Oncogene-driven non-small cell lung cancers in patients with a history of smoking lack smoking-induced mutations.

Non-small cell lung cancers (NSCLCs) in non-smokers are mostly driven by mutations in the oncogenes EGFR, ERBB2, and MET and fusions involving ALK and RET. In addition to occurring in non-smokers, alterations in these "non-smoking-related oncogenes" (NSROs) also occur in smokers. To better understand the clonal architecture and genomic landscape of NSRO-driven tumors in smokers compared to typical-smoking NSCLCs, we investigated genomic and transcriptomic alterations in 173 tumor sectors from 48 NSCLC patients. NSRO-driven NSCLCs in smokers and non-smokers had similar genomic landscapes. Surprisingly, even in patients with prominent smoking histories, the mutational signature caused by tobacco smoking was essentially absent in NSRO-driven NSCLCs, which was confirmed in two large NSCLC datasets from other geographic regions. However, NSRO-driven NSCLCs in smokers had higher transcriptomic activities related to regulation of the cell cycle. These findings suggest that, while the genomic landscape is similar between NSRO-driven NSCLC in smokers and non-smokers, smoking still affects the tumor phenotype independently of genomic alterations.

Deep whole-genome analysis of 494 hepatocellular carcinomas.

Over half of hepatocellular carcinoma (HCC) cases diagnosed worldwide are in China1-3. However, whole-genome analysis of hepatitis B virus (HBV)-associated HCC in Chinese individuals is limited4-8, with current analyses of HCC mainly from non-HBV-enriched populations9,10. Here we initiated the Chinese Liver Cancer Atlas (CLCA) project and performed deep whole-genome sequencing (average depth, 120×) of 494 HCC tumours. We identified 6 coding and 28 non-coding previously undescribed driver candidates. Five previously undescribed mutational signatures were found, including aristolochic-acid-associated indel and doublet base signatures, and a single-base-substitution signature that we termed SBS_H8. Pentanucleotide context analysis and experimental validation confirmed that SBS_H8 was distinct to the aristolochic-acid-associated SBS22. Notably, HBV integrations could take the form of extrachromosomal circular DNA, resulting in elevated copy numbers and gene expression. Our high-depth data also enabled us to characterize subclonal clustered alterations, including chromothripsis, chromoplexy and kataegis, suggesting that these catastrophic events could also occur in late stages of hepatocarcinogenesis. Pathway analysis of all classes of alterations further linked non-coding mutations to dysregulation of liver metabolism. Finally, we performed in vitro and in vivo assays to show that fibrinogen alpha chain (FGA), determined as both a candidate coding and non-coding driver, regulates HCC progression and metastasis. Our CLCA study depicts a detailed genomic landscape and evolutionary history of HCC in Chinese individuals, providing important clinical implications.

mSigHdp: hierarchical Dirichlet process mixture modeling for mutational signature discovery.

Mutational signatures are characteristic patterns of mutations caused by endogenous or exogenous mutational processes. These signatures can be discovered by analyzing mutations in large sets of samples-usually somatic mutations in tumor samples. Most programs for discovering mutational signatures are based on non-negative matrix factorization (NMF). Alternatively, signatures can be discovered using hierarchical Dirichlet process (HDP) mixture models, an approach that has been less explored. These models assign mutations to clusters and view each cluster as being generated from the signature of a particular mutational process. Here, we describe mSigHdp, an improved approach to using HDP mixture models to discover mutational signatures. We benchmarked mSigHdp and state-of-the-art NMF-based approaches on four realistic synthetic data sets. These data sets encompassed 18 cancer types. In total, they contained 3.5 × 107 single-base-substitution mutations representing 32 signatures and 6.1 × 106 small insertion and deletion mutations representing 13 signatures. For three of the four data sets, mSigHdp had the best positive predictive value for discovering mutational signatures, and for all four data sets, it had the best true positive rate. Its CPU usage was similar to that of the NMF-based approaches. Thus, mSigHdp is an important and practical addition to the set of tools available for discovering mutational signatures.

Toward clinical understanding of aristolochic acid upper-tract urothelial carcinoma.

A cluster of patients poisoned by herbal medicine in the 1990s revealed that aristolochic acid (AA) causes kidney failure and upper tract urothelial carcinoma (UTUC). Recent research demonstrated that this was not an isolated incident; on the contrary, AA exposure is widespread in East Asia. This editorial highlights research by Lu and colleagues that investigates clinical characteristics of AA and non-AA UTUCs from 90 patients in Beijing based on the AA mutational signature. The study also detected AA mutations in non-tumor tissue of AA exposed patients and showed that AA mutations can be detected in urine, which might form the basis for non-invasive tests for AA exposure.