TY - JOUR
T1 - Effect of concomitant medications with immune-modulatory properties on the outcomes of patients with advanced cancer treated with immune checkpoint inhibitors
T2 - development and validation of a novel prognostic index
AU - Buti, Sebastiano
AU - Bersanelli, Melissa
AU - Perrone, Fabiana
AU - Tiseo, Marcello
AU - Tucci, Marco
AU - Adamo, Vincenzo
AU - Stucci, Luigia S.
AU - Russo, Alessandro
AU - Tanda, Enrica T.
AU - Spagnolo, Francesco
AU - Rastelli, Francesca
AU - Pergolesi, Federica
AU - Santini, Daniele
AU - Russano, Marco
AU - Anesi, Cecilia
AU - Giusti, Raffaele
AU - Filetti, Marco
AU - Marchetti, Paolo
AU - Botticelli, Andrea
AU - Gelibter, Alain
AU - Occhipinti, Mario Alberto
AU - Ferrari, Marco
AU - Vitale, Maria Giuseppa
AU - Nicolardi, Linda
AU - Chiari, Rita
AU - Rijavec, Erika
AU - Nigro, Olga
AU - Tuzi, Alessandro
AU - De Tursi, Michele
AU - Di Marino, Pietro
AU - Conforti, Fabio
AU - Queirolo, Paola
AU - Bracarda, Sergio
AU - Macrini, Serena
AU - Gori, Stefania
AU - Zoratto, Federica
AU - Veltri, Enzo
AU - Di Cocco, Barbara
AU - Mallardo, Domenico
AU - Vitale, Maria Grazia
AU - Santoni, Matteo
AU - Patruno, Leonardo
AU - Porzio, Giampiero
AU - Ficorella, Corrado
AU - Pinato, David J.
AU - Ascierto, Paolo A.
AU - Cortellini, Alessio
N1 - Funding Information:
M.B. reports receiving research funding by Roche, Seqirus, Pfizer and Novartis and personal fees as a speaker/consultant from AstraZeneca, Novartis, Pfizer and BMS. M.T. reports receiving speaker fees and grant consultancies from AstraZeneca, Pfizer, Eli-Lilly, BMS, Novartis, Roche, MSD, Boehringer Ingelheim, Otsuka, Takeda and Pierre Fabre. A.C. reports receiving speaker fees and grant consultancies from Roche, MSD, BMS, AstraZeneca, Novartis and Astellas. R.G. reports receiving speaker fees and grant consultancies from AstraZeneca and Roche. M.G.V. reports receiving speaker fees, grant consultancies and travel support from BMS, Ipsen, Novartis, Pfizer, Astellas, Jansen and Pierre-Fabre. A.R. reports receiving grant consultancies from AstraZeneca and MSD. F.S. reports receiving speaker fees and grant consultancies from Roche, Novartis, BMS, MSD, Pierre-Fabre, Sanofi, Merck and Sunpharma. D.J.P. reports receiving lecture fees from ViiV Healthcare and Bayer Healthcare; travel expenses from BMS and Bayer Healthcare; consulting fees for Mina Therapeutics, EISAI, Roche and AstraZeneca and research funding (to institution) from MSD and BMS. P.A.A. reports receiving speaker fees and grant consultancies from BMS, Roche-Genentech, MSD, Dohme, Array, Novartis, Merck-Serono, Pierre-Fabre, Incyte, New Link Genetics, Genmab, Medimmune, AstraZeneca, Syndax, SunPharma, Sanofi, Idera, Ultimovacs, Sandoz, Immunocore, 4SC, Alkermes, Italfarmaco, Nektar and Boehringer-Ingelheim and research funds from BMS, Roche-Genentech and Array. All other authors declared no competing interests.
Funding Information:
D.J.P. is supported by grant funding from the Welcome Trust Strategic Fund ( PS3416 ) and from the NIHR Imperial Biomedical Research Centre (BRC) ITMAT Push for Impact Scheme 2019 and acknowledges infrastructural support by the Cancer Research UK Imperial Centre and the Imperial Experimental Cancer Medicine Centre (ECMC). The authors specially thank the ‘Consorzio Interuniversitario Nazionale per la Bio-Oncologia’ for support in this study.
Publisher Copyright:
© 2020 Elsevier Ltd
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2021/1
Y1 - 2021/1
N2 - Background: Concomitant medications are known to impact on clinical outcomes of patients treated with immune checkpoint inhibitors (ICIs). We aimed weighing the role of different concomitant baseline medications to create a drug-based prognostic score. Methods: We evaluated concomitant baseline medications at immunotherapy initiation for their impact on objective response rate (ORR), progression-free survival (PFS) and overall survival (OS) in a single-institution cohort of patients with advanced cancer treated with ICIs (training cohort, N = 217), and a drug-based prognostic score with the drugs resulting significantly impacting the OS was computed. Secondly, we externally validated the score in a large multicenter external cohort (n = 1012). Results: In the training cohort (n = 217), the median age was 69 years (range: 32–89), and the primary tumours were non–small-cell lung cancer (70%), melanoma (14.7%), renal cell carcinoma (9.2%) and others (6%). Among baseline medications, corticosteroids (hazard ratio [HR] = 2.3; 95% confidence interval [CI]: 1.60–3.30), systemic antibiotics (HR = 2.07; 95% CI: 1.31–3.25) and proton-pump inhibitors (PPIs) (HR = 1.57; 95% CI: 1.13–2.18) were significantly associated with OS. The prognostic score was calculated using these three drug classes, defining good, intermediate and poor prognosis patients. Within the training cohort, OS (p < 0.0001), PFS (p < 0.0001) and ORR (p = 0.0297) were significantly distinguished by the score stratification. The prognostic value of the score was also demonstrated in terms of OS (p < 0.0001), PFS (p < 0.0001) and ORR (p = 0.0006) within the external cohort. Conclusion: Cumulative exposure to corticosteroids, antibiotics and PPIs (three likely microbiota-modulating drugs) leads to progressively worse outcomes after ICI therapy. We propose a simple score that can help stratifying patients in routine practice and clinical trials of ICIs.
AB - Background: Concomitant medications are known to impact on clinical outcomes of patients treated with immune checkpoint inhibitors (ICIs). We aimed weighing the role of different concomitant baseline medications to create a drug-based prognostic score. Methods: We evaluated concomitant baseline medications at immunotherapy initiation for their impact on objective response rate (ORR), progression-free survival (PFS) and overall survival (OS) in a single-institution cohort of patients with advanced cancer treated with ICIs (training cohort, N = 217), and a drug-based prognostic score with the drugs resulting significantly impacting the OS was computed. Secondly, we externally validated the score in a large multicenter external cohort (n = 1012). Results: In the training cohort (n = 217), the median age was 69 years (range: 32–89), and the primary tumours were non–small-cell lung cancer (70%), melanoma (14.7%), renal cell carcinoma (9.2%) and others (6%). Among baseline medications, corticosteroids (hazard ratio [HR] = 2.3; 95% confidence interval [CI]: 1.60–3.30), systemic antibiotics (HR = 2.07; 95% CI: 1.31–3.25) and proton-pump inhibitors (PPIs) (HR = 1.57; 95% CI: 1.13–2.18) were significantly associated with OS. The prognostic score was calculated using these three drug classes, defining good, intermediate and poor prognosis patients. Within the training cohort, OS (p < 0.0001), PFS (p < 0.0001) and ORR (p = 0.0297) were significantly distinguished by the score stratification. The prognostic value of the score was also demonstrated in terms of OS (p < 0.0001), PFS (p < 0.0001) and ORR (p = 0.0006) within the external cohort. Conclusion: Cumulative exposure to corticosteroids, antibiotics and PPIs (three likely microbiota-modulating drugs) leads to progressively worse outcomes after ICI therapy. We propose a simple score that can help stratifying patients in routine practice and clinical trials of ICIs.
KW - Antibiotics
KW - Cancer patients
KW - Concomitant medications
KW - Corticosteroids
KW - Drugs
KW - Immune checkpoint inhibitors
KW - Immunotherapy
KW - Index
KW - Prognostic
KW - Proton-pump inhibitors
KW - Score
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U2 - 10.1016/j.ejca.2020.09.033
DO - 10.1016/j.ejca.2020.09.033
M3 - Article
AN - SCOPUS:85096157545
VL - 142
SP - 18
EP - 28
JO - European Journal of Cancer
JF - European Journal of Cancer
SN - 0959-8049
ER -