Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens

Sumana Srivatsa; Hesam Montazeri; Gaia Bianco; Mairene Coto-Llerena; Mattia Marinucci; Charlotte K Y Ng; Salvatore Piscuoglio; Niko Beerenwinkel

doi:10.1038/s41467-022-35378-z

Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens

Nat Commun. 2022 Dec 14;13(1):7748. doi: 10.1038/s41467-022-35378-z.

Authors

Sumana Srivatsa^#^{1

2}, Hesam Montazeri^#³, Gaia Bianco^#⁴, Mairene Coto-Llerena^{4

5}, Mattia Marinucci⁴, Charlotte K Y Ng^{2

6}, Salvatore Piscuoglio^{7

8}, Niko Beerenwinkel^{9

10}

Affiliations

¹ Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland.
² SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
³ Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
⁴ Visceral Surgery and Precision Medicine Research Laboratory, Department of Biomedicine, University of Basel, 4031, Basel, Switzerland.
⁵ Institute of Medical Genetics and Pathology, University Hospital Basel, 4031, Basel, Switzerland.
⁶ Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland.
⁷ Visceral Surgery and Precision Medicine Research Laboratory, Department of Biomedicine, University of Basel, 4031, Basel, Switzerland. [email protected].
⁸ Institute of Medical Genetics and Pathology, University Hospital Basel, 4031, Basel, Switzerland. [email protected].
⁹ Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland. [email protected].
¹⁰ SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland. [email protected].

^# Contributed equally.

Abstract

The development of cancer therapies is limited by the availability of suitable drug targets. Potential candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. Here, we present SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We apply SLIdR to Project DRIVE data and find both established and potential pan-cancer and cancer type-specific SL pairs consistent with findings from literature and drug response screening data. We experimentally validate two predicted SL interactions (ARID1A-TEAD1 and AXIN1-URI1) in hepatocellular carcinoma, thus corroborating the ability of SLIdR to identify potential drug targets.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Cell Line, Tumor
Humans
Neoplasms* / drug therapy
Neoplasms* / genetics
Synthetic Lethal Mutations*