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Improved detection of synthetic lethal interactions in Drosophila cells using Variable Dose Analysis (VDA)

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posted on 2025-07-31, 19:42 authored by BE Housden, Z Li, C Kelley, Y Wang, Y Hu, AJ Valvezan, BD Manning, N Perrimon
Synthetic sick or synthetic lethal (SS/L) screens are a powerful way to identify candidate drug targets to specifically kill tumor cells, but this approach generally suffers from low consistency between screens. We found that many SS/L interactions involve essential genes and are therefore detectable within a limited range of knockdown efficiency. Such interactions are often missed by overly efficient RNAi reagents. We therefore developed an assay that measures viability over a range of knockdown efficiency within a cell population. This method, called Variable Dose Analysis (VDA), is highly sensitive to viability phenotypes and reproducibly detects SS/L interactions. We applied the VDA method to search for SS/L interactions with TSC1 and TSC2, the two tumor suppressors underlying tuberous sclerosis complex (TSC), and generated a SS/L network for TSC. Using this network, we identified four Food and Drug Administration-approved drugs that selectively affect viability of TSC-deficient cells, representing promising candidates for repurposing to treat TSC-related tumors.

Funding

This work was supported by NIH Grant P01CA120964; University of Pennsylvania Orphan Disease Program Grant MDBR-15-103-LAM; and Department of Defense Grant W81XWH-16-1-0127. N.P. is a Howard Hughes Medical Institute Investigator.

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This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND): https://creativecommons.org/licenses/by-nc-nd/4.0/

Notes

This is the author accepted manuscript. The final version is available from National Academy of Sciences via the DOI in this record. Data deposition: RNAi screen data are provided in Datasets S1–S6 and in the Drosophila RNAi Screening Center (DRSC) database and are publicly available at http://www.flyrnai.org/cgi-bin/RNAi_public_screen.pl?project_id=197 and http://www.flyrnai.org/cgi-bin/RNAi_public_screen.pl?project_id=210.

Journal

Proceedings of the National Academy of Sciences

Publisher

National Academy of Sciences

Language

en

Citation

Published online 28 November 2017

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