Colorectal cancer is caused by a sequence of somatic genomic alterations affecting driver genes in core cancer pathways¹. Here, to understand the functional and prognostic impact of cancer-causing somatic mutations, we analysed the whole genomes and transcriptomes of 1,063 primary colorectal cancers in a population-based cohort with long-term follow-up. From the 96 mutated driver genes, 9 were not previously implicated in colorectal cancer and 24 had not been linked to any cancer. Two distinct patterns of pathway co-mutations were observed, timing analyses identified nine early and three late driver gene mutations, and several signatures of colorectal-cancer-specific mutational processes were identified. Mutations in WNT, EGFR and TGFβ pathway genes, the mitochondrial CYB gene and 3 regulatory elements along with 21 copy-number variations and the COSMIC SBS44 signature correlated with survival. Gene expression classification yielded five prognostic subtypes with distinct molecular features, in part explained by underlying genomic alterations. Microsatellite-instable tumours divided into two classes with different levels of hypoxia and infiltration of immune and stromal cells. To our knowledge, this study constitutes the largest integrated genome and transcriptome analysis of colorectal cancer, and interlinks mutations, gene expression and patient outcomes. The identification of prognostic mutations and expression subtypes can guide future efforts to individualize colorectal cancer therapy.

Background: Single-cell resequencing (SCRS) provides many biomedical advances in variations detection at the single-cell level, but it currently relies on whole genome amplification (WGA). Three methods are commonly used for WGA: multiple displacement amplification (MDA), degenerate-oligonucleotide-primed PCR (DOP-PCR) and multiple annealing and looping-based amplification cycles (MALBAC). However, a comprehensive comparison of variations detection performance between these WGA methods has not yet been performed. Results: We systematically compared the advantages and disadvantages of different WGA methods, focusing particularly on variations detection. Low-coverage whole-genome sequencing revealed that DOP-PCR had the highest duplication ratio, but an even read distribution and the best reproducibility and accuracy for detection of copy-number variations (CNVs). However, MDA had significantly higher genome recovery sensitivity (~84 %) than DOP-PCR (~6 %) and MALBAC (~52 %) at high sequencing depth. MALBAC and MDA had comparable single-nucleotide variations detection efficiency, false-positive ratio, and allele drop-out ratio. We further demonstrated that SCRS data amplified by either MDA or MALBAC from a gastric cancer cell line could accurately detect gastric cancer CNVs with comparable sensitivity and specificity, including amplifications of 12p11.22 (KRAS) and 9p24.1 (JAK2, CD274, and PDCD1LG2). Conclusions: Our findings provide a comprehensive comparison of variations detection performance using SCRS amplified by different WGA methods. It will guide researchers to determine which WGA method is best suited to individual experimental needs at single-cell level.

Background. Cancers arise through an evolutionary process in which cell populations are subjected to selection; however, to date, the process of bladder

Glutamate oxaloacetate transaminase 1 (GOT1) regulates cellular metabolism through coordinating the utilization of carbohydrates and amino acids to meet nutrient requirements. KRAS mutated cancer cells were recently shown to rely on GOT1 to support long-term cell proliferation. The aim of the present study was to address the role of GOT1 in the metabolic adaption of cancer cells. GOT1-null and knockdown cell lines were established through CRISPR/Cas9 and shRNA techniques. The growth properties, colony formation ability, autophagy and selected gene expression profiles were analysed. Glucose deprivation decreased the viability of the GOT1-null cells and rescue experiments were conducted with selected intermediates. The redox NADH/NAD+ homeostasis as well as lactate secretion were determined. GOT1 expression levels and correlation with survival rates were analysed in selected tumor databases. Inhibition of GOT1 sensitized the cancer cells to glucose deprivation, which was partially counteracted by oxaloacetate and phosphoenol pyruvate, metabolic intermediates downstream of GOT1. Moreover, GOT1-null cells accumulated NADH and displayed a decreased ratio of NADH/NAD+ with nutrient depletion. The relevance of GOT1 as a potential target in cancer therapy was supported by a lung adenocarcinoma RNA-seq data set as well as the GEO:GSE database of metastatic melanoma where GOT1 expression was increased. High levels of GOT1 were further linked to poor survival as analysed by the GEPIA web tool, in thyroid and breast carcinoma and in lung adenocarcinoma. Our study suggests an important role of GOT1 to coordinate the glycolytic and the oxidative phosphorylation pathways in KRAS mutated cancer cells. GOT1 is crucial to provide oxaloacetate at low glucose levels, likely to maintain the redox homeostasis. Our data suggest GOT1 as a possible target in cancer therapy.

Mouse model induced by azoxymethane (AOM) and dextran sodium sulfate (DSS) is generally accepted as an ideal object to study on the carcinogenesis mechanisms of human colorectal cancer (CRC). The genomic responses to the AOM/DSS treatment in mouse that possibly lead to elucidation of CRC pathological mechanism are still poorly understood. For the first time, we investigated the cancer genome landscape of AOM/DSS mouse model by exome sequencing, to testify its molecular faithfulness to human CRC. Of 14 neoplastic samples, 7575 somatic variants were identified, which resulted in 2507 mutant genes and exhibited a large diversity in both colorectal aberrant crypt foci (ACF) and tumors even those tissues that were gained from the similar morphology or same treatment period. Cross-species comparison of the somatic variants demonstrated the totally different patterns of variable sites, mutant genes and perturbed pathways between mouse and human CRC. We therefore come to a conclusion that the tumorigenesis at genomic level in AOM/DSS model may not be properly comparable with that in human CRC, and the molecular mechanism elicited from this animal model should be carefully evaluated.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of Coronavirus disease 2019 (COVID-19). However, the microbial composition of the respiratory tract and other infected tissues as well as their possible pathogenic contributions to varying degrees of disease severity in COVID-19 patients remain unclear. Between 27 January and 26 February 2020, serial clinical specimens (sputum, nasal and throat swab, anal swab and feces) were collected from a cohort of hospitalized COVID-19 patients, including 8 mildly and 15 severely ill patients in Guangdong province, China. Total RNA was extracted and ultra-deep metatranscriptomic sequencing was performed in combination with laboratory diagnostic assays. We identified distinct signatures of microbial dysbiosis among severely ill COVID-19 patients on broad spectrum antimicrobial therapy. Co-detection of other human respiratory viruses (including human alphaherpesvirus 1, rhinovirus B, and human orthopneumovirus) was demonstrated in 30.8% (4/13) of the severely ill patients, but not in any of the mildly affected patients. Notably, the predominant respiratory microbial taxa of severely ill patients were Burkholderia cepacia complex (BCC), Staphylococcus epidermidis, or Mycoplasma spp. (including M. hominis and M. orale). The presence of the former two bacterial taxa was also confirmed by clinical cultures of respiratory specimens (expectorated sputum or nasal secretions) in 23.1% (3/13) of the severe cases. Finally, a time-dependent, secondary infection of B. cenocepacia with expressions of multiple virulence genes was demonstrated in one severely ill patient, which might accelerate his disease deterioration and death occurring one month after ICU admission. Our findings point to SARS-CoV-2-related microbial dysbiosis and various antibiotic-resistant respiratory microbes/pathogens in hospitalized COVID-19 patients in relation to disease severity. Detection and tracking strategies are needed to prevent the spread of antimicrobial resistance, improve the treatment regimen and clinical outcomes of hospitalized, severely ill COVID-19 patients.