and multi-track copy number data using algorithms based on coherent least squares principles.

There are conflicting reports as to whether malignant peripheral nerve sheath tumor (MPNST) patients with neurofibromatosis type 1 (NF1) have worse prognosis than non-NF1 MPNST patients. Large clinical studies to address this problem are lacking due to the rareness of MPNST. We have performed meta-analyses testing the effect of NF1 status on MPNST survival based on publications from the last 50 years, including only nonoverlapping patients reported from each institution. In addition, we analyzed survival characteristics for 179 MPNST patients from 3 European sarcoma centers. The meta-analyses including data from a total of 48 studies and >1800 patients revealed a significantly higher odds ratio for overall survival (OROS) and disease-specific survival (ORDSS) in the non-NF1 group (OROS = 1.75, 95% confidence interval [CI] = 1.28–2.39, and ORDSS = 1.68, 95% CI = 1.18–2.40). However, in studies published in the last decade, survival in the 2 patient groups has been converging, as especially the NF1 group has shown improved prognosis. For our own MPNST patients, NF1 status had no effect on overall or disease-specific survival. The compiled literature from 1963 to the present indicates a significantly worse outcome of MPNST in patients with NF1 syndrome compared with non-NF1 patients. However, survival for the NF1 patients has improved in the last decade, and the survival difference is diminishing. These observations support the hypothesis that MPNSTs arising in NF1 and non-NF1 patients are not different per se. Consequently, we suggest that the choice of treatment for MPNST should be independent of NF1 status.