Abstract
Precision
medicines are increasingly being adopted as a treatment approach for cancer
patients. In this field, the use of targeted combination therapies can reduce treatment
side effects, enhance drug efficacy and critically, overcome drug resistance. Despite
combination therapies potentially offering significant patient benefit, relatively
few combinations of targeted drugs are in clinical development, in part due to
the challenge of discovering effective new target combinations.
Both
our team (1) and others (2) have previously used large-scale CRISPR-Cas9 screens to
prioritise novel therapeutic targets in cancer. Building on this, we have
established a next-generation dual guide vector tool to systematically knock
out tens of thousands of gene pairs and identify therapeutically relevant
combinations in cancers of unmet clinical need. Our vector system is composed
of two sgRNAs with a tRNA spacer, which allows processing by the cell’s
endogenous tRNA processing system, enabling simultaneous perturbation of two
targets.
Here,
we present ongoing collaborative efforts to validate synergies between potential
target combination candidates in two separate, but complementary workflows.
High throughput arrayed CRISPR screening, based on systematic target
prioritisation takes place in parallel to a lower throughput approach, using a more
target-focused and biomarker-driven experimentation. Both approaches utilise
orthogonal methodologies to validate target combinations emerging from pooled
CRISPR screens, and individually offer their own advantages, as well as generating
additional supporting evidence for targets nominated for drug discovery.
Our
findings demonstrate the effectiveness of this system for detecting synergistic
combinations and constitute a robust framework for performing efficient large-scale
genetic interaction screens for the identification of clinically relevant
target combinations. In addition, the use of complementary validation systems can
increase confidence in the selected target combinations, leading to improved
chances of clinical success.