The pan-cancer analysis of whole genomes The expansion of whole-genome sequencing studies from individual ICGC and TCGA working groups presented the opportunity to undertake a meta-analysis of genomic features across tumour types. To achieve this, the PCAWG Consortium was established. A Technical Working Group implemented the informatics analyses by aggregating the raw sequencing data from different working groups that studied individual tumour types, aligning the sequences to the human genome and delivering a set of high-quality somatic mutation calls for downstream analysis (Extended Data Fig. 1). Given the recent meta-analysis
We propose a bacteria-based microrobot (bacteriobot) based on a new fusion paradigm for theranostic activities against solid tumors. We develop a bacteriobot using the strong attachment of bacteria to Cy5.5-coated polystyrene microbeads due to the high-affinity interaction between biotin and streptavidin. The chemotactic responses of the bacteria and the bacteriobots to the concentration gradients of lysates or spheroids of solid tumors can be detected as the migration of the bacteria and/or the bacteriobots out of the central region toward the side regions in a chemotactic microfluidic chamber. The bacteriobots showed higher migration velocity toward tumor cell lysates or spheroids than toward normal cells. In addition, when only the bacteriobots were injected to the CT-26 tumor mouse model, Cy5.5 signal was detected from the tumor site of the mouse model. In-vitro and in-vivo tests verified that the bacteriobots had chemotactic motility and tumor targeting ability. The new microrobot paradigm in which bacteria act as microactuators and microsensors to deliver microstructures to tumors can be considered a new theranostic methodology for targeting and treating solid tumors.
PurposeTo build a deep learning model to diagnose glaucoma using fundus photography.DesignCross sectional case study Subjects, Participants and Controls: A total of 1,542 photos (786 normal controls, 467 advanced glaucoma and 289 early glaucoma patients) were obtained by fundus photography.MethodThe whole dataset of 1,542 images were split into 754 training, 324 validation and 464 test datasets. These datasets were used to construct simple logistic classification and convolutional neural network using Tensorflow. The same datasets were used to fine tune pre-trained GoogleNet Inception v3 model.ResultsThe simple logistic classification model showed a training accuracy of 82.9%, validation accuracy of 79.9% and test accuracy of 77.2%. Convolutional neural network achieved accuracy and area under the receiver operating characteristic curve (AUROC) of 92.2% and 0.98 on the training data, 88.6% and 0.95 on the validation data, and 87.9% and 0.94 on the test data. Transfer-learned GoogleNet Inception v3 model achieved accuracy and AUROC of 99.7% and 0.99 on training data, 87.7% and 0.95 on validation data, and 84.5% and 0.93 on test data.ConclusionBoth advanced and early glaucoma could be correctly detected via machine learning, using only fundus photographs. Our new model that is trained using convolutional neural network is more efficient for the diagnosis of early glaucoma than previously published models.
Right lobe living-donor liver transplantation (LDLT) is often not attempted in donors with anomalous portal venous branching (APVB). The authors describe their experience with portal vein (PV) reconstruction in 17 cases of APVB in right lobe LDLT. From July 1997 to December 2001, 214 right liver LDLT were performed at the Asan Medical Center. Seventeen of the donors had APVB and successfully underwent right lobectomy. The APVB were type II (trifurcation) in nine cases, type III (independent posterior segmental branching from main PV trunk) in seven, and unclassified in one. All 17 donors and recipients are alive, with good liver function. In type II APVB, the donor PV branches were obtained with separate openings that were joined as a common orifice at the back table in two, with a discoid-patch single opening in four, and with one common opening in three. In type III APVB, the donor PV were divided with two openings in four and with a discoid-patch single opening in three. The discoid-patch defect in the remnant PV was repaired with a vein patchplasty in two donors and resected with end-to-end anastomosis in five. However, one donor developed portal vein thrombosis (PVT) that was managed successfully by re-exploration and insertion of a metallic vascular stent. Of the four type III APVB obtained with two separate PV openings, the first two liver grafts were each reconstructed as double PV anastomoses. One of them required re-exploration because of PVT. In the two succeeding cases, a Y-graft interposition technique using a cryopreserved cadaveric iliac vein or the recipient's own portal confluence was successfully applied. To minimize the risk of PVT in donors with APVB, discoid-patch excision followed by repair with vein patchplasty or segmental resection should be avoided. Individual division of the PV branches creating two separate openings instead is recommended. To decrease the recipient's risk of PVT, interposition Y-graft venous reconstruction at the back table is superior to double PV anastomoses.
Cancers require telomere maintenance mechanisms for unlimited replicative potential. They achieve this through TERT activation or alternative telomere lengthening associated with ATRX or DAXX loss. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we dissect whole-genome sequencing data of over 2500 matched tumor-control samples from 36 different tumor types aggregated within the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium to characterize the genomic footprints of these mechanisms. While the telomere content of tumors with ATRX or DAXX mutations (ATRX/DAXX trunc) is increased, tumors with TERT modifications show a moderate decrease of telomere content. One quarter of all tumor samples contain somatic integrations of telomeric sequences into non-telomeric DNA. This fraction is increased to 80% prevalence in ATRX/DAXX trunc tumors, which carry an aberrant telomere variant repeat (TVR) distribution as another genomic marker. The latter feature includes enrichment or depletion of the previously undescribed singleton TVRs TTCGGG and TTTGGG, respectively. Our systematic analysis provides new insight into the recurrent genomic alterations associated with telomere maintenance mechanisms in cancer.
Many primary tumours have low levels of molecular oxygen (hypoxia), and hypoxic tumours respond poorly to therapy. Pan-cancer molecular hallmarks of tumour hypoxia remain poorly understood, with limited comprehension of its associations with specific mutational processes, non-coding driver genes and evolutionary features. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we quantify hypoxia in 1188 tumours spanning 27 cancer types. Elevated hypoxia associates with increased mutational load across cancer types, irrespective of underlying mutational class. The proportion of mutations attributed to several mutational signatures of unknown aetiology directly associates with the level of hypoxia, suggesting underlying mutational processes for these signatures. At the gene level, driver mutations in TP53, MYC and PTEN are enriched in hypoxic tumours, and mutations in PTEN interact with hypoxia to direct tumour evolutionary trajectories. Overall, hypoxia plays a critical role in shaping the genomic and evolutionary landscapes of cancer.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.