Predicting anti-PD-1 responders in malignant melanoma from the frequency of S100A9+ monocytes in the blood.

Soudabeh Rad Pour, Yago Pico de Coaña, Xavier Martinez Demorentin, Jeroen Melief, Manjula Thimma, Maria Wolodarski, David Gomez-Cabrero, Johan Hansson, Rolf Kiessling, Jesper Tegner

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


BackgroundWhile programmed cell death receptor 1 (PD-1) blockade treatment has revolutionized treatment of patients with melanoma, clinical outcomes are highly variable, and only a fraction of patients show durable responses. Therefore, there is a clear need for predictive biomarkers to select patients who will benefit from the treatment.MethodTo identify potential predictive markers for response to PD-1 checkpoint blockade immunotherapy, we conducted single-cell RNA sequencing analyses of peripheral blood mononuclear cells (PBMC) (n=8), as well as an in-depth immune monitoring study (n=20) by flow cytometry in patients with advanced melanoma undergoing treatment with nivolumab at Karolinska University Hospital. Blood samples were collected before the start of treatment and at the time of the second dose.ResultsUnbiased single-cell RNA sequencing of PBMC in patients with melanoma uncovered that a higher frequency of monocytes and a lower ratio of CD4+ T cells to monocyte were inversely associated with overall survival. Similarly, S100A9 expression in the monocytic subset was correlated inversely with overall survival. These results were confirmed by a flow cytometry-based analysis in an independent patient cohort.ConclusionOur results suggest that monocytic cell populations can critically determine the outcome of PD-1 blockade, particularly the subset expressing S100A9, which should be further explored as a possible predictive biomarker. Detailed knowledge of the biological role of S100A9+ monocytes is of high translational relevance.
Original languageEnglish (US)
Pages (from-to)e002171
JournalJournal for ImmunoTherapy of Cancer
Issue number5
StatePublished - May 8 2021

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KAUST Repository Item: Exported on 2021-05-11


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