Cancer remains a leading cause of mortality worldwide, posing a significant challenge to human health. Despite remarkable advances in cancer therapy over recent decades, therapeutic resistance remains a persistent obstacle, often resulting in poor patient outcomes (Holohan et al., 2013). Cancer cells are adept at adapting to therapeutic pressure, employing a range of genetic and non-genetic mechanisms to survive (Russo, 2024). These adaptations, while enhancing resistance, often create dependencies on specific pathways or cellular states, offering potential therapeutic targets. For instance, hyperactivation of epidermal growth factor receptor (EGFR) signaling has been strongly linked to cisplatin resistance (Oh et al., 2023). Research revealed that the transcription factor NANOG upregulated the transient receptor potential vanilloid-1 (TRPV1) channel, activating the EGFR-AKT signaling pathway and enhancing cancer cell survival (Oh et al., 2023). Notably, combining cisplatin with the TRPV1 inhibitor AMG9810 demonstrated enhanced anti-cancer effects compared to cisplatin alone in both cell and animal models (Oh et al., 2023). This highlights the potential of rational drug combinations in overcoming therapy resistance. However, the success of combination therapies hinges on a deep understanding of the mechanisms driving drug resistance and extensive experimental validation, both of which present considerable challenges in drug development. Therefore, more efficient strategies are needed to accelerate the discovery of effective drug combinations that can overcome resistance and improve clinical outcomes.

Synthetic lethality (SL) refers to a condition in which the simultaneous disruption of two or more genes leads to cell death, whereas the defect of either gene alone is tolerated (Huang et al., 2020) (Fig. 1). This concept has emerged as a promising approach for addressing drug-resistant mutations, offering substantial therapeutic benefits over traditional inhibitor-based therapies (Fong et al., 2009, Tutt et al., 2021). In cancer cells, mutations that promote survival may create new dependencies on specific pathways (O'Neil et al., 2017). By exploiting the vulnerabilities introduced by specific mutations in cancer cells, SL enables the precise targeting of malignant cells while minimizing harm to normal tissue (Bryant et al., 2005, Engstrom et al., 2023). The clinical success of poly (ADP-ribose) polymerase (PARP) inhibitors (PARPis) against susceptibility-deficient breast cancer (BRCA) has established SL as a viable and effective treatment paradigm (Singh et al., 2024) (Fig. 1). Similarly, the compensatory survival mechanisms in drug-resistant cancer cells can be leveraged as therapeutic vulnerabilities, making them ideal targets for SL-based strategy. Genome-wide screening methods have been employed to uncover vulnerability genes associated with treatment resistance, aiming to enhance therapeutic efficacy (Chen et al., 2022, Pan et al., 2017, Shu et al., 2020, Vella et al., 2024). For example, a recent preclinical study utilized SL screening in lenvatinib-resistant cells with a CRISPR-Cas9 human genome library, uncovering an SL relationship between the EGFR inhibitor gefitinib and lenvatinib in patients with high EGFR expression, successfully overcoming lenvatinib resistance and reducing toxicity to normal cells, highlighting the distinct advantages of SL-based therapies (Jin et al., 2021). Despite these promising findings, challenges remain in accurately identifying and validating potential SL gene or drug partners across diverse genetic contexts. Furthermore, determining the subset of drug-resistant patients most likely to benefit from SL-based treatments requires further refinement.

This review explores the key mechanisms driving cancer therapy resistance and examines current methodologies for SL screening and application, emphasizing their unique advantages. It also compiles preclinical evidence supporting the role of SL in overcoming cancer therapy resistance, offering insights into its therapeutic potential.