Bilateral Polydactyly in a foal

The following case report describes the diagnosis and surgery of bilateral polydactyly of unknown origin in a colt. A 7-month-old Berber colt was referred for cosmetic and curative excision of supernumerary digits. Radiographic examination revealed bilateral polydactyly and well-developed first carpal bones. Surgery consisted of an osteotomy of both second metacarpal bones combined with an amputation of the supernumerary digits. The follow-up at 18 months after surgery revealed a sound horse with an excellent cosmetic outcome

Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: a retrospective multicentric study

Aim Gastric cancer (GC) is a tumor entity with highly variant outcomes. Lymph node metastasis is a prognostically adverse biomarker. We hypothesized that GC primary tissue contains information that is predictive of lymph node status and patient prognosis and that this information can be extracted using Deep Learning (DL). Methods Using three patient cohorts comprising 1146 patients, we trained and validated a DL system to predict lymph node status directly from hematoxylin-and-eosin stained GC tissue sections. We investigated the concordance between the DL-based prediction from the primary tumor slides (aiN score) and the histopathological lymph node status (pN). Furthermore, we assessed the prognostic value of the aiN score alone and when combined with the pN status. Results The aiN score predicted the pN status reaching Area Under the Receiver Operating Characteristic curves (AUROCs) of 0.71 in the training cohort and 0.69 and 0.65 in the two test cohorts. In a multivariate Cox analysis, the aiN score was an independent predictor of patient survival with Hazard Ratios (HR) of 1.5 in the training cohort and of 1.3 and 2.2 in the two test cohorts. A combination of the aiN score and the pN status prognostically stratified patients by survival with p-values <0.05 in log-rank tests. Conclusion GC primary tumor tissue contains additional prognostic information that is accessible using the aiN score. In combination with the pN status, this can be used for personalized management of gastric cancer patients after prospective validation

Validation of Sentinel-5P TROPOMI tropospheric NO2 products by comparison with NO2 measurements from airborne imaging, ground-based stationary, and mobile car DOAS measurements during the S5P-VAL-DE-Ruhr campaign

Airborne imaging differential optical absorption spectroscopy (DOAS), ground-based stationary and car DOAS measurements were conducted during the S5P-VAL-DE-Ruhr campaign in September 2020. The campaign area is located in the Rhine-Ruhr region of North Rhine-Westphalia, Western Germany, which is a pollution hotspot in Europe comprising urban and large industrial emitters. The measurements are used to validate space-borne NO2 tropospheric vertical column density data products from the Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI). Seven flights were performed with the airborne imaging DOAS instrument for measurements of atmospheric pollution (AirMAP), providing measurements which were used to create continuous maps of NO2 in the layer below the aircraft. These flights cover many S5P ground pixels within an area of 30 km x 35 km and were accompanied by ground-based stationary measurements and three mobile car DOAS instruments. Stationary measurements were conducted by two Pandora, two zenith-sky and two MAX-DOAS instruments distributed over three target areas. Ground-based stationary and car DOAS measurements are used to evaluate the AirMAP tropospheric NO2 vertical column densities and show high Pearson correlation coefficients of 0.87 and 0.89 and slopes of 0.93 &plusmn; 0.09 and 0.98 &plusmn; 0.02 for the stationary and car DOAS, respectively. Having a spatial resolution of about 100 m x 30 m, the AirMAP tropospheric NO2 vertical column density (VCD) data creates a link between the ground-based and the TROPOMI measurements with a resolution of 3.5 km x 5.5 km and is therefore well suited to validate the TROPOMI tropospheric NO2 VCD. The measurements on the seven flight days show strong NO2 variability, which is dependent on the different target areas, the weekday, and the meteorological conditions. The AirMAP campaign dataset is compared to the TROPOMI NO2 operational off-line (OFFL) V01.03.02 data product, the reprocessed NO2 data, using the V02.03.01 of the official L2 processor, provided by the Product Algorithm Laboratory (PAL), and several scientific TROPOMI NO2 data products. The TROPOMI data products and the AirMAP data are highly correlated with correlation coefficients between 0.72 and 0.87, and slopes of 0.38 &plusmn; 0.02 to 1.02 &plusmn; 0.07. On average, TROPOMI tropospheric NO2 VCDs are lower than the AirMAP NO2 results. The slope increased from 0.38 &plusmn; 0.02 for the operational OFFL V01.03.02 product to 0.83 &plusmn; 0.06 after the improvements in the retrieval of the PAL V02.03.01 product were implemented. Different auxiliary data, such as spatially higher resolved a priori NO2 vertical profiles, surface reflectivity and the cloud treatment, are investigated using scientific TROPOMI tropospheric NO2 VCD data products to evaluate their impact on the operational TROPOMI NO2 VCD data product. The comparison of the AirMAP campaign dataset to the scientific data products shows that the choice of surface reflectivity data base has a minor impact on the tropospheric NO2 VCD retrieval in the campaign region and season. In comparison, the replacement of the a priori NO2 profile in combination with the improvements in the retrieval of the PAL V02.03.01 product regarding cloud heights has a major impact on the tropospheric NO2 VCD retrieval and increases the slope from 0.88 &plusmn; 0.06 to 1.00 &plusmn; 0.07. This study demonstrates that the underestimation of the TROPOMI tropospheric NO2 VCD product with respect to the validation dataset has been and can be further significantly improved.</p

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation