Simple and cost-effective cancer diagnosis in liquid biopsy through native tRNA sequencing

Transfer RNAs (tRNAs) are abundant, heavily-modified small non-coding RNAs that play a pivotal role in decoding genetic information, determining which transcripts are highly and poorly translated at a given moment.

Dysregulation of tRNA abundances and tRNA modifications is a well-known feature in cancer cells, leading to enhanced translation of specific oncogenic proteins.

Despite the well-established association between tRNA dysregulation and cancer progression and malignancy, tRNA abundances and their modifications are still not being used as diagnostic or prognostic markers for cancer detection or progression, mainly due to the lack of a simple, unbiased and cost-effective method to quantify tRNA abundances and their modifications.

Our laboratory has recently established Nano-tRNAseq, a pioneering method that can accurately quantify both tRNA abundances and tRNA modifications using native tRNA nanopore sequencing, for a fraction of the cost of next-generation sequencing-based approaches.

By employing a proprietary Nano-tRNAseq nanopore library preparation protocol coupled with deep learning algorithms that will rapidly classify the nanopore current signals, here we aim to establish a method that exploits tRNAs as novel biomarkers through the use of third generation sequencing technologies.

We have initial evidence of how our approach can separate cancer from non-cancer samples and possibly be used to identify the tissue of origin.

In this project, we will fully validate our technological platform for the differential analysis of low-input RNA amounts in liquid biopsy samples.

We will initially apply our method in the cancer arena, expandable to many other indications, following the advice of key stakeholders contacted so far, with the goal of laying the basis for the transfer of our technology to the marketplace, including through the creation of a new start-up company.