Article in Cancer immunology research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing 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.
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
23 authors.
Yasutoshi Takashima *Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.ORCID 0009-0009-0430-454X
Andressa Dias Costa *Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.ORCID 0000-0003-1046-4899
Naohiko Akimoto *Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.ORCID 0000-0001-9880-4143
Tomotaka Ugai *Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.ORCID 0000-0003-0182-5269
Juha P VäyrynenTranslational Medicine Research Unit, Medical Research Center Oulu, Oulu University Hospital, University of Oulu, Oulu, Finland.ORCID 0000-0002-8683-2996
Jason L HornickDepartment of Pathology, Brigham and Women's Hospital, Boston, Massachusetts.ORCID 0000-0001-6475-8345
Mari Mino-KenudsonDepartment of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.ORCID 0000-0002-9092-2265
Yuxue ZhongProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.ORCID 0009-0007-5718-7549
Satoko UgaiProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.ORCID 0009-0001-9661-9363
Koichiro HarukiProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.ORCID 0000-0002-1686-3228
Qian YaoProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.ORCID 0000-0002-4581-5989
Kosuke MatsudaProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.ORCID 0009-0009-2847-0251
Mayu HigashiokaProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.ORCID 0000-0002-3846-0470
Daniel D BuchananColorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Australia.ORCID 0000-0003-2225-6675
Amanda I PhippsPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington.ORCID 0000-0002-1446-2201
Ulrike PetersPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington.ORCID 0000-0001-5666-9318
Marios GiannakisDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.ORCID 0000-0001-9012-6982
Mingyang Song *Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.ORCID 0000-0002-1324-0316
Andrew T Chan *Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.ORCID 0000-0001-7284-6767
Jonathan A Nowak *Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.ORCID 0000-0002-0943-7407
Shuji Ogino *Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.ORCID 0000-0002-3909-2323
Funding
Statistical MethodsP01CA087969 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIASSEN, A. HEATHER, TAMIMI, RULLA M · 2000 to 2019
$77.8M
Validity of Diet and Activity Measures in WomenP01CA055075 · NCI · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI FUCHS, CHARLES S · 1991 to 2009
$41.8M
Long Term Multidisciplinary Study of Cancer in Women: The Nurses Health StudyUM1CA186107 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIASSEN, A. HEATHER, STAMPFER, MEIR · 2014 to 2023
$22.3M
Cancer Epidemiology Cohort in Male Health ProfessionalsU01CA167552 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Lorelei Mucci, Walter C. Willett · 2017 to 2026
$17.0M
Cancer Epidemiology Cohort in Male Health ProfessionalsUM1CA167552 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI WILLETT, WALTER C. · 2012 to 2016
$11.5M
Spatial Immunopathological Epidemiology of Colorectal Adenoma-Carcinoma SpectrumR01CA248857 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Jonathan Andrew Nowak, Shuji Ogino · 2020 to 2026
$6.6M
Accelerating Transdisciplinary Epidemiology of Colorectal CancerR35CA197735 · NCI · DANA-FARBER CANCER INST · PI OGINO, SHUJI · 2015 to 2021
$6.0M
The Gut Microbiome, Lifestyle, and Colorectal NeoplasiaU01CA261961 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Mingyang Song · 2022 to 2026
$3.0M
Epigenetic Events and Colorectal Cancer EpidemiologyR01CA151993 · NCI · DANA-FARBER CANCER INST · PI OGINO, SHUJI · 2010 to 2014
$2.6M
Integration of Immunology and Microbiology into Molecular Pathological Epidemiology of Colorectal CancerR50CA274122 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI UGAI, TOMOTAKA · 2023 to 2025
$506k
Integrating diet, lifestyle and tumor tissue molecular subtyping to study the role of adolescent calcium intake on the risk of early onset colorectal neoplasiaR21CA230873 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI OGINO, SHUJI, WU, KANA · 2018 to 2019
$427k
American Institute for Cancer Research (AICR)Cancer Research UK Grand Challenge Award C10674/A27140National Institutes of Health (NIH) P01 CA55075National Institutes of Health (NIH) P01 CA87969NCI NIH HHS P01 CA055075NCI NIH HHS P01 CA087969NCI NIH HHS R01 CA151993NCI NIH HHS R01 CA248857NCI NIH HHS R21 CA230873NCI NIH HHS R35 CA197735NCI NIH HHS R50 CA274122NCI NIH HHS U01 CA167552NCI NIH HHS U01 CA261961NCI NIH HHS UM1 CA167552NCI NIH HHS UM1 CA186107Prevent Cancer Foundation (PCF)
6 · The paper itself
Abstract
The immune microenvironment is a crucial component of colorectal carcinoma that has been well characterized, but much less is known about the immune microenvironment of colorectal carcinoma precursors. We hypothesized that T-cell infiltrates might differ across the colorectal neoplastic spectrum. We leveraged the prospective cohort incident-tumor biobank method, which provided formalin-fixed, paraffin-embedded tumor tissue specimens (N = 1,825) from 790 colorectal carcinoma precursors (including hyperplastic polyps, sessile serrated adenomas, traditional serrated adenomas, tubular adenomas, tubulovillous adenomas, and villous adenomas) and 1,035 colorectal carcinomas. We performed an in situ multispectral immunofluorescence assay for CD3, CD4, CD8, FOXP3 (negative, low, or high expression), PTPRC (CD45RO and CD45RA), MKI67 (Ki-67), and KRT (keratin) combined with supervised machine learning. CD3+CD4+ cells were more abundant than CD3+CD8+ cells in most precursors. In conventional adenomas, greater villous component correlated with fewer intraepithelial CD3+CD8+ cells. Serrated lesions, including hyperplastic polyps and sessile serrated lesions, exhibited higher densities of intraepithelial CD3+CD8+ cells compared with other precursors and carcinomas. Age strata of patients with precursors (including early-onset precursors) were not associated with differential T-cell infiltration patterns. Compared with invasive colorectal carcinoma, precursors generally showed higher densities of CD3+CD4+ cells and CD3+CD8+ cells with phenotypes of naive (CD45RA+CD45RO-), memory (CD45RA-CD45RO+), and regulatory (FOXP3+Low and FOXP3+High) in intraepithelial and lamina propria/stromal regions. In conclusion, T-cell infiltration patterns vary across different histopathologic types of the colorectal neoplastic spectrum from precursors to invasive carcinomas. Our findings shed light on how the tumor-immune microenvironment evolves during precursor development and progression to colorectal carcinoma.
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.
T-cell Subset Features and Distributions Evolve across the Colorectal Precancer-Cancer Spectrum. · full record | OpenQuestion