ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2026
Differences in T-cell Densities and Neighborhood Patterns in Human Colorectal Adenomas and Sessile Serrated Lesions.
Souvik Seal, Lauren R Fanning, Evan Bagley, Christine Bookhout, Elizabeth L Barry, Elizabeth C O'Quinn, Dale C Snover, David N Lewin, Silvia Guglietta, Antonis Kourtidis and 5 more
Abstract read
In one paragraphArticle in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 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 itWhat 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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
15 authors.
Souvik SealHollings Cancer Center, Medical University of South Carolina, Charleston, South Carolina.ORCID 0000-0003-3268-610X Lauren R FanningDepartment of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina.ORCID 0009-0004-5666-2254 Evan BagleyHollings Cancer Center, Medical University of South Carolina, Charleston, South Carolina.ORCID 0000-0002-5455-9470 Christine BookhoutDepartment of Pathology and Laboratory Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina.ORCID 0000-0002-1709-685X Elizabeth L BarryDepartment of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.ORCID 0000-0001-9637-3036 Elizabeth C O'QuinnBiorepository and Tissue Analysis Shared Resource, Hollings Cancer Center, Medical University of South Carolina, Charleston, South Carolina.ORCID 0009-0007-4939-5671 David N LewinDepartment of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, South Carolina.ORCID 0000-0002-0531-6018 Silvia GugliettaDepartment of Regenerative Medicine and Cell Biology, Medical University of South Carolina, Charleston, South Carolina.ORCID 0000-0001-9998-5716 Antonis KourtidisDepartment of Regenerative Medicine and Cell Biology, Medical University of South Carolina, Charleston, South Carolina.ORCID 0000-0002-8128-6391 John A BaronDepartment of Epidemiology, University of North Carolina School of Medicine, Chapel Hill, North Carolina.ORCID 0000-0003-3461-1056 Todd A MackenzieDepartment of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.ORCID 0000-0002-0215-2003 Alexander V AlekseyenkoHollings Cancer Center, Medical University of South Carolina, Charleston, South Carolina.ORCID 0000-0002-5748-2085 Kristin WallaceHollings Cancer Center, Medical University of South Carolina, Charleston, South Carolina.ORCID 0000-0002-4297-9064 Funding
Translational Science Laboratory Shared ResourceP30CA138313 · NCI · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI John J Lemasters · 2009 to 2026
$42.7MThe role of SMAD1 and SATB2 in colon patterningP20GM130457 · NIGMS · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI Evan R Delgado · 2020 to 2026
$18.7MProteomics CoreP30DK123704 · NIDDK · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI Garth R Swanson · 2020 to 2026
$8.8MThe immune contexture of colorectal adenomas and serrated polypsR01CA226086 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI WALLACE, KRISTIN · 2019 to 2023
$3.1MRole of the complement C3a receptor on immune and non immune intestinal barrier functions and microbiota in colorectal cancer developmentR01CA258882 · NCI · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI GUGLIETTA, SILVIA · 2022 to 2025
$1.8MColon cell mechanoregulation through an E-cadherin - associated RNAi machineryR01DK136658 · NIDDK · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI Antonis Kourtidis · 2024 to 2026
$1.4MEpithelial adherens junctions regulate colon cell behavior through RNAi and lncRNAsR01DK124553 · NIDDK · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI KOURTIDIS, ANTONIS · 2021 to 2024
$1.3MDivision of Cancer Prevention, National Cancer Institute (DCP, NCI) CA138313National Cancer Institute (NCI) CA226086NCI NIH HHS P30 CA138313NCI NIH HHS R01 CA226086NCI NIH HHS R01 CA258882NIDDK NIH HHS P30 DK123704NIDDK NIH HHS R01 DK124553NIDDK NIH HHS R01 DK136658NIGMS NIH HHS P20 GM130457
6 · The paper itselfAbstract
backgroundT-cell responses influence recurrence and survival in colorectal cancer. T-cell subset distributions vary by molecular phenotype and anatomic location, shaping cytotoxic or immune-cold tumor immune microenvironments. However, the T-cell contexture and their spatial proximities within preinvasive lesions are not well characterized.
methodsWe analyzed sessile serrated lesions (SSL), tubulovillous/villous adenomas (TV), and tubular adenomas (TA) from 3 studies (N = 120). Whole-slide multiplex immunofluorescence was used to quantify 8 T-cell subsets [CD4+, CD8+, helper T (Th) 1 (CD4+TBX21+), Th17 (CD4+RORC+), regulatory T cells (Treg; CD4+FOXP3+), Tc1 (CD8+TBX21+), Tc17 (CD8+RORC+), and TcTreg (CD8+FOXP3+)]. Densities were compared by histology using a generalized linear mixed model with a negative binomial distribution, including an offset for total cell area and adjusting for age, sex, anatomic location, and lesion size. Nearest neighbor (NN) analyses assessed spatial proximities of T-cell pairs across lesion types.
resultsTAs and SSLs had higher CD4+ and CD8+ T-cell densities than TVs (q <0.05). Compared with TVs, TAs also had higher Th17 cell densities, whereas SSLs showed a trend toward lower Treg densities (q = 0.06). NN analysis showed greater Treg clustering in TVs than in SSLs and TAs. In contrast, TA versus SSL comparisons demonstrated predominant CD4+ clustering with Th17, Th1, and CD8+ subsets.
conclusionsTVs exhibited lower T-cell densities and greater Treg clustering, consistent with an immune-cold environment. SSLs and TAs were more immune-infiltrated than TVs, but TAs had higher inflammation and CD4+-dominant clustering, suggesting stronger helper coordination. IMPACT: Preinvasive lesions demonstrate immune and spatial heterogeneity which may have implications for primary or secondary prevention.
Indexed as
AdenomaColorectal NeoplasmsT-Lymphocyte SubsetsAgedFemaleHumansMaleMiddle AgedT-Lymphocytes, RegulatoryTumor Microenvironment
Identifiers
PMID42112839
PMCPMC13397129
What OpenQuestion holds
Textmetadata
LicenceTDM
Read underepoch 390