Macular dystrophy associated with the mitochondrial DNA A3243G mutation: pericentral pigment deposits or atrophy? Report of two cases and review of the literature.

BACKGROUND: The A3243G point mutation in mitochondrial DNA (mtDNA) is associated with MELAS (mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes) and MIDD syndromes (maternally inherited diabetes and deafness). Both MELAS and MIDD patients can present with visual symptoms due to a retinopathy, sometimes before the genetic diagnosis is made. CASE PRESENTATION: Patient 1: 46 year-old woman with diabetes mellitus and hearing loss was referred for an unspecified maculopathy detected during screening evaluation for diabetic retinopathy. Visual acuity was 20/20 in both eyes. Fundus examination showed bilateral macular and peripapillary hyperpigmented/depigmented areas.Patient 2: 45 year-old woman was referred for recent vision loss in her left eye. History was remarkable for chronic fatigue, migraine and diffuse muscular pain. Visual acuity was 20/20 in her right eye and 20/30 in her left eye. Fundus exhibited several nummular perifoveal islands of retinal pigment epithelium atrophy and adjacent pale deposits in both eyes.Retinal anatomy was investigated with autofluorescence, retinal angiography and optical coherence tomography. Retinal function was assessed with automated static perimetry, full-field and multifocal electroretinography and electro-oculography. Genetic testing of mtDNA identified a point mutation at the locus 3243. CONCLUSION: Observation of RPE abnormalities in the context of suggestive systemic findings should prompt mtDNA testing.

Screening for ALK in non-small cell lung carcinomas: 5A4 and D5F3 antibodies perform equally well, but combined use with FISH is recommended.

OBJECTIVES: Immunohistochemistry (IHC) has become a promising method for pre-screening ALK-rearrangements in non-small cell lung carcinomas (NSCLC). Various ALK antibodies, detection systems and automated immunostainers are available. We therefore aimed to compare the performance of the monoclonal 5A4 (Novocastra, Leica) and D5F3 (Cell Signaling, Ventana) antibodies using two different immunostainers. Additionally we analyzed the accuracy of prospective ALK IHC-testing in routine diagnostics. MATERIALS AND METHODS: Seventy-two NSCLC with available ALK FISH results and enriched for FISH-positive carcinomas were retrospectively analyzed. IHC was performed on BenchMarkXT (Ventana) using 5A4 and D5F3, respectively, and additionally with 5A4 on Bond-MAX (Leica). Data from our routine diagnostics on prospective ALK-testing with parallel IHC, using 5A4, and FISH were available from 303 NSCLC. RESULTS: All three IHC protocols showed congruent results. Only 1/25 FISH-positive NSCLC (4%) was false negative by IHC. For all three IHC protocols the sensitivity, specificity, positive (PPV) and negative predictive values (NPV) compared to FISH were 96%, 100%, 100% and 97.8%, respectively. In the prospective cohort 3/32 FISH-positive (9.4%) and 2/271 FISH-negative (0.7%) NSCLC were false negative and false positive by IHC, respectively. In routine diagnostics the sensitivity, specificity, PPV and NPV of IHC compared to FISH were 90.6%, 99.3%, 93.5% and 98.9%, respectively. CONCLUSIONS: 5A4 and D5F3 are equally well suited for detecting ALK-rearranged NSCLC. BenchMark and BOND-MAX immunostainers can be used for IHC with 5A4. True discrepancies between IHC and FISH results do exist and need to be addressed when implementing IHC in an ALK-testing algorithm.

Accuracy and Precision of Head Motion Information in Multi-Channel Free Induction Decay Navigators for Magnetic Resonance Imaging.

Free induction decay (FID) navigators were found to qualitatively detect rigid-body head movements, yet it is unknown to what extent they can provide quantitative motion estimates. Here, we acquired FID navigators at different sampling rates and simultaneously measured head movements using a highly accurate optical motion tracking system. This strategy allowed us to estimate the accuracy and precision of FID navigators for quantification of rigid-body head movements. Five subjects were scanned with a 32-channel head coil array on a clinical 3T MR scanner during several resting and guided head movement periods. For each subject we trained a linear regression model based on FID navigator and optical motion tracking signals. FID-based motion model accuracy and precision was evaluated using cross-validation. FID-based prediction of rigid-body head motion was found to be with a mean translational and rotational error of 0.14±0.21 mm and 0.08±0.13(°) , respectively. Robust model training with sub-millimeter and sub-degree accuracy could be achieved using 100 data points with motion magnitudes of ±2 mm and ±1(°) for translation and rotation. The obtained linear models appeared to be subject-specific as inter-subject application of a "universal" FID-based motion model resulted in poor prediction accuracy. The results show that substantial rigid-body motion information is encoded in FID navigator signal time courses. Although, the applied method currently requires the simultaneous acquisition of FID signals and optical tracking data, the findings suggest that multi-channel FID navigators have a potential to complement existing tracking technologies for accurate rigid-body motion detection and correction in MRI.

Gene Expression Profiling of Desmoid Tumors by cDNA Microarrays and Correlation with Progression-Free Survival.

PURPOSE: Because desmoid tumors exhibit an unpredictable clinical course, translational research is crucial to identify the predictive factors of progression in addition to the clinical parameters. The main issue is to detect patients who are at a higher risk of progression. The aim of this work was to identify molecular markers that can predict progression-free survival (PFS). EXPERIMENTAL DESIGN: Gene-expression screening was conducted on 115 available independent untreated primary desmoid tumors using cDNA microarray. We established a prognostic gene-expression signature composed of 36 genes. To test robustness, we randomly generated 1,000 36-gene signatures and compared their outcome association to our define 36-genes molecular signature and we calculated positive predictive value (PPV) and negative predictive value (NPV). RESULTS: Multivariate analysis showed that our molecular signature had a significant impact on PFS while no clinical factor had any prognostic value. Among the 1,000 random signatures generated, 56.7% were significant and none was more significant than our 36-gene molecular signature. PPV and NPV were high (75.58% and 81.82%, respectively). Finally, the top two genes downregulated in no-recurrence were FECH and STOML2 and the top gene upregulated in no-recurrence was TRIP6. CONCLUSIONS: By analyzing expression profiles, we have identified a gene-expression signature that is able to predict PFS. This tool may be useful for prospective clinical studies. Clin Cancer Res; 21(18); 4194-200. ©2015 AACR.