Abstract 28P
Background
Immune checkpoint inhibitors, notably anti-PD1, have transformed melanoma therapy, yet resistance and low response rates persist as challenges. This study aimed to identify actionable biomarkers of resistance to anti-PD1 treatment and assess the efficacy of a selected target using an in vivo model.
Methods
We conducted receiver operating characteristic (ROC) analysis on a database of 1,434 samples to identify resistance-associated genes. Genes with higher expression linked to shorter survival were prioritized. In vivo studies utilized C57BL/6J mice inoculated with immunologically cold B16-F10 and YUMM1.7 melanoma cell lines. We evaluated the synergistic impact of anti-PD1 therapy and yes-associated protein 1 (YAP1) inhibition using non-photoactivated Verteporfin. Tumor volume was measured at predetermined cutoff points and normalized to body weight.
Results
YAP1 emerged as the top druggable candidate overexpressed in non-responder patients to anti-PD1 therapy (ROC AUC=0.699, FC=1.8, Mann-Whitney p=1.1E-08). In mice with YUMM1.7 melanoma, the combination of Verteporfin and mouse anti-PD1 significantly reduced tumor size compared to anti-PD1 monotherapy (p=0.008) or control (p=0.021). Verteporfin alone (p=0.425) or anti-PD1 alone (p=0.971) did not inhibit tumor growth compared to control. No significant difference was observed between cohorts in B16-F10 inoculated mice. We set up an online platform enabling the independent analysis of the 1,434 tumor tissue samples (www.rocplot.com).
Conclusions
Non-photoactivated Verteporfin, in combination with anti-PD1 treatment, showed promising cytoreductive potential in treating skin cutaneous melanoma in C57BL6/J syngeneic mice. Our analysis pipeline provides a framework for identifying future therapeutic strategies to enhance immunotherapy efficacy.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
This project was supported by National Research, Development and Innovation Office (PharmaLab, RRF-2.3.1-21-2022-00015). The work was supported by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 739593 and by a Momentum Research Grant from the Hungarian Academy of Sciences (LP-2021-14 to ZVV). Project no. RRF-2.3.1-21-2022-00003 has been implemented with the support provided by the European Union. The study was prepared with the professional support of the New National Excellence Program (ÚNKP-23-4-I-SE-5), and the Doctoral Student Scholarship Program of the Cooperative Doctoral Program (KDP-14-3/PALY-2021) of the Ministry of Innovation and Technology financed by the National Research, Development, and Innovation Fund.
Disclosure
All authors have declared no conflicts of interest.
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