Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.

Non-canonical roles of canonical telomere binding proteins in cancers

Reactivation of telomerase is a major hallmark observed in 90% of all cancers. Yet paradoxically, enhanced telomerase activity does not correlate with telomere length and cancers often possess short telomeres; suggestive of supplementary non-canonical roles that telomerase might play in the development of cancer. Moreover, studies have shown that aberrant expression of shelterin proteins coupled with their release from shortening telomeres can further promote cancer by mechanisms independent of their telomeric role. While targeting telomerase activity appears to be an attractive therapeutic option, this approach has failed in clinical trials due to undesirable cytotoxic effects on stem cells. To circumvent this concern, an alternative strategy could be to target the molecules involved in the non-canonical functions of telomeric proteins. In this review, we will focus on emerging evidence that has demonstrated the non-canonical roles of telomeric proteins and their impact on tumorigenesis. Furthermore, we aim to address current knowledge gaps in telomeric protein functions and propose future research approaches that can be undertaken to achieve this.

Reactivation of telomerase in cancer

Activation of telomerase is a critical step in the development of about 85 % of human cancers. Levels of Tert, which encodes the reverse transcriptase subunit of telomerase, are limiting in normal somatic cells. Tert is subjected to transcriptional, post-transcriptional and epigenetic regulation, but the precise mechanism of how telomerase is re-activated in cancer cells is poorly understood. Reactivation of the Tert promoter involves multiple changes which evolve during cancer progression including mutations and chromosomal re-arrangements. Newly described non-coding mutations in the Tert promoter region of many cancer cells (19 %) in two key positions, C250T and C228T, have added another layer of complexity to telomerase reactivation. These mutations create novel consensus sequences for transcription factors which can enhance Tert expression. In this review, we will discuss gene structure and function of Tert and provide insights into the mechanisms of Tert reactivation in cancers, highlighting the contribution of recently identified Tert promoter mutations.