Evidence map›Paper›PMID 41478086›Full record

ArticleESMO open2026

Predictive potential of patient-specific immunological characteristics in solid cancers: circulating monocytes, myeloid-derived suppressor cells, T cells, and the T-cell receptor repertoire.

C Zierfuss, B Niederdorfer, B Fendl, K Syböck, J Schedl, L Kohl, G Heller, E Tomasich, J M Berger, V Sunder-Plassmann and 14 more

Abstract read
In one paragraph

Article in ESMO open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors.

C ZierfussDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
B NiederdorferDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
B FendlDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
K SyböckDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
J SchedlDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
L KohlDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
G HellerDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
E TomasichDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
J M BergerDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
V Sunder-PlassmannDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
M KleinbergerDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
L GottmannDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
M KorpanDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
A M StarzerDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
I Solano HenaoDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
J FürstDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
J WolfsbergDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
M GrohmannovaDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
N DobrovitsDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
C AyDivision of Hematology and Hemostaseology, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
N VladicDivision of Hematology and Hemostaseology, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
J FurtnerChristian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Research Center for Medical Image Analysis and Artificial Intelligence, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria.
M PreusserDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
A S BerghoffDivision of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria. Electronic address: anna.berghoff@meduniwien.ac.at.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundResponse prediction to immune checkpoint inhibitors (ICIs) relies on tumor-specific biomarkers, while patient-specific characteristics are underrepresented. Therefore, we explored patient-specific immunological characteristics, including peripheral monocytes, myeloid-derived suppressor cells (MDSCs), T cells, and the T-cell receptor (TCR) repertoire, to investigate associations with survival and therapy response. PATIENTS AND

methodsPatients with solid tumors were prospectively recruited to explore the association of absolute lymphocyte and monocyte counts, leukocyte-to-lymphocyte ratio (LLR), and monocyte-to-lymphocyte ratio (MLR) with overall survival (OS). Monocytes, MDSCs, T cells, and the TCR repertoire were characterized before therapy start (baseline) and, if available, at first radiological restaging (follow-up). We analyzed their association with radiological therapy response using current guidelines, OS, and progression-free survival (PFS).

resultsA total of 1063 patients were included. High LLR [≥3.92; hazard ratio (HR) 1.48, 95% confidence interval (CI) 1.21-1.82, P < 0.001] and high MLR (≥0.31; HR 1.43, 95% CI 1.16-1.76, P < 0.001) at baseline were associated with worse OS. In 108 patients, high non-classical monocytes (NCMs) at baseline were linked to improved PFS (≥8.94%; HR 0.41, 95% CI 0.18-0.91, P = 0.03), indicating a prognostic effect independent of therapy type. High NCMs at baseline were associated with response to chemotherapy (P = 0.03), but not to ICI therapy (P = 0.47). Longitudinal analyses revealed an increase in intermediate monocytes (IMs) among ICI responders compared with ICI non-responders (P = 0.01). IMs were unaltered in chemotherapy-treated patients (P = 0.69). The TCR repertoire, T cells, and MDSCs revealed no differences between responders and non-responders, regardless of therapy.

conclusionsIn our study, monocyte subsets were associated with survival and therapy response. These results highlight the potential of monocyte subsets to serve as patient-specific, tumor-agnostic biomarkers that may help predict therapy response.

Indexed as

MonocytesMyeloid-Derived Suppressor CellsNeoplasmsReceptors, Antigen, T-CellT-LymphocytesAdultAgedFemaleHumansMaleMiddle AgedPrognosisProspective StudiesReceptors, Antigen, T-Cellimmune checkpoint inhibitionmonocytesmyeloid-derived suppressor cellspatient-specific biomarkersolid cancerT-cell receptor repertoire

Identifiers

PMID41478086
PMCPMC12804612

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