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Background <p>Targeted therapies have become mainstream anticancer treatments, although their efficacy is limited by low response rates, drug resistance, and therapy-related toxicities. Drug combinations have the potential to overcome these challenges. However, identifying effective drug combinations is a major challenge.</p> Methods <p>Focusing on melanoma, we systematically interrogated drug-drug interactions to identify synergistic and antagonistic drug combinations. We screened 23 melanoma and 2 melanocyte cell lines with a library of 61 drugs belonging to 11 different drug classes.</p> Results <p>We have identified multiple antagonistic and synergistic drug interactions. For instance, the CDK inhibitor abemaciclib strongly reduces the antiproliferative effect of nucleosides. Interestingly, this antagonistic interaction can be converted into a potent synergistic interaction by modifying the sequence of treatment; treating cells first with abemaciclib followed by nucleoside treatment results in antagonism, whereas pretreatment with nucleoside analogs followed by abemaciclib results in strong synergy. Furthermore, strong synergy is observed between combinations of Chk1 inhibitors with nucleoside analogs, especially in cell lines with defects in DNA damage response pathways, such as mutations in <i>TP53</i> or <i>CDKN2A</i>. A combination of low doses of a Chk1 inhibitor and a nucleoside analog also synergizes in a mouse model of melanoma using female mice and significantly reduces tumor growth without noticeable toxic side effects.</p> Conclusions <p>Taken together, our study underscores the importance of careful design of treatment sequence algorithms and identifies actionable drug-drug interactions for treatment of melanoma with translational potential.</p>

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Systematic interrogation of drug sensitivities in melanoma reveals potent synergistic and antagonistic drug combinations with translational potential

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月30日

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  • Pilar Ayuda-Durán,
  • Robert Hanes,
  • Leiv Rønneberg,
  • Tine Norman Alver,
  • Geir Frode Øy,
  • Sissel Hauge,
  • Aram N. Andersen,
  • Eivind Valen Egeland,
  • Siri Juell,
  • Sigve Nakken,
  • Theophilus Quachie Asenso,
  • Manuela Zucknick,
  • Randi G. Syljuåsen,
  • Gunhild M. Mælandsmo,
  • Eivind Hovig,
  • Jorrit M. Enserink

摘要

Background

Targeted therapies have become mainstream anticancer treatments, although their efficacy is limited by low response rates, drug resistance, and therapy-related toxicities. Drug combinations have the potential to overcome these challenges. However, identifying effective drug combinations is a major challenge.

Methods

Focusing on melanoma, we systematically interrogated drug-drug interactions to identify synergistic and antagonistic drug combinations. We screened 23 melanoma and 2 melanocyte cell lines with a library of 61 drugs belonging to 11 different drug classes.

Results

We have identified multiple antagonistic and synergistic drug interactions. For instance, the CDK inhibitor abemaciclib strongly reduces the antiproliferative effect of nucleosides. Interestingly, this antagonistic interaction can be converted into a potent synergistic interaction by modifying the sequence of treatment; treating cells first with abemaciclib followed by nucleoside treatment results in antagonism, whereas pretreatment with nucleoside analogs followed by abemaciclib results in strong synergy. Furthermore, strong synergy is observed between combinations of Chk1 inhibitors with nucleoside analogs, especially in cell lines with defects in DNA damage response pathways, such as mutations in TP53 or CDKN2A. A combination of low doses of a Chk1 inhibitor and a nucleoside analog also synergizes in a mouse model of melanoma using female mice and significantly reduces tumor growth without noticeable toxic side effects.

Conclusions

Taken together, our study underscores the importance of careful design of treatment sequence algorithms and identifies actionable drug-drug interactions for treatment of melanoma with translational potential.