Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale^1–3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter⁴; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation^5,6; analyses timings and patterns of tumour evolution⁷; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity^8,9; and evaluates a range of more-specialized features of cancer genomes^8,10–18.

The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.

The origins of the components of genetic coding are examined in the present study. Genetic information arose from replicator induction by metabolite in accordance with the metabolic expansion law. Messenger RNA and transfer RNA stemmed from a template for binding the aminoacyl-RNA synthetase ribozymes employed to synthesize peptide prosthetic groups on RNAs in the Peptidated RNA World. Coevolution of the genetic code with amino acid biosynthesis generated tRNA paralogs that identify a last universal common ancestor (LUCA) of extant life close to Methanopyrus, which in turn points to archaeal tRNA introns as the most primitive introns and the anticodon usage of Methanopyrus as an ancient mode of wobble. The prediction of the coevolution theory of the genetic code that the code should be a mutable code has led to the isolation of optional and mandatory synthetic life forms with altered protein alphabets.

Breast cancer is one of the most common types of cancer and is the leading cause of cancer-related death. Diagnosis of breast cancer is based on the evaluation of pathology slides. In the era of digital pathology, these slides can be converted into digital whole slide images (WSIs) for further analysis. However, due to their sheer size, digital WSIs diagnoses are time consuming and challenging. In this study, we present a lightweight architecture that consists of a bilinear structure and MobileNet-V3 network, bilinear MobileNet-V3 (BM-Net), to analyze breast cancer WSIs. We utilized the WSI dataset from the ICIAR2018 Grand Challenge on Breast Cancer Histology Images (BACH) competition, which contains four classes: normal, benign, in situ carcinoma, and invasive carcinoma. We adopted data augmentation techniques to increase diversity and utilized focal loss to remove class imbalance. We achieved high performance, with 0.88 accuracy in patch classification and an average 0.71 score, which surpassed state-of-the-art models. Our BM-Net shows great potential in detecting cancer in WSIs and is a promising clinical tool.

Osteosarcoma is predominant in the adolescent and the elderly population, but few studies have described the characteristics and prognostic factors of patients older than 60 years. In this study, the Surveillance, Epidemiology, and End Results registry database was used to identify all patients diagnosed with primary osteosarcoma from 1973 to 2014. We utilized Cox proportional hazards regression analysis to evaluate the association between patient overall survival and relevant characteristics, including gender, race, disease stage, treatment methods, primary tumor site, differentiation grade, and histologic subtype. In the data set, a total of 1139 patients with osteosarcoma older than 60 years old were identified. The overall rate of distant metastatic cases was 28.6%. Osteosarcoma occurred equally in men and women (49.5% vs 50.5%). Of all, 41.3% of tumors were located in axial location (pelvis, spine, and ribs), 34.1% of tumors were located in extremity (long or short bones of the upper or lower extremity), and 24.6% in other location (mandible, skull, and other atypical locations). Male (hazard ratio [HR] = 1.201; 95% confidence interval [CI]: 1.056-1.366), axial location (HR = 1.342; 95% CI: 1.157-1.556), distant metastasis (HR = 2.369; 95% CI: 2.015-2.785), non-surgery perform (HR = 2.108; 95% CI: 1.814-2.451) were independent risk factors for 5-year overall survival. This study revealed distinct clinicopathological features of patients with osteosarcoma older than 60 years. Male gender, tumor in axial site, nonsurgery perform, and distant metastasis indicated worse prognosis survival. Performing surgery is still an effective and reliable treatment method for patients older than 60 years.

Imaging flow cytometry (IFC) combines the imaging capabilities of microscopy with the high throughput of flow cytometry, offering a promising solution for high-precision and high-throughput cell analysis in fields such as biomedicine, green energy, and environmental monitoring. However, due to limitations in imaging framerate and real-time data processing, the real-time throughput of existing IFC systems has been restricted to approximately 1000-10,000 events per second (eps), which is insufficient for large-scale cell analysis. In this work, we demonstrate IFC with real-time throughput exceeding 1,000,000 eps by integrating optical time-stretch (OTS) imaging, microfluidic-based cell manipulation, and online image processing. Cells flowing at speeds up to 15 m/s are clearly imaged with a spatial resolution of 780 nm, and images of each individual cell are captured, stored, and analyzed. The capabilities and performance of our system are validated through the identification of malignancies in clinical colorectal samples. This work sets a new record for throughput in imaging flow cytometry, and we believe it has the potential to revolutionize cell analysis by enabling highly efficient, accurate, and intelligent measurement.