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.

Predictive drug testing of patient-derived tumor organoids (PDTOs) holds promise for personalizing treatment of metastatic colorectal cancer (mCRC), but prospective data are limited to chemotherapy regimens with conflicting results. We describe a unified framework for PDTO-based predictive testing across standard-of-care chemotherapy and biologic and targeted therapy options. In an Australian community cohort, PDTO predictions based on treatment-naive patients (n = 56) and response rates from first-line mCRC clinical trials achieve 83% accuracy for forecasting responses in patients receiving palliative treatments (18 patients, 29 treatments). Similar assay accuracy is achieved in a prospective study of third-line or later mCRC treatment, AGITG FORECAST-1 (n = 30 patients). "Resistant" predictions are associated with inferior progression-free survival; misclassification rates are similar by regimen. Liver metastases are the optimal site for sampling, with testing achievable within 7 weeks for 68.8% cases. Our findings indicate that PDTO drug panel testing can provide predictive information for multifarious standard-of-care therapies for mCRC.

The distinct molecular subtypes of lung cancer are defined by monogenic biomarkers, such as EGFR, KRAS, and ALK rearrangement. Tumor mutation burden (TMB) is a potential biomarker for response to immunotherapy, which is one of the measures for genomic instability. The molecular subtyping based on TMB has not been well characterized in lung adenocarcinomas in the Chinese population. Here we performed molecular subtyping based on TMB with the published whole exome sequencing data of 101 lung adenocarcinomas and compared the different features of the classified subtypes, including clinical features, somatic driver genes, and mutational signatures. We found that patients with lower TMB have a longer disease-free survival, and higher TMB is associated with smoking and aging. Analysis of somatic driver genes and mutational signatures demonstrates a significant association between somatic RYR2 mutations and the subtype with higher TMB. Molecular subtyping based on TMB is a potential prognostic marker for lung adenocarcinoma. Signature 4 and the mutation of RYR2 are highlighted in the TMB-High group. The mutation of RYR2 is a significant biomarker associated with high TMB in lung adenocarcinoma.

To deepen the understanding of tissue-resident microbiota in colorectal cancer (CRC), we analyzed whole-genome and transcriptome data from 937 patients. We identified 249 genera and 361 species commonly present in both tumors and adjacent normal tissues (NATs). Distinct microbial signatures were associated with anatomical location, tumor stages, hypermutation status, mutations in CRC driver and DNA damage repair genes, as well as consensus molecular subtypes (CMSs). Notably, the presence of the pks island and elevated abundance of Enterobacteriaceae were linked to poor prognosis specifically in CMS2 tumors. Finally, microbial risk scores derived from taxa present in tumor or NATs predicted patient prognosis independently of established clinico-molecular factors. Prognostic taxa were strongly associated with tumor transcriptomic pathways related to hypoxia, immune response, and metabolic status. These findings revealed the heterogeneity of tissue-resident microbiota and their critical role in CRC progression, highlighting potential avenues for targeted intervention.

Whole-genome bisulfite sequencing (WGBS) technology can provide comprehensive DNA methylation at a single-base resolution on a genome-wide scale, and is considered to be the gold standard for the detection of 5-methylcytosine (5 mC). However, the International Human Epigenome Consortium propose a full DNA methylome should have at least 30 fold redundant coverage of the reference genome from a single biological replicate. Therefore, it remains cost prohibitive for large-scale studies. To find a solution, the DNBSEQ-Tx sequencing was developed that can generate up to 6 Tb data in a single run for projects involving large-scale sequencing. In this study, we provided two WGBS library construction methods DNB_PREBSseq and DNB_SPLATseq optimized for the DNBSEQ-Tx sequencer, and demonstrated the performance of these two methods on the DNBSEQ-Tx platform, using the DNA extracted from four different cell lines. We also compared the sequencing data from these two WGBS library construction methods with HeLa cell line data from ENCODE sequenced on Illumina HiSeq X Ten and WGBS data of two other cell lines sequenced on HiSeq2500. Various quality control (QC) analyses such as the base quality scores, methylation-bias (m-bias), and conversion efficiency indicated that the data sequenced on the DNBSEQ-Tx platform met the WGBS-required quality controls. Meanwhile, our data closely resembled the coverage shown by the data generated by the Illumina platform. Our study showed that with our optimized methods, DNBSEQ-Tx could generate high-quality WGBS data with relatively good stability for large-scale WGBS sequencing applications. Thus, we conclude that DNBSEQ-Tx can be used for a wide range of WGBS research.

To further investigate the association between NAT2 polymorphisms and lung cancer susceptibility. In terms of phenotypes, we investigated the acetylator status of NAT2 polymorphisms associated with lung cancer risk. Additionally, in view of genotypes, we mainly analyzed 5 single nucleotide polymorphisms (SNPs) in NAT2 gene, namely C282T, A803G, C481T, G590A, and G857A. Twenty-six eligible studies were included in our meta-analysis by searching PubMed, Embase, and CNKI databases. We used odds ratios (ORs) with corresponding 95% confidence intervals (CIs) to evaluate the susceptibility to lung cancer associated with NAT2 polymorphisms. Overall, based on phenotypes, the pooled ORs showed no significant association between NAT2 polymorphisms and lung cancer susceptibility. In the subgroup analyses by ethnicity and source of control, there was still no significant association. In terms of genotypes, overall, no obvious relationship was observed between NAT2 polymorphisms and lung cancer risk. But increased risk of lung cancer was found in association with NAT2 C282T polymorphism (TT vs. CC + TC: OR = 1.58, 95% CI = 1.11–2.25). Our meta-analysis demonstrates that TT genotype in NAT2 C282T polymorphism may be a risk factor for lung cancer susceptibility. Additionally, the acetylator status of 5 SNPs in NAT2 gene may not be associated with lung cancer risk.