Abstract
Introduction
Immune checkpoint inhibitors (ICIs) became the standard of care for several solid tumors. A limited fraction of patients (pts) achieves a long-term benefit. Plasmatic and intracellular cholesterol levels have emerged as promising biomarkers. The aim of the present study was to determine whether cholesterol efflux capacity (CEC), mediated by serum transporters (ABCA1 and ABCG1) and passive diffusion (PD), impacts on clinical outcome of advanced non-small cell lung cancer (NSCLC) and metastatic renal cell carcinoma (mRCC) pts treated with ICIs.
Material and methods
We retrospectively enrolled advanced NSCLC and mRCC pts consecutively treated with ICIs between October 2013 and October 2018. CEC and cholesterol loading capacity (CLC) were assessed by well-established specific cell models. As primary endpoint, CEC, PD and CLC were correlated with overall survival (OS) while the effects of these parameters on progression-free survival (PFS) and clinical benefit (CB), defined as complete/partial response or stable disease, represented secondary endpoints.
Results
NSCLC accounted for 94.2% of 70 enrolled cases, and serum sample suitable for CEC and PD determination was available in 68. Blood cholesterol and serum ABCA1, ABCG1, PD and CLC were associated with outcomes (OS, PFS and CB) at univariate analysis. At the multivariate analysis, only PD confirmed its positive prognostic value in terms of OS, PFS and CB.
Conclusion
The favorable impact of cholesterol PD on clinical outcome might reflect its main conformation in mature HDL particles which potentially shape an inflamed context, ultimately promoting ICI efficacy. Further prospective studies are needed to support our findings and uncover targetable pathways.
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Data availability
The data presented in this study are available on request from the corresponding author. The data are not publicly available in order to protect patient’s privacy.
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Acknowledgements
Dr Alessio Cortellini acknowledges the support provided by the NIHR Imperial BRC.
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All the authors contributed to the study conception and design. Cholesterol analysis was performed by EF, AR and AR. Data collection was performed by FP and RS. Statistical analysis was performed by GM. The first draft of the manuscript was written by FP and SB, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Alessio Cortellini received speaker fees and grant consultancies by AstraZeneca, MSD, BMS, Roche, Novartis and EISAI. Luca Cantini is granted by ESMO with an ESMO Translational Research Fellowship. Any views, opinions, findings, conclusions or recommendations expressed in this material are those solely of the author(s) and do not necessarily reflect those of ESMO. Melissa Bersanelli received research funding (Institutional) from Roche S.p.A., Pfizer, Seqirus UK, Novartis, Bristol Myers Squibb (BMS), AstraZeneca, Sanofi Genzyme; honoraria as a speaker at scientific events and for advisory role (personal fees) from BMS, MSD, Novartis, AstraZeneca, Pierre Fabre, Pfizer, IPSEN; personal fees for copyright transfer from SciClone Pharmaceuticals, IPSEN, Pierre Fabre, MSD, Sanofi Genzyme. Marcello Tiseo: He received honoraria for advisory boards and/or speakers’ fee for AstraZeneca, Pfizer, Eli-Lilly, BMS, Novartis, Roche, MSD, Boehringer Ingelheim, Otsuka, Takeda, Pierre Fabre, Amgen and Merck, Sanofi. Research Grants from AstraZeneca and Boehringer Ingelheim. Sebastiano Buti: He received honoraria as a speaker at scientific events and advisory role by BMS, Pfizer, MSD, Ipsen, AstraZeneca and Novartis; he also received research funding from Novartis. The remaining authors have no conflicts of interest to declare.
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This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Area Vasta Emilia Nord protocol (Date September 21, 2021/No37649).
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262_2023_3398_MOESM1_ESM.jpg
Supplementary file1 (Supplementary Figure 1. Graphical representation (boxplot) of the values distribution of plasmatic cholesterol, serum ABCA1- and ABCG1-mediated CEC, cholesterol PD and CLC. ABCA-1 = ATP-binding cassette transporter A1; ABCG1 = ATP-binding cassette transporter G1; CEC = cholesterol efflux capacity; CPD = cholesterol passive diffusion; CLC = Cholesterol Loading Capacity. Unit of measurement: cholesterol = mg/dl; ABCA-1, ABCG-1, CEC, CPD and CLC: % value)
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Perrone, F., Favari, E., Maglietta, G. et al. The role of blood cholesterol quality in patients with advanced cancer receiving immune checkpoint inhibitors. Cancer Immunol Immunother 72, 2127–2135 (2023). https://doi.org/10.1007/s00262-023-03398-3
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DOI: https://doi.org/10.1007/s00262-023-03398-3