SHORT COMMUNICATION: Complementary tumor induction in neural grafts exposed to N-ethyl-N-nitrosourea and an activated myc gene

Using a combination of transplacental carcinogen exposure and retrovirus-mediated oncogene transfer into fetal brain transplants, we have studied complementary transformation by N-ethyl-N-nitrosourea (NEU) and the v-myc oncogene in the nervous system. Previous experiments had demonstrated that both agents will not induce tumors independently whereas simultaneous expression of v-H-ras and v-gag/myc exerted a powerful transforming potential in neural grafts. In order to identify other genetic alterations that co-operate with an activated myc gene, the neurotropic carcinogen NEU was used to generate mutations of cellular genes. On embryonic day 14 (ED14), pregnant donor animals (F344 rats) received a single i.v. dose of NEU (50 mg/kg). Twenty-four hours later (ED15), the fetal brains were removed, triturated and incubated with a retroviral vector carrying the v-gag/myc oncogene. Subsequently, these primary cell suspensions were transplanted stereotactically into the caudate-putamen of syngenic adult recipients. After latency periods of 3-6 months, 5 of 10 recipients harboring ED15 fetal brain transplants developed malignant, poorly differentiated neuroectodermal tumors in the grafts. No tumor development was observed in seven recipients harboring ED16 neural grafts. Cell lines were established from three tumors and the 110 kd gag/myc fusion protein encoded by the retroviral construct was identified in the tumors by Western blotting. Several candidate genes for mutational activation by NEU including the H-ras, K-ras and neu oncogenes were analyzed for specific point mutations by polymerase chain reaction (PCR) and direct DNA sequencing of the PCR products. However, no mutations were found in any of these genes. These findings lend further support to the multistep hypothesis of neoplastic transformation in the brain. The tumors induced in this model provide an interesting tool for the identification of genes that co-operate with an activated myc gene in neurocarcinogenesi

Hand use predicts the structure of representations in sensorimotor cortex.

Fine finger movements are controlled by the population activity of neurons in the hand area of primary motor cortex. Experiments using microstimulation and single-neuron electrophysiology suggest that this area represents coordinated multi-joint, rather than single-finger movements. However, the principle by which these representations are organized remains unclear. We analyzed activity patterns during individuated finger movements using functional magnetic resonance imaging (fMRI). Although the spatial layout of finger-specific activity patterns was variable across participants, the relative similarity between any pair of activity patterns was well preserved. This invariant organization was better explained by the correlation structure of everyday hand movements than by correlated muscle activity. This also generalized to an experiment using complex multi-finger movements. Finally, the organizational structure correlated with patterns of involuntary co-contracted finger movements for high-force presses. Together, our results suggest that hand use shapes the relative arrangement of finger-specific activity patterns in sensory-motor cortex

Array-based DNA methylation profiling of primary lymphomas of the central nervous system

<p>Abstract</p> <p>Background</p> <p>Although primary lymphomas of the central nervous system (PCNSL) and extracerebral diffuse large B-cell lymphoma (DLBCL) cannot be distinguished histologically, it is still a matter of debate whether PCNSL differ from systemic DLBCL with respect to their molecular features and pathogenesis. Analysis of the DNA methylation pattern might provide further data distinguishing these entities at a molecular level.</p> <p>Methods</p> <p>Using an array-based technology we have assessed the DNA methylation status of 1,505 individual CpG loci in five PCNSL and compared the results to DNA methylation profiles of 49 DLBCL and ten hematopoietic controls.</p> <p>Results</p> <p>We identified 194 genes differentially methylated between PCNSL and normal controls. Interestingly, Polycomb target genes and genes with promoters showing a high CpG content were significantly enriched in the group of genes hypermethylated in PCNSL. However, PCNSL and systemic DLBCL did not differ in their methylation pattern.</p> <p>Conclusions</p> <p>Based on the data presented here, PCNSL and DLBCL do not differ in their DNA methylation pattern. Thus, DNA methylation analysis does not support a separation of PCNSL and DLBCL into individual entities. However, PCNSL and DLBCL differ in their DNA methylation pattern from non- malignant controls.</p

Exclusion of a major role for the PTEN tumour-suppressor gene in breast carcinomas

PTEN is a novel tumour-suppressor gene located on chromosomal band 10q23.3. This region displays frequent loss of heterozygosity (LOH) in a variety of human neoplasms including breast carcinomas. The detection of PTEN mutations in Cowden disease and in breast carcinoma cell lines suggests that PTEN may be involved in mammary carcinogenesis. We here report a mutational analysis of tumour specimens from 103 primary breast carcinomas and constitutive DNA from 25 breast cancer families. The entire coding region of PTEN was screened by single-strand conformation polymorphism (SSCP) analysis and direct sequencing using intron-based primers. No germline mutations could be identified in the breast cancer families and only one sporadic carcinoma carried a PTEN mutation at one allele. In addition, all sporadic tumours were analysed for homozygous deletions by differential polymerase chain reaction (PCR) and for allelic loss using the microsatellite markers D10S215, D10S564 and D10S573. No homozygous deletions were detected and only 10 out of 94 informative tumours showed allelic loss in the PTEN region. These results suggest that PTEN does not play a major role in breast cancer formation. 1999 Cancer Research Campaig

Why is the Winner the Best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work