The concept of synthetic lethality (SL) has made a pivotal impact for the development of the anti-cancer drug olaparib, the first approved agent targeting the DNA Damage Response (DDR). Typically, SL is described as the interaction of two genes, whereby simultaneous inactivation of both genes results in cell death whereas loss of one gene can be tolerated. Given the importance of SL to develop highly selective anti-cancer therapeutics, large efforts have been undertaken to identify these interactions, both experimentally and computationally.

Here, we present a novel computational framework harnessing large-scale cell line gene inactivation screens (DepMap, Project Score), as well as patient data (TCGA), to discover known and novel SL gene pairs. Overall, we implemented six statistical tests considering gene dependency scores, genomic profiles, gene expression and patient survival as parameters. We further utilized data from public drug screening consortia to validate our top-ranking pairs.

We applied our framework to a defined target space covering genes relating to DDR, chromatin binding, cell cycle and druggable genes (overall > 2.5 M. pairs were tested). When focusing on SL partners for three DDR genes with promising inhibitors in early clinical development: ATM, ATR and DNA-PK; we noticed that chromatin modifiers were enriched in the top ranking pairs. In particular, SL interactions were predicted with histone (de)methylases, histone (de)acetyltransferases and members of SWI/SNF family.

For instance, we observed that loss of function mutations in several members of the KMT2 family, also known as MLL family, would render cancers cells more dependent on ATR or ATM. Similarly, drug sensitivity was increased for selected DDR inhibitors in cell lines with KMT2 mutations. Members of the KMT2 family play essential roles in transcription, but have recently also been shown to be recruited to DNA damage sites and may be mediators for PARP inhibitor sensitivity. The KMT2 family is frequently mutated in several cancers types such as endometrial and bladder, thus underpinning their suitability as potential selection biomarkers as well as relevance to cancer.

Furthermore, we observed that patients with mutations in EP300, a histone lysine acetyl transferase, exhibited an increase in ATR expression which may comprise a compensatory mechanism. In addition, patients with ATR and EP300 double mutation tend to have a better probability of survival, suggesting decreased tumor fitness.

In summary, we present a modular framework to predict SL gene pairs to a defined target space. Here, we focused on discovering novel SL relationships for the key DDR regulators ATM, ATR and DNA-PK. Our results provide not only new biomarker hypotheses for further validation, but also suggest that cancers with a high mutation rate in chromatin modifying genes may be efficiently targeted by DDRi.

Citation Format: Anna M. Coenen-Stass, Magda Markowska, Magdalena Budzinska-Zaniewska, Krzysztof Kolmus, Ewa Szczurek, Eike Staub. A novel computational framework predicts synthetic lethal interactions between key regulators of the DNA damage response and chromatin modifiers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1918.