Evidence map›Paper›PMID 38348048›Full record

ArticleFrontiers in immunology2024

Flow cytometry quantification of tumor-infiltrating lymphocytes to predict the survival of patients with diffuse large B-cell lymphoma.

Tiantian Yu, Zijun Y Xu-Monette, Anand Lagoo, Wen Shuai, Bangchen Wang, Jadee Neff, Luis F Carrillo, Eric D Carlsen, Sergio Pina-Oviedo, Ken H Young

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
4.2field-weighted citation impact, top 6% of its field
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

13 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Review
  12. Review
  13. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 4 institutions in 1 country.

Tiantian Yu *Hematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States.
Zijun Y Xu-Monette *Hematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States.
Anand LagooHematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States.
Wen ShuaiHematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States.
Bangchen WangDepartment of Pathology, Duke University Medical Center, Durham, NC, United States.
Jadee NeffHematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States.
Luis F CarrilloHematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States.
Eric D CarlsenHematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States.
Sergio Pina-OviedoHematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States.
Ken H YoungHematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States.
Duke Medical Center · USDuke Cancer InstituteDuke University · USDuke University Hospital · US

Funding

Genetic and Epigenetic Biomarkers for B-cell LymphomaR01CA233490 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI YOUNG, KEN H. · 2019 to 2023
$1.8M
NCI NIH HHS R01 CA233490
6 · The paper itself

Abstract

Introduction: Our previous studies have demonstrated that tumor-infiltrating lymphocytes (TILs), including normal B cells, T cells, and natural killer (NK) cells, in diffuse large B-cell lymphoma (DLBCL) have a significantly favorable impact on the clinical outcomes of patients treated with standard chemoimmunotherapy. In this study, to gain a full overview of the tumor immune microenvironment (TIME), we assembled a flow cytometry cohort of 102 patients diagnosed with DLBCL at the Duke University Medical Center. Methods: We collected diagnostic flow cytometry data, including the proportion of T cells, abnormal B cells, normal B cells, plasma cells, NK cells, monocytes, and granulocytes in fresh biopsy tissues at clinical presentation, and analyzed the correlations with patient survival and between different cell populations. Results: We found that low T cell percentages in all viable cells and low ratios of T cells to abnormal B cells correlated with significantly poorer survival, whereas higher percentages of normal B cells among total B cells (or high ratios of normal B cells to abnormal B cells) and high percentages of NK cells among all viable cells correlated with significantly better survival in patients with DLBCL. After excluding a small number of patients with low T cell percentages, the normal B cell percentage among all B cells, but not T cell percentage among all cells, continued to show a remarkable prognostic effect. Data showed significant positive correlations between T cells and normal B cells, and between granulocytes and monocytes. Furthermore, we constructed a prognostic model based on clinical and flow cytometry factors, which divided the DLBCL cohort into two equal groups with remarkable differences in patient survival and treatment response. Summary: TILs, including normal B cells, T cells, and NK cells, are associated with favorable clinical outcomes in DLBCL, and flow cytometry capable of quantifying the TIME may have additional clinical utility for prognostication.

Indexed as

Lymphocytes, Tumor-InfiltratingLymphoma, Large B-Cell, DiffuseFlow CytometryHumansMonocytesT-LymphocytesTumor MicroenvironmentDLBCLflow cytometrymicroenvironmentnormal B cellsprognosissingle-cellTILtumor-infiltrating lymphocytes

Identifiers

PMID38348048
PMCPMC10859492
OpenAlexW4391320887

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.