ArticleOncology reports2020
Exploration of the personalized immune checkpoint atlas of plasma cell dyscrasias patients using high‑dimensional single‑cell analysis.
Article in Oncology reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Immune checkpoint expression patterns on T cell subsets in light-chain amyloidosis: VISTA, PD-1, and TIGIT as potential therapeutic targets.Blood science (Baltimore, Md.) · 2024Article
- Identification of potential biomarkers from amino acid transporter in the activation of hepatic stellate cells via bioinformatics.Frontiers in genetics · 2024Article
- T cell landscape and dynamics in immunoglobulin light chain amyloidosis before and after daratumumab-based therapy.Clinical and translational medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immune checkpoint blockade endows patients with unparalleled success in conquering cancer. Unfortunately, inter‑individual heterogeneity causes failure in controlling tumors in many patients. Emerging mass cytometry technology is capable of revealing a multiscale onco‑immune landscape that improves the efficacy of cancer immunotherapy. We introduced mass cytometry to determine the personalized immune checkpoint status in bone marrow and peripheral blood samples from 3 patients with multiple myeloma, amyloid light‑chain amyloidosis, and solitary bone plasmacytoma and 1 non‑hematologic malignancy patient. The expression of 18 immune regulatory receptors and ligands on 17 defined cell populations was simultaneously examined. By single‑cell analyses, we identified the T cell clusters that serve as immunosuppressive signal source and revealed integrated immune checkpoint axes of individuals, thereby providing multiple potential immunotherapeutic targets, including programmed cell death protein 1 (PD‑1), inducible co‑stimulator (ICOS), and cluster of differentiation 28 (CD28), for each patient. Distinguishing the cell populations that function as providers and receivers of the immune checkpoint signals demonstrated a distinct cross‑interaction network of immunomodulatory signals in individuals. These in‑depth personalized data demonstrate mass cytometry as a powerful innovation to discover the systematical immune status in the primary and peripheral tumor microenvironment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.