Characterization of human telomerase reverse transcriptase promoter methylation and transcription factor binding in differentiated thyroid cancer cell lines.

Telomerase reverse transcriptase (TERT) activation plays an important role in cancer development by enabling the immortalization of cells. TERT regulation is multifaceted, and its promoter methylation has been implicated in controlling expression through alteration in transcription factor binding. We have characterized TERT promoter methylation, transcription factor binding, and TERT expression levels in five differentiated thyroid cancer (DTC) cell lines and six normal thyroid tissue samples by targeted bisulfite sequencing, ChIP-qPCR, and qRT-PCR. DTC cell lines express varying levels of TERT and exhibit TERT promoter methylation patterns similar to patterns seen in other telomerase positive cancer cell lines. The minimal promoter immediately surrounding the transcription start site is hypomethylated, while further upstream portions show dense methylation. In contrast, the TERT promoter in normal thyroid tissue is largely unmethylated throughout and expresses TERT minimally. Transcription factor binding is also affected by TERT mutation status. The E-twenty-six (ETS) factor GABPA exhibits TERT binding in the TERT mutant DTC cells only, and allele-specific methylation patterns at the minimal promoter were observed as well, which may indicate allele-specific factor recruitment at the minimal promoter. Furthermore, we identified binding sites for activators MYC and GSC in the hypermethylated upstream region, pointing to its possible importance in TERT regulation. Overall, TERT expression and telomerase activity depend on the interplay of multiple regulatory mechanisms including TERT promoter methylation, mutation status, and recruitment of transcription factors. This work explores of the interplay between these regulatory mechanisms and offers insight into cellular control of active telomerase in human cancer.

Detecting DNA cytosine methylation using nanopore sequencing.

In nanopore sequencing devices, electrolytic current signals are sensitive to base modifications, such as 5-methylcytosine (5-mC). Here we quantified the strength of this effect for the Oxford Nanopore Technologies MinION sequencer. By using synthetically methylated DNA, we were able to train a hidden Markov model to distinguish 5-mC from unmethylated cytosine. We applied our method to sequence the methylome of human DNA, without requiring special steps for library preparation.

Nanopore sequencing detects structural variants in cancer.

Despite advances in sequencing, structural variants (SVs) remain difficult to reliably detect due to the short read length (<300 bp) of 2nd generation sequencing. Not only do the reads (or paired-end reads) need to straddle a breakpoint, but repetitive elements often lead to ambiguities in the alignment of short reads. We propose to use the long-reads (up to 20 kb) possible with 3rd generation sequencing, specifically nanopore sequencing on the MinION. Nanopore sequencing relies on a similar concept to a Coulter counter, reading the DNA sequence from the change in electrical current resulting from a DNA strand being forced through a nanometer-sized pore embedded in a membrane. Though nanopore sequencing currently has a relatively high mismatch rate that precludes base substitution and small frameshift mutation detection, its accuracy is sufficient for SV detection because of its long reads. In fact, long reads in some cases may improve SV detection efficiency. We have tested nanopore sequencing to detect a series of well-characterized SVs, including large deletions, inversions, and translocations that inactivate the CDKN2A/p16 and SMAD4/DPC4 tumor suppressor genes in pancreatic cancer. Using PCR amplicon mixes, we have demonstrated that nanopore sequencing can detect large deletions, translocations and inversions at dilutions as low as 1:100, with as few as 500 reads per sample. Given the speed, small footprint, and low capital cost, nanopore sequencing could become the ideal tool for the low-level detection of cancer-associated SVs needed for molecular relapse, early detection, or therapeutic monitoring.