Immune infiltrate diversity confers a good prognosis in follicular lymphoma.
Biomarkers
/ metabolism
Biomarkers, Tumor
/ immunology
Cohort Studies
Female
Fluorescent Antibody Technique
/ methods
Humans
Kaplan-Meier Estimate
Lymphocytes, Tumor-Infiltrating
/ drug effects
Lymphoma, Follicular
/ drug therapy
Male
Prognosis
Progression-Free Survival
Rituximab
/ therapeutic use
Tumor Microenvironment
/ drug effects
Diversity
Follicular lymphoma
Multiplex
Prognosis
Spatial heterogeneity
Tumour microenvironment
Journal
Cancer immunology, immunotherapy : CII
ISSN: 1432-0851
Titre abrégé: Cancer Immunol Immunother
Pays: Germany
ID NLM: 8605732
Informations de publication
Date de publication:
Dec 2021
Dec 2021
Historique:
received:
25
08
2020
accepted:
14
04
2021
pubmed:
1
5
2021
medline:
18
11
2021
entrez:
30
4
2021
Statut:
ppublish
Résumé
Follicular lymphoma (FL) prognosis is influenced by the composition of the tumour microenvironment. We tested an automated approach to quantitatively assess the phenotypic and spatial immune infiltrate diversity as a prognostic biomarker for FL patients. Diagnostic biopsies were collected from 127 FL patients initially treated with rituximab-based therapy (52%), radiotherapy (28%), or active surveillance (20%). Tissue microarrays were constructed and stained using multiplex immunofluorescence (CD4, CD8, FOXP3, CD21, PD-1, CD68, and DAPI). Subsequently, sections underwent automated cell scoring and analysis of spatial interactions, defined as cells co-occurring within 30 μm. Shannon's entropy, a metric describing species biodiversity in ecological habitats, was applied to quantify immune infiltrate diversity of cell types and spatial interactions. Immune infiltrate diversity indices were tested in multivariable Cox regression and Kaplan-Meier analysis for overall (OS) and progression-free survival (PFS). Increased diversity of cell types (HR = 0.19 95% CI 0.06-0.65, p = 0.008) and cell spatial interactions (HR = 0.39, 95% CI 0.20-0.75, p = 0.005) was associated with favourable OS, independent of the Follicular Lymphoma International Prognostic Index. In the rituximab-treated subset, the favourable trend between diversity and PFS did not reach statistical significance. Multiplex immunofluorescence and Shannon's entropy can objectively quantify immune infiltrate diversity and generate prognostic information in FL. This automated approach warrants validation in additional FL cohorts, and its applicability as a pre-treatment biomarker to identify high-risk patients should be further explored. The multiplex image dataset generated by this study is shared publicly to encourage further research on the FL microenvironment.
Sections du résumé
BACKGROUND
BACKGROUND
Follicular lymphoma (FL) prognosis is influenced by the composition of the tumour microenvironment. We tested an automated approach to quantitatively assess the phenotypic and spatial immune infiltrate diversity as a prognostic biomarker for FL patients.
METHODS
METHODS
Diagnostic biopsies were collected from 127 FL patients initially treated with rituximab-based therapy (52%), radiotherapy (28%), or active surveillance (20%). Tissue microarrays were constructed and stained using multiplex immunofluorescence (CD4, CD8, FOXP3, CD21, PD-1, CD68, and DAPI). Subsequently, sections underwent automated cell scoring and analysis of spatial interactions, defined as cells co-occurring within 30 μm. Shannon's entropy, a metric describing species biodiversity in ecological habitats, was applied to quantify immune infiltrate diversity of cell types and spatial interactions. Immune infiltrate diversity indices were tested in multivariable Cox regression and Kaplan-Meier analysis for overall (OS) and progression-free survival (PFS).
RESULTS
RESULTS
Increased diversity of cell types (HR = 0.19 95% CI 0.06-0.65, p = 0.008) and cell spatial interactions (HR = 0.39, 95% CI 0.20-0.75, p = 0.005) was associated with favourable OS, independent of the Follicular Lymphoma International Prognostic Index. In the rituximab-treated subset, the favourable trend between diversity and PFS did not reach statistical significance.
CONCLUSION
CONCLUSIONS
Multiplex immunofluorescence and Shannon's entropy can objectively quantify immune infiltrate diversity and generate prognostic information in FL. This automated approach warrants validation in additional FL cohorts, and its applicability as a pre-treatment biomarker to identify high-risk patients should be further explored. The multiplex image dataset generated by this study is shared publicly to encourage further research on the FL microenvironment.
Identifiants
pubmed: 33929583
doi: 10.1007/s00262-021-02945-0
pii: 10.1007/s00262-021-02945-0
pmc: PMC8571143
doi:
Substances chimiques
Biomarkers
0
Biomarkers, Tumor
0
Rituximab
4F4X42SYQ6
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3573-3585Subventions
Organisme : Manchester Biomedical Research Centre
ID : IS-BRC-1215-20007
Informations de copyright
© 2021. The Author(s).
Références
Lab Invest. 2012 Sep;92(9):1342-57
pubmed: 22801299
Blood. 2021 Oct 06;:
pubmed: 34614146
Hematol Oncol. 2020 Dec;38(5):665-672
pubmed: 32627854
J Immunother Cancer. 2015 Jun 16;3:23
pubmed: 26085931
J Clin Oncol. 2009 Sep 20;27(27):4555-62
pubmed: 19652063
Haematologica. 2020 Jun;105(6):1593-1603
pubmed: 31537685
J Clin Oncol. 2014 Sep 20;32(27):3059-68
pubmed: 25113753
Blood. 2010 Jan 14;115(2):289-95
pubmed: 19901260
Cell. 2015 Jul 2;162(1):184-97
pubmed: 26095251
Blood. 2006 Nov 1;108(9):2957-64
pubmed: 16825494
Clin Cancer Res. 2007 Oct 1;13(19):5784-9
pubmed: 17908969
BMJ. 2006 May 6;332(7549):1080
pubmed: 16675816
Br J Haematol. 2016 Oct;175(1):102-14
pubmed: 27341313
J Clin Oncol. 2008 Jan 20;26(3):440-6
pubmed: 18086798
Blood. 2004 Sep 1;104(5):1258-65
pubmed: 15126323
J Clin Invest. 2010 Feb;120(2):636-44
pubmed: 20101094
Cancer Res. 2016 Jul 1;76(13):3711-8
pubmed: 27216195
Anticancer Drugs. 2002 Nov;13 Suppl 2:S3-10
pubmed: 12710585
Br J Cancer. 2015 Oct 20;113(8):1197-205
pubmed: 26439683
Hum Pathol. 2017 Jun;64:128-136
pubmed: 28414090
J Clin Oncol. 2019 Dec 1;37(34):3300-3309
pubmed: 31461379
J Clin Oncol. 2007 Aug 1;25(22):3330-6
pubmed: 17664481
J Clin Oncol. 2010 Jun 10;28(17):2902-13
pubmed: 20385990
J Histochem Cytochem. 2002 Nov;50(11):1475-86
pubmed: 12417613
Biotechniques. 2001 Dec;31(6):1272, 1274-6, 1278
pubmed: 11768655
Lab Invest. 2015 Apr;95(4):377-84
pubmed: 25599534
Cancer Res. 2015 Dec 1;75(23):5008-13
pubmed: 26573795
Blood. 2011 Nov 17;118(20):5371-9
pubmed: 21856865
Nat Rev Dis Primers. 2019 Dec 12;5(1):83
pubmed: 31831752
Br J Cancer. 2020 Feb;122(4):539-544
pubmed: 31806878
Clin Cancer Res. 2011 Jun 15;17(12):4136-44
pubmed: 21518780
Psychol Methods. 2002 Mar;7(1):19-40
pubmed: 11928888
Int J Hematol. 2010 Sep;92(2):246-54
pubmed: 20803352
Haematologica. 2011 Sep;96(9):1327-34
pubmed: 21659362
J Pathol Inform. 2013 Mar 30;4(Suppl):S4
pubmed: 23766940
J Immunother Cancer. 2016 Nov 15;4:69
pubmed: 27879971
Br J Haematol. 2012 Jan;156(2):225-33
pubmed: 22126847
Hum Pathol. 2002 Oct;33(10):968-74
pubmed: 12395368
Blood. 2005 Sep 15;106(6):2169-74
pubmed: 15933054
Oncoimmunology. 2012 Jul 1;1(4):432-440
pubmed: 22754761
Best Pract Res Clin Haematol. 2011 Jun;24(2):135-46
pubmed: 21658614
Blood Cancer J. 2015 Feb 20;5:e281
pubmed: 25700246
J Clin Exp Hematop. 2016;56(1):1-19
pubmed: 27334853
Ann Oncol. 2010 Jun;21(6):1196-1202
pubmed: 19875761
Blood. 2008 May 1;111(9):4664-7
pubmed: 18309035
Adv Anat Pathol. 2014 Jul;21(4):260-9
pubmed: 24911251