The study sought to identify genetic aberrations driving oral squamous cell carcinoma (OSCC) development among users of shammah, an Arabian preparation of smokeless tobacco. Twenty archival OSCC samples, 15 of which with a history of shammah exposure, were whole-exome sequenced at an average depth of 127×. Somatic mutations were identified using a novel, matched controls-independent filtration algorithm. CODEX and Exomedepth coupled with a novel, Database of Genomic Variant-based filter were employed to call somatic gene-copy number variations. Significantly mutated genes were identified with Oncodrive FM and the Youn and Simon’s method. Candidate driver genes were nominated based on Gene Set Enrichment Analysis. The observed mutational spectrum was similar to that reported by the TCGA project. In addition to confirming known genes of OSCC (TP53, CDKNA2, CASP8, PIK3CA, HRAS, FAT1, TP63, CCND1 and FADD) the analysis identified several candidate novel driver events including mutations of NOTCH3, CSMD3, CRB1, CLTCL1, OSMR and TRPM2, amplification of the proto-oncogenes FOSL1, RELA, TRAF6, MDM2, FRS2 and BAG1, and deletion of the recently described tumor suppressor SMARCC1. Analysis also revealed significantly altered pathways not previously implicated in OSCC including Oncostatin-M signalling pathway, AP-1 and C-MYB transcription networks and endocytosis. There was a trend for higher number of mutations, amplifications and driver events in samples with history of shammah exposure particularly those that tested EBV positive, suggesting an interaction between tobacco exposure and EBV. The work provides further evidence for the genetic heterogeneity of oral cancer and suggests shammah-associated OSCC is characterized by extensive amplification of oncogenes.

A novel coronavirus, SARS‐CoV‐2, has caused over 274 million cases and over 5.3 million deaths worldwide since it occurred in December 2019 in Wuhan, China. Here we conceptualized the temporospatial evolutionary and expansion dynamics of SARS‐CoV‐2 by taking a series of the cross‐sectional view of viral genomes from early outbreak in January 2020 in Wuhan to the early phase of global ignition in early April, and finally to the subsequent global expansion by late December 2020. Based on the phylogenetic analysis of the early patients in Wuhan, Wuhan/WH04/2020 is supposed to be a more appropriate reference genome of SARS‐CoV‐2, instead of the first sequenced genome Wuhan‐Hu‐1. By scrutinizing the cases from the very early outbreak, we found a viral genotype from the Seafood Market in Wuhan featured with two concurrent mutations (i.e., M type) had become the overwhelmingly dominant genotype (95.3%) of the pandemic 1 year later. By analyzing 4013 SARS‐CoV‐2 genomes from different continents by early April, we were able to interrogate the viral genomic composition dynamics of the initial phase of global ignition over a time span of 14 weeks. Eleven major viral genotypes with unique geographic distributions were also identified. WE1 type, a descendant of M and predominantly witnessed in western Europe, consisted of half of all the cases (50.2%) at the time. The mutations of major genotypes at the same hierarchical level were mutually exclusive, which implies that various genotypes bearing the specific mutations were propagated during human‐to‐human transmission, not by accumulating hot‐spot mutations during the replication of individual viral genomes. As the pandemic was unfolding, we also used the same approach to analyze 261 323 SARS‐CoV‐2 genomes from the world since the outbreak in Wuhan (i.e., including all the publicly available viral genomes) to recapitulate our findings over 1‐year time span. By December 25, 2020, 95.3% of global cases were M type and 93.0% of M‐type cases were WE1. In fact, at present all the five variants of concern (VOC) are the descendants of WE1 type. This study demonstrates that viral genotypes can be utilized as molecular barcodes in combination with epidemiologic data to monitor the spreading routes of the pandemic and evaluate the effectiveness of control measures. Moreover, the dynamics of viral mutational spectrum in the study may help the early identification of new strains in patients to reduce further spread of infection, guide the development of molecular diagnosis and vaccines against COVID‐19, and help assess their accuracy and efficacy in real world at real time.

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