Determination of the Cancer Genome Atlas (TCGA) Endometrial Cancer Molecular Subtypes Using the Variant Interpretation and Clinical Decision Support Software MH Guide.

BACKGROUND: The Cancer Genome Atlas (TCGA) network (United States National Cancer Institute) identified four molecular endometrial cancer (EC) subtypes using an extensive multi-method approach. The aim of this study was to determine the four TCGA EC molecular subtypes using a single-method whole-exome sequencing (WES)-based approach provided by MH Guide (Molecular Health, Heidelberg, Germany). METHODS: WES and clinical data of n = 232 EC patients were obtained from TCGA. The four TCGA EC molecular subtypes designated as (i) Mutated Polymerase ε (POLE), (ii) Microsatellite Instability (MSI), (iii) Copy Number (CN) low and, (iv) CN-high were determined using the MH Guide software. The prognostic value of the subtypes determined by MH Guide were compared with the TCGA classification. RESULTS: Analysis of WES data using the MH Guide software led to the precise identification of the four EC molecular subtypes analogous to the TCGA classification. Both approaches displayed high concordance in terms of prognostic significance. CONCLUSIONS: The multi-method-based TCGA EC molecular subtypes can reliably be reproduced by the single-method-based MH Guide approach. The easy-to-implement single-method MH Guide approach represents a promising diagnostic tool.

Reliability of algorithmic somatic copy number alteration detection from targeted capture data.

MOTIVATION: Whole exome and gene panel sequencing are increasingly used for oncological diagnostics. To investigate the accuracy of SCNA detection algorithms on simulated and clinical tumor samples, the precision and sensitivity of four SCNA callers were measured using 50 simulated whole exome and 50 simulated targeted gene panel datasets, and using 119 TCGA tumor samples for which SNP array data were available. RESULTS: On synthetic exome and panel data, VarScan2 mostly called false positives, whereas Control-FREEC was precise (>90% correct calls) at the cost of low sensitivity (<40% detected). ONCOCNV was slightly less precise on gene panel data, with similarly low sensitivity. This could be explained by low sensitivity for amplifications and high precision for deletions. Surprisingly, these results were not strongly affected by moderate tumor impurities; only contaminations with more than 60% non-cancerous cells resulted in strongly declining precision and sensitivity. On the 119 clinical samples, both Control-FREEC and CNVkit called 71.8% and 94%, respectively, of the SCNAs found by the SNP arrays, but with a considerable amount of false positives (precision 29% and 4.9%). DISCUSSION: Whole exome and targeted gene panel methods by design limit the precision of SCNA callers, making them prone to false positives. SCNA calls cannot easily be integrated in clinical pipelines that use data from targeted capture-based sequencing. If used at all, they need to be cross-validated using orthogonal methods. AVAILABILITY AND IMPLEMENTATION: Scripts are provided as supplementary information. CONTACT: gunther.jansen@molecularhealth.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Central representation of spatial and temporal surface wave parameters in the African clawed frog.

Xenopus laevis employs mechano-sensory lateral lines to, for instance, capture arthropods on the surface of turbid waters with poor visibility based on incoming wave signals. To characterise central representations of surface waves emitted from different locations, responses to several wave parameters were extracellularly recorded across brainstem, midbrain and thalamic areas. Overall, 339 of 411 statistically analysed responses showed significantly altered spike rates during the presentation of surface waves. Of these units, 45.1% were obtained in the torus semicircularis including its laminar subnucleus (23.3%) that is known to process auditory cues. Wave parameters contributing to central object representations were indicated by response rates that systematically varied with amplitude (76.3% of 160 tested units), frequency (74.4% of 270 tested units), source angle (93.7% of 79 tested units), or source distance (63.8% of 218 tested units). Map-like parameter representations were rather diffuse, yet an increased fraction of units tuned to frontal source angles was observed at deeper tissue layers (>180 µm), and an increased fraction of best neuronal responses to low wave frequencies (≤25 Hz) at rostral midbrain sections. Responses to wave frequencies remained largely robust across tested unit samples independent of source angles, and distances (N = 62). In comparison, spatial response characteristics seemed fragile across different wave frequencies in 68.3% of 41 recordings.

Lateral line units in the amphibian brain could integrate wave curvatures.

Aquatic predators like Xenopus laevis exploit mechano-sensory lateral lines to localise prey on the water surface by its wave emissions. In terms of distance, hypothetically, the source of a concentric wave could be centrally represented based on wave curvatures: for Xenopus, we present a first sample of 98 extracellularly recorded brainstem and midbrain responses to waves with curvatures ranging from 22.2-11.1 m(-1). At the frog, concurrently, wave amplitudes and their spectral composition were kept stable. Notably, 61% of 98 units displayed curvature-dependent spike rates, suggesting that wave curvatures could support an extraction of source distances in the amphibian brain.

Neural responses to water surface waves in the midbrain of the aquatic predator Xenopus laevis laevis.

Many aquatic vertebrates use mechano-sensory lateral lines to decipher water movements. The peripheral and central organization of the lateral line system has much in common with the auditory system. Therefore, it was hypothesized that the information processing of both systems could be related. Analogous to acoustic objects, for instance, object representations along the central lateral line pathway must be generated from patterns of particle motion across peripheral receivers. Thus, the lateral line offers insight into key features of neural computation beyond a specific sensory system. Here, central processing of water surface waves was described in the African clawed frog which depends on wave signals for prey detection, recognition and localization. Neural responses to surface wave stimuli were recorded in the brainstem and midbrain of Xenopus. A total of 109 units displayed either excitatory or inhibitory responses to surface waves. The response pattern distribution differed significantly across the optic tectum and torus semicircularis magnocellularis (chi-square test, P < 0.05). Stimulus frequencies from 10 to 40 Hz were represented equally across lateral line nuclei but best frequencies were systematically distributed along the rostrocaudal axis of the midbrain (chi-square test, P < 0.05). Forty-one percent of 102 widely distributed units phase locked significantly to stimulus frequencies (Rayleigh test, P < 0.05; vector strength > 0.3) and 41% of 39 tested units featured non-monotone rate-level functions. These neurones were registered mainly in the dorsal tectum and magnocellular torus semicircularis (chi-square test, P < 0.05). Across all tested nuclei, 16 of 17 discreetly distributed units showed a directional response to spatial stimulation. The results suggest midbrain subdivisions with respect to processing of stimulus timing, frequency and amplitude.