Gillman, Rhys, Schmitz, Ulf, Field, Matt A., and Hebbard, Lionel (2025) TARGET-SL: precision essential gene prediction using driver prioritisation and synthetic lethality. Briefings in Bioinformatics, 26 (3). bbaf255.

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Abstract

The ability to identify patient-specific vulnerabilities to guide cancer treatments is a vital area of research. However, predictive bioinformatics tools are difficult to translate into clinical applications due to a lack of in vitro and in vivo validation. While the increasing number of personalised driver prioritisation algorithms (PDPAs) report powerful patient-specific information, the results do not easily translate into treatment strategies. Critical in addressing this gap is the ability to meaningfully benchmark and validate PDPA predictions. To address this, we developed Tumour-specific Algorithm for Ranking GEnetic Targets via Synthetic Lethality (TARGET-SL), which utilises PDPA predictions to produce a ranked list of predicted essential genes that can be validated in vitro and in vivo. This framework employs a novel strategy to benchmark PDPAs, by comparing predictions with ground truth gene essentiality data from large-scale CRISPR-knockout and drug sensitivity screens. Importantly TARGET-SL identifies vulnerabilities that are more exclusive to individual tumours than predictions based on canonical driver genes. We further find that TARGET-SL is better at identifying sample-specific vulnerabilities than other similar tools.

Item ID:	86355
Item Type:	Article (Research - C1)
ISSN:	1477-4054
Copyright Information:	© The Author(s) 2025. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints.
Funders:	National Health and Medical Research Council of Australia (NHMRC)
Projects and Grants:	NHMRC #5121190, NHMRC #1196405
Date Deposited:	24 Jul 2025 00:36
FoR Codes:	32 BIOMEDICAL AND CLINICAL SCIENCES > 3211 Oncology and carcinogenesis > 321103 Cancer genetics @ 100%
SEO Codes:	20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions @ 100%
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