Tae‐Min Kim

Microsatellite instability (MSI) refers to the hypermutability of short repetitive sequences in the genome caused by impaired DNA mismatch repair. Although MSI has been studied for decades, large amounts of sequencing data now available allows us to examine the molecular fingerprints of MSI in greater detail. Here, we analyse ∼8,000 exomes and ∼1,000 whole genomes of cancer patients across 23 cancer types. Our analysis reveals that the frequency of MSI events is highly variable within and across tumour types. We also identify genes in DNA repair and oncogenic pathways recurrently subject to MSI and uncover non-coding loci that frequently display MSI. Finally, we propose a highly accurate exome-based predictive model for the MSI phenotype. These results advance our understanding of the genomic drivers and consequences of MSI, and our comprehensive catalogue of tumour-type-specific MSI loci will enable panel-based MSI testing to identify patients who are likely to benefit from immunotherapy.

Purpose Histologic transformation of EGFR mutant lung adenocarcinoma (LADC) into small-cell lung cancer (SCLC) has been described as one of the major resistant mechanisms for epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). However, the molecular pathogenesis is still unclear. Methods We investigated 21 patients with advanced EGFR-mutant LADCs that were transformed into EGFR TKI-resistant SCLCs. Among them, whole genome sequencing was applied for nine tumors acquired at various time points from four patients to reconstruct their clonal evolutionary history and to detect genetic predictors for small-cell transformation. The findings were validated by immunohistochemistry in 210 lung cancer tissues. Results We identified that EGFR TKI-resistant LADCs and SCLCs share a common clonal origin and undergo branched evolutionary trajectories. The clonal divergence of SCLC ancestors from the LADC cells occurred before the first EGFR TKI treatments, and the complete inactivation of both RB1 and TP53 were observed from the early LADC stages in sequenced tumors. We extended the findings by immunohistochemistry in the early-stage LADC tissues of 75 patients treated with EGFR TKIs; inactivation of both Rb and p53 was strikingly more frequent in the small-cell-transformed group than in the nontransformed group (82% v 3%; odds ratio, 131; 95% CI, 19.9 to 859). Among patients registered in a predefined cohort (n = 65), an EGFR mutant LADC that harbored completely inactivated Rb and p53 had a 43× greater risk of small-cell transformation (relative risk, 42.8; 95% CI, 5.88 to 311). Branch-specific mutational signature analysis revealed that apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like (APOBEC)-induced hypermutation was frequent in the branches toward small-cell transformation. Conclusion EGFR TKI-resistant SCLCs are branched out early from the LADC clones that harbor completely inactivated RB1 and TP53. The evaluation of RB1 and TP53 status in EGFR TKI-treated LADCs is informative in predicting small-cell transformation.

DNA copy number variations (CNVs) play an important role in the pathogenesis and progression of cancer and confer susceptibility to a variety of human disorders. Array comparative genomic hybridization has been used widely to identify CNVs genome wide, but the next-generation sequencing technology provides an opportunity to characterize CNVs genome wide with unprecedented resolution. In this study, we developed an algorithm to detect CNVs from whole-genome sequencing data and applied it to a newly sequenced glioblastoma genome with a matched control. This read-depth algorithm, called BIC-seq, can accurately and efficiently identify CNVs via minimizing the Bayesian information criterion. Using BIC-seq, we identified hundreds of CNVs as small as 40 bp in the cancer genome sequenced at 10× coverage, whereas we could only detect large CNVs (> 15 kb) in the array comparative genomic hybridization profiles for the same genome. Eighty percent (14/16) of the small variants tested (110 bp to 14 kb) were experimentally validated by quantitative PCR, demonstrating high sensitivity and true positive rate of the algorithm. We also extended the algorithm to detect recurrent CNVs in multiple samples as well as deriving error bars for breakpoints using a Gibbs sampling approach. We propose this statistical approach as a principled yet practical and efficient method to estimate CNVs in whole-genome sequencing data.

MicroRNAs (miRNAs) are endogenous noncoding RNA molecules that are involved in post-transcriptional gene silencing. Using global miRNA expression profiling, we found miR-21, -155, and 18a to be highly upregulated in rat kidneys following tubular injury induced by ischemia/reperfusion (I/R) or gentamicin administration. Mir-21 and -155 also showed decreased expression patterns in blood and urinary supernatants in both models of kidney injury. Furthermore, urinary levels of miR-21 increased 1.2-fold in patients with clinical diagnosis of acute kidney injury (AKI) (n = 22) as compared with healthy volunteers (n = 25) (p < 0.05), and miR-155 decreased 1.5-fold in patients with AKI (p < 0.01). We identified 29 messenger RNA core targets of these 3 miRNAs using the context likelihood of relatedness algorithm and found these predicted gene targets to be highly enriched for genes associated with apoptosis or cell proliferation. Taken together, these results suggest that miRNA-21 and -155 could potentially serve as translational biomarkers for detection of AKI and may play a critical role in the pathogenesis of kidney injury and tissue repair process.