One of the greatest challenges today is to select the right drug for the right patient at the right time. A lack of diagnostic tools for predicting therapy response currently hampers patient-tailored treatment decisions in the clinic with severe implications for economy and – most importantly – patient survival.
Profiling transcriptional responses to drug treatment is a key method for probing the activity of targeted therapeutics and guiding their use in the clinic. A major limitation of established gene expression profiling techniques (such as microarrays and RNA-seq) is their limited time resolution precluding the distinction of direct from secondary transcriptional responses to drug therapy. This hampers their utility for deciphering drug action and guiding patient-tailored treatment decisions.
To overcome this problem, as part of an ERC-StG project (Systematic in-vivo analysis of chromatin-associated targets in leukemia, 336860) I have co-developed thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAMseq) as a rapid, robust and highly scalable method for the unbiased quantification of changes in mRNA production upon cell perturbations. Its unique features (low input cell numbers, short treatment-to-sample time, 1-day protocol library preparation in 96-well format) may qualify SLAMseq as the first method to probe the function, efficacy and selectivity of candidate therapeutics in primary (tumor) cells with unprecedented precision and on a large scale.
In this ERC-PoC project, I propose to establish technical and commercial proof-of-concept for SLAMseq’s application in preclinical and clinical drug development and optimization, and for patient-tailored treatment selection. Upon the successful proof-of-concept for SLAMseq’s application as diagnostic tool, SLAM-Dx has the potential to revolutionize translational research and personalised medicine in a variety of disease areas.
Call for proposal
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